Communicating and Teaching Computing: the Undergraduate Ambassadors Scheme (Undergraduate)
Students will typically begin by observing the teacher's handling of the class and progress from this classroom assistant stage through small teaching tasks to at least one opportunity to undertake whole class teaching, possibly for a short part of a lesson. They will represent and promote computing and related subjects more generally as a potential university choice. Students will undertake and evaluate a special project on the basis of discussion with the teacher. This may involve a specific in-class teaching problem or an extra-curricular project such as a lunchtime club or special coaching periods for higher ability pupils. The student will keep a journal of their own progress in working in the classroom environment, and they will be asked to submit a reflective written report on the special project and other relevant aspects of the school placement experience. This format is standard within the Undergraduate Ambassadors Scheme (www.uas.ac.uk).
Embedded Systems (Postgraduate/Undergraduate)
This module provides a practice-oriented introduction to embedded real-time systems. The main topics are (1) Modelling and simulation in UML and state-of-the-art tools; (2) Basic concepts of micro-controllers; (3) Real-time systems with interrupts and schedulers; (4) Real-time operating systems: processes and communication; (5) Energy aware design and construction; (6) Debugging and testing as part of software development processes.
Real-Time and Critical Systems (Postgraduate)
Covers real-time systems, include scheduling
Real-Time and Critical Systems (Undergraduate)
Most computer systems do not sit on desks but are inside machine such as cars and medical devices. Building on modules in operating systems and software engineering, this module introduces techniques for the safety analysis of such systems and for real-time system development.
Medical Decision Support Models: Data, Knowledge and Evidence
Can data be used for decision-making? In many applications there are not enough data, key values are not directly observed or the problem requires reasoning about change. In these cases, it is better to combine data and knowledge for building a decision model.
Many medical decision problems fit this pattern. However, given the long history of clinical trials, clinicians are reluctant to assume an understanding of causes even when trials are completely impractical. Recent work on decision making in trauma surgery has shown the potential of causal models implemented using Bayesian networks. However, there are still many challenges before the use of these models can become routine.
Safety, Reliability And Risk: Modelling Accidents & Incident Causes
Analysing what can go wrong is fundamental for assessing risk in safety systems. Existing approaches have a number of deficiencies: (1) human behaviour and technical failures are poorly integrated (2) model created for system approval are often not used when a system is in operation (3) information on incidents and procedural compliance is not used to update risks.
The aim of the research is to extend existing accident-based modelling techniques to resolve these problems. Recent work has proposed a new model structure using a Bayesian network for causal modelling from accident / incident data, with the aim of predicting the likely safety / reliability improvement that would be achieved by changes in the operation of a system at a specific location.