Signal processing Signals may have to be transformed in order to Digital Signal Processing → amplify or filter out embedded information → detect patterns Markus Kuhn → prepare the signal to survive a transmission channel → prevent interference with other signals sharing a medium → undo distortions contributed by a transmission channel → compensate for sensor deficiencies → find information encoded in a different domain Computer Laboratory To do so, we also need http://www.cl.cam.ac.uk/teaching/0809/DSP/ → methods to measure, characterise, model and simulate trans- mission channels → mathematical tools that split common channels and transfor- Lent 2009 – Part II mations into easily manipulated building blocks 3 Signals Analog electronics → flow of information Passive networks (resistors, capacitors, R → measured quantity that varies with time (or position) inductances, crystals, SAW filters), non-linear elements (diodes, . . . ), U in L C U out (roughly) linear operational amplifiers → electrical signal received from a transducer Advantages: (microphone, thermometer, accelerometer, antenna, etc.) • passive networks are highly linear → electrical signal that controls a process U in over a very large dynamic range U in and large bandwidths U out Continuous-time signals: voltage, current, temperature, speed, . . . • analog signal-processing circuits U out √ require little or no power ω (= 2 πf ) t Discrete-time signals: daily minimum/maximum temperature, 0 1 / LC � t lap intervals in races, sampled continuous signals, . . . • analog circuits cause little addi- U in − U out = 1 U out d τ + C d U out tional interference R L d t −∞ Electronics (unlike optics) can only deal easily with time-dependent signals, therefore spatial signals, such as images, are typically first converted into a time signal with a scanning process (TV, fax, etc.). 2 4
Digital signal processing Syllabus Analog/digital and digital/analog converter, CPU, DSP, ASIC, FPGA. Signals and systems. Discrete sequences and systems, their types and proper- Advantages: ties. Linear time-invariant systems, convolution. Harmonic phasors are the eigen functions of linear time-invariant systems. Review of complex arithmetic. Some → noise is easy to control after initial quantization examples from electronics, optics and acoustics. → highly linear (within limited dynamic range) MATLAB. Use of MATLAB on PWF machines to perform numerical experiments and visualise the results in homework exercises. → complex algorithms fit into a single chip Fourier transform. Harmonic phasors as orthogonal base functions. Forms of the → flexibility, parameters can easily be varied in software Fourier transform, convolution theorem, Dirac’s delta function, impulse combs in the time and frequency domain. → digital processing is insensitive to component tolerances, aging, Discrete sequences and spectra. Periodic sampling of continuous signals, pe- environmental conditions, electromagnetic interference riodic signals, aliasing, sampling and reconstruction of low-pass and band-pass signals, spectral inversion. But: Discrete Fourier transform. Continuous versus discrete Fourier transform, sym- → discrete-time processing artifacts (aliasing) metry, linearity, review of the FFT, real-valued FFT. Spectral estimation. Leakage and scalloping phenomena, windowing, zero padding. → can require significantly more power (battery, cooling) → digital clock and switching cause interference 5 7 Typical DSP applications Finite and infinite impulse-response filters. Properties of filters, implementa- tion forms, window-based FIR design, use of frequency-inversion to obtain high- → communication systems → astronomy pass filters, use of modulation to obtain band-pass filters, FFT-based convolution, polynomial representation, z -transform, zeros and poles, use of analog IIR design modulation/demodulation, channel VLBI, speckle interferometry techniques (Butterworth, Chebyshev I/II, elliptic filters). equalization, echo cancellation Random sequences and noise. Random variables, stationary processes, autocor- → experimental physics → consumer electronics relation, crosscorrelation, deterministic crosscorrelation sequences, filtered random sensor-data evaluation sequences, white noise, exponential averaging. perceptual coding of audio and video on DVDs, speech synthesis, speech Correlation coding. Random vectors, dependence versus correlation, covariance, → aviation recognition decorrelation, matrix diagonalisation, eigen decomposition, Karhunen-Lo` eve trans- form, principal/independent component analysis. Relation to orthogonal transform → music radar, radio navigation coding using fixed basis vectors, such as DCT. synthetic instruments, audio effects, → security Lossy versus lossless compression. What information is discarded by human noise reduction senses and can be eliminated by encoders? Perceptual scales, masking, spatial steganography, digital watermarking, → medical diagnostics resolution, colour coordinates, some demonstration experiments. biometric identification, surveillance systems, signals intelligence, elec- Quantization, image and audio coding standards. A/ µ -law coding, delta cod- magnetic-resonance and ultrasonic tronic warfare imaging, computer tomography, ing, JPEG photographic still-image compression, motion compensation, MPEG ECG, EEG, MEG, AED, audiology video encoding, MPEG audio encoding. → engineering → geophysics Note: The last three lectures on audio-visual coding were previously part of the course “Informa- control systems, feature extraction tion Theory and Coding”. A brief introduction to MATLAB was given in “Unix Tools”. seismology, oil exploration for pattern recognition 6 8
Objectives Sequences and systems By the end of the course, you should be able to A discrete sequence { x n } ∞ n = −∞ is a sequence of numbers → apply basic properties of time-invariant linear systems . . . , x − 2 , x − 1 , x 0 , x 1 , x 2 , . . . → understand sampling, aliasing, convolution, filtering, the pitfalls of spectral estimation where x n denotes the n -th number in the sequence ( n ∈ Z ). A discrete → explain the above in time and frequency domain representations sequence maps integer numbers onto real (or complex) numbers. → use filter-design software We normally abbreviate { x n } ∞ n = −∞ to { x n } , or to { x n } n if the running index is not obvious. The notation is not well standardized. Some authors write x [ n ] instead of x n , others x ( n ). → visualise and discuss digital filters in the z -domain Where a discrete sequence { x n } samples a continuous function x ( t ) as → use the FFT for convolution, deconvolution, filtering → implement, apply and evaluate simple DSP applications in MATLAB x n = x ( t s · n ) = x ( n/f s ) , → apply transforms that reduce correlation between several signal sources we call t s the sampling period and f s = 1 /t s the sampling frequency . → understand and explain limits in human perception that are ex- A discrete system T receives as input a sequence { x n } and transforms ploited by lossy compression techniques it into an output sequence { y n } = T { x n } : → provide a good overview of the principles and characteristics of sev- discrete eral widely-used compression techniques and standards for audio- . . . , x 2 , x 1 , x 0 , x − 1 , . . . . . . , y 2 , y 1 , y 0 , y − 1 , . . . system T visual signals 9 11 Textbooks Properties of sequences A sequence { x n } is → R.G. Lyons: Understanding digital signal processing. Prentice- ∞ Hall, 2004. ( £ 45) � absolutely summable ⇔ | x n | < ∞ → A.V. Oppenheim, R.W. Schafer: Discrete-time signal process- n = −∞ ing. 2nd ed., Prentice-Hall, 1999. ( £ 47) ∞ | x n | 2 < ∞ � square summable ⇔ → J. Stein: Digital signal processing – a computer science per- n = −∞ spective. Wiley, 2000. ( £ 74) periodic ⇔ ∃ k > 0 : ∀ n ∈ Z : x n = x n + k → S.W. Smith: Digital signal processing – a practical guide for A square-summable sequence is also called an energy signal , and engineers and scientists. Newness, 2003. ( £ 40) ∞ � | x n | 2 → K. Steiglitz: A digital signal processing primer – with appli- n = −∞ cations to digital audio and computer music. Addison-Wesley, is its energy. This terminology reflects that if U is a voltage supplied to a load 1996. ( £ 40) resistor R , then P = UI = U 2 /R is the power consumed, and � P ( t ) d t the energy. → Sanjit K. Mitra: Digital signal processing – a computer-based So even where we drop physical units (e.g., volts) for simplicity in calculations, it is still customary to refer to the squared values of a sequence as power and to its approach. McGraw-Hill, 2002. ( £ 38) sum or integral over time as energy . 10 12
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