This module introduces more advanced mathematical tools that are useful for modelling real-world engineering systems and for the analysis and processing of signals.
Occurrence | Teaching period |
---|---|
A | Semester 1 2023-24 |
Subject content aims:
To introduce the techniques of multivariable calculus (including partial differentiation, coordinate transformations and multiple integrals)
To support applied modules in areas such as networks, electromagnetic fields and control theory
To provide an introduction to the Laplace transform and the Z-transform as tools for linear systems theory and analysis
To develop an awareness and understanding of the use of Fourier Transform, Fourier Series, Convolution and Correlation techniques to the study of signals and linear systems
Graduate skills aims:
To develop skills in the application of applied numeracy and algebraic techniques
Subject content learning outcomes
After successful completion of this module, students will:
Describe the use of calculus for two and three dimensional problems
Discuss the limitations of the Laplace transform in the context of engineering problems
Explain the implications of sampling signals and the basic theory of the Z-transform
Be able to demonstrate an understanding of Fourier Series and Fourier Transform techniques
Be able to demonstrate an understanding of Convolution and Correlation techniques
Be able to explain and use the theorems associated with Fourier Transform techniques
Be able to describe the use of Correlation and Convolution techniques to analyse linear time invariant systems
Be able to evaluate total derivatives and multiple integrals in two or more variables
Be able to change variables and transform the way in which a multidimensional problem is viewed
Be able to use the Laplace transform in the analysis and characterisation of linear, time-invariant systems
Be able to compare and contrast the Laplace & Fourier transforms in an engineering context
Be able to apply Fourier Transform techniques to describe the characteristics of signals
Graduate skills learning outcomes
After successful completion of this module, students will:
Be able to explain commonly encountered technical concepts concisely and accurately
Be able to select and apply a range of mathematical techniques to solve problems
Have developed skills in problem solving, critical analysis and applied mathematics
Task | % of module mark |
---|---|
Closed/in-person Exam (Centrally scheduled) | 100 |
None
Task | % of module mark |
---|---|
Closed/in-person Exam (Centrally scheduled) | 100 |
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The School of PET aims to provide some form of feedback on all formative and summative assessments that are carried out during the degree programme. In general, feedback on any written work/assignments undertaken will be sufficient so as to indicate the nature of the changes needed in order to improve the work. The School will endeavour to return all exam feedback within the timescale set out in the University's Policy on Assessment Feedback Turnaround Time. The School would normally expect to adhere to the times given, however, it is possible that exceptional circumstances may delay feedback. The School will endeavour to keep such delays to a minimum. Please note that any marks released are subject to ratification by the Board of Examiners and Senate. Meetings at the start/end of each term provide you with an opportunity to discuss and reflect with your supervisor on your overall performance to date.
Digital Signal Processing: Concepts and Applications, Bernard Mulgrew, Peter Grant and John Thompson. Palgrave Macmillan, 2nd Edition, ISBN 0333963563