Communication and signal processing
Advancing future wireless systems and AI applications to enable smarter industries, improved healthcare and global connectivity.
£6.2 Million in live research grants.
150+ research outputs a year.
World‑leading expertise shaping policy, industry and government.
Turning technical innovation into impact
Our research drives the future of wireless communications, advancing next-generation technologies and designing bespoke antennas and RF devices for demanding environments - including satellites and dense urban areas. We develop AI systems transforming industrial efficiency and clinical decision-making, and pioneer hyperspectral imaging for 3D-printed personalised prosthetics. From global connectivity to life-changing patient outcomes, we turn deep technical innovation into real, measurable human impact.
The most rewarding moment in research isn't publishing a paper; it's knowing your work could shape future technologies that improve people's lives, connect remote communities, and revolutionise industries. That's what drives everything we do.
Emad Alsusa - Group Lead and Professor of Communications and Signal Processing
Research
Our areas of research
AI-driven 6G wireless systems
Our research develops next-generation AI-enabled wireless communication systems that integrate sensing, connectivity, and intelligent decision-making for data-intensive connectivity and future industrial and medical applications. A key focus is the convergence of 6G networks, edge AI, digital twins, and integrated sensing and communication (ISAC) technologies to enable real-time, autonomous, and energy-efficient systems.
This research has transformative potential for smart manufacturing, remote healthcare, robotic automation, and critical infrastructure, supporting safer, greener, and more resilient societies through intelligent connectivity.
AI-powered machine vision systems for advanced manufacturing and sustainable circular economy
We have led several Innovate UK KTP programmes which have delivered cutting‑edge machine‑vision systems for industry.
This includes a lithium‑ion battery detection system launched in June 2024 that reduces the threat posed by batteries in waste streams, using advanced vision and machine‑learning to detect and extract hazardous items in real time. This system analyses over 500k images a day and detects 600+ cylinder batteries per hour. Now deployed across UK sites, including SWEEEP in Kent, it identifies more than 4,500 batteries daily.
Early detection of crop viral infection
This multi‑national project develops low‑cost handheld tools for farmers to detect crop viral infection early. Using multispectral imaging, machine learning and low‑power electronics, it senses biological changes in leaves with narrow‑band light sources and broadband sensors.
Targeting cassava in Africa, where viral infections can cut yields by 40%, trials show detection as early as two weeks. Funded by NSF and BBSRC, the device is portable, reconfigurable for other crops, and now in field trials in Tanzania.
Facial prostheses with real-life colour appearance
Facial prostheses are needed when patients are treated for cancers or injuries affecting the nose, lips, ears, or skin.
We are leading a five-year £6M EPSRC research programme, AMFaces across four UK universities, enabling the delivery of ultra-realistic facial prostheses to patients with the aid of modern digital imaging and 3D printing (additive manufacturing). As part of this work, we have developed a variable-geometry, goniometric hyperspectral imaging system for characterising facial skin appearance to support additive manufacturing and patient evaluation.
High-throughput spatial-spectral deep learning for histopathology and spectroscopy image analysis
This collaborative project, funded by Prostate Cancer UK and EPSRC with Chemical Engineering and The Christie Hospital, is developing and validating a clinic‑informed foundation AI model for pathology diagnosis. By integrating histopathological microscopy with infrared hyperspectral imaging, it enables objective diagnosis and risk stratification of cancer biopsies.
This dual‑modality approach reduces reliance on large annotated datasets, improves generalisation across clinics, and supports the development of clinically deployable AI tools.
Rectangular dielectric resonator antennas for multi-band applications
Recent advances in dielectric materials have positioned dielectric resonator antennas (DRAs) as a strong solution for modern wireless communications, supporting multi‑band operation across WLAN, WiMAX, satellite, 4G LTE and 5G. Compared to metallic antennas, DRAs offer wide bandwidth, compact size, high efficiency and significant design flexibility.
Our work focuses on a low‑profile rectangular DRA with a microstrip‑slot feed, enabling simultaneous excitation of at least 10 resonant modes for efficient multi‑band performance.
Terrestrial hyperspectral imaging
Hyperspectral imaging combines spatial and spectral data so that each pixel in an image of a scene or object represents a continuous radiance or reflectance spectrum.
Our development and application of a hyperspectral imaging system produced the first widely accessible sets of calibrated, high‑resolution image cubes of natural scenes for fundamental scientific research and for benchmarking modern image‑compression algorithms. These datasets are now among the most widely used natural‑scene hyperspectral image resources in published research.
Our research facilities
Alan Turing Institute
Our group works with the Alan Turing Institute through joint research in AI, machine learning, signal processing, wireless communications, and data-driven technologies, fostering interdisciplinary innovation and translating fundamental research into impactful real-world applications.
Photon Science Institute
Our group collaborates with the Manchester Photon Science Institute on terahertz technologies, advanced materials, and photonic-enabled systems, developing next-generation wireless communication solutions through interdisciplinary research spanning sensing, propagation, devices and network architectures.
Study with us
MSc in Communications and Signal Processing
The MSc in Communications and Signal Processing delivers a thorough, methodical and wide-ranging education in communications, signal processing, and microwave engineering.
Our researchers deliver components of the course, ensuring students have access to expertise at the leading-edge.
Connect with us
Our people
- Emad Alsusa - Professor of Communications and Signal Processing
- David H Foster - Professor of Vision Systems
- Danielle George - Professor of Radio Frequency Engineering
- Peter R Green- Professor of Wireless Communications
- Daniel Ka Chun So - Professor of Communication Engineering
- Zhipeng Wu - Professor of Antennas and Propagation
- Hujun Yin - Professor of Artificial Intelligence
- Khairi Hamdi - Senior Lecturer
- Fumie Costen - Lecturer
- Laith Danoon - Lecturer
- Sareh Malekpour – Lecturer (T&S)
- Kaitao Meng - Lecturer
- Zahra Mobini - Lecturer
Contact our team
For business and general inquiries, contact Professor Emad Alsusa:
Research activities
Discover more about our group's latest research activities: