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. It also covers multi-rate DSP, interpolation and decimation techniques. Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) filter design methods are critically analysed and implemented. A detailed study of advanced applications of DSP such as adaptive filtering, 2-dimensional image processing, wavelets, Discrete Cosine Transform (DCT) and sparse signal representation is included in the module. DSP devices, architectures and practical considerations for real-time DSP will be covered. The topics covered during the semester are reinforced with practical and simulation-based laboratory sessions.

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 and tutorials/laboratories

• Independent study: reading, team meetings, information gathering, student-centred learning, assignment preparation and presentation

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.

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.

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

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

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

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

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

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

MATLAB/Simulink
NI LABVIEW with Ni CompactRIO embedded control and acquisition systems
TI DSP Starter Kits

Key Website References:
GNU Octave: http://www.gnu.org/software/octave/;
Octave Online: https://octave-online.net/;
Prandoni, P. and Vetterli, M. (2008) Signal processing for communications, 1st Edn., EPFL Press.
Available free at: http://www.sp4comm.org/;
Smith, S. W. (2002) Digital Signal Processing: A Practical Guide for Engineers and Scientists, 3rd Edn., Newnes. Available free at: http://www.dspguide.com/;

IEEE Xplore Digital Library (http://ieeexplore.ieee.org/Xplore/guesthome.jsp) including:
IEEE Transactions on Signal Processing, IEEE Transactions on Image Processing, IEEE Transactions on Circuits and Systems for Video Technology, IEEE Transactions on Control Systems Technology, IEEE Signal Processing Magazine, IET Journal on Signal Processing, IET Journal on Image Processing.

Elsevier Journal on Signal Processing, Elsevier Journal on Digital Signal Processing, Elsevier Journal on Image and Vision Computing
+ additional sources as directed by the Module Tutor.

1. Demonstrate knowledge and comprehensive understanding of advanced DSP methods related to rapidly evolving research areas and systematic application of various techniques. (AHEP3: SM7M)

2. Analyse complex DSP systems using a variety of analytical tools and ability to learn new tools independently. (AHEP3: EA6M)

3. Find solutions for a wide range of DSP applications and critical evaluation of methodologies and techniques in terms of algorithm requirement and implementation platforms. (AHEP3: SM7M, EA6M, EA5m, EA7M, D10M, G1)

4. Design complex algorithms and systematically take into account non-ideal effects and practical limitations. Communicate the methodology, results and conclusions of work.done. (AHEP3:EA6M, EA7M, D9M, D10M, G1)

Written assignment in the style of a journal paper (50%) - which will assess Learning Outcomes 3 and 4. Meeting AHEP 3 Outcomes SM7M, EA6M, EA5m, EA7M, D9M, D10M, G1.

1 x 2 hour final examination (50%) which will assess Learning Outcomes 1 and 2 and 3. Meeting AHEP 3 Outcomes SM7M, EA6M, D10M.

Practice formative class tests will be undertaken during the module and formative guidance and feedback will be provided in tutorial sessions within the class.