Giuseppe Caire – författare

Delay Doppler Communications and Sensing: Principles, Advances and Applications of OTFS provides a comprehensive exploration of the principles and cutting-edge developments of delay Doppler (DD) communications and sensing with a specific focus on the advances and application of orthogonal time frequency space (OTFS). It highlights the fundamental theory for DD communications and sensing through the Zak transform, and discusses how this classic concept from quantum physics can reshape modern wireless technology. The important aspects of DD communications and sensing are introduced in detail, including transceiver design, performance analysis, and its application. In particular, this book reveals the intrinsic connections between DD domain wireless communication and sensing channels, unveiling the important interplay between communication and sensing functionalities in the DD domain. The book introduces the principles and design of communications and sensing based on the DD signal processing framework as well as delving into advanced aspects, such as DD domain MIMO and DD domain integrated sensing and communications (ISAC). By providing a comprehensive overview of both fundamental principles, cutting-edge research and practical applications, this book gives a deep understanding of the underlying concepts and techniques in Delay Doppler (DD) communications and sensing.With this book the reader will:Gain a comprehensive understanding of the Zak transform and DD domain wireless channelsUnderstand the concept of DD communications, including the popular variants of OTFSLearn implementation details of DD communications and sensingFollow state-of-the-art developments in DD communications and sensingGrasp the insights and physical interpretations of DD communications and sensingHave access to programming codes for OTFS and DD communications and sensingA comprehensive overview of both fundamental principles and cutting-edge research, giving a deep understanding of the underlying concepts and techniques in Delay Doppler (DD) communications and sensingIncludes practical content such as coding strategies, channel estimation techniques, and equalization methods, accompanied by tips and guidance for practical implementationExplores a wide range of applications, from radar sensing to DD Multiple-Input Multiple-Output (MIMO) configurations, making it relevant for researchers, engineers, and practitioners in various domainsHighlights outstanding research challenges and future directions, stimulating further exploration, and inspiring readers to contribute to the advancement of DD communications and sensing

This contributed volume contains articles written by the plenary and invited speakers from the second international MATHEON Workshop 2015 that focus on applications of compressed sensing. Article authors address their techniques for solving the problems of compressed sensing, as well as connections to related areas like detecting community-like structures in graphs, curbatures on Grassmanians, and randomized tensor train singular value decompositions. Some of the novel applications covered include dimensionality reduction, information theory, random matrices, sparse approximation, and sparse recovery. This book is aimed at both graduate students and researchers in the areas of applied mathematics, computer science, and engineering, as well as other applied scientists exploring the potential applications for the novel methodology of compressed sensing. An introduction to the subject of compressed sensing is also provided for researchers interested in the field who are not as familiar with it.

This contributed volume contains articles written by the plenary and invited speakers from the second international MATHEON Workshop 2015 that focus on applications of compressed sensing. Article authors address their techniques for solving the problems of compressed sensing, as well as connections to related areas like detecting community-like structures in graphs, curbatures on Grassmanians, and randomized tensor train singular value decompositions. Some of the novel applications covered include dimensionality reduction, information theory, random matrices, sparse approximation, and sparse recovery. This book is aimed at both graduate students and researchers in the areas of applied mathematics, computer science, and engineering, as well as other applied scientists exploring the potential applications for the novel methodology of compressed sensing. An introduction to the subject of compressed sensing is also provided for researchers interested in the field who are not as familiar with it. 

The chapters in this volume highlight the state-of-the-art of compressed sensing and are based on talks given at the third international MATHEON conference on the same topic, held from December 4-8, 2017 at the Technical University in Berlin. In addition to methods in compressed sensing, chapters provide insights into cutting edge applications of deep learning in data science, highlighting the overlapping ideas and methods that connect the fields of compressed sensing and deep learning. Specific topics covered include:Quantized compressed sensingClassificationMachine learningOracle inequalitiesNon-convex optimizationImage reconstructionStatistical learning theoryThis volume will be a valuable resource for graduate students and researchers in the areas of mathematics, computer science, and engineering, as well as other applied scientists exploring potential applications of compressed sensing.

The chapters in this volume highlight the state-of-the-art of compressed sensing and are based on talks given at the third international MATHEON conference on the same topic, held from December 4-8, 2017 at the Technical University in Berlin. In addition to methods in compressed sensing, chapters provide insights into cutting edge applications of deep learning in data science, highlighting the overlapping ideas and methods that connect the fields of compressed sensing and deep learning. Specific topics covered include:Quantized compressed sensingClassificationMachine learningOracle inequalitiesNon-convex optimizationImage reconstructionStatistical learning theoryThis volume will be a valuable resource for graduate students and researchers in the areas of mathematics, computer science, and engineering, as well as other applied scientists exploring potential applications of compressed sensing.