International Conference on Control, Artificial Intelligence, Robotics & Optimization Prague, Czech Republic, May 20-22, 2017
Int. Conf. on Control, Artificial Intelligence, Robotics & Optimization

ICCAIRO 2017: International Conference on Control, Artificial Intelligence, Robotics and Optimization
Prague, Czech Republic, May 20-22, 2017

Plenary Speakers

Plenary Speech 1:
New Cubic Spline Interpolation Methods for More Accurate Inertial Navigation

Professor Peter Z. Revesz
Department of Computer Science and Engineering
University of Nebraska-Lincoln

Abstract: Inertial navigation is the problem of estimating the flight path of a moving object based solely on acceleration or velocity measurements. Inertial navigation is especially important when GPS sensors are not feasible, for example, when the moving object is a submarine deep in the ocean. Inertial navigation may also be used by different animal species that can sense acceleration or velocity to find they way. In this presentation, we consider several algorithms for the processing of accelerometer measurement data in order to estimate the flight path of a moving object, including its future locations. We present a novel algorithm that is an improvement of other cubic spline interpolation methods and is more accurate than the older methods in estimating flight paths. 

Biography: Peter Z. Revesz holds a Ph.D. degree in Computer Science from Brown University. He was a postdoctoral fellow at the University of Toronto before joining the University of Nebraska-Lincoln, where he is a professor in the Department of Computer Science and Engineering. He is an expert in databases, data mining, big data analytics and bioinformatics. He is the author of Introduction to Databases: From Biological to Spatio-Temporal (Springer, 2010) and Introduction to Constraint Databases (Springer, 2002). Dr. Revesz held visiting appointments at the IBM T.J. Watson Research Center, INRIA, the Max Planck Institute for Computer Science, the University of Athens, the University of Hasselt, the U.S. Air Force Office of Scientific Research and the U.S. Department of State. He is a recipient of an AAAS Science & Technology Policy Fellowship, a J. William Fulbright Scholarship, an Alexander von Humboldt Research Fellowship, a Jefferson Science Fellowship, a National Science Foundation CAREER award, and a “Faculty International Scholar of the Year” award by Phi Beta Delta, the Honor Society for International Scholars.  

Plenary Speech 2:
Linear Robust Iterative Unbiased FIR Filtering

Professor Yuriy S. Shmaliy
PhD, DSc, IEEE Fellow
Department of Electronics Engineering
Universidad de Guanajuato,
DICIS, Salamanca, 36885, Mexico

Abstract: Robustness is required from an estimator when a system operates under the uncertainties in not well specified noise environments. In this regard, the Kalman filter, which requires all information about noise and initial values, is not robust. Better robustness demonstrates the unbiased finite impulse response (UFIR) filter, which completely ignores the noise statistics, the initial error statistics, and the initial state. Instead, the UFIR filter requires the optimal horizon of Nopt points to minimize the mean square error (MSE). Note that Nopt can be defined in a way much easier than that used to define the noise statistics. In this lecture, we consider the most advances iterative UFIR filtering algorithms and compare them to the Kalman filter (KF). Better robustness of the UFIR filter is shown against errors in the noise statistics, temporary model uncertainties, and measurement errors. We show that errors in the noise statistics can dramatically worsen the KF performance under the uncertainties. Applications are given for diverse kinds of linear and nonlinear problems. An overall conclusion that has been made is that the UFIR filter is more accurate in real-world applications implying model errors, while the KF is best under the ideal conditions. 

Biography: Dr. Yuriy S. Shmaliy has been a full professor in Electrical Engineering of the Universidad de Guanajuato, Mexico, since 1999. He received the B.S., M.S., and Ph.D. degrees in 1974, 1976 and 1982, respectively, from the Kharkiv Aviation Institute, Ukraine. In 1992 he received the Dr.Sc. (technical) degree from the Kharkiv Railroad Institute, Ukraine. In March 1985, he joined the Kharkiv Military University. He serves as full professor beginning in 1986 and has a Certificate of Professor, since 1993. In 1993, he founded and, by 2001, had been a director of the Scientific Center “Sichron” (Kharkiv, Ukraine) working in the field of precise time and frequency. In 2015-2016, he had been with City University London as a visiting researcher. His books Continuous-Time Signals (2006) and Continuous-Time Systems (2007) were published by Springer, New York. His book GPS-based Optimal FIR Filtering of Clock Models (2009) was published by Nova Science Publ., New York. He also edited a book Probability: Interpretation, Theory and Applications (Nova Science Publ., New York, 2012) and contributed to several books with invited chapters. Dr. Shmaliy has authored 393 Journal and Conference papers and holds 81 patents. He is IEEE Fellow; was rewarded a title, Honorary Radio Engineer of the USSR, in 1991; was listed in Outstanding People of the 20th Century, Cambridge, England in 1999; and was granted with the Royal Academy of Engineering Newton Collaboration Program Award in 2015. He currently serves on the Editorial Boards of several International Journals and is a member of the Program Committees of various Int. Symposia. His current interests include statistical signal processing, optimal estimation, and stochastic system theory.

Plenary Speech 3:
Modeling and Reconstruction of High-dimensional Data.

Prof. Dariusz JakobczakKÓBCZAK
Department of Electronics and Computer Science, Koszalin University of
Technology, Sniadeckich 2, 75-453 Koszalin, Poland

Reconstruction and extrapolation of multi-dimensional data 
represents the subject that arising in many branches of mathematics, artificial intelligence and computer science. Such a modeling connects pure mathematics with applied ciences. Artificial Intelligence similarly is situated on the border between pure mathematics and applied sciences. Our life and work are impossible without controlling, planning, time-tabling, scheduling, decision making, optimization, simulation, data analysis, risk analysis and process modeling.

Biography:  Dariusz Jacek Jakóbczak was born in Koszalin, Poland, on December 30, 1965. He graduated in mathematics (numerical methods and programming) from the University of Gdansk, Poland in 1990. He received the Ph.D. degree in 2007 in computer science from the Polish – Japanese Institute of Information Technology, Warsaw, Poland. 
From 1991 to 1994 he was a civilian programmer in the High Military School in Koszalin. He was a teacher of mathematics and computer science in the Private Economic School in Koszalin from 1995 to 1999. Since March 1998 he has worked in the Department of Electronics and Computer Science, Koszalin University of Technology, Poland and since October 2007 he has been an Assistant Professor in the Chair of Computer Science and Management in this department. His research interests connect mathematics with computer science and include computer vision, artificial intelligence, shape representation, curve interpolation, contour reconstruction and geometric modeling, numerical methods, probabilistic methods, game theory, operational research and discrete mathematics. 


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