Dr Jaime Zabalza

Research Fellow

Electronic and Electrical Engineering

Personal statement

Jaime joined the Centre for Signal and Image Processing (CeSIP) to undertake a MSc in Electronic and Electrical Engineering in 2011 and a PhD in Hyperspectral Remote Sensing (2012-2015).

He has been a Research Associate since April 2018. Previously, he took a number of research positions at the Universitat Jaume I, Universitat Politecnica de Valencia and the Energy Technological Institute in Spain.

Expertise

Has expertise in:

Digital Signal Processing

Artificial Intelligence

Machine Learning

Software Engineering

Robotics

MATLAB, C/C#/C++

Prizes and awards

IEEE Brain Data Bank Challenges and Competitions: Recipient; 8/7/2018
IEEE Brain Data Bank Challenges and Competitions: Recipient; 31/10/2017
IET V&I Best PhD Thesis Award (only one in UK per year): Recipient; 12/2016

More prizes and awards

Qualifications

PhD in Electronic and Electrical Engineering (2012-2015)

MSc in Electronic and Electrical Engineering (2011-2012)

MAS in Electrical Technology (2008-2010)

MEng in Industrial Engineering (2000-2005)

PGCert in Researcher Professional Development (2015)

Spanish Chartered Industrial Engineer (COIICV - 5288)

Publications

Folded LDA : extending the linear discriminant analysis algorithm for feature extraction and data reduction in hyperspectral remote sensing: Fabiyi Samson Damilola, Murray Paul, Zabalza Jaime, Ren Jinchang; IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2021); https://doi.org/10.1109/JSTARS.2021.3129818
A novel intelligent computational approach to model epidemiological trends and assess the impact of non-pharmacological interventions for COVID-19: Ren Jinchang, Yan Yijun, Zhao Huimin, Ma Ping, Zabalza Jaime, Hussain Zain, Luo Shaoming, Dai Qingyun, Zhao Sophia, Sheikh Aziz, Hussain Amir; IEEE Journal of Biomedical and Health Informatics Vol 24, pp. 3551-3563 (2020); https://doi.org/10.1109/JBHI.2020.3027987
2D-SSA based multiscale feature fusion for feature extraction and data classification in hyperspectral imagery: Fu Hang, Sun Genyun, Ren Jinchang, Zabalza Jaime, Zhang Aizhu, Yao Yanjuan; IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium , pp. 76-79 (2020); https://doi.org/10.1109/IGARSS39084.2020.9323776
A novel spectral-spatial singular spectrum analysis technique for near real-time in-situ feature extraction in hyperspectral imaging: Fu Hang, Sun Genyun, Zabalza Jaime, Zhang Aizhu, Ren Jinchang, Jia Xiuping; IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol 13, pp. 2214-2225 (2020); https://doi.org/10.1109/JSTARS.2020.2992230
Embedded product authentication codes in additive manufactured parts : imaging and image processing for improved scan ability: Chen Fei, Zabalza Jaime, Murray Paul, Marshall Stephen, Yu Jian, Gupta Nikhil; Additive Manufacturing (2020)
Adaptive distance-based band hierarchy (ADBH) for effective hyperspectral band selection: Sun He, Ren Jinchang, Zhao Huimin, Sun Genyun, Liao Wenzhi, Fang Zhenyu, Zabalza Jaime; IEEE Transactions on Cybernetics (2020); https://doi.org/10.1109/TCYB.2020.2977750

More publications

Teaching

Jaime looks after Small Group Tutorials, providing additional academic and general support to first-year students. He has also taught a course: "Signals and Systems" in Jilin, China.

Research interests

Signal and image processing. machine learning, artificial intelligence, data mining, deep learning, big data, hyperspectral imaging. remote sensing, computer vision 2D/3D, robotics, object detection and tracking, non-destructive inspection, condition monitoring, image stitching, smart manufacturing, human-computer interaction.

Professional activities

Remote Sensing (Journal): Peer reviewer; 2020
Remote Sensing (Journal): Guest editor; 2020
MPhil Convener: Examiner; 1/2020
Beijing University of Technology: Visiting researcher; 11/4/2019
Northeast Electric Power University: Visiting researcher; 1/4/2019
Demos on robotically enabled sensing and novel robot control for dynamic environments: Consultant; 12/3/2019

More professional activities

Projects

Remote Inspection of SNM cans GC_253: Murray, Paul (Principal Investigator) MacLeod, Charles Norman (Co-investigator) Marshall, Stephen (Co-investigator) Ren, Jinchang (Co-investigator) Zabalza, Jaime (Co-investigator); 01-Jan-2020 - 31-Jan-2020
Image Processing for Enhanced Visual Inspection of Nuclear Waste Packages GC142 Phase 2: Murray, Paul (Principal Investigator) Marshall, Stephen (Co-investigator) Ren, Jinchang (Co-investigator) West, Graeme (Co-investigator) Zabalza, Jaime (Research Co-investigator); 01-Jan-2020 - 30-Jan-2021
Oilfield data processing: Murray, Paul (Principal Investigator) Campbell, Andrew John (Co-investigator) Marshall, Stephen (Co-investigator) Ren, Jinchang (Co-investigator) Zabalza, Jaime (Co-investigator); 22-Jan-2019 - 31-Jan-2019
Flexible and Intelligent Path Planning and Control of Industrial Robots towards Autonomous Hot Forging in the Digital Manufacturing Age: Yan, Yijun (Researcher) Mineo, Carmelo (Co-investigator) Yang, Erfu (Principal Investigator) Mehnen, Jorn (Co-investigator) Zabalza, Jaime (Researcher) Fei, Zixiang (Researcher) Wong, Cuebong (Researcher); The proposed project aims to enhance the autonomous manufacturing capability of UK industry in metal forming and forging. The project brings together two departments of the University of Strathclyde, namely DMEM and EEE. It augments this knowledge with the experts of Strathclyde’s global strategic partner Nanyang Technology University (NTU, Singapore). With Industry 4.0 being currently widely acknowledged as a key driver of industrial advancement, a strong technologic shift has become apparent within industry to move towards both, more intelligence and more autonomy. Currently, hot forging and forming has benefitted only little from this shift beyond traditional automation. There is a vast opportunity to systematically transform the inherently challenging technologies, namely forming and forging into truly smart and flexible manufacturing systems.
The AFRC offers an outstanding practical background for the applied transformation of Industry 4.0 theories. This project aims at delivering practical demonstrators at TRL 6 through implementing advanced knowledge into intelligent robot behaviour and simulation environments for robotic manipulation and flexible automation into the hot forging area considering the “living” and “dirty” environment of such industries, which requires the consideration of humans, hazardous, dynamic, hot and noisy conditions which did not experience much smart automation yet.; 01-Jan-2017 - 31-Jan-2018

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Address

Electronic and Electrical Engineering
Royal College Building

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