Jun Hu - Böcker

Introduction to Structural Bioinformatics offers a complete overview on the fundamental concepts and methodologies of structural bioinformatics and computational structural biology. The book is divided into three sections, beginning with a discussion of the key principles of bioinformatics and fundamental aspects, including bioinformatics databases, multiple sequence alignment, and machine learning. Section two then moves on to structural bioinformatics, where topics include Monte Carlo simulation, protein structure prediction, RNA structure prediction, and protein design. The final section of the book focuses on experimental structural determination, where chapters focus on techniques including X-ray crystallography, nuclear magnetic resonance and cryo-electron microscopy. This is an ideal guide on key principles, methods, and the most up-to-date developments across structural bioinformatics and computational structural biology. It will be a comprehensive reference for postgraduate students, instructors, and researchers working in these and adjacent subjects.Discusses cutting-edge AI and deep-learning techniques, including AlphaFold and D-I-TASSER, along with their impact on structural bioinformaticsExplores protein and RNA structure predictionConsiders the most recent advances in the field as well as more classical physics-based approachesFeatures chapter outlines, definitions, key learning objectives, and case studies throughout the book to aid comprehension

This volume contains state of the engineering practice and recent research in the field of built infrastructure and natural hazards.

This book constitutes revised selected papers from the workshops held in conjunction with EICS 2025 and it contains This book presents a series of revised papers selected from the Workshops and Doctoral Consortium organized in conjunction with the 17th ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS 2025) which was held in Trier, Germany, during June 2025.The 17 full papers together with 16 short papers included in this book were carefully reviewed and selected from 71 submissions. They were organized in topical sections as follows:  Engineering Interactive Computing Systems Doctoral Consortium (EICS DC 2025)Engineering Interactive Systems Embedding AI Technologies (EISEAIT 2025)Experience 2.0 and Beyond – Engineering Cross Devices and Multiple Realities (EXDMR 2025)Interactive AI for Preventive Health: Personalization, Gamification, and Ethics (IAI4PH 2025)

This monograph introduces methods for handling filtering and control problems in nonlinear stochastic systems arising from network-induced phenomena consequent on limited communication capacity. Such phenomena include communication delay, packet dropout, signal quantization or saturation, randomly occurring nonlinearities and randomly occurring uncertainties.The text is self-contained, beginning with an introduction to nonlinear stochastic systems, network-induced phenomena and filtering and control, moving through a collection of the latest research results which focuses on the three aspects of:·         the state-of-the-art of nonlinear filtering and control;·         recent advances in recursive filtering and sliding mode control; and·         their potential for application in networked control systems, and concluding with some ideas for future research work. New concepts such as the randomly occurring uncertainty and the probability-constrained performance index are proposed to make the network models as realistic as possible. The power of combinations of such recent tools as the completing-the-square and sums-of-squares techniques, Hamilton‒Jacobi‒Isaacs matrix inequalities, difference linear matrix inequalities and parameter-dependent matrix inequalities is exploited in treating the mathematical and computational challenges arising from nonlinearity and stochasticity.Nonlinear Stochastic Systems with Network-Induced Phenomena establishes a unified framework of control and filtering which will be of value to academic researchers in bringing structure to problems associated with an important class of networked system and offering new means of solving them. The significance of the new concepts, models and methods presented for practical control engineering and signal processing will also make it a valuable reference for engineers dealing with nonlinear control and filteringproblems.

This monograph introduces methods for handling filtering and control problems in nonlinear stochastic systems arising from network-induced phenomena consequent on limited communication capacity. Such phenomena include communication delay, packet dropout, signal quantization or saturation, randomly occurring nonlinearities and randomly occurring uncertainties.The text is self-contained, beginning with an introduction to nonlinear stochastic systems, network-induced phenomena and filtering and control, moving through a collection of the latest research results which focuses on the three aspects of:·         the state-of-the-art of nonlinear filtering and control;·         recent advances in recursive filtering and sliding mode control; and·         their potential for application in networked control systems, and concluding with some ideas for future research work. New concepts such as the randomly occurring uncertainty and the probability-constrained performance index are proposed to make the network models as realistic as possible. The power of combinations of such recent tools as the completing-the-square and sums-of-squares techniques, Hamilton‒Jacobi‒Isaacs matrix inequalities, difference linear matrix inequalities and parameter-dependent matrix inequalities is exploited in treating the mathematical and computational challenges arising from nonlinearity and stochasticity.Nonlinear Stochastic Systems with Network-Induced Phenomena establishes a unified framework of control and filtering which will be of value to academic researchers in bringing structure to problems associated with an important class of networked system and offering new means of solving them. The significance of the new concepts, models and methods presented for practical control engineering and signal processing will also make it a valuable reference for engineers dealing with nonlinear control and filteringproblems.

This book presents neuromorphic cognitive systems from a learning and memory-centered perspective.

This book presents neuromorphic cognitive systems from a learning and memory-centered perspective.

This book is concerned with the variance-constrained optimized filtering problems and their potential applications for nonlinear time-varying dynamical systems.

These two volumes constitute the revised selected papers of the 6th International Conference, CSEI 2024, held in Haikou, China, during November 1–3, 2024. The 51 full papers included in these volumes were carefully reviewed and selected from 171 submissions.