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Computer Science Syllabus - Digital Signal Processing
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Digital Signal Processing

Lecturer: Dr M.G. Kuhn

No. of lectures: 12

Prerequisite courses: Continuous Mathematics, Numerical Analysis I, Probability
Some of the material covered in Information Theory & Coding will also help in this course.


This course teaches the basic signal processing principles necessary to understand many modern high-tech systems, with a particular view on audio-visual data compression techniques. Students will gain practical experience from numerical experiments in MATLAB-based programming assignments.


  • Signals and systems. Discrete sequences and systems, their types and properties. Linear time-invariant systems, convolution. Harmonic phasors are the eigen functions of linear time-invariant systems. Review of complex arithmetic. Some examples from electronics, optics and acoustics.

  • MATLAB. Use of MATLAB on PWF machines to perform numerical experiments and visualise the results in homework exercises.

  • Fourier transform. Harmonic phasors as orthogonal base functions. Forms of the Fourier transform, convolution theorem, Dirac's delta function, impulse combs in the time and frequency domain.

  • Discrete sequences and spectra. Periodic sampling of continuous signals, periodic signals, aliasing, sampling and reconstruction of low-pass and band-pass signals, spectral inversion.

  • Discrete Fourier transform. Continuous versus discrete Fourier transform, symmetry, linearity, review of the FFT, real-valued FFT.

  • Spectral estimation. Leakage and scalloping phenomena, windowing, zero padding.

  • Finite and infinite impulse-response filters. Properties of filters, implementation forms, window-based FIR design, use of frequency-inversion to obtain high-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 techniques (Butterworth, Chebyshev I/II, elliptic filters).

  • Random sequences and noise. Random variables, stationary processes, autocorrelation, crosscorrelation, deterministic crosscorrelation sequences, filtered random sequences, white noise, exponential averaging.

  • Correlation coding. Random vectors, dependence versus correlation, covariance, decorrelation, matrix diagonalisation, eigen decomposition, Karhunen-Loève transform, principal/independent component analysis. Relation to orthogonal transform coding using fixed basis vectors, such as DCT.

  • Lossy versus lossless compression. What information is discarded by human senses and can be eliminated by encoders? Perceptual scales, masking, spatial resolution, colour coordinates, some demonstration experiments.

  • Quantization, image and audio coding standards. A/$\mu$-law coding, delta coding, JPEG photographic still-image compression, motion compensation, MPEG video encoding, MPEG audio encoding. [2 lectures]


By the end of the course students should

  • be able to apply basic properties of time-invariant linear systems

  • understand sampling, aliasing, convolution, filtering, the pitfalls of spectral estimation

  • be able to explain the above in time and frequency domain representations

  • be competent to use filter-design software

  • be able to visualise and discuss digital filters in the z-domain

  • be able to use the FFT for convolution, deconvolution, filtering

  • be able to implement, apply and evaluate simple DSP applications in MATLAB

  • apply transforms that reduce correlation between several signal sources

  • understand and explain limits in human perception that are exploited by lossy compression techniques

  • provide a good overview of the principles and characteristics of several widely-used compression techniques and standards for audio-visual signals

Recommended reading

* Lyons, R.G. (2004). Understanding digital signal processing. Prentice-Hall (2nd ed.).
Oppenheim, A.V. & Schafer R.W. (1999). Discrete-time digital signal processing. Prentice-Hall (2nd ed.).
Stein, J. (2000). Digital signal processing - a computer science perspective. Wiley.
Salomon, D. (2002). A guide to data compression methods. Springer.

next up previous contents
Next: Human-Computer Interaction Up: Michaelmas Term 2005: Part Previous: Digital Communication II   Contents
Christine Northeast
Sun Sep 11 15:46:50 BST 2005