This 2nd edition provides an in-depth, up-to-date, unified, and comprehensive treatment of the fundamentals of the theory of active networks and its applications to feedback amplifier design. The main purpose is to discuss the topics that are of fundamental importance that transcends the advent of new devices and design tools. Intended primarily as a text in circuit theory in electrical engineering for senior and/or first year graduate students, the book also serve as a reference for researchers and practicing engineers in industry.A special feature of the book is that it bridges the gap between theory and practice, with abundant examples showing how theory solves problems. These examples are actual practical problems, not idealized illustrations of the theory. The topic on topological analysis of active networks is also expanded to benefit more discerning readers.

Active Network Analysis gives a comprehensive treatment of the fundamentals of the theory of active networks and its applications to feedback amplifiers. The guiding light throughout has been to extract the essence of the theory and to discuss those topics that are of fundamental importance and that will transcend the advent of new devices and design tools. The book provides under one cover a unified, comprehensive, and up-to-date coverage of these recent developments and their practical engineering applications. In selecting the level of presentation, considerable attention has been given to the fact that many readers may be encountering some of these topics for the first time. Thus basic introductory material has been included. The work is illustrated by a large number of carefully chosen and well-prepared examples.

This compendium contains 10 chapters written by world renowned researchers with expertise in semantic computing, genome sequence analysis, biomolecular interaction, time-series microarray analysis, and machine learning algorithms.The salient feature of this book is that it highlights eight types of computational techniques to tackle different biomedical applications. These techniques include unsupervised learning algorithms, principal component analysis, fuzzy integral, graph-based ensemble clustering method, semantic analysis, interolog approach, molecular simulations and enzyme kinetics.The unique volume will be a useful reference material and an inspirational read for advanced undergraduate and graduate students, computer scientists, computational biologists, bioinformatics and biomedical professionals.

This invaluable textbook covers the theory and circuit design techniques to implement CMOS (Complementary Metal-Oxide Semiconductor) class-D audio amplifiers integrated circuits. The first part of the book introduces the motivation and fundamentals of audio amplification. The loudspeaker's operation and main audio performance metrics explains the limitations in the amplification process. The second part of this book presents the operating principle and design procedure of the class-D amplifier main architectures to provide the performance tradeoffs. The circuit design procedures involved in each block of the class-D amplifier architecture are highlighted. The third part of this book discusses several important design examples introducing state-of-the-art architectures and circuit design techniques to improve the audio performance, power consumption, and efficiency of standard class-D audio amplifiers.

This comprehensive compendium discusses the basics of graph theory to its application, focusing on the application of graph theory to mobile communications.A mobile communication connects a mobile terminal and a base station wirelessly, and the base station enables communications all over the world via a wired and satellite communication system. This means that the mobile communication system includes wire and wireless technologies, and also hardware such as analog electric circuits, digital circuits and a software part such as computer algorithms.This useful reference text deeply studies how the network structure influences the performance of the corresponding system.

With the increasing availability of omics data and mounting evidence of the usefulness of computational approaches to tackle multi-level data problems in bioinformatics and biomedical research in this post-genomics era, computational biology has been playing an increasingly important role in paving the way as basis for patient-centric healthcare.Two such areas are: (i) implementing AI algorithms supported by biomedical data would deliver significant benefits/improvements towards the goals of precision medicine (ii) blockchain technology will enable medical doctors to securely and privately build personal healthcare records, and identify the right therapeutic treatments and predict the progression of the diseases.A follow-up in the publication of our book Computation Methods with Applications in Bioinformatics Analysis (2017), topics in this volume include: clinical bioinformatics, omics-based data analysis, Artificial Intelligence (AI), blockchain, big data analytics, drug discovery, RNA-seq analysis, tensor decomposition and Boolean network.