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6829 results in Communications and signal processing

Page 106 of 342

Robust Statistics for Signal Processing
  • Abdelhak M. Zoubir, Visa Koivunen, Esa Ollila, Michael Muma
  • Published online:
    26 October 2018
    Print publication:
    08 November 2018
  • Understand the benefits of robust statistics for signal processing with this authoritative yet accessible text. The first ever book on the subject, it provides a comprehensive overview of the field, moving from fundamental theory through to important new results and recent advances. Topics covered include advanced robust methods for complex-valued data, robust covariance estimation, penalized regression models, dependent data, robust bootstrap, and tensors. Robustness issues are illustrated throughout using real-world examples and key algorithms are included in a MATLAB Robust Signal Processing Toolbox accompanying the book online, allowing the methods discussed to be easily applied and adapted to multiple practical situations. This unique resource provides a powerful tool for researchers and practitioners working in the field of signal processing.

Smart Grid Technology
  • A Cloud Computing and Data Management Approach
  • Sudip Misra, Samaresh Bera
  • Published online:
    20 October 2018
    Print publication:
    28 March 2018
  • This comprehensive text covers fundamental concepts of smart grid technologies, integrating the tools and techniques of cloud computing and data management for application in smart grids. Different cloud and data management approaches are explained, highlighting energy management, information management, and security in the smart grid. The concepts of plug-in hybrid electric vehicle and virtual energy storage are explained in separate chapters. The text covers recent trends in cloud computing and data analytics in the field of smart grid. A glossary of important technical terms is provided for the benefit of the readers.

High-Dimensional Probability
  • An Introduction with Applications in Data Science
  • Roman Vershynin
  • Published online:
    29 September 2018
    Print publication:
    27 September 2018
  • High-dimensional probability offers insight into the behavior of random vectors, random matrices, random subspaces, and objects used to quantify uncertainty in high dimensions. Drawing on ideas from probability, analysis, and geometry, it lends itself to applications in mathematics, statistics, theoretical computer science, signal processing, optimization, and more. It is the first to integrate theory, key tools, and modern applications of high-dimensional probability. Concentration inequalities form the core, and it covers both classical results such as Hoeffding's and Chernoff's inequalities and modern developments such as the matrix Bernstein's inequality. It then introduces the powerful methods based on stochastic processes, including such tools as Slepian's, Sudakov's, and Dudley's inequalities, as well as generic chaining and bounds based on VC dimension. A broad range of illustrations is embedded throughout, including classical and modern results for covariance estimation, clustering, networks, semidefinite programming, coding, dimension reduction, matrix completion, machine learning, compressed sensing, and sparse regression.

Page 106 of 342