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Designing Networks for Innovation and Improvisation
by Matthäus P. Zylka Hauke Fuehres Andrea Fronzetti Colladon Peter A. GloorThis volume is focused on the emerging concept of Collaborative Innovation Networks (COINs). COINs are at the core of collaborative knowledge networks, distributed communities taking advantage of the wide connectivity and the support of communication technologies, spanning beyond the organizational perimeter of companies on a global scale. It includes the refereed conference papers from the 6th International Conference on COINs, June 8-11, 2016, in Rome, Italy. It includes papers for both application areas of COINs, (1) optimizing organizational creativity and performance, and (2) discovering and predicting new trends by identifying COINs on the Web through online social media analysis. Papers at COINs16 combine a wide range of interdisciplinary fields such as social network analysis, group dynamics, design and visualization, information systems and the psychology and sociality of collaboration, and intercultural analysis through the lens of online social media. They will cover most recent advances in areas from leadership and collaboration, trend prediction and data mining, to social competence and Internet communication.
Agile Enterprise Engineering: Models, Methods, Practices, Case Studies (Smart Innovation, Systems and Technologies #175)
by Sergey V. Zykov Amitoj SinghThis concise book provides a survival toolkit for efficient, large-scale software development. Discussing a multi-contextual research framework that aims to harness human-related factors in order to improve flexibility, it includes a carefully selected blend of models, methods, practices, and case studies. To investigate mission-critical communication aspects in system engineering, it also examines diverse, i.e. cross-cultural and multinational, environments. This book helps students better organize their knowledge bases, and presents conceptual frameworks, handy practices and case-based examples of agile development in diverse environments. Together with the authors’ previous books, "Crisis Management for Software Development and Knowledge Transfer" (2016) and "Managing Software Crisis: A Smart Way to Enterprise Agility" (2018), it constitutes a comprehensive reference resource adds value to this book.
Managing Software Crisis: A Smart Way to Enterprise Agility (Smart Innovation, Systems And Technologies #92)
by Sergey V. ZykovThis book discusses smart, agile software development methods and their applications for enterprise crisis management, presenting a systematic approach that promotes agility and crisis management in software engineering. The key finding is that these crises are caused by both technology-based and human-related factors. Being mission-critical, human-related issues are often neglected. To manage the crises, the book suggests an efficient agile methodology including a set of models, methods, patterns, practices and tools. Together, these make a survival toolkit for large-scale software development in crises. Further, the book analyses lifecycles and methodologies focusing on their impact on the project timeline and budget, and incorporates a set of industry-based patterns, practices and case studies, combining academic concepts and practices of software engineering.
Analysis of Safety Data of Drug Trials: An Update
by Aeilko H. Zwinderman Ton J. CleophasIn 2010, the 5th edition of the textbook, "Statistics Applied to Clinical Studies", was published by Springer and since then has been widely distributed. The primary object of clinical trials of new drugs is to demonstrate efficacy rather than safety. However, a trial in humans which does not adequately address safety is unethical, while the assessment of safety variables is an important element of the trial.An effective approach is to present summaries of the prevalence of adverse effects and their 95% confidence intervals. In order to estimate the probability that the differences between treatment and control group occurred merely by chance, a statistical test can be performed. In the past few years, this pretty crude method has been supplemented and sometimes, replaced with more sophisticated and better sensitive methodologies, based on machine learning clusters and networks, and multivariate analyses. As a result, it is time that an updated version of safety data analysis was published. The issue of dependency also needs to be addressed. Adverse effects may be either dependent or independent of the main outcome. For example, an adverse effect of alpha blockers is dizziness and this occurs independently of the main outcome "alleviation of Raynaud 's phenomenon". In contrast, the adverse effect "increased calorie intake" occurs with "increased exercise", and this adverse effect is very dependent on the main outcome "weight loss". Random heterogeneities, outliers, confounders, interaction factors are common in clinical trials, and all of them can be considered as kinds of adverse effects of the dependent type. Random regressions and analyses of variance, high dimensional clusterings, partial correlations, structural equations models, Bayesian methods are helpful for their analysis. The current edition was written for non-mathematicians, particularly medical and health professionals and students. It provides examples of modern analytic methods so far largely unused in safety analysis. All of the 14 chapters have two core characteristics, First, they are intended for current usage, and they are particularly concerned with that usage. Second, they try and tell what readers need to know in order to understand and apply the methods. For that purpose, step by step analyses of both hypothesized and real data examples are provided.
Machine Learning in Medicine
by Aeilko H. Zwinderman Ton J. CleophasMachine learning is a novel discipline concerned with the analysis of large and multiple variables data. It involves computationally intensive methods, like factor analysis, cluster analysis, and discriminant analysis. It is currently mainly the domain of computer scientists, and is already commonly used in social sciences, marketing research, operational research and applied sciences. It is virtually unused in clinical research. This is probably due to the traditional belief of clinicians in clinical trials where multiple variables are equally balanced by the randomization process and are not further taken into account. In contrast, modern computer data files often involve hundreds of variables like genes and other laboratory values, and computationally intensive methods are required. This book was written as a hand-hold presentation accessible to clinicians, and as a must-read publication for those new to the methods.
Machine Learning in Medicine - a Complete Overview
by Aeilko H. Zwinderman Ton J. CleophasThe current book is the first publication of a complete overview of machine learning methodologies for the medical and health sector. It was written as a training companion and as a must-read, not only for physicians and students, but also for any one involved in the process and progress of health and health care. In eighty chapters eighty different machine learning methodologies are reviewed, in combination with data examples for self-assessment. Each chapter can be studied without the need to consult other chapters. The amount of data stored in the world's databases doubles every 20 months, and clinicians, familiar with traditional statistical methods, are at a loss to analyze them. Traditional methods have, indeed, difficulty to identify outliers in large datasets, and to find patterns in big data and data with multiple exposure / outcome variables. In addition, analysis-rules for surveys and questionnaires, which are currently common methods of data collection, are, essentially, missing. Fortunately, the new discipline, machine learning, is able to cover all of these limitations. So far medical professionals have been rather reluctant to use machine learning. Also, in the field of diagnosis making, few doctors may want a computer checking them, are interested in collaboration with a computer or with computer engineers. Adequate health and health care will, however, soon be impossible without proper data supervision from modern machine learning methodologies like cluster models, neural networks and other data mining methodologies. Each chapter starts with purposes and scientific questions. Then, step-by-step analyses, using data examples, are given. Finally, a paragraph with conclusion, and references to the corresponding sites of three introductory textbooks, previously written by the same authors, is given.
Machine Learning in Medicine - Cookbook
by Aeilko H. Zwinderman Ton J. CleophasThe amount of data in medical databases doubles every 20 months, and physicians are at a loss to analyze them. Also, traditional methods of data analysis have difficulty to identify outliers and patterns in big data and data with multiple exposure / outcome variables and analysis-rules for surveys and questionnaires, currently common methods of data collection, are, essentially, missing. Obviously, it is time that medical and health professionals mastered their reluctance to use machine learning and the current 100 page cookbook should be helpful to that aim. It covers in a condensed form the subjects reviewed in the 750 page three volume textbook by the same authors, entitled "Machine Learning in Medicine I-III" (ed. by Springer, Heidelberg, Germany, 2013) and was written as a hand-hold presentation and must-read publication. It was written not only to investigators and students in the fields, but also to jaded clinicians new to the methods and lacking time to read the entire textbooks. General purposes and scientific questions of the methods are only briefly mentioned, but full attention is given to the technical details. The two authors, a statistician and current president of the International Association of Biostatistics and a clinician and past-president of the American College of Angiology, provide plenty of step-by-step analyses from their own research and data files for self-assessment are available at extras. springer. com. From their experience the authors demonstrate that machine learning performs sometimes better than traditional statistics does. Machine learning may have little options for adjusting confounding and interaction, but you can add propensity scores and interaction variables to almost any machine learning method.
Machine Learning in Medicine - Cookbook Three
by Aeilko H. Zwinderman Ton J. CleophasUnique features of the book involve the following. 1. This book is the third volume of a three volume series of cookbooks entitled "Machine Learning in Medicine - Cookbooks One, Two, and Three". No other self-assessment works for the medical and health care community covering the field of machine learning have been published to date. 2. Each chapter of the book can be studied without the need to consult other chapters, and can, for the readership''s convenience, be downloaded from the internet. Self-assessment examples are available at extras. springer. com. 3. An adequate command of machine learning methodologies is a requirement for physicians and other health workers, particularly now, because the amount of medical computer data files currently doubles every 20 months, and, because, soon, it will be impossible for them to take proper data-based health decisions without the help of machine learning. 4. Given the importance of knowledge of machine learning in the medical and health care community, and the current lack of knowledge of it, the readership will consist of any physician and health worker. 5. The book was written in a simple language in order to enhance readability not only for the advanced but also for the novices. 6. The book is multipurpose, it is an introduction for ignorant, a primer for the inexperienced, and a self-assessment handbook for the advanced. 7. The book, was, particularly, written for jaded physicians and any other health care professionals lacking time to read the entire series of three textbooks. 8. Like the other two cookbooks it contains technical descriptions and self-assessment examples of 20 important computer methodologies for medical data analysis, and it, largely, skips the theoretical and mathematical background. 9. Information of theoretical and mathematical background of the methods described are displayed in a "notes" section at the end of each chapter. 10. Unlike traditional statistical methods, the machine learning methodologies are able to analyze big data including thousands of cases and hundreds of variables. 11. The medical and health care community is little aware of the multidimensional nature of current medical data files, and experimental clinical studies are not helpful to that aim either, because these studies, usually, assume that subgroup characteristics are unimportant, as long as the study is randomized. This is, of course, untrue, because any subgroup characteristic may be vital to an individual at risk. 12. To date, except for a three volume introductary series on the subject entitled "Machine Learning in Medicine Part One, Two, and Thee, 2013, Springer Heidelberg Germany" from the same authors, and the current cookbook series, no books on machine learning in medicine have been published. 13. Another unique feature of the cookbooks is that it was jointly written by two authors from different disciplines, one being a clinician/clinical pharmacologist, one being a mathematician/biostatistician. 14. The authors have also jointly been teaching at universities and institutions throughout Europe and the USA for the past 20 years. 15. The authors have managed to cover the field of medical data analysis in a nonmathematical way for the benefit of medical and health workers. 16. The authors already successfully published many statistics textbooks and self-assessment books, e. g. , the 67 chapter textbook entitled "Statistics Applied to Clinical Studies 5th Edition, 2012, Springer Heidelberg Germany" with downloads of 62,826 copies. 17. The current cookbook makes use, in addition to SPSS statistical software, of various free calculators from the internet, as well as the Konstanz Information Miner (Knime), a widely approved free machine learning package, and the free Weka Data Mining package from New Zealand. 18. The above software packages with hundreds of nodes, the basic processing units including virtually all of the statistical and data mining methods, can be used not only for data analyses, but also for appropriate data stora...
Machine Learning in Medicine - Cookbook Two
by Aeilko H. Zwinderman Ton J. CleophasThe amount of data medical databases doubles every 20 months, and physicians are at a loss to analyze them. Also, traditional data analysis has difficulty to identify outliers and patterns in big data and data with multiple exposure / outcome variables and analysis-rules for surveys and questionnaires, currently common methods of data collection, are, essentially, missing. Consequently, proper data-based health decisions will soon be impossible. Obviously, it is time that medical and health professionals mastered their reluctance to use machine learning methods and this was the main incentive for the authors to complete a series of three textbooks entitled "Machine Learning in Medicine Part One, Two and Three, Springer Heidelberg Germany, 2012-2013", describing in a nonmathematical way over sixty machine learning methodologies, as available in SPSS statistical software and other major software programs. Although well received, it came to our attention that physicians and students often lacked time to read the entire books, and requested a small book, without background information and theoretical discussions and highlighting technical details. For this reason we produced a 100 page cookbook, entitled "Machine Learning in Medicine - Cookbook One", with data examples available at extras. springer. com for self-assessment and with reference to the above textbooks for background information. Already at the completion of this cookbook we came to realize, that many essential methods were not covered. The current volume, entitled "Machine Learning in Medicine - Cookbook Two" is complementary to the first and also intended for providing a more balanced view of the field and thus, as a must-read not only for physicians and students, but also for any one involved in the process and progress of health and health care. Similarly to Machine Learning in Medicine - Cookbook One, the current work will describe stepwise analyses of over twenty machine learning methods, that are, likewise, based on the three major machine learning methodologies: Cluster methodologies (Chaps. 1-3) Linear methodologies (Chaps. 4-11) Rules methodologies (Chaps. 12-20) In extras. springer. com the data files of the examples are given, as well as XML (Extended Mark up Language), SPS (Syntax) and ZIP (compressed) files for outcome predictions in future patients. In addition to condensed versions of the methods, fully described in the above three textbooks, an introduction is given to SPSS Modeler (SPSS' data mining workbench) in the Chaps. 15, 18, 19, while improved statistical methods like various automated analyses and Monte Carlo simulation models are in the Chaps. 1, 5, 7 and 8. We should emphasize that all of the methods described have been successfully applied in practice by the authors, both of them professors in applied statistics and machine learning at the European Community College of Pharmaceutical Medicine in Lyon France. We recommend the current work not only as a training companion to investigators and students, because of plenty of step by step analyses given, but also as a brief introductory text to jaded clinicians new to the methods. For the latter purpose, background and theoretical information have been replaced with the appropriate references to the above textbooks, while single sections addressing "general purposes", "main scientific questions" and "conclusions" are given in place. Finally, we will demonstrate that modern machine learning performs sometimes better than traditional statistics does. Machine learning may have little options for adjusting confounding and interaction, but you can add propensity scores and interaction variables to almost any machine learning method.
SPSS for Starters
by Aeilko H. Zwinderman Ton J. CleophasThis small book contains all statistical tests that are relevant for starters on SPSS. Each test is explained using a data example from clinical practice, including every step in SPSS and the main tables of results with an accompanying text with interpretations of the results and hints convenient for data reporting, i.e., scientific clinical articles and poster presentations.. In order to facilitate the use of this cookbook the data files of the examples are made available by the publisher on the Internet. For investigators who wish to perform their own data analyses from the very start the book can be used as a step-by-step guideline. They can enter their separate data or enter their entire data file, e.g., from Excel, simply by opening an Excel file in SPSS. SPSS statistical software is a user-friendly statistical software with many help and tutor pages. However, for the novices on SPSS an even more basic approach is welcome. The book is meant for this very purpose, and can be used without the help of a teacher. The authors are well-aware that this cookbook contains a minimal amount of text and a maximal technical details, but we believe that this property will not refrain students from mastering the SPSS software systematic, and that, instead, it will even be a help to that aim. Yet, we recommend that it be used together with the textbook "Statistics Applied to Clinical Trials" by Cleophas et al, 4th Edition, 2009, Springer Dordrecht.
SPSS for Starters and 2nd Levelers
by Aeilko H. Zwinderman Ton J. CleophasFor medical and health workers this book is a must-have, because statistical methods in these fields are vital, and no equivalent work is available. For medical and health students this is equally true. A unique point is its low threshold, textually simple and at the same time full of self-assessment opportunities. Other unique points are the succinctness of the chapters with 3 to 6 pages, the presence of entire-commands-texts of the statistical methodologies reviewed and the fact that dull scientific texts imposing an unnecessary burden on busy and jaded professionals have been left out. For readers requesting more background, theoretical and mathematical information a note section with references is in each chapter. The first edition in 2010 was the first publication of a complete overview of SPSS methodologies for medical and health statistics. Well over 100,000 copies of various chapters were sold within the first year of publication. Reasons for a rewrite were four. First, many important comments from readers urged for a rewrite. Second, SPSS has produced many updates and upgrades, with relevant novel and improved methodologies. Third, the authors felt that the chapter texts needed some improvements for better readability: chapters have now been classified according the outcome data helpful for choosing your analysis rapidly, a schematic overview of data, and explanatory graphs have been added. Fourth, current data are increasingly complex and many i mportant methods for analysis were missing in the first edition. For that latter purpose some more advanced methods seemed unavoidable, like hierarchical loglinear methods, gamma and Tweedie regressions and random intercept analyses. In order for the contents of the book to remain covered by the title, the authors renamed the book: SPSS for Starters and 2nd Levelers. Special care was, nonetheless, taken to keep things as simple as possible. Medical and health professionals tend to dislike software syntax. Therefore, virtually no syntax, but, rather, simple menu commands are given. The arithmetic is still of a no-more-than high-school level. Step-by-step analyses of different statistical methodologies are given with the help of 60 SPSS data files available through the internet. Because of the lack of time of this busy group of people, the authors have given every effort to produce a text as succinct as possible.
Statistical Analysis of Clinical Data on a Pocket Calculator
by Aeilko H. Zwinderman Ton J. CleophasThe core principles of statistical analysis are too easily forgotten in today's world of powerful computers and time-saving algorithms. This step-by-step primer takes researchers who lack the confidence to conduct their own analyses right back to basics, allowing them to scrutinize their own data through a series of rapidly executed reckonings on a simple pocket calculator. A range of easily navigable tutorials facilitate the reader's assimilation of the techniques, while a separate chapter on next generation Flash prepares them for future developments in the field. This practical volume also contains tips on how to deny hackers access to Flash internet sites. An ideal companion to the author's co-authored works on statistical analysis for Springer such as Statistics Applied to Clinical Trials, this monograph will help researchers understand the processes involved in interpreting clinical data, as well as being a necessary prerequisite to mastering more advanced statistical techniques. The principles of statistical analysis are easily forgotten in today's world of time-saving algorithms. This step-by-step primer takes researchers back to basics, enabling them to examine their own data through a series of sums on a simple pocket calculator.
Antifragile Systems and Teams
by Dave ZwiebackHow Can DevOps Make You Antifragile?All complex computer systems eventually break, despite all of the heavy-handed, bureaucratic change-management processes we throw at them. But some systems are clearly more fragile than others, depending on how well they cope with stress. In this O'Reilly report, Dave Zwieback explains how the DevOps methodology can help make your system antifragile.Systems are fragile when organizations are unprepared to handle changing conditions. As generalists adept at several roles, DevOps practitioners adjust more easily to the fast pace of change. Rather than attempt to constrain volatility, DevOps embraces disorder, randomness, and impermanence to make systems even better.This concise report covers:Why Etsy, Netflix, and other antifragile companies constantly introduce volatility to test and upgrade their systemsHow DevOps removes the schism between developers and operations, enlisting developers to deploy as well as buildUsing continual experimentation and minor failures to make critical adjustments--and discover breakthroughsHow an overreliance on measurement and automation can make systems fragileWhy sharing increases trust, collaboration, and tribal knowledgeDownload this free report and learn how the DevOps philosophy of Culture, Automation, Measurement, and Sharing makes use of changing conditions and even embarrassing mistakes to help improve your system--and your organization.Dave Zwieback has been managing large-scale, mission-critical infrastructure and teams for 17 years.
The Visual Effects Producer: Understanding the Art and Business of VFX
by Susan Zwerman Charles FinanceFirst published in 2010. Routledge is an imprint of Taylor & Francis, an informa company.
Socioinformatics - The Social Impact of Interactions between Humans and IT
by Katharina Zweig Wolfgang Neuser Volkmar Pipek Markus Rohde Ingo ScholtesSocioinformatics is a new scientific approach to study the interactions between humans and IT. These proceedings are a collection of the contributions during a workshop of the Gesellschaft für Informatik (GI). Researchers in this emerging field discuss the main aspects of interactions between IT and humans with respect to; social connections, social changes, acceptance of IT and the social conditions affecting this acceptance, effects of IT on humans and in response changes of IT, structures of the society and the influence of IT on these structures, changes of metaphysics influenced by IT and the social context of a knowledge society.
Computer Intensive Methods in Statistics
by Silvelyn Zwanzig Behrang MahjaniThis textbook gives an overview of statistical methods that have been developed during the last years due to increasing computer use, including random number generators, Monte Carlo methods, Markov Chain Monte Carlo (MCMC) methods, Bootstrap, EM algorithms, SIMEX, variable selection, density estimators, kernel estimators, orthogonal and local polynomial estimators, wavelet estimators, splines, and model assessment. Computer Intensive Methods in Statistics is written for students at graduate level, but can also be used by practitioners. Features Presents the main ideas of computer-intensive statistical methods Gives the algorithms for all the methods Uses various plots and illustrations for explaining the main ideas Features the theoretical backgrounds of the main methods. Includes R codes for the methods and examples Silvelyn Zwanzig is an Associate Professor for Mathematical Statistics at Uppsala University. She studied Mathematics at the Humboldt- University in Berlin. Before coming to Sweden, she was Assistant Professor at the University of Hamburg in Germany. She received her Ph.D. in Mathematics at the Academy of Sciences of the GDR. Since 1991, she has taught Statistics for undergraduate and graduate students. Her research interests have moved from theoretical statistics to computer intensive statistics. Behrang Mahjani is a postdoctoral fellow with a Ph.D. in Scientific Computing with a focus on Computational Statistics, from Uppsala University, Sweden. He joined the Seaver Autism Center for Research and Treatment at the Icahn School of Medicine at Mount Sinai, New York, in September 2017 and was formerly a postdoctoral fellow at the Karolinska Institutet, Stockholm, Sweden. His research is focused on solving large-scale problems through statistical and computational methods.
Computer Supported Education: 10th International Conference, CSEDU 2018, Funchal, Madeira, Portugal, March 15–17, 2018, Revised Selected Papers (Communications in Computer and Information Science #1022)
by Susan Zvacek James Uhomoibhi Bruce M. McLaren Rob ReillyThis book constitutes the thoroughly refereed proceedings of the 9th International Conference on Computer Supported Education, CSEDU 2018, held in Funchal, Madeira, Portugal, in March 2018. The 27 revised full papers were carefully reviewed and selected from 193 submissions. The papers deal with the following topics: new educational environments, best practices and case studies of innovative technology-based learning strategies, institutional policies on computer-supported education including open and distance education.
Computers Supported Education: 8th International Conference, CSEDU 2016, Rome, Italy, April 21-23, 2016, Revised Selected Papers (Communications in Computer and Information Science #739)
by Susan Zvacek James Uhomoibhi Gennaro Costagliola Bruce M. MclarenThis book constitutes the thoroughly refereed proceedings of the 8th International Conference on Computer Supported Education, CSEDU 2016, held in Rome, Italy, in April 2016. The 29 revised full papers were carefully reviewed and selected from 164 submissions. The papers deal with the following topics: new educational environments, best practices and case studies of innovative technology-based learning strategies, institutional policies on computer-supported education including open and distance education.
Computer Supported Education: 6th International Conference, CSEDU 2014, Barcelona, Spain, April 1-3, 2014, Revised Selected Papers (Communications in Computer and Information Science #510)
by Susan Zvacek Markus Helfert Maria Teresa Restivo James UhomoibhiThis book constitutes the refereed proceedings of the 6th International Conference on Computer Supported Education, CSEDU 2014, held in Barcelona, Spain, in April 2014. The 24 revised full papers presented were carefully reviewed and selected from 242 submissions. The papers address topics such as information technologies supporting learning; learning/teaching methodologies and assessment; social context and learning environments; domain applications and case studies; and ubiquitous learning.
Computer Supported Education: 7th International Conference, CSEDU 2015, Lisbon, Portugal, May 23-25, 2015, Revised Selected Papers (Communications in Computer and Information Science #583)
by Susan Zvacek Markus Helfert Maria Teresa Restivo James UhomoibhiThis book constitutes the refereed proceedings of the 6th International Conference on Computer Supported Education, CSEDU 2014, held in Barcelona, Spain, in April 2014. The 24 revised full papers presented were carefully reviewed and selected from 242 submissions. The papers address topics such as information technologies supporting learning; learning/teaching methodologies and assessment; social context and learning environments; domain applications and case studies; and ubiquitous learning.
Computers Supported Education: 9th International Conference, CSEDU 2017, Porto, Portugal, April 21-23, 2017, Revised Selected Papers (Communications in Computer and Information Science #865)
by Susan Zvacek Paula Escudeiro James Uhomoibhi Gennaro Costagliola Bruce M. McLarenThis book constitutes the thoroughly refereed proceedings of the 9th International Conference on Computer Supported Education, CSEDU 2017, held in Porto, Portugal, in April 2017. The 22 revised full papers were carefully reviewed and selected from 179 submissions. The papers deal with the following topics: new educational environments, best practices and case studies of innovative technology-based learning strategies, institutional policies on computer-supported education including open and distance education.
Industrial Communication Technology Handbook (Industrial Information Technology #8)
by Richard ZurawskiFeaturing contributions from major technology vendors, industry consortia, and government and private research establishments, the Industrial Communication Technology Handbook, Second Edition provides comprehensive and authoritative coverage of wire- and wireless-based specialized communication networks used in plant and factory automation, automotive applications, avionics, building automation, energy and power systems, train applications, and more. New to the Second Edition: 46 brand-new chapters and 21 substantially revised chapters Inclusion of the latest, most significant developments in specialized communication technologies and systems Addition of new application domains for specialized networks The Industrial Communication Technology Handbook, Second Edition supplies readers with a thorough understanding of the application-specific requirements for communication services and their supporting technologies. It is useful to a broad spectrum of professionals involved in the conception, design, development, standardization, and use of specialized communication networks as well as academic institutions engaged in engineering education and vocational training.
The Industrial Information Technology Handbook (Industrial Electronics)
by Richard ZurawskiThe Industrial Information Technology Handbook focuses on existing and emerging industrial applications of IT, and on evolving trends that are driven by the needs of companies and by industry-led consortia and organizations. Emphasizing fast growing areas that have major impacts on industrial automation and enterprise integration, the Handbook covers topics such as industrial communication technology, sensors, and embedded systems.The book is organized into two parts. Part 1 presents material covering new and quickly evolving aspects of IT. Part 2 introduces cutting-edge areas of industrial IT. The Handbook presents material in the form of tutorials, surveys, and technology overviews, combining fundamentals and advanced issues, with articles grouped into sections for a cohesive and comprehensive presentation. The text contains 112 contributed reports by industry experts from government, companies at the forefront of development, and some of the most renowned academic and research institutions worldwide. Several of the reports on recent developments, actual deployments, and trends cover subject matter presented to the public for the first time.
Human Centric Visual Analysis with Deep Learning
by Wangmeng Zuo Liang Lin Dongyu Zhang Ping LuoThis book introduces the applications of deep learning in various human centric visual analysis tasks, including classical ones like face detection and alignment and some newly rising tasks like fashion clothing parsing. Starting from an overview of current research in human centric visual analysis, the book then presents a tutorial of basic concepts and techniques of deep learning. In addition, the book systematically investigates the main human centric analysis tasks of different levels, ranging from detection and segmentation to parsing and higher-level understanding. At last, it presents the state-of-the-art solutions based on deep learning for every task, as well as providing sufficient references and extensive discussions. Specifically, this book addresses four important research topics, including 1) localizing persons in images, such as face and pedestrian detection; 2) parsing persons in details, such as human pose and clothing parsing, 3) identifying and verifying persons, such as face and human identification, and 4) high-level human centric tasks, such as person attributes and human activity understanding. This book can serve as reading material and reference text for academic professors / students or industrial engineers working in the field of vision surveillance, biometrics, and human-computer interaction, where human centric visual analysis are indispensable in analysing human identity, pose, attributes, and behaviours for further understanding.
Career Counseling: Applied Concepts Of Life Planning
by Vernon G. ZunkerThis successful and popular book has been completely updated to give you the most recent information on emerging theories, recent research, and current computer-assisted guidance programs in career counseling! Whether you are a counselor or a counselor-in-training, you'll appreciate the way Vernon Zunker presents practical techniques and real-life examples as he patiently helps you understand the theoretical models of career counseling and how to effectively counsel clients about career issues. In the Sixth Edition, you'll find Zunker's clear writing style and interesting exercises, as well as: *Five career counseling models with case illustrations *A new Chapter 8, "Self-Assessment and a Model for Using Assessment," which discusses the rationale for using self-assessment tools *A new Chapter 15, "Career Counseling for Gay, Lesbian, and Bisexual Clients," which helps familiarize you with the issues and needs of this client population *Theories of career development and counseling models-summarized in tables to help you study and prepare for the licensing examination *Web site information that points you to additional, reliable information online *And much more!