## DIGITAL SIGNAL PROCESSING

 SHE Level 4 SCQF Credit Points 20.00 ECTS Credit Points 10.00 Module Code MHH623541 Module Leader Sinan Sinanovic School School of Computing, Engineering and Built Environment Subject Electronic Engineering Trimester A (September start)

### Pre-Requisite Knowledge

Mathematics 1A,1B,2A, 2B Signals and Electronics Systems Design or equivalent

### Summary of Content

This module will introduce fundamental technologies for digital signal processing. This involves examining the nature of digital signals, discrete time convolution, impulse response, transfer function, the relationship between the s-plane and the z-plane; the design and implementation of digital filters (FIR/IIR); the analysis of processor architectures for the efficient implementation of digital signal processing algorithms; concepts and techniques used in the real world application of signal processing algorithms, introduction to adaptive filtering concepts.

### Syllabus

The teaching syllabus will cover the following areas: Review of relevant Mathematical techniques: Complex Numbers, Euler Theorem, Convergence of the Geometric Series, partial fraction expansion Discrete Time Signals and Systems: Discrete Time Signals, Discrete time Systems, LTI Systems, Causality, Stability, Frequency domain representations Z-Transform: z-transform, region of convergence, inverse z-transform using partial fractions, z-transform properties Transform Analysis of LTI Systems: Pole-zero diagrams, frequency response of LTI systems, group delay, transfer function, all pass, minimum phase, linear phase systems Structures for Discrete Time Systems: Block diagrams, FIR/IIR systems, direct forms, cascade form transposed, coefficient quantization Filter Design Techniques: Filter specifications, Impulse invariance, Bilinear transformation, FIR filter design by windowing, FIR linear phase Fourier Analysis of Signals: Complex discrete Fourier series, Discrete Fourier Transform, convolution, Fast Fourier Transform Decimation in time and frequency, sampling, spectra Introduction to Adaptive DSP: Introduction to adaptive systems with applications, Wiener-Hopf Equations, Steepest Descent

### Learning Outcomes

On completion of this module the student should be able to:Analyse systems to obtain the impulse response, transfer function, pole-zero diagrams and frequency/phase responseDesign and implement various FIR and IIR filters including digital approximations of analogue systemsUnderstand the spectral properties of digital signals using Fourier analysisCritically analyse the various computational methods and architectural properties of DSPs and general purpose microprocessorsDevelop software to simulate and implement DSP algorithms

### Teaching / Learning Strategy

The University 'Strategy for Learning' documentation has informed the learning and teaching strategy for this module. The course material will be introduced through lectures and practical exercises based on lecture material will be applied during lab and tutorial sessions. Tutorials will be used to explain and elaborate on the lecture material. Case studies will be used to demonstrate applications of signal processing. Where appropriate online demonstrations of key concepts will be utilised. The laboratory work will provide the student with support to develop signal processing implementation skills, and to develop a deeper understanding of the underlying theory. This is achieved via a series of formative exercises, then a summative project hand in. To encourage the students to be as technically innovative as possible marks are given for both technical competence and creativity. Full use will be made of GCU Learn to provide Lecture-based and related study materials, along with sample solutions of Tutorial and Laboratory exercises, thus encouraging the development of independent learning and allowing self-reflective feedback on student performance. Staff-based feedback on student performance for submitted work will be provided in line with the University feedback policy, with summative feedback and grades on the coursework assessment utilising GCU Learn. The additional interactive discussion features of GCU Learn will be utilised, as appropriate to the module, to stimulate independent and flexible student learning outwith scheduled class time.

Both the learned and the popular literature on image processing and machine vision will be used as a source of information for private study. The titles and electronic resources below are to be considered as indicative only. -360 1. Oppenheim, Schafer (2010) Discrete-Time Signal Processing 3 rd Edition Pearson 2. Hayes, Monsoon H. (1999) Digital Signal Processing, Shaums 3. Ifeachor, Jervis (2001). Digital Signal Processing, A Practical Approach. 2 nd Edition, Addison-Wesley

### Transferrable Skills

Specialist knowledge and application. Critical thinking and problem solving. Critical analysis. Communication skills, written, oral and listening. Numeracy. Effective Information retrieval and research skills. Computer literacy. Creativity, innovation & independent thinking. Knowledge of international affairs. Presentation skills. Commercial awareness

### Module Structure

Activity Total Hours
Tutorials (FT) 12.00
Tutorials (PT) 12.00
Independent Learning (FT) 120.00
Assessment (PT) 20.00
Practicals (FT) 24.00
Lectures (PT) 24.00
Assessment (FT) 20.00
Independent Learning (PT) 132.00
Practicals (PT) 12.00
Lectures (FT) 24.00

### Assessment Methods

Component Duration Weighting Threshold Description
Coursework 1 n/a 15.00 n/a Practical
Coursework 2 n/a 15.00 n/a Practical
Exam (Exams Office) 3.00 70.00 35% Exam linked to Learning Outcomes