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.