Sensing, Imaging and Signal Processing

The Group focuses on exploiting its broad expertise to address multidisciplinary measurement and signal-analysis problems in chemical, mechanical, electrical, security, and biological systems, and the building of sensor systems for users in academia, industry and healthcare.

One achievement with strong societal and research significance is their development of a novel technology for the accelerated detection of landmines. Other achievements include the measurement of focused X-ray beams and electrical capacitance tomography imaging of particle density in fluidised bed dryers, with applications in the pharmaceutical and food and energy processes industries.

Lead Researchers:

Professor Krikor Ozanyan

Krikor is a Professor of Photonic Sensors & Systems, his research interests include: (1)Sensors: Wearable inertial sensor systems for gait recognition; Semiconductor devices and materials for optical sensing (UV-VIS-IR-THz) (2) Imaging: Guided-Path Tomography and “Magic Carpet” applications in healthcare, agriculture and biometrics; Spectroscopic Optical Tomography with scanning sources; Image reconstruction from a limited number of views; Parallel computation centre-of-projection imaging of fast system dynamics (3)Signal Processing: Deep learning convolutional neural networks for spatio-temporal sensor fusion; Internet-of-Sensors: embedded data to information transfer in machine2machine sensor networks; Data analysis, incl. machine learning, of fluorescence spectra from complex systems for process monitoring; Algorithms for extraction of system properties from temporal signals.

Professor Patrick Gaydecki

Patrick is Professor of Digital Signal Processing (DSP) in the School of Electrical and Electronic Engineering. With his team, he develops systems and software in the fields of audio-based digital signal processing, imaging, insect tracking, ultrasonic and eddy current nondestructive testing, biomedical instrumentation, wearable technology and wireless instrumentation. His research has been funded by EPSRC, the DTI, the BHA, ESRC, MIMIT, TWI, The HA, and many industrial sponsors. 

 

 

Professor Bruce Grieve

Bruce is a Fellow of the Institute of Engineering & Technology, a Fellow of the Institute of Agricultural Engineers and a Fellow of the Higher Education Academy, and holds the N8 Chair in Agri-Sensors & Electronics. He manages the e-Agri Sensors Centre at Manchester. In recent years the e-Agri Sensors Centre has significantly refocused its research activities to meet the broader projected needs of agriculture and food supply. In the light of this a number of strategic enabling technologies have been identified which can facilitate innovative new approaches to crop growth and non-mammalian biotechnology. Sensor science is one of these technologies identified as having the capacity to create a paradigm shift in the future of the sector.

The Centre is based within the School of Electrical and Electronic Engineering in the Faculty of Engineering and Physical Sciences, but is necessarily multidisciplinary owing to the nature of the techniques being researched.

Dr Hujun Yin

Hujun is a senior lecturer at the university. His Research interests include: (1)Theories and applications of neural networks; self-organising systems; deep learning systems (2) Image/video processing, enhancement and recognition; face recognition (3) Nonstationary signal processing; time series analysis and prediction (4) Pattern recognition; data dimensionality reduction and manifold learning (5) Independent component analysis and blind deconvolution (6) Multidimensional data mining and visualisation (7) Neuroinformatics and bioinformatics.

 

 

Dr Alex Casson

Alex is a lecturer in the Sensing, Imaging and Signal Processing group. His research focuses on real-time signal processing in low power constrained situations. Typical applications are in brainwave monitoring, brain-computer interfaces, and transcranial stimulation. He also has extensive expertise in low power sensor nodes with onboard signal processing, particularly wearable sensors for human monitoring where signal processing is used to decrease power consumption for energy harvester powered systems.