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V John Mathews


V John Mathews

University of Utah, USA

Dr. V John Mathews is Professor and Head of the School of Electrical Engineering and Computer Science at the Oregon State University. Prior to joining Oregon State, he was with the Department of Electrical and Computer Engineering at the University of Utah from 1985 till 2015. He was the Chairman of the ECE department at Utah from 1999 to 2003. His current research interests are in nonlinear and adaptive signal processing and their applications in audio and communication systems, biomedical and neural engineering, and structural health management. He is the author of the book Polynomial Signal Processing, published by Wiley, and co-authored with Professor G. L. Sicuranza. He has published approximately 150 technical papers, and is the inventor on seven patents.
Dr. Mathews is a Fellow of IEEE and has served on many leadership positions of the IEEE Signal Processing Society. He was a Distinguished Lecturer of the IEEE Signal Processing Society for 2013 and 2014, and is the recipient of the 2014 IEEE Signal Processing Society Meritorious Service Award.


Project:  Distributed Learning and Adaptation
Sensors and computers are ubiquitous in the modern world. Billions of mobile computers in the form of smart phones are capable of communicating with each other and performing large and small-scale computations locally and in transit. They also contain a variety of sensing systems, for example, GPS sensors, cameras, accelerometers, etc. Wearable sensors can now track vital functions of the body and communicate with health care providers or remote monitoring systems. A very large number of sensors that continuously monitor the environment have also been deployed. In general, the individual sensors and computers may not have all the information necessary to fully characterize the structures and mechanisms that generated the measured signals and to extract the desired information from the measurements. Traditional approaches to learning, estimation and tracking port all the measurements to a central processor which then extracts the desired information. A distributed computing and communications modality that performs many of the computations locally and on smaller interconnected networks with information available to the local networks, and then interact with a central processor as needed has several advantages. They include the ability to perform large-scale computations over a network of relatively inexpensive and spatially distributed compute devices, reduced communication and bandwidth requirements and reduced processing latency. Our goal is to develop practical algorithms for distributed learning and adaptation over networks of inhomogeneous sensors and computing devices, characterize their performance capabilities and lay the foundation for applying the techniques in a variety of practical applications.

Stay period: MAY 2016 -