Module Catalogue 2024/25

Pre Requisite Comment

In the above two modules the mathematical techniques needed for signal analysis and the fundamental principles of signals and communication systems are explained.

Co Requisite Comment

N/A

Aims

To develop in-depth knowledge of discrete-time signal processing algorithms, and approaches to measure deterministic and random signals in the frequency domain.

To measure the computational cost of different algorithms used in time/frequency transformation.

To gain proficiency in methods to distinguish the desired signals from noise using appropriate digital filters.

Using problem-based learning to gain skills in the design and implementation of digital signal processing systems for real world applications.

This module also aims to provide underlying mathematical, statistical, and theoretical concepts of Machine Learning along with essential programming skills and expertise to design, build, and implement appropriate Machine Learning techniques for various engineering applications. The module introduces classical regression, classification, and clustering models. The module includes relevant programming exercises to complement the theoretical concepts, which will allow students to gain valuable conceptual and programming skills to build, optimise and implement these models into a range of practical engineering challenges.

Outline Of Syllabus

In semester 1 the module introduces the mathematical foundations and concepts of discrete time signals, digital signal analysis and digital filtering. Sampling and quantisation effects are discussed before moving on to time and frequency domain analysis techniques for deterministic, periodic and random waveforms. Fixed digital filter structures are introduced along with design methods/tools used to achieve the required magnitude and phase response. Adaptive filter structures are introduced as a means of achieving optimum filtering and noise cancellation for unknown and time varying systems. The computational efficiency of these techniques is explored throughout along with methods to minimise computation, including fast transforms and multi-rate systems.

In semester 2, the module introduces mathematical foundations and fundamental concepts of machine learning, including supervised, unsupervised, and clustering methods. The module focuses on mathematically formulating, and practically designing, testing and validating different types of machine learning algorithms to solve various real-world application. It also covers data processing and compression techniques to interpret key information extracted by different machine learning models. The module also exposes the ethical, privacy and security related issues of data driven machine learning models.

Intended Knowledge Outcomes

By the end of the course:
1.       Students will be able to explain and use discrete-time signal processing and frequency domain analysis based on Discrete Fourier Transform (DFT) and Fast Fourier Transform (FFT). M1
2.       Students will be capable of designing and implementing finite impulse response (FIR), infinite impulse response (IIR) and adaptive filters. M3
3.       Students will be able to explain and use methods to measure periodicity in signals and cross-correlation (matched filters) to detect signals in noise. M2
4.       Students will be able to develop optimal filtering and analysis tools based on DSP algorithms. M3
5.       Students will be able to explain the mathematical foundations of machine learning including linear algebra, statistics and probability, and calculus. M1
6.       Students will be able to define fundamental concepts of supervised, unsupervised, and clustering methods. M1
7.       The students will be able to select, design and implement the correct machine learning algorithm by distinguishing whether the given problem is a regression, classification, or clustering problem. M2 and M3
8.       Students will be able to examine and interpret key information extracted by different machine learning models. M2
9.       Students will be able to judge ethical and societal issues related to fairness, privacy, and security of data-driven machine learning models. M8

Intended Skill Outcomes

At the end of course, students will be able to:
•       Examine deterministic and random signals in time and frequency domain. M2
•       Develop Matlab based simulations of DSP systems and use them to analyse and extract information from given discrete time signals (e.g. .wav files) with realistic distortions and noise. M2,M3
•       Design and employ digital filters to separate required/desired signal from noise. M3
•       Critically appraise DSP systems in terms of performance and computational cost, to guide the design of efficient hardware/software implementation. M3
•       Algorithmically formulate and design machine Learning models to employ them in several practical applications. M2, M3
•       Design, debug, and interpret code to train, test, optimise and validate the built models and evaluate their performance. M3
•       Process, compress, and prepare data to derive and distinguish key structures and information. M2
•       Identify and appraise data biases and decision biases related to data-driven machine Learning models. M1

Teaching Rationale And Relationship

Guided online study provides the fundamental concepts of the course while the seminars and Q&A sessions reinforce understanding and application context. Lab sessions and MATLAB based exercises will develop skills in design, implementation and testing of signal processing and machine learning algorithms.

Assessment Rationale And Relationship

The summative assignment (25%) assess core understanding of course material, analysis/design skills applied to realistic DSP problems and their ability to simulate and verify algorithm performance in MATLAB.


The final NUMBAS-based exam will evaluate students' knowledge and understanding of statistical signal analysis, optimal digital filtering techniques, and the fundamental principles and applications of machine Learning techniques. Additionally, the exam will assess students' abilities to formulate, design, and select various machine learning models, as well as their capacity to interpret key information derived from these models. Furthermore, it will test students' capability in evaluating ethical and societal issues associated with data-driven machine learning models.

The formative MATLAB exercises will provide weekly feedback on their understanding of the DSP topics and their readiness to take on the assignment. The machine learning programming exercises will reinforce understanding of the machine learning methods and their practical implementation/testing. The online quizzes will provide feedback to students on their grasp of these topics and readiness for the exam.

General Notes

N/A