CE294-5-SP-CO:
Introduction to Digital Signal Processing

The details
2024/25
Computer Science and Electronic Engineering (School of)
Colchester Campus
Spring
Undergraduate: Level 5
Current
Monday 13 January 2025
Friday 21 March 2025
15
26 February 2024

 

Requisites for this module
CE141 or CE142
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Key module for

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Module description

This module introduces fundamental concepts of digital signal processing and their applications in the analysis of biomedical signals. It describes how signals can be represented as digital waveforms, explores the application of digital filtering techniques to enhance noisy signals, and looks at analysis of signals in both the time and frequency domains.


Additionally, the module explores the extraction of biomedical signal characteristics for classification tasks. The module is designed to teach Digital Signal Processing to students without prior knowledge of calculus or Fourier analysis. However, students should have an introductory mathematics course to prepare them for this module, such as Mathematics for Computing.

Module aims

The aim of this module is:



  • To provide students with an understanding of the fundamental principles of signal acquisition, signal processing, and signal classification using biomedical signals as basis.

Module learning outcomes

By the end of this module, students will be expected to be able to:



  1. Apply the fundamental concepts of digital signals to real-world systems

  2. Specify and design digital filters to solve engineering problems

  3. Analyse signals in both time and frequency domain representations

  4. Perform basic feature extraction and classification of signals

Module information

Indicative syllabus


Introduction to digital signal processing
Fundamentals of digital signal filtering
Frequency transformation and spectral analysis of signals
Applications of digital signal processing in medical applications
Feature extraction from biomedical signals
Basic machine learning techniques for classification of biomedical signals
Metrics for evaluating diagnostic test performance

Learning and teaching methods

This module will be delivered via:

  • One 2-hour lecture per week (10 weeks of Term)
  • One 2-hour laboratory per week (10 weeks of Term)
  • Two 1-hour revision lectures in Summer Term

Bibliography*

(none)

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Coursework weighting
Coursework   Progress Test (In person, closed book, invigilated Moodle Test)     50% 
Coursework   Report on Signal Processing    50% 
Exam  Main exam: In-Person, Open Book (Restricted), 120 minutes during Summer (Main Period) 
Exam  Reassessment Main exam: In-Person, Open Book (Restricted), 120 minutes during September (Reassessment Period) 

Exam format definitions

  • Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
  • In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
  • In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
  • In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary, for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.

Your department will provide further guidance before your exams.

Overall assessment

Coursework Exam
30% 70%

Reassessment

Coursework Exam
30% 70%
Module supervisor and teaching staff

 

Availability
Yes
No
Yes

External examiner

No external examiner information available for this module.
Resources
Available via Moodle
No lecture recording information available for this module.

 

Further information
Computer Science and Electronic Engineering (School of)

* Please note: due to differing publication schedules, items marked with an asterisk (*) base their information upon the previous academic year.

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