BI2BC17-Biocybernetics

Module Provider: School of Biological Sciences
Number of credits: 20 [10 ECTS credits]
Level:5
Terms in which taught: Autumn / Spring term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites: BI2SP16 Signal Processing
Modules excluded:
Current from: 2022/3

Module Convenor: Prof William Harwin
Email: w.s.harwin@reading.ac.uk

Type of module:

Summary module description:

The module introduces students to mathematical concepts in biomedical engineering. In particular the module introduces the concept of Cybernetics and how it can be applied in animals, humans and machines. The lectures will develop mathematical techniques introduced in the first year including constructing and solving differential equations, feedback, learning, adaptive systems, and optimization.  Both linear and nonlinear mathematical techniques will be explored.  Lectures will be supplemented with exercises and labs primarily based on Matlab and Simulink to help reinforce the concepts and allow rapid visualization of ideas.

Aims:

To introduce the cybernetics and its application in complex systems including humans and animals. The aim is to introduce students to some key techniques to help us to understand such complex system. These techniques can then be used to build system models that will help us to understand how they work and predict their future states.

Assessable learning outcomes:

By the end of this module the students should be able to:

• Understand the difference between linear and non-linear system models


• Describe a linear or non-linear system as interacting block in a diagrams and, in the case of linear systems, to reduce these blocks to their simplest form.   


• Do a frequency analysis of linear systems and sketch out Bode plots.


• Understand stability of linear systems and sketch out root locus plots.


• Define the state-space of both linear and non-linear systems in terms of differential equations and predict future states of these systems


• Assess performance of complex non-linear models including new generations of machine learning algorithms.

Additional outcomes:

Gain experience in programming, numerical techniques and symbolic algebra using toolboxes within Matlab and Simulink.

Outline content:

Block diagrams and differential equations with examples (e.g. resonance circuits and mass spring damper systems). This concept will be extended to frequency analysis using root locus and stability analysis using Bode plots. Course will look at the control of machines and regulation in animals based on feedback. Simple methods in state-space analysis will be considered including using numerical integration (Runge-Kutta, Euler etc) to model the time course of any linear or non-linear state-space model. The course will also consider methods to fit models and evaluate their resulting performance. This concept then progresses to non-linear models of complex systems including multi-layered perceptrons and looks and how modern machine learning algorithms are able to recognise people’s faces or distinguish between different patterns of movement.                           

Brief description of teaching and learning methods:

The module comprises lectures, drop-in sessions, exercises, labs and guided independent study.

Contact hours:

	Autumn	Spring	Summer
Lectures	20	20
Guided independent study:	80	80
Total hours by term	100	100	0
Total hours for module	200

Summative Assessment Methods:

Method	Percentage
Written exam	70
Practical skills assessment	30

Summative assessment- Examinations:

There will be one exam lasting 3 hours.

The examination for this module will require a narrowly defined time window and is likely to be held in a dedicated exam venue.

Summative assessment- Coursework and in-class tests:

There will be 1 assignment. Submission date in week 1 of Spring term.

Formative assessment methods:

In class exercises, take home exercises.

Penalties for late submission:

The Support Centres will apply the following penalties for work submitted late:

• where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of five working days;
• where the piece of work is submitted more than five working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

The University policy statement on penalties for late submission can be found at: https://www.reading.ac.uk/cqsd/-/media/project/functions/cqsd/documents/cqsd-old-site-documents/penaltiesforlatesubmission.pdf
You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Assessment requirements for a pass:
40%

Reassessment arrangements:
Examination

Additional Costs (specified where applicable):

1) Required text books:  

2) Specialist equipment or materials:  

3) Specialist clothing, footwear or headgear:  

4) Printing and binding:  

5) Computers and devices with a particular specification:  

6) Travel, accommodation and subsistence:  

Last updated: 31 January 2023