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Keynote Speaker

Below is the keynote speakers of ICDSP 2017; For speakers of ICDSP 2018, more details will be updated soon...

Prof. Erchin Serpedin
Texas A&M University, College Station, Texas, USA

Erchin Serpedin received the specialization degree in signal processing and transmission of information from Ecole Superieure D'Electricite (SUPELEC), Paris, France, in 1992, the M.Sc. degree from the Georgia Institute of Technology, Atlanta, in 1992, and the Ph.D. degree in electrical engineering from the University of Virginia, Charlottesville, in January 1999. He is currently a professor in the Department of Electrical and Computer Engineering at Texas AM University, College Station. He is the author of 3 research monographs, 1 textbook, 140 journal papers and 250 conference papers, and serves currently as associate editor for the IEEE Signal Processing Magazine and as editor-in-chief of EURASIP Journal on Bioinformatics and Systems Biology, an online journal edited by Springer-Nature. He served as associate editor for a dozen of journals such as IEEE Transactions on Signal Processing, IEEE Transactions on Communications, IEEE Transactions on Information Theory, Signal Processing (Elsevier), EURASIP Journal on Advances in Signal Processing, Physical Communication (Elsevier), and IEEE Signal Processing Letters. His research interests include signal processing, wireless communications, bioinformatics, and machine learning. He is an IEEE Fellow.

Speech Title: Timing Synchronization and Node Localization in Wireless Sensor Networks

Abstract: Wireless sensor networks consist of a large number of sensor nodes, capable of on-board sensing and data processing, that are employed to observe some phenomenon of interest. With their desirable properties of flexible deployment, resistance to harsh environment and lower implementation cost, wireless sensor networks envisage a plethora of applications in diverse areas such as industrial process control, battlefield surveillance, health monitoring, and target localization and tracking. This presentation will focus on deriving efficient estimators and performance bounds for the clock parameters in wireless sensor networks. Identifying the close connections between the problems of node localization and clock synchronization, we also address in this presentation the problem of joint estimation of an unknown node's location and clock parameters by incorporating the effect of imperfections in node oscillators. A review of the state-of-the-art results and open research problems will be also presented.

Prof. Momoh Jimoh Eyiomika Salami,
Department of Electrical and Computer Engineering, International Islamic University of Malaysia, Kuala Lumpur, Malaysia

Prof. Dr. Momoh Jimoh Eyiomika Salami is Professor of Signal and Image Processing, International Islamic University Malaysia Professor at the Department of Mechatronics. He has authored/co-authored more than 100 publications in both local and international journals and conference proceedings as well as being one of the contributors. He had contributed a chapter in recently published book entitled "The Mechatronics Handbook" edited by Prof. Bishop. His research interests include Digital Signal and Image Processing, Intelligent Control Systems Design and instrumentation. Prof. Momoh is a senior member of IEEE.

Speech Title: Evolution and Application of Signal (Data) Prediction

Abstract: From time immemorial human beings have shown desire to predict the future of many phenomena that pose danger to them and their properties. Examples of such could be kaleidoscopic weather, capricious earthquake, turbulent stock market, and inconsistent heartbeat just to name a few. Rudolf Wolf (1848) devised a method for the daily estimation of solar activity by counting the number of individual spots and groups of spots on the sun surface which can be considered a classical approach of measuring the level of disturbances in the earth's magnetic field: solar activity can cause satellite drag, telecommunication outages, huge electrical currents in power lines as well as radio wave propagation problem. Yule (1927) introduced regression analysis for the prediction of sunspots in order to obtain more accurate primary periodicity in sunspot numbers as well as to search for additional periodicities in the data. It is important to note that the Yule method is reminiscent to the data fitting procedure of Baron de Prony (1795) which is a method of fitting an exponential model to a set of data representing pressure and volume of a gas. In another development, Walker (1931) generalized Yule approach (modeling of disturbed harmonics data) by proposing autoregressive model (applicable to any form of data) which was applied to the analysis of the atmospheric data, leading to the discovery of the Walker circulation. The combination of these two techniques led to what is known as Yule-Walker equation (YWE) which forms the basis of signal modelling or linear prediction. YWE and its variants have been widely used in many fields of science, engineering and economics. Signal (Data) prediction has therefore evolved to be an important aspect of digital signal processing which has been successfully used to solve variety of problems in communication, control, biomedicine, geophysics, atmospheric science, and this list could continue. Signal models, which could be either parametric (with an assumption of certain functional form) or nonparametric techniques, entail efficient mathematical representation of data or signal so as to extract desirable information from either the signal or the system that generates it. Though several signal models are available, both the AR and ARMA models are widely used in many application areas as they possess some desirable properties. Consequently, a lot of research efforts have been concentrated on these two models since the last four decades. This presentation examines the theoretical framework of signal models, especially with respect to the AR and ARMA models. The shortcomings of these statistical approaches are then discussed. In recent times, there seems to be paradigm shift in signal modeling approach. The availability of very power computer combined with the emergence of machine learning, typified by artificial neural networks and genetic algorithms has made it possible to develop complex algorithms for the estimating of the model parameters. The exploitation of this so called intelligent signal modeling approach is also examined here. The presentation is concluded by evaluating the performance of aforementioned techniques in transient signal analysis, earthquake prediction, consumer load prediction for theft classification and detection, seamless mobility in wireless communication, and parameter estimation of time-varying signals.

Plenary Speaker

Assoc. Prof. Ir. Dr. Ahmad Rasdan Ismail
Faculty of Creative Technology and Heritage, Universiti Malaysia Kelantan, Malaysia

Assoc. Prof. Ir. Dr. Ahmad Rasdan Ismail is currently an academician at Universiti Malaysia Kelantan (UMK) Bachok Campus. He graduated his PhD in Mechanical Engineering (Industrial Ergonomics), Master of Science in Manufacturing Systems Engineering and a Bachelor Degree in Mechanical Engineering from Universiti Kebangsaan Malaysia. Currently, he is a Head of Occupational Safety Health Environment Management Unit Registrar Office of Universiti Malaysia Kelantan. His research interest in the area of ergonomics, human factor and safety. He has been a principal consultant and project leader for many private and government projects from multinational oil and gas companies for many years.

He had translated his research finding in 105 international and local journals and 188 proceedings conferences and managed to publish 1 book as main author, 4 books (as the Editor) as well as 5 chapters in distinguish ergonomics books which were published by Taylor and Francis, CRC. So far he has published more than 200 scientific papers in his field of interest including international scholarly journals and conferences. According to the google scholar, Dr Ahmad Rasdan Ismail's articles have been cited exceeding 374, H index at 10 and i10- index 7.


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