Foreword by James L. Massey. Codes, Graphs, and Systems is an excellent reference for both academic researchers and professional engineers working in the fields of communications and signal processing. A collection of contributions from world-renowned experts in coding theory, information theory, and signal processing, the book provides a broad perspective on contemporary research in these areas. Survey articles are also included. Specific topics covered include convolutional codes and turbo codes; detection and equalization; modems; physics and information theory; lattices and geometry; and behaviors and codes on graphs. Codes, Graphs, and Systems is a tribute to the leadership and profound influence of G. David Forney, Jr. The 35 contributors to the volume have assembled their work in his honor.
This book stresses the connection between, and the applications of, design theory to graphs and codes. Beginning with a brief introduction to design theory and the necessary background, the book also provides relevant topics for discussion from the theory of graphs and codes.
Coding theory, system theory, and symbolic dynamics have much in common. A major new theme in this area of research is that of codes and systems based on graphical models. This volume contains survey and research articles from leading researchers at the interface of these subjects.
Fundamentals of Codes, Graphs, and Iterative Decoding is an explanation of how to introduce local connectivity, and how to exploit simple structural descriptions. Chapter 1 provides an overview of Shannon theory and the basic tools of complexity theory, communication theory, and bounds on code construction. Chapters 2 - 4 provide an overview of "classical" error control coding, with an introduction to abstract algebra, and block and convolutional codes. Chapters 5 - 9 then proceed to systematically develop the key research results of the 1990s and early 2000s with an introduction to graph theory, followed by chapters on algorithms on graphs, turbo error control, low density parity check codes, and low density generator codes.
This book shares key insights into system performance and management analytics, demonstrating how the field of analytics is currently changing and how it is used to monitor companies’ efforts to drive performance.Managing business performance facilitates the effective accomplishment of strategic and operational goals, and there is a clear and direct correlation between using performance management applications and improved business and organizational results. As such, performance and management analytics can yield a range of direct and indirect benefits, boost operational efficiency and unlock employees’ latent potential, while at the same time aligning services with overarching goals.The book addresses a range of topics, including software reliability assessment, testing, quality management, system-performance management, analysis using soft-computing techniques, and management analytics. It presents a balanced, holistic approach to viewing the world from both a technical and managerial perspective by considering performance and management analytics. Accordingly, it offers a comprehensive guide to one of the most pressing issues in today’s technology-dominated world, namely, that most companies and organizations find themselves awash in a sea of data, but lack the human capital, appropriate tools and knowledge to use it to help them create a competitive edge.
This three volume book set constitutes the proceedings of the Third International Conference on Machine Learning for Cyber Security, ML4CS 2020, held in Xi’an, China in October 2020. The 118 full papers and 40 short papers presented were carefully reviewed and selected from 360 submissions. The papers offer a wide range of the following subjects: Machine learning, security, privacy-preserving, cyber security, Adversarial machine Learning, Malware detection and analysis, Data mining, and Artificial Intelligence.
Provides a thorough introduction to the development, operation, maintenance, and troubleshooting of mobile communications systems Mobile Communications Systems Development: A Practical Introduction for System Understanding, Implementation, and Deployment is a comprehensive “how to” manual for mobile communications system design, deployment, and support. Providing a detailed overview of end-to-end system development, the book encompasses operation, maintenance, and troubleshooting of currently available mobile communication technologies and systems. Readers are introduced to different network architectures, standardization, protocols, and functions including 2G, 3G, 4G, and 5G networks, and the 3GPP standard. In-depth chapters cover the entire protocol stack from the Physical (PHY) to the Application layer, discuss theoretical and practical considerations, and describe software implementation based on the 3GPP standardized technical specifications. The book includes figures, tables, and sample computer code to help readers thoroughly comprehend the functions and underlying concepts of a mobile communications network. Each chapter includes an introduction to the topic and a chapter summary. A full list of references, and a set of exercises are also provided at the end of the book to test comprehension and strengthen understanding of the material. Written by a respected professional with more than 20 years’ experience in the field, this highly practical guide: Provides detailed introductory information on GSM, GPRS, UMTS, and LTE mobile communications systems and networks Describes the various aspects and areas of the LTE system air interface and its protocol layers Covers troubleshooting and resolution of mobile communications systems and networks issues Discusses the software and hardware platforms used for the development of mobile communications systems network elements Includes 5G use cases, enablers, and architectures that cover the 5G NR (New Radio) and 5G Core Network Mobile Communications Systems Development is perfect for graduate and postdoctoral students studying mobile communications and telecom design, electronic engineering undergraduate students in their final year, research and development engineers, and network operation and maintenance personnel.
The symposia in applied mathematics have been held under the auspices of the American Mathematical Society and others since 1967. This books connects coding theory with actual applications in consumer electronics and with other areas of mathematics. It covers in detail the mathematical foundations of digital data storage and makes connections to symbolic dynamics, linear systems, and finite automata. It also explores the use of algebraic geometry within coding theory and examines links with finite geometry, statistics, and theoretical computer science.
Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.
Presented here is an integrated approach - perhaps the first in its class - of advanced Rigid Body Kinematics with the object-oriented C++ code that implements the rigid body objects and brings them to life. Thinking in terms of objects is the natural way of thinking. The concept of object has existed in Science for centuries. More recently, objects were introduced in Computation, and object-oriented programming languages were created. Yet the concept of object is not routinely used when teaching Science, and the idea that objects can come alive in a computer has not yet been fully exploited.This book is Volume 2 of the multi-volume series "Articulated Robot Mechanics and C++ Code". Volume 1 is "Vectors, Matrices and C++ Code", published in 2004. Volumes to be published are "Articulated Robot Dynamics and C++ Code", and "Articulated Robot Control and C++ Code". More volumes may be added in the future.This book integrates advanced rigid body Kinematics with object-oriented concepts and the actual code implementing them. It is both a textbook and a software release, complete with software documentation and the mathematical background that supports the code. The source code is included by download and readers can use it for their own programming. The reader will need a basic knowledge of Physics, particularly Mechanics, and Algebra and Trigonometry. Familiarity with C++ is not required because a course on C++ is included in Volume 1. You should read this book if you are a developer who needs an advanced background in rigid body Kinematics, a student of Physics or Engineering who needs to learn C++, a scientist who needs to write advanced code but can't waste time developing the basics, or you just need ready-to-use C++ source code for your project.
