Prerequisite courses: Continuous Mathematics, Numerical Analysis I, Probability
Some of the material covered in Information Theory & Coding will also help in this course.
Aims
This course teaches the basic signal processing principles necessary
to understand many modern high-tech systems. Students will gain
practical experience from numerical experiments in MATLAB-based
programming assignments.
Lectures
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, averaging, noise reduction filters,
exponential averaging, periodic averaging.
Objectives
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
Recommended books
* 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.
