Bücher veröffentlicht von Springer New York

Pattern recognition presents one of the most significant challenges for scientists and engineers, and many different approaches have been proposed. The aim of this book is to provide a self-contained account of probabilistic analysis of these approaches. The book includes a discussion of distance measures, nonparametric methods based on kernels or nearest neighbors, Vapnik-Chervonenkis theory, epsilon entropy, parametric classification, error estimation, free classifiers, and neural networks. Wherever possible, distribution-free properties and inequalities are derived. A substantial portion of the results or the analysis is new. Over 430 problems and exercises complement the material.

In this book the author presents the dynamical systems in infinite dimension, especially those generated by dissipative partial differential equations. This book attempts a systematic study of infinite dimensional dynamical systems generated by dissipative evolution partial differential equations arising in mechanics and physics and in other areas of sciences and technology. This second edition has been updated and extended.

Of the many different approaches to solving partial differential equations numerically, this book studies difference methods. Written for the beginning graduate student in applied mathematics and engineering, this text offers a means of coming out of a course with a large number of methods that provide both theoretical knowledge and numerical experience. The reader will learn that numerical experimentation is a part of the subject of numerical solution of partial differential equations, and will be shown some uses and taught some techniques of numerical experimentation.Prerequisites suggested for using this book in a course might include at least one semester of partial differential equations and some programming capability. The author stresses the use of technology throughout the text, allowing the student to utilize it as much as possible. The use of graphics for both illustration and analysis is emphasized, and algebraic manipulators are used when convenient. This is the second volume of a two-part book.

This book describes several techniques to address variation-related design challenges for analog blocks in mixed-signal systems-on-chip. The methods presented are results from recent research works involving receiver front-end circuits, baseband filter linearization, and data conversion. These circuit-level techniques are described, with their relationships to emerging system-level calibration approaches, to tune the performances of analog circuits with digital assistance or control. Coverage also includes a strategy to utilize on-chip temperature sensors to measure the signal power and linearity characteristics of analog/RF circuits, as demonstrated by test chip measurements. Describes a variety of variation-tolerant analog circuit design examples, including from RF front-ends, high-performance ADCs and baseband filters;Includes built-in testing techniques, linked to current industrial trends;Balances digitally-assisted performance tuning with analog performance tuning and mismatch reduction approaches;Describes theoretical concepts as well as experimental results for test chips designed with variation-aware techniques.

For the first two editions of the book Probability (GTM 95), each chapter included a comprehensive and diverse set of relevant exercises. While the work on the third edition was still in progress, it was decided that it would be more appropriate to publish a separate book that would comprise all of the exercises from previous editions, in addition to many newexercises.Most of the material in this book consists of exercises created by Shiryaev, collected and compiled over the course of many years while working on many interesting topics. Many of the exercises resulted from discussions that took place during special seminars for graduate and undergraduate students.  Many of the exercises included in the book contain helpful hints and other relevant information.Lastly, the author has included an appendix at the end of the book that contains a summary of the main results, notation and terminology from Probability Theory that are used throughout the present book.  This Appendix also contains additional material from Combinatorics, Potential Theory and Markov Chains, which is not covered in the book, but is nevertheless needed for many of the exercises included here.

This book covers some biostatistical methods and several case studies useful to interpret and analyze dental research in the areas of orofacial pain and temporomandibular disorders. It will guide practitioners in these fields who would like to interpret research findings or find examples on the design of clinical investigations. After an introduction dealing with the basic issues, the central sections of the textbook are dedicated to the different types of investigations in sight of specific goals researchers may have. The final section contains a recent approach based on nonparametric permutation tests which can be adopted in many practical situations. The field of orofacial pain and temporomandibular disorders is emerging as one of the most critical areas of clinical research in dentistry. Due to the complexity of clinical pictures, the multifactorial etiology, and the importance of psychosocial factors in all aspects of the TMD practice, clinicians often find it hard to appraise their modus operandi, and researchers must constantly increase their knowledge in epidemiology and medical statistics. Indeed, proper methodological designs are fundamental to reaching high levels of internal and external validity of findings in this specific area.

This book is intended as an undergraduate senior level or beginning graduate level text for mathematical logic. There are virtually no prere- quisites, although a familiarity with notions encountered in a beginning course in abstract algebra such as groups, rings, and fields will be useful in providing some motivation for the topics in Part III. An attempt has been made to develop the beginning of each part slowly and then to gradually quicken the pace and the complexity of the material. Each part ends with a brief introduction to selected topics of current interest. The text is divided into three parts: one dealing with set theory, another with computable function theory, and the last with model theory. Part III relies heavily on the notation, concepts and results discussed in Part I and to some extent on Part II. Parts I and II are independent of each other, and each provides enough material for a one semester course. The exercises cover a wide range of difficulty with an emphasis on more routine problems in the earlier sections of each part in order to familiarize the reader with the new notions and methods. The more difficult exercises are accompanied by hints. In some cases significant theorems are devel- oped step by step with hints in the problems. Such theorems are not used later in the sequence.

"e;Mechanics is one ofthe branches ofphysics in which the number ofprinciples is at once very few and very rich in useful consequences. On the other hand, there are few sciences which have required so much thought-the conquest of a few axioms has taken more than 2000 years. "e;-Rene Dugas, A History 0/ Mechanics Introductory courses in engineering mechanics (statics and dynamics) are generally found very early in engineering curricula. As such, they should provide the student with a thorough background in the basic fundamentals that form the foundation for subsequent work in engi- neering analysis and design. Consequently, our primary goal in writing Statics for Engineers and Dynamics for Engineers has been to develop the fundamental principles of engineering mechanics in a manner that the student can readily comprehend. With this comprehension, the student thus acquires the tools that would enable him/her to think through the solution ofmany types ofengineering problems using logic and sound judgment based upon fundamental principles. Approach We have made every effort to present the material in a concise but clear manner. Each subject is presented in one or more sections fol- lowed by one or more examples, the solutions for which are presented in a detailed fashion with frequent reference to the basic underlying principles. A set of problems is provided for use in homework assign- ments.

This is the first volume of a comprehensive two-volume treatment of mechanics intended for students of civil and mechanical engineering. Used for several years in courses at Bradley University, the text presents statics in a clear and straightforward way and emphasizes problem solving. More than 350 examples clarify the discussion. The diskette included with the book contains EnSolve, a program written by the authors for solving problems in engineering mechanics. The program runs on Macintosh and PC-DOS computers and includes the following: - a unit converter for SI to US units and vice versa - a graphics program for plotting functions and data - a set of numerical subroutines The graphics module will, among other features, fit smooth splines between data, plot regression lines and curves, and change scales -- including from arithmetic to log and log-log. The numerical routines will, for example, find roots of polynomials, solve systems of equations, invert matrices, differentiate and integrate, and solve boundary-value problems.

This book gives an elementary treatment of the basic material about graph spectra, both for ordinary, and Laplace and Seidel spectra. The text progresses systematically, by covering standard topics before presenting some new material on trees, strongly regular graphs, two-graphs, association schemes, p-ranks of configurations and similar topics. Exercises at the end of each chapter provide practice and vary from easy yet interesting applications of the treated theory, to little excursions into related topics. Tables, references at the end of the book, an author and subject index enrich the text. Spectra of Graphs is written for researchers, teachers and graduate students interested in graph spectra. The reader is assumed to be familiar with basic linear algebra and eigenvalues, although some more advanced topics in linear algebra, like the Perron-Frobenius theorem and eigenvalue interlacing are included.

With the rise of systems biology as an approach in biochemistry research, using high throughput techniques such as mass spectrometry to generate metabolic profiles of cancer metabolism is becoming increasingly popular. There are examples of cancer metabolic profiling studies in the academic literature; however they are often only in journals specific to the metabolomics community. This book will be particularly useful for post-graduate students and post-doctoral researchers using this pioneering technique of network-based correlation analysis. The approach can be adapted to the analysis of any large scale metabolic profiling experiment to answer a range of biological questions in a range of species or for a range of diseases.

There are two main approaches in the theory of network error correction coding. In this SpringerBrief, the authors summarize some of the most important contributions following the classic approach, which represents messages by sequences similar to algebraic coding, and also briefly discuss the main results following the other approach, that uses the theory of rank metric codes for network error correction of representing messages by subspaces. This book starts by establishing the basic linear network error correction (LNEC) model and then characterizes two equivalent descriptions. Distances and weights are defined in order to characterize the discrepancy of these two vectors and to measure the seriousness of errors. Similar to classical error-correcting codes, the authors also apply the minimum distance decoding principle to LNEC codes at each sink node, but use distinct distances. For this decoding principle, it is shown that the minimum distance of a LNEC code at each sink node can fully characterize its error-detecting, error-correcting and erasure-error-correcting capabilities with respect to the sink node. In addition, some important and useful coding bounds in classical coding theory are generalized to linear network error correction coding, including the Hamming bound, the Gilbert-Varshamov bound and the Singleton bound. Several constructive algorithms of LNEC codes are presented, particularly for LNEC MDS codes, along with an analysis of their performance. Random linear network error correction coding is feasible for noncoherent networks with errors. Its performance is investigated by estimating upper bounds on some failure probabilities by analyzing the information transmission and error correction. Finally, the basic theory of subspace codes is introduced including the encoding and decoding principle as well as the channel model, the bounds on subspace codes, code construction and decoding algorithms.

Applied Computer Science presents a unique approach for introductory courses that will engage students with relevant topics from a variety of disciplines, encourage their natural creativity, and prepare them for independent projects. Lab assignments are accessible and carefully sequenced for maximum impact. Students are able to write their own code in building solutions and Python is used to minimize any language barrier for beginners. Problems involving visualization are emphasized throughout with interactive graphics, image files, and plots of generated data. This text aims to establish a core learning experience around which any number of other learning objectives could be included. The text is presented in seven (7) chapters where each chapter contains three (3) problems and each problem develops five (5) specific lab assignments, plus additional questions and discussion. This approach seeks to leverage the immediate feedback provided by the computer to help students as they work toward writing code creatively. All labs will scale to available hardware and free software could be used for the entire course, if desired.