Compressed sensing is an exciting, rapidly growing field, attracting considerable attention in electrical engineering, applied mathematics, statistics and computer science. This book provides the first detailed introduction ...

Buy Now From Amazon

Compressed sensing is an exciting, rapidly growing field, attracting considerable attention in electrical engineering, applied mathematics, statistics and computer science. This book provides the first detailed introduction to the subject, highlighting recent theoretical advances and a range of applications, as well as outlining numerous remaining research challenges. After a thorough review of the basic theory, many cutting-edge techniques are presented, including advanced signal modeling, sub-Nyquist sampling of analog signals, non-asymptotic analysis of random matrices, adaptive sensing, greedy algorithms and use of graphical models. All chapters are written by leading researchers in the field, and consistent style and notation are utilized throughout. Key background information and clear definitions make this an ideal resource for researchers, graduate students and practitioners wanting to join this exciting research area. It can also serve as a supplementary textbook for courses on computer vision, coding theory, signal processing, image processing and algorithms for efficient data processing.

  • Used Book in Good Condition
  • Used Book in Good Condition

Similar Products

A Mathematical Introduction to Compressive Sensing (Applied and Numerical Harmonic Analysis)Sampling Theory: Beyond Bandlimited SystemsDeep Learning (Adaptive Computation and Machine Learning series)Sparse Modeling: Theory, Algorithms, and Applications (Chapman & Hall/Crc Machine Learning & Pattern Recognition)Sparse Representations and Compressive Sensing for Imaging and Vision (SpringerBriefs in Electrical and Computer Engineering)Statistical Learning with Sparsity: The Lasso and Generalizations (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent SystemsSparse and Redundant Representations: From Theory to Applications in Signal and Image Processing