This open access Brief introduces the basic principles of control theory in a concise self-study guide. It complements the classic texts by emphasizing the simple conceptual unity of the subject. A novice can quickly see how and why the different parts fit together. The concepts build slowly and naturally one after another, until the reader soon has a view of the whole. Each concept is illustrated by detailed examples and graphics. The full software code for each example is available, providing the basis for experimenting with various assumptions, learning how to write programs for control analysis, and setting the stage for future research projects. The topics focus on robustness, design trade-offs, and optimality. Most of the book develops classical linear theory. The last part of the book considers robustness with respect to nonlinearity and explicitly nonlinear extensions, as well as advanced topics such as adaptive control and model predictive control. New students, as well as scientists from other backgrounds who want a concise and easy-to-grasp coverage of control theory, will benefit from the emphasis on concepts and broad understanding of the various approaches.

Advances in communications technology continue to accelerate. To maintain the competitive edge in such a dynamic environment, today's managers, professionals and engineers can expect to be challenged daily to keep pace with the technical and organizational issues, opportunities and threats surrounding the operation and management of any communications system. The purpose of this book is to enable these people to detect, understand, handle and control a communications system during a crisis.

• Integrated use of real-world examples.
• Numerous case studies illustrate how actual disasters are detected, studies, and successfully controlled.
• Delineates the procedures required for the smooth and safe operation of telecommunications, broadcasting and computer systems during a crisis.

Mit seinem Workshop 2017 zum Thema "Logistik und Echtzeit" bietet der GI/GMA/ITG-Fachausschuss Echtzeitsysteme Wissenschaftlern, Nutzern und Herstellern ein Forum, auf dem neue Trends und Entwicklungen zu folgenden Programmschwerpunkten vorgestellt werden: Eingebettete Systeme, Echtzeitkommunikation, Leistungssteuerung und -bewertung, Logistik und Echtzeit sowie funktionale Sicherheit. Berichte zu aktuellen Anwendungen und zur Ausbildung runden die Publikation ab. 

There are many textbooks on algorithms focusing on big-O notation and basic design principles. This book offers a unique approach to taking the design and analyses to the level of predictable practical efficiency, discussing core and classic algorithmic problems that arise in the development of big data applications, and presenting elegant solutions of increasing sophistication and efficiency. Solutions are analyzed within the classic RAM model, and the more practically significant external-memory model that allows one to perform I/O-complexity evaluations. Chapters cover various data types, including integers, strings, trees, and graphs, algorithmic tools such as sampling, sorting, data compression, and searching in dictionaries and texts, and lastly, recent developments regarding compressed data structures. Algorithmic solutions are accompanied by detailed pseudocode and many running examples, thus enriching the toolboxes of students, researchers, and professionals interested in effective and efficient processing of big data.

How long does it take for your website to load? Web performance is just as critical for small and medium-sized websites as it is for massive websites that receive tons of hits. Before you pour money and time into rewriting your code or replacing your infrastructure, first consider a reverse-caching proxy server like Varnish. With this practical book, you'll learn how Varnish can give your website or API an immediate performance boost. Varnish mimicks the behavior of your webserver, caches its output in memory, and serves the result directly to clients without having to access your webserver. If you're a web developer familiar with HTTP, this book helps you master Varnish basics, so you can get up and running in no time. You'll learn how to use the Varnish Configuration Language and HTTP best practices to achieve faster performance and a higher hit rate.Understand how Varnish helps you gain optimum web performance Use HTTP to improve the cache-ability of your websites, web applications, and APIs Properly invalidate your cache when the origin data changes Optimize access to your backend servers Avoid common mistakes when using Varnish in the wild Use logging and debugging tools to examine the behavior of Varnish

This book is both a reference for engineers and scientists and a teaching resource, featuring tutorial chapters and research papers on feature extraction. Until now there has been insufficient consideration of feature selection algorithms, no unified presentation of leading methods, and no systematic comparisons.

This book contains the full papers on which the invited lectures of the 4th International Conference on Geotechnical Earthquake Engineering (4ICEGE) were based. The conference was held in Thessaloniki, Greece, from 25 to 28 June, 2007. The papers offer a comprehensive overview of the progress achieved in soil dynamics and geotechnical earthquake engineering, examine ongoing and unresolved issues, and discuss ideas for the future.

What is combinatorial optimization? Traditionally, a problem is considered to be c- binatorial if its set of feasible solutions is both ?nite and discrete, i. e. , enumerable. For example, the traveling salesman problem asks in what order a salesman should visit the cities in his territory if he wants to minimize his total mileage (see Sect. 2. 2. 2). The traveling salesman problem’s feasible solutions - permutations of city labels - c- prise a ?nite, discrete set. By contrast, Differential Evolution was originally designed to optimize functions de?ned on real spaces. Unlike combinatorial problems, the set of feasible solutions for real parameter optimization is continuous. Although Differential Evolution operates internally with ?oating-point precision, it has been applied with success to many numerical optimization problems that have t- ditionally been classi?ed as combinatorial because their feasible sets are discrete. For example, the knapsack problem’s goal is to pack objects of differing weight and value so that the knapsack’s total weight is less than a given maximum and the value of the items inside is maximized (see Sect. 2. 2. 1). The set of feasible solutions - vectors whose components are nonnegative integers - is both numerical and discrete. To handle such problems while retaining full precision, Differential Evolution copies ?oating-point - lutions to a temporary vector that, prior to being evaluated, is truncated to the nearest feasible solution, e. g. , by rounding the temporary parameters to the nearest nonnegative integer.

This book provides an original graph theoretical approach to the fundamental properties of wireless mobile ad-hoc networks. This approach is combined with a realistic radio model for physical links between nodes to produce new insight into network characteristics like connectivity, degree distribution, hopcount, interference and capacity. The book establishes directives for designing ad-hoc networks and sensor networks. It will interest the academic community, and engineers who roll out ad-hoc and sensor networks.

Fuzzy Cognitive Maps (FCM) constitute cognitive models in the form of fuzzy directed graphs consisting of two basic elements: the nodes, which basically correspond to “concepts� bearing different states of activation depending on the knowledge they represent, and the “edges� denoting the causal effects that each source node exercises on the receiving concept expressed through weights. Weights take values in the interval [-1,1], which denotes the positive, negative or neutral causal relationship between two concepts. An FCM can be typically obtained through linguistic terms, inherent to fuzzy systems, but with a structure similar to the neural networks, which facilitates data processing, and has capabilities for training and adaptation. During the last 10 years, an exponential growth of published papers in FCMs was followed showing great impact potential. Different FCM structures and learning schemes have been developed, while numerous studies report their use in many contexts with highly successful modeling results.   The aim of this book is to fill the existing gap in the literature concerning fundamentals, models, extensions and learning algorithms for FCMs in knowledge engineering. It comprehensively covers the state-of-the-art FCM modeling and learning methods, with algorithms, codes and software tools, and provides a set of applications that demonstrate their various usages in applied sciences and engineering.

Machine learning is concerned with the analysis of large data and multiple variables. It is also often more sensitive than traditional statistical methods to analyze small data. The first and second volumes reviewed subjects like optimal scaling, neural networks, factor analysis, partial least squares, discriminant analysis, canonical analysis, fuzzy modeling, various clustering models, support vector machines, Bayesian networks, discrete wavelet analysis, association rule learning, anomaly detection, and correspondence analysis. This third volume addresses more advanced methods and includes subjects like evolutionary programming, stochastic methods, complex sampling, optional binning, Newton's methods, decision trees, and other subjects. Both the theoretical bases and the step by step analyses are described for the benefit of non-mathematical readers. Each chapter can be studied without the need to consult other chapters. Traditional statistical tests are, sometimes, priors to machine learning methods, and they are also, sometimes, used as contrast tests. To those wishing to obtain more knowledge of them, we recommend to additionally study (1) Statistics Applied to Clinical Studies 5th Edition 2012, (2) SPSS for Starters Part One and Two 2012, and (3) Statistical Analysis of Clinical Data on a Pocket Calculator Part One and Two 2012, written by the same authors, and edited by Springer, New York.

                                                                                                                                  

This book illustrates in detail how digital video can be utilized throughout a design process, from the early user studies, through making sense of the video content and envisioning the future with video scenarios, to provoking change with video artifacts. The text offers first-hand case studies in both academic and industrial contexts, and is complemented by video excerpts. It is a must-read for those wishing to create value through insightful design.

Approaches in Integrative Bioinformatics provides a basic introduction to biological information systems, as well as guidance for the computational analysis of systems biology. This book also covers a range of issues and methods that reveal the multitude of omics data integration types and the relevance that integrative bioinformatics has today. Topics include biological data integration and manipulation, modeling and simulation of metabolic networks, transcriptomics and phenomics, and virtual cell approaches, as well as a number of applications of network biology. It helps to illustrate the value of integrative bioinformatics approaches to the life sciences. This book is intended for researchers and graduate students in the field of Bioinformatics. Professor Ming Chen is the Director of the Bioinformatics Laboratory at the College of Life Sciences, Zhejiang University, Hangzhou, China. Professor Ralf Hofestädt is the Chair of the Department of Bioinformatics and Medical Informatics, Bielefeld University, Germany.

This book is devoted to a novel approach for dimensionality reduction based on the famous nearest neighbor method that is a powerful classification and regression approach. It starts with an introduction to machine learning concepts and a real-world application from the energy domain. Then, unsupervised nearest neighbors (UNN) is introduced as efficient iterative method for dimensionality reduction. Various UNN models are developed step by step, reaching from a simple iterative strategy for discrete latent spaces to a stochastic kernel-based algorithm for learning submanifolds with independent parameterizations. Extensions that allow the embedding of incomplete and noisy patterns are introduced. Various optimization approaches are compared, from evolutionary to swarm-based heuristics. Experimental comparisons to related methodologies taking into account artificial test data sets and also real-world data demonstrate the behavior of UNN in practical scenarios. The book contains numerous color figures to illustrate the introduced concepts and to highlight the experimental results.  

Nikolaos Karras behandelt die Senkung des Kühlenergiebedarfs batterieelektrischer Fahrzeuge als Beitrag zur Reichweitensteigerung. Kernaspekt ist hierbei die Untersuchung neuer, effizienterer Wassermantelgeometrien für Elektromotoren, die über eine hohe Wärmeabfuhr bei gleichzeitig geringem Druckverlust verfügen. Neben gängigen Geometrien werden auch alternative Strukturen untersucht. Die Analyse und Beurteilung erfolgt rein numerisch durch ein validiertes 3D-Simulationswerkzeug. Zur zusätzlichen Entlastung des Fahrzeugenergiespeichers werden weitere Kühlkreislauf- bzw. Fahrzeugparameter betrachtet. Für die Bewertung der einzelnen Maßnahmen dient eine 1D-Simulationsumgebung des Elektrofahrzeugs. Dessen Validierung erfolgt durch Klimawindkanal- sowie Streckentests. Abschließend wird die Basis-Fahrzeugkonfiguration mit der optimierten Fahrzeugkonfiguration verglichen.

There are many books on the use of numerical methods for solving engineering problems and for modeling of engineering artifacts. In addition there are many styles of such presentations ranging from books with a major emphasis on theory to books with an emphasis on applications. The purpose of this book is hopefully to present a somewhat different approach to the use of numerical methods for - gineering applications. Engineering models are in general nonlinear models where the response of some appropriate engineering variable depends in a nonlinear manner on the - plication of some independent parameter. It is certainly true that for many types of engineering models it is sufficient to approximate the real physical world by some linear model. However, when engineering environments are pushed to - treme conditions, nonlinear effects are always encountered. It is also such - treme conditions that are of major importance in determining the reliability or failure limits of engineering systems. Hence it is essential than engineers have a toolbox of modeling techniques that can be used to model nonlinear engineering systems. Such a set of basic numerical methods is the topic of this book. For each subject area treated, nonlinear models are incorporated into the discussion from the very beginning and linear models are simply treated as special cases of more general nonlinear models. This is a basic and fundamental difference in this book from most books on numerical methods.

This textbook teaches crucial statistical methods to answer research questions using a unique range of statistical software programs, including MINITAB and R. This textbook is developed for undergraduate students in agriculture, nursing, biology and biomedical research. Graduate students will also find it to be a useful way to refresh their statistics skills and to reference software options. The unique combination of examples is approached using MINITAB and R for their individual strengths. Subjects covered include among others data description, probability distributions, experimental design, regression analysis, randomized design and biological assay. Unlike other biostatistics textbooks, this text also includes outliers, influential observations in regression and an introduction to survival analysis. Material is taken from the author's extensive teaching and research in Africa, USA and the UK. Sample problems, references and electronic supplementary material accompany each chapter.

Learn to write C++ programs by interfacing a computer to a wide range of popular and fundamental real-world technologies. Unique and original approach to use the PC to do real things- not just number crunching and graphics – but writing programs to interact with the outside world. Learn C++ programming in an enjoyable and powerful way. Includes a purpose-designed circuit board

“We live in the age of data. In the last few years, the methodology of extracting insights from data or "data science" has emerged as a discipline in its own right. The R programming language has become one-stop solution for all types of data analysis. The growing popularity of R is due its statistical roots and a vast open source package library. The goal of “Beginning Data Science with R� is to introduce the readers to some of the useful data science techniques and their implementation with the R programming language. The book attempts to strike a balance between the how: specific processes and methodologies, and understanding the why: going over the intuition behind how a particular technique works, so that the reader can apply it to the problem at hand. This book will be useful for readers who are not familiar with statistics and the R programming language.

Today, as hundreds of genomes have been sequenced and thousands of proteins and more than ten thousand metabolites have been identi?ed, navigating safely through this wealth of information without getting completely lost has become crucial for research in, and teaching of, molecular biology. Consequently, a considerable number of tools have been developed and put on the market in the last two decades that describe the multitude of potential/putative interactions between genes, proteins, metabolites, and other biologically relevant compounds in terms of metabolic, genetic, signaling, and other networks, their aim being to support all sorts of explorations through bio-data bases currently called Systems Biology. As a result, navigating safely through this wealth of information-processing tools has become equally crucial for successful work in molecular biology. To help perform such navigation tasks successfully, this book starts by providing an extremely useful overview of existing tools for ?nding (or designing) and inv- tigating metabolic, genetic, signaling, and other network databases, addressing also user-relevant practical questions like • Is the database viewable through a web browser? • Is there a licensing fee? • What is the data type (metabolic, gene regulatory, signaling, etc. )? • Is the database developed/maintained by a curator or a computer? • Is there any software for editing pathways? • Is it possible to simulate the pathway? It then goes on to introduce a speci?c such tool, that is, the fabulous “Cell - lustrator 3. 0� tool developed by the authors.

Data Driven Decisions: A Practical Toolkit for Library and Information Professionals is a simple, jargon-free guide to using data for decision making in library services. The book walks readers step-by-step through each stage of implementing, reviewing and embedding data driven decisions in their organisation, providing accessible visualisations, top tips, and downloadable tools to support readers on their data journey. Staring with the absolute basics of using data, the author creates a framework for building skills and knowledge slowly until the reader is comfortable with even complex uses of data. The book begins with an exploration of explore the foundations of data driven decisions in libraries including a look at the impact of the current financial climate on resources, theoretical foundations of data collection and analysis, and how this book can be used in practice. The next section takes readers through the data driven decisions model, providing the guide for understanding and manual for implementation of the model. Finally, the book provides further perspectives and reading surrounding analysis and implementation of data driven decisions. This section aims to give supplementary and focused information on different areas of data driven decisions which can be included in processes once the reader understands the foundation of the book from earlier chapters. Highly practical and written in an accessible style, this book is an essential resource for librarians and information professionals who increasingly need to justify decisions on programmes and services through quantifiable data.

Peer to Peer Accommodation networks presents a new conceptual framework which offers an initial explanation for the continuing and rapid success of 'disruptive innovators’ and their effects on the international hospitality industry, with a specific focus on Airbnb, in the international context. Using her first-hand experience as a host on both traditional holiday accommodation webpages and a peer-to-peer accommodation network, respected tourism academic Sara Dolnicar examines possible reasons for the explosive success of peer to peer accommodation networks, investigates related topics which are less frequently discussed – such as charitable activities and social activism – and offers a future research agenda. Using first hand empirical results, this text provides much needed insight into this ‘disruptive innovator’ for those studying and working within the tourism and hospitality industries. This book discusses a wealth of issues including: * The disruptive innovation model - the criteria for identifying and understanding new disruptive innovators, and how peer-to-peer accommodation networks comply with these; * The factors postulated to drive the success of these networks and the celebration of variation; * Who are genuine networks members, tourist motivators and the chance of the ‘perfect match’; * Pricing, discrimination and stimulation of the creation of new businesses.

AN FT BUSINESS BOOK OF THE MONTH The book that will make you rethink everything you thought you knew about data, privacy and the future of Big Tech. 'We are currently living in a moment of extreme pessimism about data. This book will change your mind.' Almost everything we do generates data. Digital technology is now so pervasive that it's very hard to escape its influence, and with that growth comes fear. But whatever the news has told you about data and technology, think again. Data expert and tech insider Sam Gilbert shows that, actually, this data revolution could be the best thing that ever happened to us. Good Data examines the incredible new ways this information explosion is already helping us – whether that's combating inequality, creating jobs, advancing the frontiers of knowledge or protecting us from coronavirus – and explains why the best is yet to come. Data touches everything, from our biggest hates (online advertising) to our greatest loves (our pets), and in this fascinating new book, Gilbert explores how, if we can embrace the revolution (even the ads), we could all live vastly improved lives. We are standing on the edge of greatness, we just need to know how to get there.

The aim of this book is to unlock the power of the freeware R language to advanced university students and researchers dealing with whole-rock geochemistry of (meta-) igneous rocks. The first part covers data input/output, calculation of commonly used indexes and plotting in R. The core of the book then focusses on the presentation and practical implementations of modelling techniques used for fingerprinting processes such as partial melting, fractional crystallization, binary mixing or AFC using major-, trace-element and radiogenic isotope data. The reader will be given a firm theoretical basis for forward/reverse modelling, followed by exercises dealing with typical problems likely to be encountered in real life, and their solutions using R. The concluding sections demonstrate, using practical examples, how a researcher can proceed in developing a realistic model simulating natural systems. The appendices outline the fundamentals of the R language and provide a quick introduction to the open-source R-package GCDkit for interpretation of whole-rock geochemical data from igneous and metamorphic rocks.