We assume that technologies will improve our lives, work and quality of life. For many people such benefits are undoubtedly true. However, does this apply to everyone? A House of Lords report in 2023 proposed that the "digital divide is undermining efforts to improve UK productivity, economic growth and socio-economic inclusion" and that groups likely to be most effected included the elderly, with 31 per cent of people aged over 65 not using the internet at home, compared with only 4 per cent of those aged 35-44 (https://publications.parliament.uk/pa/ld5803/ldselect/ldcomm/219/21902.htm ).  

A useful project would be to explore options for research in this area. Perhaps formulate a survey strategy, questionnaire, workshop, or proposed data sources that might be used for subsequent exploration. Proposals would need to be informed by background research and considerate of research rigour and ethics. 

The Internet of Things and Digital Twins are becoming hot industry topics. It would be great for us to build our capabilities in these fields, with a view to expanding our teaching and research options. A useful project would be to explore low cost (even free!) options to that would enable academic researchers and students to explore topics such as medium/long range wireless networks (such as LoRa), IoT system simulation, or Digital Twin development. 

ChatGPT has been in the news since it was released in November 2022. It has been the object of a lot of hype (a user-friendly combination of Google and Wikipedia; the ability to collate and draw conclusions from mountains of data) as well as disaster predictions (the end of traditional academic assignments).

But what can it do? And how do people see it? There are many questions that could be asked.

When given a complex prompt (more than just a request for factual information) ChatGPT will give a different response each time it is asked. Investigate the variations in response to the same prompt delivered repeatedly. Do the responses converge or diverge? You will need to generate several suitable prompts, and investigate the responses to each over an extended period of time.

Because it is designed to do nothing more than deliver plausible-sounding text, ChatGPT has (as yet) no concept of truth or factual accuracy. This means that it will sometimes produce factually inaccurate statements (‘hallucinations’) not derived from its training data (the Internet as at Summer 2021 – which probably contained more than a few inaccuracies, both accidental and deliberate). If these ‘hallucinations’ are uncritically added to the pool of (mis)information available to humanity, they will act like ‘fake news’ and pollute our data sources. How frequent are these hallucinations? Do they occur more frequently in some subject areas, and less frequently in others?

What do people think of ChatGPT? Does their opinion depend on age/gender/educational background…? Will it be a tool to make life easier? Or a threat to privacy? What evidence is there to support each of these two opposing positions, or any other position on ChatGPT?

Investigate how ChatGPT compares with Google Bard, Microsoft Bing (or any other easily available AI chatbot). On what sort of tasks do they perform differently, and how, and why? Are the differences significant/important? You may find it best to investigate and compare performance on a small number of tasks.

Investigate the proliferation of various website security/cookie/privacy policies.

How are they affecting the user experience?

How are they affecting the browsing experience?

Does this have a detrimental impact for organisations and e-commerce?

Do you have a solution? What does the literature tell you? I have "heard" rumours about a single sign-up universal system that would enable users to accept preferences one time only.

There are a number of approaches you could take to this project but it would probably fit a theoretical approach.

Government agencies are increasingly making data available to the public and researchers. Examples include higher education data published by HESA, socio-economic data published by ONS, and health data published by NHS. Such data might provide a useful source for analysis, research and planning. A project could investigate what data is available in a certain field, and then explore how it might be used, for example by being prepared and interrogated using Python or R. Alternatively, a potential use could be decided, such as predicting the number of total hip and knee replacement operations needed in the next ten years, and then looking at how this might be predicted and what existing data might be used.

The rise in connected autonomous vehicles presents challenges such as software reliability, operational trust and determination of accountability. A major concern is cyber security. Vehicle-attack opportunities are still emerging and not yet fully understood.

1) Assessing the threat and perceptions of automotive cyber risk. Vehicles will increasingly need to communicate with one another and to cloud systems. With the rise in connectivity, come the increasing risk of cyber breaches. Attacks could potentially affect data privacy, vehicle security or even vehicle safety. But how great is the danger? What are the perceptions of the car users? And how should we measure these? These are questions that automotive manufactures will need to increasingly ask in the next few years. 

2) Devising and testing automotive cyber attack scenarios. The Controller Area Network (CAN) is a crucial in-vehicle network used by safety crucial components, such as transmission, engine performance and braking. It is possible to read packets from the car’s CAN (as well as inject malicious packets), though the car manufacturers keep the meaning of the data secret.

Projects could investigate what CAN data sets might be available for testing attack detection, or investigate how attacks might be simulated, or devise algorithms to detect suspicious anomalies in data which might indicate an attack. One option would be to use a laptop based based CAN simulator (such as the free Kvaser CAN King) and CAN Python libraries with pre-gathered CAN logs (which we already collected). 

You are using a system to store project suggestions from members of staff in a format that students can access, and so select themselves a final year project. Chris Bowers has put a lot of work into developing this system, but I'm sure there is room to make it even better. This project will involve finding out what improvements users would like to see, implementing as many as possible of them, and evaluating the outcome. It may involve developing a completely new system, or (with Chris Bowers' agreement) improving his system.

An interesting feature of the Microsoft Kinect depth sensor is the occasional/spurious appearance of 'orbs' in the infra-red camera output.  A number of reports have been made about this, and we have seen them a few times ourselves when working with the device.  Setting aside the heated discussion as to what these 'orbs' are (as they cannot be normally seen by the naked eye), it would be interesting to see how the depth sensor component of the Kinect interprets these anomalies (ie in terms of the resulting point-cloud data).

To this end, we would like you to Design-Build-Test an application that detects orbs in the infra-red camera image, and upon detection, it triggers a recorder, so that the Kinect's point-cloud data output and infra-red video output is captured to disk.

Although the artefact (ie your software) will be a key deliverable, we will also need to see:

• a requirements specification and plan, developed through background research/study and discussions with your supervisor
• a detailed and reasoned software design
• a development plan/log based on the above design
• a test strategy and log, showing the planned approach to verification and the results of your tests.  As a part of this, you will need to include a method for 'cheating' the Kinect sensor using an infra-red element of a scene that is not in the visible spectrum.

Due to the ubiquitous and pervasive nature of mobile devices they are prone to being exposed to a wide variety of potentially hazardous environments and situations. There is a growing interest in being able to monitor and detect exposure to such environments. An example of this is the inclusion of water ingress detectors in many mobile devices that change colour when exposed to water. 

This project aims to extend the kinds of hazards that can be detected by mobile devices. In particular it should look at impact forces and generate notifications of potentially harmful forces. The project would consist of development and evaluation of potential detection techniques using existing sensors and to develop a working prototype logging and notification application.

This project will investigate the feasibility of using Evolutionary Computation to generate QR codes which contain aesthetically appealing patterns or shapes. 

QR codes typically appear to contain a random black and white grid of squares. However this pattern is carefully crafted to ensure that a URL can be encoded in a robust fashion. Its therefore very difficult to create a QR code with a desirable pattern or structure.

This project will attempt to create QR codes that contain patterns of black and white squares that form an identifiable pattern or shape that is aesthetically appealing. The resulting QR codes may have a significant commercial value as compared to apparently randomly generated codes.