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MODULE DESCRIPTOR
Module Title
Advanced Digital System Design
Reference EN4542 Version 4
Created August 2021 SCQF Level SCQF 10
Approved SCQF Points 15
Amended August 2021 ECTS Points 7.5

Aims of Module
To enable student to study the advanced techniques and methods for digital systems and to provide students with the ability to analyse, design an implement advanced digital systems.

Learning Outcomes for Module
On completion of this module, students are expected to be able to:
1 Design, implement and evaluate the performance of digital systems.
2 Analyse and critically assess the design of advanced digital systems.
3 Synthesise solutions to engineering problems using computational intelligence based digital systems.
4 Analyse and evaluate solutions of computational intelligence based digital systems using computer simulation.

Indicative Module Content
Advanced techniques for processor design: pipelined processors, very long instruction word (VLIW) processors, superscalar processors, vector processors, parallel processors and supercomputers. Digital signal processing design: fixed and floating point processing, digital signal processors (architecture, instruction set, features and applications) and operation design. Computational Intelligence based digital system: artificial neural networks (biological basis, perceptron, back-propagation, feedback), fuzzy logic (fuzzification, inference engine, defuzzification), evolutionary artificial neural networks and other current methods.

Module Delivery
This module is taught using a structure programme of lectures, tutorials, laboratory exercises and student-centred learning.

Indicative Student Workload Full Time Part Time
Contact Hours 51 51
Non-Contact Hours 99 99
Placement/Work-Based Learning Experience [Notional] Hours N/A N/A
TOTAL 150 150
Actual Placement hours for professional, statutory or regulatory body    

ASSESSMENT PLAN
If a major/minor model is used and box is ticked, % weightings below are indicative only.
Component 1
Type: Coursework Weighting: 50% Outcomes Assessed: 1, 4
Description: Coursework.
Component 2
Type: Examination Weighting: 50% Outcomes Assessed: 2, 3
Description: Closed book examination.

MODULE PERFORMANCE DESCRIPTOR
Explanatory Text
The module has 2 components and to gain an overall pass a minimum D grade must be achieved in each component. The component weighting is as follows: C1 is worth 50% and C2 is worth 50%.
Examination:
Coursework: A B C D E F NS
A A A B B E E
B A B B C E E
C B B C C E E
D B C C D E E
E E E E E E F
F E E E E F F
NS Non-submission of work by published deadline or non-attendance for examination

Module Requirements
Prerequisites for Module None.
Corequisites for module None.
Precluded Modules None.

INDICATIVE BIBLIOGRAPHY
1 DU, K.L. and SWAMY, M.N.S., 2006. Neural Networks in a Softcomputing Framework. London: Springer.
2 HAYKIN, S., 2008. Neural Networks and Learning Machines: A Comprehensive Foundation. 3rd ed. London: Pearson Education.
3 NEGNEVITSKY, M., 2011. Artificial Intelligence: A Guide to Intelligent Systems. 3rd ed. Harlow: Addison-Wesley.
4 HENNESSY, J.L. and PATTERSON, D.A., 2011. Computer Architecture: A Quantitative Approach. 5th ed. Amsterdam, Netherlands: Morgan Kaufmann.
5 CHASSAING, R. and REAY, D.S., 2008. Digital Signal Processing and Applications with the TMS320C6713 and TMS320C6416 DSK. 2nd ed. Hoboken, NJ: Wiley-Blackwell.

Robert Gordon University, Garthdee House, Aberdeen, AB10 7QB, Scotland, UK: a Scottish charity, registration No. SC013781