COMP3210 Advanced Computer Networks

Module Overview

This module is designed to be a follow-up module to the Computer Science or ITO second year introductory networking module.

The module is split fairly evenly between two areas, each principlally delivered by a separate lecturer. The wireless part reviews wireless technologies and their application in areas such as sensor networking. The coursework gives students the opportunity to get hands-on experience with wireless networking for small, embedded devices. The other part of the module reviews emerging networking technologies, which might include topics such as future routing protocols, IPv6 transition, and software-defined networking.

Students should consider taking this module if they are interested in learning about networking systems and their architectures in more detail than covered in the introductory module, and exploring emerging topics which are delivered as part of ECS' research-led teaching philosophy.

Aims & Objectives

Aims

Knowledge and Understanding

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

Operation of wireless networks
Emerging topics in computer networks
A range of network architectures and protocols

Subject Specific Intellectual

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

Design and critically analyse networking protocols for a range of technologies and scenarios

Subject Specific Practical

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

Build a wireless networking demonstrator for an embedded system

Syllabus

Wireless networking:

Bluetooth, 802.11 standards
Information theory, bandwidth, multiple access
Wireless sensor networks
Wireless roaming; eduroam
Mobile IPv6; host and network mobility

Emerging networking technologies:

Host configuration and service discovery principles
Future routing architectures
IPv6 deployment scenarios and challenges, IPv6 transition/integration
Advanced IP multicast, including IPv6 multicast and SSM
Software-defined networking
Delay-tolerant networking
Future home network architectures
IP network management and monitoring
Network security; intrusion detection and prevention

Learning & Teaching

Learning & teaching methods

Activity	Description	Hours
Lecture	Regular lecture slots.	21
Tutorial	Tutorial support on the coursework and specific lecture topics	8
Seminar	Guest lectures from industry experts	4
Tutorial	Revision (last teaching week)	3

Assessment

Assessment methods

Method	Hours	Percentage contribution
Wireless networking for embedded systems	-	30%
Exam	2 hours	70%

Referral Method: By examination

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COMP3208 Social Computing

Module Overview

The aim of this module is to introduce the fundamental concepts and computational techniques used in social computing. In a broad sense, social computing includes all situations where social interactions are supported by computers. More specifically, in this module we will focus on three main areas: crowdsourcing, online auctions (including online advertising), and recommender systems. The module has a large practical component where you will learn how to solve a problem using crowdsourcing, and you will learn how to set up such experiments and dealing with incentives. In addition, you will learn about technologies such as auctions and algorithms for recommender systems. Note that this module does not cover social networking as this is covered elsewhere.

Aims & Objectives

Aims

Knowledge and Understanding

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

concepts and example applications from social computing, including crowdsourcing, recommender systems, and online auctions
incentives in crowdsourcing applications
applications in crowdsourcing
the auctions used in online advertising

Subject Specific Intellectual

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

use recommender technologies such as item-based and user-based collaborative filtering techniques
describe the most important techniques and issues in designing, building and modelling social computing systems

Subject Specific Practical

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

set up social computing experiments and analyse the results using a scientific approach

Syllabus

Crowdsourcing

Human computation
Participatory sensing
Citizen science
Amazon Mechanical Turk and other platforms

Web analytics and Experimental design

A/B split testing
Latin squares

Incentives and monetary payments in crowdsourcing
Prediction markets
Reputation systems

User-based collaborative filtering
Item-based collaborative filtering

Online auctions

Sponsored search
Display advertising

Learning & Teaching

Learning & teaching methods

Activity	Description	Hours
Lecture		36

Assessment

Assessment methods

Method	Hours	Percentage contribution
implementation and analysis of social computing system	-	30%
Exam	2 hours	70%

Referral Method: By examination

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COMP3207 Cloud Application Development

Module Overview

During the first two years of the degree students gain experience in a variety of 'traditional' programming languages in procedural, functional and object-oriented flavours. This module addresses the design and use of scripting languages for a contemporary cloud-based computing application.

Explore the role of scripting languages in cloud applications
Introduce Python and Javascript and their applications
Provide experience in cloud computing
Provide an appreciation of new concepts in a rapidly developing field

Aims & Objectives

Aims

Knowledge and Understanding

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

The role of scripting languages
The syntax and semantics of languages such as Python and JavaScript
Cloud computing and its advantages and disadvantages

Subject Specific Intellectual

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

Compare and contrast the features and capabilities of scripting languages used for cloud computing applications
Compare and contrast scripting languages with other programming languages
Select an appropriate scripting language for the development of a given cloud-based application
Read programs in a range of scripting languages

Subject Specific Practical

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

Design and implement a cloud-based application

Syllabus

Cloud computing

introduction and examples
advantages and disadvantages
taxonomy of cloud computing: PaaS, SaaS, IaaS

Python

overview, introduction and examples
advantages and disadvantages
Google App Engine

JavaScript

client-side web scripting: DOM and AJAX
server-side applications: node.js

Learning & Teaching

Learning & teaching methods

Activity	Description	Hours
Lecture		36

Assessment

Assessment methods

Method	Hours	Percentage contribution
Individual Assignment	-	40%
Group Assignment	-	60%

Referral Method: By set coursework assignment(s)

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COMP3206 Machine Learning

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.

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

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

Activity	Description	Hours
Lecture	Lectures using whiteboard and slides	20
Computer Lab	Timetables computer lab sessions during weeks 8 and 9	6

Assessment

Assessment methods

The coursework items will be varied in scope and will require different degrees of effort. Marks will be distributed accordingly, and the distribution will be made clear to students in advance.

Method	Hours	Percentage contribution
Assignment on implementing machine learning algorithms	-	20%
Exam	2 hours	80%

Referral Method: By examination

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COMP3204 Computer Vision

Module Overview

The challenge of computer vision is to develop a computer based system with the capabilities of the human eye-brain system. It is therefore primarily concerned with the problem of capturing and making sense of digital images. The field draws heavily on many subjects including digital image processing, artificial intelligence, computer graphics and psychology.

This course will explore some of the basic principles and techniques from these areas which are currently being used in real-world computer vision systems and the research and development of new systems.

Objectives:

To develop the students' understanding of the basic principles and techniques of image processing and image understanding.
To develop the students' skills in the design and implementation of computer vision software

Aims & Objectives

Aims

Knowledge and Understanding

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

Human and computer vision systems
Current approaches to image formation and image modelling
Current approaches to basic image processing and computer vision

Subject Specific Intellectual

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

Analyse and design a range of algorithms for image processing and computer vision
Develop and evaluate solutions to problems in computer vision

Subject Specific Practical

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

Implement basic image processing algorithms

Syllabus

The human eye-brain system as a model for computer vision
Image formation: sampling theorem, Fourier transform and Fourier analysis
Image models
Basic image processing: Sampling and quantisation, Brightness and colour, Histogram operations, Filters and convolution, Frequency domain processing
Edge detection
Boundary and line extraction
Building machines that see: constraints, robustness, invariance and repeatability
Fundamentals of machine-learning: classification and clustering
Understanding covariance, eigendecomposition and PCA
Feature extraction
Interest point detection
Segmentation
2-D Shape representation
Local features
Image matching
Large-scale image search and feature indexing
Understanding image data and performing classification and recognition
3D vision systems
Recovering depth from multiple views
Practical examples, including: biometric systems (recognising people), industrial computer vision, etc

Learning & Teaching

Learning & teaching methods

Activity	Description	Hours
Lecture	Lectures and demonstrations of material	24
Tutorial	Students develop and present implementations of basic material	12

Assessment

Assessment methods

Method	Hours	Percentage contribution
Working with OpenIMAJ to achieve basic computer vision analysis	-	10%
Build basic technique	-	10%
Group coursework	-	20%
Exam	2 hours	60%

Referral Method: By examination and a new coursework assignment

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COMP3203 Serious Games

Module Overview

The aim of this module is to introduce students to the field of learning design and Serious Games and to provide students with practical experience of the design, development, delivery and evaluation of a modest game-based e-learning activity.

Aims & Objectives

Aims

Knowledge and Understanding

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

Identify key concepts and principles in learning theory and cognitive psychology that underpin teaching, learning, and information processing
Identify the principles underlying the systems engineering of instructional materials and environments
Identify the elements of a serious game

Subject Specific Intellectual

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

Analyse the instructional design of a series of learning activities in a game
Design a serious game to achieve a specified objective by following an instructional systems design method

Transferable and Generic

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

Combine conflicting theories and requirements to develop an effective serious game

Syllabus

The instructional systems engineering (ISE) development lifecycle and its application to the analysis, design, production, and evaluation of instructional materials and serious games.
Key pedagogical components of instructional materials, and serious games.
Key technical components of serious games: Data structures; Games Engines; User Experience.
Tools and techniques for the analysis, design, production, and evaluation of serious games.

Learning & Teaching

Learning & teaching methods

Activity	Description	Hours
Lecture		36
Computer Lab		12

Assessment

Assessment methods

Concept of a Serious Game: Students individually design a serious game.

Prototype Serious Game: Students in groups design and develop a small prototype serious game.

Class Test: Supervised unseen restricted-time open-book individual exercise (detailed case study, short-answer and multiple-choice question), 90 minute duration.

Method	Hours	Percentage contribution
Concept for a Serious Game	-	30%
Prototype for a Serious Game	-	40%
Class test	-	30%

Referral Method: By set coursework assignment(s)

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ELEC3207 Nanoelectronic Devices

Module Overview

This module aims to provide an in-depth understanding of complimentary metal oxide semiconductor field effect transistors (CMOS) device physics and of the process flows used to fabricate CMOS transistors. It will discuss all important issues related to scaling down the transistor size into the nanometer regime, such as high-k dielectrics and FINFETs. The teaching will be complemented with a finite element simulation of the MOS scaling which will bring into practice many of the above improvements.

Aims & Objectives

Aims

Knowledge and Understanding

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

Understand the fundamental device physics of semiconductors.
Understand the operation principle of CMOS transistors.

Subject Specific Intellectual

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

Construct the process flows to fabricate CMOS transistors.

Subject Specific Practical

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

Simulate the performance of CMOS transistors

Syllabus

Nanoelectronics

Technology roadmap of nano-electronics (Moore's law)

Scaling of devices and technology jump

Energy band structure in Silicon

Metal Oxide Semicoductor Field Effect Transistors (MOSFET)

Basic MOSFET Operation

Threshold Voltage and Subthreshold Slope

Current/voltage characteristics

Finite Element Modelling of MOS

Advanced CMOS transistors scaling

Challenge of the CMOS technologies

High-k dielectrics and Gate stack

Future interconnect

FINFET and architecture

Design for Variability

Mobility enhancement

Learning & Teaching

Learning & teaching methods

Activity	Description	Hours
Lecture		24
Computer Lab		12

Assessment

Assessment methods

Students can get good experiences by simulating the MOSFET characteristics after learning the fundamental principles in the lectures. Simulations and lecturing are complementary each other, and students can get more insights in understanding the MOSFETs. The learning outcomes include the capabilities to simulate unknown new device performance for their future jobs in CMOS or even beyond-CMOS industries.

Method	Hours	Percentage contribution
SILVACO finite element simulation	-	30%
Exam	2 hours	70%

Referral Method: By examination

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ELEC3206 Digital Control System Design

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.

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

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

Introduction to optimal control
Finite horizon LQR
Inifte Horzion LQR

Learning & Teaching

Learning & teaching methods

Activity	Description	Hours
Lecture		36
Tutorial		12

Assessment

Assessment methods

Method	Hours	Percentage contribution
Exam	2 hours	100%

Referral Method: By examination

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ELEC3205 Control System Design

Module Overview

This module aims

To develop skills for design of linear multivariable control systems by pole placement.
To introduce basic nonlinear system analysis and design methods.

Aims & Objectives

Aims

Knowledge and Understanding

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

Design controller using frequency domain methods
Analyse linear dynamical systems by state space methods.
Derive state space representation from a given transfer function representation.
Check controllability/observability by rank test of the controllability/observability matrix.
Design pole placement state-feedback controller in the state space setting, also with observers in the loop.
Model, analyse, and synthesise nonlinear dynamical systems.
Derive state space representations for nonlinear systems from first principles.
Analyse stability of nonlinear autonomous systems by state space methods.
Analyse nonlinear input--output systems by describing functions.

Syllabus

Frequency Domain Methods for Controller Design

Lead-lag compensator
Introduction to loop shaping

State-space representations for linear systems

Transfer function canonical realisations
State space representations

Structural properties

Controllability and state transfer
Observability and state estimation

Multivariable control by pole placement

Pole placement by state feedback
Elements of optimal control

State estimation

Observer design by pole placement

Joint observer-controller schemes
Nonlinear systems and mathematical modelling
Introduction to the phase plane analysis method
Stability and Lyapunov analysis

Lyapunov indirect method
Lyapunov direct method
Lasalle’s Theorem

Describing functions
Nonlinear control system design

Design via linearisation
Design via feedback linearisation
Introduction to Lyapunov based design method

Learning & Teaching

Learning & teaching methods

Activity	Description	Hours
Lecture		36

Assessment

Assessment methods

Method	Hours	Percentage contribution
2 problem sheets, containing 3 questions each.	-	20%
	-	%
Exam	2 hours	80%

Referral Method: By examination

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ELEC3203 Digital Coding and Transmission

Module Overview

to expand knowledge of techniques for information transmission via discrete (digital) channels, which have a wide area of applications, ie distributed computer systems, instrumentation and control systems, as well as communication systems of all types.
to introduce the basic concepts and applications of information theory and show its importance.
to develop skills in communications performance evaluation and digital transmission system design.
to concentrate attention on the application of the various analytical techniques.
to link theoretical concepts with cutting edge industrial standards.

Aims & Objectives

Aims

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

understanding essential building blocks in modern digital communications systems.
understanding techniques for improving the efficiency and reliability of multimedia information.
design and characterise digital transmission components and schemes using Monte Carlo simulation.
bridge theoretical analysis with practical simulations and prototypes
understanding various design trade-offs and practical implementation constraints

Syllabus

Information Theory
- information and entropy
- coding of memoryless sources: Shannon-Fano / Huffman coding
- sources with memory: Markov model
- coding of sources with memory
- channel model and information across channels
- average mutual information and channel capacity
Digital Modulation and Optimal Reception
- quadrature amplitude modulation
- optimal transmit / receive filtering
- ASK/PSK constellations, eye diagram
- channel distortions and their influence on reception
- synchronisation, equalisation
- adaptive equalisation
Source Coding
- linear and non-linear quantisation, companding
- rate-distortion theory, predictive coding
- prediction gain, parametric and analysis-by-synthesis speech coding
- inter-frame video coding, motion compensation
- intra-frame video coding, transform coding
Channel Coding
- convolutional coding
- Viterbi decoding
- block coding
- hybrid ARQ
Coding versus Modulation Tradeoff
- bandwidth and power trade-off plane
- bandwidth efficient and power efficient design
- trellis coded modulation
- bit interleaved coded modulation
- system design in communications standards

Learning & Teaching

Learning & teaching methods

Activity	Description	Hours
Lecture		36
Tutorial		12

Assessment

Assessment methods

Method	Hours	Percentage contribution
Exam	2.5 hours	100%

Referral Method: By examination

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