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Random Matrix Methods for Machine Learning
This book presents a unified theory of random matrices for applications in machine learning, offering a large-dimensional data vision that exploits concentration and universality phenomena. This enables a precise understanding, and possible improvements, of the core mechanisms at play in real-world machine learning algorithms. The book opens with a thorough introduction to the theoretical basics of random matrices, which serves as a support to a wide scope of applications ranging from SVMs, through semi-supervised learning, unsupervised spectral clustering, and graph methods, to neural networks and deep learning. For each application, the authors discuss small- versus large-dimensional intuitions of the problem, followed by a systematic random matrix analysis of the resulting performance and possible improvements. All concepts, applications, and variations are illustrated numerically on synthetic as well as real-world data, with MATLAB and Python code provided on the accompanying website.
Random Matrix Methods for Machine Learning
This unified random matrix approach to large-dimensional machine learning covers applications from power detection to deep neural networks.
Random Matrix Methods for Wireless Communications
Blending theoretical results with practical applications, this book provides an introduction to random matrix theory and shows how it can be used to tackle a variety of problems in wireless communications. The Stieltjes transform method, free probability theory, combinatoric approaches, deterministic equivalents and spectral analysis methods for statistical inference are all covered from a unique engineering perspective. Detailed mathematical derivations are presented throughout, with thorough explanation of the key results and all fundamental lemmas required for the reader to derive similar calculus on their own. These core theoretical concepts are then applied to a wide range of real-world problems in signal processing and wireless communications, including performance analysis of CDMA, MIMO and multi-cell networks, as well as signal detection and estimation in cognitive radio networks. The rigorous yet intuitive style helps demonstrate to students and researchers alike how to choose the correct approach for obtaining mathematically accurate results.
Random Matrix Methods for Wireless Communications
An introduction to random matrix theory and its applications to real-world problems in signal processing and wireless communications.
Orthogonal Frequency Division Multiple Access Fundamentals and Applications
Supported by the expert-level advice of pioneering researchers, Orthogonal Frequency Division Multiple Access Fundamentals and Applications provides a comprehensive and accessible introduction to the foundations and applications of one of the most promising access technologies for current and future wireless networks. It includes authoritative cove
Radio Engineering
Software radio ideally provides the opportunity to communicate with any radio communication standard by modifying only the software, without any modification to hardware components. However, taking into account the static behavior of current communications protocols, the spectrum efficiency optimization, and flexibility, the radio domain has become an important factor. From this thinking appeared the cognitive radio paradigm. This evolution is today inescapable in the modern radio communication world. It provides an autonomous behavior to the equipment and therefore the adaptation of communication parameters to better match their needs. This collective work provides engineers, researchers and radio designers with the necessary information from mathematical analysis and hardware architectures to design methodology and tools, running platforms and standardization in order to understand this new cognitive radio domain.
Mathematical Foundations for Signal Processing, Communications, and Networking
Mathematical Foundations for Signal Processing, Communications, and Networking describes mathematical concepts and results important in the design, analysis, and optimization of signal processing algorithms, modern communication systems, and networks. Helping readers master key techniques and comprehend the current research literature, the book offers a comprehensive overview of methods and applications from linear algebra, numerical analysis, statistics, probability, stochastic processes, and optimization. From basic transforms to Monte Carlo simulation to linear programming, the text covers a broad range of mathematical techniques essential to understanding the concepts and results in sign...
Low-Power Computer Vision
Energy efficiency is critical for running computer vision on battery-powered systems, such as mobile phones or UAVs (unmanned aerial vehicles, or drones). This book collects the methods that have won the annual IEEE Low-Power Computer Vision Challenges since 2015. The winners share their solutions and provide insight on how to improve the efficiency of machine learning systems.
Wireless Algorithms, Systems, and Applications
This book constitutes the refereed proceedings of the 8th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2013, held in Zhangjiajie, China, in August 2013. The 25 revised full papers presented together with 18 invited papers were carefully reviewed and selected from 80 submissions. The papers cover the following topics: effective and efficient state-of-the-art algorithm design and analysis, reliable and secure system development and implementations, experimental study and testbed validation, and new application exploration in wireless networks.
Random Matrix Methods for Wireless Communications
"Blending theoretical results with practical applications, this book provides an introduction to random matrix theory and shows how it can be used to tackle a variety of problems in wireless communications. The Stieltjes transform method, free probability theory, combinatoric approaches, deterministic equivalents and spectral analysis methods for statistical inference are all covered from a unique engineering perspective. Detailed mathematical derivations are presented throughout, with thorough explanation of the key results and all fundamental lemmas required for the reader to derive similar calculus on their own. These core theoretical concepts are then applied to a wide range of real-world problems in signal processing and wireless communications, including performance analysis of CDMA, MIMO and multi-cell networks, as well as signal detection and estimation in cognitive radio networks. The rigorous yet intuitive style helps demonstrate to students and researchers alike how to choose the correct approach for obtaining mathematically accurate results"--
