Our Computing, Informatics and Applications (CIA) research group has a wide remit that includes scientific data processing, smart technology and web/internet technologies. We are particularly interested in how computational and artificial intelligence (AI) methods can be used to improve people's lives and increase productivity.
The group has specific expertise in image and signal processing, data visualisation, mobile devices, 2D/3D modelling, distributed computing, wireless environments, the application of sound and imaging technology in assisted living, online virtual environments, the Internet of Things (IoT), and sensor technology. Much of the research the group undertakes is applied/collaborative, and includes links with industrial partners.
If you would like to find out more about our research, please contact Dr Jin Zhang at firstname.lastname@example.org
For postgraduate research enquiries, please contact Dr Christina Luca at email@example.com
For external income generation, please contact Dr George Wilson at firstname.lastname@example.org
Our work is primarily focussed in the following research areas:
Our Imaging Technologies and Acoustics team focuses on AI-based navigation, image and signal processing systems, 2D/3D image modelling, neural networks, inverse problems, numerical modelling of physical processes (electromagnetics, acoustics, psychoacoustics, quantum mechanics), statistical methods, ray-tracing, architectural acoustics, data sonification, data visualisation, auditory display, sound synthesis, mobile devices and technologies for supporting visual impairment. It also looks at computer aided design (CAD) tools and methods applied to digital systems modelling and rapid prototyping using VHDL and FPGAs..
Our AI, Machine Learning, Data Science and Applications team has a wide range of research interests that include: data analysis and machine learning techniques; up-to-date theoretical and practical developments and hardware platforms for AI; signal processing; remote sensing for the IoT and how these converge in the emergence of intelligent systems; advanced statistics; machine learning for structured data and graphs; data integration, probabilistic system;, algorithms for massive datasets; large-scale optimisation.
Our IoT, Machine Learning and Cloud Computing team is interested in IoT, smart environments, pervasive healthcare, Human Computer Interaction (HCI), adaptive computational systems, advanced internet and mobile technologies, AI, smart sensors and advanced spectral analysis in biomechanics and biomedical sciences.
Our Semantic Web and Educational Technologies team investigates business information systems, open linked data, intelligent internet search and knowledge modelling, online virtual environments, image processing and data visualisation.
Our sustainability group focuses on use of data analysis and machine learning techniques to aid climate change related problems of air pollution, natural disasters, vegetation, food production, healthcare, transportation, and agricultural and industrial sustainability. It proposes sustainable solutions in different domains with the help of AI, IoT and emerging technologies such as Blockchain and Fog Computing. In the healthcare domain, the group also looks at sustainable care for all. In the agricultural domain, it looks at smart farming, precision agriculture and precision livestock farming.
Jin Zhang (PhD Fixed Broadband Wireless Communications) has expertise in signal processing, data communications, remote sensing and the IoTs. Her research interests include electronic systems modelling, the study and design for development of novel artificial metamaterials components in the mid-infrared range and lower spectral range, the design of quasi-optical components for millimetre-wave astronomy and the electromagnetic modelling and the simulations.
Her work has been published in high impact journals and conference publications and reflects continued collaborative work with other HE institutes (Cardiff, UCL). She has secured external funding as principle investigator for two Royal Society China-UK Science Network Grants on behalf of the UK Government Office for Science (GO-Science) and by the China Scholarship Council (CSC) on behalf of the Ministry of Education in China. She has recently been awarded European Space Agency (ESA) funding as co-investigator for a major collaborative research project.
George Wilson (PhD Isotope Geochemistry) teaches courses in programming techniques, artificial intelligence and distributed computing, whilst his research is focused on spatial data processing, search semantics, Bayesian techniques and fuzzy rule-based systems. He has academically refereed journal and conference publications in most areas of his expertise.
George has jointly managed a number of successful KTP/KEEP/InnovateUK income generating initiatives, promoted training courses for schools and industry (networking, neural networks), and has been involved in EU funding bid preparation, all reflecting an interest in external income generation from alternative sources.
Cristina Luca (PhD Formal Languages) worked for several years in industry as a senior software engineer prior to her academic career. Experienced in ontological modelling and mobile development, she teaches programming, software engineering and Web application development. Her research interests are in the semantic web and the integration of ontologies in software applications utilising machine learning.
Cristina has co-authored several textbooks on programming and algorithms and published and peer reviewed papers on semantic query languages, linked data, and formal languages. She is interested in promoting income generation through knowledge transfer (involvement in European and KTP projects) and other sources (BCS funding for educational school support).
Lakshmi Saheer (PhD Computer Science and Artificial Intelligence) has international industrial and academic research experience in AI, especially in the fields of data science and machine learning. She has successfully applied data analysis, machine learning and signal processing techniques on diverse domains like air quality, climate change and sustainability, various speech and audio applications, affective computing, medical & healthcare and IoT.
Lakshmi has several awards and grants worth more than £150K under her belt. She is the author and reviewer of several international conference and journal publications. She has been involved in various European projects with international collaborations.
Marcian Cirstea (Professor, PhD Digital Control) has interests mainly in digital systems, with a focus on artificial intelligence and the use of computer aided design tools and methods applied to digital systems modelling and rapid prototyping using VHDL and FPGAs, including generative design. He has co-authored over 145 peer reviewed papers and several technical books, and has delivered several international tutorials in these subject areas.
Aside from his duties as Head of School, Marican's professional activities include coordinating an European project consortium, participating in several knowledge transfer partnerships, chairing a few IEEE international conferences and acting as Associate Editor for the IEEE Transactions on Industrial Informatics. He is interested in knowledge transfer schemes and short training courses.
Silvia Cirstea (PhD Imaging Technologies) has more than fifteen years’ research experience in mathematical and software modelling for imaging technologies, acoustics, radio communications and medical applications. Her areas of expertise include image and signal processing, neural networks, inverse problems, numerical modelling of physical processes (electromagnetics, acoustics, psycho-acoustics, quantum mechanics), statistical methods, SLAM and ray tracing.
Silvia has a keen interest in assisted living applications for the visually impaired and has been involved in successful EU and Innovate UK projects.
Stiphen Chowdhury (MSc Software Engineering) has a mix of academic and commercial experience involving various Java/C++ programming projects. His research interests are in Artificial Intelligence especially feature weighting in clustering algorithms, supervised and unsupervised learning.
Razvan-Ioan Dinita (PhD Cloud Computing) has expertise in Cloud software development, optimisation and security, web and software engineering/security and programming languages. His research is focused in these areas and he has authored a number of relevant peer-reviewed publications and presented his work at international conferences. He is interested in generating external income through EU and other sources, delivery of short training courses, and knowledge transfer schemes (recently involved in several KEEP projects).
Arooj Fatima (PhD The Semantic Web) has 12 years' industrial background in the IT sector (database design, evaluation of user interfaces for software applications) and is an experienced programmer in web and mobile application development. Her research interests include AI (semantic data technologies, natural language software interfaces) and user experience design techniques. She is interested in generating external income in these areas of research through the delivery of short training courses and knowledge transfer schemes.
Mahdi Maktab-Dar-Oghaz's (PhD Computer Vision and Machine Learning) expertise includes use of video analytics, artificial intelligence, and computer vision used in both small scale (medical image processing) and largescale (crowd safety and security) applications. His interests extend to deep learning and convolutional neural networks applied to these scenarios. He has published several articles in various international journals and conferences, and has been involved in collaborative projects (cyber Security, video analytics) including a European (H2020) project.
Ian van der Linde (PhD Human/Computer Vision) has research interests that include image processing and vision science, computing methods in psychology, neuropsychological testing, and evolutionary algorithms. Ian is particularly interested in external income generation to fund research studentships and fellowships.
Domenico Vicinanza (PhD Physics) is a public speaker and professional music composer and orchestrator. He worked for seven years at CERN as a scientific associate. He is a co-author of more than 130 publications and his expertise includes high-energy particle detector and grid technology, network-enabled audio-video communication, audio-music technology and smart sensors.
Domenico's main research interest is in auditory display (sound engineering for scientific data analysis, including space, satellite and biomechanics) and non-linear dynamics with applications to human movement science, sport science and experimental psychology. He has active collaborations with CERN and NASA and is interested in generating external income through professional consultancy, delivery of short training courses and knowledge transfer schemes in all areas of his expertise/research.
Javad Zarrin (PhD Computer Science - Distributed Computing) has spent several years in industry working as senior software engineer and project leader, and in academia in various research-based roles. His research interests are in the broad area of distributed systems and his recent interests include Decentralized Systems, Machine Learning, Optimization, Scheduling, Resource Management, Networks, Datacentre, Blockchain, Stream Processing, Data Platforms, NLP, Graph, Simulation, HPC, Cloud, IoT, Fog and Large-Scale Computing.
Javad is an IEEE member, authored several journal/conference papers, and served as PC, TPC, keynote speaker, and reviewer for various events and venues. He has also contributed to the UK and European research projects including EPSRC, FP7, Horizon2020, FCT, and FEDER projects.
Members of our research group have participated in the following projects and partnerships:
We have also undertaken other KTPs and KEEPs worth up to £130k with many local and national companies including Sovereign Installations Ltd, Glazing Vision, LMK Thermosafe, Papershrink Ltd, Calex Electronics and I-Dash Ltd.
Members of our research group have also organised the following:
Funded by Innovate UK, Silvia Cirstea, Razvan Dinita and Javad Zarrin from the School of Computing and Information Science worked with industry partner TR Control Solutions to tackle the challenge of reducing errors in the construction industry.
Availability of data in the construction industry is a major barrier, as most building workers do not get their instructions via digital devices and records of errors are not kept in digital form linked to the construction specification. To address this problem, the project developed a framework to collect and link up data from a number of sources, including workers’ profiles and understanding of task, captured via a web app, and construction task information extracted from specification models and design documents. Artificial Intelligence techniques were used to devise automatic generation of questionnaires to assess operatives' knowledge and understanding of the task.
A machine learning framework, based on Support Vector Machines, was developed and shown to be able to predict the likelihood of construction defects with sufficient reliability and accuracy on a pilot data set. Following the successful completion of this feasibility study with our partners in April 2020, collaboration continues to develop the framework into a commercial product.
Bednarik, R., …, van der Linde, I., 2020. EMIP: The eye-movements in programming dataset. Science of Computer Programming. doi:10.1016/j.scico.2020.102520
Viera, A., van der Linde, I., Bright, P., Wilkins, A., 2020. Preference for lighting chromaticity in migraine with aura. Headache, 60(6), pp. 1124-1131.doi:10.1111/head.13801
Sapkota, R. P., van der Linde, I., & Pardhan, S., 2020. How does aging influence object-location and name-location binding during a visual short-term memory task? Aging & Mental Health, 24(1), pp. 63-72, doi:10.1080/13607863.2018.1515887
Bright, P., & van der Linde, I., 2020. Comparison of methods for estimating premorbid intelligence. Neuropsychological Rehabilitation, 30(1), pp. 1-14, doi: 10.1080/09602011.2018.1445650
Aggius-Vella, E., Kolarik, A. J., Gori, M., Cirstea, S., Campus, C., Moore, B. C. J., Pardhan, S., 2020. Comparison of auditory spatial bisection and minimum audible angle in front, lateral, and back space. Scientific Reports, 10, p. 6279. doi:10.1038/s41598-020-62983-z
Kolarik, A. J., Raman, R., Moore, B. C. J., Cirstea, S., Gopalakrishnan, S., Pardhan, S., 2020. The accuracy of auditory spatial judgments in the visually impaired is dependent on sound source distance. Scientific Reports, 10, p. 7169, doi:10.1038/s41598-020-64306-8
Hameed, N., Shabut, A., Hameed, F., Cirstea, S., Harriet, S., Hossain, A., 2020. Mobile-based Skin Lesions Classification Using Convolution Neural Network. Annals of Emerging Technologies in Computing, 4(2), pp. 26-37. doi:10.33166/AETiC.2020.02.003
Smith, L., Stubbs, B., Hu, L., Veronese, N., Vancampfort, D., Williams, G., Vicinanza, D., Jackson, S., Ying, L., López Sánchez, G. F., and Yang, L., 2019. Is active transport and leisure time physical activity associated with inflammatory markers in US adults: Cross-sectional analyses from NHANES. Journal of Physical Activity and Health, 16(7), pp. 540-546, doi:10.1123/jpah.2018-0423
Moseley, P., Savini, G., Saenz, E., Zhang, J., and Ade, P., 2019. Detailed characterization of a lenster - A mm-wave flat lens. IEEE Transactions on Antennas and Propagation, 67(5), pp. 3178-3184 doi:10.1109/TAP.2019.2902435
Zhao, G., Savini, G., Saenz, E., Zhang, J., Ade, P., 2019. A dual-port THz time domain spectroscopy system optimized for recovery of a sample's Jones matrix. Scientific Reports, 9. doi:10.1038/s41598-019-39322-y
Oghaz, M. M., Maarof, M. A., Rohani, M. F., Zainal, A., & Shaid, S. Z. M., 2019. An optimized skin texture model using gray-level co-occurrence matrix. Neural Computing and Applications, 31, pp. 1835-1853. doi:10.1007/s00521-017-3164-8
Hameed N., Hameed F., Shabut A., Khan S., Cirstea S., Hossain A., 2019, An Intelligent Computer-Aided Scheme for Classifying Multiple Skin Lesions. Computers, 8(3), p. 62. doi:10.3390/computers8030062
Vicinanza, D., Newell, K. M., Irwin, G., Smith, L., & Williams, G. K., 2018. Limit cycle dynamics of the gymnastics longswing. Human Movement Science, 57, pp. 217-226. doi:10.1016/j.humov.2017.12.014
van der Linde, I., Bright, P., 2018. A genetic algorithm to find optimal reading test word subsets for estimating full-scale IQ. PLOS ONE, 13(10), e0205754. doi:10.1371/journal.pone.0205754
Bright, P., Hale, E., Gooch, V. J., Myhill, T., & van der Linde, I., 2018. The National Adult Reading Test: Restandardisation against the Wechsler Adult Intelligence Scale—Fourth edition. Neuropsychological Rehabilitation, 28(6), pp. 1019-1027. doi:10.1080/09602011.2016.123112
Kettouch, M. Luca, C., Hobbs, M., 2018. Mediator-based framework for keyword search over semi-structured and linked data. Journal of Intelligent Information Systems, 52(2), pp. 311-335. doi:10.1007/s10844-018-0536-1
Zarrin, J., L. Aguiar, R., P. Barraca, J., 2018. Resource discovery for distributed computing systems: A comprehensive survey. Journal of Parallel and Distributed Computing, 113, pp. 127-166. doi:10.1016/j.jpdc.2017.11.010
Williams, G. K., & Vicinanza, D., 2017. Coordination in gait: Demonstration of a spectral approach. Journal of Sports Sciences, 36(15), pp. 1768-1775. doi:10.1080/02640414.2017.1416974
Moseley, P., Savini, G., Zhang, J., Ade, P., 2017. Dual focus polarisation splitting lens. Optics Express, 25(21), pp. 25363-25373. doi:10.1364/OE.25.025363
Sapkota, R. P., van der Linde, I., Lamichhane, N., Upadhyaya, T., & Pardhan, S., 2017. Patients with Mild Cognitive Impairment Show Lower Visual Short-Term Memory Performance in Feature Binding Tasks. Dementia and Geriatric Cognitive Disorders Extra, 7(1), pp. 74-86, doi:10.1159/000455831
Campbell, W., Paterson, J., & van der Linde, I., 2017. Listener preferences for alternative dynamic-range-compressed audio configurations. Journal of the Audio Engineering Society, 65(7/8), pp. 540-551. doi:10.17743/jaes.2017.0019
Kolarik, A. J., Raman, R., Moore, B. C., Cirstea, S., Gopalakrishnan, S., & Pardhan, S., 2017. Partial visual loss affects self-reports of hearing abilities measured using a modified version of the speech, spatial, and qualities of hearing questionnaire. Frontiers in Psychology, 8, p. 561. doi:10.3389/fpsyg.2017.00561
Kolarik, A. J., Pardhan, S., Cirstea, S., & Moore, B. C., 2017. Auditory spatial representations of the world are compressed in blind humans. Experimental Brain Research, 235(2), pp. 597-606. doi:10.1007/s00221-016-4823-1
Williams, G., Aggio, D., Vicinanza, D., Stubbs, B., Kerr, C., Johnstone, J., Roberts, J., Smith, L., 2017. Prospective associations between measures of gross and fine motor coordination in infants and objectively measured physical activity and sedentary behaviour in childhood. Medicine, 96(46), e8424. doi:10.1097/MD.0000000000008424
Zarrin, J., L. Aguiar, R., P. Barraca, J., 2017. HARD: Hybrid Adaptive Resource Discovery for Jungle Computing. Journal of Network and Computer Applications, 90, pp. 42-73. doi:10.1016/j.jnca.2017.04.014
Zarrin, J., L. Aguiar, R., P. Barraca, J., 2017. Manycore simulation for peta-scale system design: Motivation, tools, challenges and prospects. Simulation Modelling Practice and Theory, 72, pp. 168-201. doi:10.1016/j.simpat.2016.12.014
Oghaz, M. M., Maarof, M. A., Rohani, M. F., Zainal, A., & Shaid, S. Z. M., 2017. A Hybrid Color Space for Skin Recognition for Real-Time Applications. Journal of Computational and Theoretical Nanoscience, 14(4), pp. 1852-1861. doi:10.1166/jctn.2017.6516
Sapkota, R. P., Pardhan, S., & van der Linde, I., 2016. Spatiotemporal proximity effects in visual short-term memory examined by target–nontarget analysis. Journal of Experimental Psychology: Learning, Memory and Cognition, 42(8), pp. 1304-1315. doi:10.1037/xlm0000238
McGonigle, C., van der Linde, I., Pardhan, S., Engel, S. A., Mallen, E. A., & Allen, P. M., 2016. Myopes experience greater contrast adaptation during reading. Vision Research, 121, pp. 1-9. doi:10.1016/j.visres.2016.01.001
Kolarik, A. J., Moore, B. C., Zahorik, P., Cirstea, S., & Pardhan, S., 2016. Auditory distance perception in humans: a review of cues, development, neuronal bases, and effects of sensory loss. Attention, Perception, & Psychophysics, 78(2), pp.373-395. doi:10.3758/s13414-015-1015-1
Stanciu, A., Cirstea, M. N., & Moldoveanu, F. D., 2016. Analysis and evaluation of PUF-based SoC designs for security applications. IEEE Transactions on Industrial Electronics, 63(9), pp. 5699-5708. doi:10.1109/TIE.2016.2570720
Folea, S. C., Mois, G., Muresan, C. I., Miclea, L., De Keyser, R., & Cirstea, M. N., 2016. A portable implementation on industrial devices of a predictive controller using graphical programming. IEEE Transactions on Industrial Informatics, 12(2), pp. 736-744. doi:10.1109/TII.2016.2532118
Zarrin, J., L. Aguiar, R., P. Barraca, J., 2016. ElCore: Dynamic elastic resource management and discovery for future large-scale manycore enabled distributed systems. Microprocessors and Microsystems, 46(B), pp. 221-239. doi:10.1016/j.micpro.2016.06.007
Yaghoubyan, S. H., Maarof, M. A., Zainal, A., Kiani, M. J., Rad, F., & Oghaz, M. M., 2016. A Robust Keypoint Descriptor Based on Tomographic Image Reconstruction Using Heuristic Genetic Algorithm and Principal Component Analysis Techniques. Journal of Computational and Theoretical Nanoscience, 13(8), pp. 5554-5568. doi:10.1166/jctn.2016.5453
Zarrin, J., L. Aguiar, R., P. Barraca, J., 2015. Dynamic, scalable and flexible resource discovery for large-dimension many-core systems. Future Generation Computer Systems, 53, pp. 119-129. doi:10.1016/j.future.2014.12.011
Oghaz, M. M., Maarof, M. A., Zainal, A., Rohani, M. F., & Yaghoubyan, S. H., 2015. A hybrid color space for skin detection using genetic algorithm heuristic search and principal component analysis technique. PLOS ONE, 10(8), e0134828. doi:10.1371/journal.pone.0134828