Team Project (Undergraduate)
A group simulation project to be taken by all third year MEng students registered for an MEng programme of study in Electronic Engineering. The module aims to give students experience in: * commercial quality simulation packages * working as a team * problem solving skills * using simulation tools * implementing a design or development project using simulation tools.
Team Project (Undergraduate)
This is a group project where you will undertake an extended examination of a given topic. The assessment will be by group report and group viva, followed by an individual viva where candidates will discuss their contribution. The project tests group-working skills, technical ability and organisation, communication and evaluation skills. This team project, undertaken with supervision from a member of academic staff, requires students to undertake scientific investigation on a chosen research topic. The roles within the group should be complementary and all members will be assessed on both their individual contribution and also on the final group thesis. Group members will have to work together on project management issues (eg efficient and effective division of work) and to plan for the integration of individual contributions in the bigger picture of the team project. The project is seen as an excellent indicator of a student's overall ability to carry out a serious piece of work, and consequently employers are likely to be impressed by competence shown. It will give you a topic of conversation at your job interview. This module is compulsory for the degree title G401 MSci in Computer Science. Online information is available from https://intranet.dcs.qmul.ac.uk/courses/coursenotes/projects/bsc/ Not open to Associate Students.
Research Interests:Overall Research Aim: How can an understanding of biological systems help us build better technology?
One of the accepted Grand Challenges of science is to understand the workings of the human brain. This is a problem that has many aspects and many levels, but one of the most promising is to study the brain as a computational system, and in particular to study the lower level processing components, such as early vision and sensory-motor integration which are both experimentally accessible and lend themselves to algorithmic description. The development of such biologically validated algorithms also allows their transfer to build functional computer artefacts mimicking natural behaviours. Research in my lab is focused on three main areas.
1 Understanding early visual processing
How does our brain start to process the visual signal, and can we build accurate and experimentally validated mathematical models for this process?
Working with Alan Johnston at Psychology at UCL we have developed and promoted a gradient based framework for understanding neural structure and human performance in a range of low-level visual tasks such as motion and spatial orientation. Our mathematical models have allowed us to predict and validate a number of novel optical illusions. There are opportunities to extend this work to look at other low level processes such as stereovision. We have recently started to develop a mathematical framework to examine human visual attention, and to test this model against human performance.
2 Sensory motor integration
Can we develop and test mathematical models that explain how perceptions lead to actions?
Our work on integration examined initially a novel insect behaviour, active motion camouflage, and developed the first explicit neural model for this stealth strategy. Using the model developed it was possible to incorporate it into a virtual environment and show for the first time that humans are also susceptible to this camouflaging behaviour. Examination of human sensory motor performance also allowed the development of an award winning Internet based security system using computer mouse based signatures, which we showed for the first time to be both reproducible and biometric.
3 Closing the perception action loop
How can we create computers able to recognise human facial expressions or gestures and so interact more naturally with the users?
Closing the perception action loop in human computer interaction is an extension and integration of our previous work, using biologically inspired methods and computer vision techniques to develop adaptive systems capable of tracking faces, recognising facial expressions and generating photo realistic avatars, so supporting affective computing applications. This work is extending currently to include the automated recovery of body language in an effort to improve interaction, and will form the basis of software to be incorporated and field tested in companion robotics developed as part of the Framework 7 project LIREC. Work with Psychology UCL, funded under the Cognitive Systems programme will develop techniques to create photo realistic graphical avatars, which will be used to examine properties such as imitation. Through such systems we can attempt in the near future to understand many of the higher-level cognitive processes in the human brain concerned with affective communications, social intelligence and empathy.
Public dialogue on Computer Science and AI
Research on understanding the brain and the development of artificial intelligence has been identified by the UK Government as an issue that the public have concerns about. Integral to my research agenda is a successful and ongoing engagement with the public and media to demystify the subject and inspire the next generation of researchers. Projects include the online Humans Vs AI Olympics Sodarace, based on the BAFTA winning sodaconstructor website, and also the cs4fn project which aims to develop a magazine and website to inspire and inform school children (and others) about the broad field of Computer Science. This outreach work has been selected for the Royal Society Summer Exhibition in 2005 (Sodarace) and 2007 (The perception deception, maths made optical illusions)