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Electronics and Computer Science

ELEC6128 EMECS MSc Project

Module Overview

Aims & Objectives

Aims

Learning & Teaching

Learning & teaching methods

ActivityDescriptionHours
Lecture36
Tutorial12

Assessment

Assessment methods

MethodHoursPercentage contribution

Referral Method: By examination

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ELEC6240 Digital Control System Design (MSc)

Module Overview

 To introduce the student to the fundamentals of control theory as applied to digital controllers or sampled data control systems in general. To familiarise the student with the use of the MATLAB Control Toolbox.

This module will be taught together with ELEC3206: Digital Control System. This module will have higher requirements on the desired learning outcomes which will be assessed by a different set of coursework.

 

Aims & Objectives

Aims

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

  • z transform analysis of sampled data feedback loops
  • stability theorems and root locus techniques
  • A suite of techniques for digital controller design
  • Optimal control design method

Subject Specific Intellectual

Having successfully completed this module, you will be able to:

  • Demonstrate awareness of the key implementation issues in digital control systems design

Syllabus

  • Introduction
  • Basics of z transform theory
    • inverse z transform
    • convolution
    • recursion relation
    • realisability
  • Sampling and reconstruction of signals
    • zero order hold/D->A conversion
    • Shannon's sampling theorem; aliasing and folding
    • choice of the sampling period in sampled-data control systems
    • pulse transfer function and analysis of control systems
    • mapping of poles and zeroes
  • Case study: PID digital control
  • Continuous-time state-space systems and their discretization
    • controllability and observability under discretization
    • intersample behaviour
  • Realization theory
    • canonical forms
    • minimality
    • internal- and BIBO-stability, and relation between the two
  • Controller design via pole placement
    • continuous-time-based design techniques
    • deadbeat control
  • Case study: root-locus based digital control design
  • Observers and their use in state-feedback loops
    • Observer-based controllers
    • the separation principle
  • Optimal control design
    • Finite horizon LQR
    • Inifte Horzion LQR

Learning & Teaching

Learning & teaching methods

ActivityDescriptionHours
Lecture36
Tutorial12

Assessment

Assessment methods

MethodHoursPercentage contribution
1 coursework on evaluation of specified research papers-20%
Exam2 hours80%

Referral Method: By examination

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ELEC6229 Advanced Systems and Signal Processing

Module Overview

This module aims to introduce to the students advanced model based signal processing methods and systems design theories, with illustrative case studies to demonstrate how the knowledge obtained in this module can be used in some challenging real life applications.

Aims & Objectives

Aims

Subject Specific Intellectual

Having successfully completed this module, you will be able to:

  • estimate unknown system parameters from noisy measurement data
  • estimate system state information from noisy measurements
  • evaluate the performance of a stochastic system using Monte Carlo methods
  • design and implement model based control systems
  • apply the model based signal processing and system design methods to real life applications

Syllabus

The course will cover the following topics:

  • Review of mathematical background
    • Review of state space modelling
    • Review of linear algebra
    • Review of probability
  • Stochastic simulation and Monte Carlo method
    • Random Number Generation
    • Monte Carlo method
    • Stochastic simulation using Monte Carlo simulation
  • Stochastic signal processing, focusing on
    • Estimation problem and least squares
    • Kalman filtering and Extended Kalman filtering
    • Particle Filtering
  • Advanced system control theory
    • Optimal Control: LQR and LQG
    • Receding horizon methods
  • A case study: next generation health care – electrical stimulation and robotic-assisted upper-limb stroke rehabilitation

Learning & Teaching

Learning & teaching methods

ActivityDescriptionHours
Lecture36
Tutorial12

Assessment

Assessment methods

MethodHoursPercentage contribution
Take home test-10%
Coursework-20%
Coursework-20%
Coursework-20%
Coursework-30%

Referral Method: By set coursework assignment(s)

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COMP6231 Foundations of Artificial Intelligence

Module Overview

This course introduces the fundamental concepts of artificial intelligence (AI) and contains a coursework assignment to give you hands-on experience with the techniques.

This unit aims to give a broad introduction to the rapidly-developing field of AI covering a range of approaches (modern, classical, symbolic, and statistical). This should prepare students for specialist options in semester 2.

  • classical and modern approaches to AI
  • the principal achievements and shortcomings of AI.
  • the main techniques that have been used in AI, and theirrange of applicability
  • the philosophical basis of AI
  • challenges for the future of AI 

Aims & Objectives

Aims

Syllabus

  • Introduction to AI
    • Flavours of AI: strong and weak, neat and scruffy, symbolic and sub-symbolic, knowledge-based and data-driven.
    • The computational metaphor. What is computation? Church-Turing thesis. The Turing test. Searle's Chinese room argument.
  • Search
    • Finding satisfactory paths: depth-first and breadth-first, iterative deepening, local search and heuristic search. Finding optimal paths: branch and bound, dynamic programming, A*.
  • Representing Knowledge
    • Production rules, monotonic and non-monotonic logics, semantic nets, frames and scripts, description logics.
  • Reasoning and Control
    • Data-driven and goal-driven reasoning.
  • Reasoning under Uncertainty
    • Probabilities, conditional independence, causality, Bayesian networks, belief propagation.
  • Machine Learning
    • Inductive and deductive learning, unsupervised and supervised learning, reinforcement learning, concept learning from examples, Quinlan's ID3, classification and regression trees, Bayesian methods.

Learning & Teaching

Learning & teaching methods

ActivityDescriptionHours
Lecturemain delivery of taught material: conventional lectures. (Lecture time also used for group presentations and discussion)36

Assessment

Assessment methods

referral is 3 hr exam.

MethodHoursPercentage contribution
Main coursework: search methods and extension (games, planning or learning)-35%
group presentations-15%
Exam1.5 hours50%

Referral Method: By examination

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COMP6229 Machine Learning (MSc)

Module Overview

This module aims to introduce the mathematical foundations for machine learning and a set of representative approaches to address data-driven problem solving in computer science and artificial intelligence.

This module will be taught together with COMP3206: Machine Learning. This module will have higher requirements on the desired learning outcomes which will be assessed by a different set of coursework.

Aims & Objectives

Aims

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

  • Underlying mathematical principles from probability, linear algebra and optimisation
  • The relationship between machine learning and neurophysiology

Subject Specific Intellectual

Having successfully completed this module, you will be able to:

  • Characterise data in terms of explanatory models
  • Use data to reinforce one/few among many competing explanatory hypotheses
  • Gain a critical appreciation of the latest research issues

Subject Specific Practical

Having successfully completed this module, you will be able to:

  • Systematically work with data to learn new patterns or concepts
  • Gain facility in working with algorithms to handle data sets in a scientific computing environment

Syllabus

  • Historical Perspective
    • Biological motivations: the McCulloch and Pitts neuron, Hebbian learning.
    • Statistical motivations
  • Theory
    • Generalisation: What is learning?
    • The power of machine learning methods: what is a learning algorithm? what can they do?
  • Probability
    • Probability as representation of uncertainty in models and data
    • Bayes Theorem and its applications
    • Law of large numbers and the Gaussian distribution
    • Markov and graphical models
  • Supervised Learning
    • Classification using Bayesian principles
    • Perceptron Learning
    • Support Vector Machines and Kernel methods
    • Neural networks/multi-layer perceptrons (MLP)
    • Features and discriminant analysis
  • Linear Algebra
    • Using matrices to find solutions of linear equations
    • Properties of matrices and vector spaces
    • Eigenvalues, eigenvectors and singular value decomposition
  • Data handling and unsupervised learning
    • Principal Components Analysis (PCA)
    • Blind source separation using Independent Components Analysis (ICA)
    • K-Means clustering
    • Spectral clustering
    • Manifold learning
  • Regression and Model-fitting Techniques
    • Linear regression
    • Polynomial Fitting
    • Kernel Based Networks
  • Optimisation
    • Convexity
    • 1-D minimisation
    • Gradient methods in higher dimensions
    • Constrained optimisation
    • Dynamic Programming
  • Case Studies
    • Example applications: Speech, Vision, Natural Language, Bioinformatics.

Learning & Teaching

Learning & teaching methods

ActivityDescriptionHours
LectureLectures using whiteboard and slides20
Computer LabTimetables computer labs during weeks 8 and 96

Assessment

Assessment methods

MethodHoursPercentage contribution
Assignment on implementing machine learning algorithms-20%
-%
Exam2 hours80%

Referral Method: By examination

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COMP6060 MSc Project for DTC Complex Systems Simulation

Module Overview

Aims & Objectives

Aims

Learning & Teaching

Learning & teaching methods

ActivityDescriptionHours
Lecture36
Tutorial12

Assessment

Assessment methods

MethodHoursPercentage contribution

Referral Method: By examination

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WEBS6203 Interdisciplinary Thinking

Module Overview

[NB This modules is called Interdisciplinary Studies. Please correct.]

This module is offered in the context of a multi-disciplinary programme that requires students to both demonstrate appropriate appreciation of disciplines which are foreign to them (including an understanding of current research and research methods, an awareness of the current limits of knowledge in that discipline) and an appreciation of the possibilities of multi- and inter-disciplinary research opportunities.

No specific pre-requisites.

Aims & Objectives

Aims

  • the concepts in two different disciplines (of your choice) that are applicable to studies of the Web
  • the differences in disciplinary approaches to Web analysis
  • related methodologies and techniques to a range of practical applications
  • The issues surrounding navigating the languages of different disciplines
  • Case studies in the application of interdisciplinary approaches to real-world problems
  • Methods for constructing arguments from multi-disciplinary perspectives
  • Critical analysis in an interdisciplinary setting

Syllabus

This module addresses a large number of problems in web science, chosen by the students as individuals or in groups. Previous issues have included the following:

  • What factors influence credibility on the Web?
  • Does information want to be free
  • What is misinformation and how and why does it spread on the Web?
  • Does the Web have gatekeepers?
  • Identity authenticity or identity anonymity?
  • Should the web’s infrastructure directly protect any of the following: payments, privacy or piracy?
  • The rights of individuals in the media spotlight to privacy in the digital environment appears confused and lacking direction. Is there a balance to be struck here and if so on what principles should it be based?
  • What effect is the Web having on University operation?
  • How can we create/utilise and spread Web memes e.g. viral videos for government to improve public health/public understanding of an issue?
  • Should there be an international law of the web as there is an international law of the sea?
  • Who is shaping the education web - students or faculty?
  • What are the barriers to total adoption of the semantic web across different industries?
  • Will true media convergence require a more open, and less 'walled' publishing web?
  • Assess the problems associated with the development of a coherent policy for the regulation of Internet content in the EU.
  • Can the Web reduce poverty? 

Learning & Teaching

Learning & teaching methods

ActivityDescriptionHours
LectureOne lecture per week10
TutorialStudent-led study groups, once per week10

Assessment

Assessment methods

MethodHoursPercentage contribution
Poster pitch: students make short presentations to their peers presenting an overview of the interdisciplinary analysis of their chosen Web Science. A version of their poster will be printed and on display-0%
Poster: Interdisciplinary Coursework #1 Students have the opportunity to revise the poster they presented earlier in the week, if they choose.-10%
Peer review of draft individual reports (in pairs) -0%
Multidisciplinary Investigation Based on Private Reading, Individual Interdisciplinary Coursework #2-90%

Referral Method: By set coursework assignment(s)

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COMP2202 Database and Database Applications

Module Overview

To give students an understanding of the role of database systems in information management, the theoretical and practical issues that influence the design, implementation and applications of database management systems and languages.

Aims & Objectives

Aims

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

  • Understand the role of database systems in information management.
  • Understand the concept of data modelling.
  • Understand the theoretical properties of relational databases

Subject Specific Intellectual

Having successfully completed this module, you will be able to:

  • To be able to apply entity-relationship modelling
  • To be able to normalise data.
  • To be able to use SQL to create, update and query a database

Syllabus

  • Rationale behind Database Systems
  • Database Modelling using the Entity-Relationship Model
  • Database System Architecture
  • Data Models and Data Sublanguages
  • SQL
  • Database Management Issues: concurrency, security, integrity
  • Application of Database
  • No SQL databases

Learning & Teaching

Learning & teaching methods

ActivityDescriptionHours
LectureEach week you will have 3 one hour lecturers, you will need to prepare for these. Where appropriate for the activity Slide will be up in advance and direct reading will be provided.33
Computer LabA computer laboratories for 4 weeks to practice your skills. Attendance is expected as this is your opportunity to get help and feedback on the practical aspects of the course, which you will need in order to complete the coursework.12

Assessment

Assessment methods

The initial coursework will be on line from week one. However a hand-in of the database design is required in week 3. Formative feedback and in class discussion of an appropriate design will be given in week 4. This will then be used to answer the remaining part of the assignment. None engagement in the excise will limit your understanding of the problem to be addressed.

MethodHoursPercentage contribution
Database design and implement-20%
Exam2 hours80%

Referral Method: By examination

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COMP1201 Algorithmics

Module Overview

This is a core module for computer science and software engineers.  It teaches the basic data structures and algorithms which underpins modern software engineering.  Without these algorithms most software would be hopelessly slow to the point of unusability.  The course also teaches the principles behind the algorithms and data structures and the software engineering lessons which data structures and algorithms teach us.

Aims & Objectives

Aims

Knowledge and Understanding

Having successfully completed the module, you will be able to demonstrate knowledge and understanding of:

A1.  Knowledge of common data structures and algorithms

A2.  Understanding of time complexity

A3.  Understanding of how to code data structures using object oriented methods

Intellectual Skills

Having successfully completed the module, you will be able to:

B1.  Choose the most appropriate data structure for a particular problem

B2.  Understand the operation of a number of important computer algorithms using those structures

B3.  Understand how to evaluate an algorithm for efficiency

B4.  Choose an appropriate algorithmic strategy to solve a problem

Subject Specific Skills

Having successfully completed the module, you will be able to:

C1.  Have a greater confidence to write programs in Java

C2.  Be able to code a simple data structure

C3.  Be able to use data structures to build complex algorithms

Employability/Transferable/Key Skills

Having successfully completed the module, you will be able to:

D1. Be able to solve problems algorithmically

Syllabus

  • Introduction
    • Data Objects, Data Structures, Complex Data Structures
  • Algorithm Analysis
    • The big O
    • Correctness
  • Algorithm Design and Strategies
    • Brute Force, Depth-First, Breadth-First, DFID, Best-first, Greedy, Divide and Conquer, Dynamic Programming, Branch and Bound
  • Simple Data Structures
    • List, Stack, Queue, Tree, Tree traversal
  • Sorting
    • Selection Sort, Insertion Sort, Shellsort, MergeSort, QuickSort, Bucket Sort, Radix sort, External sorting
  • Searching
    • Sequential Search, Handling Failure, Binary Search, Binary Tree Search,
  • Advanced Tree Structures
    • AVL Trees, Retaining Balance, Single Rotation, Double Rotation
    • Splay Trees, Red-black Trees, B-trees
  • Hash tables
    • Terminology, Hash table size, Hash function collision resolution, Separate chaining, Open Addressing, Re-hashing
  • Priority Queues (Heaps)
    • Terminology, Simple implementations, Binary heaps, Heap sort
  • Graphs
    • Terminology, Adjacency Matrix and List, Connectivity, Breadth vs Depth first search, Topological sort, Shortest path algorithms, Unweighted graphs, Breadth first search, Weighted Graphs, Minimum Spanning Tree, Prim's algorithm, Biconnectivity, Articulation points
  • Geometric algorithms
    • Convex hull, …

Learning & Teaching

Learning & teaching methods

ActivityDescriptionHours
Lecture36
Tutorial12

Assessment

Assessment methods

MethodHoursPercentage contribution
Assessed Tutorials-15%
Exam2 hours85%

Referral Method: By examination

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COMP1206 Programming 2

Module Overview

The aim of this module is to teach the students advanced programming techniques using Java in order to support its use on other modules. C will also be taught in order to introduce explicit memory allocation and the use of pointers.

Aims & Objectives

Aims

Intellectual Skills

Having successfully completed the module, you will be able to:

B1.  Construct Java applications with Graphical User Interfaces in Swing and AWT

B2.  Construct multi-threaded Java applications

B3.  Use persistent storage for Java applications

B4.  Use pointers to manipulate dynamically allocated storage in C

B5.  Perform testing on Java programs using JUnit

Syllabus

  • Graphical User Interface Programming
    • Writing Swing and AWT user interfaces
    • User interfaces Components
    • Event Handling
    • Graphics in User Interfaces
  • Control Flow and the Java Virtual Machine
    • JVM overview
    • Exceptions and exception handling
    • Recursion in the JVM
    • Multi-threading and synchronisation
  • Storage and Files in Java
    • Garbage Collection
    • Strings and Character Encodings
    • Input/Output and Object Serialisation
  • Validation and Verification
    • Black Box Testing
    • White Box Testing
    • Integration Testing Strategies
  • The C programming language
    • Introduction to the language
    • Pointers and pointer arithmetic
    • Data structures and arrays
    • Comparison with Java
  • Patterns

Learning & Teaching

Learning & teaching methods

ActivityDescriptionHours
Lecture36
Computer Lab12

Assessment

Assessment methods

MethodHoursPercentage contribution
Coursework Assignment-75%
Laboratory Exercises-25%

Referral Method: By set coursework assignment(s)

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