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Amit Konar Emotion Recognition. A Pattern Analysis Approach


A timely book containing foundations and current research directions on emotion recognition by facial expression, voice, gesture and biopotential signals This book provides a comprehensive examination of the research methodology of different modalities of emotion recognition. Key topics of discussion include facial expression, voice and biopotential signal-based emotion recognition. Special emphasis is given to feature selection, feature reduction, classifier design and multi-modal fusion to improve performance of emotion-classifiers. Written by several experts, the book includes several tools and techniques, including dynamic Bayesian networks, neural nets, hidden Markov model, rough sets, type-2 fuzzy sets, support vector machines and their applications in emotion recognition by different modalities. The book ends with a discussion on emotion recognition in automotive fields to determine stress and anger of the drivers, responsible for degradation of their performance and driving-ability. There is an increasing demand of emotion recognition in diverse fields, including psycho-therapy, bio-medicine and security in government, public and private agencies. The importance of emotion recognition has been given priority by industries including Hewlett Packard in the design and development of the next generation human-computer interface (HCI) systems. Emotion Recognition: A Pattern Analysis Approach would be of great interest to researchers, graduate students and practitioners, as the book Offers both foundations and advances on emotion recognition in a single volume Provides a thorough and insightful introduction to the subject by utilizing computational tools of diverse domains Inspires young researchers to prepare themselves for their own research Demonstrates direction of future research through new technologies, such as Microsoft Kinect, EEG systems etc.

10124.13 РУБ

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Mourad Elloumi Pattern Recognition in Computational Molecular Biology. Techniques and Approaches


A comprehensive overview of high-performance pattern recognition techniques and approaches to Computational Molecular Biology This book surveys the developments of techniques and approaches on pattern recognition related to Computational Molecular Biology. Providing a broad coverage of the field, the authors cover fundamental and technical information on these techniques and approaches, as well as discussing their related problems. The text consists of twenty nine chapters, organized into seven parts: Pattern Recognition in Sequences, Pattern Recognition in Secondary Structures, Pattern Recognition in Tertiary Structures, Pattern Recognition in Quaternary Structures, Pattern Recognition in Microarrays, Pattern Recognition in Phylogenetic Trees, and Pattern Recognition in Biological Networks. Surveys the development of techniques and approaches on pattern recognition in biomolecular data Discusses pattern recognition in primary, secondary, tertiary and quaternary structures, as well as microarrays, phylogenetic trees and biological networks Includes case studies and examples to further illustrate the concepts discussed in the book Pattern Recognition in Computational Molecular Biology: Techniques and Approaches is a reference for practitioners and professional researches in Computer Science, Life Science, and Mathematics. This book also serves as a supplementary reading for graduate students and young researches interested in Computational Molecular Biology.

10498.36 РУБ

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Webb Andrew R. Statistical Pattern Recognition


Statistical pattern recognition relates to the use of statistical techniques for analysing data measurements in order to extract information and make justified decisions. It is a very active area of study and research, which has seen many advances in recent years. Applications such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition, all require robust and efficient pattern recognition techniques. This third edition provides an introduction to statistical pattern theory and techniques, with material drawn from a wide range of fields, including the areas of engineering, statistics, computer science and the social sciences. The book has been updated to cover new methods and applications, and includes a wide range of techniques such as Bayesian methods, neural networks, support vector machines, feature selection and feature reduction techniques.Technical descriptions and motivations are provided, and the techniques are illustrated using real examples. Statistical Pattern Recognition, 3rd Edition: Provides a self-contained introduction to statistical pattern recognition. Includes new material presenting the analysis of complex networks. Introduces readers to methods for Bayesian density estimation. Presents descriptions of new applications in biometrics, security, finance and condition monitoring. Provides descriptions and guidance for implementing techniques, which will be invaluable to software engineers and developers seeking to develop real applications Describes mathematically the range of statistical pattern recognition techniques. Presents a variety of exercises including more extensive computer projects. The in-depth technical descriptions make the book suitable for senior undergraduate and graduate students in statistics, computer science and engineering. Statistical Pattern Recognition is also an excellent reference source for technical professionals. Chapters have been arranged to facilitate implementation of the techniques by software engineers and developers in non-statistical engineering fields. www.wiley.com/go/statistical_pattern_recognition

11746.45 РУБ

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Elaine Knuth Trading Between the Lines. Pattern Recognition and Visualization of Markets


Insights into a pattern-based method of trading that can increase the likelihood of profitable outcomes While most books on chart patterns, or pattern recognition, offer detailed discussion and analysis of one type of pattern, the fact is that a single pattern may not be very helpful for trading, since it often does not give a complete picture of the market. What sets Trading Between the Lines apart from other books in this area is author Elaine Knuths identification of sets of patterns that give a complete analysis of the market. In it, she identifies more complex chart patterns, often several patterns combined over multiple time frames, and skillfully examines these sets of patterns called «constellations» in relation to one another. These constellations turn sets of individual patterns into a more manageable set of patterns, where the relationship between them can lead to tactical trading opportunities. Shows how to apply complex patterns to specific trades and identify opportunities as well entry and exit points Markets covered include commodities, equities, and indexes Presents an effective trading approach based on real market cycles-as opposed to computer simulations-that are found in active markets Moving beyond the simple identification of basic patterns to identifying pattern constellations, this reliable resource will give you a better view of what is really going on in the market and help you profit from the opportunities you uncover.

3889.55 РУБ

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CCPS (Center for Chemical Process Safety) Guidelines Combustible Dust Hazard Analysis


This book describes how to conduct a Combustible Dust Hazard Analysis (CDHA) for processes handling combustible solids. The book explains how to do a dust hazard analysis by using either an approach based on compliance with existing consensus standards, or by using a risk based approach. Worked examples in the book help the user understand how to do a combustible dust hazards analysis.

9723.89 РУБ

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Jan Flusser Moments and Moment Invariants in Pattern Recognition


Moments as projections of an image’s intensity onto a proper polynomial basis can be applied to many different aspects of image processing. These include invariant pattern recognition, image normalization, image registration, focus/ defocus measurement, and watermarking. This book presents a survey of both recent and traditional image analysis and pattern recognition methods, based on image moments, and offers new concepts of invariants to linear filtering and implicit invariants. In addition to the theory, attention is paid to efficient algorithms for moment computation in a discrete domain, and to computational aspects of orthogonal moments. The authors also illustrate the theory through practical examples, demonstrating moment invariants in real applications across computer vision, remote sensing and medical imaging. Key features: Presents a systematic review of the basic definitions and properties of moments covering geometric moments and complex moments. Considers invariants to traditional transforms – translation, rotation, scaling, and affine transform – from a new point of view, which offers new possibilities of designing optimal sets of invariants. Reviews and extends a recent field of invariants with respect to convolution/blurring. Introduces implicit moment invariants as a tool for recognizing elastically deformed objects. Compares various classes of orthogonal moments (Legendre, Zernike, Fourier-Mellin, Chebyshev, among others) and demonstrates their application to image reconstruction from moments. Offers comprehensive advice on the construction of various invariants illustrated with practical examples. Includes an accompanying website providing efficient numerical algorithms for moment computation and for constructing invariants of various kinds, with about 250 slides suitable for a graduate university course. Moments and Moment Invariants in Pattern Recognition is ideal for researchers and engineers involved in pattern recognition in medical imaging, remote sensing, robotics and computer vision. Post graduate students in image processing and pattern recognition will also find the book of interest.

10423.66 РУБ

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Gary Kinzel L. Kinematics, Dynamics, and Design of Machinery


Kinematics, Dynamics, and Design of Machinery, Third Edition, presents a fresh approach to kinematic design and analysis and is an ideal textbook for senior undergraduates and graduates in mechanical, automotive and production engineering Presents the traditional approach to the design and analysis of kinematic problems and shows how GCP can be used to solve the same problems more simply Provides a new and simpler approach to cam design Includes an increased number of exercise problems Accompanied by a website hosting a solutions manual, teaching slides and MATLAB® programs

9749.19 РУБ

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Mike Cheung W.-L. Meta-Analysis. A Structural Equation Modeling Approach


Presents a novel approach to conducting meta-analysis using structural equation modeling. Structural equation modeling (SEM) and meta-analysis are two powerful statistical methods in the educational, social, behavioral, and medical sciences. They are often treated as two unrelated topics in the literature. This book presents a unified framework on analyzing meta-analytic data within the SEM framework, and illustrates how to conduct meta-analysis using the metaSEM package in the R statistical environment. Meta-Analysis: A Structural Equation Modeling Approach begins by introducing the importance of SEM and meta-analysis in answering research questions. Key ideas in meta-analysis and SEM are briefly reviewed, and various meta-analytic models are then introduced and linked to the SEM framework. Fixed-, random-, and mixed-effects models in univariate and multivariate meta-analyses, three-level meta-analysis, and meta-analytic structural equation modeling, are introduced. Advanced topics, such as using restricted maximum likelihood estimation method and handling missing covariates, are also covered. Readers will learn a single framework to apply both meta-analysis and SEM. Examples in R and in Mplus are included. This book will be a valuable resource for statistical and academic researchers and graduate students carrying out meta-analyses, and will also be useful to researchers and statisticians using SEM in biostatistics. Basic knowledge of either SEM or meta-analysis will be helpful in understanding the materials in this book.

4610.29 РУБ

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Nikolay Linde Emotion-Image Therapy. Analysis and Implementation


Emotion-Image therapy (EIT) is a new method of psychotherapy, which addresses any chronic negative emotional state of the individual. EIT allows to solve many psychological and psychosomatic problems (such as phobias, anxieties, allergies, asthma, and much more).This book provides a theoretical basis of EIT, practical approaches and cases, imaginative exercises, and a dictionary of images interpretations.

400 РУБ

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Salah Khalfallah Structural Analysis 1. Statically Determinate Structures


Using a general approach, this book supports the student to enable mastery of the methods of analysis of isostatic and hyperstatic structures. To show the performance of the methods of analysis of the hyperstatic structures, selected beams, gantries and reticular structures are selected and subjected to a comparative study by the different methods of analysis of the hyperstatic structures.

10696.27 РУБ

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Richard Brereton G. Chemometrics for Pattern Recognition


Over the past decade, pattern recognition has been one of the fastest growth points in chemometrics. This has been catalysed by the increase in capabilities of automated instruments such as LCMS, GCMS, and NMR, to name a few, to obtain large quantities of data, and, in parallel, the significant growth in applications especially in biomedical analytical chemical measurements of extracts from humans and animals, together with the increased capabilities of desktop computing. The interpretation of such multivariate datasets has required the application and development of new chemometric techniques such as pattern recognition, the focus of this work. Included within the text are: ‘Real world’ pattern recognition case studies from a wide variety of sources including biology, medicine, materials, pharmaceuticals, food, forensics and environmental science; Discussions of methods, many of which are also common in biology, biological analytical chemistry and machine learning; Common tools such as Partial Least Squares and Principal Components Analysis, as well as those that are rarely used in chemometrics such as Self Organising Maps and Support Vector Machines; Representation in full colour; Validation of models and hypothesis testing, and the underlying motivation of the methods, including how to avoid some common pitfalls. Relevant to active chemometricians and analytical scientists in industry, academia and government establishments as well as those involved in applying statistics and computational pattern recognition.

11998.84 РУБ

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Richard Brereton G. Chemometrics for Pattern Recognition


Over the past decade, pattern recognition has been one of the fastest growth points in chemometrics. This has been catalysed by the increase in capabilities of automated instruments such as LCMS, GCMS, and NMR, to name a few, to obtain large quantities of data, and, in parallel, the significant growth in applications especially in biomedical analytical chemical measurements of extracts from humans and animals, together with the increased capabilities of desktop computing. The interpretation of such multivariate datasets has required the application and development of new chemometric techniques such as pattern recognition, the focus of this work. Included within the text are: ‘Real world’ pattern recognition case studies from a wide variety of sources including biology, medicine, materials, pharmaceuticals, food, forensics and environmental science; Discussions of methods, many of which are also common in biology, biological analytical chemistry and machine learning; Common tools such as Partial Least Squares and Principal Components Analysis, as well as those that are rarely used in chemometrics such as Self Organising Maps and Support Vector Machines; Representation in full colour; Validation of models and hypothesis testing, and the underlying motivation of the methods, including how to avoid some common pitfalls. Relevant to active chemometricians and analytical scientists in industry, academia and government establishments as well as those involved in applying statistics and computational pattern recognition.

12446.57 РУБ

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Fernando Diz Modern Security Analysis. Understanding Wall Street Fundamentals


A legendary value investor on security analysis for a modern era This book outlines Whitmans approach to business and security analysis that departs from most conventional security analysts. This approach has more in common with corporate finance than it does with the conventional approach. The key factors in appraising a company and its securities: 1) Credit worthiness, 2) Flows—both cash and earnings, 3) Long-term outlook, 4) Salable assets which can be disposed of without compromising the going concern, dynamics, 5) Resource conversions such as changes in control, mergers and acquisitions, going private, and major changes in assets or in liabilities, and 6) Access to capital. Offers the security analysis value approach Martin Whitman has used successfully since 1986 Details Whitmans unconventional approach to security analysis and offers information on the six key factors for appraising a company Contains the three most overemphasized factors used in conventional securities investing Written by Martin J. Whitman and Fernando Diz, Modern Security Analysis meets the challenge of todays marketplace by taking into account changes to regulation, market structures, instruments, and the speed and volume of trading.

4861.94 РУБ

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Irini Moustaki Latent Variable Models and Factor Analysis. A Unified Approach


Latent Variable Models and Factor Analysis provides a comprehensive and unified approach to factor analysis and latent variable modeling from a statistical perspective. This book presents a general framework to enable the derivation of the commonly used models, along with updated numerical examples. Nature and interpretation of a latent variable is also introduced along with related techniques for investigating dependency. This book: Provides a unified approach showing how such apparently diverse methods as Latent Class Analysis and Factor Analysis are actually members of the same family. Presents new material on ordered manifest variables, MCMC methods, non-linear models as well as a new chapter on related techniques for investigating dependency. Includes new sections on structural equation models (SEM) and Markov Chain Monte Carlo methods for parameter estimation, along with new illustrative examples. Looks at recent developments on goodness-of-fit test statistics and on non-linear models and models with mixed latent variables, both categorical and continuous. No prior acquaintance with latent variable modelling is pre-supposed but a broad understanding of statistical theory will make it easier to see the approach in its proper perspective. Applied statisticians, psychometricians, medical statisticians, biostatisticians, economists and social science researchers will benefit from this book.

6974.48 РУБ

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Thomas Au P. A Modern Approach to Graham and Dodd Investing


An updated approach to classic security analysis The principles of value investing outlined by Graham and Dodd in the 1940s continues to be used today by individuals and companies who face challenging investment decisions. A Modern Approach to Graham and Dodd Investing examines the classic Graham and Dodd approach to valuation and updates it for the twenty-first century. Thomas Au, a credentialed analyst with a leading insurance company and an ex-Value Line analyst, reworks the basics of value investing from net present value, financial statement analysis, and return on capital to return and leverage, asset allocation, and diversification. Through case studies and real-time analysis, A Modern Approach to Graham and Dodd Investing presents readers with examples that will make analysis and portfolio theory more relevant and powerful. Thomas P. Au (Hartford, CT) is a Vice President and Portfolio Manager for the investment arm of a large insurance and healthcare provider. His specialty is emerging and international markets. He received his BA, cum laude, with a double major in economics and history, from Yale University, and an MBA in finance from New York University.

5510.2 РУБ

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Mohamed Daoudi 3D Face Modeling, Analysis and Recognition


3D Face Modeling, Analysis and Recognition presents methodologies for analyzing shapes of facial surfaces, develops computational tools for analyzing 3D face data, and illustrates them using state-of-the-art applications. The methodologies chosen are based on efficient representations, metrics, comparisons, and classifications of features that are especially relevant in the context of 3D measurements of human faces. These frameworks have a long-term utility in face analysis, taking into account the anticipated improvements in data collection, data storage, processing speeds, and application scenarios expected as the discipline develops further. The book covers face acquisition through 3D scanners and 3D face pre-processing, before examining the three main approaches for 3D facial surface analysis and recognition: facial curves; facial surface features; and 3D morphable models. Whilst the focus of these chapters is fundamentals and methodologies, the algorithms provided are tested on facial biometric data, thereby continually showing how the methods can be applied. Key features: • Explores the underlying mathematics and will apply these mathematical techniques to 3D face analysis and recognition • Provides coverage of a wide range of applications including biometrics, forensic applications, facial expression analysis, and model fitting to 2D images • Contains numerous exercises and algorithms throughout the book

9599.07 РУБ

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Edward R. Dougherty Error Estimation for Pattern Recognition


This book is the first of its kind to discuss error estimation with a model-based approach. From the basics of classifiers and error estimators to distributional and Bayesian theory, it covers important topics and essential issues pertaining to the scientific validity of pattern classification. Error Estimation for Pattern Recognition focuses on error estimation, which is a broad and poorly understood topic that reaches all research areas using pattern classification. It includes model-based approaches and discussions of newer error estimators such as bolstered and Bayesian estimators. This book was motivated by the application of pattern recognition to high-throughput data with limited replicates, which is a basic problem now appearing in many areas. The first two chapters cover basic issues in classification error estimation, such as definitions, test-set error estimation, and training-set error estimation. The remaining chapters in this book cover results on the performance and representation of training-set error estimators for various pattern classifiers. Additional features of the book include: • The latest results on the accuracy of error estimation • Performance analysis of re-substitution, cross-validation, and bootstrap error estimators using analytical and simulation approaches • Highly interactive computer-based exercises and end-of-chapter problems This is the first book exclusively about error estimation for pattern recognition. Ulisses M. Braga Neto is an Associate Professor in the Department of Electrical and Computer Engineering at Texas A&M University, USA. He received his PhD in Electrical and Computer Engineering from The Johns Hopkins University. Dr. Braga Neto received an NSF CAREER Award for his work on error estimation for pattern recognition with applications in genomic signal processing. He is an IEEE Senior Member. Edward R. Dougherty is a Distinguished Professor, Robert F. Kennedy ’26 Chair, and Scientific Director at the Center for Bioinformatics and Genomic Systems Engineering at Texas A&M University, USA. He is a fellow of both the IEEE and SPIE, and he has received the SPIE Presidents Award. Dr. Dougherty has authored several books including Epistemology of the Cell: A Systems Perspective on Biological Knowledge and Random Processes for Image and Signal Processing (Wiley-IEEE Press).

10124.13 РУБ

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Stephen Bach Managing Human Resources. Resource Management in Transition


This revised edition is a comprehensive, authoritative set of essays. It is more detailed and analytical than the mainstream treatments of HRM. As in previous editions, Managing Human Resources analyses HRM, the study of work and employment, using an integrated multi-disciplinary approach. The starting point is a recognition that HRM practice and firm performance are influenced by a variety of institutional arrangements that extend beyond the firm. The consequences of HRM need to incorporate analysis of employees and other stakeholders as well as the implications for organizational performance.

4499.3 РУБ

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Leszek Szczecinski Bit-Interleaved Coded Modulation. Fundamentals, Analysis and Design


Presenting a thorough overview of bit-interleaved coded modulation (BICM), this book introduces the tools for the analysis and design of BICM transceivers. It explains in details the functioning principles of BICM and proposes a refined probabilistic modeling of the reliability metrics–the so-called L-values–which are at the core of the BICM receivers. Alternatives for transceiver design based on these models are then studied. Providing new insights into the analysis of BICM, this book is unique in its approach, providing a general framework for analysis and design, focusing on communication theoretic aspects of BICM transceivers. It adopts a tutorial approach, explains the problems in simple terms with the aid of multiple examples and case studies, and provides solutions using accessible mathematical tools. The book will be an excellent resource for researchers in academia and industry: graduate students, academics, development engineers, and R & D managers. Key Features: Presents an introduction to BICM, placing it in the context of other coded modulation schemes Offers explanations of the functioning principles and design alternatives Provides a unique approach, focusing on communication theory aspects Shows examples and case studies to illustrate analysis and design of BICM Adopts a tutorial approach, explaining the problems in simple terms and presenting solutions using accessible mathematical tools

10498.36 РУБ

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Clifton A. Ericson, II Hazard Analysis Techniques for System Safety


Explains in detail how to perform the most commonly used hazard analysis techniques with numerous examples of practical applications • Includes new chapters on Concepts of Hazard Recognition, Environmental Hazard Analysis, Process Hazard Analysis, Test Hazard Analysis, and Job Hazard Analysis • Updated text covers introduction, theory, and detailed description of many different hazard analysis techniques and explains in detail how to perform them as well as when and why to use each technique • Describes the components of a hazard and how to recognize them during an analysis • Contains detailed examples that apply the methodology to everyday problems

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Saul Stahl Real Analysis. A Historical Approach


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Frank Shih Y. Image Processing and Pattern Recognition. Fundamentals Techniques


A comprehensive guide to the essential principles of image processing and pattern recognition Techniques and applications in the areas of image processing and pattern recognition are growing at an unprecedented rate. Containing the latest state-of-the-art developments in the field, Image Processing and Pattern Recognition presents clear explanations of the fundamentals as well as the most recent applications. It explains the essential principles so readers will not only be able to easily implement the algorithms and techniques, but also lead themselves to discover new problems and applications. Unlike other books on the subject, this volume presents numerous fundamental and advanced image processing algorithms and pattern recognition techniques to illustrate the framework. Scores of graphs and examples, technical assistance, and practical tools illustrate the basic principles and help simplify the problems, allowing students as well as professionals to easily grasp even complicated theories. It also features unique coverage of the most interesting developments and updated techniques, such as image watermarking, digital steganography, document processing and classification, solar image processing and event classification, 3-D Euclidean distance transformation, shortest path planning, soft morphology, recursive morphology, regulated morphology, and sweep morphology. Additional topics include enhancement and segmentation techniques, active learning, feature extraction, neural networks, and fuzzy logic. Featuring supplemental materials for instructors and students, Image Processing and Pattern Recognition is designed for undergraduate seniors and graduate students, engineering and scientific researchers, and professionals who work in signal processing, image processing, pattern recognition, information security, document processing, multimedia systems, and solar physics.

11998.84 РУБ

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