Signal Matrix

The second volume will deal with a presentation of the main matrix and tensor decompositions and their properties of uniqueness, as well as very useful tensor networks for the analysis of massive data. Parametric estimation algorithms will be presented for the identification of the main tensor decompositions. After a brief historical review of the compressed sampling methods, an overview of the main methods of retrieving matrices and tensors with missing data will be performed under the low rank hypothesis. Illustrative examples will be provided.

This book takes a comprehensive study on turbo message passing algorithms for structured signal recovery, where the considered structured signals include 1) a sparse vector/matrix (which corresponds to the compressed sensing (CS) problem), 2) a low-rank matrix (which corresponds to the affine rank minimization (ARM) problem), 3) a mixture of a sparse matrix and a low-rank matrix (which corresponds to the robust principal component analysis (RPCA) problem).

This book takes a comprehensive study on turbo message passing algorithms for structured signal recovery, where the considered structured signals include 1) a sparse vector/matrix (which corresponds to the compressed sensing (CS) problem), 2) a low-rank matrix (which corresponds to the affine rank minimization (ARM) problem), 3) a mixture of a sparse matrix and a low-rank matrix (which corresponds to the robust principal component analysis (RPCA) problem).

This book describes a fast, accurate and flexible modelling methodology for PCBs. The model uses the concept of tensorial analysis of networks (TAN) based on Kron's and Kron-Branin's methods adapted for the EMC use by O.Maurice. The TAN approach is applied to the PCB SI and electromagnetic compatibility (EMC) analysis.

Signal Processing: A Mathematical Approach is designed to show how many of the mathematical tools the reader knows can be used to understand and employ signal processing techniques in an applied environment. Assuming an advanced undergraduate- or graduate-level understanding of mathematics―including familiarity with Fourier series, matrices, probability, and statistics

This book is concerned with the aim of analysing biological signals to extract useful information. The book will provide the reader with an understanding of biological signals and digital signal analysis techniques such as conditioning, filtering, feature extraction, classification and statistical validation for solving practical biological signal analysis problems using MATLAB.