Imaging, Data and Communications

Research Theme

Novel Computing and Beyond CMOS Hardware

Sensor Signal Processing

Aim

High-Performance Kernel Learning Processors for 6G Sensing: From Algorithmic Models to ASICs

Objectives

1. Develop analytical models that characterise the stability, convergence behaviour, and error performance of kernel-based online learning algorithms under the high-speed conditions expected in 6G sensing systems.
2. Design and optimise high-speed kernel learning algorithms that exploit sparsity, feature selection, and reduced model complexity to enable real-time sensor signal processing.
3. Create hardware-efficient architectures implementing the proposed algorithms, investigating trade-offs between throughput, energy consumption, silicon area, and learning accuracy for ASIC deployment.
4. Validate prototype ASICs, performing real-time testing using representative 6G sensing data to demonstrate end-to-end functionality and performance.

Description

This project will also be supervised by Prof George Goussetis.

This project investigates how kernel-based online learning can operate under the extreme data rates, tight latency, and dense sensing environments expected in 6G systems. Although well suited to continuously evolving sensor data, kernel online learning is fundamentally constrained by nonlinear parameter-update loops that become computational bottlenecks at 6G-class speeds. The core problem is the absence of analytical understanding and hardware-efficient formulations that explain these limits and indicate how they can be overcome.

The research will develop models that characterize stability, complexity, and error behaviour under realistic 6G operating conditions, revealing the constraints and sparsity structures that determine real-time feasibility. These insights will guide the exploration of algorithmic variants and architectural principles capable of supporting high-speed, low-energy kernel adaptation.

Validation will use representative 6G sensing workloads, establishing a clear pathway from problem characterization to hardware-ready design principles suitable for future ASIC implementations.

[1] M. Scarpiniti et al., “Nonlinear spline adaptive filtering,” Signal Process., vol. 93, no. 4, pp. 772–783, 2013.

[2] W. Liu et al., “The kernel least-mean-square algorithm,” IEEE Trans. Signal Process., vol. 56, no. 2, pp. 543–

554, 2008.

[3] W. D. Parreira et al., “Stochastic behavior analysis of the Gaussian kernel least-mean-square algorithm,” IEEE

Trans. Signal Process., vol. 60, no. 5, pp. 2208–2222, 2012.

[4] N. J. Fraser et al., “FPGA implementations of kernel normalised least mean squares processors,” ACM Trans.

[5] M. T. Khan and O. Gustafsson, “ASIC implementation trade-offs for high-speed LMS and block LMS adaptive

[6] M. T. Khan et al., “Optimal complexity architectures for pipelined distributed arithmetic-based LMS adaptive filter,” IEEE Trans. Circuits Syst. Regul. Pap., vol. 66, no. 2, pp. 630–642, 2018.

[7] M. T. Khan and O. Gustafsson, “Stochastic analysis of LMS algorithm with delayed block coefficient adaptation,”(arXiv:2306.00147)

[8] M. T. Khan and R. A. Shaik, "High-Throughput and Improved-Convergent Design of Pipelined Adaptive DFE for 5G Communication," in IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 68, no. 2, pp. 652-656, Feb. 2021

[9] M. T. Khan, H. E. Yantır, K. N. Salama and A. M. Eltawil, "Architectural Trade-Off Analysis for Accelerating LSTM Network Using Radix-r OBC Scheme," in IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 70, no. 1, pp. 266-279, Jan. 2023

[10] M. T. Khan and M. A. Alhartomi, "Digit-Serial DA-Based Fixed-Point RNNs: A Unified Approach for Enhancing Architectural Efficiency," in IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 5, pp. 8240-8254, May 2025.

[11] Boyang Chen, M. T. Khan, et al., “COMET: Co-Optimization of a CNN Model using Efficient-Hardware OBC Techniques” (arXiv:2510.03516)

Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. 

Further information on English language requirements for EU/Overseas applicants.

Home fee rate and stipend are available for this position.

Research Theme

Multi-Agent systems and Data Intelligence

Aim

Develop a framework to integrate and enhance multi-agent sensor data with prior geospatial intelligence to provide robust situational awareness via personalised (AR/VR/Speech UI) and overview (maps, data feeds) interfaces.

Objectives

1. Develop a decentralised edge computing architecture for geospatial data processing, able to accommodate rapid updates (e.g. sensors), for delivering dynamic situational awareness.
2. Investigate personalised HCI for different operator roles (e.g. VR/AR interfaces; speech-first hands-free; tangible; egocentric vs allocentric frames of reference).
3. Assess role-specific HCI adaptations via AI Agents (i.e. the ability for any user to use AI to design their own interface to the system)
4. Integrate and examine AI techniques for data validation and correction

Description

This research seeks to develop a decentralised edge computing solution to enhance situational awareness in multi-agent systems by integrating live sensor data with geospatial intelligence and real-time modelling.

Remote sensors deployed on robots and in the environment—capturing location, video, audio, and IMU data—will be fused with existing datasets such as LiDAR point clouds, digital surface models to provide personalized situational awareness.

A modular, extensible API will support analyses such as real-time visibility modelling for covert or signal-optimized route planning. AI-driven natural language queries will allow users to interact with the system.

Interfaces will include AR, tablets, and hands-free operation via speech recognition, augmented with LLM-based semantic processing and spatial audio. A key research focus will be integrating AI agents to allow end users—without programming skills—to easily design custom interfaces suited to their needs at any time.

UK 2:1. in computer science / computer systems or MSc in geographical information science.

Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. 

Further information on English language requirements for EU/Overseas applicants.

Home fee rate and stipend available for this position.

Aim

This PhD project focuses on developing a novel hybrid control framework that combines Model Predictive Control (MPC) and Behavioural Cloning (BC) to enable robust, real-time locomanipulation for navigating and surveillance of complex structures using legged robots. The project aims to train legged robots with motor skills that enable them to perform autonomous surveillance. Specifically, we focus on enabling dynamic motions through MPC, and the versatile behaviour needed to open doors or clear path tasks through BC in quadruped and humanoid robots equipped with manipulators.

Objectives

1. Design contact-implicit stochastic MPC frameworks that can efficiently compute policy gradients and handle uncertainty in contact events;
2. Develop a novel diffusion-based learning framework for MPC controllers that can clone dynamic behaviours such as opening doors and path clearance during surveillance operations;
3. Integrate BL techniques with MPC controllers to execute dynamic surveillance operations autonomously; and
4. Apply this integrated framework to real-time surveillance on steel and cluttered structures with legged robots.

Description

Model Predictive Control (MPC) has demonstrated remarkable capabilities in enabling agile robotic behaviors—most notably, dynamic maneuvers such as backflips in Boston Dynamics’ Atlas robot. However, conventional MPC methods remain constrained by local optima, limiting their ability to plan complex motion and contact sequences, particularly in cluttered or uncertain environments. These limitations are especially evident in loco-manipulation tasks, where both mobility and interaction with the environment are required.
 

Moreover, existing whole-body MPC frameworks are largely deterministic, which makes them ill-suited for real-world uncertainties, especially in contact-rich scenarios. On the other hand, behavioural cloning (BC) via diffusion policies has recently shown impressive success in learning the diversity of manipulation behaviors but struggles to scale to more whole-body behaviours where balance and dynamics are critical. Fundamentally, diffusion policies capture and learn the complex distributions present in human behaviours by learning the de-noising process from collected data.

This PhD research will explore a hybrid approach, combining the structure and real-time feasibility of MPC with the flexibility and autonomous capabilities of BC, to enable robust and versatile surveillance on legged robots. Concretely, this project aims to enable legged robots to move around complex environments, requiring them to open doors and remove debris autonomously.

The project builds on advances in robot motor intelligence, differential contact simulation, and model predictive control developed internally in the RoMI lab. It will also advance our current research efforts in Neural Conditioning Probability (NCP) for behavioural cloning.

Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. 

Further information on English language requirements for EU/Overseas applicants.

Home fee rate and stipend are available for this position.

Prof Majid Safari received the Ph.D. degree in electrical and computer engineering from the University of Waterloo, Waterloo, ON, Canada, in 2011. He is currently a Professor of optical and wireless communications and the Deputy Head of Institute for Imaging, Data, and Communications, The University of Edinburgh, Edinburgh, UK. He has authored or coauthored more than 150 papers. His main research interests include the application of optics, information theory, signal processing in optical, wireless, and quantum communications. Some of his current research works include designing 6G Optical wireless networks as part of the EPSRC program grant TOWS, developing single-photon avalanche diode based receivers for classical communication, and the design of novel communication schemes for nonlinear long-haul fibre-optic channels. Prof Safari has been an Associate Editor for IEEE Transactions on Communications and Associate Editor for IEEE Communication Letters. He was the recipient of Mitacs Fellowship, Canada and prestigious grants from Leverhulme Trust and EPSRC, UK. He was the recipient of Best Paper Awards from IEEE GLOBECOM 2022 and IEEE ICC 2023.

• 2011: PhD in Electrical and Computer Engineering from University of Waterloo, Canada
• 2005: MSc in Electrical Engineering from Sharif University of Technology
• 2003: BSc in Electrical and Computer Engineering from University of Tehran

Course Organiser:

* Digital Communication 4: 2013-present

* Digital Communications Fundamentals (MSc): 2013-present

* Digital System Design 2: 2015-present

Other Courses:

* Analogue Mixed Signal Laboratory 3: 2018-2020

* Engineering Mathematics 2A: 2014-2016

* Electrical Engineering 1: 2017-2019

http://www.skper.org/

Dr Podilchak received the B.A.Sc. degree in Engineering Science from the University of Toronto, ON, Canada, in 2005, the M.A.Sc. and the Ph.D. degrees in Electrical Engineering from Queen’s University, Kingston, ON, Canada, in 2008 and 2013, respectively, where he was an Assistant Professor, from 2013 to 2015.

He then joined Heriot-Watt University, Edinburgh U.K., in 2015, as an Assistant Professor,and became an Associate Professor in 2017.

His research was supported by the H2020 Marie Skłodowska-Curie European Research Fellowship.

He currently serves as a Lecturer with the European School of Antennas and a Senior Lecturer with The University of Edinburgh, School of Engineering.

He is a registered professional engineer (P.Eng.). He has had industrial experience as a computer programmer and designed 24 and 77GHz automotive radar systems with Samsung and Magna Electronics.

Recent industry experience also includes the design of high frequency surfacewave radar systems, professional software design, and implementation for measurements in anechoic chambers with the Canadian Department, National Defence and the SLOWPOKE Nuclear Reactor Facility.

He has also designed new compact multiple-input–multiple-output(MIMO) antennas for wideband military communications and highly compact circularly polarized antennas for microsatellites with COMDEV International, and new wireless power transmission and millimeter-wave automotive radar systems with Samsung.

His research interests include surface waves, leaky-wave antennas, metasurfaces, UW Bantennas, phased arrays, and CMOS integrated circuits.

Dr.Podilchak was a recipient of many best paper awards and scholarships; most notably research fellowships from the IEEE Antennas and Propagation Society and the IEEE Microwave Theory and Techniques Society.

He has received the Outstanding Dissertation Award for the Ph.D. degree from Queen’s University.

He also received a Postdoctoral Fellowship from the Natural Sciences and Engineering Research Council of Canada (NSERC) and four Young Scientist Awards from the International Union of Radio Science (URSI).

In 2011 and 2013, he received the Student Paper Award from the IEEE International Symposium on Antennas and Propagation, the Best Paper Prize for Antenna Design from the European Conference on Antennas and Propagation for his work on CubeSat antennas, in 2012, and the European Microwave Prize for his research on surface waves and leaky-wave antennas, in 2016.

He was bestowed a Visiting Professorship Award from Sapienza University of Rome, in 2017 and 2019. He was also the Founder and the First Chairman of the IEEE Antennas and Propagation Society and the IEEE Microwave Theory and Techniques Joint SocietyJoint Chapter of the IEEE Kingston Section in Canada as well as the IEEE U.K. and Ireland Section in Scotland. In recognition of these services, he has received the Outstanding Volunteer Award from the IEEE, in 2015.

He is an Associate Editor of the journal IET Electronic Letters. He was recognized as an Outstanding Reviewer of the IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION by the IEEE Antennas and Propagation Society, in 2014.

• BASc degree in Engineering Science, Uversity of Toronto, ON, Canada, 2005
• MASc Electrical Engineering, Queen’s University, Kingston, ON, Canada, in 2008
• PhD Electrical Engineering, Queen’s University, Kingston, ON, Canada, 2013

Awards and Scholarship

• IEEE Antennas and Propagation Society Research Fellowship
• IEEE Microwave Theory and Techniques Society Research Fellowship
• Outstanding Dissertation Award for the PhD degree from Queen’s University
• Postdoctoral Fellowship from the Natural Sciences and Engineering Research Council of Canada (NSERC)
• Four Young Scientist Awards from the International Union of Radio Science (URSI)
• Student Paper Award from the IEEE International Symposium on Antennas and Propagation 2011 and 2013
• Best Paper Prize for Antenna Design from the European Conference on Antennas and Propagation for his work on CubeSat antennas, 2012
• European Microwave Prize for his research on surface waves and leaky-wave antennas, 2016
• Visiting Professorship Award from Sapienza University of Rome, 2017 and 2019
• Outstanding Volunteer Award from the IEEE, 2015

• Registered Professional Engineer, PEng
• Founder and the First Chairman of the IEEE Antennas and Propagation Society
• Founder and the First Chairman of the IEEE Microwave Theory and Techniques Society
• Outstanding Volunteer Award from the IEEE, 2015
• Associate Editor of the journal IET Electronic Letters
• Outstanding Reviewer of the IEEE Transactions on Antennas and Propogation, 2014

• Assistant Professor, Queen’s University, Kingston, ON, Canada, 2013-2015
• Assistant Professor, Heriot-Watt University, Edinburgh, Scotland, 2015-2017
• Associate Professor, Heriot-Watt University, Edinburgh, Scotland, 2017-2019
• Lecturer, European School of Antennas present
• Senior Lecturer in Radio Frequency, School of Engineering, University of Edinburgh

Wide ranging industrial experience including:

• Computer programmer - designed 24GHz and 77GHz automotive radar systems with Samsung and Magna Electronics
• Design of high frequency surface wave radar systems, professional software design, and implementation for measurements in anechoic chambers with the Canadian Department, National Defence and the SLOWPOKE Nuclear Reactor Facility
• Designed new compact multiple-input–multiple-output(MIMO) antennas for wideband military communications and highly compact circularly polarized antennas for microsatellites with COMDEV International
• Designed new wireless power transmission and millimeter-wave automotive radar systems with Samsung

• 1962-1966 Heriot-Watt University, Edinburgh BSc (Hons), 2nd year class medal
• 1972-1975 PhD University of Edinburgh "The Design of Surface Acoustic Wave Filters and Applications in Future Communication Systems"
• Honorary DEng, Heriot-Watt University
• Honorary DEng, Edinburgh Napier University

• Fellow Royal Academy of Engineering (FREng)
• Fellow Institution Engineering and Technology (FIET)
• Eurasip Fellow
• Fellow Royal Society of Edinburgh (FRSE)
• Fellow IEEE
• Honorary Professorial Fellow Head School of Engineering and Electronics, 2002-2007
• Regius Professor of Engineering 2007 - 2009
• Professor of Electronic Signal Processing to 2007
• University Curator of Patronage, 2003-2006
• International Professional Awards and Recognition Faraday Medal, Institution of Electrical Engineers (IEE), 2004
• Fellow of Institute of Electrical and Electronics Engineers, New York (FIEEE)
• IEEE Signal Processing Society distinguished lecturer on DSP for Mobile Communications, 1998
• President, European Association for Signal, Speech and Image Processing (EURASIP), 2000-2002
• National Professional Fellowships Fellow of Royal Academy of Engineering (FREng)
• Fellow of Royal Society of Edinburgh (FRSE).
• Fellow of Institution of Electrical Engineers, London (FIEE)
• Committee Chairs Electronics and Electrical Engineering Panel for the 2001 UK research assessment exercise (RAE)
• Royal Society of Edinburgh Electronics and Electrical Engineering Sectional Committee, 1999-2003
• Mechanisms for Excellence group of the Scottish Science Advisory Committee, 2002-date
• Proceedings IEE Editorial Advisory Panel, IEE 1990-98
• Editorial Advisory Panel, IEE Electronics and Communications Engineering Journal, 1996-2002
• EPSRC Communications Signal Processing and Coding programme, assessment 1993-95 and monitoring panels 1996-97
• Conference Committee Chairs Technical programme, IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), Glasgow in May 1989
• European Signal Processing conference (EUSIPCO 94), Edinburgh
• Paper Premia Awards Bulgin premium, Institution of Electronic and Radio Engineers (IERE) in 1974 and again in 1977
• IERE Lord Mountbatten premium in 1982
• IEE Marconi premium 1992
• IEE J Langham Thompson premium 1992
• Commercial activities Executive Committee, Mobile Virtual Centre of Excellence in Mobile and Personal Communications, 2002-2004
• Board Member, Mobile Virtual Centre of Excellence in Mobile and Personal Communications, 1997-date
• Independent Member, Strategic Advisory Board to the UK Defence Technology Centre in Electromagnetic Remote Sensing, 2003-date
• Board Member of Edinburgh Technology Transfer Centre, 2003-date

Personal Webpage

• PhD in Signal Processing, University of Edinburgh, January 2010

• Personal Webpage
• Curriculum Vitae

https://edin.ac/3ZnHPqx

• Bachelor of Engineering (1st Class)
• PhD (Electrical Engineering)

• FIEEE
• EURASIP Fellow
• MIEE CEng

• Google Scholar Publications List: https://scholar.google.com/citations?user=XbQGWCUAAAAJ&hl=en&oi=ao
• Edinburgh Research Explorer: https://edin.ac/3ZnHPqx

• M.Eng., M.A., Ph.D.

• Statistical Signal Processing, concerning the utilisation of stochastic nonstationarity in single and multi-channel blind signal separation and deconvolution.

• Institute for Digital Communications (IDCoM)