Branislav Vuksanovic was born in Osijek, Croatia in 1962. He graduated from the University of Belgrade, Serbia with degree in Electrical and Power Engineering. He holds MSc degree in Measurement and Instrumentation from South Bank University, London and a PhD in Active Noise Control from the University of Huddersfield, UK.
Previously, he worked as a Project Engineer for Croatian Electricity Board in Osijek, Croatia. During his academic career he worked as a Research Fellow at Sheffield and Birmingham Universities on Optical Brain Imaging and Medical Video Compression projects. He also worked as a Lecturer at the University of Derby where he was a member of Sensors and Controls Research Group. Currently he works as a Senior Lecturer at the University of Portsmouth, School of Engineering. He has published papers in the field of active noise control, biomedical signal processing and pattern recognition for intrusion detection and knowledge based authentication. He also published one book in Digital Electronics and Microcontrollers field.
Dr Branislav Vuksanovic is a member of IET, ILT and IACSIT. His current research interests are in the application of pattern recognition techniques for power systems and analysis of ground penetrating radar and ECG data.
Abstract: Ground Penetrating Radar (GPR) is the general term applied to techniques which employ radio waves, typically in the MHz and GHz range, to map structures and features buried in the ground or in man-made structures. Currently, GPR surveys include on-site GPR measurements followed by an interactive use of geophysics or image processing packages and interpretation of obtained results by skilful and experienced human GPR operator. Subtleties in processing and interpretation of GPR data mean that surveyors and GPR data analysts may often fail to detect targets that could otherwise be clearly resolved. GPR measurements can suffer from excessive amount of noise or clutter making them extremely difficult to understand and interpret, even to well-trained and skilful GPR operators. Current research in this field focuses on improving GPR hardware but even more so on developing and advancing techniques for GPR image processing and analysis using modern machine vision and artificial intelligence algorithms and techniques.
This talk will introduce GPR technology and expose some issues related to the use of this technology in engineering as well as in some other areas of human activity. GPR capabilities, some problems and developments in the field will be discussed with particular focus on the use of signal and image processing techniques for the analysis of GPR data and images acquired through simulations and real measurements.
Sunil Vadera is the Dean of the School of Computing, Science and Engineering at the University of Salford in Greater Manchester, UK. Sunil was Chair of the UK BCS Knowledge Discovery and Data Mining Symposium held in Salford in 2009, A Programme Chair of the IFIP conference on Intelligent Information Processing in 2010, 2012,2016. His research has been published in some of the leading outlets, including the Computer Journal, ACM Transactions on Knowledge Discovery from Data, ACM Computing Surveys, Expert Systems Journal, Foundations of Science, and IEEE Transactions of Power Systems. Sunil was Chair of the British Computer Society Academic Accreditations Committee, that has responsibility for professional accreditation of programmes in the UK, from 2007-2009. He holds a PhD in Computer Science from the University of Manchester, is a Fellow of the BCS and was awarded the BDO best British Indian Scientist and Engineer in 2014 and the Amity Award for Research in 2018 in recognition of his contributions to the field.
Sunil Vadera has led a number of projects in applying data mining and machine learning for problems in Energy, Finance, and Policy over the last decade, including:
• Developing new models for real time sensor validation of gas turbines
• Data mining of near miss data for the health and safety executive
• Analysis of SMART meters data for British Gas
• A major FP7 funded project on Self-Learning Energy Efficient Buildings and Open Spaces
• Analysing factors affecting children in need and troubled families
• Sub-prime lending aimed at improving financial inclusion
• Data mining for predicting client churn for a major Software House.
Abstract: Advances in deep learning have started a new AI revolution that is transforming our world. Applications in speech recognition, self-driving cars and publicity around systems such Alpha Go defeating human Go champions has ignited interest from the public, academia and industry. Convolutional neural networks, that can take images as input, learn to identify key features and perform classification are the heart of many of the proposed applications in medical diagnosis such as detecting breast cancer, predicting Alzheimer’s disease and grading brain tumours. These neural networks can, however, be very large, taking up memory and requiring significant computational resources. For example, one of the most highly cited networks, AlexNet has over 62 million parameters that need to be learned. Hence, this keynote explores methods for reducing the size of such networks without compromising performance. The talk will summarise existing methods for pruning neural networks, including direct methods, and Optimal Brain Damage, and describe our recent work on a new framework, based on the use of multi-armed bandits such as Thomson Sampling and Upper Confidence Bounds. The talk will conclude with the results of an empirical evaluation of the new methods over several benchmark data sets such as ImageNet, MNIST, CIFAR and Street View House Numbers.
Marat Akhmet is a professor of mathematics at Middle East Technical University (Ankara, Turkey) known for his research on the chaos and bifurcation theory in differential equations and hybrid systems with applications in physics, neural networks, biology, medicine and economics . Born in Kazakhstan, he studied at Aktobe State University. He received his doctorate in 1984 at Kiev University . He has been awarded a Science Prize of TUBITAK (Turkey, 2015), for best achievments in scientific research. He is an author of four books: "Principles of Discontinuous Dinamical Systems", Springer, 2010, "Nonlinear Hybrid Continuous Discrete-Time Models", Atlantis Press (Springer), 2011, "Neural networks with Discontinuous Impact Activations," Springer, 2014, and "Replication of Chaos in Neural Networks, Economics and Physics", Springer&HEP, 2015. His has solved the Second Peskin conjecture for Integrate-and-fire biological oscillators, has introduced and developed theory of differential equations with piecewise constant argument of generalized type, many aspects of discontinous dynamical systems. The last decade his main subject of research is input-output analysis of chaos and irregular behavior of hybrid neural networks.
Abstract: The collective behavior of biological and chemical oscillators is a fascinating topic that has attracted a lot of attention in the last 50 years. The integrate-and-fire model of the cardiac pacemaker was developed by C. Peskin to a population of identical pulse-coupled oscillators. It was conjectured that the model self-synchronizes such that: (C1) For arbitrary initial conditions, the system approaches a state in which all the oscillators are firing synchronously. (C2) This remains true even when the oscillators are not quite identical. The conjecture (C1) is solved by C. Peskin for a system with two oscillators, and R. Mirollo and S. Strogatz for the generalized model of two and more oscillators. In the present research we give a solution of the conjecture (C2). A new way of analysis that can be employed to solve many interesting problems is proposed. All results are derived for the most general setting. The talk will consists of main results, simulations and discussion of possible extensions. Applications for neural networks are discussed.