Signal Analysis for Computing - CO662

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Module delivery information

Location Term Level1 Credits (ECTS)2 Current Convenor3 2021 to 2022
Canterbury
Autumn 6 15 (7.5) checkmark-circle

Overview

This module will provide the student with an understanding of basic principles of signals; introduce digitisation methods such as sampling, quantisation and coding; describe and apply signal analysis techniques, such as segmentation, noise reduction, filtering, spectral analysis, feature extraction and classification (including recognition and decision making) to solve practical signal analysis problems using Matlab.

Details

Contact hours

Total contact hours: 30
Private study hours: 120
Total study hours: 150

Method of assessment

13.1 Main assessment methods
1 piece of coursework (40 hours) (50%)
2 hour unseen exam (50%)

13.2 Reassessment methods
Like for like.

Indicative reading

R. Palaniappan, "Biological Signal Analysis," BookBoon, 2010, http://bookboon.com/en/textbooks/it-programming/introduction-to-biological-signal-analysis. The free to download ebook has the core material on signal analysis and classification.
I. McLoughlin, "Applied Speech and Audio Processing," Cambridge University Press, 2009
B. W. Schuller, “Intelligent Audio Analysis,” Springer, 2013
L. Sornmo and P. Laguna, “Bioelectrical Signal Processing in Cardiac and Neurological Applications,” Elsevier Academic Press, 2005
R.M. Rangayyan, “Biomedical Signal Analysis, 2nd ed.,” IEEE-Wiley Press, 2015
S. Mitra, “Digital Signal Processing: A Computer-based Approach, 4th ed.,” McGraw-Hill, 2010

See the library reading list for this module (Medway)

Learning outcomes

8. The intended subject specific learning outcomes.
On successfully completing the module students will be able to:
8.1 Demonstrate a systematic understanding of basic principles of digital signals
8.2 Describe and comment upon the different categories of digital signals ?
8.3 Identify and apply pre- and post- processing techniques, such as conditioning, filtering, feature extraction, classification and hypothesis testing techniques for various types of signals
8.4 Demonstrate the ability to use Matlab for analysis and visualisation of digital signals
8.5 Apply their knowledge and understanding to initiate and carry out real world signal analysis problem solving

9. The intended generic learning outcomes.
On successfully completing the module students will be able to:
9.1 Make effective use of general computing facilities
9.2 Engage with research literature and other information sources
9.3 Communicate technical issues clearly in written formats
9.4 Manage their own learning and development, including time management and organisational skills

Notes

  1. Credit level 6. Higher level module usually taken in Stage 3 of an undergraduate degree.
  2. ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
  3. The named convenor is the convenor for the current academic session.
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