![Course Overview
2
• Topics:
– Fourier Transforms and Digital Filters 6 letures [me]
– Random signals, Optimal Filtering and Signal
Modelling 6 lectures [Simon Godsill]
– Pattern recognition 4 lectures [Zoubin
Ghahramani]
• Handouts:
– New: typos, please, e-mail
op205@cam.ac.uk
– www-sigproc.eng.cam.ac.uk/~op205
– Feedback welcome
• Natural extension of 3F1](https://image.slidesharecdn.com/introductiontodsp-240828084846-0d442e0b/85/introduction_to_digital_signap_process-pptx-2-320.jpg)
introduction_to_digital_signap_process.pptx
- 1. Introduction to Digital Signal Processing (DSP) 1 Elena Punskaya www-sigproc.eng.cam.ac.uk/~op205 Some material adapted from courses by Prof. Simon Godsill, Dr. Arnaud Doucet, Dr. Malcolm Macleod and Prof. Peter Rayner
- 2. Course Overview 2 • Topics: – Fourier Transforms and Digital Filters 6 letures [me] – Random signals, Optimal Filtering and Signal Modelling 6 lectures [Simon Godsill] – Pattern recognition 4 lectures [Zoubin Ghahramani] • Handouts: – New: typos, please, e-mail op205@cam.ac.uk – www-sigproc.eng.cam.ac.uk/~op205 – Feedback welcome • Natural extension of 3F1
- 3. Books 3 Books: – J.G. Proakis and D.G. Manolakis, Digital Signal Processing 3rd edition, Prentice-Hall. – R.G Lyons, Understanding Digital Signal processing, 2nd edition, Prentice-Hall. (Amazon’s top-selling for five straight years) Material covered – maths (why is it difficult?) – exams – examples papers – help
- 4. Digital: operating by the use of discrete signals to represent data in the form of numbers Signal: a parameter (electrical quantity or effect) that can be varied in such a way as to convey information Processing: a series operations performed according to programmed instructions changing or analysing information which is measured as discrete sequences of numbers What is Digital Signal Processing? 4
- 5. The Journey 5 “Learning digital signal processing is not something you accomplish; it’s a journey you take”. R.G Lyons, Understanding Digital Signal processing
- 6. Applications of DSP - Radar Radar and Sonar: Examples 1)target detection – position and velocity estimation 2) tracking 6
- 7. Applications of DSP - Biomedical Biomedical: analysis of biomedical signals, diagnosis, patient monitoring, preventive health care, artificial organs Examples: 1)electrocardiogram (ECG) signal – provides doctor with information about the condition of the patient’s heart 2)electroencephalogram (EEG) signal – provides Information about the activity of the brain 7
- 8. Applications of DSP - Speech Speech applications: Examples 1) noise reduction – reducing background noise in the sequence produced by a sensing device (microphone) 2) speech recognition – differentiating between various speech sounds 8 3) synthesis of artificial speech – text to speech systems for blind
- 9. Applications of DSP - Communications Communications: Examples 1) telephony – transmission of information in digital form via telephone lines, modem technology, mobile phones 2) encoding and decoding of the information sent over a physical channel (to optimise transmission or to detect or correct errors in transmission) 9
- 10. Applications of DSP – Image Processing 2) compression - reducing the redundancy in the image data to optimise transmission / storage Image Processing: Examples 1)content based image retrieval – browsing, searching and retrieving images from database 2) image enhancement 10
- 11. Applications of DSP – Music Music Applications: Examples: 1) Recording 3) Manipulation (mixing, special effects) 2) Playback 11
- 12. Applications of DSP - Multimedia Multimedia: generation storage and transmission of sound, still images, motion pictures Examples: 1) digital TV 2) video conferencing 12
- 13. DSP Implementation - Operations To implement DSP we must be able to: DSP Digital Signal 13 Digital Signal 1) perform numerical operations including, for example, additions, multiplications, data transfers and logical operations either using computer or special-purpose hardware Input Output
- 14. 14 DSP chips • Introduction of the microprocessor in the late 1970's and early 1980's meant DSP techniques could be used in a much wider range of applications. DSP chip – a programmable device, with its own native instruction code designed specifically to meet numerically-intensive requirements of DSP capable of carrying out millions of floating point operations per second Bluetooth headset Household appliances Home theatre system
- 15. DSP Implementation – Digital/Analog Conversion Digital Signal DSP Digital Signal Reconstruction Analog 15 Signal 2) convert the digital information, after being processed back to an analog signal - involves digital-to-analog conversion & reconstruction (recall from 1B Signal and Data Analysis) e.g. text-to-speech signal (characters are used to generate artificial sound) To implement DSP we must be able to:
- 16. DSP Implementation –Analog/Digital Conversion Digital Signal Sampling Digital Signal DSP Analog Signal 16 To implement DSP we must be able to: 3) convert analog signals into the digital information - sampling & involves analog-to-digital conversion (recall from 1B Signal and Data Analysis) e.g. Touch-Tone system of telephone dialling (when button is pushed two sinusoid signals are generated (tones) and transmitted, a digital system determines the frequences and uniquely identifies the button – digital (1 to 12) output
- 17. DSP Implementation DSP Digital Signal Digital Signal Reconstruction Analog Signal Sampling Analog Signal 17 To implement DSP we must be able to: perform both A/D and D/A conversions e.g. digital recording and playback of music (signal is sensed by microphones, amplified, converted to digital, processed, and converted back to analog to be played
- 18. Limitations of DSP - Aliasing Gjendemsjø, A. Aliasing Applet, Connexions, http://cnx.org/content/m11448/1.14 Most signals are analog in nature, and have to be sampled loss of information • we only take samples of the signals at intervals and don’t know what happens in between aliasing cannot distinguish between higher and lower frequencies (recall from 1B Signal and Data Analysis) Sampling theorem: to avoid aliasing, sampling rate must be at least twice the maximum frequency component (`bandwidth’) of the signa1l8
- 19. 19 • Sampling theorem says there is enough information to reconstruct the signal, which does not mean sampled signal looks like original one correct reconstruction is not just connecting samples with straight lines needs antialias filter (to filter out all high frequency components before sampling) and the same for reconstruction – it does remove information though Limitations of DSP - Antialias Filter Each sample is taken at a slightly earlier part of a cycle (recall from 1B Signal and Data Analysis)
- 20. 20 Limitations of DSP – Frequency Resolution Most signals are analog in nature, and have to be sampled loss of information • we only take samples for a limited period of time limited frequency resolution does not pick up “relatively” slow changes (recall from 1B Signal and Data Analysis)
- 21. 21 Limitations of DSP – Quantisation Error Most signals are analog in nature, and have to be sampled loss of information • limited (by the number of bits available) precision in data storage and arithmetic quantisation error smoothly varying signal represented by “stepped” waveform (recall from 1B Signal and Data Analysis)
- 22. Why still do it? • Digital system can be simply reprogrammed for other applications / ported to different hardware / duplicated (Reconfiguring analog system means hadware redesign, testing, verification) • DSP provides better control of accuracy requirements (Analog system depends on strict components tolerance, response may drift with temperature) • Digital signals can be easily stored without deterioration (Analog signals are not easily transportable and often can’t be processed off-line) • More sophisticated signal processing algorithms can be implemented (Difficult to perform precise mathematical operations in analog form) 22 Advantages of Digital over Analog Signal Processing