Böcker i Foundations and Trends (R) in Signal Processing-serien

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.

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.

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.

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.

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.

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.

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.