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This book constitutes the thoroughly refereed conference proceedings of the International Workshop on Face and Facial Expression Recognition from Real World Videos in conjunction with the 22nd International Conference on Pattern Recognition held in Stockholm, Sweden, in August 2014. The 11 revised full papers were carefully reviewed and selected from numerous submissions and cover topics such as Face Recognition, Face Alignment , Facial Expression Recognition, and Facial Images.
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This book constitutes the refereed conference proceedings of the 8th International Conference on Image and Graphics, ICIG 2015 held in Tianjin, China, in August 2015. The 164 revised full papers and 6 special issue papers were carefully reviewed and selected from 339 submissions. The papers focus on various advances of theory, techniques and algorithms in the fields of images and graphics.
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The book presents a comprehensive view on Flow-Aware Networking. It starts with a brief overview of the known QoS architectures based on the concept of a flow. Then, the original FAN concept is presented, along with its variations proposed by the authors. The next chapter covers a very valuable feature of the FAN architecture, namely its ability to assure net neutrality. The chapters that follow will discuss, in detail, a variety of issues making the FAN concept implementable, including congestion control, fairness, resilience to failures, service differentiation and degradation. The final chapter presents the test implementation of the FAN router, including the environment used and performance tests. Chapters are supplemented with problems to solve, along with their solutions. The pedagogical character of the book is supported by a number of illustrative examples contained in most of the chapters. At the end of the book, a glossary of the key terms is included, along with a comprehensive bibliography. Flow-based traffic management is currently becoming a mainstream. There is plenty of Quality of Service (QoS) techniques based on flows. Software-Defined Networking with its dominant protocol OpenFlow also follows this trend. Flow-Aware Networking (FAN) is a promising QoS architecture. Information on FAN can be found in various research papers. It is, therefore highly scattered. This book gathers practically all relevant information regarding FAN and puts it together. Quality of Service assurance is one of the key challenges of today’s Internet. The existing approaches to provide QoS do not meet expectations of network operators, managers and users although numerous efforts in this area have been reported. One of the most promising concepts is the Flow-Aware Network (FAN). FAN can play a key role in assuring the net neutrality, smoothly combining interests of all the involved parties. The authors of the proposal have been involved in FAN research practically since its inception at the start of the 21st century. The book reports the wide experiences the authors accumulated in the subject area during the work on common FAN-related projects conducted with the team of James Roberts that proposed the original FAN concept as well as other leading research groups in Europe and the USA. One of the aims of the book is to accompany courses taught by the authors.
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Christofer F. Daiberl explores how to enhance the productivity of services delivered by a network of co-providers. Harnessing empirical insights and synthesizing contributions from service design, information systems, and engineering, the author develops a systematic productivity improvement technique. The technique supports practitioners to iteratively discover and seize opportunities to enhance productivity for their own organization, customers, and relevant co-providers. Reflecting on the overall results, five general design principles are proposed that support the development of new artifacts fostering truly productive services in a networked world. Contents The concepts of networked service delivery and networked service productivity Approaches for improving productivity from a network perspective The networked service productivity improvement technique Design principles for improving networked service productivity Target Groups Researchers and students in the field of service management and information systems Service managers and practitioners aiming to enhance the productivity of networked service offerings The Author Christofer F. Daiberl received his PhD from the Friedrich-Alexander-Universität Erlangen-Nürnberg, where he worked as a Research Associate for Prof. Dr. Kathrin M. Möslein at the Chair of Information Systems – Innovation & Value Creation. .
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This book treats various methods for stability analysis and controller design of local model networks (LMNs). LMNs have proved to be a powerful tool in nonlinear dynamic system identification. Their system architecture is more suitable for controller design compared to alternative approximation methods. The main advantage is that linear controller design methods can be, at least locally, applied and combined with nonlinear optimization to calibrate stable state feedback as well as PID controller. The calibration of stable state-feedback controllers is based on the closed loop stability analysis methods. Here, global LMIs (Linear Matrix Inequalities) can be derived and numerically solved. For LMN based nonlinear PID controllers deriving global LMIs is not possible. Thus, two approaches are treated in this book. The first approach works iteratively to get LMIs in each iteration step. The second approach uses a genetic algorithm to determine the PID controller parameters where for each individual the stability is checked. It allows simultaneous enhancement of (competing) optimization criteria. About the author Christian Mayr received the M.S. degree in mechanical engineering, the Ph.D. degree in technical sciences from TU Wien, Vienna, Austria, in 2009 and 2013, respectively. Since 2013 he is with AVL List GmbH, Graz, Austria. First as Development Engineer, from 2017 as Project Manager, in 2020 as Team Leader and since 2021 Department Manager for Virtualization Application.
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This unique and useful textbook presents a comprehensive review of the essentials of image data mining, and the latest cutting-edge techniques used in the field. The coverage spans all aspects of image analysis and understanding, offering deep insights into areas of feature extraction, machine learning, and image retrieval. The theoretical coverage is supported by practical mathematical models and algorithms, utilizing data from real-world examples and experiments. Topics and features: Describes essential tools for image mining, covering Fourier transforms, Gabor filters, and contemporary wavelet transforms Develops many new exercises (most with MATLAB code and instructions) Includes review summaries at the end of each chapter Analyses state-of-the-art models, algorithms, and procedures for image mining Integrates new sections on pre-processing, discrete cosine transform, and statistical inference and testing Demonstrates how features like color, texture, and shape can be mined or extracted for image representation Applies powerful classification approaches: Bayesian classification, support vector machines, neural networks, and decision trees Implements imaging techniques for indexing, ranking, and presentation, as well as database visualization This easy-to-follow, award-winning book illuminates how concepts from fundamental and advanced mathematics can be applied to solve a broad range of image data mining problems encountered by students and researchers of computer science. Students of mathematics and other scientific disciplines will also benefit from the applications and solutions described in the text, together with the hands-on exercises that enable the reader to gain first-hand experience of computing.
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This book constitutes the proceedings of the 23rd International Conference on Descriptional Complexity of Format Systems, DCFS 2021, which was supposed to take place in Seoul South Korea in June 2021, but the conference was canceled due to the COVID-19 crisis. The 16 full papers presented in this volume were carefully reviewed and selected from 21 submissions. The conference focus to all aspects of descriptional complexity-the costs of description of objects in various computational models such as Turing machines, pushdown automata, finite automata or grammars.
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