Biography: Dr. Zhao is a Professor at the Department of Electrical Engineering and Computer Science, Cleveland State University. He earned his Ph.D. at University of California, Santa Barbara in 2002. He has over 180 peer-reviewed publications. Dr. Zhao’s research spans from dependable distributed systems to human centered smart systems. His research has been funded by the US NSF, US Department of Transportation, Ohio Bureau of Workers’ Compensation, Ohio Department of Higher Education, and Ohio Development Services Agency. He has delivered more than 10 keynotes, tutorials, public talks and demonstrations in various conferences, industry and academic venues. Dr. Zhao is an associate editor for IEEE Access and PeerJ Computer Science, and a member of the editorial board of several international journals, including Computers, Applied System Innovation, Internal Journal of Parallel, Emergent and Distributed Systems. He is currently an IEEE Senior Member and serves on the executive committee of the IEEE Cleveland Section.
Topic: Reducing Lower-Back Injuries with a Privacy-Aware Compliance Tracking System
Abstract: Lost productivity from lower back injuries in workplaces costs over $100 billion per year in the United States alone. A significant fraction of such workplace injuries is the result of workers not following best practices. In this talk, Dr. Zhao will present the design, implementation, and evaluation of a novel computer-vision-based system that aims to increase the workers’ compliance to best practices in using proper body mechanics. The system consists of inexpensive programmable depth sensors, smartwatches, and smartphones. The system is designed to track the activities of consented workers using the depth sensors, alert them discreetly on detection of noncompliant activities, and produce cumulative reports on their performance. Essentially, the system provides a valuable set of services for both workers and administrators toward a healthier and, therefore, more productive workplace.
Biography: Dr. Tatiana Yakovleva has graduated with honors from the Moscow Engineering-Physics Institute and completed her Ph.D in 1982. She was awarded by the Royal British Society Postdoctoral fellowship in 1993. In 1915 she got a degree of Doctor of Science in Physics and Mathematics. The scientific interests cover the issues of elaboration of mathematical signal processing techniques that are applicable in various fields of science, such as nonlinear optics, wave front reversal, the light and ultrasound waves scattering in inhomogeneous medium. During the last few years the main subject of her research is connected with the theoretical investigations of the Rice statistical distribution peculiarities. The mathematical methods having been developed by Dr. Tatiana Yakovleva allow efficient stochastic signals processing, the separation of the informative and noise signals’ components and high-precision reconstruction of the useful signal against the background of noise. The methods of the so-called two-parameter analysis of Rician signals elaborated by Dr. Yakovlevaform a theoretical basis for new information technologies and can be applied in a wide spectrum of scientific and technical tasks, such as optical phase measurements, magnetic-resonance visualization, radar signals’ analysis, range-measuring, etc. She has published more than 120 papers in reputed journals.
Topic: The Rice Distribution Peculiarities as a Basis for New Approaches to Stochastic Data Analysis
Abstract: The Rice distribution has recently become a subject of increasing interest because of its wide applicability: a lot of scientific and technical problems connected with the signal’s envelope, or amplitude analysis are known to be adequately described by the Rice statistical model. These problems include the magnetic-resonance visualization, the radar, radio and sonar signals analysis, etc. The Speech proposes a thorough theoretical review of the problem of stochastic data analysis within the Rice statistical model. It provides a rigorous mathematical analysis of the Rice statistical distribution’s properties, such as a uniqueness of the likelihood function’s maximum, the stable character of this distribution, etc. The revealed peculiarities of the Rice distribution have become a prerequisite for the development of new techniques of the Rician signals’ analysis implying the joint signal and noise accurate evaluation. These techniques couldbe efficiently applied in various information technologies.