An increasing complexity of models used to predict real-world systems leads to the need for algorithms to replace complex models with far simpler ones, while preserving the accuracy of the predictions. This two-volume handbook covers methods as well as applications. This second volume focuses on applications in engineering, biomedical engineering, computational physics and computer science.
An increasing complexity of models used to predict real-world systems leads to the need for algorithms to replace complex models with far simpler ones, while preserving the accuracy of the predictions. This two-volume handbook covers methods as well as applications. This second volume focuses on applications in engineering, biomedical engineering, computational physics and computer science.
An increasing complexity of models used to predict real-world systems leads to the need for algorithms to replace complex models with far simpler ones, while preserving the accuracy of the predictions. This two-volume handbook covers methods as well as applications. This first volume focuses on real-time control theory, data assimilation, real-time visualization, high-dimensional state spaces and interaction of different reduction techniques.
This book addresses the state of the art of reduced order methods for modelling and computational reduction of complex parametrised systems, governed by ordinary and/or partial differential equations, with a special emphasis on real time computing techniques and applications in various fields. Consisting of four contributions presented at the CIME summer school, the book presents several points of view and techniques to solve demanding problems of increasing complexity. The focus is on theoretical investigation and applicative algorithm development for reduction in the complexity – the dimension, the degrees of freedom, the data – arising in these models. The book is addressed to graduate students, young researchers and people interested in the field. It is a good companion for graduate/doctoral classes.
Many applications in the emerging smart environments, such as smart cities and intelligent transportation, require an accurate situational awareness, i.e., information about presence and location of all users in the environment. A reliable situational awareness is based on robust communications and sensing techniques, as well as large-scale deployment. Therefore, an integration of sensing functionality is envisioned as a native capability of next generation wireless networks. This work provides a novel radio sensing approach for the localization of non-cooperative users, referred to as multipath-enhanced device-free localization (MDFL). For localization, MDFL exploits user-induced variations in the received power of static signal components, i.e., line-of-sight and multipath components. Due to its low computational complexity, the proposed MDFL approach represents a suitable technique for the emerging perceptive wireless networks and can help to enable ubiquitous radio sensing.
Compiled by leading authorities, Aerospace Navigation Systems is a compendium of chapters that present modern aircraft and spacecraft navigation methods based on up-to-date inertial, satellite, map matching and other guidance techniques. Ranging from the practical to the theoretical, this book covers navigational applications over a wide range of aerospace vehicles including aircraft, spacecraft and drones, both remotely controlled and operating as autonomous vehicles. It provides a comprehensive background of fundamental theory, the utilisation of newly-developed techniques, incorporates the most complex and advanced types of technical innovation currently available and presents a vision for future developments. Satellite Navigation Systems (SNS), long range navigation systems, short range navigation systems and navigational displays are introduced, and many other detailed topics include Radio Navigation Systems (RNS), Inertial Navigation Systems (INS), Homing Systems, Map Matching and other correlated-extremalsystems, and both optimal and sub-optimal filtering in integrated navigation systems.
This book details some of the major developments in the implementation of compressive sensing in radio applications for electronic defense and warfare communication use. It provides a comprehensive background to the subject and at the same time describes some novel algorithms. It also investigates application value and performance-related parameters of compressive sensing in scenarios such as direction finding, spectrum monitoring, detection, and classification.
A valuable addition to the Wiley Series in Microwave and Optical Engineering Today's modern wireless mobile communications depend on adaptive "smart" antennas to provide maximum range and clarity. With the recent explosive growth of wireless applications, smart antenna technology has achieved widespread commercial and military applications. The only book available on the topic of adaptive antennas using digital technology, this text reflects the latest developments in smart antenna technology and offers timely information on fundamentals, as well as new adaptive techniques developed by the authors. Coupling electromagnetic aspects of antenna design with signal processing techniques designed to promote accurate and efficient information exchange, the text presents various mechanisms for characterizing signal-path loss associated with signal propagation, particularly for mobile wireless communications systems based on such techniques as joint space-frequency adaptive processing. In clear, accessible language, the authors: * explain the difference between adaptive antennas and adaptive signal processing * Illustrate the procedures for adaptive processing using directive elements in a conformal array * clarify multistage analysis procedure which combines electromagnetic analysis with signal processing * present a survey of the various models for characterizing radio wave propagation in urban and rural environments * describe a method wherein it is possible to identify and eliminate multipath without spatial diversity * optimize the location of base stations in a complex environment The text is an excellent resource for researchers and engineers working in electromagnetics and signal processing who deal with performance improvement of adaptive techniques, as well as those who are concerned with the characterization of propagation channels and applications of airborne phased arrays.
Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains. The book focuses on three primary aspects of data clustering: Methods, describing key techniques commonly used for clustering, such as feature selection, agglomerative clustering, partitional clustering, density-based clustering, probabilistic clustering, grid-based clustering, spectral clustering, and nonnegative matrix factorization Domains, covering methods used for different domains of data, such as categorical data, text data, multimedia data, graph data, biological data, stream data, uncertain data, time series clustering, high-dimensional clustering, and big data Variations and Insights, discussing important variations of the clustering process, such as semisupervised clustering, interactive clustering, multiview clustering, cluster ensembles, and cluster validation In this book, top researchers from around the world explore the characteristics of clustering problems in a variety of application areas. They also explain how to glean detailed insight from the clustering process—including how to verify the quality of the underlying clusters—through supervision, human intervention, or the automated generation of alternative clusters.
The LNCS journal Transactions on Large-Scale Data- and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing (e.g., computing resources, services, metadata, data sources) across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. This, the 45th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains eight revised selected regular papers. Topics covered include data analysis, information extraction, blockchains, and big data.
