Bücher der Reihe Foundations and Trends (R) in Signal Processing

Provides a historical overview of deep learning and focuses on its applications in object recognition, detection, and segmentation, which are key challenges of computer vision and have numerous applications to images and videos.

Written in a tutorial style, readers from both classical and quantum backgrounds will find this an enlightening treatise on the topic. This monograph is a comprehensive and accessible overview of a complex problem for students and researchers in signal processing.

Considers the cell-free network architecture that is designed to reach the goal of uniformly high data rates everywhere. The authors introduce the concept of a cell-free network before laying out the foundations of what is required to design and build such a network.

Introduces the reader to the research and practical aspects behind the approach of learning the characteristics of the acoustic environment directly from the data rather than using a predefined physical model. This book provides a comprehensive overview and insights into this burgeoning area of acoustic developments.

Focuses on a technique called Network Time Distribution, which is often more cost-effective than GPS-based timing. The technique uses a master/slave construction to synchronize the time throughout devices on a network. To do this, two-way message exchange is required which can be subject to network delays.

Presents a wide swath of biomedical image reconstruction algorithms under a single framework. The book offers a brief survey of six decades of research. The underpinning theory of the techniques are described and practical considerations for designing reconstruction algorithms for use in biomedical systems form the central theme of each chapter.

Reviews several recent compressed sensing advancements in wireless networks with the aim of improving the quality of signal reconstruction or detection while reducing the use of energy, radio, and computation resources.

Provides the starting point to the literature that every engineer new to machine learning needs. This book offers a basic and compact reference that describes key ideas and principles in simple terms and within a unified treatment, encompassing recent developments and pointers to the literature for further study.

Provides an overview of the low-dimensional models of structure signals, along with its relevant theories and low-complexity algorithms of signal recovery. The book further reviews the recent advancements of low-dimensional-model-based electromagnetic imaging in various applied areas.

The second part of the two-part monograph Fundamentals of Source and Video Coding. This part describes the application of the techniques described in the first part to video coding. In doing so it provides a description of the fundamentals concepts of video coding and, in particular, the signal processing in video encoders and decoders.

Introduces and reviews Sparse Sensing, a technique that is proving to be an efficient and cost-effective method for data collection. The book provides the reader with a comprehensive overview of this technique and a framework that can be used in implementing the technique in practical sensing systems.

Provides a survey, tutorial development, and discussion of four highly stylized examples of sensing and decision making in social networks: social learning for interactive sensing; tracking the degree distribution of social networks; sensing and information diffusion; and coordination of decision making via game-theoretic learning.

Explores investment in financial assets treated as a signal processing and optimization problem. The book explores such connections and capitalizes on the existing mathematical tools developed in wireless communications and signal processing to solve real-life problems arising in the financial markets.

Considers the estimation of covariance matrices in non-standard conditions including heavy-tailed distributions and outlier contamination. Prior knowledge on the structure of these matrices is exploited in order to improve the estimation accuracy.

A handbook of known formulas which directly relate to information measures and estimation measures. This book provides intuition and draws connections between these formulas, highlights some important applications, and motivates further explorations. The main focus is on such formulas in the context of the additive Gaussian noise model.

Bivariate Markov processes play a central role in the theory and applications of estimation, control, queuing, biomedical engineering, and reliability. This book presents some of the fundamentals of the theory of bivariate Markov processes, and reviews the parameters and signal estimation approaches that are associated with these Markov processes.

Introduces the fundamentals of Markovian modeling in image segmentation as well as providing a brief overview of recent advances in the field. Segmentation is considered in a common framework, called image labelling, where the problem is reduced to assigning labels to pixels.

Introduces the expectation-maximization (EM) algorithm and provides an intuitive and mathematically rigorous understanding of this method. The book describes in detail two of the most popular applications of EM: estimating Gaussian mixture models, and estimating hidden Markov models.

Explores the fundamental subject of source coding. Based on a simple and accessible presentation of the fundamentals of information and rate distortion theory, the authors describe the subjects of entropy coding and quantization as well as predictive and transform coding.

Provides an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks. This is a timely and important book for researchers and students with an interest in deep learning methodology.

Offers an introduction to redundant signal representations called frames. These representations have recently emerged as a powerful tool in the signal processing toolbox, spurred by a host of recent applications requiring some level of redundancy. The book asks, why and where should one use frames? Anywhere where redundancy is a must.

Offers a gentle and novel introduction to Reproducing Kernel Hilbert Spaces theory. The book also presents several classical applications, and concludes by focusing on recent developments in the machine learning literature concerning embeddings of random variables.