This module provides an advanced study of modern digital signal processing (DSP) and a deep understanding of some of its major applications. The module starts with time and frequency domain analysis of discrete-time signals and systems. z-transforms are studied in detail and their applications are discussed. Fourier transformation of discrete-time signals is included in the module and the computational complexity of the transforms is critically analysed. Analogue to digital and digital to analogue conversion processes are discussed in detail. Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) filter design methods are critically analysed and implemented. A detailed study of DSP to solve complex problems using adaptive filtering, 2-dimensional image processing, audio processing, wavelets, and Discrete Cosine Transform (DCT) is included in the module.

A 2500-word assignment in the style of a journal paper weighted at 50%, assessing learning outcomes 1 and 2. A literature review on a specific topic to be performed, solutions for a complex real-life problem to be obtained using DSP and critical evaluation of results as well as methodologies used to be included in the paper. Meeting AHEP 4 Outcomes M2, M3, M4

A 2-hour examination weighted at 50%, assessing learning outcomes 3 and 4. Several questions to be answered based on topics covered in the module to demonstrate a comprehensive knowledge and understanding of methodologies and techniques applicable to digital signal processing. Meeting AHEP 4 Outcomes M1, M2

Professional Body requirements mean that a minimum overall score of 50% is required to pass a module, with each element of assessment requiring a minimum mark of 40% unless otherwise stated.

This module will enable students to gain understanding, apply knowledge, analyse and evaluate problems and create solutions through a variety of activities, including problem-based lectures, tutorials, laboratories and independent study.

1. Demonstrate comprehensive knowledge and understanding of advanced DSP methods related to rapidly evolving research areas and systematic application of various techniques. (AHEP 4: M4)

Knowledge and Understanding

2. Develop DSP-based solutions for complex real-life problems and provide critical evaluation of methodologies and techniques used including signal analysis in both time and frequency domains. (AHEP 4: M2, M3)

Application

3. Design digital filters for use in a wide range of DSP applications and employ appropriate decision-making to solve complex problems. (AHEP 4: M1)

Problem Solving

4. Apply appropriate analytical techniques and use engineering judgements to critically evaluate complex DSP systems. (AHEP 4: M2)

Analysis

Software tools (such as MATLAB, LabVIEW etc.) to develop DSP-based solutions.

Key Website References:

Prandoni, P. and Vetterli, M. (2008) Signal processing for communications, 1st Edn., EPFL Press.

Available at: http://www.sp4comm.org/;

Smith, S. W. (2002) Digital Signal Processing: A Practical Guide for Engineers and Scientists, 3rd Edn., Newnes. Available at: http://www.dspguide.com/;

IEEE Xplore Digital Library (https://ieeexplore.ieee.org/Xplore/home.jsp) including:

IEEE Transactions on Signal Processing, IEEE Transactions on Image Processing, IEEE Transactions on Circuits and Systems for Video Technology, IEEE Signal Processing Magazine, IET Journal on Signal Processing, and IET Journal on Image Processing.

Broughton, S.A. and Bryan, K. (2018) Discrete Fourier Analysis and Wavelets: Applications to Signal and Image Processing. John Wiley & Sons.

Farhang-Boroujeny, B. (2013) Adaptive filters: Theory and Applications. John Wiley & Sons.

Giron-Sierra, J.M. (2017) Digital Signal Processing with MATLAB Examples: Signals and Data, Filtering, Non-stationary

Signals, Modulation, Volume 1. Springer Singapore.

Giron-Sierra, J.M. (2017) Digital Signal Processing with MATLAB Examples: Decomposition, Recovery, Data-Based

Actions, Volume 2. Springer Singapore.

Giron-Sierra, J.M. (2017) Digital Signal Processing with MATLAB Examples: Model-Based Actions and Sparse

Representation, Volume 3. Springer Singapore.

Gonzalez, R.C. and Woods, R.E. (2017) Digital Image Processing, Pearson Higher Ed.

Gopi, E.S. (2018) Multi-Disciplinary Digital Signal Processing: A Functional Approach. Springer International Publishing.

Kumar, R.T. (2015) Digital Signal Processing, Oxford University Press.

Kuo, S.M., Lee, B.H. and Tian, W. (2013) Real-time Digital Signal Processing: Fundamentals, Implementations and Applications. John Wiley & Sons.

Mitra, S.K. (2011) Digital Signal Processing: A Computer-based Approach, McGraw-Hill.

Sundararajan, D. (2016) Discrete Wavelet Transform: A Signal Processing Approach. John Wiley & Sons.

With the wide-spread use of digital systems and smart devices, Digital Signal Processing (DSP) is being used to solve complex problems in a diverse range of applications. This module will discuss different DSP techniques which can be used to analyse discrete-time systems, extract valuable information from signals and improve overall system’s performance.