Digital Signal Processing

An introductory course on digital signal processing basics for students in computer science and engineering, designed to teach students the fundamentals of digital signal processing theroy and show the basics of DSP application. Topics covered: discrete-time signals and systems, frequency domain analysis of DT signals, DTFT and DFT, FFT algorithm basics, FFT applications in spectral analysis, digital filtering, FIR/IIR filter design and implementation, digital signal processors overview, fundamentals of DT random signals, basic autocorrelation and PSD estimation techniques, 2D signal processing, advanced DSP techniques overview.,

An introductory course on digital signal processing basics for students in computer science and engineering, designed to teach students the fundamentals of digital signal processing theroy and show the basics of DSP application. Topics covered: discrete-time signals and systems, frequency domain analysis of DT signals, DTFT and DFT, FFT algorithm basics, FFT applications in spectral analysis, digital filtering, FIR/IIR filter design and implementation, digital signal processors overview, fundamentals of DT random signals, basic autocorrelation and PSD estimation techniques, 2D signal processing, advanced DSP techniques overview.,


Lecture contents
DT signals and systems review. Review of DT signals and systems basics: discretisation in time and in amplitude, inherently discrete signals, DT system properties: linearity, shift-invariance. Description of LTI systems: impulse response, step response, difference equations, linear convolution (4h).

Frequency-domain methods. Concept of frequency in DT domain, periodicity definition, DTFT properties (periodic and limited energy signals), DFT definition and properties. DFT implementations (FFT) and applications. Windowing theory and applications. Circular convolution vs. linear convolution (5h).

Review and test I.(1h+1h)
Instantaneous spectrum. Application of FFT and windowing to analysis of signals with changing properties (1h).

Z-transform. Review of z-transform basics, applications to analysis of DT signals and systems. Z-domain transfer function, differential equation of a system and its frequency characteristics (2h).

Filter design. FIR filter design by window and optimisation methods. IIR filter design from CT prototype (impulse invariance and bilinear transform) and optimisation methods. Filter implementation problems (2h).
Digital signal processors. The idea of DSP. Architectural properties (pipelining, parallelism, hardware enhancements, Harvard memory architecture). DSP56002 as an example of typical DSP: arithmetic, addressing, programming overview (2h).

Introduction to random DT signals. Random signal properties. Random process and its realization. Autocorrelation and PSD. Filtering of random signals. Estimation of parameters. Applications of random signal theory: signal modelling, system modelling, detection (3h).

Review and test II. (1h+1h)
Two-dimensional signal processing. 2D signals. 2D FFT - definition and properties. Filtering of 2D signals. Application to image processing (3h).

Advanced techniques overview. Examples of practical applications and implementations of the ideas and algorithms learned at the lecture - audio processing, video compression, radar, sonar, telecommunication (2h).

Summary and review (2h).

Laboratory contents

1. Matlab as a DSP tool (2h)


2. DT signals, systems, frequency concept (4h)
   
3. Spectral analysis of deterministic signals (4h)


4. Instantaneous spectrum and spectrogram (4h)


5. Digital filtering and filter design (4h)


6. Digital signal processors (basic programming)


7. Random signals


8. Image processing

Basic

1. Alan V. Oppenheim, Roland Schäfer. Disrete-time signal processing. (any edition, including previous version entitled Digital signal processing)

Optional

1. Steven W. Smith, The Scientist and Engineer`s Guide to Digital Signal Processing, www.dspguide.com