10 papers
Empirical Minimal-Realisation Compression of Deep Neural Networks via Controllability-Observability Tests
Anis Hamadouche, Amir Hussain
Deep neural networks often contain substantial hidden-state redundancy, but most compression methods operate directly on weights, neurons, or quantised representations without expl…
Lyapunov-Guided Training for Hardware-Safe Neural Networks Under Fixed-Point Arithmetic
Anis Hamadouche, Amir Hussain
Low-precision neural networks are attractive for resource-constrained hardware, but fixed-point arithmetic introduces failure modes that are often hidden by idealised quantisation…
Audio-Visual Speech Enhancement: Architectural Design and Deployment Strategies
Anis Hamadouche, Haifeng Luo, Mathini Sellathurai +2
Real-time audio-visual speech enhancement (AVSE) is a key enabler for immersive and interactive multimedia services, yet its performance is tightly constrained by network latency,…
Antenna Health-Aware Selective Beamforming for Hardware-Constrained DFRC Systems II
Anis Hamadouche, Tharm Ratnarajah, Christos Masouros +2
This study introduces an innovative beamforming design approach that incorporates the reliability of antenna array elements into the optimization process, termed "antenna health-aw…
Antenna Health-Aware Selective Beamforming for Hardware-Constrained DFRC Systems I
Anis Hamadouche, Tharm Ratnarajah, Christos Masouros +2
This paper addresses the optimization challenges in dual-functional radar-communication (DFRC) systems with a focus on array selection and beamforming in dynamic and heterogeneous…
Efficient Dual-Blind Deconvolution for Joint Radar-Communication Systems Using ADMM: Enhancing Channel Estimation and Signal Recovery in 5G mmWave Networks
Anis Hamadouche, Mathini Sellathurai
This paper introduces a novel framework for jointly estimating unknown radar channels and transmit signals in millimeter-wave (mmWave) Joint Radar-Communication (JRC) systems, a pr…