Signal Processing and AI

Our research in Advanced Intelligent Signal Processing is world renowned.

We provide cutting edge, explainable, interpretable and verifiable Artificial Intelligence (AI) for the next generation of autonomous systems. Our research covers the development of novel theory and applications in the fields of public security and healthcare.

Signal Processing and AI testing footage

Our world-leading research informs our teaching, supervision and mentoring. We produce highly employable, multi-skilled graduates and postgraduates. Our graduates are very successful and work all over the globe. We supervise and teach talented students to the highest standards through our PhD, MSc, MEng and BEng Programmes.

We are actively involved and regularly contribute to the world premier forums in Signal Processing. We have international and national collaborations within academia and industry. Our research has led to technology transfer and exploitations in security and surveillance, biometrics, and medical technologies. Our research projects have resulted in spin-out companies in the creative industries.

We have received funding from research councils and industry, including:

Engineering and Physical Sciences Research Council (EPSRC)
Biotechnology and Biological Sciences Research Council (BBSRC)
Medical Research Council (MRC)
Wellcome Trust
EU FP7 (7th Framework Programme)
InnovateUK
Newton Fund
Fera Science
Ministry of Defence (MoD)
Thales
Neura Technologies
Neura Health Group Ltd

Intelligent health

We have a strong tradition of developing image analysis techniques. The techniques that we develop have potential for high impact in clinical practice. We take concepts from feasibility studies through to clinical translation and commercial exploitation. We work with clinical and industrial partners.

Our research uses advances in AI and machine learning. It has led to the development of new systems that can revolutionise healthcare. These include new approaches to:

understanding health risks
predicting disease progression
creating personalised health interventions for improved patient outcomes

These approaches allow us to develop innovative tools to support clinicians and reduce spending.

We can achieve this tremendous potential by developing effective, trustworthy machine learning applications. These applications should be implementable in real healthcare contexts.

We work with multi-disciplinary groups of machine learning experts, clinicians and engineers. Together, we develop human-centred machine learning solutions that transform care pathways and improve health outcomes.

Our current projects on Intelligent Health include:

Next Generation 3D histology for earlier prostate cancer detection
Advanced applications of ocular optical coherence tomography (VELOCITY)
Diabetic retinopathy detection using machine learning
Investigation into mitochondrial dysfunction in facial appearance and ageing
Automated detection and assessment of pain and distress in preterm babies using deep learning
Application of artificial intelligence to predict effectiveness and adverse events to biologic and non-biological systemic therapy in psoriasis
Fuzzy neural inference system applied for the classification of cancerous colonic mucosa cell images
Prediction of nodal spread of breast cancer by using an artificial neural network

Machine Learning for Signal Processing

Our research focuses on the mathematical foundations and practical applications of signal processing algorithms. The algorithms are able to learn, reason and act. It bridges the boundary between theory and application. It allows for development of novel theoretically-inspired methodologies. These target both longstanding and emergent signal processing applications.

The core of MLSP lies in its use of intelligent, nonlinear and non-Gaussian signal processing methodologies. It combines these with convex and non-convex optimisation.

MLSP encompasses new theoretical frameworks for statistical signal processing. These include:

neural networks
Hidden Markov Models (HMMs)
independent component analysis (ICA)
nonnegative matrix factorisation (NMF)
Tensor factorisation
Bayesian methods

MLSP couples these with information-theoretic learning.

Novel developments in these areas are specialised in the processing of a variety of signal modalities. This includes audio, speech, bio-signals, images, multispectral, and video.

More than statistics

Statistics is organised information, but intelligence is more than that. It is not enough to solve a problem by giving the most ‘frequent’ answer. We are developing theories and demonstrable algorithms for statistical machine learning that truly integrate signal processing and computational intelligence.

Our algorithms are able to discover knowledge for themselves. They learn from new information whenever unseen data is captured. Many researchers discount the need for human intervention in data analysis. Our work goes further. It provides the freedom to combine human intuition and machine intelligence.

Our current projects in Machine Learning for Signal Processing include:

signal separation (eg single- and multi-channel recordings, audio source separation, bio-signal separation including EEG, ECG and MEG)
speech recognition, activity recognition, event detection
inference and prediction of hidden patterns in signals/images
compressive sensing and sparse information retrieval
biomedical data analysis
computational social scene analysis
pervasive audio-visual sensing

Computer Vision and Biometrics Recognition

Our research takes place at the cutting-edge of security and intelligent surveillance technologies.

Predictions acknowledge that by the year 2020, global databases will hold information on over a billion people. This will result in many challenges to the whole subject area. These challenges mainly relate to the reliability of the systems. This is particularly true when the raw image data contains imperfections. These can include poor light, imperfect equipment, or even deliberate and fraudulent alteration.

abstract image of an eye and computer processing chart

Computer vision

Our Computer Vision research leads the international field in the extraction of object behaviour models and dynamic face models from image sequences and real time video. Our work has been widely applied to people and object tracking, human gesture and pattern recognition.-

Our current projects in computer vision include:

statistical machine learning
time series analysis
multi-modal data fusion
biologically inspired vision
image compression
multi-camera networks
image analysis
video tracking
3D reconstruction from 2D images
automated monitoring of lameness in dairy cattle

Biometric recognition

The work involves multi-modal techniques and fusion of data at the feature level. Our models can be applied to biometric recognition systems. They have led to efficiencies in complexity, speed and accuracy. These efficiencies result in recognition rates which show a robustness against variation in image size. This is due to efficient image feature extraction and classification.

Current projects in biometrics recognition include: