P. S. Neelakanta – författare

With quantum leaps in science and technology occurring at breakneck speed, professionals in virtually every field face a daunting task-practicing their discipline while keeping abreast of new advances and applications in their filed. In no field is this more applicable than in the rapidly growing field of telecommunications engineering. Practicing engineers who work with ATM technology on a daily basis must not only keep their skill sharp in areas such as ATM network interfaces, protocols, and standards, but they must also stay informed, about new classes of ATM applications.A Textbook on ATM Telecommunications gives active telecommunications engineers the advantage they need to stay sharp in their field. From the very basics of ATM to state-of-the-art applications, it covers the gamut of topics related to this intriguing switching and multiplexing strategy.Starting with an introduction to telecommunications, this text combines the theory underlying broadband communications technology with applied practical instruction and lessons gleaned from industry. The author covers fundamental communications and network theory, followed by applied ATM networking. Each chapter includes design exercises as well as worked examples .A Textbook on ATM Telecommunications includes examples of design and implementation-making it an ideal took for both aspiring and practicing telecommunication professionals.Features

Neural Network Modeling offers a cohesive approach to the statistical mechanics and principles of cybernetics as a basis for neural network modeling. It brings together neurobiologists and the engineers who design intelligent automata to understand the physics of collective behavior pertinent to neural elements and the self-control aspects of neurocybernetics. The theoretical perspectives and explanatory projections portray the most current information in the field, some of which counters certain conventional concepts in the visualization of neuronal interactions.

Information theoretics vis-a-vis neural networks generally embodies parametric entities and conceptual bases pertinent to memory considerations and information storage, information-theoretic based cost-functions, and neurocybernetics and self-organization. Existing studies only sparsely cover the entropy and/or cybernetic aspects of neural information.Information-Theoretic Aspects of Neural Networks cohesively explores this burgeoning discipline, covering topics such as:Shannon information and information dynamics neural complexity as an information processing system memory and information storage in the interconnected neural web extremum (maximum and minimum) information entropy neural network training non-conventional, statistical distance-measures for neural network optimizations symmetric and asymmetric characteristics of information-theoretic error-metrics algorithmic complexity based representation of neural information-theoretic parameters genetic algorithms versus neural information dynamics of neurocybernetics viewed in the information-theoretic plane nonlinear, information-theoretic transfer function of the neural cellular units statistical mechanics, neural networks, and information theory semiotic framework of neural information processing and neural information flow fuzzy information and neural networks neural dynamics conceived through fuzzy information parameters neural information flow dynamics informatics of neural stochastic resonanceInformation-Theoretic Aspects of Neural Networks acts as an exceptional resource for engineers, scientists, and computer scientists working in the field of artificial neural networks as well as biologists applying the concepts of communication theory and protocols to the functioning of the brain. The information in this book explores new avenues in the field and creates a common platform for analyzing the neural complex as well as artificial neural networks.

Information theoretics vis-a-vis neural networks generally embodies parametric entities and conceptual bases pertinent to memory considerations and information storage, information-theoretic based cost-functions, and neurocybernetics and self-organization. Existing studies only sparsely cover the entropy and/or cybernetic aspects of neural information.Information-Theoretic Aspects of Neural Networks cohesively explores this burgeoning discipline, covering topics such as:Shannon information and information dynamicsneural complexity as an information processing systemmemory and information storage in the interconnected neural webextremum (maximum and minimum) information entropyneural network trainingnon-conventional, statistical distance-measures for neural network optimizationssymmetric and asymmetric characteristics of information-theoretic error-metricsalgorithmic complexity based representation of neural information-theoretic parametersgenetic algorithms versus neural informationdynamics of neurocybernetics viewed in the information-theoretic planenonlinear, information-theoretic transfer function of the neural cellular unitsstatistical mechanics, neural networks, and information theorysemiotic framework of neural information processing and neural information flowfuzzy information and neural networksneural dynamics conceived through fuzzy information parametersneural information flow dynamicsinformatics of neural stochastic resonanceInformation-Theoretic Aspects of Neural Networks acts as an exceptional resource for engineers, scientists, and computer scientists working in the field of artificial neural networks as well as biologists applying the concepts of communication theory and protocols to the functioning of the brain. The information in this book explores new avenues in the field and creates a common platform for analyzing the neural complex as well as artificial neural networks.

Information theoretics vis-a-vis neural networks generally embodies parametric entities and conceptual bases pertinent to memory considerations and information storage, information-theoretic based cost-functions, and neurocybernetics and self-organization. Existing studies only sparsely cover the entropy and/or cybernetic aspects of neural information.Information-Theoretic Aspects of Neural Networks cohesively explores this burgeoning discipline, covering topics such as:Shannon information and information dynamicsneural complexity as an information processing systemmemory and information storage in the interconnected neural webextremum (maximum and minimum) information entropyneural network trainingnon-conventional, statistical distance-measures for neural network optimizationssymmetric and asymmetric characteristics of information-theoretic error-metricsalgorithmic complexity based representation of neural information-theoretic parametersgenetic algorithms versus neural informationdynamics of neurocybernetics viewed in the information-theoretic planenonlinear, information-theoretic transfer function of the neural cellular unitsstatistical mechanics, neural networks, and information theorysemiotic framework of neural information processing and neural information flowfuzzy information and neural networksneural dynamics conceived through fuzzy information parametersneural information flow dynamicsinformatics of neural stochastic resonanceInformation-Theoretic Aspects of Neural Networks acts as an exceptional resource for engineers, scientists, and computer scientists working in the field of artificial neural networks as well as biologists applying the concepts of communication theory and protocols to the functioning of the brain. The information in this book explores new avenues in the field and creates a common platform for analyzing the neural complex as well as artificial neural networks.

Neural Network Modeling offers a cohesive approach to the statistical mechanics and principles of cybernetics as a basis for neural network modeling. It brings together neurobiologists and the engineers who design intelligent automata to understand the physics of collective behavior pertinent to neural elements and the self-control aspects of neurocybernetics. The theoretical perspectives and explanatory projections portray the most current information in the field, some of which counters certain conventional concepts in the visualization of neuronal interactions.

Neural Network Modeling offers a cohesive approach to the statistical mechanics and principles of cybernetics as a basis for neural network modeling. It brings together neurobiologists and the engineers who design intelligent automata to understand the physics of collective behavior pertinent to neural elements and the self-control aspects of neurocybernetics. The theoretical perspectives and explanatory projections portray the most current information in the field, some of which counters certain conventional concepts in the visualization of neuronal interactions.