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Library of Congress Cataloging-in-Publication Data
Data acquisition and signal processing for smart sensors / Nikolay V. Kirianaki . . . [et al.].
p. cm.
Includes bibliographical references and index.
ISBN 0-470-84317-9 (alk. paper)
1. Detectors. 2. Microprocessors. 3. Signal processing. 4. Automatic data collection
systems. I. Kirianaki, Nikolai Vladimirovich.
TA165. D38 2001
681.2–dc21 2001046912
British Library Cataloguing in Publication Data
A catalogue record for this book is available from the British Library
ISBN 0 470 84317 9
Typeset in 10/12pt Times by Laserwords Private Limited, Chennai, India
Printed and bound in Great Britain by Biddles Ltd, Guildford King’s Lynn
This book is printed on acid-free paper responsibly manufactured from sustainable forestry,
in which at least two trees are planted for each one used for paper production.](https://image.slidesharecdn.com/19527-250510084747-695587e1/85/Data-Acquisition-and-Signal-Processing-for-Smart-Sensors-Nikolay-V-Kirianaki-7-320.jpg)
![INTRODUCTION
Rapid advances in IC technologies have brought new challenges to the physical design
of integrated sensors and micro-electrical-mechanical systems (MEMS). Microsystem
technology (MST) offers new ways of combining sensing, signal processing and actu-
ation on a microscopic scale and allows both traditional and new sensors to be realized
for a wide range of applications and operational environments. The term ‘MEMS’ is
used in different ways: for some, it is equivalent to ‘MST’, for others, it comprises
only surface-micromechanical products. MEMS in the latter sense are seen as an
extension to IC technology: ‘an IC chip that provides sensing and/or actuation func-
tions in addition to the electronic ones’ [1]. The latter definition is used in this
book.
The definition of a smart sensor is based on [2] and can be formulated as: ‘a smart
sensor is one chip, without external components, including the sensing, interfacing,
signal processing and intelligence (self-testing, self-identification or self-adaptation)
functions’.
The main task of designing measuring instruments, sensors and transducers has
always been to reach high metrology performances. At different stages of measurement
technology development, this task was solved in different ways. There were technolog-
ical methods, consisting of technology perfection, as well as structural and structural-
algorithmic methods. Historically, technological methods have received prevalence
in the USA, Japan and Western Europe. The structural and structural-algorithmic
methods have received a broad development in the former USSR and continue devel-
oping in NIS countries. The improvement of metrology performances and extension
of functional capabilities are being achieved through the implementation of particular
structures designed in most cases in heuristic ways using advanced calculations and
signal processing. Digital and quasi-digital smart sensors and transducers are not the
exception.
During measurement different kinds of measurands are converted into a limited
number of output parameters. Mechanical displacement was the first historical type
of such (unified) parameters. The mercury thermometer, metal pressure gauge, pointer
voltmeter, etc. are based on such principles [3]. The amplitude of an electric current or
voltage is another type of unified parameter. Today almost all properties of substances
and energy can be converted into current or voltage with the help of different sensors.
All these sensors are based on the use of an amplitude modulation of electromagnetic
processes. They are so-called analog sensors.
Digital sensors appeared from a necessity to input results of measurement into
a computer. First, the design task of such sensors was solved by transforming an](https://image.slidesharecdn.com/19527-250510084747-695587e1/85/Data-Acquisition-and-Signal-Processing-for-Smart-Sensors-Nikolay-V-Kirianaki-17-320.jpg)
![xvi INTRODUCTION
analog quantity into a digital code by an analog-to-digital converter (ADC). The
creation of quasi-digital sensors, in particular, frequency sensors, was another very
promising direction [3]. Quasi-digital sensors are discrete frequency–time-domain
sensors with frequency, period, duty-cycle, time interval, pulse number or phase shift
output. Today, the group of frequency output sensors is the most numerous among all
quasi-digital sensors (Figure I). Such sensors combine a simplicity and universatility
that is inherent to analog devices, with accuracy and noise immunity, proper to
sensors with digital output. Further transformation of a frequency-modulated signal was
reduced by counting periods of a signal during a reference time interval (gate). This
operation exceeds all other methods of analog-to-digital conversion in its simplicity
and accuracy [4].
Separate types of frequency transducers, for example, string tensometers or induction
tachometers, have been known for many years. For example, patents for the string
distant thermometer (Patent No. 617 27, USSR, Davydenkov and Yakutovich) and
the string distant tensometer (Patent No. 21 525, USSR, Golovachov, Davydenkov
and Yakutovich) were obtained in 1930 and 1931, respectively. However, the output
frequency of such sensors (before digital frequency counters appeared) was measured
by analog methods and consequently substantial benefit from the use of frequency
output sensors was not achieved practically.
The situation has changed dramatically since digital frequency counters and
frequency output sensors attracted increasing attention. As far back as 1961 Professor
P.V. Novitskiy wrote: ‘. . . In the future we can expect, that a class of frequency
sensors will get such development, that the number of now known frequency sensors
will exceed the number of now known amplitude sensors. . .’ [3]. Although frequency
output sensors exist practically for any variables, this prognosis has not yet been fully
justified for various reasons.
With the appearance in the last few years of sensor microsystems and the heady
development of microsystem technologies all over the world, technological and cost
factors have increased the benefits of digital and quasi-digital sensors. Modern tech-
nologies are able to solve rather complicated tasks, concerned with the creation of
different sensors. Up to now, however, there have still been some major obstacles
preventing industries from largely exploiting such sensors in their systems. These are
only some subjective reasons:
Time Interval
6% Pulse Number
2%
Duty-Cycle
12%
Frequency
35%
Digital
45%
Figure I Classification of sensors from discrete group in terms of output signals (IFSA, 2001)](https://image.slidesharecdn.com/19527-250510084747-695587e1/85/Data-Acquisition-and-Signal-Processing-for-Smart-Sensors-Nikolay-V-Kirianaki-18-320.jpg)
![INTRODUCTION xvii
• The lack of awareness of the innovation potential of modern methods for frequency-
time conversion in many companies, as processing techniques have mainly been
developed in the former Soviet Union.
• The tendency of companies to return, first of all, major expenditures, invested in
the development of conventional ADCs.
• The lack of emphasis placed on business and market benefits, which such measuring
technologies can bring to companies etc.
Today the situation has changed dramatically. According to Intechno Consulting,
the non-military world market for sensors has exceeded expectations with US$32.5
billion in 1998. By 2003, this market is estimated to grow at an annual rate of 5.3%
to reach US$42.2 billion. Under very conservative assumptions it is expected to reach
US$50–51 billion by 2008; assuming more favourable but still realistic economic
conditions, the global sensor market volume could even reach US$54 billion by 2008.
Sensors on a semiconductor basis will increase their market share from 38.9% in 1998
to 43% in 2008. Strong growth is expected for sensors based on MEMS-technologies,
smart sensors and sensors with bus capabilities [5]. It is reasonable to expect that silicon
sensors will go on to conquer other markets, such as the appliances, telecommunications
and PC markets [6].
We hope that this book will be a useful and relevant resource for anyone involved
in the design of high performance and highly efficient digital smart sensors and data
acquisition systems.](https://image.slidesharecdn.com/19527-250510084747-695587e1/85/Data-Acquisition-and-Signal-Processing-for-Smart-Sensors-Nikolay-V-Kirianaki-19-320.jpg)
![Data Acquisition and Signal Processing for Smart Sensors
Nikolay Kirianaki, Sergey Yurish, Nestor Shpak, Vadim Deynega
Copyright 2002 John Wiley Sons Ltd
ISBNs: 0-470-84317-9 (Hardback); 0-470-84610-0 (Electronic)
1
SMART SENSORS FOR
ELECTRICAL AND
NON-ELECTRICAL, PHYSICAL
AND CHEMICAL VARIABLES:
TENDENCIES AND
PERSPECTIVES
The processing and interpretation of information arriving from the outside are the main
tasks of data acquisition systems and measuring instruments based on computers. Data
acquisition and control systems need to get real-world signals into the computer. These
signals come from a diverse range of transducers and sensors. According to [7] ‘Data
Acquisition (DAQ) is collecting and measuring electrical signals from sensors and
transducers and inputting them to a computer for processing.’ Further processing can
include the sensors’ characteristic transformation, joint processing for many parame-
ters as well as statistical calculation of results and presenting them in a user-friendly
manner.
According to the output signal, sensors and transducers can be divided into potential
(amplitude), current, frequency, pulse-time and code. As a result, the task of adequate
sensor interfacing with PCs arises before the developers and users of any data acqui-
sition systems. Therefore special attention must be paid to the problems of output
conversion into a digital format as well as to high accuracy and speed conversion
methods.
In general, a sensor is a device, which is designed to acquire information from an
object and transform it into an electrical signal. A classical integrated sensor can be
divided into four parts as shown in Figure 1.1. The first block is a sensing element
(for example, resistor, capacitor, transistor, piezo-electric material, photodiode, resistive
bridge, etc.). The signal produced from the sensing element itself is often influenced by
noise or interference. Therefore, signal-conditioning and signal-processing techniques
such as amplification, linearization, compensation and filtering are necessary (second
block) to reduce sensor non-idealities.](https://image.slidesharecdn.com/19527-250510084747-695587e1/85/Data-Acquisition-and-Signal-Processing-for-Smart-Sensors-Nikolay-V-Kirianaki-26-320.jpg)
![2 1 SMART SENSORS: TENDENCIES AND PERSPECTIVES
Sensing
element
Signal
conditioning
and processing
A/D
converter
Sensor-bus
interface
Figure 1.1 Classical integrated sensors
Sometimes if certain sensing elements are used on the same chip, a multiplexer is
necessary. In cases of data acquisition, the signal from the sensor must be in a serial
or parallel digital format. This function can be realized by the analog-to-digital or
frequency-to-digital converter. The last (but not least) block is a sensor-bus interface. A
data acquisition system can have a star configuration in which each sensor is connected
to a digital multiplexer. When using a large number of sensors, the total cable length
and the number of connections at the multiplexer can become very high. For this reason
it is much more acceptable to have a bus-organized system, which connects all data
sources and receivers. This bus system handles all data transports and is connected to
a suitable interface that sends accumulated data to the computer [8].
A smart sensor block diagram is shown in Figure 1.2. A microcontroller is typically
used for digital signal processing (for example, digital filtering), analog-to-digital or
frequency-to-code conversions, calculations and interfacing functions. Microcontrollers
can be combined or equipped with standard interface circuits. Many microcontrollers
include the two-wire I2
C bus interface, which is suited for communication over short
distances (several metres) [9] or the serial interface RS-232/485 for communication
over relatively long distances.
However, the essential difference of the smart sensor from the integrated sensor with
embedded data-processing circuitry is its intelligence capabilities (self-diagnostics, self-
identification or self-adaptation (decision-making)) functions. As a rule, these functions
are implemented due to a built-in microcontroller (microcontroller core (‘microcon-
troller like’ ASIC) or application-specific instruction processor (ASIP)) or DSP. New
functions and the potential to modify its performance are the main advantages of smart
sensors. Due to smart sensor adaptability the measuring process can be optimized for
maximum accuracy, speed and power consumption. Sometimes ‘smart sensors’ are
called ‘intelligent transducers’.
At present, many different types of sensors are available. Rapid advancement of
the standard process for VLSI design, silicon micromachining and fabrication provide
the technological basis for the realization of smart sensors, and opens an avenue that
Sensing
element
Signal
conditioning µK
Figure 1.2 Smart sensor](https://image.slidesharecdn.com/19527-250510084747-695587e1/85/Data-Acquisition-and-Signal-Processing-for-Smart-Sensors-Nikolay-V-Kirianaki-27-320.jpg)
![1 SMART SENSORS: TENDENCIES AND PERSPECTIVES 3
can lead to custom-integrated sensors to meet the new demands in performance, size
and cost. This suggests a smooth merging of the sensor and electronics and the fabri-
cation of complete data acquisition systems on a single silicon chip. The essential
issue is the fabrication compatibility of the sensor, sensor-related analogue micro-
electronic circuits and digital interface circuits [10]. In fact, for any type of silicon
sensing element and read-out circuitry, a process can be developed to merge them onto
a single chip. However, process development is very expensive and therefore only a
huge production volume will pay off the development cost. Successful integrated-sensor
processes must have an acceptable complexity and/or applicability for a wide range of
sensors [11]. MEMS technologies allow the miniaturization of sensors and, at the same
time, integration of sensor elements with microelectronic functions in minimal space.
Only MEMS technologies make it possible to mass-produce sensors with increasing
cost-effectiveness while improving their functionality and miniaturizing them.
Of course, the implementation of the microcontroller in one chip together with the
sensing element and signal-conditioning circuitry is an elegant and preferable engi-
neering solution. However, the combination of monolithic and hybrid integration with
advanced processing and conversional methods in many cases achieves magnificent
technical and metrological performances for the shorter time-to-market period without
additional expenditures for expensive CAD tools and the lengthy smart sensor design
process. For implementation of smart sensors with hybrid-integrated processing elec-
tronics, hardware minimization is a necessary condition to achieve a reasonable price
and high reliability. In this case, we have the so-called ‘hybrid smart sensor’ in which
a sensing element and an electronic circuit are placed in the same housing.
Frequency–time-domain sensors are interesting from a technological and fabrication
compatibility point of view: the simplifications of the signal-conditioning circuitry and
measurand-to-code converter, as well as metrology performances and the hardware for
realization. The latter essentially influences the chip area. Such sensors are based on
resonant phenomena and variable oscillators, whose information is embedded not in
the amplitude but in the frequency or the time parameter of the output signal. These
sensors have frequency (fx), period (Tx = 1/fx), pulse width (tp), spacing interval
(ts), the duty cycle (tp/Tx), online time ratio or off-duty factor (Tx/tp), pulse number
(N), phase shift (ϕ) or single-time interval (τ) outputs. These informative parame-
ters are shown in Figure 1.3. Because these informative parameters have analog and
digital signal properties simultaneously, these sensors have been called ‘quasi-digital’.
Frequency output sensors are the most numerous group among all quasi-digital sensors
(see Figure I, Introduction). Let us consider the main advantages of frequency as the
sensor’s output signal.
• High noise immunity. In frequency sensors, it is possible to reach a higher accuracy
in comparison with analog sensors with analog-to-code conversion. This property
of high noise immunity proper to a frequency modulation is apparently the principal
premise of frequency sensors in comparison with analog ones. The frequency signal
can be transmitted by communication lines for a much greater distance than analog
and digital signals. The frequency signal transmitted practically represents a serial
digital signal. Thus, all the advantages of digital systems are demonstrated. Also,
only a two-wire line is necessary for transmission of such a signal. In compar-
ison with the usual serial digital data transmission it has the advantage of not](https://image.slidesharecdn.com/19527-250510084747-695587e1/85/Data-Acquisition-and-Signal-Processing-for-Smart-Sensors-Nikolay-V-Kirianaki-28-320.jpg)
![1 SMART SENSORS: TENDENCIES AND PERSPECTIVES 5
All this makes the design and usage of different frequency–time-domain smart sensors
very efficient.
The most important properties of smart sensors have been well described in [9]. Here
we will briefly describe only the basic focus points for an intelligent frequency–time-
domain smart sensor design.
• Adaptability. A smart sensor should be adaptive in order to optimize the measuring
process. For example, depending on the measuring conditions, it is preferable to
exchange measurement accuracy for speed and conversely, and also to moderate
power consumption, when high speed and accuracy are not required. It is also desir-
able to adjust a clock crystal oscillator frequency depending on the environment
temperature. The latter also essentially influences the following focus point–the
accuracy.
• Accuracy. The measuring error should be programmable. Self-calibration will allow
reduction of systematic error, caused, for example, by the inaccuracy of the system
parameters. The use of statistical algorithms and composited algorithms of the
weight average would allow reduction of random errors caused, for instance, by
interference, noise and instability.
• Reliability. This is one of the most important requirements especially in industrial
applications. Self-diagnostics is used to check the performance of the system and
the connection of the sensor wires.
For analysis of quasi-digital smart sensors, it is expedient to use the classification,
shown in Figure 1.4. Depending on conversion of the primary information into
frequency, all sensors are divided into three groups: sensors with measurand-to-
frequency conversion; with measurand-to-voltage-to-frequency conversion; and with
measurand-to-parameter-to-frequency conversion.
1. Sensors with x(t) → F(t) conversion. These are sensors that generate a frequency
output. Electronic circuitry might be needed for the amplification of the impedance
matching, but it is not needed for the frequency conversion step itself. Measuring
information like the frequency or the frequency-pulse form is most simply obtained
in inductive, photoimpulse, string, acoustic and scintillation sensors, since the
principle of operation allows the direct conversion x(t) → F(t). One group of
Quasi-digital
sensors
x(t )→F(t ) x(t )→V(t )→F(t ) x(t )→P(t )→F(t )
Figure 1.4 Quasi-digital smart sensor classification (x(t) — measurand; F(t) — frequency;
V (t) — voltage, proportional to the measurand; P (t) — parameter)](https://image.slidesharecdn.com/19527-250510084747-695587e1/85/Data-Acquisition-and-Signal-Processing-for-Smart-Sensors-Nikolay-V-Kirianaki-30-320.jpg)
![6 1 SMART SENSORS: TENDENCIES AND PERSPECTIVES
such sensors is based on resonant structures (piezoelectric quartz resonators, SAW
(surface acoustic wave) dual-line oscillators, etc.) whereas another group is based
on the periodic geometrical structure of the sensors, for example, angle encoders.
2. Sensors with x(t) → V (t) → F(t) conversion. This group has a numerous number
of different electric circuits. These are Hall sensors, thermocouple sensors and
photosensors based on valve photoelectric cells. When a frequency output is
required, a simple voltage-to-frequency or current-to-frequency conversion circuit
can be applied to obtain the desired result.
3. Sensors with x(t) → P (t) → F(t) conversion. The sensors of this group are rather
manifold and numerous. These are the so-called electronic-oscillator based sensors.
Such sensors are based on the use of electronic oscillators in which the sensor
element itself is the frequency-determining element. These are inductive, capacity
and ohmic parametric (modulating) sensors.
Parametric (modulating) sensors are devices that produce the primary information
by way of respective alterations of any electrical parameter of some electrical circuit
(inductance, capacity, resistance, etc.), for which it is necessary to have an external
auxiliary power supply. Examples of such types of sensors are pressure sensors based
on the piezoresistive effect and photodetectors based on the photoelectric effect.
In turn, self-generating sensors are devices that receive a signal immediately by way
of a current i(t) or voltage V (t) and do not require any source of power other than the
signal being measured. Examples of such types of sensors are Seebeck effect based
thermocouples and photoeffect based solar cells. Self-generating sensors are also called
‘active’ sensors, while modulating sensors are called ‘passive’ sensors.
The signal power of modulating sensors is the largest and, therefore, from the noise-
reduction point of view their usage is recommended.
The distinctiveness of these three sensor groups (Figure 1.4) is the absence of
conventional ADCs. In order to design digital output smart sensors, it is expedient
to use a microcontroller for the frequency-to-code conversion. The production of such
smart sensors does not require extra technological steps. Moreover, modern CAD tools
contain microcontroller cores and peripheral devices as well as voltage-to-frequency
converters (VFC) in the library of standard cells. So, for example, the Mentor Graphic
CAD tool includes different kinds of VFCs like AD537/650/652, the CAD tool from
Protel includes many library cells of different Burr–Brown VFCs.
In comparison with the data-capturing method using traditional analog-to-digital
converters (ADC), the data-capturing method using VFC has the following advan-
tages [12]:
• Simple, low-cost alternative to the A/D conversion.
• Integrating input properties, excellent accuracy and low nonlinearity provide perfor-
mance attributes unattainable with other converter types, make VFC ideal for high
noise industrial environments.
• Like a dual-slope ADC, the VFC possesses a true integrating input and features
the best, much better than a dual-slope converter, noise immunity. It is especially](https://image.slidesharecdn.com/19527-250510084747-695587e1/85/Data-Acquisition-and-Signal-Processing-for-Smart-Sensors-Nikolay-V-Kirianaki-31-320.jpg)
![1 SMART SENSORS: TENDENCIES AND PERSPECTIVES 7
important in industrial measurement and data acquisition systems. While a succes-
sive approximation ADC takes a ‘snapshot’ in time, making it susceptible to noise
peaks, the VFC’s input is constantly integrating, smoothing the effects of noise or
varying input signals.
• It has universality. First, its user-selected voltage input range (± supply). Second,
the high accuracy of the frequency-to-code conversion (up to 0.001%). The error
of such conversion can be neglected in a measuring channel. This is not true for
traditional analog-to-digital conversion. The ADC error is commensurable with the
sensor’s error, especially if we use modern high precision sensors with relative
error up to 0.01%.
• When the data-capturing method with a VFC is used, a frequency measurement
technique must also be chosen which meets the conversion speed requirements.
While it is clearly not a ‘fast’ converter in a common case, the conversion speed of
a VFC system can be optimized by using efficient techniques. Such optimization
can be performed due to advanced methods of frequency-to-code conversion, quasi
pipeline data processing in a microcontroller and the use of novel architectures
of VFC.
While pointing out the well-known advantages of frequency sensors, it is necessary
to note, that the number of physical phenomena, on the basis of which sensors with
frequency and digital outputs can be designed, is essentially limited. Therefore, analog
sensors with current and voltage outputs have received broad dissemination. On the
one hand, this is because of the high technology working off analog sensor units,
and also because of the heady development of analog-to-digital conversion in the last
few years. On the other hand, voltage and current are widely used as unified standard
signals in many measuring and control systems.
An important role is played by the technological and cost factors in the choice of
sensor. Therefore, the question, what sensors are the best–frequency or analog–is not
enough. With the appearance of sensor microsystems and the heady development of
microsystem technologies all over the world, technological and cost factors need to be
modified for the benefit of frequency sensors.
Sensor types with the highest demand volumes are temperature sensors, pressure
sensors, flow sensors, binary position sensors (proximity switches, light barriers,
reflector-type photosensors), position sensors, chemical sensors for measurement in
liquids and gases, filling sensors, speed and rpm sensors, flue gas sensors and fire
detectors worldwide. The fastest growing types of sensors include rain sensors,
thickness sensors, sensors that measure the quality of liquids, navigation sensors, tilt
sensors, photodetectors, glass breakage sensors, biosensors, magnetic field sensors and
motion detectors [5].
The frequency–time-domain sensor group is constantly increasing. First, it is con-
nected with the fast development of modern microelectronic technologies, secondly
with the further development of methods of measurement for frequency-domain param-
eters of signals and methods for frequency-to-code conversion, and thirdly, with advan-
tages of frequency as the informative parameter of sensors and transducers. Today it is
difficult to find physical or chemical variables, for which frequency output or digital
sensors do not exist. Of course, this book cannot completely describe all existing](https://image.slidesharecdn.com/19527-250510084747-695587e1/85/Data-Acquisition-and-Signal-Processing-for-Smart-Sensors-Nikolay-V-Kirianaki-32-320.jpg)
![8 1 SMART SENSORS: TENDENCIES AND PERSPECTIVES
Radiant
Mechanical
Thermal
Electrical
Magnetic
Chemical
Signal
domains
Figure 1.5 Six signal domains
sensors and their principles of operation. For readers wishing to learn more about
smart sensor development history we would like to recommend the article [6].
This review aims to illustrate state-of-the-art frequency–time-domain IC sensors
with high metrology performances, and also to formulate the basic requirements for
such an important smart sensor’s unit, as the frequency-to-code converter.
Frequency–time domain-sensors can be grouped in several different ways. We will
group them according to the measurand domains of the desired information. There are
six signal domains with the most important physical parameters shown in Figure 1.5.
Electrical parameters usually represent a signal from one of the non-electrical signal
domains.
1.1 Temperature IC and Smart Sensors
Temperature sensors play an important role in many measurements and other integrated
microsystems, for example, for biomedical applications or self-checking systems and
the design for the thermal testability (DfTT). IC temperature sensors take advantage
of the variable resistance properties of semiconductor materials. They provide good
linear frequency, the duty-cycle or pulse width output proportional to the temperature
typically in the range from −55 ◦
C to +150 ◦
C at a low cost. These devices can
provide direct temperature readings in a digital form, thus eliminating the need for an
ADC. Because IC sensors can have a memory, they can be very accurately calibrated,
and may operate in multisensor environments in applications such as communications
networks. Many IC sensors also offer communication protocols for use with bus-type
data acquisition systems; some also have addressability and data storage and retrieval
capabilities.
Smart temperature sensors need to be provided with some kind of output digital
signal adapted to microprocessors and digital-processing systems. This signal can be
a time-signal type, where the measurement is represented by the duty-cycle or the](https://image.slidesharecdn.com/19527-250510084747-695587e1/85/Data-Acquisition-and-Signal-Processing-for-Smart-Sensors-Nikolay-V-Kirianaki-33-320.jpg)
![1.1 TEMPERATURE IC AND SMART SENSORS 9
frequency ratio, or the fully digital code that is sent to the processor in a serial way
through the digital bus [13]. Some important restraints, caused by the integration of
sensing and digital-processing function on the same chip are [14] (a) the limited chip
area, (b) the tolerances of the device parameters and (c) the digital interference. Perfor-
mances of some integrated temperature sensors are shown in Table 1.1.
Since CMOS is still the most extensively used technology the integration of temper-
ature sensors in high-performance, low-cost digital CMOS technologies is preferred in
order to allow signal conditioning and digital processing on the same chip [13].
In the framework of the COPERNICUS EC project CP0922, 1995–1998, THER-
MINIC (THermal INvestigations of ICs and Microstructures), the research group from
Technical University of Budapest has dealt for several years with the design problem
of small-size temperature sensors that must be built into the chip for thermal moni-
toring [18–21]. One such sensor is based on a current-to-frequency converter [18,19].
The analog signal of the current output CMOS sensor is converted into a quasi-digital
one using a current-to-frequency converter. The block diagram of the frequency output
sensor is shown in Figure 1.6. The Iout output current and its ‘copy’ generated by a
current mirror the charge and discharge the capacitor Cx. The signal of the capacitor
is led to a differential comparator the reference voltage of which is switched between
the levels VC and VD [19]. The resulting frequency is
f =
Iout
2 · Cx(VC − CD)
. (1.1)
The sensitivity is −0.808%/◦
C. The output frequency 0.5–1.3 MHz is in a conve-
nient range. The complete circuit requires only an area of 0.018 mm2
using the ECPD
1 µm CMOS process. The low sensitivity on the supply voltage is a remarkable feature:
±0.25 V charge in VDD results in only ±0.28% charge in the frequency. The latter
corresponds to the ±0.35 ◦
C error. The total power consumption of this sensor is about
200 µW [20].
The characteristic of this sensor is quite linear and the output frequency of these
sensors can be approximately written as
fout = f20Cels exp(γ (TCels − 20
◦
C)), (1.2)
Table 1.1 Performances of some integrated temperature sensors
Sensor Output type Characteristic Area, mm2
IC technology
[15] Digital I → F converter + DSP 4.5 CMOS
[16] Duty-cycle Duty-cycle-modulated 5.16 Bipolar
[17] Frequency I → F converter 6 Bipolar
Temperature-to-current
converter
Current-to-frequency
converter
I = 5 ...15 mA f = 0.5 ...1.5 MHz
Figure 1.6 Block diagram of the temperature frequency output sensor](https://image.slidesharecdn.com/19527-250510084747-695587e1/85/Data-Acquisition-and-Signal-Processing-for-Smart-Sensors-Nikolay-V-Kirianaki-34-320.jpg)
![10 1 SMART SENSORS: TENDENCIES AND PERSPECTIVES
in the −50 · · · +120 ◦
C temperature range, where γ is the sensitivity, f20Cels is the
nominal frequency related to T = 20 ◦
C. Using the AMS 0.8 µm process, the area
consumption is 0.005 mm2
[21]. The THSENS-F [22] sensor characteristic and sensor
layout based on these researches are shown in Figure 1.7 and 1.8 respectively. This
sensor can be inserted into CMOS designs, which can be transferred and re-used as cell
(layout level) entities or as circuit netlists with transistor sizes. The sensor’s sensitivity
is ≈ −0.8%/◦
C; the temperature range is −50 · · · +150 ◦
C; the accuracy is ≈ ±2 ◦
C
for (0 . . . 120 ◦
C). The two latter parameters depend on the process.
If the temperature sensors described above are inserted into a chip design, additional
circuitry must be implemented in order to provide access to such sensors [21]. Built-in
temperature sensors can be combined with other built-in test circuitry. The boundary
scan architecture [23] is suitable for monitoring temperature sensors. This architecture
has led to the standard IEEE 1149.1 and is suitable for incorporating frequency output
temperature sensors.
400
600
800
1000
1200
1400
1600
1800
2000
−100 −50 0 50
Temperature [Celsius]
100 150 200
Frequency
[kHz]
Figure 1.7 Sensor characteristic (output frequency vs. temperature) (Reproduced by permission
of MicReD)
Figure 1.8 Sensor layout (Reproduced by permission of MicReD)](https://image.slidesharecdn.com/19527-250510084747-695587e1/85/Data-Acquisition-and-Signal-Processing-for-Smart-Sensors-Nikolay-V-Kirianaki-35-320.jpg)
![1.1 TEMPERATURE IC AND SMART SENSORS 11
A further interesting fully CMOS temperature sensor designed by this research team
is based on the temperature dependence of internal thermal diffusion constant of silicon.
In order to measure this diffusion constant an oscillating circuit is used in which the
frequency-determining element is realized by a thermal delay line. The temperature
difference sensors used in this delay line are Si-Al thermopiles. This circuit is the
Thermal-Feedback Oscillator (TFO). The frequency of this oscillator is directly related
to the thermal diffusion constant and thus to the temperature. This constant can be
defined as
Dth = λ/c, (1.3)
where λ is the thermal conductivity and c is the unit-volume heat capacitance. This
diffusion constant shows a reasonably large (−0.57%/◦
C) temperature dependence on
the silicon. In order to measure this diffusion constant, oscillating circuits were used
in which the frequency-determining feedback element is realized by a thermal time-
delay line. If the feedback element is a thermal two-port (thermal delay line) then the
frequency of the oscillator is directly related to the thermal diffusion constant and thus
shows similar temperature dependence as the thermal diffusion constant [18].
The thermal delay line requires, however, a significant power input. Because of this
disadvantage, the circuit is not really suitable for online monitoring purposes [19].
However, these sensors and the sensor principle can probably be used for other appli-
cations.
It is also necessary to note the low-power consumption smart temperature sensor
SMT 160-30 from Smartec (Holland) [24]. It is a sophisticated full silicon sensor with
a duty-cycle modulated square-wave output. The duty-cycle of the output signal is
linearly related to the temperature according to the equation:
DC =
tp
Tx
= tp · fx = 0.320 + 0.00470 · t, (1.4)
where tp is the pulse duration; Tx is the period; fx is the frequency; t is the temperature
in ◦
C. This sensor is calibrated during the test and burn-in of the chip. The sensor
characteristic is shown in Figure 1.9.
One wire output can be directly connected to all kinds of microcontrollers without
the A/D conversion. The temperature range is −45 ◦
C–+150 ◦
C, the best absolute
−40
−20
0
0 0.2 0.4
Duty cycle
0.6 0.8 1
20
40
60
80
100
120
140
Temperature
[°C]
Figure 1.9 Sensor characteristic (temperature vs. duty-cycle) (Reproduced by permission of
Smartec)](https://image.slidesharecdn.com/19527-250510084747-695587e1/85/Data-Acquisition-and-Signal-Processing-for-Smart-Sensors-Nikolay-V-Kirianaki-36-320.jpg)
![12 1 SMART SENSORS: TENDENCIES AND PERSPECTIVES
accuracy, including all errors is ±0.7 ◦
C, the relative error is 0.47%, the frequency range
is 1–4 kHz. The CMOS output of the sensor can handle cable length up to 20 metres.
This makes the SMT 160-30 very useful for remote sensing and control applications.
This smart temperature sensor represents a significant totally new development in
transducer technology. Its novel on-chip interface meets the progressively stringent
demands of both the consumer and industrial electronics sectors for a temperature
sensor directly connectable to the microprocessor input and thus capable of direct and
reliable communication with microprocessors.
In applications where more sensors are used, easy multiplexing can be obtained
by using more microprocessor inputs or by using simple and cheap digital multi-
plexers.
The next specialized temperature sensor is interesting due to the high metrology
performances (high accuracy). It is the SBE 3F temperature sensor with an initial
accuracy of 0.001 ◦
C and typically stable to 0.002 ◦
C per year [25]. It is used
for custom-built oceanographic profiling systems or for high-accuracy industrial
and environmental temperature-monitoring applications. Depth ratings to 6800
and 10 500 metres (22 300 and 34 400 ft) are offered to suit different application
requirements.
The sensing element is a glass-coated thermistor bead, pressure-protected in a thin-
walled 0.8 mm diameter stainless steel tube. Exponentially related to the temperature,
the thermistor resistance is the controlling element in the optimized Wien bridge oscil-
lator circuit. The resulting sensor frequency is inversely proportional to the square root
of the thermistor resistance and ranges from approximately 2 to 6 kHz, corresponding
to temperatures from −5 to +35 ◦
C.
In speaking about digital output IC and smart temperature sensors it is necessary
to mention interesting developments of companies such as Analog Devices, Dallas
Semiconductor and National Semiconductor.
The TMP03/TMP04 are monolithic temperature detectors from Analog Devices
[26,27] that generate a modulated serial digital output that varies in direct propor-
tion to the temperature of the device. The onboard sensor generates a voltage precisely
proportional to the absolute temperature, which is compared with the internal voltage
reference and the input to a precision digital modulator. The ratiometric encoding
format of the serial digital output is independent from the clock drift errors common to
most serial modulation techniques such as voltage-to-frequency converters. The overall
accuracy is ±1.5 ◦
C (typical) from −25 ◦
C to +100 ◦
C, with good transducer linearity.
The digital output of the TMP04 is CMOS/TTL compatible, and is easily interfaced to
the serial inputs of the most popular microprocessors. The open-collector output of the
TMP03 is capable of sinking 5 mA. The TMP03 is best suited for systems requiring
isolated circuits, utilizing optocouplers or isolation transformers.
The TMP03/TMP04 are powerful, complete temperature measurement systems on a
single chip. The onboard temperature sensor follows the footsteps of the TMP01 low-
power programmable temperature controller, offering excellent accuracy and linearity
over the entire rated temperature range without correction or calibration by the user.
The sensor output is digitized by the first-order sigma-delta modulator, also known
as the ‘charge balance’ type analog-to-digital converter. This type of converter utilizes
the time-domain oversampling and a high accuracy comparator to deliver 12 bits of
effective accuracy in the extremely compact circuit.](https://image.slidesharecdn.com/19527-250510084747-695587e1/85/Data-Acquisition-and-Signal-Processing-for-Smart-Sensors-Nikolay-V-Kirianaki-37-320.jpg)
![1.1 TEMPERATURE IC AND SMART SENSORS 13
Basically, the sigma-delta modulator consists of an input sampler, a summing net-
work, an integrator, a comparator, and a 1-bit DAC. Similar to the voltage-to-frequency
converter, this architecture creates in effect a negative feedback loop whose intent is to
minimize the integrator output by changing the duty-cycle of the comparator output in
response to input voltage changes. The comparator samples the output of the integrator
at a much higher rate than the input sampling frequency, called the oversampling. This
spreads the quantization noise over a much wider band than that of the input signal,
improving the overall noise performance and increasing the accuracy.
The modulated output of the comparator is encoded using a circuit technique (patent
pending), which results in a serial digital signal with a mark-space ratio format that
is easily decoded by any microprocessor into either degrees centigrade or degrees
Fahrenheit values, and is readily transmitted or modulated over a single wire. It is
very important that this encoding method avoids major error sources common to other
modulation techniques, as it is clock-independent.
The AD7818 (single-channel) and AD7817 (4-channel) [28,29] are on-chip temper-
ature sensors with 10-bit, single and four-channel A/D converters. These devices
contain an 8 ms successive-approximation converter based around a capacitor DAC,
an on-chip temperature sensor with an accuracy of ±1 ◦
C, an on-chip clock oscillator,
inherent track-and-hold functionality and an on-chip reference (2.5 V ± 0.1%). Some
other digital temperature sensors from Analog Devices [30] are shown in Table 1.2.
Dallas Semiconductor offers a broad range of factory-calibrated 1-, 2-, 3- Wire or
SPI buses temperature sensors/thermometers that can provide straightforward thermal
management for a vast array of applications. This unparalleled product line includes
a variety of ‘direct-to-digital’ temperature sensors that have the accuracy and features
to easily improve system performance and reliability [31]. These devices reduce the
component count and the board complexity by conveniently providing digital data
without the need for dedicated ADCs. These sensors are available with accuracies
ranging from ±0.5 ◦
C to ±2.5 ◦
C (guaranteed over wide temperature and power-supply
ranges), and they can operate over a temperature range of −50 ◦
C to +125 ◦
C.
The conversion time range for the temperature into a digital signal is 750 ms–1.2 s.
The 1-Wire and 2-Wire devices have a multi-drop capability, which allows multiple
sensors to be addressed on the same bus. In addition, some devices (DS1624, DS1629
and DS1780) combine temperature sensing with other valuable features including
Table 1.2 Digital temperature sensors from Analog Devices
Type Description
AD7414 SMBus/I2
C digital temperature sensor in 6-pin SOT with SMBus alert and
over temperature pin
AD7415 SMBus/I2
C digital temperature sensor in 5-pin SOT
AD7416 Temperature-to-digital converter, I2
C, 10-bit resolution, −55 ◦
C to +125 ◦
C,
±2 ◦
C accuracy
AD7417 4-channel, 10-bit ADC with on-chip temperature-to-digital converter, I2
C,
±1 ◦
C accuracy
AD7418 Single-channel, 10-bit ADC with on-chip temperature-to-digital converter, I2
C,
±1 ◦
C accuracy
AD7814 10-bit digital temperature sensor in 6-lead SOT-23
AD7816 10-bit ADC, temperature monitoring only in an SOIC/µSOIC package](https://image.slidesharecdn.com/19527-250510084747-695587e1/85/Data-Acquisition-and-Signal-Processing-for-Smart-Sensors-Nikolay-V-Kirianaki-38-320.jpg)
![14 1 SMART SENSORS: TENDENCIES AND PERSPECTIVES
EEPROM arrays, real-time clocks and CPU monitoring. One more interesting feature
of Dallas Semiconductor’s temperature sensors is that they are expandable from 9 to
13 bits or user configurable to 9, 10, 11 or 12 bits resolution.
Dallas Semiconductor’s DS1616 Temperature Data Recorder with the 3-Input Analog
to Digital Converter adds the potential for three powerful external sensors to the
base design of the DS1615 Temperature Data Recorder. It permits logging of not
only the temperature, but also the humidity, the pressure, the system voltage, external
temperature sensors, or any other sensor with the analog voltage output. The DS1616
provides all of the elements of a multi-channel data acquisition system on one chip.
It measures the selected channels at user-programmable intervals, then stores the data
and a time/date stamp in the nonvolatile memory for later downloading through one
of the serial interfaces.
National Semiconductor also proposes some digital temperature sensors [32] with
different temperature ranges from −55 ◦
C up to +150 ◦
C: SPI/MICROWIRE plus the
sign digital temperature sensor LM70 (10-bit) and LM74 (12-bit); the digital temper-
ature sensor and the thermal watchdog with the two-wire (I2
CTM
Serial Bus) interface
LM75 (±3 ◦
C); digital temperature sensors and the thermal window comparator with
the two-wire interface LM76 (±1 ◦
C), LM77 (±1.5 ◦
C) and LM92 (±0.33 ◦
C). The
sensors LM70, LM74 and LM75 include the delta-sigma ADC.
The window-comparator architecture of the sensors eases the design of the temper-
ature control systems conforming to the ACPI (advanced configuration and power
interface) specification for personal computers.
Another example of a digital output sensor is +GF+ SIGNET 2350 [33] with
a temperature range from −10 to +100 ◦
C and accuracy ±0.5 ◦
C. The temperature
sensor’s digital output signal allows for wiring distances between sensor and temper-
ature transmitter of up to 61 m. An integral adapter allows for the integration of the
sensor and the transmitter into a compact assembly.
1.2 Pressure IC and Smart Sensors and Accelerometers
Similar to temperature sensors, pressure sensors are also very widely spread. In Europe,
the first truly integrated pressure sensor was designed in 1968 by Gieles at Philips
Research Laboratories [34], and the first monolithic integrated pressure sensor with
digital (i.e., frequency) output was designed and tested in 197l at Case Western Reserve
University [35] as part of a programme addressing biomedical applications. Miniature
silicon diaphragms, with the resistance bridge at the centre of the diaphragm and sealed
to the base wafer with gold–tin alloy, were developed for implant and indwelling
applications.
Pressure sensors convert the external pressure into an electrical output signal. To
accomplish this, semiconductor micromachined pressure sensors use the monolithic
silicon-diffused piezoresistors. The resistive element, which constitutes the sensing
element, resides in the thin silicon diaphragm. Applying pressure to the silicon dia-
phragm causes its deflection and changes the crystal lattice strain. This affects the free
carrier mobility, resulting in a change of the transducer’s resistance, or piezoresistivity.
The diaphragm thickness as well as the geometrical shape of resistors, is determined
by the tolerance range of the pressure. Advantages of these transducers are:](https://image.slidesharecdn.com/19527-250510084747-695587e1/85/Data-Acquisition-and-Signal-Processing-for-Smart-Sensors-Nikolay-V-Kirianaki-39-320.jpg)
![1.2 PRESSURE IC AND SMART SENSORS AND ACCELEROMETERS 15
• high sensitivity
• good linearity
• minor hysteresis phenomenon
• small response time.
The output parameters of the diffused piezoresistors are temperature dependent and
require the device to be compensated if it is to be used over a wide temperature range.
However, with occurrence smart sensors and MEMS, the temperature error can be
compensated using built-in temperature sensors.
Most of today’s MEMS pressure transducers produced for the automotive market
consist of the four-resistor Wheatstone bridge, fabricated on a single monolithic die
using bulk etch micromachining technology. The piezoresistive elements integrated
into the sensor die are located along the periphery of the pressure-sensing diaphragm,
at points appropriate for strain measurement [36].
Now designers can choose between two architectures for sensor compensation: the
conventional analog sensor signal processing or digital sensor signal processing. The
latter is characterized by full digital compensation and an error-correction scheme. With
a very fine geometry, mixed-signal CMOS IC technologies have enabled the incorpo-
ration of the sophisticated digital signal processor (DSP) into the sensor compensator
IC. The DSP was designed specifically to calculate the sensor compensation, enabling
the sensor output to realize all the precision inherent in the transducer.
As considered in [37] ‘as the CMOS process and the microcontroller/DSP tech-
nology have become more advanced and highly integrated, this approach may become
increasingly popular. The debate continues as to whether the chip area and the circuit
overhead of standard microprocessor designs used for this purpose will be competi-
tive with less flexible (but smaller and less costly) dedicated DSP designs that can be
customized to perform the specific sensor calibration function’.
The integrated pressure sensor shown in Figure 1.10 uses a custom digital signal
processor and nonvolatile memory to calibrate and temperature-compensate a family
of pressure sensor elements for a wide range of automotive applications.
This programmable signal conditioning engine operates in the digital domain using
a calibration algorithm that accounts for higher order effects beyond the realm of
most analog signal conditioning approaches. The monolithic sensor provides enhanced
features that typically were implemented off the chip (or not at all) with traditional
analog signal conditioning solutions that use either laser or electronic trimming. A
specially developed digital communication interface permits the calibration of the indi-
vidual sensor module via connector pins after the module has been fully assembled
and encapsulated. The post-trim processing is eliminated, and the calibration and the
module customization can be performed as an integral part of the end-of-line testing
by completion of the manufacturing flow. The IC contains a pressure sensor element
that is coprocessed in a submicron, mixed-signal CMOS wafer fabrication step and
can be scaled to a variety of automotive pressure-sensing applications [37].
Now, let us give some state-of-the-art and industrial examples of modern pressure
sensors and transducers. Major attention has been given to the creation of pressure
sensors with frequency output in the USSR [38,39]. The first of them was based on
the usage of VFC and had an accuracy up to 1%, the effective range of measuring](https://image.slidesharecdn.com/19527-250510084747-695587e1/85/Data-Acquisition-and-Signal-Processing-for-Smart-Sensors-Nikolay-V-Kirianaki-40-320.jpg)
![16 1 SMART SENSORS: TENDENCIES AND PERSPECTIVES
Figure 1.10 Monolithic pressure sensor (Reproduced by permission of Motorola)
frequencies 0–2 kHz in the pressure range 0–40 MPa. The second was founded on
the usage of the piezoresonator. The connection of this device into a self-oscillator
circuit receives a frequency signal, proportional to the force. The relation between the
measured pressure p and the output frequency signal f is expressed by the following
equation:
p = (f − f0)/Kp; Kp = KF · Seff, (1.5)
where f0 is the frequency at p = 0; f is the measurand frequency; Kp is the conversion
factor of pressure-to-frequency; KF is the force sensitivity factor; Seff is the membrane’s
effective area.
The silicon pressure sensor based on bulk micromachining technology and the VFC
based on CMOS technology was described in [40]. It has 0–40 kPa measuring pressure
range, 280–380 kHz frequency output range and main error ±0.7%.
The Kulite company produces the frequency output pressure transducer ETF-1-1000.
The sensor provides an output, which can be interfaced directly to a digital output.
The transducer uses a solid-state piezoresistive sensing element, with excellent relia-
bility, repeatability and accuracy. The pressure range is 1.7–350 bar, output frequency
is 5–20 kHz, the total error band is ±2%. Other examples are pressure transducers
VT 1201/1202 from Chezara (Ukraine) with 15–22 kHz frequency output range and
standard error ±0.25% and ±0.15% accordingly.
The shining example of a sensor with x(t) → V (t) → F(t) conversion is the pres-
sure sensor from ADZ Sensortechnik GmbH (Germany). The IC LM 331 was used
as the VFC. The output frequency of the converter can be calculated according to the
following equation:
fout =
(Uin − Uoffset) · R6
2.09V · R4 · R8 · C6
, (1.6)
where
Uin = Pabs · 0.533
V
bar
+ 0.5V (1.7)
The measuring range is 0–8.8 bar, the frequency range is 1–23 kHz.](https://image.slidesharecdn.com/19527-250510084747-695587e1/85/Data-Acquisition-and-Signal-Processing-for-Smart-Sensors-Nikolay-V-Kirianaki-41-320.jpg)
![1.2 PRESSURE IC AND SMART SENSORS AND ACCELEROMETERS 17
The model SP550 from Patriot [41] is a rugged pressure transducer which provides
full-scale output of 1–11 kHz. The output frequency can be offset to provide the output
of any span between 1 kHz to 150 kHz. In this transducer the strain–guage signal is
converted into the frequency via the precision monolithic VFC and the operational
amplifier.
Geophysical Research Corporation has announced the Amerada
Quartz Pressure
Transducer [42]. The transducer employs a rugged crystalline quartz sensor that res-
ponds to the stress created by the pressure. This response is in the form of a change
in the resonant frequency created by the applied pressure. The pressure dependence of
the sensor is slightly non-linear but is easily corrected during the calibration using a
third-order polynomial function. In addition, the crystalline quartz is considered to be
perfectly elastic which contributes to the excellent repeatability that is characteristic of
this technology. Two additional quartz sensors are employed, one to measure temper-
ature, the second acting as a stable reference signal. The temperature measurement is
used for the dynamic temperature compensation of the pressure crystal while the refer-
ence signal is used as a stable timing base for the frequency counting. The pressure
range is up to 10 000 psia, the accuracy up to ±0.02% FS [42]. The output pressure
and temperature frequencies range between 10 kHz and 60 kHz.
The high-accuracy (0.01%) fibre-optic pressure transducers have been developed by
ALTHEN GmbH by applying optical technology to resonator-based sensors [43].
Further development of microelectronic technologies and smart sensors has declined
in the rise of high-precision (up to 0.01%) digital output pressure sensors and trans-
ducers. Some of which are described below.
The Paroscientific, Inc. Digiquartz
Intelligent Transmitter [44] consists of a unique
vibrating quartz crystal pressure transducer and a digital interface board in the integral
package. Commands and data requests are sent via the RS-232 channel and the trans-
mitter returns data via the same two-way bus. Digital outputs are provided directly in
engineering units with typical accuracy of 0.01% over a wide temperature range. The
use of a frequency output quartz temperature sensor for the temperature compensation
yields the achievable full-scale accuracy of 0.01% over the entire operating temperature
range. The output pressure is fully thermally compensated using the internally mounted
quartz crystal specifically designed to provide a temperature signal. All transmitters
are programmed with calibration coefficients for full plug-in interchangeability. The
Intelligent Transmitter can be operated either as a stand-alone standard output pressure
sensor with the display, or as a fully integrated addressable computer-controlled system
component. Transducers use crystalline quartz as the key sensing elements for both
the pressure and the temperature because of its inherent stability and precision char-
acteristics. The pressure-sensing element is a quartz beam, which changes frequency
under the axial load. The transferred force acts on the quartz beam to give a controlled,
repeatable and stable change in the resonator’s natural frequency, which is measured
as the transducer output. The load-dependent frequency characteristic of the quartz
crystal beam can be characterized by a simple mathematical model to yield highly
precise measurements of the pressure and pressure-related parameters. The output is a
square wave frequency [44].
Other examples are intelligent pressure standards (series 960 and 970) [45]. In
the 960 series, the pressure is measured via the change in the resonant frequency
of the oscillating quartz beam by the pressure-induced stress. QuartzonixTM
pressure](https://image.slidesharecdn.com/19527-250510084747-695587e1/85/Data-Acquisition-and-Signal-Processing-for-Smart-Sensors-Nikolay-V-Kirianaki-42-320.jpg)
![18 1 SMART SENSORS: TENDENCIES AND PERSPECTIVES
standards produce the output frequency between 30 and 45 kHz and can achieve accu-
racy of ±0.01% FS. The precise thermal compensation is provided via the integrated
quartz temperature sensor used to measure the operating temperature of the transducer.
The 970 series uses a multi-drop, 9600 baud ASCII character RS-485 type interface,
allowing a network of up to 31 transducers on the same bus. The output pressure
measurement is user programmable for both the pressure units and update rate.
The resonant pressure transducer RPT 200 (frequency, RS-232/485 outputs) and the
digital output pressure sensor RPT 301 (selectable output RS-232 or RS-485) series
with ±0.01% FS accuracy are produced by Druck Pressure Measurement [46].
Another very popular silicon sensor from the mechanical signal domain is the
accelerometer. The measurement of acceleration or one of its derivative properties
such as vibration, shock, or tilt has become very commonplace in a wide range of
products. The types of sensor used to measure the acceleration, shock, or tilt include
the piezo film, the electromechanical servo, the piezoelectric, the liquid tilt, the bulk
micromachined piezoresistive, the capacitive, and the surface micromachined capaci-
tive. Each has distinct characteristics in the output signal, the development cost, and
the type of the operating environment in which it best functions [47]. The piezoelectric
has been used for many years and the surface micromachined capacitive is relatively
new. To provide useful data, the first type of accelerometers require the proper signal-
conditioning circuitry. Over the last few years, the working range of these devices has
been broadened to include frequencies from 0.1 Hz to above 30 kHz.
Capacitive spring mass accelerometers with integrated electronics that do not require
external amplifiers are proposed by Rieker Inc. These accelerometers of the Sieka series
are available with analog DC output, digital pulse-width modulated, or frequency-
modulated outputs [48].
The surface micromachined products provide the sensor and the signal-conditioning
circuitry on the chip, and require only a few external components. Some manufacturers
have taken this approach one step further by converting the analog output of the analog
signal conditioning into a digital format such as a duty-cycle. This method not only
lifts the burden of designing the fairly complex analog circuitry for the sensor, but also
reduces the cost and the board area [47].
A very simple circuit can be used to measure the acceleration on the basis of
ADXL202/210 accelerometers from Analog Devices. Both have direct interface to
popular microprocessors and the duty-cycle output with 1 ms acquisition time [49].
For interfacing of the accelerometer’s analog output (for example, ADXL05) with
microcontrollers, Analog Devices proposes acceleration-to-frequency circuits based on
AD654 VFC to provide a circuit with a variable frequency output. A microcontroller
can then be programmed to measure the frequency and compute the applied accelera-
tion [50].
1.3 Rotation Speed Sensors
There are many known rotation speed sensing principles and many commercially avail-
able sensors. The overwhelming majority of such sensors is from the magnetic signal
domain (Hall-effect and magnetoresistor-based sensors) and the electrical signal domain
(inductive sensors). According to the nature, passive and active electromagnetic sensors
are from the frequency-time domain. Pulses are generated on its output. The frequency](https://image.slidesharecdn.com/19527-250510084747-695587e1/85/Data-Acquisition-and-Signal-Processing-for-Smart-Sensors-Nikolay-V-Kirianaki-43-320.jpg)
![1.3 ROTATION SPEED SENSORS 19
is proportional to the measured parameter. In such sensors the flow mesh is connected
with the angle of rotation by the following equation:
= m · cos θ, (1.8)
then the induced emf in the sensor sensitive element is
e = −
dϕ
dt
= ψm · sin θ
dθ
dt
= Em sin θ (1.9)
and can be determined by the instantaneous frequency in any moment of time
f =
1
2π
·
dθ
dt
(1.10)
which is proportional to the instantaneous angular speed
ω =
dθ
dt
(1.11)
The current averaging of the angular speed ω on the interval h, measured in the units
2π represents the frequency of rotation and is determined by Steklov’s function [51]:
n(t) =
1
2πh
t+0.5h
t−0.5h
ω(τ)dτ (1.12)
This equation reflects only a common idea of the current average of some function
on its argument. Using the frequency measurement of the rotational speed the choice
of mathematical expression should agree with experiment. In this case, the rotational
speed and the angular velocity should be interlinked with the expression relevant to
the physics of the observable process and the requirements of their measurement in
a concrete system. For the description of rotational speed, it is expedient to use the
expression of the flowing average:
n(t) =
1
2πh
t
t−h
ω(τ)dτ =
1
2πh
h
0
ω(t − τ)dτ (1.13)
The measuring frequency of the rotational speed is given by
nx = fx ·
60
Z
(1.14)
where Z is the number of modulation rotor’s (encoder’s) gradations.
For modern applications the rotation speed sensor should provide the digital or the
quasi-digital output compatible with standard technologies. This means that the sensor
and the signal-processing circuitry (the microcontroller core) can be realized in the
same chip. An excellent solution in many aspects is when this signal is a square-wave
output of an oscillator, the frequency of which is linearly dependent on the rotating
speed and carries the information about it.
The semiconductor active position sensor of relaxation type designed by the
authors together with the Autoelectronic company (Kaluga, Russia) can serve as an](https://image.slidesharecdn.com/19527-250510084747-695587e1/85/Data-Acquisition-and-Signal-Processing-for-Smart-Sensors-Nikolay-V-Kirianaki-44-320.jpg)
![20 1 SMART SENSORS: TENDENCIES AND PERSPECTIVES
example [52,53]. It was developed on the basis of the crankshaft position sensor. Its
principle of action is based on the effect of the continuous suppression of oscillations
of the high frequency generator by passing each metal plate of the modulating rotor
in front of the active sensor element and its subsequent resumption. Due to that,
rectangular pulses with constant amplitude (+Vcc) are continuously formed on the
sensor output. The frequency of these pulses is proportional to the rotating speed. If
the metal tooth of the rotor-modulator comes nearer to the active element (generator
coil), the logic level ‘1’ (+Vcc) is formed as the sensor output. When the active sensor
part appears between the teeth, the logic level ‘0’ is formed as the output. Thus, the
active sensor forms the pulsing sequence, the frequency of which is proportional or
equal to the rotating speed. This sensor does not require any additional buffer devices
for the tie-in measuring system and has a very easy interface to the microsystem.
Moreover, the sensor meets the requirements of the technological compatibility with
other components of the microsystem.
The circuit diagram of the Active Sensor of Rotation Speed (ASRS) is shown in
Figure 1.11. It consists of a high frequency generator (f = 1 MHz), a sensing element
(the generator coil), an amplifier, a voltage stabilizer and an output forming transistor
with an opened collector [54]. The “chip wire” technology was used in the sensor
design, which combines the advantages of both monolithic and hybrid integrated tech-
nologies. All electronics was realized in a single chip, only the inductance, two resistors
and the stabilitron were implemented in accordance with hybrid technology.
The ASRS is shown in Figure 1.12 and the sensor’s output waveforms in Figure 1.13.
The amplitude of the signal is constant and does not depend on temperatures and the
direction of the rotation. The online time ratio Q = 2 (50% duty-cycle independent of
the distance). But in a frequency range of more than 50 000 rpm, the pulse width will
be increased.
The comparative features of modern non-contact sensors of different principles of
function are shown in Table 1.3. Here sensors A5S07/08/09 are made by BR Braun
(Germany), DZXXXX by Electro Corporation (USA); VT1855, OO020 by NIIFI
(Penza, Russia); 4XXXX by Trumeter (UK); LMPC by Red Lion Controls (USA).
R5
Vcc
T1 T2
C1
C2
T3
R1
R2
R3
R4
R6
R7
L1
External
components
Figure 1.11 Circuit diagram of the Active Sensor of Rotation Speed (ASRS)](https://image.slidesharecdn.com/19527-250510084747-695587e1/85/Data-Acquisition-and-Signal-Processing-for-Smart-Sensors-Nikolay-V-Kirianaki-45-320.jpg)
![22 1 SMART SENSORS: TENDENCIES AND PERSPECTIVES
Table 1.3 Comparative features of non-contact sensors of rotation speed
Sensors Freq. range,
kHz
Supply voltage,
V
Current consumption,
mA
Type
ASRS 0–50 4.5–24 7–15 active
A5S07 0.5–25 8–28 15+load current Hall-effect
A5S08/09 0.5–25 8–25 15 Hall-effect
DZ375 0–5 4.5–16 20–50 magnetic
DZH450 0–5 4.5–30 20 Hall-effect
DZP450 1–10 4.5–16 50 Hall-effect
VT1855 0.24–160 27 3 inductive
OO 020 0.24–720 27 100 photo
4TUC 0.3–2 10–30 200 mag./inductive
4TUN 0.3–2 6.2–12 3 mag./inductive
45515 0.002–30 25 20 Hall-effect
LMPC up to 10 9–17 25 mag./inductive
Active semiconductor sensors are not influenced by run-out and external magnetic
fields in comparison with Hall-effect sensors. With Hall-effect sensors, it is necessary
to take into account the availability of the initial level of the output signal between
electrodes of the Hall’s element by absence of the magnetic field and its drift. It is
especially characteristic for a broad temperature range. A Hall-effect rotation speed
sensor needs encoders with magnetic pole teeth.
Another good example of a smart sensor for rotation speed is the inductive posi-
tion, speed and direction active microsensor MS1200 from CSEM (Switzerland) [55].
The output can switch up to 1 mA and is compatible with CMOS digital circuits,
in particular with microprocessors. The frequency range is 0–40 kHz, the air-gap is
0–3 mm. The core is a sensor chip with one generator coil and two sets of detection
coils (Figure 1.14).
The detection coils are connected in a differential arrangement, to reject the common
mode signal. The sensor also includes an electronic interface, which is composed of
Logic
B
A
Supply(VDD)
Ground(VSS)
Sensor chip
Generator micro-coil
Detection micro-coils
Demod.
Osc.
Driver
Demod.
Direction
4x
Metallic target
Figure 1.14 MS1200 functional block diagram (Reproduced by permission of POSIC S.A.,
Neuchatel, Switzerland)](https://image.slidesharecdn.com/19527-250510084747-695587e1/85/Data-Acquisition-and-Signal-Processing-for-Smart-Sensors-Nikolay-V-Kirianaki-47-320.jpg)
![1.4 INTELLIGENT OPTO SENSORS 23
a high frequency excitation for the generator coil and two read-out channels for the
two sets of detection coils (channels A and B). The read-out electronics extract the
amplitude variation of the high frequency signal due to the presence of a metallic target.
The output stage is a first-order low-pass filter and a comparator. For a nominal target
period of 2 mm, the outputs are two channels in quadrature (A quad B) as well as a
direction signal and a speed signal (4X interpolation). It is composed of two silicon
chips, one for the integrated microcoil and the other for the integrated interface circuit.
The sensor produces a two-channel digital output, as well as a direction signal.
1.4 Intelligent Opto Sensors
Next, we shall examine a technique of delivering the output from optical (light) sensors
into the frequency–time (quasi-digital) domain. Light is a real-world signal that is often
measured either directly or used as an indicator of some other quantity. Most light-
sensing elements convert light into an analog signal in the form of the current or the
voltage, then a photodiode current can be converted into the frequency output. Light
intensity can vary over many orders of the magnitude, thus complicating the problem
of maintaining resolution and signal-to-noise ratio over a wide input range. Converting
the light intensity to a frequency overcomes limitations imposed on the dynamic range
by the supply voltage, the noise and the ADC resolution.
One such device is a low-cost programmable silicon opto sensor TSL230/235/245
from Texas Instruments with a monolithic light-to-frequency converter [56]. The output
of these devices is a square wave with a frequency (0–1 MHz) that is linearly propor-
tional to the light intensity of the visible and short infra red radiation. Additionally the
devices provide programming capability for the adjustment of the input sensitivity and
the output scaling. These capabilities are effected by a simple electronic technique,
switching in different numbers of the 100 elements of the photodiode matrix. For costs
reasons, the low-cost microcontroller with a limited frequency range may be used for
the frequency-to-digital converter due to the output scaling capability. Options are an
undivided pulse train with the fixed pulse width or the square wave (50% duty-cycle)
divided by 2, 10 or 100 outputs. Light levels of 0.001 to 100 000 µW/am2
can be
accommodated directly without filters [56].
Since the conversion is performed on-chip, effects of external interference such as
noise and leakage currents are minimized and the resulting noise immune frequency
output is easily transmitted even from remove locations to other parts of the system.
The isolation is easily accomplished with optical couples or transformers.
Another interesting example is the integrated smart optical sensors developed in
Delft University of Technology [57,58]. Integrated on-chip colour sensors have been
designed and fabricated to provide a digital output in the IS2 bus format. The readout
of photodiodes in the silicon takes place in such a way that pulse series are gener-
ated with the pulse frequency proportional to the optical intensity (luminance) and the
duty-cycle to the colour (chrominance). The colour information is obtained using the
wavelength dependence of the absorption coefficient in the silicon in the optical part
of the spectrum, so no filters are required. The counters and the bus interface have
been realized in a bipolar and CMOS version with enhanced resolution which is being
investigated.](https://image.slidesharecdn.com/19527-250510084747-695587e1/85/Data-Acquisition-and-Signal-Processing-for-Smart-Sensors-Nikolay-V-Kirianaki-48-320.jpg)
![Tax, taks, subst. belasting, schatting, tol, requisitie, zware plicht of
taak, proef; Tax verb. belasten, afpersen, beschuldigen, berispen,
vragen, op de proef stellen: Graduated income tax =
progressieve inkomstenbelasting; Inheritance tax = successie
belasting; Poll tax = hoofdelijke omslag; The tax on his patience
= de proef waarop zijn geduld werd gesteld; A tax on profits and
incomes = belasting opbedrijfs-, en andere inkomsten; Heavy
taxes were levied = er werden zware belastingen geheven; I was
taxed with having offended him = werd beschuldigd; Tax-
collector = Tax-gatherer = belastingontvanger; Taxpayer =
belastingschuldige; Taxability = belastbaarheid; adj. Taxable;
Taxameter, taksamətə, afstandswijzer (for indicating cab fares);
Taxation, takseiš’n, belasting, het belasten. Zie Taxer.
Taxel, taks’l, Am. das.
Taxer, taksə, zetter (v. de belastingen), vroeger ambtenaar aan de
hoogeschool te Cambridge, die bij de uitdeeling, voor goede maat en
juist gewicht zorgde.
Taxicab, taksikab, autotax (atax).
Taxidermist, taksidɐ̂ mist, opzetter; Taxidermy = de kunst dieren
op te zetten.
Taxin, taksin, harsachtige stof uit taxusbladeren; Taxus, taksəs,
taxus.
Tea, tî, thee, aftreksel, een maaltijd, avondeten der kinderen; Tea
verb. de tea gebruiken: Beef tea = bouillon; Five o’clock tea;
[565]High, Meat tea = thee, souper met allerlei vleezen; Sweet
tea = thee met gebak, vruchten, sandwiches; Will you make (the)
tea? = thee zetten; To take tea = drinken; Tea-board = theeblad;
Tea-caddy = theekistje; Tea-cake = theekoekje; Tea-canister =](https://image.slidesharecdn.com/19527-250510084747-695587e1/85/Data-Acquisition-and-Signal-Processing-for-Smart-Sensors-Nikolay-V-Kirianaki-52-320.jpg)
![Teeter, tîtə, wippen (Am.).
Teeth, tîth, Meerv. van Tooth = tand; Teeth verb. (tîdh) = tanden
krijgen: From one’s teeth = niet van harte; In the teeth of the
westerly gale = vlak tegen in; He did it in the teeth of opposition
= trots allen tegenstand; In the teeth of the doctor’s
prohibition = geheel in tegen; He cast those reproaches in my
teeth = wierp mij voor de voeten; He escaped by the skin of his
teeth = ontsnapte ternauwernood (Job. XIX, 30 ); Teeth-
drawing (fig.) = bellen moeren; Teething, tîdhiŋ, het tanden
krijgen.
Teetotal, tîtout’l, onthoudend: Teetotal drinks = alcoholvrije;
Teetotalism = geheelonthouding; Teetotaller = geheelonthouder.
Teetotum, tîtout’m, A-al tolletje: To spin a teetotum.
Tegument, tegjument, omhulsel, huid, zaadhuid, vleugeldeksel;
adj. Tegumentary.
Tehee, tîhî, subst. gegichel; Tehee verb. gichelen; interj. hihi. [566]
Teheran, tehərân; Teignmouth, teinməth, tînməth.
Teil, tîl, lindeboom (= Teil-tree).
Teind, tînd = Tithe (Schotl.).
Teith, tîth; Telamon, teləm’n, mannenfiguur als zuil, At as.
Teledu, telədû, Javaansche bunzing.
Telegram, teləgram, telegram; adj. Telegrammic; Telegraph,
teləgraf, subst. telegraaf; Telegraph verb. telegrafeeren, seinen:
Telegraph-cable; Telegraph-operator = Telegrapher,](https://image.slidesharecdn.com/19527-250510084747-695587e1/85/Data-Acquisition-and-Signal-Processing-for-Smart-Sensors-Nikolay-V-Kirianaki-56-320.jpg)
![Templar, templə, tempelier; student in de rechten, advocaat of
jurist: Order of “Good Templars” = vereeniging tot het verleenen
van wederzijdschen steun bij ouderdom, etc.
Temple, temp’l, tempel, godshuis, slaap (van het hoofd): Inner-
temple, Middle-temple (Londensche colleges voor opleiding van
juristen).
Templet, templət, schabloon, vormhout. [567]
Tempo, tempou, tempo, maat (Meerv. Tempi).
Temporal, tempər’l, tijdelijk, tijds - -, wereldlijk; slaap - -:
Temporal bone = slaapbeen; Temporal Lords = wereldlijke, ter
onderscheiding v. geestelijke, pairs van Engeland; Temporality =
tijdelijk of wereldlijk bezit; Temporalities = temporaliën, inkomsten
der geestelijken uit land, tienden, enz.; Temporariness, subst. v.
Temporary = tijdelijk, niet duurzaam; Temporize, tempəraiz, zich
naar de omstandigheden schikken, den gunstigen tijd afwachten,
trachten tijd te winnen: Temporizer = iemand die met de wolven
meehuilt, de huik naar den wind hangt, etc.
Tem(p)se, tems, zeef, vergiet(test); Tem(p)se-bread = brood van
fijngebuild meel.
Tempt, tem(p)t, verleiden, verlokken, in verzoeking brengen: He
tempted me into giving up my plan = verlokte mij; Temptable =
te verlokken; Temptation, tem(p)teiš’n, verlokking, verzoeking,
aanvechting: To lead into temptation; He yielded to
temptation = bezweek voor; Tempter = verleider: The Tempter
= de duivel; Tempting = verleidelijk; subst. Temptingness;
Temptress = verleidster.](https://image.slidesharecdn.com/19527-250510084747-695587e1/85/Data-Acquisition-and-Signal-Processing-for-Smart-Sensors-Nikolay-V-Kirianaki-60-320.jpg)
, donkerroode Spaansche wijn; Tent verb. van tenten
voorzien, tenten opslaan, onder tenten wonen; sondeeren, peilen of
openhouden van eene wond; Tent-bed = ledikant met hemel;
Tent-cloth; Tent-maker = tentenmaker; Tent-pin; Tent-pole;
Tent-rope.
Tentacle, tentək’l, zuig- of tastorgaan; Tentacular, tentakjulə,
zuig …, tast …; Tentaculate(d), tentakjulit(-eitid), van zuigorganen
voorzien.
Tentative, tentətiv, subst. proeve, proef, poging; adj. pogend,
beproevend.
Tenter, tentə, subst. spanraam, spanhaak, voeldraad; Tenter verb.
opspannen, rekken: To be on the tenter(s) = op heete kolen
zitten; To keep on the tenter(s) = in angstige spanning houden;
Tenter-ground = plaats voor het stellen van spanramen; Tenter-
hook = spanhaak: We are on (the) tenter-hooks = on the
tenter(s).
Tenuifolius, tenjuifouljəs, met fijne en smalle bladen; Tenuity,
tənjûiti, fijnheid, dunheid, ijlheid; Tenuous, tenjuəs, dun, ijl, fijn,
klein.
Tenure, tenjə, eigendomsrecht, leendiensten, bezit: Tenure of life
= levenstijd; Tenure of office = diensttijd.
Tepefaction, tepifakš’n, matige verwarming; Tepefy = matig
verwarmen, lauw worden; Tepid, tepid, lauw; subst. Tepidity =
Tepidness.](https://image.slidesharecdn.com/19527-250510084747-695587e1/85/Data-Acquisition-and-Signal-Processing-for-Smart-Sensors-Nikolay-V-Kirianaki-64-320.jpg)
![Terrestrial, tərestriəl, ondermaansch, aard—, land—; subst.
aardbewoner: Terrestrial animal; Terrestrial globe.
Terret, terət, de ring waar doorheen de leidsels worden gestoken.
Terrible, terib’l, verschrikkelijk, ontzagwekkend, ijselijk, kolossaal;
subst. Terribleness.
Terrier, teriə, terrier.
Terrific, tərifik, schrik- of ontzagwekkend; Terrify, terifai, doen
verschrikken, schokken: I was terrified to death = schrikte me
dood.
Territorial, teritôriəl, territoriaal; subst. soldaat behoorende bij het
Territorial Army (Territorial Force) i e. een corps van
vrijwilligers, dat in 1908 de oude Volunteers verving; Territorialize
= vergrooten; tot een territory maken (Amer.); Territoried,
teritərid, land in eigendom hebbende; Territory, teritəri, gebied,
(ook fig.), landstreek, bereik; gebied met minder dan 60.000
inwoners en zonder vertegenwoordiging in het Congres (Amer.).
Terror, terə, schrik, ontsteltenis: The King of Terrors = de Dood;
The Reign of Terror = het Schrikbewind; I am struck with
terror, terror-struck, terror-stricken = van schrik verpletterd;
Terrorism = schrikbewind; Terrorist = lid van het schrikbewind,
terrorist; Terrorize, terəraiz, schrik aanjagen, door schrik dwingen.
Terry, teri, soort van pluche; Terry-velvet = soort katoenfluweel.
Terse, tɐ̂ s, beknopt, kort en bondig; subst. Terseness.
Tertian, tɐ̂ š’n, derdedaagsch: Tertian fever; Tertiary, tɐ̂ šəri,
tertiair: Tertiary epoch, formation. [569]](https://image.slidesharecdn.com/19527-250510084747-695587e1/85/Data-Acquisition-and-Signal-Processing-for-Smart-Sensors-Nikolay-V-Kirianaki-67-320.jpg)
![Thames, temz, Theems: He will not set the Thames on fire. Zie
Fire.
T(h)ammuz, t(h)aməz, vierde maand van het Joodsche burgerlijk
jaar.
Than, dhan, dan (alléén na comparatieven): He is older than I by
seven years.
Thane, thein, Angelsaksische titel der grootere grondbezitters tot de
12de eeuw; Thanedom; Thane-lands; Thaneship.
Thanet, thanət.
Thank, thaŋk, subst. dank (thans steeds meervoud); Thank verb.
danken, bedanken (dikwijls ironisch): Thanks to thee, I am safe
= ik ben veilig, dank zij u; Thanks = ik dank u; No, thanks! =
dank u; geen dank; Thanks to your eagerness = dank zij; Thanks
be to God = God zij dank; To give thanks = danken (na den
maaltijd); To return thanks = dank betuigen; No thank you = ik
dank u; Thank you, yes = alstublieft; Thank you for nothing =
ik zou je danken; Thank God we are rid of him = Goddank; He has
only to thank himself for = ’t is zijn eigen schuld, dat…; I’ll
thank you to shut the door = doe alstublieft de deur dicht; I’ll
thank you not to do it = gij doet me [570]plezier als gij het laat;
I’ll thank you for the potatoes (for a cup of tea) = mag ik
alstublieft; Thank-offering = dankoffer; Thanksgiver = bedanker,
dankzegger; Thanksgiving = dankzegging (aan God);
Thanksgiving-day = dankdag; Thankee = dank u; Thankful =
dankbaar; subst. Thankfulness; Thankless = ondankbaar:
Thankless task; subst. Thanklessness; Thankworthiness,
subst. v. Thankworthy = dankenswaard, verdienstelijk.](https://image.slidesharecdn.com/19527-250510084747-695587e1/85/Data-Acquisition-and-Signal-Processing-for-Smart-Sensors-Nikolay-V-Kirianaki-71-320.jpg)
![Theorem, thîər’m, theorema; adj. Theorematic(al).
Theoretic(al), thîəretik(’l), theoretisch; Theoretics = het
theoretisch gedeelte eener wetenschap; Theorist, thîərist,
theoreticus; Theorize = theoretiseeren: Theorizer; Theory, thîəri,
theorie: His practice falls short of his theory = zijne praktijk
haalt niet bij zijne theorie.
Theosophy, thiosəfi, theosophie.
Therapeutic(al), therəpjûtik(’l), therapeutisch; Therapeutics =
therapie.
There, dhêa, daar, er: When did he leave there = wanneer is hij
vandaar vertrokken; You are right (wrong) there = daar hebt ge
gelijk (ongelijk) aan; Here [571]and there = hier en daar; Then
and there = op dat zelfde oogenblik (= There and then); I am
all there = ik weet drommels goed wat ik doe; There you are! =
klaar is ’t; alstublieft; There is a horse for you = dat is nog eens
een paard! I am no match for you there = in dat opzicht kan ik
niet tegen u op; You have got a tile off and are not all there = je
bent niet recht bij het hoofd, en weet niet wat je doet; That to me is
everything! So there! = nu weet je het; en daarmede basta! Stop,
there’s a good fellow = dan ben je een beste; Thereabout(s) =
daaromtrent: A guilder or thereabouts = een gulden of
daaromtrent; Thereafter = daarna, volgens dat, daarnaar;
Thereanent = met betrekking tot dat punt; Thereat = daar, om die
reden, bovendien: He is poor and a fool thereat = en een dwaas
op den koop toe; Thereby = daarnevens, daardoor, diensvolgens,
daaromtrent; Therefor = hiervoor; Therefore = daarom, daarvoor,
met dat doel; Therefrom = daarvan, daaruit; Therein = daarin,
hierin; Thereinto = daarin; Thereof, dhêrov, hier- of daarvan;
Thereon = hier- of daarop, er op; Thereout = daaruit; Thereto =
daar- of hiertoe, buiten en behalve; Thereunder = daaronder;](https://image.slidesharecdn.com/19527-250510084747-695587e1/85/Data-Acquisition-and-Signal-Processing-for-Smart-Sensors-Nikolay-V-Kirianaki-75-320.jpg)
Data Acquisition and Signal Processing for Smart Sensors Nikolay V. Kirianaki
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- 5. Data Acquisition and Signal Processing for Smart Sensors Nikolay Kirianaki, Sergey Yurish, Nestor Shpak, Vadim Deynega Copyright 2002 John Wiley & Sons Ltd ISBNs: 0-470-84317-9 (Hardback); 0-470-84610-0 (Electronic) DATA ACQUISITION AND SIGNAL PROCESSING FOR SMART SENSORS
- 6. DATA ACQUISITION AND SIGNAL PROCESSING FOR SMART SENSORS Nikolay V. Kirianaki and Sergey Y. Yurish International Frequency Sensor Association, Lviv, Ukraine Nestor O. Shpak Institute of Computer Technologies, Lviv, Ukraine Vadim P. Deynega State University Lviv Polytechnic, Ukraine
- 7. Copyright 2002 by John Wiley & Sons, Ltd Baffins Lane, Chichester, West Sussex, PO19 1UD, England National 01243 779777 International (+44) 1243 779777 e-mail (for orders and customer service enquiries): cs-books@wiley.co.uk Visit our Home Page on http://www.wileyeurope.com All Rights Reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except under the terms of the Copyright, Designs and Patents Act 1988 or under the terms of a licence issued by the Copyright Licensing Agency Ltd, 90 Tottenham Court Road, London, UK W1P 0LP, without the permission in writing of the Publisher. Neither the authors nor Jon Wiley & Sons Ltd accept any responsibility or liability for loss or damage occasioned to any person or property through using the material, instructions, methods or ideas contained herein, or acting or refraining from acting as a result of such use. The authors and Publisher expressly disclaim all implied warranties, including merchantability of fitness for any particular purpose. Designations used by companies to distinguish their products are often claimed as trademarks. In all instances where John Wiley & Sons is aware of a claim, the product names appear in initial capital or capital letters. Readers, however, should contact the appropriate companies for more complete information regarding trademarks and registration. Other Wiley Editorial Offices John Wiley & Sons, Inc., 605 Third Avenue, New York, NY 10158-0012, USA Wiley-VCH Verlag GmbH, Pappelallee 3, D-69469 Weinheim, Germany John Wiley & Sons Australia, Ltd, 33 Park Road, Milton, Queensland 4064, Australia John Wiley & Sons (Asia) Pte Ltd, 2 Clementi Loop #02-01, Jin Xing Distripark, Singapore 129809 John Wiley & Sons (Canada) Ltd, 22 Worcester Road, Rexdale, Ontario M9W 1L1, Canada Library of Congress Cataloging-in-Publication Data Data acquisition and signal processing for smart sensors / Nikolay V. Kirianaki . . . [et al.]. p. cm. Includes bibliographical references and index. ISBN 0-470-84317-9 (alk. paper) 1. Detectors. 2. Microprocessors. 3. Signal processing. 4. Automatic data collection systems. I. Kirianaki, Nikolai Vladimirovich. TA165. D38 2001 681.2–dc21 2001046912 British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library ISBN 0 470 84317 9 Typeset in 10/12pt Times by Laserwords Private Limited, Chennai, India Printed and bound in Great Britain by Biddles Ltd, Guildford King’s Lynn This book is printed on acid-free paper responsibly manufactured from sustainable forestry, in which at least two trees are planted for each one used for paper production.
- 8. CONTENTS Preface ix List of Abbreviations and Symbols xiii Introduction xv 1 Smart Sensors for Electrical and Non-Electrical, Physical and Chemical Variables: Tendencies and Perspectives 1 1.1 Temperature IC and Smart Sensors 8 1.2 Pressure IC and Smart Sensors and Accelerometers 14 1.3 Rotation Speed Sensors 18 1.4 Intelligent Opto Sensors 23 1.5 Humidity Frequency Output Sensors 24 1.6 Chemical and Gas Smart Sensors 24 Summary 27 2 Converters for Different Variables to Frequency-Time Parameters of the Electric Signal 29 2.1 Voltage-to-Frequency Converters (VFCs) 29 2.2 Capacitance-to-Period (or Duty-Cycle) Converters 47 Summary 50 3 Data Acquisition Methods for Multichannel Sensor Systems 51 3.1 Data Acquisition Method with Time-Division Channelling 52 3.2 Data Acquisition Method with Space-Division Channelling 55 3.3 Smart Sensor Architectures and Data Acquisition 57 3.4 Main Errors of Multichannel Data-Acquisition Systems 59 3.5 Data Transmission and Error Protection 61 3.5.1 Essence of quasi-ternary coding 62 3.5.2 Coding algorithm and examples 62 3.5.3 Quasi-ternary code decoding 65 Summary 67
- 9. vi CONTENTS 4 Methods of Frequency-to-Code Conversion 69 4.1 Standard Direct Counting Method (Frequency Measurement) 70 4.2 Indirect Counting Method (Period Measurement) 74 4.3 Combined Counting Method 79 4.4 Method for Frequency-to-Code Conversion Based on Discrete Fourier Transformation 82 4.5 Methods for Phase-Shift-to-Code Conversion 85 Summary 86 5 Advanced and Self-Adapting Methods of Frequency-to-Code Conversion 89 5.1 Ratiometric Counting Method 89 5.2 Reciprocal Counting Method 94 5.3 M/T Counting Method 94 5.4 Constant Elapsed Time (CET) Method 96 5.5 Single- and Double-Buffered Methods 96 5.6 DMA Transfer Method 97 5.7 Method of Dependent Count 98 5.7.1 Method of conversion for absolute values 99 5.7.2 Methods of conversion for relative values 100 5.7.3 Methods of conversion for frequency deviation 104 5.7.4 Universal method of dependent count 104 5.7.5 Example of realization 105 5.7.6 Metrological characteristics and capabilities 107 5.7.7 Absolute quantization error q 107 5.7.8 Relative quantization error δq 109 5.7.9 Dynamic range 110 5.7.10 Accuracy of frequency-to-code converters based on MDC 112 5.7.11 Calculation error 114 5.7.12 Quantization error (error of method) 114 5.7.13 Reference frequency error 114 5.7.14 Trigger error 115 5.7.15 Simulation results 117 5.7.16 Examples 120 5.8 Method with Non-Redundant Reference Frequency 121 5.9 Comparison of Methods 123 5.10 Advanced Method for Phase-Shift-to-Code Conversion 125 Summary 126 6 Signal Processing in Quasi-Digital Smart Sensors 129 6.1 Main Operations in Signal Processing 129 6.1.1 Adding and subtraction 129 6.1.2 Multiplication and division 130 6.1.3 Frequency signal unification 132 6.1.4 Derivation and integration 135
- 10. CONTENTS vii 6.2 Weight Functions, Reducing Quantization Error 136 Summary 142 7 Digital Output Smart Sensors with Software-Controlled Performances and Functional Capabilities 143 7.1 Program-Oriented Conversion Methods Based on Ratiometric Counting Technique 145 7.2 Design Methodology for Program-Oriented Conversion Methods 150 7.2.1 Example 158 7.3 Adaptive PCM with Increased Speed 161 7.4 Error Analysis of PCM 164 7.4.1 Reference error 165 7.4.2 Calculation error 171 7.4.3 Error of T02 forming 173 7.5 Correction of PCM’s Systematic Errors 174 7.6 Modified Method of Algorithm Merging for PCMs 175 Summary 182 8 Multichannel Intelligent and Virtual Sensor Systems 183 8.1 One-Channel Sensor Interfacing 183 8.2 Multichannel Sensor Interfacing 184 8.2.1 Smart rotation speed sensor 185 8.2.2 Encoder 187 8.2.3 Self-adaptive method for rotation speed measurements 188 8.2.4 Sensor interfacing 190 8.3 Multichannel Adaptive Sensor System with Space-Division Channelling 193 8.4 Multichannel Sensor Systems with Time-Division Channelling 197 8.5 Multiparameters Sensors 199 8.6 Virtual Instrumentation for Smart Sensors 199 8.6.1 Set of the basic models for measuring instruments 201 8.7 Estimation of Uncertainty for Virtual Instruments 215 Summary 224 9 Smart Sensor Design at Software Level 225 9.1 Microcontroller Core for Smart Sensors 225 9.2 Low-Power Design Technique for Embedded Microcontrollers 227 9.2.1 Instruction selection and ordering 234 9.2.2 Code size and speed optimizations 234 9.2.3 Jump and call optimizations 236 9.2.4 Cycle optimization 237 9.2.5 Minimizing memory access cost 239 9.2.6 Exploiting low-power features of the hardware 240 9.2.7 Compiler optimization for low power 241 Summary 244
- 11. viii CONTENTS 10 Smart Sensor Buses and Interface Circuits 245 10.1 Sensor Buses and Network Protocols 245 10.2 Sensor Interface Circuits 248 10.2.1 Universal transducer interface (UTI) 248 10.2.2 Time-to-digital converter (TDC) 252 Summary 253 Future Directions 255 References 257 Appendix A What is on the Sensors Web Portal? 267 Glossary 269 Index 275
- 12. PREFACE Smart sensors are of great interest in many fields of industry, control systems, biomed- ical applications, etc. Most books about sensor instrumentation focus on the classical approach to data acquisition, that is the information is in the amplitude of a voltage or a current signal. Only a few book chapters, articles and papers consider data acquisition from digital and quasi-digital sensors. Smart sensors and microsensors increasingly rely on resonant phenomena and variable oscillators, where the information is embedded not in the amplitude but in the frequency or time parameter of the output signal. As a rule, the majority of scientific publications dedicated to smart sensors reflect only the tech- nological achievements of microelectronics. However, modern advanced microsensor technologies require novel advanced measuring techniques. Because data acquisition and signal processing for smart sensors have not been adequately covered in the literature before, this book aims to fill a significant gap. This book is based on 40 years of the authors’ practical experience in the design and creation of sensor instrumentation as well as the development of novel methods and algorithms for frequency–time-domain measurement, conversion and signal processing. Digital and quasi-digital (frequency, period, duty-cycle, time interval and pulse number output) sensors are covered in this book. Research results, described in this book, are relevant to the authors’ international research in the frame of different RD projects and International Frequency Sensor Association (IFSA) activity. Who Should Read this Book? This book is aimed at PhD students, engineers, scientists and researchers in both academia and industry. It is especially suited for professionals working in the field of measuring instruments and sensor instrumentation as well as anyone facing new challenges in measuring, and those involved in the design and creation of new digital smart physical or chemical sensors and sensor systems. It should also be useful for students wishing to gain an insight into this rapidly expanding area. Our goal is to provide the reader with enough background to understand the novel concepts, principles and systems associated with data acquisition, signal processing and measurement so that they can decide how to optimize their sensor systems in order to achieve the best technical performances at low cost.
- 13. x PREFACE How this Book is Organized This book has been organized into 10 chapters. Chapter 1, Smart sensors for electrical and non-electrical, physical and chemical quantities: the tendencies and perspectives, describes the main advantages of frequency–time-domain signals as informative parameters for smart sensors. The chapter gives an overview of industrial types of smart sensors and contains classifications of quasi-digital sensors. Digital and quasi-digital (frequency, period, duty-cycle, time interval and pulse number output) sensors are considered. Chapter 2, Converters for different variables to frequency–time parameters of elec- tric signals, deals with different voltage (current)-to-frequency and capacitance-to- period (or duty-cycle) converters. Operational principles, technical performances and metrological characteristics of these devices are discussed from a smart sensor point of view in order to produce further conversion in the quasi-digital domain instead of the analog domain. The open and loop (with impulse feedback) structures of such converters are considered. (Figures 2.11, 2.12, 2.13, 2.14, 2.15 and some of the text appearing in Chapter 2, section 2.1, are reproduced from New Architectures of Inte- grated ADC, PDS ’96 Proceedings. Reproduced by permission of Maciej Nowinski.) Chapter 3, Data acquisition methods for multichannel sensor systems, covers multi- channel sensor systems with cyclical, accelerated and simultaneous sensor polling. Data acquisition methods with time-division and space-division channelling are described. The chapter contains information about how to calculate the time-polling cycle for a sensor and how to analyse the accuracy and speed of data acquisition. Main smart sensor architectures are considered from a data acquisition point of view. Data transmit- ting and error protection on the basis of quasi-ternary cyclic coding is also discussed. Chapter 4, Methods of frequency-to-code conversion for smart sensors, discusses traditional methods for frequency (period)-to-code conversion, including direct, indi- rect, combined, interpolation, Fourier conversion-based counting techniques as well as methods for phase-shift-to-code conversion. Such metrological characteristics as quan- tization error, conversion frequency range and conversion speed as well as advantages and disadvantages for each of the methods are discussed and compared. Chapter 5, Advanced and self-adapting methods of frequency-to-code conversion, discusses reciprocal, ratiometric, constant elapsed time (CET), M/T, single-buffered, double-buffered and DMA transfer advanced methods. Comparative and cost-effective analyses are given. Frequency ranges, quantization errors, time of measurement and other metrological performances as well as hardware and software requirements for realization from a smart sensor point of view are described. This chapter is very important because it also deals with the concepts, principles and nature of novel self- adapting methods of dependent count (MDC) and the method with non-redundant reference frequency. The chapter covers main metrological performances including accuracy, conversion time, frequency range as well as software and hardware for MDC realization. Advanced conversion methods for frequencies ratio, deviations and phase shifts are also described. Finally, some practical examples and modelling results are presented. Chapter 6, Signal processing for quasi-digital smart sensors, deals with the main frequency signal manipulations including multiplication, division, addition, subtraction, derivation, integration and scaling. Particular attention has been paid to new methods
- 14. PREFACE xi of frequency multiplication and scaling with the aim of frequency signal unification. Different wave shapes (sine wave, sawtooth, triangular and rectangular) of a sensor’s output are considered. It is also shown how the weight function averaging can be used for noise and quantization error reduction. Chapter 7, Digital output smart sensors with software-controlled performances and functional capabilities, discusses program-oriented methods for frequency-, period-, duty-cycle-, time-interval-, phase-shift- and pulse-number-to-code conversion and digital smart sensors. The design methodology for optimal program-oriented conversion methods, correction of systematic errors and the modified method of algorithms merging are considered. Examples are given. This chapter also describes specific errors and features. Chapter 8, Multichannel intelligent and virtual sensor systems, describes smart sensor systems with time- and space-division frequency channelling. Both are based on the method of dependent count. Comparative analysis is given. Performances and features are illustrated by an ABS smart sensor microsystem example. Multiparame- ters sensors are also considered. The chapter includes information about virtual sensor instrumentation and how to estimate the total error of arranged system. Definitions and examples (temperature, pressure, rotation speed virtual instruments) are given. Chapter 9, Smart Sensor Design at Software level, deals with embedded microcon- troller set instruction minimization for metering applications (to save chip area) and low-power design techniques–optimal low-power programming (for power consump- tion reduction). Many practical ‘hints’ (e.g. instruction selection and ordering, jump, call and cycle optimization, etc.), recommendations and examples are given. Chapter 10, Smart sensor buses and interface circuits, describes sensor buses and network protocols from the smart sensor point of view. Modern sensor interface circuits are discussed. Particular attention has been given to the Universal Transducer Inter- face (UTI) and Time-to-Digital Converter (TDC), which allow low-cost interfacing with different analog sensors elements such as Pt resistors, thermistors, potentiometer resistors, capacitors, resistive bridges, etc. and convert analog sensor signals to the quasi-digital domain (duty-cycle or time interval). Finally, we discuss what the future might bring. References. Apart from books, articles and papers, this section includes a large collection of appropriate Internet links, collected from the Sensors Web Portal launched by the authors.
- 15. LIST OF ABBREVIATIONS AND SYMBOLS δq program-specified relative quantization error q absolute quantization error Df specified measuring range of frequencies fx measurand frequency f0 reference frequency F greater of the two frequencies fx and f0 f lower of the two frequencies fx and f0 fbound lower frequency limit Fbound upper frequency limit m counter capacity Nδ number, determined by the error δ = 1/Nδ Nx number of periods of lower frequency f T period of greater frequency (T = 1/F) τ period of lower frequency (τ = 1/f ) Tq quantization window T0 reference gate time interval ABS antilock braking system ADC analog-to-digital converter ALU arithmetic logic unit ASIC application specific integrated circuit ASIP application specific instruction processor CAD computer-aided design CMOS complementary metal oxide semiconductor CT counter DAC digital-to-analog converter DAQ data acquisition DFT discrete Fourier transformation DSP digital signal processor FCC frequency-to-code converter FPGA field-programmable gate array FS full scale GUI graphical user interface LCF Liapunov characteristic function
- 16. xiv LIST OF ABBREVIATIONS AND SYMBOLS MDC method of dependent count µK microcontroller µP microprocessor MSM multichip module PCA programable counter array PCM program-oriented conversion method PWM pulse width modulation RAM random access memory ROM read-only memory VFC voltage-to-frequency converter VLSI very large scale integration
- 17. INTRODUCTION Rapid advances in IC technologies have brought new challenges to the physical design of integrated sensors and micro-electrical-mechanical systems (MEMS). Microsystem technology (MST) offers new ways of combining sensing, signal processing and actu- ation on a microscopic scale and allows both traditional and new sensors to be realized for a wide range of applications and operational environments. The term ‘MEMS’ is used in different ways: for some, it is equivalent to ‘MST’, for others, it comprises only surface-micromechanical products. MEMS in the latter sense are seen as an extension to IC technology: ‘an IC chip that provides sensing and/or actuation func- tions in addition to the electronic ones’ [1]. The latter definition is used in this book. The definition of a smart sensor is based on [2] and can be formulated as: ‘a smart sensor is one chip, without external components, including the sensing, interfacing, signal processing and intelligence (self-testing, self-identification or self-adaptation) functions’. The main task of designing measuring instruments, sensors and transducers has always been to reach high metrology performances. At different stages of measurement technology development, this task was solved in different ways. There were technolog- ical methods, consisting of technology perfection, as well as structural and structural- algorithmic methods. Historically, technological methods have received prevalence in the USA, Japan and Western Europe. The structural and structural-algorithmic methods have received a broad development in the former USSR and continue devel- oping in NIS countries. The improvement of metrology performances and extension of functional capabilities are being achieved through the implementation of particular structures designed in most cases in heuristic ways using advanced calculations and signal processing. Digital and quasi-digital smart sensors and transducers are not the exception. During measurement different kinds of measurands are converted into a limited number of output parameters. Mechanical displacement was the first historical type of such (unified) parameters. The mercury thermometer, metal pressure gauge, pointer voltmeter, etc. are based on such principles [3]. The amplitude of an electric current or voltage is another type of unified parameter. Today almost all properties of substances and energy can be converted into current or voltage with the help of different sensors. All these sensors are based on the use of an amplitude modulation of electromagnetic processes. They are so-called analog sensors. Digital sensors appeared from a necessity to input results of measurement into a computer. First, the design task of such sensors was solved by transforming an
- 18. xvi INTRODUCTION analog quantity into a digital code by an analog-to-digital converter (ADC). The creation of quasi-digital sensors, in particular, frequency sensors, was another very promising direction [3]. Quasi-digital sensors are discrete frequency–time-domain sensors with frequency, period, duty-cycle, time interval, pulse number or phase shift output. Today, the group of frequency output sensors is the most numerous among all quasi-digital sensors (Figure I). Such sensors combine a simplicity and universatility that is inherent to analog devices, with accuracy and noise immunity, proper to sensors with digital output. Further transformation of a frequency-modulated signal was reduced by counting periods of a signal during a reference time interval (gate). This operation exceeds all other methods of analog-to-digital conversion in its simplicity and accuracy [4]. Separate types of frequency transducers, for example, string tensometers or induction tachometers, have been known for many years. For example, patents for the string distant thermometer (Patent No. 617 27, USSR, Davydenkov and Yakutovich) and the string distant tensometer (Patent No. 21 525, USSR, Golovachov, Davydenkov and Yakutovich) were obtained in 1930 and 1931, respectively. However, the output frequency of such sensors (before digital frequency counters appeared) was measured by analog methods and consequently substantial benefit from the use of frequency output sensors was not achieved practically. The situation has changed dramatically since digital frequency counters and frequency output sensors attracted increasing attention. As far back as 1961 Professor P.V. Novitskiy wrote: ‘. . . In the future we can expect, that a class of frequency sensors will get such development, that the number of now known frequency sensors will exceed the number of now known amplitude sensors. . .’ [3]. Although frequency output sensors exist practically for any variables, this prognosis has not yet been fully justified for various reasons. With the appearance in the last few years of sensor microsystems and the heady development of microsystem technologies all over the world, technological and cost factors have increased the benefits of digital and quasi-digital sensors. Modern tech- nologies are able to solve rather complicated tasks, concerned with the creation of different sensors. Up to now, however, there have still been some major obstacles preventing industries from largely exploiting such sensors in their systems. These are only some subjective reasons: Time Interval 6% Pulse Number 2% Duty-Cycle 12% Frequency 35% Digital 45% Figure I Classification of sensors from discrete group in terms of output signals (IFSA, 2001)
- 19. INTRODUCTION xvii • The lack of awareness of the innovation potential of modern methods for frequency- time conversion in many companies, as processing techniques have mainly been developed in the former Soviet Union. • The tendency of companies to return, first of all, major expenditures, invested in the development of conventional ADCs. • The lack of emphasis placed on business and market benefits, which such measuring technologies can bring to companies etc. Today the situation has changed dramatically. According to Intechno Consulting, the non-military world market for sensors has exceeded expectations with US$32.5 billion in 1998. By 2003, this market is estimated to grow at an annual rate of 5.3% to reach US$42.2 billion. Under very conservative assumptions it is expected to reach US$50–51 billion by 2008; assuming more favourable but still realistic economic conditions, the global sensor market volume could even reach US$54 billion by 2008. Sensors on a semiconductor basis will increase their market share from 38.9% in 1998 to 43% in 2008. Strong growth is expected for sensors based on MEMS-technologies, smart sensors and sensors with bus capabilities [5]. It is reasonable to expect that silicon sensors will go on to conquer other markets, such as the appliances, telecommunications and PC markets [6]. We hope that this book will be a useful and relevant resource for anyone involved in the design of high performance and highly efficient digital smart sensors and data acquisition systems.
- 20. Data Acquisition and Signal Processing for Smart Sensors Nikolay Kirianaki, Sergey Yurish, Nestor Shpak, Vadim Deynega Copyright 2002 John Wiley Sons Ltd ISBNs: 0-470-84317-9 (Hardback); 0-470-84610-0 (Electronic) INDEX absolute quantization error 73, 79, 100, 107 absolute temperature 12 absorption coefficient 23 acceleration-to-frequency circuits 18 Accelerometers 14, 18 Accuracy 4, 5, 91, 112 acoustic gas sensor 26 active sensor 19, 21 Active Sensor of Rotation Speed (ASRS) 20 ActiveX 204 Adaptability 19 Adaptive PCM 161–65 Adding 129 Advanced Configuration and Power Interface (ACPI) 14 Advanced Methods 89, 120, 122, 126, 153 amplifier hysteresis 78 amplitude-frequency characteristic 138 analog-to-digital converter (ADC) 6, 12, 14, 41 angle encoders 6 angular position sensor 21 speed 19 anti-lock braking system (ABS) 185–90, 192 Application-Specific Instruction Processor (ASIP) 227 approximating error 116 architectural-level power estimation 229 ASIC 2, 49, 225 atomic frequency standard 115 automated software development tools 228, 241 automobile sensors network 185 automotive applications 15, 183, 245 automotive-network interface 245 averaging windows 136 Dirichlet, see Weight Functions, Dirichlet Base Clock Accuracy 60 base energy cost 229–33, 241, 243 estimation 230 binary position sensors 7 biomedical applications 8 biosensors 7 block codes 62 boundary scan architecture 10 bulk micromachined piezoresistive 18 bus architecture 59, 229, CAN 183, 246–47 controller 247 D2 B 246–47 I2 C 2, 13, 14, 246–47 IS2 23, 246–47 Hart 246 SPI 13, 14 CAD tools 6, 225 caesium frequency standards 115 calculating error 61, 105, 112, 114, 171–72 calibration algorithm 15 CANOpen 247 capacitance-to-period converter 49 Capacitive cell 24 spring mass accelerometers 18 carbon steel 26 central processing unit (CPU) 225 centralized architecture 192 charge balance technique 42 Chemical sensor 7, 24 signal domain 8 chemisorbing polymer films 24 chrominance 23 circuit state effect 232, 234
- 21. 276 INDEX circuit-breakers 211 clock oscillator 13 code 61–67 block length 61 Bose-Chaudhuri-Hocquenghem (BCH), 62 combination 61, 64–66 correcting 62, 65 cyclic 61, 62, 63, 65, 66, 68 distance 63, 65, 66 Golay 62–64, Hamming 62 iterated 62 redundancy 62, 65, 67 coding algorithm 61, 62 efficiency 62, 65 Combined Counting Method 79 ComponentWorks 2.0 203 condition jump instructions 235, 236, 238 conductivity 11, 199 confidence interval 221, 223 constant elapsed time (CET) method 70, 96, 123 Control Unit 81 Controller Area Network (CAN) 183 conversion cycles 29, 32, 33, 42, 47, 94, 107, 177 speed 7, 46, 164, 174, 203 conversion current-to-frequency 6 voltage-to-frequency 5, 35 correcting ability 62, 66 correlation dependence 167 crankshaft position sensor 20 criterion conditional 153, 157, 158 preference 151, 153, 156, 157, 158 unconditional 153, 156, 158 crystalline quartz 17 DAQ Board 59, 67, 205 Data acquisition 1 systems 1, 3, 7, 8, 14, 51–53, 55, 59–62, 67, 197 data capturing method 6, 7 data coding 62 logger 199 transfer instruction 230 transmission 3, 61 data-acquisition rate 86, 183, 184 decoding algorithm 62 degrees centigrade 13 Fahrenheit 13 delta-sigma analog-to-digital converter 14 Derivation 135 digital filtering 2 interface circuits 3 modulator 12 digital signal processing (DSP) 2, 105, 129, 183 direct counting method 52, 60, 70–73, 92, 121 Direct Memory Access (DMA) 70, 97, 124 Dirichlet window 108, 109, 117, 118, 120, 137, 141 Discrete Fourier Transform (DFT) 82, 83 discretization step 217, 220, 221 distributed control systems 248 intelligence 183 distribution anti-modal 221 Erlang 221 trapezium 217, 221–23 triangular 72, 75, 108, 115, 217, 221 uniform 72, 74, 75, 105, 108, 115, 173, 221–23 Division 76, 89, 122, 130, 165, 171–72 DMA transfer method 70, 97, 124 double buffered measurement method (DB) 70, 96, 97, 123 dual-slope converters 41, 43 duty cycle modulated square-wave output 11 duty factor 3, 29, 42, 46, 77 dynamic average power 121 range 4, 23, 41, 76, 110–11, temperature compensation 17, 30 effect photoelectric 6 piezoresistive 6 Seebeck 6 Elbert’s converters 43, 45, 46 electrical signal domain 8, 18 Electrochemical oscillations 26 electrochemical reactions 26 electronic nose 24, 199 Embedded microcontroller 129, 143, 153, 200, 227
- 22. INDEX 277 energy domains 4 error correction 66, 67, 174 error of method 100 Error of Rounding 77, 90, 105, 164, 171 Error of Wavefront Forming 164, 175 Error of Wavetail Forming 164, 173–74 Error Protection 61, 68 external interrupt processing 239 fibre-optic pressure transducers 17 finite impulse response filter (FIR) 136 firing-pins 211–14 flow mesh 19 free-running timer 97 frequency deviation 104 function 217–18, 220–23 multiplier 130–33 ratio 100–03 frequency instability long-term 73, 114 short-term 73, 114 Frequency Reference Error 73, 165, 168–69 Frequency Signals Unification 132 frequency-determining element 6, 11 frequency-time domain sensors 3, 4, 7, 27 frequency-to-code converter 8, 52, 54, 55, 57, 105, 112, 136, 143, 160 gas-filled cell 26 gate time 71 Gaussian distribution law 221 generating polynomial 62–64, 66, 67 Graphical User Interface (GUI) 200–02 Harvard architecture 225 high-Q piezoelectric quartz crystal 26 hybrid technology 20 hybrid-integrated processing electronics 3 hydrogen peroxide 26 I/O interface 203, 253 IEEE 1149.1 standard 10 IEEE-488 interfaces 200, 246 impedance matching 5 indirect counting method 69, 74, 77, 79, 123 input sampler 13 input signal noise 77 instruction level power model 229, 233 instruction set 225–27, 229 instruction-level power analysis 229 integrated quality factor 157 integrated microcoil 23 Integrated Smart-Sensor Bus (IS2 ), see IS2 Integration 135–36 intelligent pressure standards 17 intelligent sensor, see smart sensor Interface Circuits 245, 248 inter-instruction effects 229, 241 interpolation method 69, 79, 86, 92 Interrupt Response Delay Error 164, 173 Shift Error 164, 173–75 inverse Fourier transform 137 Inversed VFC 41, 43 Jitter 87 Karp’s algorithm 233 LabVIEW software module 200–01 LabWindows/CVI 201 left low bound determination 153, 156 Liapunov characteristic functions (LCF) 217 Light intensity 23 light-sensing elements 23 linear block cyclic separable codes 62 linear-broken distribution 221 low-power programming style 228, 235, 237, 242, 244 luminance 23 M/T method 70, 94–96, 123 magnetic field sensors 7 signal domain 8, 18 magnetoresistor 18 manipulation absolute-phase 62 relative-phase 62 mass transport processes 26 MCS-51 microcontroller family 225 measurand-to-frequency conversion 5 measurand-to-parameter-to-frequency conversion 6 measurand-to-voltage-to-frequency conversion 6 measurement standard 165 measuring system 200 memory access cost 239–40 η-method 217
- 23. 278 INDEX method of accelerated sensors polling 53–54 method of algorithm merging 175 method of coincidence 70, 125 method of recirculation 69 method of delayed coincidences 70 Method of dependent count (MDC) 70, 98, 124 Method with Non-Redundant Reference Frequency 121 MI-Bus 246 Michigan Parallel Standard (MPS) 246 Michigan Serial Standard (MSS) 246 microcontroller core 225 mode idle 240, 243 power down 240, 243 Modified Method of Algorithm Merging 175 monolithic light-to-frequency converter 23 monolithic silicon diffused piezoresistors 14 monolithic temperature detectors 12 morphological matrix 154 tensor 154 motion detectors 7 multichannel sensor interfacing 184–85 multichannel sensor systems 51, 197–98 multilayers architecture 183–84 multiparameters sensor 199 navigation sensors 7 negative feedback loop 13 Network Protocols 245–46 noise attenuation 42 immunity 3, 45, 47 noise-resistant signal transmission 61 non-contact sensors 20, 22 non-linear phase selectors 65 non-redundant reference frequency 121–23 nuclear magnetic resonance 127 numeration error 171 off-duty factor 3 offset 249, 251 on-chip reference 13 on-line time ratio 3 optical intensity 23 optimization call 236 code 234 compiler 228, 241 cycle 237 jump 236 local 244 speed 234–35, 243–44 optimizing compilers 227–28, 242, 244 Opto Sensors 23 oven-controlled crystal oscillator 73 parallel data transmission 59 information processing 184 parametric (modulating) sensors 6 partial matrix algorithm schemes 175 passive sensors 6 peak detector 232 period-modulated oscillator 248 phase manipulation digit-by-digit 62, 64, 65 Phase Shift-to-Code Conversion 85, 86, 125 phase-frequency characteristics 221 phosphoric acid 26 photo diode matrix 23 photodetectors 6, 7 photosensors 6, 7 piece-wise linear approximation 217 piezo film 18 piezoresistivity 14 polling cyclic synchronous 52 software controlled asynchronous 52 polynomial function 17 position sensor 7, 19, 21 power consumption graph 233–34 probability distribution 217, 219, 221–23 programmable counter array 148 program-oriented conversion methods (PCM) 143 proximity switches 7 pulse width modulation (PWM) 26 quantile multiplier 217 quantization error 60, 72, 91, 107, 109, 165 noise 13 time 52 window 98 quartz beam 17 Quasi-Ternary Code Decoding 65 quasi-pipelining data processing 203 Quasi-Ternary Coding 62–64, 66
- 24. INDEX 279 quasi-ternary cyclic code 65, 66 quaziparallel algorithms 184 rain sensors 7 ratiometric counting technique 145, 150 encoding format 12 Rayleigh distribution 217 reciprocal counting method 70, 94, 95, 123 reference time interval 90, 92, 95, 147, 173 relaxation oscillator 42 Reliability 5 Residual Matrixes 64–65 resistance bridge 14 Resonant pressure transducer 18 structures 6 rotation acceleration 21 angle 19 speed 18–22 rotor-modulator rounding error 77, 105, 171 rubidium frequency standards 115 sampling time 251 self-adaptation 2, 59 Self-Adapting Methods 89 self-checking systems 8 Self-diagnostic 2, 5 self-generating sensors 6 self-identification 2 self-testing 193 semiconductor active position sensor 19 sensing diaphragm 15 sensor bus standard 253 sensor networks 183 Sensor analog 3, 7 digital 7 flow 7 gas 24–26 frequency 3, 4, 7 Hall 18, 21, 22, 186 humidity 24 pressure 14–18 quasi-digital 3, 5 system 183, 193, 197 temperature 8–14 sensor-driven process control 247 sensors array 27 Sensors Web Portal 27 short infra red radiation 23 Si-Al thermopiles 11 sigma-delta modulator 12 signal conditioning 1, 15 processing 1, 2, 15, 129 redundancy 62, 65, 67 silicon diaphragms 14 silicon micromachining 2 Simpson distribution law 72, 75, 108 single buffered measurement method (SB) 96–97, 123 smart sensor 2 architecture 57–59 systems 200, 224 Smart Sensors Buses 245 software level design 225 software low-power design 225, 228, 244 solar cells 6 sound velocity 26 Space-Division Channelling 55, 57 stand-alone instrument 200 star configuration 2 static analysis 226 Steklov’s function 19 step approximation 217 straight-line segment 217, 220–21 strain measurement 15 strain-gage signal 17 Subtraction 129–30 successive-approximation converter 13 surface acoustic wave (SAW) 6, 26 surface micromachined capacitive 18 synchronized VFC 41 syndromes 66 Systematic Error 146, 173–75 Tachometer 211, 215 Temperature Data Recorder 14 Temperature-to-Digital Converter 13 thermal compensation 18 conductivity 11 delay line 11 diffusion constant 11 management 13 monitoring 9 noise 167 watchdog 14 Thermal-Feedback Oscillator (TFO) 11 thermocouple sensors 6 thickness sensors 7
- 25. 280 INDEX three-phase differential method of measurement 248 tilt sensors 7 time cycle polling 52 Time-Dividing Channelling 52 time-domain oversampling 12 time-interval measurement 69 Time-to-Digital Converter (TDC) 252–53 time-window counting 69 tracking error 116 transducer, intelligent 2 transferred polynomial 66 transmission rate 61 trigger error 60, 77, 86, 112, 115–16 hysteresis 115 Truncation error 171–72 Tuning Algorithm 213 two-wire interface 14 ultrasound transmitter 26 unit-volume heat capacitance 11 Universal Transducer Interface (UTI) 248–52 valve photoelectric cells 6 vapour condensation 26 vibrating quartz crystal pressure transducer 17 virtual counter 102, 147, 159 measuring channel 201–03 instruments 200–03 tachometric system 211, 214 voltage-to-frequency converter (VFC) 6, 7, 29–47 Weight Functions 136–41 Dirichlet (-shaped) 137 graded-triangular 137, 140 optimal 137, 141 trapezoidal 137, 140–41 Wheatstone bridge 15 Wien Bridge oscillator circuit 12 window-comparator architecture 14
- 26. Data Acquisition and Signal Processing for Smart Sensors Nikolay Kirianaki, Sergey Yurish, Nestor Shpak, Vadim Deynega Copyright 2002 John Wiley Sons Ltd ISBNs: 0-470-84317-9 (Hardback); 0-470-84610-0 (Electronic) 1 SMART SENSORS FOR ELECTRICAL AND NON-ELECTRICAL, PHYSICAL AND CHEMICAL VARIABLES: TENDENCIES AND PERSPECTIVES The processing and interpretation of information arriving from the outside are the main tasks of data acquisition systems and measuring instruments based on computers. Data acquisition and control systems need to get real-world signals into the computer. These signals come from a diverse range of transducers and sensors. According to [7] ‘Data Acquisition (DAQ) is collecting and measuring electrical signals from sensors and transducers and inputting them to a computer for processing.’ Further processing can include the sensors’ characteristic transformation, joint processing for many parame- ters as well as statistical calculation of results and presenting them in a user-friendly manner. According to the output signal, sensors and transducers can be divided into potential (amplitude), current, frequency, pulse-time and code. As a result, the task of adequate sensor interfacing with PCs arises before the developers and users of any data acqui- sition systems. Therefore special attention must be paid to the problems of output conversion into a digital format as well as to high accuracy and speed conversion methods. In general, a sensor is a device, which is designed to acquire information from an object and transform it into an electrical signal. A classical integrated sensor can be divided into four parts as shown in Figure 1.1. The first block is a sensing element (for example, resistor, capacitor, transistor, piezo-electric material, photodiode, resistive bridge, etc.). The signal produced from the sensing element itself is often influenced by noise or interference. Therefore, signal-conditioning and signal-processing techniques such as amplification, linearization, compensation and filtering are necessary (second block) to reduce sensor non-idealities.
- 27. 2 1 SMART SENSORS: TENDENCIES AND PERSPECTIVES Sensing element Signal conditioning and processing A/D converter Sensor-bus interface Figure 1.1 Classical integrated sensors Sometimes if certain sensing elements are used on the same chip, a multiplexer is necessary. In cases of data acquisition, the signal from the sensor must be in a serial or parallel digital format. This function can be realized by the analog-to-digital or frequency-to-digital converter. The last (but not least) block is a sensor-bus interface. A data acquisition system can have a star configuration in which each sensor is connected to a digital multiplexer. When using a large number of sensors, the total cable length and the number of connections at the multiplexer can become very high. For this reason it is much more acceptable to have a bus-organized system, which connects all data sources and receivers. This bus system handles all data transports and is connected to a suitable interface that sends accumulated data to the computer [8]. A smart sensor block diagram is shown in Figure 1.2. A microcontroller is typically used for digital signal processing (for example, digital filtering), analog-to-digital or frequency-to-code conversions, calculations and interfacing functions. Microcontrollers can be combined or equipped with standard interface circuits. Many microcontrollers include the two-wire I2 C bus interface, which is suited for communication over short distances (several metres) [9] or the serial interface RS-232/485 for communication over relatively long distances. However, the essential difference of the smart sensor from the integrated sensor with embedded data-processing circuitry is its intelligence capabilities (self-diagnostics, self- identification or self-adaptation (decision-making)) functions. As a rule, these functions are implemented due to a built-in microcontroller (microcontroller core (‘microcon- troller like’ ASIC) or application-specific instruction processor (ASIP)) or DSP. New functions and the potential to modify its performance are the main advantages of smart sensors. Due to smart sensor adaptability the measuring process can be optimized for maximum accuracy, speed and power consumption. Sometimes ‘smart sensors’ are called ‘intelligent transducers’. At present, many different types of sensors are available. Rapid advancement of the standard process for VLSI design, silicon micromachining and fabrication provide the technological basis for the realization of smart sensors, and opens an avenue that Sensing element Signal conditioning µK Figure 1.2 Smart sensor
- 28. 1 SMART SENSORS: TENDENCIES AND PERSPECTIVES 3 can lead to custom-integrated sensors to meet the new demands in performance, size and cost. This suggests a smooth merging of the sensor and electronics and the fabri- cation of complete data acquisition systems on a single silicon chip. The essential issue is the fabrication compatibility of the sensor, sensor-related analogue micro- electronic circuits and digital interface circuits [10]. In fact, for any type of silicon sensing element and read-out circuitry, a process can be developed to merge them onto a single chip. However, process development is very expensive and therefore only a huge production volume will pay off the development cost. Successful integrated-sensor processes must have an acceptable complexity and/or applicability for a wide range of sensors [11]. MEMS technologies allow the miniaturization of sensors and, at the same time, integration of sensor elements with microelectronic functions in minimal space. Only MEMS technologies make it possible to mass-produce sensors with increasing cost-effectiveness while improving their functionality and miniaturizing them. Of course, the implementation of the microcontroller in one chip together with the sensing element and signal-conditioning circuitry is an elegant and preferable engi- neering solution. However, the combination of monolithic and hybrid integration with advanced processing and conversional methods in many cases achieves magnificent technical and metrological performances for the shorter time-to-market period without additional expenditures for expensive CAD tools and the lengthy smart sensor design process. For implementation of smart sensors with hybrid-integrated processing elec- tronics, hardware minimization is a necessary condition to achieve a reasonable price and high reliability. In this case, we have the so-called ‘hybrid smart sensor’ in which a sensing element and an electronic circuit are placed in the same housing. Frequency–time-domain sensors are interesting from a technological and fabrication compatibility point of view: the simplifications of the signal-conditioning circuitry and measurand-to-code converter, as well as metrology performances and the hardware for realization. The latter essentially influences the chip area. Such sensors are based on resonant phenomena and variable oscillators, whose information is embedded not in the amplitude but in the frequency or the time parameter of the output signal. These sensors have frequency (fx), period (Tx = 1/fx), pulse width (tp), spacing interval (ts), the duty cycle (tp/Tx), online time ratio or off-duty factor (Tx/tp), pulse number (N), phase shift (ϕ) or single-time interval (τ) outputs. These informative parame- ters are shown in Figure 1.3. Because these informative parameters have analog and digital signal properties simultaneously, these sensors have been called ‘quasi-digital’. Frequency output sensors are the most numerous group among all quasi-digital sensors (see Figure I, Introduction). Let us consider the main advantages of frequency as the sensor’s output signal. • High noise immunity. In frequency sensors, it is possible to reach a higher accuracy in comparison with analog sensors with analog-to-code conversion. This property of high noise immunity proper to a frequency modulation is apparently the principal premise of frequency sensors in comparison with analog ones. The frequency signal can be transmitted by communication lines for a much greater distance than analog and digital signals. The frequency signal transmitted practically represents a serial digital signal. Thus, all the advantages of digital systems are demonstrated. Also, only a two-wire line is necessary for transmission of such a signal. In compar- ison with the usual serial digital data transmission it has the advantage of not
- 29. 4 1 SMART SENSORS: TENDENCIES AND PERSPECTIVES . . . 1 j 2 N tp ts . . . Tx = 1/fx Figure 1.3 Informative parameters of frequency–time-domain sensors requiring any synchronization. A frequency signal is ideal for high noise industrial environments. • High output signal power. The sensor’s signal can be grouped into six energy domains: electrical, thermal, mechanical, chemical, radiant and magnetic. Electrical signals are currently the most preferred signal form. Therefore, sensor design is focused on developing transducers that convert the signal from one or other energy domain into a quantity in the electrical domain. From the power point of view, the section from a sensor output up to an amplifier input is the heaviest section in a measuring channel for transmitting signals. Here the signal is transmitted by a very small level of energy. The losses, originating in this section, cannot be filled any more by signal processing. Output powers of frequency sensors are, as a rule, considerably higher. In this case, the power affecting the generated frequency stability is the oscillation (reactive) power of the oscillating loop circuit and due to the higher quality factor of the oscillating loop its power is higher. • Wide dynamic range. Because the signal is in the form of the frequency, the dynamic range is not limited by the supply voltage and noise. A dynamic range of over 100 dB may be easily obtained. • High accuracy of frequency standards. The frequency reference, for example, crystal oscillators, can be made more stable than the voltage reference. This can be explained in the same way as information properties of amplitude-modulated and frequency-modulated signals. • Simplicity of commutation and interfacing. Parasitic emf, transient resistances and cross-feed of channels in analog multiplexers by analog sensors are reduced to the occurrence of complementary errors. The frequency-modulated signal is not sensitive to all the above listed factors. Multiplexers for frequency sensors and transducers are simple enough and do not introduce any errors into observed results. • Simplicity of integration and coding. The precise integration in time of frequency sensors’ output signals can be realized simply enough. The adding pulse counter is an ideal integrator with an unlimited measurement time. The frequency signal can be processed by microcontrollers without any additional interface circuitry.
- 30. 1 SMART SENSORS: TENDENCIES AND PERSPECTIVES 5 All this makes the design and usage of different frequency–time-domain smart sensors very efficient. The most important properties of smart sensors have been well described in [9]. Here we will briefly describe only the basic focus points for an intelligent frequency–time- domain smart sensor design. • Adaptability. A smart sensor should be adaptive in order to optimize the measuring process. For example, depending on the measuring conditions, it is preferable to exchange measurement accuracy for speed and conversely, and also to moderate power consumption, when high speed and accuracy are not required. It is also desir- able to adjust a clock crystal oscillator frequency depending on the environment temperature. The latter also essentially influences the following focus point–the accuracy. • Accuracy. The measuring error should be programmable. Self-calibration will allow reduction of systematic error, caused, for example, by the inaccuracy of the system parameters. The use of statistical algorithms and composited algorithms of the weight average would allow reduction of random errors caused, for instance, by interference, noise and instability. • Reliability. This is one of the most important requirements especially in industrial applications. Self-diagnostics is used to check the performance of the system and the connection of the sensor wires. For analysis of quasi-digital smart sensors, it is expedient to use the classification, shown in Figure 1.4. Depending on conversion of the primary information into frequency, all sensors are divided into three groups: sensors with measurand-to- frequency conversion; with measurand-to-voltage-to-frequency conversion; and with measurand-to-parameter-to-frequency conversion. 1. Sensors with x(t) → F(t) conversion. These are sensors that generate a frequency output. Electronic circuitry might be needed for the amplification of the impedance matching, but it is not needed for the frequency conversion step itself. Measuring information like the frequency or the frequency-pulse form is most simply obtained in inductive, photoimpulse, string, acoustic and scintillation sensors, since the principle of operation allows the direct conversion x(t) → F(t). One group of Quasi-digital sensors x(t )→F(t ) x(t )→V(t )→F(t ) x(t )→P(t )→F(t ) Figure 1.4 Quasi-digital smart sensor classification (x(t) — measurand; F(t) — frequency; V (t) — voltage, proportional to the measurand; P (t) — parameter)
- 31. 6 1 SMART SENSORS: TENDENCIES AND PERSPECTIVES such sensors is based on resonant structures (piezoelectric quartz resonators, SAW (surface acoustic wave) dual-line oscillators, etc.) whereas another group is based on the periodic geometrical structure of the sensors, for example, angle encoders. 2. Sensors with x(t) → V (t) → F(t) conversion. This group has a numerous number of different electric circuits. These are Hall sensors, thermocouple sensors and photosensors based on valve photoelectric cells. When a frequency output is required, a simple voltage-to-frequency or current-to-frequency conversion circuit can be applied to obtain the desired result. 3. Sensors with x(t) → P (t) → F(t) conversion. The sensors of this group are rather manifold and numerous. These are the so-called electronic-oscillator based sensors. Such sensors are based on the use of electronic oscillators in which the sensor element itself is the frequency-determining element. These are inductive, capacity and ohmic parametric (modulating) sensors. Parametric (modulating) sensors are devices that produce the primary information by way of respective alterations of any electrical parameter of some electrical circuit (inductance, capacity, resistance, etc.), for which it is necessary to have an external auxiliary power supply. Examples of such types of sensors are pressure sensors based on the piezoresistive effect and photodetectors based on the photoelectric effect. In turn, self-generating sensors are devices that receive a signal immediately by way of a current i(t) or voltage V (t) and do not require any source of power other than the signal being measured. Examples of such types of sensors are Seebeck effect based thermocouples and photoeffect based solar cells. Self-generating sensors are also called ‘active’ sensors, while modulating sensors are called ‘passive’ sensors. The signal power of modulating sensors is the largest and, therefore, from the noise- reduction point of view their usage is recommended. The distinctiveness of these three sensor groups (Figure 1.4) is the absence of conventional ADCs. In order to design digital output smart sensors, it is expedient to use a microcontroller for the frequency-to-code conversion. The production of such smart sensors does not require extra technological steps. Moreover, modern CAD tools contain microcontroller cores and peripheral devices as well as voltage-to-frequency converters (VFC) in the library of standard cells. So, for example, the Mentor Graphic CAD tool includes different kinds of VFCs like AD537/650/652, the CAD tool from Protel includes many library cells of different Burr–Brown VFCs. In comparison with the data-capturing method using traditional analog-to-digital converters (ADC), the data-capturing method using VFC has the following advan- tages [12]: • Simple, low-cost alternative to the A/D conversion. • Integrating input properties, excellent accuracy and low nonlinearity provide perfor- mance attributes unattainable with other converter types, make VFC ideal for high noise industrial environments. • Like a dual-slope ADC, the VFC possesses a true integrating input and features the best, much better than a dual-slope converter, noise immunity. It is especially
- 32. 1 SMART SENSORS: TENDENCIES AND PERSPECTIVES 7 important in industrial measurement and data acquisition systems. While a succes- sive approximation ADC takes a ‘snapshot’ in time, making it susceptible to noise peaks, the VFC’s input is constantly integrating, smoothing the effects of noise or varying input signals. • It has universality. First, its user-selected voltage input range (± supply). Second, the high accuracy of the frequency-to-code conversion (up to 0.001%). The error of such conversion can be neglected in a measuring channel. This is not true for traditional analog-to-digital conversion. The ADC error is commensurable with the sensor’s error, especially if we use modern high precision sensors with relative error up to 0.01%. • When the data-capturing method with a VFC is used, a frequency measurement technique must also be chosen which meets the conversion speed requirements. While it is clearly not a ‘fast’ converter in a common case, the conversion speed of a VFC system can be optimized by using efficient techniques. Such optimization can be performed due to advanced methods of frequency-to-code conversion, quasi pipeline data processing in a microcontroller and the use of novel architectures of VFC. While pointing out the well-known advantages of frequency sensors, it is necessary to note, that the number of physical phenomena, on the basis of which sensors with frequency and digital outputs can be designed, is essentially limited. Therefore, analog sensors with current and voltage outputs have received broad dissemination. On the one hand, this is because of the high technology working off analog sensor units, and also because of the heady development of analog-to-digital conversion in the last few years. On the other hand, voltage and current are widely used as unified standard signals in many measuring and control systems. An important role is played by the technological and cost factors in the choice of sensor. Therefore, the question, what sensors are the best–frequency or analog–is not enough. With the appearance of sensor microsystems and the heady development of microsystem technologies all over the world, technological and cost factors need to be modified for the benefit of frequency sensors. Sensor types with the highest demand volumes are temperature sensors, pressure sensors, flow sensors, binary position sensors (proximity switches, light barriers, reflector-type photosensors), position sensors, chemical sensors for measurement in liquids and gases, filling sensors, speed and rpm sensors, flue gas sensors and fire detectors worldwide. The fastest growing types of sensors include rain sensors, thickness sensors, sensors that measure the quality of liquids, navigation sensors, tilt sensors, photodetectors, glass breakage sensors, biosensors, magnetic field sensors and motion detectors [5]. The frequency–time-domain sensor group is constantly increasing. First, it is con- nected with the fast development of modern microelectronic technologies, secondly with the further development of methods of measurement for frequency-domain param- eters of signals and methods for frequency-to-code conversion, and thirdly, with advan- tages of frequency as the informative parameter of sensors and transducers. Today it is difficult to find physical or chemical variables, for which frequency output or digital sensors do not exist. Of course, this book cannot completely describe all existing
- 33. 8 1 SMART SENSORS: TENDENCIES AND PERSPECTIVES Radiant Mechanical Thermal Electrical Magnetic Chemical Signal domains Figure 1.5 Six signal domains sensors and their principles of operation. For readers wishing to learn more about smart sensor development history we would like to recommend the article [6]. This review aims to illustrate state-of-the-art frequency–time-domain IC sensors with high metrology performances, and also to formulate the basic requirements for such an important smart sensor’s unit, as the frequency-to-code converter. Frequency–time domain-sensors can be grouped in several different ways. We will group them according to the measurand domains of the desired information. There are six signal domains with the most important physical parameters shown in Figure 1.5. Electrical parameters usually represent a signal from one of the non-electrical signal domains. 1.1 Temperature IC and Smart Sensors Temperature sensors play an important role in many measurements and other integrated microsystems, for example, for biomedical applications or self-checking systems and the design for the thermal testability (DfTT). IC temperature sensors take advantage of the variable resistance properties of semiconductor materials. They provide good linear frequency, the duty-cycle or pulse width output proportional to the temperature typically in the range from −55 ◦ C to +150 ◦ C at a low cost. These devices can provide direct temperature readings in a digital form, thus eliminating the need for an ADC. Because IC sensors can have a memory, they can be very accurately calibrated, and may operate in multisensor environments in applications such as communications networks. Many IC sensors also offer communication protocols for use with bus-type data acquisition systems; some also have addressability and data storage and retrieval capabilities. Smart temperature sensors need to be provided with some kind of output digital signal adapted to microprocessors and digital-processing systems. This signal can be a time-signal type, where the measurement is represented by the duty-cycle or the
- 34. 1.1 TEMPERATURE IC AND SMART SENSORS 9 frequency ratio, or the fully digital code that is sent to the processor in a serial way through the digital bus [13]. Some important restraints, caused by the integration of sensing and digital-processing function on the same chip are [14] (a) the limited chip area, (b) the tolerances of the device parameters and (c) the digital interference. Perfor- mances of some integrated temperature sensors are shown in Table 1.1. Since CMOS is still the most extensively used technology the integration of temper- ature sensors in high-performance, low-cost digital CMOS technologies is preferred in order to allow signal conditioning and digital processing on the same chip [13]. In the framework of the COPERNICUS EC project CP0922, 1995–1998, THER- MINIC (THermal INvestigations of ICs and Microstructures), the research group from Technical University of Budapest has dealt for several years with the design problem of small-size temperature sensors that must be built into the chip for thermal moni- toring [18–21]. One such sensor is based on a current-to-frequency converter [18,19]. The analog signal of the current output CMOS sensor is converted into a quasi-digital one using a current-to-frequency converter. The block diagram of the frequency output sensor is shown in Figure 1.6. The Iout output current and its ‘copy’ generated by a current mirror the charge and discharge the capacitor Cx. The signal of the capacitor is led to a differential comparator the reference voltage of which is switched between the levels VC and VD [19]. The resulting frequency is f = Iout 2 · Cx(VC − CD) . (1.1) The sensitivity is −0.808%/◦ C. The output frequency 0.5–1.3 MHz is in a conve- nient range. The complete circuit requires only an area of 0.018 mm2 using the ECPD 1 µm CMOS process. The low sensitivity on the supply voltage is a remarkable feature: ±0.25 V charge in VDD results in only ±0.28% charge in the frequency. The latter corresponds to the ±0.35 ◦ C error. The total power consumption of this sensor is about 200 µW [20]. The characteristic of this sensor is quite linear and the output frequency of these sensors can be approximately written as fout = f20Cels exp(γ (TCels − 20 ◦ C)), (1.2) Table 1.1 Performances of some integrated temperature sensors Sensor Output type Characteristic Area, mm2 IC technology [15] Digital I → F converter + DSP 4.5 CMOS [16] Duty-cycle Duty-cycle-modulated 5.16 Bipolar [17] Frequency I → F converter 6 Bipolar Temperature-to-current converter Current-to-frequency converter I = 5 ...15 mA f = 0.5 ...1.5 MHz Figure 1.6 Block diagram of the temperature frequency output sensor
- 35. 10 1 SMART SENSORS: TENDENCIES AND PERSPECTIVES in the −50 · · · +120 ◦ C temperature range, where γ is the sensitivity, f20Cels is the nominal frequency related to T = 20 ◦ C. Using the AMS 0.8 µm process, the area consumption is 0.005 mm2 [21]. The THSENS-F [22] sensor characteristic and sensor layout based on these researches are shown in Figure 1.7 and 1.8 respectively. This sensor can be inserted into CMOS designs, which can be transferred and re-used as cell (layout level) entities or as circuit netlists with transistor sizes. The sensor’s sensitivity is ≈ −0.8%/◦ C; the temperature range is −50 · · · +150 ◦ C; the accuracy is ≈ ±2 ◦ C for (0 . . . 120 ◦ C). The two latter parameters depend on the process. If the temperature sensors described above are inserted into a chip design, additional circuitry must be implemented in order to provide access to such sensors [21]. Built-in temperature sensors can be combined with other built-in test circuitry. The boundary scan architecture [23] is suitable for monitoring temperature sensors. This architecture has led to the standard IEEE 1149.1 and is suitable for incorporating frequency output temperature sensors. 400 600 800 1000 1200 1400 1600 1800 2000 −100 −50 0 50 Temperature [Celsius] 100 150 200 Frequency [kHz] Figure 1.7 Sensor characteristic (output frequency vs. temperature) (Reproduced by permission of MicReD) Figure 1.8 Sensor layout (Reproduced by permission of MicReD)
- 36. 1.1 TEMPERATURE IC AND SMART SENSORS 11 A further interesting fully CMOS temperature sensor designed by this research team is based on the temperature dependence of internal thermal diffusion constant of silicon. In order to measure this diffusion constant an oscillating circuit is used in which the frequency-determining element is realized by a thermal delay line. The temperature difference sensors used in this delay line are Si-Al thermopiles. This circuit is the Thermal-Feedback Oscillator (TFO). The frequency of this oscillator is directly related to the thermal diffusion constant and thus to the temperature. This constant can be defined as Dth = λ/c, (1.3) where λ is the thermal conductivity and c is the unit-volume heat capacitance. This diffusion constant shows a reasonably large (−0.57%/◦ C) temperature dependence on the silicon. In order to measure this diffusion constant, oscillating circuits were used in which the frequency-determining feedback element is realized by a thermal time- delay line. If the feedback element is a thermal two-port (thermal delay line) then the frequency of the oscillator is directly related to the thermal diffusion constant and thus shows similar temperature dependence as the thermal diffusion constant [18]. The thermal delay line requires, however, a significant power input. Because of this disadvantage, the circuit is not really suitable for online monitoring purposes [19]. However, these sensors and the sensor principle can probably be used for other appli- cations. It is also necessary to note the low-power consumption smart temperature sensor SMT 160-30 from Smartec (Holland) [24]. It is a sophisticated full silicon sensor with a duty-cycle modulated square-wave output. The duty-cycle of the output signal is linearly related to the temperature according to the equation: DC = tp Tx = tp · fx = 0.320 + 0.00470 · t, (1.4) where tp is the pulse duration; Tx is the period; fx is the frequency; t is the temperature in ◦ C. This sensor is calibrated during the test and burn-in of the chip. The sensor characteristic is shown in Figure 1.9. One wire output can be directly connected to all kinds of microcontrollers without the A/D conversion. The temperature range is −45 ◦ C–+150 ◦ C, the best absolute −40 −20 0 0 0.2 0.4 Duty cycle 0.6 0.8 1 20 40 60 80 100 120 140 Temperature [°C] Figure 1.9 Sensor characteristic (temperature vs. duty-cycle) (Reproduced by permission of Smartec)
- 37. 12 1 SMART SENSORS: TENDENCIES AND PERSPECTIVES accuracy, including all errors is ±0.7 ◦ C, the relative error is 0.47%, the frequency range is 1–4 kHz. The CMOS output of the sensor can handle cable length up to 20 metres. This makes the SMT 160-30 very useful for remote sensing and control applications. This smart temperature sensor represents a significant totally new development in transducer technology. Its novel on-chip interface meets the progressively stringent demands of both the consumer and industrial electronics sectors for a temperature sensor directly connectable to the microprocessor input and thus capable of direct and reliable communication with microprocessors. In applications where more sensors are used, easy multiplexing can be obtained by using more microprocessor inputs or by using simple and cheap digital multi- plexers. The next specialized temperature sensor is interesting due to the high metrology performances (high accuracy). It is the SBE 3F temperature sensor with an initial accuracy of 0.001 ◦ C and typically stable to 0.002 ◦ C per year [25]. It is used for custom-built oceanographic profiling systems or for high-accuracy industrial and environmental temperature-monitoring applications. Depth ratings to 6800 and 10 500 metres (22 300 and 34 400 ft) are offered to suit different application requirements. The sensing element is a glass-coated thermistor bead, pressure-protected in a thin- walled 0.8 mm diameter stainless steel tube. Exponentially related to the temperature, the thermistor resistance is the controlling element in the optimized Wien bridge oscil- lator circuit. The resulting sensor frequency is inversely proportional to the square root of the thermistor resistance and ranges from approximately 2 to 6 kHz, corresponding to temperatures from −5 to +35 ◦ C. In speaking about digital output IC and smart temperature sensors it is necessary to mention interesting developments of companies such as Analog Devices, Dallas Semiconductor and National Semiconductor. The TMP03/TMP04 are monolithic temperature detectors from Analog Devices [26,27] that generate a modulated serial digital output that varies in direct propor- tion to the temperature of the device. The onboard sensor generates a voltage precisely proportional to the absolute temperature, which is compared with the internal voltage reference and the input to a precision digital modulator. The ratiometric encoding format of the serial digital output is independent from the clock drift errors common to most serial modulation techniques such as voltage-to-frequency converters. The overall accuracy is ±1.5 ◦ C (typical) from −25 ◦ C to +100 ◦ C, with good transducer linearity. The digital output of the TMP04 is CMOS/TTL compatible, and is easily interfaced to the serial inputs of the most popular microprocessors. The open-collector output of the TMP03 is capable of sinking 5 mA. The TMP03 is best suited for systems requiring isolated circuits, utilizing optocouplers or isolation transformers. The TMP03/TMP04 are powerful, complete temperature measurement systems on a single chip. The onboard temperature sensor follows the footsteps of the TMP01 low- power programmable temperature controller, offering excellent accuracy and linearity over the entire rated temperature range without correction or calibration by the user. The sensor output is digitized by the first-order sigma-delta modulator, also known as the ‘charge balance’ type analog-to-digital converter. This type of converter utilizes the time-domain oversampling and a high accuracy comparator to deliver 12 bits of effective accuracy in the extremely compact circuit.
- 38. 1.1 TEMPERATURE IC AND SMART SENSORS 13 Basically, the sigma-delta modulator consists of an input sampler, a summing net- work, an integrator, a comparator, and a 1-bit DAC. Similar to the voltage-to-frequency converter, this architecture creates in effect a negative feedback loop whose intent is to minimize the integrator output by changing the duty-cycle of the comparator output in response to input voltage changes. The comparator samples the output of the integrator at a much higher rate than the input sampling frequency, called the oversampling. This spreads the quantization noise over a much wider band than that of the input signal, improving the overall noise performance and increasing the accuracy. The modulated output of the comparator is encoded using a circuit technique (patent pending), which results in a serial digital signal with a mark-space ratio format that is easily decoded by any microprocessor into either degrees centigrade or degrees Fahrenheit values, and is readily transmitted or modulated over a single wire. It is very important that this encoding method avoids major error sources common to other modulation techniques, as it is clock-independent. The AD7818 (single-channel) and AD7817 (4-channel) [28,29] are on-chip temper- ature sensors with 10-bit, single and four-channel A/D converters. These devices contain an 8 ms successive-approximation converter based around a capacitor DAC, an on-chip temperature sensor with an accuracy of ±1 ◦ C, an on-chip clock oscillator, inherent track-and-hold functionality and an on-chip reference (2.5 V ± 0.1%). Some other digital temperature sensors from Analog Devices [30] are shown in Table 1.2. Dallas Semiconductor offers a broad range of factory-calibrated 1-, 2-, 3- Wire or SPI buses temperature sensors/thermometers that can provide straightforward thermal management for a vast array of applications. This unparalleled product line includes a variety of ‘direct-to-digital’ temperature sensors that have the accuracy and features to easily improve system performance and reliability [31]. These devices reduce the component count and the board complexity by conveniently providing digital data without the need for dedicated ADCs. These sensors are available with accuracies ranging from ±0.5 ◦ C to ±2.5 ◦ C (guaranteed over wide temperature and power-supply ranges), and they can operate over a temperature range of −50 ◦ C to +125 ◦ C. The conversion time range for the temperature into a digital signal is 750 ms–1.2 s. The 1-Wire and 2-Wire devices have a multi-drop capability, which allows multiple sensors to be addressed on the same bus. In addition, some devices (DS1624, DS1629 and DS1780) combine temperature sensing with other valuable features including Table 1.2 Digital temperature sensors from Analog Devices Type Description AD7414 SMBus/I2 C digital temperature sensor in 6-pin SOT with SMBus alert and over temperature pin AD7415 SMBus/I2 C digital temperature sensor in 5-pin SOT AD7416 Temperature-to-digital converter, I2 C, 10-bit resolution, −55 ◦ C to +125 ◦ C, ±2 ◦ C accuracy AD7417 4-channel, 10-bit ADC with on-chip temperature-to-digital converter, I2 C, ±1 ◦ C accuracy AD7418 Single-channel, 10-bit ADC with on-chip temperature-to-digital converter, I2 C, ±1 ◦ C accuracy AD7814 10-bit digital temperature sensor in 6-lead SOT-23 AD7816 10-bit ADC, temperature monitoring only in an SOIC/µSOIC package
- 39. 14 1 SMART SENSORS: TENDENCIES AND PERSPECTIVES EEPROM arrays, real-time clocks and CPU monitoring. One more interesting feature of Dallas Semiconductor’s temperature sensors is that they are expandable from 9 to 13 bits or user configurable to 9, 10, 11 or 12 bits resolution. Dallas Semiconductor’s DS1616 Temperature Data Recorder with the 3-Input Analog to Digital Converter adds the potential for three powerful external sensors to the base design of the DS1615 Temperature Data Recorder. It permits logging of not only the temperature, but also the humidity, the pressure, the system voltage, external temperature sensors, or any other sensor with the analog voltage output. The DS1616 provides all of the elements of a multi-channel data acquisition system on one chip. It measures the selected channels at user-programmable intervals, then stores the data and a time/date stamp in the nonvolatile memory for later downloading through one of the serial interfaces. National Semiconductor also proposes some digital temperature sensors [32] with different temperature ranges from −55 ◦ C up to +150 ◦ C: SPI/MICROWIRE plus the sign digital temperature sensor LM70 (10-bit) and LM74 (12-bit); the digital temper- ature sensor and the thermal watchdog with the two-wire (I2 CTM Serial Bus) interface LM75 (±3 ◦ C); digital temperature sensors and the thermal window comparator with the two-wire interface LM76 (±1 ◦ C), LM77 (±1.5 ◦ C) and LM92 (±0.33 ◦ C). The sensors LM70, LM74 and LM75 include the delta-sigma ADC. The window-comparator architecture of the sensors eases the design of the temper- ature control systems conforming to the ACPI (advanced configuration and power interface) specification for personal computers. Another example of a digital output sensor is +GF+ SIGNET 2350 [33] with a temperature range from −10 to +100 ◦ C and accuracy ±0.5 ◦ C. The temperature sensor’s digital output signal allows for wiring distances between sensor and temper- ature transmitter of up to 61 m. An integral adapter allows for the integration of the sensor and the transmitter into a compact assembly. 1.2 Pressure IC and Smart Sensors and Accelerometers Similar to temperature sensors, pressure sensors are also very widely spread. In Europe, the first truly integrated pressure sensor was designed in 1968 by Gieles at Philips Research Laboratories [34], and the first monolithic integrated pressure sensor with digital (i.e., frequency) output was designed and tested in 197l at Case Western Reserve University [35] as part of a programme addressing biomedical applications. Miniature silicon diaphragms, with the resistance bridge at the centre of the diaphragm and sealed to the base wafer with gold–tin alloy, were developed for implant and indwelling applications. Pressure sensors convert the external pressure into an electrical output signal. To accomplish this, semiconductor micromachined pressure sensors use the monolithic silicon-diffused piezoresistors. The resistive element, which constitutes the sensing element, resides in the thin silicon diaphragm. Applying pressure to the silicon dia- phragm causes its deflection and changes the crystal lattice strain. This affects the free carrier mobility, resulting in a change of the transducer’s resistance, or piezoresistivity. The diaphragm thickness as well as the geometrical shape of resistors, is determined by the tolerance range of the pressure. Advantages of these transducers are:
- 40. 1.2 PRESSURE IC AND SMART SENSORS AND ACCELEROMETERS 15 • high sensitivity • good linearity • minor hysteresis phenomenon • small response time. The output parameters of the diffused piezoresistors are temperature dependent and require the device to be compensated if it is to be used over a wide temperature range. However, with occurrence smart sensors and MEMS, the temperature error can be compensated using built-in temperature sensors. Most of today’s MEMS pressure transducers produced for the automotive market consist of the four-resistor Wheatstone bridge, fabricated on a single monolithic die using bulk etch micromachining technology. The piezoresistive elements integrated into the sensor die are located along the periphery of the pressure-sensing diaphragm, at points appropriate for strain measurement [36]. Now designers can choose between two architectures for sensor compensation: the conventional analog sensor signal processing or digital sensor signal processing. The latter is characterized by full digital compensation and an error-correction scheme. With a very fine geometry, mixed-signal CMOS IC technologies have enabled the incorpo- ration of the sophisticated digital signal processor (DSP) into the sensor compensator IC. The DSP was designed specifically to calculate the sensor compensation, enabling the sensor output to realize all the precision inherent in the transducer. As considered in [37] ‘as the CMOS process and the microcontroller/DSP tech- nology have become more advanced and highly integrated, this approach may become increasingly popular. The debate continues as to whether the chip area and the circuit overhead of standard microprocessor designs used for this purpose will be competi- tive with less flexible (but smaller and less costly) dedicated DSP designs that can be customized to perform the specific sensor calibration function’. The integrated pressure sensor shown in Figure 1.10 uses a custom digital signal processor and nonvolatile memory to calibrate and temperature-compensate a family of pressure sensor elements for a wide range of automotive applications. This programmable signal conditioning engine operates in the digital domain using a calibration algorithm that accounts for higher order effects beyond the realm of most analog signal conditioning approaches. The monolithic sensor provides enhanced features that typically were implemented off the chip (or not at all) with traditional analog signal conditioning solutions that use either laser or electronic trimming. A specially developed digital communication interface permits the calibration of the indi- vidual sensor module via connector pins after the module has been fully assembled and encapsulated. The post-trim processing is eliminated, and the calibration and the module customization can be performed as an integral part of the end-of-line testing by completion of the manufacturing flow. The IC contains a pressure sensor element that is coprocessed in a submicron, mixed-signal CMOS wafer fabrication step and can be scaled to a variety of automotive pressure-sensing applications [37]. Now, let us give some state-of-the-art and industrial examples of modern pressure sensors and transducers. Major attention has been given to the creation of pressure sensors with frequency output in the USSR [38,39]. The first of them was based on the usage of VFC and had an accuracy up to 1%, the effective range of measuring
- 41. 16 1 SMART SENSORS: TENDENCIES AND PERSPECTIVES Figure 1.10 Monolithic pressure sensor (Reproduced by permission of Motorola) frequencies 0–2 kHz in the pressure range 0–40 MPa. The second was founded on the usage of the piezoresonator. The connection of this device into a self-oscillator circuit receives a frequency signal, proportional to the force. The relation between the measured pressure p and the output frequency signal f is expressed by the following equation: p = (f − f0)/Kp; Kp = KF · Seff, (1.5) where f0 is the frequency at p = 0; f is the measurand frequency; Kp is the conversion factor of pressure-to-frequency; KF is the force sensitivity factor; Seff is the membrane’s effective area. The silicon pressure sensor based on bulk micromachining technology and the VFC based on CMOS technology was described in [40]. It has 0–40 kPa measuring pressure range, 280–380 kHz frequency output range and main error ±0.7%. The Kulite company produces the frequency output pressure transducer ETF-1-1000. The sensor provides an output, which can be interfaced directly to a digital output. The transducer uses a solid-state piezoresistive sensing element, with excellent relia- bility, repeatability and accuracy. The pressure range is 1.7–350 bar, output frequency is 5–20 kHz, the total error band is ±2%. Other examples are pressure transducers VT 1201/1202 from Chezara (Ukraine) with 15–22 kHz frequency output range and standard error ±0.25% and ±0.15% accordingly. The shining example of a sensor with x(t) → V (t) → F(t) conversion is the pres- sure sensor from ADZ Sensortechnik GmbH (Germany). The IC LM 331 was used as the VFC. The output frequency of the converter can be calculated according to the following equation: fout = (Uin − Uoffset) · R6 2.09V · R4 · R8 · C6 , (1.6) where Uin = Pabs · 0.533 V bar + 0.5V (1.7) The measuring range is 0–8.8 bar, the frequency range is 1–23 kHz.
- 42. 1.2 PRESSURE IC AND SMART SENSORS AND ACCELEROMETERS 17 The model SP550 from Patriot [41] is a rugged pressure transducer which provides full-scale output of 1–11 kHz. The output frequency can be offset to provide the output of any span between 1 kHz to 150 kHz. In this transducer the strain–guage signal is converted into the frequency via the precision monolithic VFC and the operational amplifier. Geophysical Research Corporation has announced the Amerada Quartz Pressure Transducer [42]. The transducer employs a rugged crystalline quartz sensor that res- ponds to the stress created by the pressure. This response is in the form of a change in the resonant frequency created by the applied pressure. The pressure dependence of the sensor is slightly non-linear but is easily corrected during the calibration using a third-order polynomial function. In addition, the crystalline quartz is considered to be perfectly elastic which contributes to the excellent repeatability that is characteristic of this technology. Two additional quartz sensors are employed, one to measure temper- ature, the second acting as a stable reference signal. The temperature measurement is used for the dynamic temperature compensation of the pressure crystal while the refer- ence signal is used as a stable timing base for the frequency counting. The pressure range is up to 10 000 psia, the accuracy up to ±0.02% FS [42]. The output pressure and temperature frequencies range between 10 kHz and 60 kHz. The high-accuracy (0.01%) fibre-optic pressure transducers have been developed by ALTHEN GmbH by applying optical technology to resonator-based sensors [43]. Further development of microelectronic technologies and smart sensors has declined in the rise of high-precision (up to 0.01%) digital output pressure sensors and trans- ducers. Some of which are described below. The Paroscientific, Inc. Digiquartz Intelligent Transmitter [44] consists of a unique vibrating quartz crystal pressure transducer and a digital interface board in the integral package. Commands and data requests are sent via the RS-232 channel and the trans- mitter returns data via the same two-way bus. Digital outputs are provided directly in engineering units with typical accuracy of 0.01% over a wide temperature range. The use of a frequency output quartz temperature sensor for the temperature compensation yields the achievable full-scale accuracy of 0.01% over the entire operating temperature range. The output pressure is fully thermally compensated using the internally mounted quartz crystal specifically designed to provide a temperature signal. All transmitters are programmed with calibration coefficients for full plug-in interchangeability. The Intelligent Transmitter can be operated either as a stand-alone standard output pressure sensor with the display, or as a fully integrated addressable computer-controlled system component. Transducers use crystalline quartz as the key sensing elements for both the pressure and the temperature because of its inherent stability and precision char- acteristics. The pressure-sensing element is a quartz beam, which changes frequency under the axial load. The transferred force acts on the quartz beam to give a controlled, repeatable and stable change in the resonator’s natural frequency, which is measured as the transducer output. The load-dependent frequency characteristic of the quartz crystal beam can be characterized by a simple mathematical model to yield highly precise measurements of the pressure and pressure-related parameters. The output is a square wave frequency [44]. Other examples are intelligent pressure standards (series 960 and 970) [45]. In the 960 series, the pressure is measured via the change in the resonant frequency of the oscillating quartz beam by the pressure-induced stress. QuartzonixTM pressure
- 43. 18 1 SMART SENSORS: TENDENCIES AND PERSPECTIVES standards produce the output frequency between 30 and 45 kHz and can achieve accu- racy of ±0.01% FS. The precise thermal compensation is provided via the integrated quartz temperature sensor used to measure the operating temperature of the transducer. The 970 series uses a multi-drop, 9600 baud ASCII character RS-485 type interface, allowing a network of up to 31 transducers on the same bus. The output pressure measurement is user programmable for both the pressure units and update rate. The resonant pressure transducer RPT 200 (frequency, RS-232/485 outputs) and the digital output pressure sensor RPT 301 (selectable output RS-232 or RS-485) series with ±0.01% FS accuracy are produced by Druck Pressure Measurement [46]. Another very popular silicon sensor from the mechanical signal domain is the accelerometer. The measurement of acceleration or one of its derivative properties such as vibration, shock, or tilt has become very commonplace in a wide range of products. The types of sensor used to measure the acceleration, shock, or tilt include the piezo film, the electromechanical servo, the piezoelectric, the liquid tilt, the bulk micromachined piezoresistive, the capacitive, and the surface micromachined capaci- tive. Each has distinct characteristics in the output signal, the development cost, and the type of the operating environment in which it best functions [47]. The piezoelectric has been used for many years and the surface micromachined capacitive is relatively new. To provide useful data, the first type of accelerometers require the proper signal- conditioning circuitry. Over the last few years, the working range of these devices has been broadened to include frequencies from 0.1 Hz to above 30 kHz. Capacitive spring mass accelerometers with integrated electronics that do not require external amplifiers are proposed by Rieker Inc. These accelerometers of the Sieka series are available with analog DC output, digital pulse-width modulated, or frequency- modulated outputs [48]. The surface micromachined products provide the sensor and the signal-conditioning circuitry on the chip, and require only a few external components. Some manufacturers have taken this approach one step further by converting the analog output of the analog signal conditioning into a digital format such as a duty-cycle. This method not only lifts the burden of designing the fairly complex analog circuitry for the sensor, but also reduces the cost and the board area [47]. A very simple circuit can be used to measure the acceleration on the basis of ADXL202/210 accelerometers from Analog Devices. Both have direct interface to popular microprocessors and the duty-cycle output with 1 ms acquisition time [49]. For interfacing of the accelerometer’s analog output (for example, ADXL05) with microcontrollers, Analog Devices proposes acceleration-to-frequency circuits based on AD654 VFC to provide a circuit with a variable frequency output. A microcontroller can then be programmed to measure the frequency and compute the applied accelera- tion [50]. 1.3 Rotation Speed Sensors There are many known rotation speed sensing principles and many commercially avail- able sensors. The overwhelming majority of such sensors is from the magnetic signal domain (Hall-effect and magnetoresistor-based sensors) and the electrical signal domain (inductive sensors). According to the nature, passive and active electromagnetic sensors are from the frequency-time domain. Pulses are generated on its output. The frequency
- 44. 1.3 ROTATION SPEED SENSORS 19 is proportional to the measured parameter. In such sensors the flow mesh is connected with the angle of rotation by the following equation: = m · cos θ, (1.8) then the induced emf in the sensor sensitive element is e = − dϕ dt = ψm · sin θ dθ dt = Em sin θ (1.9) and can be determined by the instantaneous frequency in any moment of time f = 1 2π · dθ dt (1.10) which is proportional to the instantaneous angular speed ω = dθ dt (1.11) The current averaging of the angular speed ω on the interval h, measured in the units 2π represents the frequency of rotation and is determined by Steklov’s function [51]: n(t) = 1 2πh t+0.5h t−0.5h ω(τ)dτ (1.12) This equation reflects only a common idea of the current average of some function on its argument. Using the frequency measurement of the rotational speed the choice of mathematical expression should agree with experiment. In this case, the rotational speed and the angular velocity should be interlinked with the expression relevant to the physics of the observable process and the requirements of their measurement in a concrete system. For the description of rotational speed, it is expedient to use the expression of the flowing average: n(t) = 1 2πh t t−h ω(τ)dτ = 1 2πh h 0 ω(t − τ)dτ (1.13) The measuring frequency of the rotational speed is given by nx = fx · 60 Z (1.14) where Z is the number of modulation rotor’s (encoder’s) gradations. For modern applications the rotation speed sensor should provide the digital or the quasi-digital output compatible with standard technologies. This means that the sensor and the signal-processing circuitry (the microcontroller core) can be realized in the same chip. An excellent solution in many aspects is when this signal is a square-wave output of an oscillator, the frequency of which is linearly dependent on the rotating speed and carries the information about it. The semiconductor active position sensor of relaxation type designed by the authors together with the Autoelectronic company (Kaluga, Russia) can serve as an
- 45. 20 1 SMART SENSORS: TENDENCIES AND PERSPECTIVES example [52,53]. It was developed on the basis of the crankshaft position sensor. Its principle of action is based on the effect of the continuous suppression of oscillations of the high frequency generator by passing each metal plate of the modulating rotor in front of the active sensor element and its subsequent resumption. Due to that, rectangular pulses with constant amplitude (+Vcc) are continuously formed on the sensor output. The frequency of these pulses is proportional to the rotating speed. If the metal tooth of the rotor-modulator comes nearer to the active element (generator coil), the logic level ‘1’ (+Vcc) is formed as the sensor output. When the active sensor part appears between the teeth, the logic level ‘0’ is formed as the output. Thus, the active sensor forms the pulsing sequence, the frequency of which is proportional or equal to the rotating speed. This sensor does not require any additional buffer devices for the tie-in measuring system and has a very easy interface to the microsystem. Moreover, the sensor meets the requirements of the technological compatibility with other components of the microsystem. The circuit diagram of the Active Sensor of Rotation Speed (ASRS) is shown in Figure 1.11. It consists of a high frequency generator (f = 1 MHz), a sensing element (the generator coil), an amplifier, a voltage stabilizer and an output forming transistor with an opened collector [54]. The “chip wire” technology was used in the sensor design, which combines the advantages of both monolithic and hybrid integrated tech- nologies. All electronics was realized in a single chip, only the inductance, two resistors and the stabilitron were implemented in accordance with hybrid technology. The ASRS is shown in Figure 1.12 and the sensor’s output waveforms in Figure 1.13. The amplitude of the signal is constant and does not depend on temperatures and the direction of the rotation. The online time ratio Q = 2 (50% duty-cycle independent of the distance). But in a frequency range of more than 50 000 rpm, the pulse width will be increased. The comparative features of modern non-contact sensors of different principles of function are shown in Table 1.3. Here sensors A5S07/08/09 are made by BR Braun (Germany), DZXXXX by Electro Corporation (USA); VT1855, OO020 by NIIFI (Penza, Russia); 4XXXX by Trumeter (UK); LMPC by Red Lion Controls (USA). R5 Vcc T1 T2 C1 C2 T3 R1 R2 R3 R4 R6 R7 L1 External components Figure 1.11 Circuit diagram of the Active Sensor of Rotation Speed (ASRS)
- 46. 1.3 ROTATION SPEED SENSORS 21 Figure 1.12 Active Sensor of Rotation Speed (ASRS) Figure 1.13 Waveforms of sensor’s output horizontal scale 2.0 ms/div; vertical scale 5V/div Active, magnetic and Hall-effect sensors are more suitable for the determination of the object status ‘Stop’ (a shaft is stopping). The advantage of active semiconductor sensors is the possibility of operation with the non-magnetic modulating rotor’s teeth (steel, copper, brass, aluminium, nickel, iron). Therefore, the modulating rotor can be made of plastic and its teeth — of the metallized coating. It essentially raises the manufacturability and decreases the cost value. With the exception of the non-contact rotation speed sensing, such sensors can be used like an angular position sensor, a position sensor, a metallic targets counter and an end-switch. In addition, a smart sensor on this basis allows the measurement of the rotation acceleration.
- 47. 22 1 SMART SENSORS: TENDENCIES AND PERSPECTIVES Table 1.3 Comparative features of non-contact sensors of rotation speed Sensors Freq. range, kHz Supply voltage, V Current consumption, mA Type ASRS 0–50 4.5–24 7–15 active A5S07 0.5–25 8–28 15+load current Hall-effect A5S08/09 0.5–25 8–25 15 Hall-effect DZ375 0–5 4.5–16 20–50 magnetic DZH450 0–5 4.5–30 20 Hall-effect DZP450 1–10 4.5–16 50 Hall-effect VT1855 0.24–160 27 3 inductive OO 020 0.24–720 27 100 photo 4TUC 0.3–2 10–30 200 mag./inductive 4TUN 0.3–2 6.2–12 3 mag./inductive 45515 0.002–30 25 20 Hall-effect LMPC up to 10 9–17 25 mag./inductive Active semiconductor sensors are not influenced by run-out and external magnetic fields in comparison with Hall-effect sensors. With Hall-effect sensors, it is necessary to take into account the availability of the initial level of the output signal between electrodes of the Hall’s element by absence of the magnetic field and its drift. It is especially characteristic for a broad temperature range. A Hall-effect rotation speed sensor needs encoders with magnetic pole teeth. Another good example of a smart sensor for rotation speed is the inductive posi- tion, speed and direction active microsensor MS1200 from CSEM (Switzerland) [55]. The output can switch up to 1 mA and is compatible with CMOS digital circuits, in particular with microprocessors. The frequency range is 0–40 kHz, the air-gap is 0–3 mm. The core is a sensor chip with one generator coil and two sets of detection coils (Figure 1.14). The detection coils are connected in a differential arrangement, to reject the common mode signal. The sensor also includes an electronic interface, which is composed of Logic B A Supply(VDD) Ground(VSS) Sensor chip Generator micro-coil Detection micro-coils Demod. Osc. Driver Demod. Direction 4x Metallic target Figure 1.14 MS1200 functional block diagram (Reproduced by permission of POSIC S.A., Neuchatel, Switzerland)
- 48. 1.4 INTELLIGENT OPTO SENSORS 23 a high frequency excitation for the generator coil and two read-out channels for the two sets of detection coils (channels A and B). The read-out electronics extract the amplitude variation of the high frequency signal due to the presence of a metallic target. The output stage is a first-order low-pass filter and a comparator. For a nominal target period of 2 mm, the outputs are two channels in quadrature (A quad B) as well as a direction signal and a speed signal (4X interpolation). It is composed of two silicon chips, one for the integrated microcoil and the other for the integrated interface circuit. The sensor produces a two-channel digital output, as well as a direction signal. 1.4 Intelligent Opto Sensors Next, we shall examine a technique of delivering the output from optical (light) sensors into the frequency–time (quasi-digital) domain. Light is a real-world signal that is often measured either directly or used as an indicator of some other quantity. Most light- sensing elements convert light into an analog signal in the form of the current or the voltage, then a photodiode current can be converted into the frequency output. Light intensity can vary over many orders of the magnitude, thus complicating the problem of maintaining resolution and signal-to-noise ratio over a wide input range. Converting the light intensity to a frequency overcomes limitations imposed on the dynamic range by the supply voltage, the noise and the ADC resolution. One such device is a low-cost programmable silicon opto sensor TSL230/235/245 from Texas Instruments with a monolithic light-to-frequency converter [56]. The output of these devices is a square wave with a frequency (0–1 MHz) that is linearly propor- tional to the light intensity of the visible and short infra red radiation. Additionally the devices provide programming capability for the adjustment of the input sensitivity and the output scaling. These capabilities are effected by a simple electronic technique, switching in different numbers of the 100 elements of the photodiode matrix. For costs reasons, the low-cost microcontroller with a limited frequency range may be used for the frequency-to-digital converter due to the output scaling capability. Options are an undivided pulse train with the fixed pulse width or the square wave (50% duty-cycle) divided by 2, 10 or 100 outputs. Light levels of 0.001 to 100 000 µW/am2 can be accommodated directly without filters [56]. Since the conversion is performed on-chip, effects of external interference such as noise and leakage currents are minimized and the resulting noise immune frequency output is easily transmitted even from remove locations to other parts of the system. The isolation is easily accomplished with optical couples or transformers. Another interesting example is the integrated smart optical sensors developed in Delft University of Technology [57,58]. Integrated on-chip colour sensors have been designed and fabricated to provide a digital output in the IS2 bus format. The readout of photodiodes in the silicon takes place in such a way that pulse series are gener- ated with the pulse frequency proportional to the optical intensity (luminance) and the duty-cycle to the colour (chrominance). The colour information is obtained using the wavelength dependence of the absorption coefficient in the silicon in the optical part of the spectrum, so no filters are required. The counters and the bus interface have been realized in a bipolar and CMOS version with enhanced resolution which is being investigated.
- 49. Other documents randomly have different content
- 50. Tat, tat, frivolité maken; subst. paatje; peuter: Tiny tats = kleine peuters; To give tit for tat = met gelijke munt betalen; Tatting = frivolité. Ta-ta, tâtâ, Zie Ta. Tatar, tâtâ, Tatarian, Tatary, tâtəri = Tartaar, Tartaarsch, Tartarije. Tatta, tatə, scherm v. bamboesmat voor deuren en ramen (Indië). Tatter, tatə, subst. vod, lap, lomp, flard; Tatter verb. in flarden scheuren: All in tatters; Tatterdemalion, tatədimeilj’n, tatədimalj’n, havelooze kerel. Tattersall’s, tatəsôlz, Tattersall’s stallen in Londen met clubgebouw voor ’t afrekenen van weddingschappen. Tattie, tati = Tatta. Tattle, tat’l, subst. gebabbel, gesnap; Tattle verb. babbelen, snappen: So runs the tattle = zoo loopt het praatje; Tattler = snapper; Tattling = babbelziek. Tattoo, tətû, subst. taptoe; tatoueering; Tattoo verb. tatoueeren: To beat the devil’s tattoo = (van ongeduld) met de vingers op de tafel trommelen (ook trappelen van ongeduld). Taught, tôt, imperf. en p.p. van to teach. Taunt, tônt, tânt, subst. schimp, hoon, smaad, schamper gezegde; adj. hoog (van masten); verb. hoonen, beschimpen; Taunt- masted; Taunter. Taunton, tônt’n.
- 51. Tauriform, tôriföm, als een stier gevormd of gebouwd; Taurine, tôr(a)in, runderachtig, tot een stierengevecht behoorend; Taurus, tôrəs, stier (Dierenriem). Taut, tôt, strak, gespannen, vol dienstijver, keurig: I hope these lines will find you pretty taut = in goeden welstand; Taut and trim = in vol ornaat (fig.); Tauten = strak worden, aanhalen; mooi maken; Tautness = strakheid, etc. Tautologic(al), tôtəlodžik(’l), tautologisch; Tautology, tôtolədži, tautologie. Tavern, tavən, herberg, wijnhuis, kroeg; Tavern-bush = de krans of struik (vroeger als uithangteeken van herbergen): Good wine needs no (tavern-)bush = goede wijn behoeft geen krans; Tavern-keeper = logementhouder, etc. Tavistock, tavistok. Taw, tô, subst. knikker, knikkerspel; Taw verb. witlooien: To play at taw; To taw a person’s hide = afranselen; Tawer, Tawyer = witlooier. Tawdriness, tôdrinəs, subst. v. Tawdry, tôdri, smakeloos, bont, opzichtig, waardeloos: The Tawdry binding detracts somewhat from the value of the work = de smakelooze en opzichtige band; Tawdry dress = opgedirkte kleeding. Tawniness, tôninəs, subst. van Tawny, tôni, taankleurig, getaand. Taws(e), tôz, soort karwats: I was thrashed with the eight- tongued taws = met de karwats met acht riemen; “Getting the taws” means: Thrashing (in een Schotsche school).
- 52. Tax, taks, subst. belasting, schatting, tol, requisitie, zware plicht of taak, proef; Tax verb. belasten, afpersen, beschuldigen, berispen, vragen, op de proef stellen: Graduated income tax = progressieve inkomstenbelasting; Inheritance tax = successie belasting; Poll tax = hoofdelijke omslag; The tax on his patience = de proef waarop zijn geduld werd gesteld; A tax on profits and incomes = belasting opbedrijfs-, en andere inkomsten; Heavy taxes were levied = er werden zware belastingen geheven; I was taxed with having offended him = werd beschuldigd; Tax- collector = Tax-gatherer = belastingontvanger; Taxpayer = belastingschuldige; Taxability = belastbaarheid; adj. Taxable; Taxameter, taksamətə, afstandswijzer (for indicating cab fares); Taxation, takseiš’n, belasting, het belasten. Zie Taxer. Taxel, taks’l, Am. das. Taxer, taksə, zetter (v. de belastingen), vroeger ambtenaar aan de hoogeschool te Cambridge, die bij de uitdeeling, voor goede maat en juist gewicht zorgde. Taxicab, taksikab, autotax (atax). Taxidermist, taksidɐ̂ mist, opzetter; Taxidermy = de kunst dieren op te zetten. Taxin, taksin, harsachtige stof uit taxusbladeren; Taxus, taksəs, taxus. Tea, tî, thee, aftreksel, een maaltijd, avondeten der kinderen; Tea verb. de tea gebruiken: Beef tea = bouillon; Five o’clock tea; [565]High, Meat tea = thee, souper met allerlei vleezen; Sweet tea = thee met gebak, vruchten, sandwiches; Will you make (the) tea? = thee zetten; To take tea = drinken; Tea-board = theeblad; Tea-caddy = theekistje; Tea-cake = theekoekje; Tea-canister =
- 53. theebus; Tea-chest = theekist; Tea-cloth = theekleedje; Tea- cosy = theemuts; Tea-cup = theekopje: The tea-cup and saucer school in literature and art = de peuterige, burgerlijke methode in letteren en kunst; A storm in a tea-cup, (tea-pot) = een storm in een glas water; Tea-dealer = theehandelaar; Tea-equipage, ekwipidž, theeservies; Tea-fight = theevisite; Tea-garden = theetuin; Tea-gown = japon bij Afternoon Tea; Tea-kettle = theeketel; Tea-peg = koude thee met soda water (Br. Ind.); Tea- pot = trekpot; Tea-rose; Tea-service (Tea-set); Tea-shrub; Tea-spoon = theelepeltje; Tea-taster = theeproever; Tea-table; Tea-things = theegoed; Tea-towels = theedoeken; Tea-tray = theeblad; Tea-urn = theeurn; Tea-water. Teach, tîtš, onderwijzen, leeren: I’ll teach you manners = zal je mores leeren; To teach a person wit = door schade en schande leeren; Teachable = wat te onderwijzen is, leerzaam; subst. Teachableness; Teacher = onderwijzer: Assistant teacher = hulponderwijzer; Pupil teacher = kweekeling; Teachership = onderwijzers- of leeraarsbetrekking; The teaching profession = de onderwijzers (als corps). Teague, tîg, Ier (spottend) = Teaguelander. Teak, tîk, teakhout = Teak-wood. Teal, tîl, wintertaling. Team, tîm, span, bespanning, toom, vlucht, groep v. elf personen (cricket of voetbal); Team-work = veldarbeid door trekvee (Amer.); Teamster = voerman; Teamwise = als een span. Tear, tîə, subst. traan: Her tears started unbidden = schoten haar in de oogen; The tears trickled down her cheeks = biggelden langs; To be all in tears (= To be drowned in tears) = in tranen
- 54. baden; He burst into tears = barstte in schreien uit; To draw tears from = ontlokken; To shed tears = storten; Tear-drop = traan; Tear-stained = beschreid; Tearful = tranen stortend, treurig. Tear, têə, subst. scheur, spleet; Tear verb. scheuren, trekken, losrukken, openrijten, tieren, vliegen: The donkey ran away full tear = ging ervan door; To be on a tear = aan den rol zijn; The wear and tear of the engine = de slijtage; He tore his hair and clothes = rukte zijne haren uit en verscheurde zijne kleederen; He could not tear himself away from his wife = kon zich niet losrukken; The flesh was torn from the bones = gescheurd van; All at once the children came tearing in = binnenstormen; He tore through the pages = vloog er doorheen; Don’t tear it to pieces, rags = scheur het niet aan stukken of flarden; The cloth was torn up into several pieces = werd gescheurd; He went up and down, tearing and swearing = tierend en vloekend; To be in a tearing passion = woest zijn; A tearing old Tory = vurige, felle. Tease, tîz, subst. plager, plaaggeest; Tease verb. plagen, sarren, kwellen, kaarden: What a tease you are = wat ben je een plaaggeest; Teaser = plager, lastig geval: That’s a teaser = daar zitten we mee verlegen. Teat, tît, tepel, uier. Teazel, Teazle, tîz’l, weverskaarde (plant). Tebeth, tîbeth, tebeth, tiende maand van het godsdienstige jaar der Joden. Tec, tek, samentr. van Detective.
- 55. Technic, teknik, subst. techniek, kunstvaardigheid; adj. Technic(al) = technisch: Technical education = vakonderwijs; Technical classes = cursussen voor vakonderwijs; Technical instruction act = wet op het vakonderwijs; Technicality, Technicalness = technische eigenaardigheid of uitdrukking; Technics = techniek; Technique, teknîk, de techniek of methode eens kunstenaars; Technological, teknəlodžik’l, technologisch; Technology, teknolədži, technologie. Techy, tetši, knorrig, gemelijk, lichtgeraakt. Tectrices, tektrisîz, dekveeren. Ted, ted, keeren (van gemaaid gras); Tedder = machine om het pas gemaaide gras te keeren. Teddy, tedi: Teddy Bear = beertje (kinderspeelgoed). Te Deum, tîdîəm, Te-Deum. Tedious, tîdjəs, vervelend, saai, verdrietig, lastig; subst. Tediousness = Tedium, tîdj’m. Tee, tî, doel, aardhoopje, vanwaar de bal geslagen wordt bij het Golfspel; doel waarnaar quoits of curling-stones worden geworpen; Tee verb. den bal daar van af slaan. Teem, tîm, zwanger zijn van, werpen, zich voortplanten, vol zijn, overvloed hebben van: This part of the country teems with gold = bevat veel goud. Teens, tînz, de jaren v. dertien tot negentien: In her teens = beneden twintig (maar minstens twaalf); Out of her teens = boven de negentien; Since his middle teens = sedert zijn 14de, 15de jaar.
- 56. Teeter, tîtə, wippen (Am.). Teeth, tîth, Meerv. van Tooth = tand; Teeth verb. (tîdh) = tanden krijgen: From one’s teeth = niet van harte; In the teeth of the westerly gale = vlak tegen in; He did it in the teeth of opposition = trots allen tegenstand; In the teeth of the doctor’s prohibition = geheel in tegen; He cast those reproaches in my teeth = wierp mij voor de voeten; He escaped by the skin of his teeth = ontsnapte ternauwernood (Job. XIX, 30 ); Teeth- drawing (fig.) = bellen moeren; Teething, tîdhiŋ, het tanden krijgen. Teetotal, tîtout’l, onthoudend: Teetotal drinks = alcoholvrije; Teetotalism = geheelonthouding; Teetotaller = geheelonthouder. Teetotum, tîtout’m, A-al tolletje: To spin a teetotum. Tegument, tegjument, omhulsel, huid, zaadhuid, vleugeldeksel; adj. Tegumentary. Tehee, tîhî, subst. gegichel; Tehee verb. gichelen; interj. hihi. [566] Teheran, tehərân; Teignmouth, teinməth, tînməth. Teil, tîl, lindeboom (= Teil-tree). Teind, tînd = Tithe (Schotl.). Teith, tîth; Telamon, teləm’n, mannenfiguur als zuil, At as. Teledu, telədû, Javaansche bunzing. Telegram, teləgram, telegram; adj. Telegrammic; Telegraph, teləgraf, subst. telegraaf; Telegraph verb. telegrafeeren, seinen: Telegraph-cable; Telegraph-operator = Telegrapher,
- 57. təlegrəfə, teləgrafə, telegrafist; Telegraphic, teləgrafik, telegrafisch: Telegraphic address = telegramadres; Telegraphist, təlegrəfist, teləgrafist, telegrafist(e); Telegraphy, təlegrefi, teləgrafi, telegraphie. Telemachus, təleməkɐs. Teleological, teliəlodžik’l, tîliəlodžik’l, teleologisch; Teleology, teliolədži, tîliolədži, teleologie. Telepathic, teləpathik, telepathisch: Card-guessing, bank-note- finding and the various other forms of telepathic hide and seek; Telepathy, təlepəthi, teləpathi, telepathie. Telepheme, teləfîm, telephonisch bericht; Telephone, teləfoun, subst. telephoon; Telephone verb. telephoneeren; Telephonic = telephonisch; Telephonist, təlefənist, teləfounist, telephonist(e); Telephony, təlefəni, teləfouni, telephonie. Telescope, teləskoup, subst. telescoop, verrekijker; soort van langwerpige spiraalschelp; Telescope verb. in elkander schuiven, zooals een telescoop: The book tries to be an encyclopaedia, telescoped into a dictionary = in een woordenboek saamgevat; The first and the second carriage were telescoped (into each other) = werden in elkander geschoven; Telescope-table = uittrektafeltje; Telescopic(al), teləskojpik(’l), telescopisch, in- en uitschuifbaar. Telesia, təlîžə, varieteit van saffier. Tell, tell, vertellen, mededeelen, melden, berichten, bevelen, optellen, onderscheiden, uitwerking hebben, indruk maken, klikken: I can tell = ik kan je verzekeren; Who can tell? = wie weet; Never tell me = maak me niet wijs; Can you tell the clock yet? =
- 58. kun je al op de klok kijken; I had my fortune told by an old gipsy = liet me waarzeggen; Don’t tell stories (fibs) = jok nu niet; Every shot told = was raak; He never preached “I told you so” = hij zanikte nooit van “Dat heb ik je wel gezegd”; They numbered 100 all told = met hun allen; That tells against you = pleit tegen je; You can tell this wine from vinegar only by the label = slechts onderscheiden van; It is difficult to tell good paste from diamonds = simili van diamant te onderscheiden; That tells for something = dat is lang niet mis, telt mee; You can tell them off by hundreds = aanwijzen bij honderden; One of the clerks was always told off to sleep in the house = werd aangewezen; I shall tell the priests on you = u verklappen aan; The heavy work has told on his constitution = zijn gestel aangegrepen; His troubles have told on him = hem erg aangegrepen; The speech told on the hearers = maakte indruk; To tell out = uittellen; Told out = op, blut; This told with him = dit hielp (bij hem); Tell-tale, subst. babbelaar, verklikker, klikspaan; adj. babbelziek, lasterend, verraderlijk; They are tellable on the fingers = die kan je op de vingers tellen; Teller = verteller, teller, stemopnemer, klerk in een bank die met de klanten rekent; harde slag. Zie Telling. Tellina, təlainə, platschelpen. Telling, teliŋ: With telling effect = met goed gevolg, krachtige uitwerking; A telling phrase = kernachtig; A telling speech = een rede, die pakt; That’s telling(s) = dat mag ik niet zeggen. Tellurian, təl(j)ûriən, aardsch —; bewoner der aarde; Telluric = tellurisch; Tellurion, təl(j)ûriən, tellurium; Tellurium, təl(j)ûriəm, tellurium (Chemie). Telotype, telətaip, druktelegraaf.
- 59. Telpher, telfə, subst. inrichting voor electrisch kabelvervoer; adj. behoorende tot Telpherage, telfəridž, vervoer door electriciteit; Telpher-line = electr. kabelspoorlijn. Temerity, təmeriti, vermetelheid, roekeloosheid. Temper, tempə, subst. aard, natuur, temperament, humeur, gemoed, prikkelbaarheid, opvliegendheid, hardheid (v. metaal); Temper verb. matigen, regelen, verzachten, doen bedaren, temperen, harden: Equal, Even temper = gelijkmatig humeur; Hot temper = drift; The temper of the nation = stemming; Your friend is not a good (is a horrid) temper = heeft geen gemakkelijk (een vreeselijk) humeur; He kept his temper better than we had supposed = bleef bedaarder; He lost his temper = raakte uit zijn humeur, verloor zijn geduld; He was in a black temper = verschrikkelijk slecht gehumeurd; What a temper you are in! = hè, wat ben jij knorrig; You must keep him in temper = hem in zijn humeur houden; He was out of temper this morning = uit zijn humeur; Temperament = gestel, geaardheid, temperament; Temperance = matigheid, gematigdheid, onthouding: Temperance-bar = koffiehuis voor onthouders; Temperance-meeting; Temperance-society = matigheids- of afschaffersgenootschap; Temperate, tempərit, bedaard, kalm, gematigd: Temperate zone = gematigde luchtstreek; subst. Temperateness; Temperature, tempərətjə, temperatuur: To take one’s temperature = opnemen; Tempered: Good-, Ill- tempered; Even-tempered = van gelijkmatig humeur; Quick- tempered = opvliegend; A sweet-tempered girl = zacht. Tempest, tempəst, hevige storm, orkaan, zwaar weer: Tempest- beaten = door de stormen gebeukt; Tempest-tossed = door de stormen geslingerd; Tempestuous, tempestjuəs, stormachtig, hevig; subst. Tempestuousness.
- 60. Templar, templə, tempelier; student in de rechten, advocaat of jurist: Order of “Good Templars” = vereeniging tot het verleenen van wederzijdschen steun bij ouderdom, etc. Temple, temp’l, tempel, godshuis, slaap (van het hoofd): Inner- temple, Middle-temple (Londensche colleges voor opleiding van juristen). Templet, templət, schabloon, vormhout. [567] Tempo, tempou, tempo, maat (Meerv. Tempi). Temporal, tempər’l, tijdelijk, tijds - -, wereldlijk; slaap - -: Temporal bone = slaapbeen; Temporal Lords = wereldlijke, ter onderscheiding v. geestelijke, pairs van Engeland; Temporality = tijdelijk of wereldlijk bezit; Temporalities = temporaliën, inkomsten der geestelijken uit land, tienden, enz.; Temporariness, subst. v. Temporary = tijdelijk, niet duurzaam; Temporize, tempəraiz, zich naar de omstandigheden schikken, den gunstigen tijd afwachten, trachten tijd te winnen: Temporizer = iemand die met de wolven meehuilt, de huik naar den wind hangt, etc. Tem(p)se, tems, zeef, vergiet(test); Tem(p)se-bread = brood van fijngebuild meel. Tempt, tem(p)t, verleiden, verlokken, in verzoeking brengen: He tempted me into giving up my plan = verlokte mij; Temptable = te verlokken; Temptation, tem(p)teiš’n, verlokking, verzoeking, aanvechting: To lead into temptation; He yielded to temptation = bezweek voor; Tempter = verleider: The Tempter = de duivel; Tempting = verleidelijk; subst. Temptingness; Temptress = verleidster.
- 61. Ten, ten, tien: There were ten of them = They were ten = ze waren met hun tienen; The ten Tribes = tien stammen Israëls; Nine in ten = negen van de tien; Ten to one = tien tegen een; Tenfold = tienvoudig; Ten-pin = kegel: Ten-pin alley = kegelbaan (Amer.); Ten-spot = een tien (kaartspel); He made many ten-strikes = gooide dikwijls alle tien; Ten-seater = fiets voor tien; Tenner = bankbiljet van 10 £; Tenth = tiende (deel). Tenability, tenəbiliti, subst. v. Tenable, tenəb’l, houdbaar, verdedigbaar: The scholarship is tenable for one year = de beurs geldt voor; subst. Tenableness. Tenace, tenis, de “fourchette” (de hoogste en op twee na de hoogste kaart) in handen van den vierden speler (Whist); Tenace- minor = kleine “fourchette” (de hoogste en op drie na de hoogste kaart). Tenacious, təneišəs, vasthoudend, sterk, hardnekkig, kleverig, taai: He has got a tenacious memory = sterk geheugen; He is tenacious of whatever he gets = houdt vast; To be tenacious of life = taai zijn; Tenaciousness = Tenacity, tənasiti, vasthoudendheid, kleverigheid, getrouwheid. Tenail(le), təneil, tangwerk ter dekking v. een courtine (Vestingb.); Tenaillon, təneiljon, klein tangwerk. Tenancy, ten’nsi, subst. huur, pacht; Tenant, subst. huurder, pachter, bewoner; Tenant verb. in pacht of huur hebben, bewonen: Tenant at will = opzegbare huurder; Tenant for life; Tenant in capite, Tenant in chief = huurder direct van de kroon; Tenant in tail = houder van een pachthoeve, die bij versterf op bepaalde erfgenamen (in pacht) overgaat; Tenantable = geschikt om ge- of verhuurd te worden, bewoonbaar; subst. Tenantableness;
- 62. Tenantless = onverhuurd, leeg; Tenantry = de gezamenlijke pachters of huurders. Tench, tenš, muithond, zeelt. Tend, tend, bewaken, verzorgen, letten op, denken om; strekken, streven, zich richten; bijdragen, om het anker zwaaien: That way instruction ought to tend = dien kant moet het onderwijs uit; Tendency = strekking, neiging, aanleg voor. Tender, tendə, aanbod, offerte, inschrijving; betaalmiddel (Legal tender); Tender verb. aanbieden, inschrijven: He made a tender of his friendship, services = bood aan; Private tender = onderhandsche inschrijving; To let by public tender = publiek uitbesteden; To tender cordial thanks = hartelijk dank zeggen; To tender for the dredging of a harbour = inschrijven op; To tender and contract for = aannemen (v. een werk). Tender, tendə, teeder, zwak, zacht, malsch, teergevoelig, vriendelijk, zorgvuldig; Tender verb. zacht maken, hoogschatten: At a tender age = op jeugdigen leeftijd; The tender passion(s) = de liefde; He was still tender of her, though she had betrayed him = hij hield nog van haar; Tender-foot = gevoelige voet; nieuweling (Australië); Tender-hearted = teergevoelig; subst. Tender- heartedness; Tender-loin = filet; Tender-minded = teerhartig; Tenderling = vertroetelde lieveling; een van de eerste hoorns van een hert; Tenderness = teederheid, vriendelijkheid, bezorgdheid voor (met of). Tender, tendə, tender (v. een locomotief), (sleep)bootje, oppasser. Tendon, tend’n, pees: Tendon of Achilles. Tendril, tendril, rank (van klimplanten).
- 63. Tenebrae, tenəbrî, donkere metten, die op Woensd., Donderd. en Vrijdag van de week vóór Paschen tegen den avond gezongen worden; Tenebrosity = duisternis; Tenebrous = duister. Tenement, tenəment, woning, huis, stel vertrekken door één gezin bewoond, pachthoeve; Tenement-house = huis, dat bij gedeelten aan verschillende gezinnen verhuurd wordt; Tenemental, tenəment’l, verpacht of verhuurbaar, huur … = Tenementary, tenəmentəri. Tenerife, tenərif. Tenet, tenət, leerstuk, beginsel. Tennessee, tenəsî. Tennis, tenis, tennis; Tennis-ball; Tennis-court = tennisbaan; Tennis-net; Tennis-racket. Tennyson, tenis’n. Tenon, tenən, subst. pen, pin, tap, neut ter verbinding; Tenon verb. met een tenon verbinden. Tenor, tenə, subst. gang, loop, richting, inhoud, geest, wezen, afschrift, tenor, altviool: The tenor of this man’s life = de richting van zijn leven; The tenor of this work = bedoeling of hoofdgedachte: The even tenor of the session was never ruffled = de gelijkmatige gang. Tense, tens, tijd (gramm.). Tense, tens, streng. strak, gespannen; subst. Tenseness; Tensibility = rekbaarheid; adj. Tensible; Tensile, tens(a)il, rekbaar, spannings - -: Tensile force = spankracht; Tension, tenš’n,
- 64. spanning, gespannenheid, spankracht, groote inspanning; Tensive, spannend; Tensor, tensə, spanspier. Tent, tent, subst. tent, kap, overtrek, wiek [568](om eene wond open te houden), donkerroode Spaansche wijn; Tent verb. van tenten voorzien, tenten opslaan, onder tenten wonen; sondeeren, peilen of openhouden van eene wond; Tent-bed = ledikant met hemel; Tent-cloth; Tent-maker = tentenmaker; Tent-pin; Tent-pole; Tent-rope. Tentacle, tentək’l, zuig- of tastorgaan; Tentacular, tentakjulə, zuig …, tast …; Tentaculate(d), tentakjulit(-eitid), van zuigorganen voorzien. Tentative, tentətiv, subst. proeve, proef, poging; adj. pogend, beproevend. Tenter, tentə, subst. spanraam, spanhaak, voeldraad; Tenter verb. opspannen, rekken: To be on the tenter(s) = op heete kolen zitten; To keep on the tenter(s) = in angstige spanning houden; Tenter-ground = plaats voor het stellen van spanramen; Tenter- hook = spanhaak: We are on (the) tenter-hooks = on the tenter(s). Tenuifolius, tenjuifouljəs, met fijne en smalle bladen; Tenuity, tənjûiti, fijnheid, dunheid, ijlheid; Tenuous, tenjuəs, dun, ijl, fijn, klein. Tenure, tenjə, eigendomsrecht, leendiensten, bezit: Tenure of life = levenstijd; Tenure of office = diensttijd. Tepefaction, tepifakš’n, matige verwarming; Tepefy = matig verwarmen, lauw worden; Tepid, tepid, lauw; subst. Tepidity = Tepidness.
- 65. Teraph, terəf (mv. Teraphim), huisgod der oude Israëlieten. Terce, tɐ̂ s, ± 190,830 L. (wijn, brandewijn, azijn, olie, etc.) ook ± 158,963 L.; ± 137,892 of ± 152,407 K.G. (vleesch voor schepen); Terce-major = de drie hoogste kaarten. Tercel(et), tɐ̂ səl(et), mannetjesvalk. Tercentenary, tɐ̂ sentənəri, subst. en adj. driehonderdjarige (gedenkdag). Tercet, tɐ̂ set, drieregelig gedichtje. Terebinth, terəbinth, terpentijnboom: Oil of Terebinth = terpentijnolie; Terebinthine, terəbinthin, terpentijn .… Teredine, terədin, Teredo, tərîdou, paalworm. Tergiversate, tɐ̂ dživəseit, uitvluchten zoeken, draaien; Tergiversation, draaierij, uitvlucht, afvalligheid. Term, tɐ̂ m, subst. grens, beperking, termijn, vervaldag, betaaldag, collegetijd, berechtingstijd, lid, uitdrukking, bewoording (In plain terms); Terms = voorwaarden, condities, honorarium, prijs, schoolgeld; stonden (med.); Term verb. noemen, benoemen, uitdrukken: A naval term = zeemansuitdrukking; To bring to terms = tot toegeven noodzaken; Couldn’t you come to terms? = het eens worden; Why don’t you express yourself in more definite terms = niet juister; I got on terms with him = kwam op goeden voet; They married on equal terms = in gemeenschap van goederen; To be on even terms = gelijk staan; To be on good terms = goed met elkaar; They are on intimate terms, terms of intimacy = zeer intiem met elkaar, op intiemen voet; To be on poor terms = niet al te best met elkaar; What are your terms? = wat vraagt u daarvoor? Term of life (of a person’s natural
- 66. life) = levensduur; Term of office = diensttijd; Term of payment = betalingstermijn. Termagant, tɐ̂ məg’nt, subst. helleveeg; adj. kijfachtig. Terminable, tɐ̂ minəb’l, begrensbaar; subst. Terminableableness; Terminal, subst. einde, grens, uiterste; adj. eindigend, begrenzend: Terminal station = kopstation; Terminate, tɐ̂ minit, begrensd, beperkt; Terminate verb. tɐ̂ mineit, begrenzen, eindigen, een einde maken aan: I shall soon be terminated with you = tusschen ons beiden zal het gauw uit zijn; Termination = grens, einde, begrenzing: To draw to a termination = ten einde loopen; Terminative = begrenzend, uitsluitend; Terminology, terminologie; Terminus = grenssteen, eindstation. Termite, tɐ̂ mait, witte mier. Ternary, tɐ̂ nəri, subst. drietal, groep van drie; adj. van of bij drieën, drietallig. Terpsichore, tɐ̂ pksikərî, Terpsichore; Terpsichorean, tɐ̂ psikərîən, dans —. Terra, terə, de aarde: Terra del Fuego, terədelfjûgou, Vuurland; Terra alba = pijpaarde; Terra-cotta = terracotta; Terra firma = vaste grond; Terra incognita = onbekend land. Terrace, teris, subst. terras, plat dak; Terrace verb. tot terrassen vormen. Terrapin, terəpin, moerasschildpad (Amer.). Terraqueous, təreikwiəs, tərakwiəs, uit land en water bestaande.
- 67. Terrestrial, tərestriəl, ondermaansch, aard—, land—; subst. aardbewoner: Terrestrial animal; Terrestrial globe. Terret, terət, de ring waar doorheen de leidsels worden gestoken. Terrible, terib’l, verschrikkelijk, ontzagwekkend, ijselijk, kolossaal; subst. Terribleness. Terrier, teriə, terrier. Terrific, tərifik, schrik- of ontzagwekkend; Terrify, terifai, doen verschrikken, schokken: I was terrified to death = schrikte me dood. Territorial, teritôriəl, territoriaal; subst. soldaat behoorende bij het Territorial Army (Territorial Force) i e. een corps van vrijwilligers, dat in 1908 de oude Volunteers verving; Territorialize = vergrooten; tot een territory maken (Amer.); Territoried, teritərid, land in eigendom hebbende; Territory, teritəri, gebied, (ook fig.), landstreek, bereik; gebied met minder dan 60.000 inwoners en zonder vertegenwoordiging in het Congres (Amer.). Terror, terə, schrik, ontsteltenis: The King of Terrors = de Dood; The Reign of Terror = het Schrikbewind; I am struck with terror, terror-struck, terror-stricken = van schrik verpletterd; Terrorism = schrikbewind; Terrorist = lid van het schrikbewind, terrorist; Terrorize, terəraiz, schrik aanjagen, door schrik dwingen. Terry, teri, soort van pluche; Terry-velvet = soort katoenfluweel. Terse, tɐ̂ s, beknopt, kort en bondig; subst. Terseness. Tertian, tɐ̂ š’n, derdedaagsch: Tertian fever; Tertiary, tɐ̂ šəri, tertiair: Tertiary epoch, formation. [569]
- 68. Tertullian, tɐ̂ tɐlj’n, Tertulianus. Terza-rima, tɐ̂ tsə-rîmə, tercine; Terzetto, ter-tsətou, terzet (muz.). Tessellar, tesələ, geruit; Tesselated, tesəleitid, geruit, als mozaïek; Tessellation = ruit- of mozaïekwerk; Tessera, tesərə, klein kubusje van marmer, etc. voor mozaïekwerk; Tesseral = Tessellar. Test, test, subst. toets, toetssteen, onderzoek, proef, reagens, oordeel, kroes tot zuiveren van metaal; Test verb. toetsen, beproeven, keuren, onderzoeken, attesteeren: Crucial test = vuurproef (fig.); That’s a fair test = een geschikte opgaaf (bij een examen, b.v.); To apply a severe test to = aan een streng onderzoek onderwerpen; To put to the test = op de proef stellen; To stand the test = de proef doorstaan; Test Act = Eng. wet van 1678–1828, die voor ambtenaren een eed voorschreef waarbij ze betuigden niet Katholiek te zijn; State tested unadulterated liquor = van rijkswege gekeurde, onvervalschte sterke drank; Test-paper = lakmoespapier; Test-tube = reageerbuisje; Test-types = letters om de gezichtsscherpte te keuren; Testable = wat geattesteerd kan worden, in staat een testament te maken of getuigenis af te leggen; Tester = toetser. Zie Tester. Testacea, testeišə, schelpdieren; Testacean, subst. schelpdier; adj. tot de schelpdieren behoorende; Testaceous, testeišəs, schaal - -; bruingeel: Testacean animals = schaaldieren. Testament, testəment, testament, verbond: New Testament; Old Testament; Testamental, Testamentary, testəment’l, testəmentəri, testamenteel; Testamur, təsteimə, testimonium; Testate, testit, subst. die een testament gemaakt heeft; adj. een testament nalatend: To die testate; Testator, testeitə, Testatrix, testeitriks, erflater, erflaatster.
- 69. Tester, testə, oude shilling (onder Henry VIII); vierkante ledikantshemel, plat klankbord (kansel): Tester-bed = met een hemel. Zie Test. Testicle, testik’l, zaadbal; Testiculate, təstikjulit, Testicular, təstikjulə, gelijk een bal. Testification, testifikeiš’n, getuigenis; Testifier = getuige; Testify, testifai, plechtig verklaren, getuigen, getuigenis afleggen: I shall never testify against you = tegen u getuigen; He testified to my good conduct = hij gaf getuige van. Testimonial, testimounj’l, subst. getuigschrift, verklaring, attestatie, hulde, huldeblijk; ook adj.: Testimonial dinner = feestmaaltijd ter eere van; Testimonial letter; Testimony, testiməni, getuigenis, betuiging, openbaring, Gods woord: In testimony whereof = ten bewijze waarvan; To bear testimony = getuigenis afleggen; To call in testimony = tot getuige roepen. Testiness, testinəs, subst. v. Testy, testi, eigenzinnig, knorrig, gemelijk, prikkelbaar. Tetchiness, tetšinəs, subst. van Tetchy = knorrig, gemelijk. Tether, tedhə, subst. touw waaraan een grazend dier is gebonden, speelruimte, bevoegdheid; Tether verb. vastbinden, beperken: He came to the end of his tether = zijne middelen waren uitgeput; To go to the end of one’s tether = zoo ver gaan als men kan; To tether a person by a short rope = iemand kort houden. Tetra, tetrə, (in samenst.), vier: Tetrachord = halve octaaf (van c tot f of van g tot c); viersnarige lier; Tetradactyl(e), tetrədaktil, viervingerig; Tetradiapason, tetrədaiəpeiz’n, viervoudige octaaf; Tetragon, tetrəgon, vierhoek; adj. Tetragonal, tətragən’l;
- 70. Tetrahedron, tetrəhîdr’n, tetrəhedr’n, regelmatig viervlak; Tetrameter, tətramətə, viervoetige versregel; Tetrapetalous, tetrəpetəlɐs, vierbladig; Tetrapod = vierpootig; Tetrapteran, tətraptər’n, viervleugelig (insect); Tetrapterous, tətraptərɐs, viervleugelig; Tetrarch, tetrâk, tîtrâk, gouverneur van het vierde van een wingewest (Rom.): Tetrarchate, tetrâkit, tîtrâkit, Tetrarchy, tetrâki, grondgebied van een T.; Tetrastich, tetrəstik, tətrastik, vierregelig gedicht. Tetter, tetə, subst. naam voor verschillende huidziekten (Eating tetter = lupus); Tetter verb. eene huidziekte bezorgen. Teuton, tjût’n, iemand v. Teutonischen stam; Teutonic, tjutonik, Teutonisch, Germaansch: Teuton languages = Germaansche talen; Teutonicism, tjutonisizm, Germaansch idioom; Teutonize = germaniseeren. Teviot, tiviət; Tewk(e)sbury, tjûksb’ri; Texan, teks’n; Texas, teksəs; (The) Texel, dhəteks’l. Text, tekst, tekst, onderwerp, inhoud; Textbook = handboek, schoolboek; Text-hand = groot loopend schrift; Textual = volgens den tekst; Textualist = schriftgeleerde, iemand die zich streng aan den tekst houdt. Textile, tekst(a)il, subst. geweven stof; adj. geweven: Textile industry; Texture, tekstjə, het weefsel, structuur. Thackeray, thakər(e)i; Thaddeus, thədîəs, thadiəs; Thaisa, theiizə, thəîzə. Thaler, tâlə, thaler. Thales, theilîz; Thalia, thəlaiə, Thalia: Thalian = komisch; Thaliard, thaliəd.
- 71. Thames, temz, Theems: He will not set the Thames on fire. Zie Fire. T(h)ammuz, t(h)aməz, vierde maand van het Joodsche burgerlijk jaar. Than, dhan, dan (alléén na comparatieven): He is older than I by seven years. Thane, thein, Angelsaksische titel der grootere grondbezitters tot de 12de eeuw; Thanedom; Thane-lands; Thaneship. Thanet, thanət. Thank, thaŋk, subst. dank (thans steeds meervoud); Thank verb. danken, bedanken (dikwijls ironisch): Thanks to thee, I am safe = ik ben veilig, dank zij u; Thanks = ik dank u; No, thanks! = dank u; geen dank; Thanks to your eagerness = dank zij; Thanks be to God = God zij dank; To give thanks = danken (na den maaltijd); To return thanks = dank betuigen; No thank you = ik dank u; Thank you, yes = alstublieft; Thank you for nothing = ik zou je danken; Thank God we are rid of him = Goddank; He has only to thank himself for = ’t is zijn eigen schuld, dat…; I’ll thank you to shut the door = doe alstublieft de deur dicht; I’ll thank you not to do it = gij doet me [570]plezier als gij het laat; I’ll thank you for the potatoes (for a cup of tea) = mag ik alstublieft; Thank-offering = dankoffer; Thanksgiver = bedanker, dankzegger; Thanksgiving = dankzegging (aan God); Thanksgiving-day = dankdag; Thankee = dank u; Thankful = dankbaar; subst. Thankfulness; Thankless = ondankbaar: Thankless task; subst. Thanklessness; Thankworthiness, subst. v. Thankworthy = dankenswaard, verdienstelijk.
- 72. That, dhat, gene, die, dat; opdat: That is me (I) = dat ben ik; He is a good fellow for all that = toch een goede vent; And that for this reason = en wel om deze reden; Nothing follows, nothing that is, which is of any real weight = namelijk niets; While his family, his mother that is, were living in D. = zijn moeder namelijk; No human being ever spoke like that = op zoo’n manier; This is horrible, that is = dit is bepaald af grijselijk; Mrs Quilp that is = de tegenwoordige; Mrs Corney that was = de vroegere; He has been here, but what of that? = wat zou dat, bewijst dat; I was the eldest son and not much of a help at that = en trouwens als zoodanig nog geen groote steun; ‘Christmas comes but once a year’, and adds the cynic, ‘once too often at that’ = en dat is trouwens nog één keer te vaak; I send you word that you may be prepared = opdat gij voorbereid zijt; It is not that I believe = niet omdat ik geloof; Do tell me, that’s a good girl = dan ben je eene beste meid; That much is certain = zooveel; I am that sorry = het spijt me zóó! I suppose you are worth all that = wel zóó rijk; I am tougher than that = daar ben ik te taai voor. Thatch, thatš, subst. dakstroo, dakriet, stroodak, hut; Thatch verb. met stroo of riet dekken; Thatcher = rietdekker. Thaumatrope, thômətroup, thaumatroop; Thaumaturge, thômətɐ̂ dž, wonderdoener = Thaumaturgist; Thaumaturgic(al), thômətɐ̂ džik(’l), wonderdadig; Thaumaturgy, thômətɐ̂ dži, wonderdoenerij. Thaw, thô, subst. dooi; Thaw verb. dooien, ontdooien (ook fig.): Silver thaw = ijzel; The thaw set in = het begon te dooien; It thaws. The, dhə, dhi (vóór een klinker), dhî (met nadruk), de, het: The more the merrier = hoe meer zieltjes hoe meer vreugde; The sooner the better = hoe eerder hoe beter; The more I see you
- 73. the better I like you = hoe meer ik u zie, hoe meer ik van u houd; The more so, as I do not know him = des te meer omdat; The rather = temeer; So this the grand-daughter, is it? = je kleindochter; The Lady Grace Eveleigh = Freule G. E. (meer officieel dan zonder ’t lidwoord); The Douglasses’ house = der familie D. Theatre, thîətə, theater, schouwburg, toeneel, medische gehoorzaal: Theatre of war = oorlogstooneel; Theatre-goers = bezoekers; Theatrical, thiatrik’l, theatraal: Theatricals = tooneelvertooningen: Private theatricals = liefhebberijtooneel; Theatricality, thîatrikaliti, theatrale manier van doen, vertoon. Theban, thîb’n, subst. en adj. Thebaan(sch): Theban year = 365 d. en 6 u; Thebes, thîbz, Thebe; Thecla, theklə. Thee, dhî, u (object van Thou): They thee and thou each other = spreken elkaar aan met je en jou. Theft, theft, diefstal. Theina, thiainə, Theine, thî-in, theeïne. Their, dhêə, hun, haar; Theirs = van hen, van haar: These books are theirs = zijn de hunne. Theism, thîizm, theïsme; Theist; adj. Theistic(al). Them, dhem, hen, haar (object van They); Themselves, dh’mselvz, zich zelven, zij zelven. Thematic, thimatik, thematisch; Theme, thîm, onderwerp, thema, stam, (gram.). Themis, thîmis, Themis.
- 74. Then, dhen, adj. toenmalig; adv. en conj. toen, dan, later, alsdan, daarom, diensvolgens, derhalve: The then measures were insufficient = toen genomen; You might have had a bad fall then = daar had je leelijk kunnen vallen; What did he say then? = wat zei hij daar toch; Did he laugh at you? Then he ought not = maar dat moest hij niet; I hear his footstep, then he is back = dus is hij terug; I think, then I exist = ik denk, dus besta ik; By then = tegen dien tijd; On then! = vooruit! If he sees us, what then? = wat zou dat, wat hindert dat; Now and then = nu en dan; Every now and then = telkens; Now then, what can you say to the contrary? = welnu; Then and there = onmiddellijk, op staanden voet; Till then = tot dien tijd, tot zoolang; Not until then = eerst toen; Thence, vandaar, derhalve: From then = van uit die plaats; Thenforth, dhensföth, Thenforward, dhensföwəd, van dien tijd af aan. Theobald, thîəbôld. Theocracy, thiokrəsi, theocratie; Theocrat; Theocratic(al) = theocratisch; Theodicy, thiodisi, theodicee. Theodora, thîədôrə; Theodore, thîədö. Theogony, thiogəni, theogonie; Theologian, thiəloudž’n, godgeleerde; Theologic(al), thîəlodžik(’l), theologisch: The theological virtues are: Faith, Hope and Charity = de goddelijke deugden zijn: Geloof, Hoop en Liefde; Theologize, thiolədžaiz, theologiseeren; Theology, thiolədži, theologie: Natural theology = de kennis Gods uit Zijne werken. Theophilus. thiofilɐs. Theorbo, thiöbou, theorbe, groote basluit.
- 75. Theorem, thîər’m, theorema; adj. Theorematic(al). Theoretic(al), thîəretik(’l), theoretisch; Theoretics = het theoretisch gedeelte eener wetenschap; Theorist, thîərist, theoreticus; Theorize = theoretiseeren: Theorizer; Theory, thîəri, theorie: His practice falls short of his theory = zijne praktijk haalt niet bij zijne theorie. Theosophy, thiosəfi, theosophie. Therapeutic(al), therəpjûtik(’l), therapeutisch; Therapeutics = therapie. There, dhêa, daar, er: When did he leave there = wanneer is hij vandaar vertrokken; You are right (wrong) there = daar hebt ge gelijk (ongelijk) aan; Here [571]and there = hier en daar; Then and there = op dat zelfde oogenblik (= There and then); I am all there = ik weet drommels goed wat ik doe; There you are! = klaar is ’t; alstublieft; There is a horse for you = dat is nog eens een paard! I am no match for you there = in dat opzicht kan ik niet tegen u op; You have got a tile off and are not all there = je bent niet recht bij het hoofd, en weet niet wat je doet; That to me is everything! So there! = nu weet je het; en daarmede basta! Stop, there’s a good fellow = dan ben je een beste; Thereabout(s) = daaromtrent: A guilder or thereabouts = een gulden of daaromtrent; Thereafter = daarna, volgens dat, daarnaar; Thereanent = met betrekking tot dat punt; Thereat = daar, om die reden, bovendien: He is poor and a fool thereat = en een dwaas op den koop toe; Thereby = daarnevens, daardoor, diensvolgens, daaromtrent; Therefor = hiervoor; Therefore = daarom, daarvoor, met dat doel; Therefrom = daarvan, daaruit; Therein = daarin, hierin; Thereinto = daarin; Thereof, dhêrov, hier- of daarvan; Thereon = hier- of daarop, er op; Thereout = daaruit; Thereto = daar- of hiertoe, buiten en behalve; Thereunder = daaronder;
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