The University of Southampton
×
Electronics and Computer Science

COMP6206 Advanced Computer Vision

Module Overview

To capitalise on image processing and computer vision skills presented in part 3, to ready students for practical implementation in commerce or in research. Computer Science, Electrical and Electronic Engineering students are all welcome on this course (if you hear a prerequisite of Signal Processing is stated, this is not true).

This course has always been hugely enjoyed by the students (and the staff!) as it is a mix of theory and implementation and capitalises on presentation and group project work.

Aims & Objectives

Aims

To build working computer vision systems

To appreciate the stock of technique available for computer vision

To learn the principles of developing and applying computer vision

To practice (and perfect!) presentation tectniques and group coursework

Syllabus

  • Feature Extraction
    • Further techniques in parametric and non-parametric feature extraction including advance Hough transform techniques and active contour models.
  • Feature Description
    • How to describe extracted features for purposes of further analysis and in feature recognition.
  • Image Interpretation
    • Syntactic and symbolic image interpretation and analysis.
  • Image Restoration
    • Beyond the Weiner filter. Least mean squares and extensions and maximum entropy restoration.
  • 3D Imaging
    • Calibration, epipolar constraint, coordinate systems. Active and passive ranging systems.
  • Morphology
    • Binary image processing and image geometry

Learning & Teaching

Learning & teaching methods

ActivityDescriptionHours
LectureOverview of computer vision, overview of implementation and tutorial on basic techniques3
LectureStudents present implementations of advanced material18
Specialist LabGroup coursework, e.g o build a mobile phone based biometric system, or to build a system for and to spoof a face localisation system15

Assessment

Assessment methods

MethodHoursPercentage contribution
Lecture material-60%
Group coursework-40%

Referral Method: By examination

Share this module FacebookTwitterWeibo

COMP6205 Web Development

Module Overview

The aims of the module are

  • To provide students with the opportunity to improve their understanding of web development, and their judgement of the effectiveness of different development techniques, both in theory and in practice.
  • To cover important techniques and issues in designing, building and deploying robust large scale web systems
  • To consider development methods and patterns which enhance maintainability and testability, such as web components, MVC, ORM, HTML template engines, and automated web testing
  • To familiarise students with the ASP.NET web development framework, and compare this with other frameworks and approaches to web development.

Aims & Objectives

Aims

Knowledge and Understanding

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

  • Modern web standards, technologies, and techniques, including content management systems and responsive web design
  • The ASP.NET web development framework, including ASP.NET MVC
  • Similarities and differences with alternatives such as Enterprise Java, OO PHP, Python/Django, and Web Forms
  • Techniques for deploying and testing web sites, and for enhancing their performance and scalability

Subject Specific Intellectual

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

  • Evaluate alternative server-side frameworks, and contrast client-side and server-side web programming
  • Model and manage web performance using a range of methods
  • Explain the limitations of partitioning and parallelism in improving web performance

Subject Specific Practical

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

  • Design and build ASP.NET MVC web sites using professional web development tools such as IDEs, HTML template engines, test automation, and Object-Relational Mapping software

Syllabus

  • Review of modern web standards such as HTML5 and CSS3
    • web templates and template engines
    • responsive web design
  • Web Information Architecture and Content Management Systems
    • intranet search techniques, use of metadata
    • examples of CMS/Portals such as Sharepoint and Drupal
  • Web Development using ASP.NET
    • underlying .NET technologies such as C#, ASP, and LINQ
    • ASP.NET Razor and MVC
    • comparison with other approaches such as:  Java Enterprise (JSP, JDBC), Python/Django, Object Oriented PHP, and ASP.NET Web Forms
    • comparison of client-side versus server-side programming
  • Patterns and methods to enhance maintainability and testability
    • dependability injection and inversion of control
    • Model-View-Controller (MVC) and variants (MV*)
    • object relational mapping (ORM)
  • Business Logic
    • maintaining web state (page, session, and application lifetime and scope)
    • persistence using Entity Framework and LINQ
    • techniques for validating input data in each tier and their benefits
  • Testing, deployment and configuration
    • private, test and public builds
    • web site hosting
    • classification and management of detected errors
    • range and use of web test automation tools
  • Performance modelling and management
    • partitioning and parallelism, Amdahl’s law
    • performance modelling and benchmarking
    • graceful degradation (admission control, disabling recommendations)

Learning & Teaching

Learning & teaching methods

Pre-requisites

Professional web sites are constructed using standards such as HTML5 and CSS3.  They typically connect to a back-end database, either directly or using an API.  In addition, you should have some understanding of networking and security, for example familiarity with HTTPS.

Web development also involves the use of modern object oriented languages such as C#, Java, JavaScript and PHP (OO from version 5 onwards).  It is expected you will be comfortable with using language features such as inheritance and interfaces as associative arrays and iterators.  You will, moreover, be comfortable with the language of design patterns, including the classic Model-View-Controller (MVC).

There will be a diagnostic test at the start of this module.  Students who have some minor gaps in their background knowledge will be given directed reading to help them catch up, and given the opportunity to participate in a study group.  Students with more significant gaps will be advised to reconsider their choice of this option.

ActivityDescriptionHours
Lecture36

Assessment

Assessment methods

MethodHoursPercentage contribution
An ASP.NET MVC Web Development Exercise-30%
Exam2 hours70%

Referral Method: By examination

Share this module FacebookTwitterWeibo

COMP6203 Intelligent Agents

Module Overview

This unit gives a broad introduction to the new and rapidly expanding field of agent-based computing. It introduces the key concepts and models of the field, dealing both with the individual agents and with their interactions. Particular emphasis is placed on automated negotiation, cooperation and on-line auctions, and students are required to program a trading agent in Java which will compete in a class tournament within a simulated trading environment.

Aims & Objectives

Aims

Learning outcomes are:

  • Understand motivations for, and approriate use of, agent-based computing.
  • Understand main agent models in use today and their grounding in artificial intelligence research.
  • Understand main agent decision making frameworks for cooperative and competetive environments.
  • Be able to apply these approaches to deploy an agent within a simulated agent trading environment.
  • Be able to analyse and critique the performance of this deployed agent.

Syllabus

Topics covered are:

  • Introduction to agent-based computing
    • Motivations for agent-based computing
    • Key concepts and models of reasoning (symbolic, reactive and practical)
    • Rational decision making and handling uncertainty
  • Agent Interactions
    • Models of coordination (DCOP and the max-sum algorithm)
    • Models of competitive behaviour (game theory and mechanism design)
    • Computational markets (auctions)
  • Agent design and implementation
    • Structuring agent models in code
    • Deploying agents within a simulated environment
    • Practical reasoning strategies for computational markets

Learning & Teaching

Learning & teaching methods

ActivityDescriptionHours
LectureLectures of core academic course content.18
Demonstration or Examples SessionIntroduction to trading agent competition - downloading and running initial simulator and agent.2
TutorialDiscussion student developed strategies for the trading agent competition.4

Assessment

Assessment methods

MethodHoursPercentage contribution
Trading agent competition-40%
Exam1.5 hours60%

Referral Method: By examination, with the original coursework mark being carried forward

Share this module FacebookTwitterWeibo

COMP6202 Evolution of Complexity

Module Overview

Evolution by natural selection has created amazingly complex and sophisticated solutions to some very difficult problems - how exactly does it achieve this? - and how can we harness this capability for engineering artificial systems and computational problem solving?

This module has three complementary aims:

  1. To provide knowledge of the theory, mechanisms and processes of evolution, and the factors affecting evolvability of complex systems, paying particular attention to the algorithmic assumptions underlying different aspects of the models and theory. (This unit introduces basic biological topics to a computer science/numerate audience and assumes no biological background/pre-requisites.)
  2. To provide knowledge of and experience with using computational methods inspired by natural evolution and population dynamics.
  3. To place this knowledge and these techniques within the broader framework of general computational optimization techniques.

The content includes key concepts, tools and approaches in:

  • basic aspects of evolutionary biology,
  • techniques in artificial evolutionary computation,
  • and scientific exchange between the two disciplines: e.g. how artificial evolutionary algorithms help us understand the capabilities and limitations of biological evolution, and how current topics in evolutionary biology inspire new solutions to evolvability and scalability in engineering.

 

This module is intended as an optional module for appropriate part 4 undergraduates and MSc students. Prior completion of specific modules is not a prerequisite for enrolment. This unit introduces basic biological topics to a computer science/numerate audience and assumes no biological background/pre-requisites. However, the module does involve considerable biological as well as computational material, and would suit students with an interest in the theory of evolution and competence in programming.

Aims & Objectives

Aims

Syllabus

  • Introduction to algorithmic concepts of evolution
    • What is evolution?
    • Natural selection and adaptation
    • Evolutionary algorithms
    • Algorithm variants (genetic algorithms, evolutionary strategies, etc.)
  • Computational optimisation methods
    • Combinatorial optimisation (e.g. TSP, graph-colouring)
    • Heuristic techniques (e.g., Simulated Annealing, gradient ascent)
  • Evolvability of Complex Systems
    • Concepts of evolutionary difficulty (fitness landscapes, epistasis, adaptation and constraint)
    • Modularity
    • Sexual recombination and evolvability
    • Algorithmic possibilities for evolution
    • Artificial Life models, open-ended evolution
    • Developmental representations
  • Advanced topics
    • Wright versus Fisher (mass selection, shifting balance theory)
    • Units of selection
    • Interactions among species (coevolution, coadaptation)
    • Compositional evolution (hybridisation, major evolutionary transitions)
    • Macro-evolution (saltations, gradualism, extinction, trends, diversity, species and speciation)

Learning & Teaching

Learning & teaching methods

ActivityDescriptionHours
LectureMain delivery of taught material - conventional lectures.36
Specialist LabThis is effectively 'office hours' where I make myself available for assisting with coursework questions. An hour per week needs to be timetabled but no room booking is needed.12

Assessment

Assessment methods

referral needs 3hr exam.

MethodHoursPercentage contribution
warm-up coursework (not assessed): code a genetic algorithm-%
main coursework: reimplement a selected paper and extend -50%
Exam1.5 hours50%

Referral Method: By examination

Share this module FacebookTwitterWeibo

ELEC6216 Personal Multimedia Communications

Module Overview

The course endeavours to practice the fundamentals of wireless communications, to review the most recent trends and techniques in the broad field of Mobile Multimedia Communications and to equip the participants with basic skills required to design such systems.

The course is taught to the MSc students in Wireless Communications and Part IV Master of Engineering students in Electronics.

Students taking this course are expected to have the basic knowledge of digital communications and wireless communications.

Aims & Objectives

Aims

When completing this module, you will be expected to be able to:

  • be familiar with the architecture of mobile multimedia systems;
  • have a good knowledge about the various types of wireless communication channels as well as their modelling;
  • employ the skills of Monte Carlo simulation to predict the performance of some specific wireless communication schemes based on the above-mentioned wireless communication channel models;
  • be able to apply appropriate simulation tools for simulating the bit error rate (BER) or/and throughput performance of wireless communication schemes;
  • be able to analyse the constraints of some wireless communications techniques;
  • be familiar with the principles of a range of wireless communications systems as well as their implementation challenges;
  • employ the knowledge of some novel techniques that have been employed or may be employed in wireless communications systems.

Syllabus

Students are required to complete a range of compulsory tasks on the topics, including:

  • Additive white Gaussian noise (AWGN) channel modelling and simulation;
  • Analysis and simulation of the BER performance for BPSK, QPSK, MQAM, etc. modulation schemes, when communicating over AWGN channels;
  • Modelling and simulation of uncorrelated Rayleigh/Rician fading channels;
  • Analysis and simulation of the BER performance for BPSK, QPSK, MQAM, etc. modulation schemes, when communicating over uncorrelated Rayleigh/Rician fading channels;
  • Modelling and simulation of correlated Rayleigh/Rician fading channels, pilot-based channel estimation;
  • Simulation of the BER performance of BPSK, QPSK, MQAM, etc. modulation schemes, when communicating over correlated Rayleigh/Rician fading channels;
  • Principles and simulation of adaptive modulation over wireless channels.

Students are also required to complete one to several optional tasks on the topics, including:

  • Code-division multiple-access (CDMA), which mainly focuses on the evaluation of the performance of some CDMA schemes with various single- and multiuser detection strategies, when communicating over various wireless channels. The possible CDMA schemes may include DS-CDMA, FHMA, THMA, multicarrier CDMA, etc.;
  • Antenna techniques, which may consider the issues, such as transmit/receive diversity, space-time coding, MIMO transceiver design, MIMO space-time processing algorithms, beamforming and transmitter preprocessing, relay/cooperative communications, etc.;
  • Multicarrier and Orthogonal Frequency-division Multiplexing (OFDM), which may considers the issues, such as performance of downlink OFDMA and uplink SC-FDMA, uplink/downlink resource allocation, MIMO OFDM, techniques for mitigation of peak-to-average power ratio, techniques for inter-carrier interference suppression, etc.;
  • Source coding, which may work on the issues such as compression of voice, image, video, etc. source signals, various compression algorithms, compression efficiency, complexity, etc.;
  • Channel coding, which may include the investigation of, such as linear error-control codes (BCH code, convolutional code, LDPC, etc.), non-linear error-control codes (constant-weight code, Hadamard code, etc.), various decoding algorithm, viterbi algorithm, turbo decoding, iterative decoding, etc.;
  • Joint source-channel coding, which considers the issues such as channel optimised source coding, source-controlled channel decoding, joint source-channel decoding, iterative source-channel decoding, etc.;
  • Channel estimation and prediction, which studies the issues associated with channel estimation and prediction as well as various channel estimation and prediction algorithms in wireless communications;
  • Interference mitigation, which deals with the issues, such as interference suppression in various wireless systems, multiuser detection in CDMA systems, algorithms for suppressing partial-band interference, multiuser interference, multitone interference, etc.

Learning & Teaching

Learning & teaching methods

ActivityDescriptionHours
LectureIntroduce and revise the principles of the topics related to the course.24
TutorialProvide students knowledge about the advanced techniques in wireless communications. 12

Assessment

Assessment methods

MethodHoursPercentage contribution
Assessed by progress report, individual presentation, final report -100%

Referral Method: By set coursework assignment(s)

Share this module FacebookTwitterWeibo

ELEC6214 Advanced Wireless Communications Networks and Systems

Module Overview

ELEC6214 Advanced Wireless Communication Networks and Systems is designed:

  • To introduce the student to the most recent techniques in the broad field of Wireless Communication
  • To equip the student with basic skills required to design such systems as well as to work for future wireless systems

This module is taught in Semester 2. It is particularly aimed at equiping our MSc Wireless Communications and MEng Electronic Engineering with Wireless Communication students with advanced comminucation theory and technologies, vital for a successful career in digital economy.

This is a key taught module for the MSc Wireless Communication and MEng Electronic Engineering with Wireless Communication programmes course and is complemntary to the core semester 2 module, ELEC6216 Personal Multimedia Communications, which is hands-on coursework based. In particular, this module offers the students fundamental theory of wireless communications, which diretly benefits their "hands-on" practice in ELEC6216, in order to develop the vital transferable practical skills for working in the information industry.

For undergraduate students, the prerequisites for this module are satisfied by having taken ELEC3203 Digital Coding and Transmission OR ELEC3204 Wireless and Optical Communications.

For postgraduate students, the prerequisites for this module are satisfied by the prerequisites of their programme. However, some additional background reading will be required for students that do not have a background in the topics covered by ELEC3203 and ELEC3204.

Aims & Objectives

Aims

After taking this module, the student will master

  • The fundamentals of mobile wireless channels, and the limitations of mobile channels imposed on communication systems
  • Advanced modulation and transmission techniques, and practical channel coding schemes
  • The architectures of mobile communications, and recent standard mobile systems, such as the fourth generation (4G) system
  • The foundation of understanding and working for future generation of wireless systems
  • Theory and practice of Information Transfer in our information society, and the critical transferable skills to work in other information industries

Syllabus

Mobile fundaments

  • Multiple access techniques: frequency division multiple access (FDMA), time division multiple access (TDMA), code division multiple access (CDMA), space division multiple access (SDMA);
  • Space-time processing: multiple antenna techniques, diversity and multiplexing gains, multiple-input multiple-output (MIMO) systems.

Mobile radio channels

  • Pathloss, large-scale fading, small-scale fading; Power budge of mobile links;
  • Doppler spread and coherent time, delay spread and coherent bandwith; flat fading and frequency selective fading.

Modulation and transmission

  • Digital modulation overview and digital modulation schemes, spectral efficiency and implementation complexity, power efficiency and green communication;
  • Carrier and clock recovery, coherent receiver and non-cohernet receiver;
  • Adaptive signal processing for communication, channel equalisation, combating interference, and multi-user detection;
  • Multi-carrier orthogonal frequency division multiplexing (OFDM) and single-carrier block transmission with frequency domain equalisation.

Practical channel coding schemes

  • The fundamentals of forward error correction (FEC) coding, convolutional coding, linear block coding, hard-decision channel decoding, soft-decision channel decoding;
  • Turbo principle, turbo coding, turbo decoding-detection, near-capacity three-stage concatenatedturbo transceiver.

MIMO technology

  • The fundamentals of MIMO, diversity and multiplexing gains, beamforming gain, SDMA based multi user system;
  • Vertical Bell Lab layered space-time (V-BLAST), space-time block codes (STBCs), Linear dispersion codes (LDCs), spatial modulation (SM) and space-shift keying (SSK), and space-time shift keying (STSK) - a unified MIMO;
  • Acquisition of MIMO channel state information (CSI), state-of-the-art near-capacity MIMO systems.

Existing and future wireless systems and standards

  • 1st generation (1G) system, 2G system, 3G system, and 4G system;
  • Beyond 4G (B4G) system, massive MIMO, millimeter wave communication, optical wireless.

Learning & Teaching

Learning & teaching methods

ActivityDescriptionHours
LectureDelivery of basic syllabus by lecturing.36
TutorialDelivery of advanced topics by seminars as well as discussion and revisions.12

Assessment

Assessment methods

MethodHoursPercentage contribution
Exam2 hours100%

Referral Method: By examination

Share this module FacebookTwitterWeibo

ELEC6213 Image Processing

Module Overview

This module is useful to introduce:

-Image processing and its relation to signal processing.

-Image transformations for filtering, coding and etc.

-histogram processing algorithms to enhance image qualities and visibility.

-Theories analysing and understanding images using feature extraction, segmentation, and texture modelling.

-linear and nonlinear methods for shape registration, noise reduction and restoration.

-Image classification and object recognition.

-Edge detection

Aims & Objectives

Aims

-To learn how images can be digitised and stored in computers

-To know and understand how computers can process digital images

-To learn how to do linear and nonlinear filtering on images

-To know of the relation to signal processing and other fields

-To learn how to extract features from images

-To learn how to use features to classify images for recognition

-To learn what segmentation is and how to do segmentation in digital images

Syllabus

  • Overview [1];
  • Image acquisition and sampling theory [1];
  • Image transformations [2]:Fourier, Discrete Cosine and Wavelet;
  • Histogram processing and linear filtering [1];
  • Point processing and operations [1];
  • Calculus of variations and Lagrange miltipliers [2];
  • Active contours [4]: Kass Model and Level Set formulation;
  • Geodesic Active contours [2];
  • Shape Registeration [1];
  • Image noise reduction[1];
  • Anisotropic Diffusion [1];
  • Image Restoration [3]: Wiener Filter and total variation;
  • Shape description [3];
  • Image Classifcation and Recognition[1];

Learning & Teaching

Learning & teaching methods

ActivityDescriptionHours
Lecture24
Computer Lab24

Assessment

Assessment methods

MethodHoursPercentage contribution
Practical laboratory work-30%
Exam2 hours70%

Referral Method: By examination

Share this module FacebookTwitterWeibo

ELEC6212 Biological Inspired Robotics

Module Overview

This module lies at the intersection of robotics and biology. Through the abstraction of design principles from biological systems, it is possible to develop a range of core competences, inlcuding mechatronic systems, sensor and actuator technologies. By taking this module students will get an understanding of adaptivity and autonomy of animals through robotics, and have the opportunity to design, build and test novel robotic applications which are more adaptive, maneuverable, resilient, and energy efficient than current designs. Previous robots developed have included the Formica swarm robot, mobile platforms, swiming and flying robots, robotic heads and a range of advanced sensors.

Aims & Objectives

Aims

Transferable and Generic

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

  • Work effectively as a group in a professional manner

Subject Specific Practical

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

  • Complete a self-directed design and build project relating to biologically inspired robotics.
  • Develop skills related to the design, construction and testing of advanced robotic systems.

Disciplinary Specific

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

  • Have a deep understanding of bilogically inspired robotics and its current impact on robotic research.

Syllabus

A number of introductory lectures will cover the following, prior to students developing their own designs.

  • What is a biologically Inspired Robotic System, and its advantages and disadvantages
  • Mobility systems (legs, swiming and flying system)
  • Sensors (tactile, vision, electronic nose, etc)
  • Swarm robotics
  • Control architectures
  • Emerging fields of study.

The purpose of the 3-4 introductory lectures is is give the students an appreciation of the scope of the subject and provide the knowledge and understanding to allow the groups to undertake research to allow the definition of the individual projects.

Learning & Teaching

Learning & teaching methods

ActivityDescriptionHours
LectureThe initial seminars will provide an introduction to the module and Biologically Inspired Robotics. Students will be encouraged to gain background knowledge through directed reading. The lecture slots will also be used for the student's elevator pitches and final presentation. 8

Assessment

Assessment methods

MethodHoursPercentage contribution
Quality of the Initial Plan.-5%
Technical Execution-40%
Documentation-30%
Individual contribution to Wiki or video-20%
Individual reflection-5%

Referral Method: There is no referral opportunity for this syllabus in same academic year

Share this module FacebookTwitterWeibo

ELEC6211 Project Preparation

Module Overview

The Project Preparation Module will prepare students for the summer Masters Project. It will give students a grounding in the research methods and techniques they will need in order for them to plan and successfully execute and complete their project.

 

Part of the Module involves identifying potential areas of research, negotiating with the Programme Leader and potential supervisors.  This will involve general research and the review of literature and the identification of a research question or questions.  They will then develop a research plan which will be presented in summary form via a submitted coursework and a Poster presentation.  This will be presented via the Module Poster Exhibition at the end of the semester.

 

It is expected that as part of this module the students will undertake appropriate preparatory study for their summer project, e.g. learning specific new analytical, simulation or other technical skills where necessary.  The module also teaches students what it is to be a professional practitioner, examining ethical and legal issues around professional practice.

The aim of this module is for students to demonstrate appropriate mastery of research methods, including an understanding of how to perform critical evaluation of a review of the literature, leading to the creation of a project plan to explore identified research question(s).

Aims & Objectives

Aims

Knowledge and Understanding

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

  • The general background to the subject of your summer project topic.
  • Demonstrate their knowledge and understanding of professional codes of practice, legal, social, cultural and ethical issues and an awareness of societal and environmental impact;
  • The general background to the subject of your summer project topic.
  • The concepts of project planning and risk management

Subject Specific Intellectual

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

  • Demonstrate their knowledge and understanding of appropriate qualitative and/or quantitative research methods;

Transferable and Generic

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

  • Identify and describe appropriate research questions, and devise the means to address those questions through subsequent research/project work;
  • Carry out a critical review of literature, including current developments, analysing them and identifying limitations and avenues for further research and development;
  • Be able to present a project plan to a public audience;
  • • Understand where necessary how to assess and obtain ethical approval for a project.

Syllabus

The syllabus will develop skills required for students to be able to undertake research in the area of their identified summer project.  It will also introduce them to specific tools, the appropriate research and data collection methods, as well as the quantitative or qualitative analytical methods required.  It will also cover the planning and development methods necessary to deliver a viable Project Outline for the work over the summer term.

Development of the project research topic:

1. Literature review:

  • Reading and summarising relevant articles
  • Critical analysis and evaluation of research
  • Identification of themes and comparators
  • Writing review documents

2. Scientific method and nature of evidence

  • Experimental methods
  • Design methods
  • Simulation techniques
  • Data collection and management for quantitative data
  • Human participants: expert reviews, focus groups, questionnaires and interviews
  • Analytical techniques and tools
  • Statistical techniques for analysing data

3. Ethical issues

  • Ethical and societal challenges
  • Positions and model of ethics
  • Business ethics and corporate responsibility
  • Personal and workplace ethics.

4. Legal and professional issues

  • Ownership, copyright and patent
  • workplace legislation
  • data protection
  • privacy and security
  • environmental responsibility.
  • Health and Safety: Good practise and policy

5. Professional Issues

  • Professional societies.
  • Codes of conduct and practice;

6. Project management and report writing

  • Project planning and management
  • Risk analysis
  • Report structure and style
  • Report writing

Learning & Teaching

Learning & teaching methods

ActivityDescriptionHours
LectureLectures on a range of topics, some general in nature, some specific to the cohorts.10
Project supervisionWeekly meetings with supervisors after project allocation has been performed.4
TutorialProgramme specific discussion sessions related to the general nature of each area, as well as the allocation of project supervisors.3

Assessment

Assessment methods

This assessment will require the use of a second examiner and moderation, on an individual case-by-case basis, as for other projects.

MethodHoursPercentage contribution
Literature Review-40%
Project Plan and methodology-30%
Poster Presentation -30%

Referral Method: By set coursework assignment(s)

Share this module FacebookTwitterWeibo

COMP6201 E-Business Strategy

Module Overview

This course exposes students to the problems and methods of strategic management of large scale e-business systems. These are systems whose continuing operation and evolution is vital for the business or organisation that they serve. IT managers and CIOs must ensure that systems are effective and cost-effective, that new projects give a good return, and that emerging technologies are evaluated and, where appropriate, adopted in an orderly manner. Similarly, emerging risks such as security threats must be evaluated and addressed using appropriate and cost-effective techniques.

About half of the course is devoted to directed reading, presentations and reports on significant technologies. The other half concerns case-studies in enterprise and e-business systems.

Aims & Objectives

Aims

Knowledge and Understanding

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

  • Describe the principles of strategic management
  • Describe systems architecture and technologies for systems integration

Subject Specific Intellectual

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

  • Define and quantify the benefits, costs, risks and time-scales associated with new strategic e-business or IT initiatives.
  • Compare and evaluate alternative e-business strategies and technologies
  • Justify and promote strategic initiatives, such as adopting a new e-business system or technology

Transferable and Generic

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

  • Prepare and deliver senior management reports and presentations

Syllabus

 1.    Technology-enabled business transformation, alignment of business and IT

2.    Approaches to strategy creation, SWOT analysis, senior management presentations

3.    IT governance, risk management, reporting structure

4.    Software economics, cost management, ROI, time to pay back, outsourcing, utility computing, business and IT metrics, dashboards and balanced scorecards

5.    Enterprise computing, middleware, business applications (ERP, SCM, CRM, CMS)

6.    The role of CIOs and consultants

7.    Advanced presentation and report writing skills for business

8.    Case studies based on use of e-business techniques and technology in a range of organisations

Learning & Teaching

Learning & teaching methods

Lectures, Directed Reading, Student Presentations and Reports

ActivityDescriptionHours
LectureLectures will present the core material for the module. In addition, some classes may be used for students to present their work for assessment.24
TutorialExtra activities will take place in tutorial sessions, where for example students may practice for their assessed presentations.12

Assessment

Assessment methods

MethodHoursPercentage contribution
Evaluate an existing business, its IT/e-business systems, recommend and justify changes & improvements to it (6 pages)-30%
Working individually, and in a group of two students, present your proposal to senior management (3 minutes individual pitch, 7 minutes as a pair)-30%
Working in a group of 2 students, write a report to evaluate and justify a potential new or improved IT/e-business solution & possible suppliers (8 pages)-40%

Referral Method: By set coursework assignment(s)

Share this module FacebookTwitterWeibo

Pages