Kommunikationsteknik / telekommunikation

Signal processing traditionally relies on classical statistical modeling techniques. Such model-based methods utilize mathematical formulations that represent the underlying physics, prior information and additional domain knowledge. Simple classical models are useful but sensitive to inaccuracies and may lead to poor performance when real systems display complex or dynamic behavior. More recently, deep learning approaches that use highly parametric deep neural networks (DNNs) are becoming increasingly popular. Deep learning systems do not rely on mathematical modeling, and learn their mapping from data, which allows them to operate in complex environments. However, they lack the interpretability and reliability of model-based methods, typically require large training sets to obtain good performance, and tend to be computationally complex. Model-based signal processing methods and data-centric deep learning each have their pros and cons. These paradigms can be characterized as edges of a continuous spectrum varying in specificity and parameterization. The methodologies that lie in the middle ground of this spectrum, thus integrating model-based signal processing with deep learning, are referred to as model-based deep learning, and are the focus here. This monograph provides a tutorial style presentation of model-based deep learning methodologies. These are families of algorithms that combine principled mathematical models with data-driven systems to benefit from the advantages of both approaches. Such model-based deep learning methods exploit both partial domain knowledge, via mathematical structures designed for specific problems, as well as learning from limited data. The monograph includes running signal processing examples, in super-resolution, tracking of dynamic systems, and array processing. It is shown how they are expressed using the provided characterization and specialized in each of the detailed methodologies. The aim is to facilitate the design and study of future systems at the intersection of signal processing and machine learning that incorporate the advantages of both domains. The source code of the numerical examples are available and reproducible as Python notebooks.

The official publication of the Railway Signal Association, this journal provides in-depth coverage of issues and developments in railway signaling technology, including articles on equipment, components, maintenance, testing, and more. Aimed at professionals in the field, this journal also offers valuable insights for anyone interested in the latest advances in railway safety and communications.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it.This work is in the "public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.

This textbook presents the fundamental concepts and theories in electromagnetic theory in a very simple, systematic, and comprehensive way. The book is written in a lucid manner so that they are able to understand the realization behind the mathematical concepts which are the backbone of this subject. All the subject fundamentals and related derivations are discussed in an easy and comprehensive way to make the students strong about the basics of the electromagnetic theory. The philosophy of presentation and material content in the book is based on concept-based approach toward the subject. The key features also lies in the solutions of several interesting numerical problems so that the students should have the idea of the practical usages of the subject. The book benefits students who are taking introductory courses in electromagnetic wave and field theory for applications in communication engineering.

This book presents some of the most advanced leading-edge technology for the fourth Industrial Revolution -- known as ¿Industry 4.0.¿ The book provides a comprehensive understanding of the interconnections of AI, IoT, big data and cloud computing as integral to the technologies that revolutionize the way companies produce and distribute products and the way local governments deliver their services. The book emphasizes that at every phase of the supply chain, manufactures are found to be interweaving AI, robotics, IoT, big data/machine learning, and cloud computing into their production facilities and throughout their distribution networks. Equally important, the authors show how their research can be applied to computer vision, cyber security, database and compiler theory, natural language processing, healthcare, education and agriculture.Presents the fundamentals of AI, IoT, and cloud computing and how they can be incorporatedin Industry 4.0 applicationsMotivates readers to address challenges in the areas of speech communication and signal processingProvides numerous examples, case studies, technical descriptions, and approaches of AI/ML

This book introduces a unique perspective on the use of data from popular emerging technologies and the effect on user quality of experience (QoE). The term data is first refined into specific types of data such as financial data, personal data, public data, context data, generated data, and the popular big data. The book focuses the responsible use of data, with consideration to ethics and wellbeing, in each setting. The specific nuances of different technologies bring forth interesting case studies, which the book breaks down into mathematical models so they can be analyzed and used as powerful tools. Overall, this perspective on the use of data from popular emerging technologies and the resulting QoE analysis will greatly benefit researchers, educators and students in fields related to ICT studies, especially where there is additional interest in ethics and wellbeing, user experience, data management, and their link to emerging technologies.