Course Catalogue

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This course introduces the concepts and techniques of digital signal processing (DSP) and demonstrates some interesting and useful practical applications of DSP. It also provides practical experience in using Python in analysis and design of DSP systems and algorithms. Taught material is programming language agnostic ensuring that the skills and knowledge acquired remain relevant even as underlying implementation technologies evolve. However, this material is generously supplemented with reference implementations of the algorithms described written in the Python programming language. The course concludes with a free project where students are encouraged to connect sensor modules to a microcontroller board to achieve an aim of their choice, then, using the signal processing techniques acquired in the course, to design, implement and test a digital filter which demonstrably satisfies their stated requirements.

2 hours of flipped classroom teaching per week: Online videos and extensive lab session with problem-based learning.

Mandatory Entry Requirements

None

Recommended Entry Requirements

None

None.

None.

50% Examination

50% Assignment (3 assignments at 12.5%, 12.5% and 25%)

Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. It is not possible to offer reassessment of the coursework and the mark achieved will be counted towards the final course grade for both the main exam and the resit.

It is not practicable to offer reassessment in any aspect of this course that requires practical or group work.

This class is heavily lab based. The assignments are based on the principle of problem based learning.

The aims of this course are to:

■ Introduce the concepts and techniques of digital signal processing (DSP);

■ Demonstrate some interesting and useful practical applications of DSP;

■ Provide practical experience in using DSP software in analysis and design of DSP systems and algorithms;

■ Develop energy-efficient implementations of DSP systems by understanding the impact of different implementation choices;

■ Integrate interdisciplinary knowledge and skills into DSP such as audio, medical and communications;

■ Understand safety issues of the interaction between humans and DSP systems and to mitigate risk in this context;

■ Apply the theory, concepts and techniques learned in the lecture material to the solution of incompletely specified ("real world"), complex problems;

■ Evaluate the suitability of various technologies in the acquisition and implementation of DSP solutions;

■ Use empirical techniques to distil a requirement from observed data and by understanding its relationship to the desired outcome;

■ Demonstrate the importance of testing whether system requirements have been satisfied and to introduce methods to perform such testing;

■ Develop the participants capacity to function effectively as part of a team;

■ Communicate outcomes and achievement by both formal, technical, analytical reporting and in a manner appropriate to non-technical audiences.

By the end of this course students will be able to:

■ use the Fourier transform to filter signals, such as audio, biomedical and communication signals

■ critically evaluate literature concerning DSP algorithms and their application

■ design and create FIR filters from a desired frequency response arising from complex interdisciplinary projects

■ design and create IIR filters on the basis of an analogue prototype and apply them in complex low latency applications

■ design and create matched filters for biomedical, communication and other complex interdisciplinary problems

■ create system specifications and requirements analysis of a product in both the time domain (latency) and frequency domain (frequency response)

■ make ethical choices when processing human data

■ make decisions on the safety of laboratory equipment particularly when interacting with human subjects

■ compare and judge different DSP methodologies in the solution of a given "real world" design challenge

■ create optimal filters for best performance in a given problem including due consideration to efficiency and the capacity to implement the solution on the minimal hardware

■ use Python as a filter design tool

■ create object oriented DSP processing code in Python, thus promoting reuse and sustainability

■ critically evaluate the performance of such solutions in terms of satisfying product requirements

■ test and measure the capacity of a DSP system to satisfy latency requirements

■ analyse interdisciplinary problems and to provide a solution to the problem

■ formally report the DSP deliverable using the highest standard of graphical and written content

communicate the benefits of their work to a non-technical audience through multimedia

Students must attend the degree examination and submit at least 75% by weight of the other components of the course's summative assessment.

Note that these are minimum requirements: good students will achieve far higher participation/submission rates.  Any student who misses an assessment or a significant number of classes because of illness or other good cause should report this by completing a MyCampus absence report.