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Cognitive Computing Models
in Communication Systems

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Cognitive Computing Models
in Communication Systems
Budati Anil Kumar
ECE Department, Gokaraju Rangaraju Institute of
Engineering & Technology, Hyderabad, India
S. B. Goyal
Faculty of Information Technology, City University, Malaysia
and
Sardar M.N. Islam
The American University of Ras Al Khaimah (AURAK), United Arab Emirates

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10 9 8 7 6 5 4 3 2 1

v
Contents
Preface xi
Acknowledgement xiii
1 Design of a Low-Voltage LDO of CMOS Voltage Regulator
for Wireless Communications 1
S. Pothalaiah, Dayadi Lakshmaiah, B. Prabakar Rao,
D. Nageshwar Rao, Mohammad Illiyas and G. Chandra Sekhar
1.1 Introduction 2
1.2 LDO Controller Arrangement and Diagram Drawing 2
1.2.1 Design of the LDO Regulator 4
1.2.1.1 Design of the Fault Amplifier 4
1.2.1.2 Design of the MPT Phase 8
1.3 Conclusion 14
References 14
2 Performance Analysis of Machine Learning and Deep Learning
Algorithms for Smart Cities: The Present State and Future
Directions 15
Pradeep Bedi, S. B. Goyal, Sardar MN Islam, Jia Liu
and Anil Kumar Budati
2.1 Introduction 16
2.2 Smart City: The Concept 16
2.3 Application Layer 18
2.3.1 Smart Homes and Buildings 18
2.3.1.1 Smart Surveillance 18
2.3.2 Smart Transportation and Driving 19
2.3.3 Smart Healthcare 19
2.3.4 Smart Parking 19
2.3.5 Smart Grid 19
2.3.6 Smart Farming 19
2.3.7 Sensing Layer 20
2.3.8 Communication Layer 20

vi Contents
2.3.9 Data Layer 20
2.3.10 Security Layer 21
2.4 Issues and Challenges in Smart Cities: An Overview 21
2.5 Machine Learning: An Overview 22
2.5.1 Supervised Learning 22
2.5.2 Support Vector Machines (SVMs) 22
2.5.3 Artificial Neural Networks 23
2.5.4 Random Forest 24
2.5.5 Naïve Bayes 25
2.6 Unsupervised Learning 26
2.7 Deep Learning: An Overview 26
2.7.1 Autoencoder 27
2.7.2 Convolution Neural Networks (CNNs) 27
2.7.3 Recurrent Neural Networks (RNNs) 28
2.8 Deep Learning vs Machine Learning 29
2.9 Smart Healthcare 30
2.9.1 Evolution Toward a Smart Healthcare Framework 30
2.9.2 Application of ML/DL in Smart Healthcare 31
2.10 Smart Transport System 33
2.10.1 Evolution Toward a Smart Transport System 33
2.10.2 Application of ML/DL in a Smart Transportation
System 34
2.11 Smart Grids 36
2.11.1 Evolution Toward Smart Grids 36
2.11.2 Application of ML/DL in Smart Grids 38
2.12 Challenges and Future Directions 40
2.13 Conclusion 41
References 41
3 Application of Machine Learning Algorithms and Models
in 3D Printing 47
Chetanpal Singh
3.1 Introduction 48
3.2 Literature Review 50
3.3 Methods and Materials 65
3.4 Results and Discussion 69
3.5 Conclusion 70
References 72

Contents vii
4 A Novel Model for Optimal Reliable Routing Path
Prediction in MANET 75
S.R.M. Krishna, S. Pothalaiah and R. Santosh
4.1 Introduction 76
4.2 Analytical Hierarchical Process Technique 77
4.3 Mathematical Models and Protocols 78
4.3.1 Rough Sets 78
4.3.1.1 Pawlak Rough Set Theory Definitions 78
4.3.2 Fuzzy TOPSIS 79
4.4 Routing Protocols 80
4.4.1 Classification of Routing Paths 80
4.5 RTF-AHP Model 81
4.5.1 Rough TOPSIS Fuzzy Set Analytical
Hierarchical Process Algorithm 81
4.6 Models for Optimal Routing Performance 83
4.6.1 Genetic Algorithm Technique 84
4.6.2 Ant Colony Optimization Technique 84
4.6.3 RTF-AHP Model Architecture Flow 84
4.7 Results and Discussion 85
4.8 Conclusion 88
References 88
5 IoT-Based Smart Traffic Light Control 91
Sreenivasa Rao Ijjada and K. Shashidhar
5.1 Introduction 92
5.2 Scope of the Proposed Work 93
5.3 Proposed System Implementation 94
5.4 Testing and Results 99
5.5 Test Results 100
5.6 Conclusion 104
References 105
6 Differential Query Execution on Privacy Preserving Data
Distributed Over Hybrid Cloud 107
Sridhar Reddy Vulapula, P. V. S. Srinivas and Jyothi Mandala
6.1 Introduction 107
6.2 Related Work 108
6.3 Proposed Solution 110

viii Contents
6.3.1 Data Transformation 110
6.3.2 Data Distribution 113
6.3.3 Query Execution 114
6.4 Novelty in the Proposed Solution 115
6.5 Results 115
6.6 Conclusion 119
References 120
7 Design of CMOS Base Band Analog 123
S. Pothalaiah,Dayadi Lakshmaiah, Bandi Doss,
Nookala Sairam and K. Srikanth
7.2 Proposed Technique of the BBA Chain
for Reducing Energy Consumption 125
7.3 Channel Preference Filter 130
7.4 Programmable Amplifier Gain 132
7.5 Executed Outcomes 133
7.6 Conclusion 135
References 135
8 Review on Detection of Neuromuscular Disorders Using
Electromyography 137
G. L. N. Murthy, Rajesh Babu Nemani, M. Sambasiva Reddy
and M. K. Linga Murthy
8.2 Materials 139
8.3 Methods 140
8.4 Conclusion 142
References 142
9 Design of Complementary Metal–Oxide Semiconductor Ring
Modulator by Built-In Thermal Tuning 145
P. Bala Murali Krishna, Satish A., R. Yadgiri Rao,
Mohammad Illiyas and I. Satya Narayana
9.2 Device Structure 147
9.3 DC Performance 149
9.4 Small-Signal Radiofrequency Assessments 149
9.5 Data Modulation Operation (High Speed) 150
9.6 Conclusions and Acknowledgments 152
References 153

Contents ix
10 Low-Power CMOS VCO Used in RF Transmitter 155
D. Subbarao, Dayadi Lakshmaiah, Farha Anjum,
G. Madhu Sudhan Rao and G. Chandra Sekhar
10.2 Transmitter Architecture 157
10.3 Voltage-Controlled Ring Oscillator Design 158
10.4 CMOS Combiner 161
10.5 Conclusion 163
References 163
11 A Novel Low-Power Frequency-Modulated Continuous
Wave Radar Based on Low-Noise Mixer 165
Dayadi Lakshmaiah, Bandi Doss, J.V.B. Subrmanyam,
M.K. Chaitanya, Suresh Ballala, R. Yadagirir Rao
and I. Satya Narayana
11.2 FMCW Principle 168
11.3 Results 174
11.4 Conclusion 178
References 179
12 A Highly Integrated CMOS RF Tx
Used for IEEE 802.15.4 181
Dayadi Lakshmaiah, Subbarao, C.H. Sunitha,
Nookala Sairam and S. Naresh
12.2 Related Work 182
12.3 Simulation Results and Discussion 185
12.4 Conclusion 186
References 187
13 A Novel Feedforward Offset Cancellation Limiting
Amplifier in Radio Frequencies 189
Dayadi Lakshmaiah, L. Koteswara Rao, I. Satya Narayana,
B. Rajeshwari and I. Venu
13.2 Hardware Design 190
13.2.1 Limiting Amplifier 190
13.2.2 Offset Extractor 192
13.2.3 Architecture and Gain 192
13.2.4 Quadrature Detector 192
13.2.5 Sensitivity 194

x Contents
13.3 Experimental Results 195
13.4 Conclusion 195
References 196
14 A Secured Node Authentication and Access Control Model
for IoT Smart Home Using Double-Hashed Unique Labeled
Key-Based Validation 199
Sulaima Lebbe Abdul Haleem
14.2 Challenges in IoT Security and Privacy 203
14.2.1 Heterogeneous Communication and Devices 203
14.2.2 Physical Equipment Integration 204
14.2.3 Resource Handling Limitations 204
14.2.4 Wide Scale 204
14.2.5 Database 204
14.3 Background 209
14.4 Proposed Model 210
14.4.1 Communication Flow 214
14.4.1.1 IoT Node and Registration Authority 214
14.4.1.2 User and Local Authorization Authority 215
14.5 Results 215
14.6 Conclusion 218
14.7 Claims 218
References 219
Index 221

xi
Preface
Domain-specific system architectures such as software and hardware
are attracting attention for use in overcoming the stagnation of size
scaling of memory and domain functions in wireless communication
systems. The need for improvements in performance to lower latency,
and for faster simulation and power efficiency requires dedicated soft-
ware and hardware focused on accelerating key applications. This type
of system is widespread, and artificial intelligence, hardware descrip-
tion languages, machine learning, neural networks, advanced computer
algorithms, and deep learning are especially becoming mainstream in
all areas. The rapid growth of applications and system software is also
reflected in hardware system architectures, signal processing speeds,
wired/wireless communication systems, computational algorithms,
and data storage/transmission systems.
Ensuring the security and efficiency of communication system design
and implementation is a top priority. Recent research has been aimed at a
higher degree of autonomy of such systems in architecture/system design,
implementation, and optimization, especially in areas such as advanced
system architecture, digital signal processing, communication systems,
and the internet. This poses new challenges for implementation and valida-
tion. Therefore, much research is being conducted in the area of embedded
security and autonomous software systems of things and various aspects
of communication systems and computing technologies. To this end, this
book provides a comprehensive overview of current research on cognitive
models in communication systems and computing. Furthermore, it aims
to fill in the gap between various communication systems and solutions by
providing current models and computing technologies, their applications,
the strengths and limitations of existing methods, and future directions in
this area.

xii Preface
The main purpose of this book is to publish the latest research papers
focusing on problems and challenges in the areas of data transmis-
sion technology, computer algorithms, artificial intelligence (AI)-based
devices, computer technology, and their solutions to motivate researchers.
Therefore, it will serve as an instant ready reference for researchers and
professionals working in the area of Cognitive Models.
The Editors
July 2022

xiii
Acknowledgement
We, the editors of Cognitive Models in Communication Systems and
Computing Methods, wish to acknowledge the hard work, commitment and
dedication of the authors who have contributed their wonderful chapters
to this book within the stipulated time frame.
Furthermore, we would like to convey our special gratitude to Dr.
Prasenjit Chatterjee, Dean (Research and Consultancy) of MCKV Institute
of Engineering, West Bengal, India, for his consistent support and guid-
ance at each stage of the book’s development.
We wish to bestow our best regards to all the reviewers for providing
constructive comments to the authors to improve their chapters to meet
the publisher’s standard, quality and coherence. A successful book publica-
tion is the integrated result of more people than the people granted credit
as editor and author and we acknowledge these unsung heroes.
Finally, we, the editors, acknowledge everyone who helped us directly
and indirectly.
Budati Anil Kumar
S. B. Goyal
Sardar M.N. Islam
August 2022

1
Budati Anil Kumar, S. B. Goyal and Sardar M.N. Islam. Cognitive Computing Models
in Communication Systems, (1–14) © 2022 Scrivener Publishing LLC
1
Design of a Low-Voltage LDO
of CMOS Voltage Regulator for
Wireless Communications
S. Pothalaiah1
, Dayadi Lakshmaiah2
*, B. Prabakar Rao3
,
D. Nageshwar Rao4
, Mohammad Illiyas5
and G. Chandra Sekhar6
1
Electronics and Communication Engineering Dept., Vignana Bharathi Institute
of Technology, Hyderabad, India
2
Electronics and Communication Engineering Dept., Sri Indu Institute
of Engineering and Technology, Hyderabad, India
3
Electronics and Communication Engineering Dept., JNTUH, Hyderabad, India
4
Electronics and Communication Engineering Dept., TKRCET, Hyderabad, India
5
Electronics and Communication Engineering Shadan College of Engineering and
Technology, Hyderabad, India
6
Electronics and Communication Engineering Dept., Sri Indu Institute
of Engineering and Technology, Hyderabad, India
Abstract
In this document, a low-voltage, low-dropout (LDO) voltage regulator process is
proposed and executed by means of a 0.25-µm complementary metal–oxide semi-
conductor (CMOS). This debate of a 3- to 5-V, 50-mA small CMOS give up a single
1-pF compensating capacitor in a linear voltage regulator. Tentative outcomes prove
that the highest yield load current is 50 mA and the control yield electrical energy is
2.8 V. The controller provides a total weight fleeting reaction by lower than a 5-mV
overshoot in addition to the undershoot. The lively outline part is 358.28 µm ×
243.30 µm. Voltage regulators are used to provide a stable power supply voltage
independent of load impedance, input voltage variations, temperature, and time.
LDO regulators are distinguished by their ability to maintain regulation with small
differences between the supply voltage and the load voltage. LDO is used for wireless
communications and satellite. In this chapter, the authors discuss how LDO works.
*Corresponding author: laxmanrecw@gmail.com

2 Cognitive Computing Models in Communication Systems
Keywords: LDO, low-voltage regulator, CMOS, linear controller, power supply
circuits, regulator
1.1 Introduction
A low-dropout (LDO) controller is a direct current linear electrical energy
regulator that is able to run through extremely minute input–output discrep-
ancies of electrical energy. Claim intended small-voltage, small fall-away reg-
ulator is rising since rising consists of convenient electronics, i.e., mobile or
radio. The same manufacturing also has self-profiling relevance [1]. Lately,
growing requirement meant for handy plus sequence operate yield contain
required circuit toward work below short-voltage situations, and elevated
current competence have as well grown essential toward capitalizing the life
span of battery [1]. The regulator ought to enclose a tiny dynamic region.
Low drop-out aim has turned into additional demand owing to the ris-
ing insistence of high-performance small dropouts, of which small-voltage
fast-instantaneous LDOs are particularly significant methods to pick up
the traditional LDO configuration contain to be projected. However, with
structural restriction, which is the major problem in concurrently achieving
steadiness, more output voltage correctness plus small retort point, at a halt
can’t exist defeat [2]. The structural restraint of traditional small dropouts is
mostly due to the connected solitary pole–zero termination scheme, into the
break off capacitor through the elevated corresponding sequence confron-
tation necessary for obtaining small regularity pole–zero termination. The
resulting sphere expansion was not satisfactorily elevated toward reaching a
fine line and load system, and the loop gain bandwidth was not satisfactorily
large for little reaction time in adding, essential elevated equivalent series
resistance (ESR) introduces useless response. A low-voltage plan is moreover
imperfect due to the voltage buffer surrounded by traditional LDOs [3]. An
additional enhancement on top of the traditional structure is not easy due to
the limitation of the constancy of LDO. Therefore, to accomplish fine speci-
fications, a novel LDO among the extremely easy circuit configurations was
engaged. The organization has twice over pole–zero termination schemes,
along with a blueprint providing how save for present is a instant.
1.2 LDO Controller Arrangement and Diagram
Drawing
The configuration of the planned LDO is displayed below (Figure 1.1). It
is self-possessed into two phases: the first phase, as in a traditional LDO,

Low Power CMOS Voltage Regulator 3
comprise an error electronic equipment applied to supply fault electronic
equipment for voltage regulation, while the second phase is a common
source amplifier that incorporates a lot of the output sway. Thanks to the
cascade design, amplification depends on the harvest of electrical energy
gain of the two gain phases.
The circuit in Figure 1.2 shows a liability amplifier of the discrepancy
couple M2 and M3 through dynamic loads M4 and M5, whereas the sec-
ond gain phase is the common source (CS) phase M6 in the bias current
spring M7. The output swing of the second phase was greatly enhanced
compared to the source admire into the revolving ON/OFF power tran-
sistor, and so this arrangement is appropriate for low-voltage LDOs. The
current mirrors M, M7, and M8 offer current bias for the two phases.
Maximum power transfer (MPT) was planned to function within the
saturation state at fall away. While the voltage gain of the MPT is less than
unanimity, the gain is not tainted because of the error electronic equipment
in addition to the second gain phase. A mere gain of 60 dB was achieved in
the projected plan, which is adequate for high-quality line and load regula-
tions [1]. In the design blueprint, for fine transitory reaction presentation,
the transistor dimensions reached centimeters, which generates larger gate
capacitors. At gate, the swing rate of MPT and freq reaction for low drop-
out disadvantage meant of designed LDO Vin workings as of 3–5 V, it pro-
jected LDOs control range.
Rw
Rr
Vout
Cout
lout
Cc
Rc
negative gain stage
power transistor
MPT
Vout
error amplifier
Vm
Vp
voltage reference
Vin
Vref
input voltage supply
+
–
+
–
Figure 1.1 Schematic diagram of the low dropout control [2].

1.2.1 Design of the LDO Regulator
Intent of small dropout is capable of subdivided keen on the drawing of
power transistor (MPT) and intent of two-phase operational amplifier.
1.2.1.1 Design of the Fault Amplifier
During this part, the method was modernized to allow determination of
the first-cut design of the second-phase operational amplifier. The tender
estimate approached 70% of the design method. The two-phase opera-
tional amplifier was subsequently developed (Table 1.1).
BW, bandwidth; GB, gain bandwidth; ICMR, input common mode
range; Pdiss
, power dissipation; Cl
, load capacitor; SR, swing rate; Vout
, output
voltage; Vdd
, drain voltage; Vss
, source voltage
Cc
= 0.22XCL = (2.2/10) * 10p = 2.2 pF ≈ 3 pF (1.1)
Thereafter, the smallest value of the extremity current I5
was determined
based on the swing rate requirements.
Vin Ibias
M2
Vref
M1
M3
M7
M8
M4 M5 M6 MPT
Rf2
Rf1
Cf2 Cout
RESR
Vout
Figure 1.2 Representation of the planned low-dropout (LDO) control device [3–5].

M1
30uA
Vin -
M2
M3 M4 M6
Vdd=2.5v
Vin +
M8 M5
30uA
M7
Vss=-2.5v
CL=10pF
Vout
Cc=2.8pF
Figure 1.3 Diagram of fault amplifier [5–8].
Table 1.1 Requirements for the design of the two-phase
operational amplifier [5–8].
Parameter Symbol Value
Operational amplifier gain Av ≥2,220 V/V
Gain GB 5 MHz
BW Pdiss ≤1.2 mW
Power
Indulgence Cl 10 pF
Load
Capacitance SR ≥10 V/µs
Swing rate Vout ±2 V
Output voltage
Range ICMR −2.5 to 3.2
Input common +2.5 V
Mode range Vdd +2.5 V
Positive Vss
Voltage −2.5 V

I5
= SR(Cc
)
=10 * 106
* 3p (1.2)
=30 µA
The characteristic ration of M3 is able to be resolved with the require-
ment meant for optimistic input common mode series.
S3
= (W/L)3 = I5
/(K3
)[Vdd
− Vin(max)
− |VT03
|(max) + VT(min)
]2 = 15
S4 = S3 (1.3)
The requirements of the transconductance i/p transistor are resolved
with the idea of Cc
and GB. Transconductance, gm1
, was computed using
the following equation:
gm1
= GB * Cc
= 94.24 µs (1.4)
The feature percentage (W/L)1
was directly calculated using gm1
as
follows:
The aspect ratio (W/L)1
was
S1
= (W/L)1
= S2
= gm12
(K1′
) (I5
) = 3 (1.5)
The required sequence is now obtained near computing the diffusion
electrical energy of transistor M5. Due to the unfavorable ICMR equation,
VDS5
was computed using the following association:
VDS5
= Vin(min)
− Vss
− (I5
)1/2
− Vt1(max)
(β1
) = 0.35 V = 350 mV
(1.6)
With the obtained VDS5
, (W/L)5
can then be extracted using the equa-
tion below:
S5
= 2I5
K5′
(VDS5
)2
= 4.5 (1.7)

Thereafter, the first stage of the operational amplifier is done.
Subsequently, the production phase is planned. Designed for a stage edge
of 60, the location of the productivity extremity is understood to be toward
2.2 times the GB, after which 0 is positioned on the slightest 10 times supe-
rior than GB. The transconductance, gm6
, can then be resolved using the
following relation:
gm6
≥ 10gm1
= 942.4 μs (1.8)
Thus, used for realistic stage edge, the value of gm6
is about 10 times the
input stage transconductance gm1
. Present are two likely approaches near
implementation of the design of M6 (i.e., W6
/L6
and I6
). The primary one
was to attain good mirror of the first-phase current mirror loads of M3 and
M4, such that VGS4
= VGS6
; then:
Assume gm6
= 942.4 μs and manipulate gm4
:
10 * 5M * 3p = 150 μs.
We utilized the following equation to obtain
S6
= S4
* gm6
gm4
= 94 (1.9)
Consider gm6
and S6
describing direct current I6
with the following
expression:
I6
= gm6
2 2 * (K6′
) (W/L)6
= 198.14 μA (1.10)
The mechanism dimension of M7 was determined using the equation
below:
S7
= (W/L)7
= (W/L)5
* (I6
/I5
) = 14 (1.11)
Setting test Vmin(out)
even though the W/L of M7 is great sufficient is pos-
sibly not essential. The value of Vmin(out)
is
Vmin(out)
= VDS7(sat)
= √((2 * I6
)/(K7
* S7
)) = 0.3 (1.12)

Since are fewer variously necessary? Now the first-cut design is complete.
1.2.1.2 Design of the MPT Phase
Underneath are the steps during the MPT phase (Table 1.2).
MPTL (modular plug terminal length); MPTW (modular plug terminal
width); MPTM (modular plug terminal mosfet); VREF
, voltage reference;
COUT
, output of capacitance; Rf1
, feedback resistor 1; Rf2
, feedback resistor 2;
Cf2
, feedback capacitor 2.
Yield correctness of the planned low saturation is elevated by consid-
ering the outcome of the equalized electrical energy, here couple of strat-
egies needed for high-quality corresponding M2–M3 and M4–M5. The
offset electrical energy due to great changes in the fault amplifier output,
LDO condensed inside planned LDO owing to the near increase phase
created through M6 and M7. Owing to the easy path arrangement, the
harvest sound of the planned LDO was small. A fixed resistor was used for
M3
Vdd = 2.5v
30uA
4um / 0.25um
Vin -
Vin +
M1
1um / 0.25um
M2
1um / 0.25um
M4 M6
4um / 0.25 um
Cc=2.8pF
24um / 0.25um
Vout
CL=10pF
M7
8um / 0.25 um
2um /0.25um
M5
M8
2um /0.25um
Vss = -2.5v
30uA
Figure 1.4 Intent of the two-stage operational amplifier.

constant poles and zeros; thus, no combination noise is forced on the fault
amplifier. In addition, production noise of fault amplifier preserve exists
minimized in great gm2
and gm3
.
The planned LDO was carried out using 0.25-µm CMOS tools.
The complete chip outline is shown in Figure 1.13, with the region just
Table 1.2 Design requirements of maximum power transfer (MPT)
[1–8].
Parameter Parameter symbol Value
Length MPTL 2.5 μm
Width MPTW 0.625 μm
Reproduction feature MPTM 3,000
Orientation output voltage VREF
0.93 V
Capacitance bias COUT
20 μF
Bias resistance Rf1
50 kΩ
Bias resistance Rf2
100 kΩ
Bias capacitance Cf2
01 pF
Vin
3 to 5 V
voltage reference
Vref
0.93V
Vm
Vp
Vout
error amplifier
negative gain stage
Cc
MPT
power transistor
Icut
1pF
Cout
100 K ohm Rc
Rr
50 K ohm Rw
30 m ohm
Vout
20 uF
+
–
+
–
+
–
Figure 1.5 Complete blueprint of the maximum power transfer (MPT) stage [9].

359.28 µm * 243.3 µm. The LDO able to use as 3-5V, which covers a wide
variety of classic series electrical energy (Figures 1.3, 1.4, 1.5).
Figure 1.6 shows the input/output distinctiveness of the 2.8-V LDO
control device. LDO o/p voltage start stabilize on 2.8V, i/p voltage is 3v.
The leave low-loss voltage is 200 mV (3–2.8 V) at 50 mA.
An I/P voltage of 3 V and a partiality current of 30 μA are useful for low
loss. The inactive current is practical toward 129 μA, as revealed in Figure 1.7.
Remaining toward high loop gain provides via plan organization
and great range of the transistor, and the line and load extents are fine.
5.0
4.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
Voltage
(V)
0.0 0.5 1.0 1.5 2.0 2.5
volt(v)
volt(v)
3.0 3.5 4.0 4.5 5.0
v(out)
v(in)
Voltage
(V)
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0
2.0v
2.5
2.0
1.5
1.0
0.5
0.0
Figure 1.6 Dropout voltage.
Time (ms)
3.0
2.5
2.0
1.5
1.0
0.5
0.0
Current
(uA)
0
50
100
129uA
i1(req)
T-Spice1
(0.00, 129.10u) (0.00, 129.10u) dx=0.00 dy=0.00 m=lnf
Figure 1.7 Inactive current.

The designed line and load conventions were 1.85 mV/V and 56.4 μV/mA,
respectively, as displayed in Figures 1.8, 1.9, 1.10 and 1.11.
The load regulations of LDO were calculated via an N-channel metal–
oxide semiconductor (NMOS) freight because control through the 1-ms
time stage illustrated a 50-mA current when ON and a 0-mA current when
OFF. Vin
remained stable with a 5-V direct current.
The pass response was 44.5 μs. Power provide refusal was 68.3 dB, while
operational by 3 V (as shown in Figure 1.12).
The gain of LDO was invariable at 55.03 dB (as shown in Figure 1.13),
and the stage at 438.7 kHz was 64.1°.
Voltage
(V)
Current
(mA)
Time (ms)
Time (ms)
0.0 0.5 1.0 1.5 2.0 2.5 3.0
0.0 0.5 1.0 1.5 2.0 2.5 3.0
0.0
0.5
1.0
1.5
2.0
2.5
3.0
55
45
35
25
15
5
-5
Figure 1.8 Line regulation.
Vin Ibios
M2
Vref
M8 M1
M3
M7
Rf1
Rf2
M4 M5 M6 MPT
Vout
Cf2 Cout
R
RESR
V
switch
Figure 1.9 Low dropout (LDO) in the N-channel metal–oxide semiconductor (NMOS)
control as load [10].

(V
oltage
V
)
Current
(mA)
Time (ms)
0.0 0.5 1.0 1.5 2.0 2.5 3.0
Time (ms)
0.0 0.5 1.0 1.5 2.0 2.5 3.0
0.0
0.5
1.0
1.5
2.0
2.5
3.0
55
45
35
25
15
5
-5
Figure 1.10 Load guideline.
55
45
35
25
15
5
-5
50
40
30
20
10
0
Voltage
(V)
Current
(mA)
Time (ms)
Time (ms)
0.500 0.510 0.520 0.530 0.540 0.550 0.560
0.500 0.510 0.520 0.530 0.540 0.550 0.560
2.0015
2.0010
2.0005
2.0000
2.7995
2.7990
dx=44.53us v(vout)
Figure 1.11 Complete load transitory response [10].

The effectiveness of the low-voltage dropout regulator was inadequate
due to the inactive current and the I/P and O/P voltage, given below:
Effectiveness at 3-V I/P = ((10 * V0
)/((IO + Iq
) * Vi
)) * 100 = 93%
(1.13)
Effectiveness at 5-V I/P = 55.85%.
Voltage
Magnitude
(dB)
0
-50
-100
10 100 1k 10k 100k 1M 10M
Frequency (Hz)
-68.35dB
vdb(vout)
y1=68.68 y2=-68.35 dy=328.54m
psrr
Figure 1.12 Power supply rejection ratio (PSRR) of the projected low dropout [10].
Frequency (Hz)
10 100 1k 10k 100k 1M 10M 100M
Frequency (Hz)
10 100 1k 10k 100k 1M 10M 100M
Voltage
Phase
(deg)
200
150
100
50
0
64.13deg
Voltage
Magnitude
(dB)
50
0
-50
55.03 dB 55.05 dB
429.70k
vdb(vout1)
Figure 1.13 Gain and stage edge of low dropout [10].

1.3 Conclusion
A small dropout directive through a recompense capacitor was planned.
The design was easy, but showed superior organization and proposed a
double pole–zero termination scheme. It satisfied the majority of the char-
acteristic conditions of a profitable LDO. Tentative fallout planned LDO
have tiny overshoot and undershoot while have brilliant line and load con-
vention. Though planned propose has drawback is deliberate freight tem-
porary retort. Intended low voltage drop be fitting intended for powering
awake small-voltage complementary metal oxide semiconductor diverse
functions, that need elevated accuracy supply voltage also small recupera-
tion speed of calculated perform. LDO is used in communication.
References
1. AI-Shoyoukh, M., A Transient-Enhanced Low-Quiescent Current Low-
Dropout Regulator. IEEE J. Solid-State Circuit, 42–49, 8, Aug. 2007.
2. Rincon-Mora, G.A., A Low-Voltage, Low Quiescent Current, Low Drop-Out
Regulator. IEEE J. Solid-State Circuits, 33, 36–44, Jan. 1998.
3. Leung, K.N., A Low-Voltage CMOS Low-Dropout Regulator with Enhanced
Loop Response, IEEE, 2011.
4. Rincon-Mora, G.A., Optimized Frequency-Shaping Circuit Topologies for
LDOs. IEEE Trans. Circuits Syst. II Analog Digit. Signal Process., 45–51, 6,
703–708, Jun. 2008.
5. Rincon-Mora, G.A., Study and Design of Low Drop-Out Regulators, IJER,
2012.
6. Simpson, C., Linear Regulators: Theory of Operation and Compensation, in
Proceeding: National Semiconductor Application Note, vol. 1148s, pp. 1–12,
May 2015.
7. Simpson, C., A User’s Guide to Compensating Low-Dropout Regulators, in:
National Semiconductor Power Management Applications, pp. 1–14, 2017.
8. Kugelstadt, T., Fundamental Theory of PMOS Low-dropout Voltage
Regulator, in: Texas Instruments Application Report, Apr. 2011.
9. Lee, B.S., Technical Review of Low Dropout Voltage. Regulator Operation
and Performance, in: Texas Instruments Application Report, pp. 1–25, Aug.
2012.

15
Budati Anil Kumar, S. B. Goyal and Sardar M.N. Islam. Cognitive Computing Models
in Communication Systems, (15–46) © 2022 Scrivener Publishing LLC
2
Performance Analysis of Machine
Learning and Deep Learning
Algorithms for Smart Cities: The
Present State and Future Directions
Pradeep Bedi1
, S. B. Goyal2
*, Sardar MN Islam3
, Jia Liu4
and Anil Kumar Budati5
1
Galgotias University, Greater Noida, Uttar Pradesh, India
2
Faculty of Information Technology, City University, Petaling Jaya, Malaysia
3
lSlLC & Director of Decision Sciences and Modelling Program,
Victoria University, Melbourne, Australia
4
School of Cyberspace, Hangzhou Dianzi University, Zhejiang, China
5
Department of ECE, GRIET, Hyderabad, Telengana, India
Abstract
To manage growing urbanization, there is the development of smart cities with
an aim for environment preservation, improvement of the socio-economical stan-
dard of living of people by adopting technological advancement in information and
communication technology (ICT). For design, implementation, and deployment of
smart cities leads to an exploration of artificial intelligence (AI), machine learning
(ML), and deep learning (DL). In this work, the application of machine learning or
deep learning is explored for applications of smart cities such as smart transporta-
tion systems (STSs), smart grids (SGs), smart healthcare, etc. Major challenges that
are faced while designing smart city plans are such as the plant should be energy
efficient network architecture, privacy-preserving as well as data needed to be effi-
ciently analyzed of big data. To explore more accurate and precise decision-making
system ML/AI techniques have shown their proficiency in the improvement of effi-
ciency as well as to deploy low-cost smart network architecture design and manage-
ment. In this chapter, an analytical study will be presented with the application of
AI, ML, and DL in different sectors/application areas of smart cities. The main aim
*Corresponding author: drsbgoyal@gmail.com, https://orcid.org/0000-0002-8411-7630

is to focus on and explore the efficiency level of ML/AI techniques. This chapter will
also provide an in-depth analysis of innovative development, deployment, analysis,
security, and management in smart cities. So, this chapter will help in the explora-
tion of research challenges and future direction for researchers.
Keywords: Machine learning, deep learning, smart cities, smart grids, IoT
2.1 Introduction
To manage the challenges of growing urbanization, smart cities, which uti-
lize information and communication technologies (ICT), are being devel-
oped to deploy and promote sustainable development practices. The design,
implementation, and deployment of smart cities lead to an exploration
of artificial intelligence (AI), machine learning (ML), and deep learning
(DL). In this work, the application of machine learning and deep learning
is explored for smart city applications, such as smart transportation sys-
tems (STSs), smart grids (SGs), smart healthcare, etc. Major challenges to
be faced when designing smart cities include the plant’s energy-efficient
network architecture, the preservation of privace, and the efficient analysis
of big data. To explore more accurate and precise decision-making sys-
tems, ML/AI techniques have been shown to be proficient in improving
efficiency and deploying low-cost smart network architecture design and
management. In this chapter, an analytical review is presented on the appli-
cation of AI, ML, and DL in different sectors/application areas of smart cit-
ies. The main aim is to focus on and explore the efficiency level of ML/AI
techniques. This chapter also provides an in-depth analysis of smart cities’
innovative development, deployment, analysis, security, and management.
Hence, this chapter will help in the exploration of research challenges and
suggest future directions for researchers.
2.2 Smart City: The Concept
Advancements in automatic sensors and their reliability in terms of pre-
cision measurement have made complete city automation possible by
deploying 5G-based cellular technology along with mobile crowd sensing-
based equipment. The objective of city automation cannot be achieved
without considering these technological innovations [1].
The smart city concept is implemented by taking into account cer-
tain data sets that are collected by making use of various sensors, and the

Analysis of ML/DL Algorithms for Smart Cities 17
accumulated data are transferred to the corresponding processing frame-
work for analytical calculations and data transmission. The data process-
ing and modification is undertaken in the application layer of the working
framework. The security of architectural framework of the smart city is
addressed in the security layer [2].
The smart city has a large number of applications from smart mobility
applications that improve traffic efficiency and reduce CO2
emissions, to
automated streetlights, smart healthcare, etc. The working architecture of a
smart city comprises the following key five layers (Figure 2.1) [3, 4]:
• Application
• Sensing
• Communication
• Data Analysis
• Security aspects
Application Layer
(Smart City, Smart Grid, Smart Transportation)
Communication Layer
(WLAN, WBAN, Cellular Networks)
Sensing Layer
(Smart Sensors)
Data Layer
(Data Analysis, Visualization and Storage)
Security
Layer
(Privacy,
Authentication,
Integrity,
Access
Control)
Figure 2.1 Five aspects of designing the architecture of smart cities [8].

2.3 Application Layer
The interactive layer between the smart city and the people using it, which
are mostly the clients or inhabitants of the city, is the application layer. The
misuse of resources can be reduced by the use of ubiquitous and continu-
ous monitoring that enhances security and safety measures. There are sev-
eral methods to enhance the interaction between data sets and their users,
the most common of which is direct interaction, which involves the use
of an online platform that collaborates with smartphone applications, or
indirect interaction, which involves the use of actuators for environmental
control. Several research studies have focused on the interactive environ-
ment between the clients and the associated data sets and concluded that
interoperability is the technical challenge in the applications designed for
smart city implementation. Several services face challenges, such as smart
transportation, smart homes and smart healthcare hospitals, and so on that
form a part of homogeneous and integrative services. The invention of the
electric vehicle (EV) and its pervasiveness, which depends on the electrical
utility grid, is a vast area for smart application development and improve-
ment, which depends on the IoT platform [5–14]. The myriad applications
of smart cities are as follows:
2.3.1 Smart Homes and Buildings
One of the application of smart city deployment is smart homes or build-
ings. Homes that have the capability to reduce human effort and provide a
comfortable living environment are known as smart homes. There are two
conceptual levels of smart buildings:
• Physical level: the smart building’s community has the func-
tionalities of wired and wireless networking, inclusive of the
transportation system along with power grid control.
• Virtual level: the levels involve smart applications, such as
information sharing between the clients, cooperation, and
interactive environment applications.
2.3.1.1 Smart Surveillance
Today’s growing urban culture has increased the need for surveillance and
security systems. Security is an important concern nowadays; however,
its importance has increased substantially with increased data transfer

strategies. Modern security surveillance systems are complex in architec-
ture. Data transfer speed, robustness, and automated analysis are other
issues which are a part of security.
2.3.2 Smart Transportation and Driving
To enhance the safety, efficiency, and experience of traveling for both pas-
sengers and drivers, the smart transportation system provides vehicles
with smart devices, such as sensors, which have high-speed communica-
tion, computing, and processing capabilities. To reduce additional wiring
inside the vehicle, the wireless sensor network is used inside the vehicle.
2.3.3 Smart Healthcare
The grouping of sensor data is a smarter way to offer healthcare services
than to utilize sensors and actuators to enhance the quality of life and to
create healthier communities for feasible cities. Many medical, social, and
behavioral fields can be improvised toward smart health applications.
Human gait activity recognition is very important for orthopedic health
monitoring of individuals, particularly the elderly, which is a typical smart
health application area.
2.3.4 Smart Parking
To optimize parking space usage, improve the efficiency of parking opera-
tions and help reduce traffic congestion, smart parking systems have been
introduced in urban areas. On-road sensors like magnetometers and RFID
tags or light sensors and off-road sensors, like cameras, are used to identify
available parking spaces in smart parking systems.
2.3.5 Smart Grid
The idea behind smart grids is to collect data in an automated fashion and
analyze the behavior of electricity consumers and suppliers to improve effi-
ciency, as well as the economical use of electricity. Smart grids are able to
detect sources of power outages more quickly and at individual household
levels like a nearby solar panel, making distributed energy systems possible.
2.3.6 Smart Farming
With the increase in the world’s population, the demand for food is increas-
ing. Governments are helping farmers to use advanced techniques and

research to increase food production. Smart farming is one of the fastest
growing fields in IoT. Sensing for soil moisture and nutrients, controlling
water usage for plant growth, and determining custom fertiliser are some
of the uses of IoT.
2.3.7 Sensing Layer
The application of the smart cities requires several applications involving
sensors along with actuators that function as physical signal recording
units for recording environmental radiation, temperature, etc. A smart
city monitors the myriad physical parameters, which relies on the utmost
precision, accuracy, and sensitivity of the equipment. There are constant
interactions between users and their environment. There is flexibility and
expandability due to addition of sensing layer. The devices used have to be
monitored in relation to robustness, security concerns, nonintrusiveness,
and ecological amicability. Adaptability and security concerns have to be
monitored and solved efficiently to ensure the successful implementation
of automated smart city projects [15].
2.3.8 Communication Layer
The data acquired from the sensing layer are preprocessed and aggregated
and delivered to the other layers with the aid of the communication layer,
which provides communication between devices in the field by detecting
parameters with the cloud platform. This link is required to support low-
latency, high-throughput, flexible, and secure communication. As a result,
the developers are forced into trade-offs, to the requirements of their appli-
cations. Scant power availability mainly hampers the capabilities of com-
munication. When we compare the proficiency and scarceness effect of the
communication, it is more profound than in the data layer more energy is
required as compared to sensing layer. The most economical solution is to
create trade-offs among the information rate, latency, and transmission to
prolong the battery lifetime of sensing devices (which host the front-end
electronic communication equipment) [16].
2.3.9 Data Layer
In this layer, the collected data or sensed data from the communication
layer are processed. The processing of data includes meaningful con-
version, analysis, prediction, or forecasting. For this, data mining algo-
rithms are used that require high computational resources. This high-level

processing is not possible at the sensing layer with its limited capacity and
energy level [17].
2.3.10 Security Layer
This layer is integrated with all the other layers to provide privacy security,
which is a primary concern for every smart application. There are many
techniques to cope with security issues. These security issues relate to con-
fidentiality, access control, authentication, and integrity. There are many
algorithms, such as Advanced Encryption Standard (AES), Elliptic Curve
Cryptography (ECC), and blockchain cryptography, etc., that can protect
data and users from security attacks [18, 19].
2.4 Issues and Challenges in Smart Cities:
An Overview
The smart city architectural design incorporates smart lighting, intelligent
transportation system, smart health, hospital availability, etc. The proj-
ects involve the data associated with various sensors across the city that
Key challenges
of smart cities
Availability
of Resources
Fault Tolerent
Social
Inclusion of
People
Training to
citizens
Data security
and privacy
Complex and
cost-effective
Infrastructure
Figure 2.2 Key challenges faced when deploying a smart city plan.

involves further processing and communicating platforms. The working
framework is analyzed to be operational in a low cost-efficient manner
along with innovative and subtle projects. However, multiple challenges
associated with such a platform have been detected (Figure 2.2). Table 2.1
overviews some of the most prevailing problems that stand in the way of
smart city development [20].
2.5 Machine Learning: An Overview
Recently, for solving more complex statistical prediction problems,
machine learning has been adopted by many researchers. The mecha-
nisms include machine learning algorithms, artificial intelligence-based
advanced techniques, and the basic structure are that rely on which is the
factual identification procedure. The framework is expected to bring about
results requiring no human intercession [21, 22].
2.5.1 Supervised Learning
To attain the learning objective in this case, the various data sets and their
exact labels are utilized to function as an input parameter to machine learn-
ing algorithms and, hence, is named supervised learning. The algorithm is
trained for the convergence and approximation of the key function y = f(x).
The function comprises x as the data input example and y as the associated
label. Further output variables form the reason that supervised learning
has been categorized as classification and regression models. The regres-
sion models yield outputs in uninterrupted form as in predicting tasks. The
other is referred to as the classification type when the output variables are
grouping and categorical type, such as color or shape, etc. However, most
of the platforms these days make use of the prediction technology based
on supervised learning algorithms. A few examples are the random forest
method, support vector machines, linear regression models, and logistic
regression types which are categorized as supervised learning [21].
2.5.2 Support Vector Machines (SVMs)
A support vector machine (SVM) is a technology designed and developed
following regression and also for classification tasks. The methodology
develops a hyperplane (line) whose main function is to achieve segregation
and grouping of data that is trained into different classes. As the generation
of the hyperplane expands the classes and the distance between them, it

becomes more likely to generalise that information in the otherwise invis-
ible data (Figure 2.3). The SVM is the best technique for the prediction of
time-series statistics (data) as it does not lead to data overflow. It comprises
of best classification technique with accurate training and organizing of
data sets into classes.
Various studies have concluded that the SVM technique is preferrable
for data segregation and classification purposes and is less capable of mak-
ing precise assumptions from the data set. The SVM technique may be a
linear or nonlinear approach wherein determination of hyperplane forms
the linear model approach where data are converted into line format.
2.5.3 Artificial Neural Networks
Artificial neural network-based predictive algorithms follow the concept
of human brain neuron cells for learning mechanisms and training pur-
poses. The algorithms are trained to generate certain transfer functions
for attaining proper data set classification and increasing the entity num-
ber. Various functions in the combinational form are processed using
ANN model architecture as a classifier, and further for diverse data sets
variables, their precision is also calculated. The real-world problems
form the part of multidimensional data sets, and their classification and
grouping are an inexplicable part of the prediction process. The learning
and training sets are analyzed from the process. The accurate testing of
these records makes it easier to categorise the data sets. The neural net-
works are trained with similar information, and during the process, the
backpropagation algorithms do the calculation for directing the neural
framework (Figure 2.4).
hyperplane
Support Vectors
Figure 2.3 Support vector machine architecture.

2.5.4 Random Forest
The regression and classification procedure is very easily enacted by the
random forest learning mechanism (Figure 2.5). The method follows the
bagging method which describes the combinational algorithm perfor-
mance that produces outcomes with an improved accuracy of results.
The decision trees are acted upon by algorithms that enable the shap-
ing of these trees and combining the principles of these decision trees to
bring about the collective learning technique for enabling precise predic-
tion. This combination of various classifiers that are weak and collectively
built up robust classifiers has the effect of producing a more literal pre-
diction and deep learning of various data sets. This learning architectural
framework provides randomized input of data sets for operation derived
from weak decision trees that helps in the generation of learning rules.
Therefore, these rules are generated as a grouping of weak classifiers to
build up a robust one.
Random forest-based predictive analysis is carried out on testing data
set statistics. The output from the learning algorithm creates a yield from
the analysis in the format of classes or labels. The various decision trees
work in accordance to create the stronger learner, referred to as bagging.
The data that are generated after the application of the algorithm have cer-
tain information already available known as Out of Bag (OOB) data. This
OOB data availability stands differently for various decision trees and their
distinctive training samples are the key factors for such differences. The
OBB, specific to each decision tree, assists in more proficient data set eval-
uation, thereby improving the learning mechanism through forest predic-
tive algorithms.
Outputs
Inputs
Input
Layer
Hidden
Layers
Output
Layer
Figure 2.4 Artificial neural network architecture.

2.5.5 Naïve Bayes
The Bayes theorem forms the basis for laying out the architectural design
framework for the naïve Bayes learning technology for the classification
of data sets that are unknown (Figure 2.6). This learning and prediction
algorithm utilizes the previous data set structure to realize future ones.
This architecture forms a probabilistic learning technique that incorpo-
rates algorithms for grasping uncertainties in an exceptionally principled
approach, which is done by keeping the deciding factors of future results.
Randomly
choosing
dataset
Aggregate Strategy
Feature Space
RS1 RS2 RSn
RS1 RS2 RSn
Final Decision
Figure 2.5 Random forest architecture.
File
Reader
Partitioning Predictor Scorer
Learner
Figure 2.6 Naïve Bayes architecture.

The algorithm is strong enough against noise in input data sets, which
forms the base for its capability to solve the predictive and diagnostic
issues in the data sets.
2.6 Unsupervised Learning
The next learning technology, which deals with input data variables with-
out any output label being defined, is known as the unsupervised learning
technique. The training of the learning mechanism makes use of only the
input statistical variables. The algorithm focuses on studying the pattern
of those inputs and making further predictions. This type of learning is
categorized as the clustering technique and association method to perform
significant tasks. Common clustering algorithms include [22]:
• Hierarchical clustering: constructs a multilevel hierarchy of
clusters by creating a cluster tree.
• K-means clustering: partitions data into k distinct clusters,
depending on the distance to the centroid of a cluster.
• Gaussian mixture models: models clusters as a mixture of
multivariate normal density components.
• Self-organizing maps: employs neural networks that learn
the topology and distribution of the data.
• Hidden Markov models: uses observed data to recover the
sequence of states.
2.7 Deep Learning: An Overview
Deep learning, a part of artificial intelligence, is derived from machine
learning that is created with more layers of algorithms or networks. It is
similar to machine learning but consists of a large algorithm or network.
Deep learning architecture is similar to the human brain as all the nerves
or neurons are connected to the brain in a complex manner and process
all the complex data generated. Similar to the human brain, deep learn-
ing is designed to handle complex data with the help of large algorithms
[23, 24].
There are many deep learning approaches which have been designed,
some of which are discussed in the following.

2.7.1 Autoencoder
An autoencoder is designed by combining an encoder and decoder type
of neural network, as shown in Figure 2.7. Raw input data are fed into
encoder units where features are extracted, and these features are fed into
the decoder to reconstruct the data from the extracted features. While
training the deep autoencoder model, the divergence of the encoder and
decoder is reduced gradually. The feature extraction by the encoder and
reconstruction by the decoder is not supervised information. Different
types of deep autoencoders, such as denoising autoencoders and sparse
autoencoders, focus on researchers.
2.7.2 Convolution Neural Networks (CNNs)
Convolution neural networks (CNNs) are a type of deep neural network
designed to interpret in a similar way to the human visual system (HVS).
CNNs have made great achievements in the field of computer vision.
Recently, it has been applied in other fields also. Many recent human-
computer interfaces are designed using CNNs. CNNs have advantages over
feed-forward networks because they are capable of finding feature local-
ities. Thus, it is capable of extracting features and processes. CNNs can
work on 2-dimensional (2D) as well as 3-dimensional (3D) data in which
input data are converted into matrices (Figure 2.8).
Outputs
Inputs
Encoder Decoder
Figure 2.7 Architecture of an autoencoder.

2.7.3 Recurrent Neural Networks (RNNs)
Recurrent neural networks (RNNs) are a type of neural network architec-
ture designed for sequential data and is especially used for time series pre-
dictive problems. In these networks, the output of the previous state is fed
into the current state, whereas, in all traditional networks, all inputs and
outputs are independent of each other. In RNNs, the hidden state remem-
bers information about the sequence. The structure of an RNN is shown
in Figure 2.9. Standard RNNs can handle only limited-length sequences.
A modified version of RNNs has been proposed to solve such an issue,
termed long short-term memory (LSTM), gated recurrent unit (GRU), etc.
Feature Extraction
Outputs
Convolution Maxpooling
Classification
Figure 2.8 Architecture of CNN.
Outputs
Inputs
Recurrent Network
Hidden Layers
Figure 2.9 Architecture of RNN.

2.8 Deep Learning vs Machine Learning
Deep learning, a subset of machine learning, has several advantages over
machine learning algorithms. Deep learning presents data differently to
machine learning. In machine learning, structured data are always required
as an input to be processed whereas in deep learning, data do not need to
be structured [25]. In deep learning, artificial neural networks (ANNs) are
taken as a base algorithm.
Some of the major advantages of deep learning over machine learning
are discussed as follows:
• Labeled data are required by machine learning for learn-
ing as well as producing results. It is required to learn/teach
the machine as to whether the output is incorrect. On the
other hand, deep learning does not always need labelled data
for learning as its architecture is multilayered. So, data are
placed in the hierarchy and the network learns from its own
mistakes. But sometimes, deep learning also gives incorrect
results as the data provided may not be good enough for
decision making. So, data are a deciding factor that deter-
mines the performance level of both.
• Machine learning is an evolution from artificial intelligence,
whereas deep learning evolved from machine learning with
deep architecture.
• Thousands of data points are used in machine learning
whereas millions of data are required in deep learning.
• Machine learning output is a numerical value, whereas deep
learning output can be in any form, either numerical, image,
signal, etc.
• In machine learning, different algorithms such as kernel
learning, ensemble learning, etc., algorithms are used to
learn and predict the output. But in deep learning, the base
algorithm is a neural network which is used to process data,
interpret data features, and establish the relations among
them to predict or generate results.
• A deep learning algorithm solves complex machine learning
problems.
• Machine learning does not perform better over large and
complex data whereas deep learning gives better output for
large and complex data or big data.

So, in this research work, deep learning is adopted to train and learn
such a large amount of data.
2.9 Smart Healthcare
2.9.1 Evolution Toward a Smart Healthcare Framework
As depicted in Figure 2.10, the following steps healthcare framework is
designed including steps as:
• Data acquisition: different sensors are deployed over the
patient’s body to collect data and transmit it to the control
unit.
• Control: the acquired data is collected and sent via the
Internet for medical analysis.
• Data analysis: This step is generally performed remotely.
All the collected data are stored at the cloud server where
the analyst can access it, perform an assessment and send a
report to the patient. The patient can also access the cloud
server.
Medical Server
ECG Sensor
EEG Sensor Blood pressure
and
temperature
sensor
Motion Sensor
Control Unit
Data Analytics,
Monitoring and
Commands
Figure 2.10 Healthcare framework.

The healthcare sector is facing several issues in relation to the cost-
effective real-time assessment and diagnosis of patients and in providing
timely treatment (Figure 2.11). The diagnosis and assessment of disease is
one of the critical concerns of a healthcare system. The healthcare system
is facing several challenges during data collection, control, and decision
making, despite the rapid development of technology [26]. To address
these challenges associated with smart and secure diagnosis and assess-
ment tools, machine learning or deep learning tools are used. These appli-
cations will help to establish collaborative knowledge for the discovery and
predictive analysis of a patient’s report, whereas many research works have
collaborated on a specific domain such as heart disease diagnosis, brain
tumour detection, etc. [27].
2.9.2 Application of ML/DL in Smart Healthcare
Some of the contributions of researchers to the field of smart healthcare
monitoring are detailed in Table 2.1.
After analyzing the above contributions of machine learning to a smart
healthcare monitoring system, the following objectives for future research
have been derived while working on healthcare assessment tasks:
• To design and develop platform-independent applications.
• To improve the interoperability for data handling and
maintenance.
• To handle high-dimensional data acquisition.
• To handle channel abnormalities (such as noise) on patient’s
real-time data (such as EEG, ECG, CT scan, etc.) for accu-
rate analysis by doctors remotely.
• To deploy healthcare assistance at home to save the patient
time and money.
IoT and ML in
Healthcare
Realtime
Monitoring
Chronic
Disease
Monitoring
Telemedicine Rehabilation
Figure 2.11 Application of IoT/ML in healthcare.

Table
2.1
Machine
learning
application
evaluation
in
healthcare
assessment.
Ref
Application
Features
Technique
Result
Limitations
[28]
Cardiovascular
diseases
predictive
model
Android-based
application
with
cloud
storage
SVM,
KNN
and
the
Naïve
Bayes
87%
Accuracy
Domain-specific
model
[29]
Health
Assessment
Assessment
model
using
ML
AdaBoost
95%
Accuracy
Real-time
application
is
not
supported
[30]
Heart
Disease
Prediction
Application
of
Deep
learning
Ensemble
Deep
Learning
98.5%
Accuracy
Doesn’t
handle
missing
data,
data
redundancy
issues.
[31]
Human
activity
recognition
Smartphone
sensor-based
data
collection.
Application
of
ML
to
recognise
human
activities.
SVM,
k-NN,
ANN,
Decision
Tree
98%
Accuracy
Unable
to
handle
uncertainty
[32]
Risk
assessment
Discussed
objectives
and
application
of
ML
and
blockchain
for
health
risk
assessment.
ML
and
blockchain
-
-
[33]
Heart
Disease
Prediction
Assessment
model
using
ML
Random
forest
89%
Accuracy
Cannot
handle
large
feature
sets
[34]
Secure
ECG
Monitoring
Biometric
security
ML
and
biometrics
for
assessment
Cost-effective
Domain-specific
model

• To design a secure and efficient framework to preserve the
privacy of sensitive information.
• To develop an efficient, secure model for secure access to
electronic health records stored on the cloud.
• To improve the quality, safety, performance, and account-
ability of the entire system for development in the direction
of a smart city.
2.10 Smart Transport System
2.10.1 Evolution Toward a Smart Transport System
The digital era in which we currently live is underpinned by information
and communication technology (ICT) and affects every aspect of life, for
example lifestyle, working style, ways of thinking, etc. Development is a
continuous process, so with small incremental changes, day by day differ-
ent aspects of a person’s life are also gradually improving.
Nowadays, people are aware of innovative technology and how it can
be used to support a person’s ability to work smart; thus this era can also
be called the era of smart things. This is a progressive movement from
intelligent transportation systems (ITSs) to smart transport systems (STSs)
[35]. ITS have become procurable due to smart city technologies [36]. The
advent of ICT has revolutionized the field of transportation.
To design and implement smart transportation systems, smart sensors
and communication models are deployed for a smart and automated trans-
portation system. The design should improve efficiency in terms of cost,
time, accuracy, safety, and security. To facilitate this, roadside infrastruc-
ture should be upgraded with smart sensors and communication models.
Smart sensors should also be deployed in vehicles for vehicle-to-vehicle or
vehicle-to-infrastructure communication. These communication enhance-
ments will provide diverse services to customers. Some major applications
of the smart transportation system are illustrated in Figure 2.12.
There are several challenges associated with the deployment of smart
applications in a smart transportation system, which are identified in this
section and illustrated in Figure 2.13 [37]. These challenges can be resolved
by integrating machine learning concepts [38–40].

2.10.2 Application of ML/DL in a Smart Transportation
System
Some of the contributions of researchers in the field of a smart transporta-
tion system are as follows (Table 2.2):
STS
Automated
Fare
Collection
System
Smart
Parking
Intelligent
Signal System
Real-time
Traffic
Monitoring
Real-time
Route
Selection
Real-time
Survillence
Real-time
Vehicle
Location
Figure 2.12 Framework of a smart transport system.
•
•
•
•
•
Issues in Smart
Transportation
Loss or damage of sensors
Damage to control units
Communication loss
Diversion from smart route
Data Privacy and security
Figure 2.13 Challenges in a smart transportation system.

Table
2.2
Machine
learning
application
evaluation
in
a
smart
transportation
system.
Ref
Application
Features
Technique
Result
Limitations
[41]
Congestion
Control
Vehicle
speed
was
predicted
Long
Short-Term
Memory
(LSTM)
approach
was
used
84–95%
Accuracy
Univariate
method
[42]
Traffic
Management
Analyzed
the
dynamic
pattern
of
traffic
Online
Incremental
ML
0.07
mean
absolute
error
(MAE)
Traffic
flow
was
only
predicted.
Congestion
control
was
not
discussed.
[43]
Traffic
Management
Analyzed
the
dynamic
pattern
of
traffic
Deep
Learning
and
SVM
5%
error
rate
Local
monitoring
[44]
Traffic
Management
Flow
and
speed
analysis
of
traffic
Multi-layer
neural
network
4%
error
rate
Lower
accuracy
and
high
time
consumption
[45]
Smart
Parking
Space
prediction
for
parking
Deep
extreme
learning
model
60%
accuracy
Low
performance
rate
[46]
Road
safety
Accident
prediction
ML
approaches
such
as
SVM,
ensemble
learning,
deep
neural
network,
etc.
90%
precision
Trained
with
one
data
set
[47]
Vehicle
monitoring
Carbon
emission
prediction
based
on
traffic
flow
ML
and
DL
90%
Accuracy
Only
focused
on
traffic
features
to
evaluate
carbon
emission

After analyzing the above contributions of machine learning in smart
transportation monitoring systems, the following objectives for future
research were derived while working on smart transportation deployment
and management [48]:
• To design and develop geographically dependent applica-
tions for decision making.
• To improve the existing infrastructure rather than creating
a new one.
• To incorporate an intelligent and smart management sys-
tem, which improves productivity despite limited resources.
• To deploy secure transactions and updates for smart parking
or an automated fare collection system.
• To design a framework that analyses traffic flow and acci-
dent probabilities.
• Quick and smart accident detection system.
• To improve the efficiency level by integrating ML/IoT/
Blockchain technologies.
2.11 Smart Grids
2.11.1 Evolution Toward Smart Grids
The modern world depends on electricity. The power grid network system
is considered the backbone of a city, delivering power to make everyday
operations possible [49]. Every smart city is built on two pillars: one is
sustainability and the other is clean energy and smart grids can be consid-
ered as a fundamental element of the smart city. To transform a traditional
grid system into a smart grid, there is a need to merge information and
communication technology. The existing power grid architecture delivers
the energy produced from large generation stations to the end consumer.
The stages in the delivery of electric services are shown in Figure 2.14. The
traditional grid system comprises the following:
Generation Station: Energy generation power plants convert different
forms of energy into electricity. Fossil fuels, nuclear power, and renewable
energy sources (RES) are examples of natural resources. In the last few
years, RES has attracted a lot of attention because the conversion losses
are minimal. The produced electricity is then transported to the consumer
via the transmission and distribution network. Power plants are usually
constructed near raw material sites, for example, thermal power plants are

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Cha-dokoro Tea-place.
Cha-ire Tea-jar; literally, “tea-put in.”
Cha-no-yu A tea-party.
Chigai-dana
A shelf, one half of which is on a different plane
from the other.
Chōdzu-ba Privy; literally, “hand water-place.”
Chōdzu-
bachi
A convenience near the privy for washing the
hands.
Chu-nuri Middle layer of plaster.
Dai-jū-
no
A pan for holding burning charcoal, used in
replenishing the hibachi.
Daiku A carpenter.
Daimio A feudal lord.
Dodai The foundation-sill of a house.
Dodai-
ishi
Foundation stone.
Do-ma
Earth-space. A small unfloored court at the
entrance the house.
Fukuro-dana. Cupboard; literally, “pouch-shelf.”
Fumi-ishi Stepping-stone.
Furo A small culinary furnace, also a bath-tub.
Furosaki
biyō-bu.
A two-fold screen placed in front of the furo.
Fusuma A sliding screen between rooms.
Fū-tai
The bands which hang down in front of a
kake-mono; literally, “wind-band.”
Futon A quilted bed-cover.
Ge-dan Lower step.
Genka The porch at the entrance of a house.
Geta Wooden clogs.
Goyemon buro A form of bath-tub.
Habakari Privy.

Hagi A kind of rush.
Hashira A post.
Hashira
kakushi
A long narrow picture to hang on post in
room; literally, “post-hide.”
Hibachi
A brazier for holding hot coals for warming the
apartments.
Hibashi Metal tongs.
Hikite
A recessed catch in a screen for sliding it back
and forth.
Hi-no-ki A species of pine.
Hisashi A small roof projecting over a door or window.
Hon-gawara True tile.
[pg 353]
Ichi-yo-dana A kind of shelf.
Iri-kawa. The space between the verandah and room.
Ishi-dōrō. A stone lantern.
Ji-bukuro. Cupboard.
Jin-dai-sugi “Cedar of God's age.”
Jinrikisha A two-wheeled vehicle drawn by a man.
Ji-zai A hook used for hanging pots over the fire.
Jō-dan Upper step. Raised floor in house.
Kago Sedan chair.
Kaikōsha Name of a private school of architecture.
Kake-mono Hanging picture.
Kaki Fence.
Kamado Kitchen range.
Kami-dana A shelf in the house for Shin-tō shrine.
Kami-no-ma Higher room.
Kamoi Lintel.
Kara-kami Sliding screen between rooms.
Kawarake Unglazed earthen ware.
Kaya A kind of grass used for thatch.
Kaya Mosquito netting.

Kazari-kugi Ornamental headed nails.
Kaze-obi
The bands which hang down in front of the
kake-mono; literally, “wind-band.”
Keshō-no-
ma
Toilet-room.
Keyaki A kind of hard wood.
Kō-ka Privy; literally, “back frame.”
Koshi-bari A kind of paper used for a dado.
Kuguri-do A small, low door in a gate.
Kura A fire-proof store-house.
Kuro-moji-
gaki
A kind of ornamental fence.
Ma-bashira Middle post.
Mado Window.
Ma-gaki A fence made of bamboo.
Magari-gane A carpenter's iron square.
Maki-mono Pictures that are kept rolled up, not hung.
Maki-mono-dana Shelf for make-mono.
Makura Pillow.
Miki-dokkuri Bottle for offering wine to gods.
Mochi A kind of bread made of glutinous rice.
Mon Badge, or crest.
Mune Ridge of roof.
Naka-tsubo Middle space.
Nan-do. Store-room. Pantry.
Neda-maruta Cross-beams to support floor.
Nedzumi-
bashira
Cross-beam at end of building; literally, “rat-
post.”
Nikai-bari
Horizontal beam to support second-story
floor.
Noren Curtain. Hanging screen.
Nuki
A stick passed through mortised holes to bind
together upright posts.
Nuri-yen A verandah unprotected by amado.

Ochi-yen A low platform.
Oshi-ire Closet; literally, “push, put in.”
Otoshi-kake Hanging partition.
Ramma
Open ornamental work over the screens which form
the partitions in the house.
Ro Hearth, or fire-place, in the floor.
Ro-ka Corridor. Covered way.
Sake Fermented liquor brewed from rice.
Samisen A guitar with three strings.
Samisen-
tsugi
A peculiar splice for joining timber.
Samurai Military class privileged to wear two swords.
Sashi-
mono-ya
Cabinet-maker.
Setsu-in Privy; literally, “snow-hide.”
Shaku
A wooden tablet formerly carried by nobles
when in presence of the Emperor.
Shaku A measure of ten inches. Japanese foot.
Shichirin A brazier for cooking purposes.
Shikii
The lower grooved beam in which the door or
screens slide.
Shin-tō The primitive religion of Japan.
Shita-nuri The first layer of plaster.
Shō-ji The outside door-sash covered with thin paper.
Sode-gaki A small ornamental fence adjoining a house.
Sudare A shade made of split bamboo or reeds.
Sugi Cedar.
Sumi-sashi A marking-brush made of wood.
Sumi-tsubo
An ink-pot used by carpenters in lieu of the
chalk-line.
Sun One tenth of a Japanese foot.
Sunoko A platform made of bamboo.

Tabako-bon
A box or tray in which fire and smoking utensils
are kept.
Tamari-no-
ma
Anteroom.
Tansu Bureau.
Taruki A rafter of the roof.
Tatami A floor-mat.
Ten-jō Ceiling.
Te-shoku Hand-lamp.
To-bukuro
A closet in which outside doors are stowed
away.
Tokkuri A bottle.
Toko The floor of the tokonoma.
Toko-
bashira
The post dividing the two bays or recesses in
the guestroom.
Tokonoma A bay, or recess, where a picture is hung.
Tori-i
A portal, or structure of stone or wood, erected
in front of a Shin-tō temple.
Tsubo An area of six feet square.
Tsugi-no-
ma
Second room.
Tsui-tate A screen of one leaf set in a frame.
Tsume-sho. A servant's waiting-room.
Usukasumi-dana A name for shelf; literally, “thin mist-shelf,”
Uwa-nuri The last layer of plaster.
Watari A passage; literally, “to cross over.”
Yane Roof.
Yane-
shita
Roof-beams.
Yashiki
A lot of ground upon which a house stands. An
enclosure for a Daimio's residence.
Yedo-
gawara
Yedo tile.
Yen A coin; equals one dollar.

Yen-gawa Verandah.
Yen-riyo Reserve.
Yen-
zashiki
End-parlor.
Yō-ba Privy; literally, “place for business.”
Yoshi A kind of reed.
Yoshi-do A screen made of yoshi.
Yu-dono Bath-room.
Yuka-
shita
The beams supporting the first floor.

1.
2.
Footnotes
Transactions of the Asiatic Society of Japan, vol. v., part i.
p. 207.
It may be well to state here that most of the good and
reliable contributions upon Japan are to be found in the
Transactions of the English and German Asiatic Societies
published in Yokohama; also in the pages of the Japan
“Mail,” in the now extinct Tokio “Times,” and in a most
excellent but now defunct magazine called the
“Chrysanthemum,” whose circulation becoming vitiated by
the theological sap in its tissues, finally broke down
altogether from the dead weight of its dogmatic leaves.
Among the many valuable papers published in these
Transactions of the Asiatic Society of Japan, is one by
Thomas R. H. McClatchie, Esq., on “The Feudal Mansions of
Yedo,” vol. vii. part iii. p. 157, which gives many important
facts concerning a class of buildings that is rapidly
disappearing, and to which only the slightest allusion has
been made in the present work. The reader is also referred
to a Paper in the same publication by George Cawley, Esq.,
entitled “Some Remarks on Constructions in Brick and
Wood, and their Relative Suitability for Japan,” vol. vi. part

3.
4.
ii. p. 291; and also to a Paper by R. H. Brunton, Esq., on
“Constructive Art in Japan,” vol. ii. p. 64; vol. iii. part ii. p.
20.
Professor Huxley has said in one of his lectures, that if all
the books in the world were destroyed, with the exception
of the Philosophical Transactions, “it is safe to say that the
foundations of Physical Science would remain unshaken,
and that the vast intellectual progress of the last two
centuries would be largely though incompletely recorded.”
In a similar way it might almost be said of the Japan “Mail,”
that if all the books which have been written by foreigners
upon Japan were destroyed, and files of the Japan “Mail”
alone preserved, we should possess about all of value that
has been recorded by foreigners concerning that country.
This journal not only includes the scholarly productions of
its editor, Capt. F. Brinkley, as well as an immense mass of
material from its correspondents, but has also published
the Transactions of the Asiatic Society of Japan in advance
ot the Society's own publications.
Still another English writer says: “It is unpleasant to live
within ugly walls; it is still more unpleasant to live within
unstable walls: but to be obliged to live in a tenement
which is both unstable and ugly is disagreeable in a tenfold
degree.” He thinks it is quite time to evoke legislation to
remedy these evils, and says: “An Englishman's house was
formerly said to be his castle; but in the hands of the
speculating builder and advertising tradesman, we may be
grateful that it does not oftener become his tomb.”
Fig. 12 represents the frame-work of an ordinary two-
storied house. It is copied from a Japanese carpenter's
drawing, kindly furnished the writer by Mr. Fukuzawa, of
Tokio, proper corrections in perspective having been made.
The various parts have been lettered, and the dimensions

5.
given in Japanese feet and inches. The Japanese foot is,
within the fraction of an inch, the same as ours, and is
divided into ten parts, called sun. The wood employed in
the frame is usually cedar or pine. The corner posts, as well
as the other large upright posts, called hashira (H), are
square, and five sun in thickness; these are tenoned into
the plate upon which they rest. This plate is called do-dai
(D); it is made of cedar, and sometimes of chestnut. The
do-dai is six sun square, and rests directly on a number of
stones, which are called do-dai-ishi (D,1). Between the
hashira come smaller uprights, called ma-bashira (M)
(hashira changed to bashira for euphony); these are two
sun square. Through these pass the cross-pieces called
nuki; these are four sun wide and one sun thick. To these
are attached the bamboo slats as substitutes for laths. The
horizontal beam to support the second-story floor is called
the nikaibari (Ni); this is of pine, with a vertical thickness of
one foot two sun, and a width of six tenths of a sun. The
rafters of the roof, called yane-shita (Ya), in this frame are
nine feet long, three sun wide, and eight tenths of a sun in
thickness. Cross-beams (T), from the upper plate from
which spring posts to support the ridge-pole, are called
taruki. The first floor is sustained by posts that rest on
stones embedded in the ground, as well as by a beam
called yuka-shita (Yu); this is secured to the upright beams
at the height of one and one-half or two feet above the do-
dai. The upper floor-joists are of pine, two inches square;
the flooring boards are six tenths of a sun in thickness, and
one foot wide. The lower floor-joists, called neda-maruta
(Ne), are rough round sticks, three sun in diameter, hewn
on opposite sides. On top of these rest pine boards six
tenths of a sun in thickness.
The accompanying sketches will illustrate the various stages
in the construction of the ceiling.

6.
7.
General Francis A. Walker, in his Lowell Lectures on the
United States Census for 1880, shows that carpenters
constitute the largest single body of artisans working for
the supply of local wants. He shows that the increase of
this body from decade to decade is far behind what it
should be if it increased in the ratio of the population; and
though this fact might excite surprise, he shows that it is
due to the enormous increase in machine-made material,
such as doors, sashes, blinds, etc.; in other words, to the
making of those parts which in former times trained a man
in delicate work and accurate joinery.
There is no question but that in England apprentices serve
their time at trades more faithfully than with us;
nevertheless, the complaints that go up in the English press
in regard to poor and slovenly work show the existence of a
similar class of impostors, who defraud the public by
claiming to be what they are not. The erratic Charles
Reade, in a series of letters addressed to the “Pall Mall
Gazette,” on builders' blunders, inveighs against the British
workmen as follows: “When last seen, I was standing on
the first floor of the thing they call a house, with a blunder
under my feet,—unvarnished, unjoined boards; and a
blunder over my head,—the oppressive, glaring plaster-
ceiling, full of the inevitable cracks, and foul with the smoke
of only three months' gas.”
In regard to sash windows, he says: “This room is lighted
by what may be defined ‘the unscientific window.’ Here, in
this single structure, you may see most of the intellectual
vices that mark the unscientific mind. The scientific way is
always the simple way; so here you have complication on
complication,—one half the window is to go up, the other
half is to come down. The maker of it goes out of his way
to struggle with Nature's laws; he grapples insanely with
gravitation, and therefore he must use cords and weights

8.
9.
10.
11.
12.
13.
and pulleys, and build boxes to hide them in. He is a great
hider. His wooden frames move up and down wooden
grooves, open to atmospheric influence. What is the
consequence? The atmosphere becomes humid; the
wooden frame sticks in the wooden box, and the
unscientific window is jammed. What, ho! Send for the
Curse of Families, the British workman! On one of the cords
breaking (they are always breaking), send for the Curse of
Families to patch the blunder of the unscientific builder.”
A Government bureau called the Kaitakushi, now
fortunately extinct, established in Yezo, the seat of its
labors, one or two saw-mills; but whether they are still at
work I do not know.
A structure of stone or wood, not unlike the naked frame-
work of a gate, erected in front of shrines and temples.
This sketch was made from a photograph taken for this
work, at the suggestion of Dr. W. S. Bigelow, by Percival
Lowell, Esq.
We have characterized as a ridge-roof that portion which
has truncate ends,—in other words, the form of a gable,—
and which receives special methods of treatment. The line
of demarcation between the long reach of thatch of the
roof proper and the ridge-roof is very distinct.
An odor which at home we recognize as “Japanesy,” arising
from the wood-boxes in which Japanese articles are
packed.
In the plan (fig. 97) P is an eight-mat room; D and L are
six-mat rooms; S is a four and one-half mat room; S, H,
and St. are three-mat rooms; S B, and F are two-mat
rooms.

14.
15.
16.
17.
The following is a brief explanation of the names of the
rooms given in plan fig. 99: Agari-ba (Agari, “to go up; ”
ba, “place”), Platform, or place to stand on in coming out of
the Bath. Cha-dokoro, Tea-place; Ge-dan, Lower Step; ō-
dan, Upper Step; Iri-kawa, Space between verandah and
room; Kami-no-ma, Upper place or room; Tsugi-no-ma,
Next place or room; Kesho-no-ma, Dressing-room (Kesho,
—“adorning the face with powder”). Nan-do, Store-room;
Naka-tsubo, Middle space, Oshi-ire, Closet (literally, “push,”
“put in”); Ro-ka, Corridor, Covered way; Tamari, Ante-
chamber; Tsume-sho, Waiting-room for servants; Yu-dono,
Bath-room; Yen-zashiki, End parlor; Watari,—“to cross
over;” Sunoko, Bamboo shelf or platform.
See chapter viii. for further considerations regarding the
matter.
A correspondent in the “Pall Mall Gazette,” in protesting
against the attempt to impose European clothing on those
people who are accustomed to go without any, says: “In
many parts of India there is a profound suspicion of the
irreligiousness of clothing. The fakir is distressed even by
the regulation rag upon which the Government modestly
insists, and a fully dressed fakir would be scouted. The late
Brahmo minister, Chesub Chunder Sen, expressed the belief
that India would never accept a Christ in hat and boots.
The missionary should remember that clothes-morality is
climatic, and that if a certain degree of covering of the
body has gradually become in the Northwest associated
with morality and piety, the traditions of tropical countries
may have equally connected elaborate dress rather with the
sensualities of Solomon in his glory than with the purity of
the lily as clothed by Nature.”
Rein says: “The cleanliness of the Japanese is one of his
most commendable qualities. It is apparent in his body, in

18.
19.
20.
21.
22.
his house, in his workshop, and no less in the great
carefulness and exemplary exactness with which he looks
after his fields.”
From the name tokonoma, which means “bed-place,”
literally “bed of floor,” it is supposed that in ancient times
the bed was made or placed in this recess.
In this connection it may be interesting to mention the
various names applied to the privy by the Japanese, with a
free translation of the same as given me by Mr. A. S.
Mihara: Setsu-in, “snow-hide;” Chodsu-ba,“place to wash
hands” (the chōdzu-bachi, a convenience for washing the
hands, being always near the privy); Benjo and Yo-
ba,“place for business;” Ko-ka,“ back-frame.” Habakari is a
very common name for this place; the word Yen-riyo,
though not applied to this place, has the same meaning, it
implies reserve.
These words with their meanings certainly indicate a great
degree of refinement an delicacy in the terms applied to
the privy.
The ordinary form of verandah is called yen, or yen-gawa.
In Kishiu it is called simply yen, while in Tokio it is called
yen-gawa. A low platform is called an ochi-yen; a platform
that can be raised or lowered is called an age-yen. When
the platform has no groove for the rain-doors on the outer
edge, it is called a nuri-yen,—nuri meaning wet, the rain in
this case beating in and wetting the verandah. A little
platform made of bamboo, which may be used as a shelf
for plants, is called sunoko.
A gate-like structure seen in front of all shrines and
temples.
This legend is from a work entitled “Chikusan Teizoden.”

23.
24.
25.
26.
Professor Atkinson, in the Journal of the Asiatic Society, vol.
vi. part i.; Dr. Geerts, ibid., vol. vii. part iii.
Dr. O. Korschelt has made an extremely valuable
contribution to the Asiatic Society of Japan, on the water-
supply of Tokio. Aided by Japanese students, he has made
many analyses of well-waters and waters from the city
supply, and shows that, contrary to the conclusions of
Professor Atkinson, the high-ground wells are on the whole
much purer than those on lower ground. Dr. Korschelt also
calls attention to the great number of artesian wells sunk in
Tokio, by means of bamboo tubes driven into the ground.
The ordinary form of well is carried down thirty or forty feet
in the usual way, and then at the bottom bamboo tubes are
driven to great depths, ranging from one hundred to two
hundred feet and more. He speaks of a number of these
wells in Tokio and the suburbs as overflowing. There is one
well not far from the Tokio Daigaku which overflows; and a
very remarkable sight it is to see the water pouring over a
high well-curb and flooding the ground in the vicinity. He
shows that pure water may be reached in most parts of
Tokio by means of artesian wells; and to this source the city
must ultimately look for its water-supply.
For further particulars concerning this subject, the reader is
referred to Dr. Korschelt's valuable paper in the
Transactions of the Asiatic Society of Japan, vol. xii. part iii.,
p. 143.
The pier-glaas is happily unknown in Japan; a small disk of
polished metal represents the mirror, and is wisely kept in a
box till needed!
Transactions of the Asiatic Society of Japan, vol. i. p. 20.

27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
Owing to the sensible civil service of England, scholars and
diplomates are appointed to these duties in the East; and
as a natural result all the honors,—political, commercial,
and literary,—have, with few exceptions, been won by
Englishmen.
Transactions of the Asiatic Society of Japan, vol. ix. part ii.
p. 191.
Ibid., vol. x. Supplement.
Ibid., vol. iii. part ii. p. 131.
In Anam I noticed that the bed-rooms were indicated by
hanging cloth partition as well as by those made of
matting.
Transactions of the Asiatic Society of Japan, vol. vi. part i.
p. 109.
Satow gives quite a different rendering of this passage.
Translations of the Asiatic Society of Japan, vol. ii. p. 119.
Transactions of the Asiatic Society of Japan, vol. iii. part ii.
In Mr. Aston's translation this word is printed “heart,” but
evidently this must be a misprint.
It is lamentable to reflect how many monstrous designs
have been perpetrated under the general name of Gothic,
which are neither in spirit nor letter realized the character
of Mediaeval art. In London these extraordinary ebullitions
of uneducated taste generally appear in the form of
meeting-houses, music-halls, and similar places of popular
resort. Showy in their general effect, and usually
overloaded with meretricious ornament, they are likely

37.
38.
enough to impose upon an uninformed judgment, which is
incapable of discriminating between what Mr. Ruskin has
called the “Lamp of Sacrifice,”—one of the glories of ancient
art,—and the lust of profusion which is the bane of modern
design.—Eastlake's Hints on Household Taste, p. 21.
Notes of a visit to Hachijô, in 1878. By F. V. Dickins and
Ernest Satow. Transactions of the Asiatic Society of Japan,
vol. vi. part iii. p. 435.
Vol. iv. p. 68.

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