Research Projects

3D graphical visualisation for biofeedback therapy

3D computer visualisation for medical purposes has become a growing field of science. These techniques have had huge impact in medical practice, from education to diagnosis and training. Driven by improvements in technology and increases in computing power, 3D visualisation and imaging are now being accepted clinically for diagnosis. Moreover, this technology is now powerful enough to describe precise and real time information of the anatomy that can be used to guide surgery. Virtual reality technologies that employ advanced 3D visualisation tools have been also used in physical rehabilitation.

This PhD project aims to address the challenges in rehabilitation. The EDA team (consisting of clinicians, academics and industry) has a strong record in developing clinical devices, providing visual biofeedback to patients. These devices combine measurements from instrumentation, a simulation model and a 3D-visualisation software interface. Existing EDA technology will be adapted to measure swallowing parameters. A simulation model will be developed mimicking the swallowing dynamics, which will be translated into a 3D visualisation to provide real-time biofeedback to the patient.

Proposals in other areas of rehabilitation using 3D visualisation and bio feedback are also welcome

Candidates should have, or are expecting to obtain in the near future, a First Class or good 2.1 Honours Degree in Digital Media, Computer Science, Engineering or a related discipline. An appropriate degree at Masters level will be an advantage.

Please contact Dr Jim Ang (csa8@kent.ac.uk) for more details.

A Psychological Approach to Biometric Feature Selection

Selecting which features are used for biometric recognition from images such as faces has relied on algorithmically assessing the performance of both individual and combinations of features. In this project we shall utilise results from Psychology as to how humans make an identity in a surveillance context, in terms of what features are used and furthermore when they are used as person approaches. This project will investigate software classification systems inspired by human methods of personal identification and assess if these methods can enhance conventional systems.

Candidates should have, or are expecting to obtain in the near future, a First Class or good 2.1 Honours Degree in Electronic Engineering, Computer Science or a related discipline. An appropriate degree at Masters level will be an advantage.

Please contact Dr. Richard Guest (r.m.guest@kent.ac.uk) for more details.

Ageing-Adaptive Face Recognition

Face recognition is now an important tool in key applications such as border control and forensic investigations. This project is concerned with developing new technologies for automatic recognition of people using their facial images when there may be a large time difference between the images that are compared. Human ageing significantly affects the performance of face recognition systems in these crucial application domains. The proposed research aims to develop a thorough understanding of the impact of the human aging process on the performance of face recognition systems and through this understanding develop systems and strategies for management of biometric information to ensure universal access to cost-effective biometrics-enabled authentication and forensic services.

Please contact Dr. Farzin Deravi (f.deravi@kent.ac.uk) for more details.

Applying Adaptive Systems to Biometric Recognition Problems

Existing adaptive system technologies such as weightless neural network techniques are limited by the number of pattern classes over which they may be employed since each additional class incurs a significant memory penalty. They may thus be employed for tasks such as character recognition where a limited number of distinct classes is present but may not be employed in problem domains, such as one-to-many biometric identification, where the number of classes is far higher. This project will investigate techniques for applying such technology to biometric systems via the development of a multi-classifier configuration for weightless networks where the component classifiers are trained on differing subsets of the pattern classes available. The entire ensemble is subsequently able to address the one-to-many identification problem. This project will specifically address fingerprint recognition.

Please contact Dr. Gareth Howells (w.g.j.howells@kent.ac.uk) for more details.

Augmented Reality Interaction

Augmented reality (AR) is a technology which blends digital information (e.g. virtual objects, graphical user interfaces, etc.) in the physical environment, allowing the users to experience a “mixed reality” world. In other words, AR can augment real-world environments with computer-generated sensory input such audio video, 3D graphics and other digital information. In a smart manufacturing environment, a AR headset can display contextually relevant information to manufacturing workers to perform their tasks more efficiently and safely.
The PhD project will deal with a range of engineering and design issues of AR headset, as well as other forms of user interaction modalities for healthcare domains. For instance, can AR be designed to help support patients with certain psychological conditions, e.g. anxiety and phobia? Another research area of interest is the assessment of user’s cognitive and affective states through monitoring of body movements and physiological markers through sensors built into the AR headset or external sensors.

Please contact Dr. Jim Ang (c.s.ang@kent.ac.uk) for more details.

Automatic Biometric Recognition of Wildlife

Gold Crested Newts are an endangered and protected species and their close monitoring has become an important aspect of the worldwide attempts for the preservation. This project aims to explore if the patterns on the belly of these newts can be used for their individual identification. Techniques similar to those used in human biometric recognition will be adapted to the extraction of features and classification of newt belly patterns to provide means for their monitoring. The issues of ageing and uniqueness for these patterns will also be explored. The project is likely to result in significant new findings in both areas of conservation and pattern recognition. This project will be conducted in association with the Durrell Institute for Conservation and Ecology.

Please contact Dr. Sanaul Hoque (s.hoque@kent.ac.uk) for more details.

Biometric Exception Handling using Shamir’s Secret Sharing Algorithm

A major problem with any biometric system is how to deal with situations where a person is unable to provide a sample from the given modality. For many practical systems a solution to this problem is essential to prevent large scale exclusion of sections of the population from given services (such as travel) or compromise of the security of the system employing the biometric. Multi-biometric systems offer a possible solution to this problem by allowing a person to select a modality or set of modalities from which to provide a sample. However, the biometric system employing these modalities needs to be designed to provide a secure and integrated whole. Shamir’s secret sharing algorithm offers a possible solution to this problem by ensuring that authentication only occurs when a given number of biometric modalities match the given user but offer the flexibility that an arbitrary subsets of modalities may be employed. An additional security feature is that the comprise of fewer than the required number of biometric modalities does not compromise the security of the overall system. This project will address the practical issues related to producing a working implementation of such a system.

Please contact Dr. Gareth Howells (w.g.j.howells@kent.ac.uk) for more details.

Biometric Identity Verification using BCH Codes

This project will investigate a means of addressing the significant Achilles’ heel of existing biometric systems in that compromised access to biometric template data contained within one system will potentially compromise all systems and data protected by the biometrics contained within the templates. The storage of template biometric data, however encrypted or secured, necessarily places any system secured via that template at risk. As an alternative approach, biometric feature data may be encoded with BCH codes with only the check bits of the codes being stored. Reverse engineering of the biometric data thus becomes a highly challenging task and security is improved. This project will consist of an evaluation of this technique followed by the creation of a practical demonstrator.

Please contact Dr. Gareth Howells (w.g.j.howells@kent.ac.uk) for more details.

Biometric Liveness Detection and its Evaluation

Face recognition is now an important tool in key applications such as border control. Increasingly there is a range of applications where it is desirable to deploy face recognition in unsupervised and remote applications where it may be possible for fraudsters to present photographs or other artefacts representing the faces of genuine users and thereby gain unauthorized access. A range of technologies have emerged for detecting such sensor-level spoofing attacks and determining the genuine liveness of the biometric samples presented to the sensor however objective assessment of these technologies have so far not been placed on an objective footing. This project is concerned with developing new technologies for liveness detection, as well as methodologies and protocols for objective assessment of liveness detection and counter-spoofing technologies with a focus on facial biometrics.

Please contact Dr. Farzin Deravi (f.deravi@kent.ac.uk) for more details.

Biometric/Signature Variation due to Capture Device

Anyone who has used a back-projected write-on device for the donation of a signature will know that the visual appearance of the resultant signature often differs significantly from a paper-and-pen based representation. As these devices (PDAs, dedicated signature capture tablets etc.) become more widespread, it is important to analyse variations that occur due to the device, ‘ink’ feedback, writing surface and user interface for use in biometric authentication scenarios. This work seeks to establish these variations to enable an understanding of expected performance and to explore best practice as far as user interface/writing surface design is concerned in order to ensure maximum performance.

Please contact Dr. R.M. Guest (r.m.guest@kent.ac.uk) for more details.

Brain Computer Interfaces for biometrics and affect monitoring

This project will explore the use of biosignals, such as EEG, as a source of information for assessing the identity, attention and emotional state of individuals. Use of games-grade Brain Computer Interfaces is explored as a source of biometric information and for establishing the attention of subjects and their level of stress and anxiety. This information can then be used to develop better and more reliable interfaces for intelligent applications. The resulting techniques can also be used to produce novel systems to aid the treatment and rehabilitation of patients and severely disabled people.

Please contact Farzin Deravi (f.deravi@kent.ac.uk) for more details.

Device agnostic activity recognition in an IoT rich world

Significant work has been done in developing systems that can accurately detect human activities using sensing technologies embedded in the environment (IoT) and / or worn by the user. Such systems have been explored in the context of smart homes, where the smart environment is capable of adapting to the activities their participants are performing, or in the context of health and wellbeing where unusual changes in daily activities can be indicators of changes in user’s health.
However, with the fast growth in the IoT industry. such sensing technologies tend to have a very short life-span (new smart devices with new capabilities are developed every year), there is real challenge in developing activity recognition techniques that can be translated across devices that may potentially have new capabilities. The aim of the project is to investigate the challenges of developing activity recognition models that can be transferred across devices that have potentially different capabilities, either automatically, or with minimum user involvement. The vision is the develop an activity recognition system that would evolve over time, as new technologies are developed and older technologies are decommissioned without the need for full re-training of the system.

Candidates should have, or are expecting to obtain in the near future, a First Class or good 2.1 Honours Degree in Computer Science, Engineering or a related discipline. An appropriate degree at Masters level will be an advantage.

Please contact Dr Christos Efstratiou (c.efstratiou@kent.ac.uk) for more details.

Display and performance of creative media in public space

Ubiquitous digital technologies enable creative practices to be easily embedded into everyday life. These technologies may be out-of-the-box solutions but are often custom-made. We can use both, out-of-the-box and custom-made technologies, to mash tangible and virtual objects, produce hybrid textual (and textural) experiences, visualise flows and movement, etc. These technologies are part of a socio-economic acceleration process, but can we use them to design creative mediated experiences that slow the acceleration down and offer people the opportunity of engaging with them and with others in public space?

The PhD student is expected to: (i) analyse existing out-of-the-box and custom-made design solutions; (ii) prototype media installations that slow people down in public space (e.g. e-literature and hyper-textuality in physical environments; visualisation/sonification of flows in urban/natural environments); (iii) test the designed prototypes (e.g. alpha/beta tests with groups); and (iv) evaluate them in the context of embodied perception (e.g. using sentiment analysis, position tracking, visual ethnography).

Strong first degree and Master’s degree in digital media, interaction design or related discipline. Candidates should ideally have strong software skills, and experience with tangible interfaces, media and interaction design.

Please contact Dr Rocio von Jungenfeld (r.von-jungenfeld@kent.ac.uk) for more details.

Exploring smartphone photography as means of detecting and influencing mental wellbeing

With the wide use of smartphone devices, photography has become a prominent activity for users.
Momentary smartphone photography can be considered as a rich dataset that can reveal the experiences of users on a daily basis. Furthermore, reviewing of older photos has been shown to influence peoples psychological attitudes and general mood. In this work the aim is explore two key research questions: firstly whether photos captured by smartphone users can be used to discover their changes in affective state (positive or negative mood), and secondly whether a smart system can be devised to serve selective photo memories as a form of intervention to influence user’s affective state. The focus of the work will be on the development of machine learning algorithms that can discovery associations between photo content and meta data, and the users affective state, and the design of automated interventions using appropriate ML techniques. The intention of this work is to explore the applicability of such techniques for people suffering from depression or bipolar disorder.

Please contact Dr Christos Efstratiou (c.efstratiou@kent.ac.uk) for more details.

Facial Recognition using a Variety of Sources

Facial biometric systems most frequently use images that are controlled in terms of subject pose and environmental factors such as background. Very often matching is required on photographic samples that do not meet these ideals, for example when automatically matching two images uploaded to Facebook. This project will investigate the performance of automatic systems to verify facial images collected across a wide range of scenarios and sources, and devise a mitigation framework to maximise verification ability.

Please contact Dr. Richard Guest (r.m.guest@kent.ac.uk) for more details.

Interactive Face Recognition

One important step in face recognition is the acquisition of a normalised image of the face. This project is concerned with developing techniques to allow a user to facilitate the face recognition process through the acquisition of high quality images. As part of the project algorithms will be developed to dynamically assess the quality of the image and this is then used to provide feedback to the user to make necessary adjustments, including adjusting his position relative to the camera, for optimum performance. The algorithms and interfaces developed will be tested using databases of facial images as well as live sessions to prove their effectiveness.

Please contact Dr. Farzin Deravi (f.deravi@kent.ac.uk) for more details.

Investigation of Techniques for the Direct Generation of Encryption Keys from Electronic Circuits (Icmetrics)

The digital revolution has transformed the way we create, destroy, share, process and manage information, bringing many benefits in its wake and an ever increasing number of embedded consumer and communication devices are at the heart of this revolution. However, such technology has also increased the opportunities for fraud and other related crimes to be committed. Therefore, as the adoption of such technologies expands, it becomes vital to ensure the integrity and authenticity of electronic digital systems and to manage, control access to and verify their identity. The University of Kent has developed novel techniques for the generation of encryption keys directed from properties associated with the software and hardware associated with a particular device, termed ICmetrics. ICmetrics represents an exciting new approach for generating unique identifiers for embedded devices enabling secure encrypted communication between devices potentially significantly reducing both fraudulent activity such as eavesdropping and device cloning. The use of ICMetric authentication represents a novel concept of regulating access to devices and is explicitly aimed at providing protection at the especially vulnerable points where data access is initiated. Specifically, the aim is to investigate appropriate integrated encryption and digital signature facilities to protect data from unauthorised access, forgery and tampering. Project objectives are to evaluate available feature sets for a range of ICmetric measurements and determine those suitable for application in the direct encryption technology and further to develop a set of prototype tools for demonstrating the effectiveness of the proposed approach. The project will explore further the potential of ICmetric technology by the use of novel measurement features and exploitation scenarios such as those associated with Cloud Computing.

Please contact Dr. Gareth Howells (w.g.j.howells@kent.ac.uk) for more details.

Investigation of Template-Free Biometric based Encryption Technology

Compromised access to confidential data is a growing source of serious crime encompassing a wide variety of areas including medical, legal and law enforcement agencies. As the adoption of digital technologies expands, it becomes vital to minimise fraudulent use of digital data by ensuring their integrity and authenticity and in managing and controlling access to their shared contents. This project will build on the substantial novel work already undertaken at the University of Kent into template-free biometric technology. The developed technology offers the advantages of:-

The development of biometrically based data encryption techniques which offer the significant advantage of not requiring the storage of potentially compromising biometric templates.
The development of prototype tools to allow the practical exploitation of such technology in order to realise significant benefits.
The development of techniques for the distributed management and processing of data to establish trust for the producers, distributors, consumers and users of documents containing confidential data.

Please contact Dr. Gareth Howells (w.g.j.howells@kent.ac.uk) for more details.

Merging Model Driven Software Engineering and Adaptive Systems for Autonomous Vehicle Design

This project will bring together several previously independent domains of Computer Science and systems engineering to address the significant challenges in engineering a robust and reliable autonomous vehicle capable of adapting to a rapidly changing dynamic environment. Specifically, it proposes to combine techniques derived from Model Driven Development and Adaptive Systems to develop a software architecture capable of “high-level” adaptation rather than the low level adaptation typically exhibited by current adaptive systems. In the proposed solution, high level models of the system would be subject to the governance of an overarching adaptive system which would be able to modify the models to cover a rapidly changing environment which may be significantly at variance to that originally envisioned. This ability is due to a class of artificial neural system developed at the University of Kent which is able to model high level language concepts by integrating formal mathematics within the traditional architecture of an artificial neural system combined with an intelligent agent approach. The fundamental research hypothesis of the projectl is that the combination of advantages from Model Driven Software Engineering, Adaptive systems, autonomous agents, multiple processor core computing, embedded systems and control engineering can be fused to produce a hybrid technique that is better able to generate, manipulate and modify model transformations than any technique in its own right. The significant novelty of the project is the application of generalised adaptive systems to the problem, these serve to reduce the complexity and quantities inherent in defining transformations rules for each individual case. This has the beauty not only of its generality and adaptability but its inherent simplicity means it is extremely well suited for operation in a low power and limited resource environment.

Please contact Dr. Gareth Howells (w.g.j.howells@kent.ac.uk) for more details.

Novel Approaches to Static Signature Verification

Very often the only form of archive signature data is as a static image rather than the time series data used in dynamic signature verification. This project aims to comprehensively explore methods for the verification of static signatures and develop comparison methods based on other image-based biometric technologies such as fingerprint and facial recognition algorithms.

Please contact Dr. Richard Guest (r.m.guest@kent.ac.uk) for more details.

Novel Behavioural Modalities for Biometric Recognition

Biometrics-enabled person recognition systems hold the promise of providing reliable authentication and protection of personal identity for a range of applications. This project involves an investigation of new modalities using biosignals such as EEG and gaze trajectory for the recognition of individuals. The proposed research programme will explore the development of systems and solutions to establish extract and classify information from such biosignals to see if the identity of individuals can be reliably established from them. Additionally liveness of biometric samples may also be assessed through such data thus countering the possibility of spoofing. The focus of the research will be the development of spoof-resistant acquisition strategies for person recognition.

Please contact Dr. Farzin Deravi (f.deravi@kent.ac.uk) for more details.

Participation, perception and play in interactive environments

The design of interactive environments can help us understand how people perceive and play with digital technologies and experience technologically mediated environments. Perception and participation are idiosyncratic, so how can we design comprehensive interactive tangible environments that explore, for instance, the visual-haptic characteristics of colour or the agency of light beams? How can we bring colour-perception or assemblage theories into interactive environment design?

The PhD student is expected to: (i) analyse current trends and developments in internet of things and interaction technologies; (ii) prototype visual/haptic interfaces or light reacting environments/systems; (iii) produce site-specific tests of the designed systems (e.g. alpha/beta tests with groups); and (iv) evaluate the systems in the context of engagement and play (e.g. using sentiment analysis, position tracking, visual ethnography).

Please contact Dr Rocio von Jungenfeld (r.von-jungenfeld@kent.ac.uk) for more details.

Passive activity sensing for assistive living using IoT

Current state-of-the-art in assistive living relies heavily on the use of wearable sensing technologies. However, real-world deployments of wearable technologies for the elderly have had only limited success. In this project we aim to explore a new paradigm where the daily activities of people are monitored passively through sensing technologies that are embedded within the environment, without the need for any form of wearable device or active user intervention. Specifically, the aim of this project is to rely on a combination of embedded sensing technologies involving audio sensing, RF sensing and presence (PIR) sensing that work collaboratively to accurately track the daily activities of elderly participants.

Research challenges that would be investigated are:
Audio sensing for activity detection: Employing deep learning models to identify changes in contextual activities performed at home.
Multi modal sensor fusion: Exploring the fusion of audio, RF sensing and presence sensing to accurate detect activities in shared environments with multiple occupants.

Passive sensing infrastructure: Explore the challenges of embedding passive sensing ML algorithms in resource constraint sensing devices.
Our team in the School of Engineering and Digital Arts is in close collaboration with industrial partners involved in the deployment of IoT (Internet of Things) technologies for assistive living. The project will benefit by access to real-world deployments of the relevant technologies in elderly peoples’ homes.

Please contact Dr Christos Efstratiou (c.efstratiou@kent.ac.uk) for more details.

Person Recognition from High-Resolution Skin Texture

Recently the possibility of using high resolution images of skin textures have been suggested as a means for improving face recognition accuracy. In this project algorithms will be developed for skin texture recognition based on spatial and frequency domain features. Algorithms will be tested on a database of high-res facial images. Additionally the robustness of this approach to involuntary and deliberate changes to skin conditions will be assessed.

Please contact Dr. Farzin Deravi (f.deravi@kent.ac.uk) for more details.

Portability and creative media projections in public space

In this project you will investigate how interactive designs can bring media out of portable electronic devices (e.g. screens, headphones) and into the public sphere where they can be shared with others. The work will involve the co-creation and display of content in public learning spaces (e.g. park, garden) and the design of an expanded media experience for public space. The designs should be conceived to enable people to participate in hybrid digital-tangible environments are made of media, projections and interactive things.

The PhD student is expected to: (i) analyse current trends and developments in projection, recording and interaction technologies; (ii) prototype locative, recording, interactive and projection systems; (iii) produced site-specific tests of the designed systems (e.g. alpha/beta tests with groups); and (iv) evaluate the systems in the context of engagement and situated learning (e.g. using sentiment analysis, position tracking, visual ethnography).

Please contact Dr Rocio von Jungenfeld (r.von-jungenfeld@kent.ac.uk) for more details.

Recognition of Emotional States from Facial Images

Face recognition systems are now increasingly used as a means for reliable recognition of individuals to enhance security and prevent identity theft. This project will explore the use of facial images as a source of information for assessing the emotional state of individuals. Use of video information from a number of channels including different colour and thermal imaging sources is explored for establishing the liveness of subjects and their level of attention, stress and anxiety. This information can then be used to develop better and more reliable interfaces for intelligent applications.

Please contact Dr. Farzin Deravi (f.deravi@kent.ac.uk) for more details.

Sensing of Quality of Life for people with long term health conditions using mobile technologies

Medical interventions on people with long term health conditions require an assessment of how quality of life (QoL) for the patient may be altered or improved. The aim of the project is to explore the design of low energy sensing and classification systems that can detect changes in QoL from low level sensor data captured by people’s smartphones and wearable devices. Of particular interest would be the detection of daily social patterns of users using mobile sensing technologies, and how those can be used to estimate their perceived QoL. Through the longitudinal collection of mobile and wearable sensor traces, the aim is to develop machine learning algorithms that can detect potential changes in the perceived quality of life through the analysis of the sensor data.

Please contact Dr Christos Efstratiou (c.efstratiou@kent.ac.uk) for more details.

Signature Ageing and Stability

The human signature is widely used behavioural biometric modality. Automated systems rely on using algorithms to assess similarity between samples, but by its very nature, intra-person samples will vary. Signatures and writing can also vary over time. The aims of this project are twofold: firstly to assess the short- to mid- term variations in writing and signatures to establish the validity of enrolment templates. Secondly, the project will look at longer term data to consider the implication of ageing on the construction of writing.

Please contact Dr. R.M. Guest (r.m.guest@kent.ac.uk) for more details.

Skin-like sensors for human computer interactions

Electrophysiology research has drawn a great attention for advancing human-computer interaction (HCI), in addition to its main contributions in health monitoring. Recently, in collaboration with Dr Yeo from Georgia Tech (https://sites.google.com/view/yeogroup) we are researching a class of technology for low profile, soft, and stretchable electronics (‘skin-like electronics’), which provides comfortable, conformal integration to the human skin for recording of physiological signals. Without the use of conductive gels and adhesives, ultrathin elastomeric membranes provided sufficient adhesion force to mount electrodes on the skin, purely via van der Waals interactions; same force that geckos use to stick to walls. Currently, we are interested in exploring how skin-like EMG sensors can be used for monitoring swallowing and chewing. We measure deglutition (action of swallowing) behaviour to demonstrate game-based, user-controlled feedback (see video : https://www.youtube.com/watch?v=PIBkpdqbGjA). We have also used skin-like EEG for brain-computer interaction control.

In this PhD project, you will be exploring novel use of these skin-like sensors for a range of HCI applications. Candidates should ideally have strong software skills, including machine learning and software app development.

Please contact Dr. Jim (CS) Ang (C.S.Ang@kent.ac.uk) for more details.

Stereoscopic vision and proprioception in VR environments

VR headsets offer new avenues for the creative exploration of affect at individual and collective level, and for experiencing our place in the world and our relationships with others. Based on stereoscopic, proprioceptive and binaural principles, VR makes us question where our bodies are and in which environments we inscribe our actions. On this account, can we apply the prevailing premise of environments as products of actions to VR, and if so what new VR experiences can we design?

The PhD student is expected to: (i) analyse prevailing theories on the construction of environments and the interrelation of agents and things; (ii) prototype VR experiences based on these theories (stereo-vision, sound environments, haptic/proprioception); (iii) test the designed prototypes (e.g. alpha/beta tests with groups); and (iv) evaluate them in the context of embodied perception (e.g. using sentiment analysis, position tracking, visual ethnography).

Strong first degree and Master’s degree in digital media, interaction design or related discipline. Candidates should have strong software skills, and experience with tangible interfaces, media and interaction design.

Please contact Dr Rocio von Jungenfeld (r.von-jungenfeld@kent.ac.uk) for more details.

Tangible Media and Mixed Reality

A tangible user interface allows the users to interact with digital information through the physical environment. This could involve the use of various sensors and actuators to manipulate and perceive digital information, giving it physical form. This interaction technique is closely related to the idea of mixed reality. Mixed reality refers to the blending of physical and virtual worlds to produce visualisations in which physical and virtual objects co-exist and interact with each other.

There are various application domains of this research area, including learning/training, healthcare, art installations, museum displays, games/entertainment, etc.

The digital media group at Kent has been carrying out research in the use of tangible media for people with dementia at care homes and day centres. We are also currently investigating the use of this technology for education in rural regions of developing country.

PhD projects looking into the study, design/development, and evaluation of novel tangible media in healthcare, education, and other relevant application domains are welcomed. These projects typically require skills in the interaction between software (mobile app, Web, 3D game engine, etc) and hardware (arduino, raspberry pi, kinect/leap motion, various sensors such as physiological and environmental sensors, etc).

Candidates should have, or are expecting to obtain in the near future, a First Class or good 2.1 Honours Degree in Computer Science, Electronic Engineering or a related discipline.

Please contact Jim Ang (c.s.ang@kent.ac.uk) for more details.

Validation Techniques for Complex Devices

This project seeks to bridge the gap between the observed performance of hardware and the predicted performance of system models for the same hardware. Although accurate modelling of all possible facets of hardware behaviour is an unrealisable goal, it is nevertheless possible to approximate an acceptable performance for a model by emphasising model characteristics whose significance is greatest regarding the specified performance of the hardware within a given problem domain. The project will proceed by concurrently, but independently, generating a hardware solution for a given problem domain accompanied by a formal model of the said solution. It is subsequently proposed to address the issue of modelling errors for the given systems via two distinct approaches:-

An empirical investigation will proceed to compare the actual behavioural characteristics observed from the hardware with the predicted results of the associated model. A number of metrics will be investigated to ascertain the most contributing factors to system deficiencies. Subsequently the data will be used to enhance the original modelling process.
A formal mathematical approach to comparing system and actual behaviour will be employed. Here the emphasis will be on the application of formal logic to represent the requirements of the system model. The novel significance is that the logic will be designed to emphasise the required characteristics of the model and allow it freedom to be inaccurate where such inaccuracies do not impinge on system performance. Theorem proving tools will be developed to ensure that model and hardware behaviour are acceptably close.These two approaches will be initially compared and contrasted; finally resulting in a suite of validation tools which may be employed to accurately address complex system validation issues. This dual approach will meet the need of ensuring acceptable modelling performance whilst not overburdening the modelling approach with hardware performance details which will not impact on system performance.

Please contact Dr. Gareth Howells (w.g.j.howells@kent.ac.uk) for more details.

Intelligent interactions

Featured story

3D graphical visualisation for biofeedback therapy

A Psychological Approach to Biometric Feature Selection

Ageing-Adaptive Face Recognition

Applying Adaptive Systems to Biometric Recognition Problems

Augmented Reality Interaction

Automatic Biometric Recognition of Wildlife

Biometric Exception Handling using Shamir’s Secret Sharing Algorithm

Biometric Identity Verification using BCH Codes

Biometric Liveness Detection and its Evaluation

Biometric/Signature Variation due to Capture Device

Brain Computer Interfaces for biometrics and affect monitoring

Device agnostic activity recognition in an IoT rich world

Display and performance of creative media in public space

Exploring smartphone photography as means of detecting and influencing mental wellbeing

Facial Recognition using a Variety of Sources

Interactive Face Recognition

Investigation of Techniques for the Direct Generation of Encryption Keys from Electronic Circuits (Icmetrics)

Investigation of Template-Free Biometric based Encryption Technology

Merging Model Driven Software Engineering and Adaptive Systems for Autonomous Vehicle Design

Novel Approaches to Static Signature Verification

Novel Behavioural Modalities for Biometric Recognition

Participation, perception and play in interactive environments

Passive activity sensing for assistive living using IoT

Person Recognition from High-Resolution Skin Texture

Portability and creative media projections in public space

Recognition of Emotional States from Facial Images

Sensing of Quality of Life for people with long term health conditions using mobile technologies

Signature Ageing and Stability

Skin-like sensors for human computer interactions

Stereoscopic vision and proprioception in VR environments

Tangible Media and Mixed Reality

Validation Techniques for Complex Devices