Cambridge Scholars Publishing High-Resolution Noisy Signal and Image Processing

The book introduces valuable new data analysis methods in time and space, and provides many examples and recommendations for new developments. It will teach the reader how to use powerful, but very flexible, tools, frequently referred to as Kolmogorov-Zurbenko Filters. The main construction of these tools is derived from spectral concepts where natural laws occur. Rather than forcing models on data, they allow us to discover the nature of phenomena hidden within the data. The methods outlined here are capable of obtaining accurate results within very noisy environments. Their extremely accurate spectral diagnostics permits the separation of different sources of influences within the data. Treating each source separately can achieve highly accurate explanations of the total picture. For example, this approach is able to identify the most dangerous moments and locations for hurricanes and tornados. About the Author Igor Zurbenko received a PhD in Applied Statistics and a doctorate of Probability and Statistics from Moscow State University. He is currently Full Professor at the Department of Biometry and Statistics of the School of Public Health at the University at Albany, USA. He has authored and co-authored over 200 papers and 10 books on theoretical and applied statistics, covering their applications in biostatistics, environmental pollution, atmospheric sciences, climate change and other disciplines. Devin Smith is a PhD student studying network inference methods for neural spike-train data at Rensselaer Polytechnic Institute, New York. He received an MSc in Biostatistics from the SUNY University at Albany’s School of Public Health, where he worked on hyperspectral image processing techniques. Amy Potrzeba-Macrina received her PhD from the University at Albany in 2013. Her research interests include spectral analysis and forecasting with applications to atmospheric variables. Barry Loneck received his MSSA and PhD from Case Western Reserve University, USA. While serving as Associate Professor in the University at Albany’s School of Social Welfare, he completed degrees in Mathematics (BS) and Biostatistics (MS), and is currently completing a PhD in Biostatistics. His work focuses on an approach to compute confidence intervals for the Kolmogorov-Zurbenko periodogram to detect significant differences in signal frequencies between populations. Edward Valachovic, PhD, is an Assistant Professor at the Department of Epidemiology and Biostatistics in the School of Public Health at the University at Albany, State University of New York. His work focuses on the advancement of statistical theory and analysis methods, particularly in the field of spatiotemporal time series analysis, and applications of these and general statistical methods to epidemiology, public health, and other fields of research. Mingzeng Sun earned his PhD in Biostatistics from the State University of New York at Albany. His work focuses on parametric data analysis, non-parametric data analysis and creating a rolling variance (RLV) algorithm and a rolling variation estimation (RVE) algorithm to perform spatial boundary detection on jet stream data.