Teaching Responsibility

LJMU Schools involved in Delivery:

LJMU Partner Taught

Learning Methods

Lecture

Practical

Module Offerings

6601SLT-FEB-PAR

6601SLT-JUL-PAR

Aims

This module is intended to provide students with a good appreciation of the mathematical concepts necessary to apply digital signal and image processing algorithms to a range of engineering problems.

Learning Outcomes

1.
Characterise analogue and digital systems using appropriate transforms, impulse response and convolution
2.
Design and implement digital filters
3.
Process and compress images using appropriate techniques
4.
Apply DSP to a range of applications

Module Content

Outline Syllabus:Signals and Systems - Foundations, Architecture Requirements and Characteristics Use of MATLAB Fundamentals – Linear Systems, Convolution and Properties of Convolution Transforms – Fourier: CTFT, DTFT, DFT, FFT; Laplace and Z-transforms Digital Filters - Basic Concepts Finite Impulse Response filters (FIR) - Design, Fourier Series Approximation Analogue Prototypes - Butterworth, Chebyshev, Elliptic; Analogue-To-Analogue Transforms Infinite Impulse Response filters (IIR) - Design, Bilinear, Impulse Invariant Transforms Digital Filters - Implementation, Algorithms & Finite Word Effects ADCs & DACs - Sample and Hold, Antialiasing Multirate Signal Processing – Interpolation and Decimation Time Frequency Analysis – Short term Fourier series, Wavelets, Filter-Banks 2D Signal Processing – Representation of images, image compression, 2D transforms
Additional Information:This module will provide students with a sound grasp of the theory and applications of modern signal and image processing. UNESCO Sustainable Development Goals Quality Education Gender Equality Industry, Innovation and Infrastructure Partnerships for the Goals UK SPEC AHEP 4 CEng. M1 Apply a comprehensive knowledge of mathematics, statistics, natural science and engineering principles to the solution of complex problems. Much of the knowledge will be at the forefront of the particular subject of study and informed by a critical awareness of new developments and the wider context of engineering. M2 Formulate and analyse complex problems to reach substantiated conclusions. This will involve evaluating available data using first principles of mathematics, statistics, natural science and engineering principles, and using engineering judgment to work with information that may be uncertain or incomplete, discussing the limitations of the techniques employed. M3 Select and apply appropriate computational and analytical techniques to model complex problems, discussing the limitations of the techniques employed. M6 Apply an integrated or systems approach to the solution of complex problems. M12 Use practical laboratory and workshop skills to investigate complex problems. IEng. B1 Apply knowledge of mathematics, statistics, natural science and engineering principles to broadly-defined problems. Some of the knowledge will be informed by current developments in the subject of study. B2 Analyse broadly-defined problems reaching substantiated conclusions using first principles of mathematics, statistics, natural science and engineering principles. B3 Select and apply appropriate computational and analytical techniques to model broadly-defined problems, recognising the limitations of the techniques employed. B6 Apply an integrated or systems approach to the solution of broadly-defined problems. B12 Use practical laboratory and workshop skills to investigate broadly-defined problems.

Assessments

Portfolio