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Books in Physical sciences and engineering

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    • The Algebraic Theory of Switching Circuits

      • 1st Edition
      • Gr. C. Moisil
      • English
      The Algebraic Theory of Switching Circuits covers the application of various algebraic tools to the delineation of the algebraic theory of switching circuits for automation with contacts and relays. This book is organized into five parts encompassing 31 chapters. Part I deals with the principles and application of Boolean algebra and the theory of finite fields (Galois fields). Part II emphasizes the importance of the sequential operation of the automata and the variables associated to the current and to the contacts. This part also tackles the recurrence relations that describe operations of the network and the principles of the so-called characteristic equations. Part III reviews the study of networks with secondary elements other than ordinary relays, while Part IV focuses on the fundamentals and application of multi-position contacts. Part V considers several topics related to circuit with electronic elements, including triodes, pentodes, transistors, and cryotrons. This book will be of great value to practicing engineers, mathematicians, and workers in the field of computers.

    • Probability Inequalities in Multivariate Distributions

      • 1st Edition
      • Y. L. Tong
      • Z. W. Birnbaum + 1 more
      • English
      Probability Inequalities in Multivariate Distributions is a comprehensive treatment of probability inequalities in multivariate distributions, balancing the treatment between theory and applications. The book is concerned only with those inequalities that are of types T1-T5. The conditions for such inequalities range from very specific to very general. Comprised of eight chapters, this volume begins by presenting a classification of probability inequalities, followed by a discussion on inequalities for multivariate normal distribution as well as their dependence on correlation coefficients. The reader is then introduced to inequalities for other well-known distributions, including the multivariate distributions of t, chi-square, and F; inequalities for a class of symmetric unimodal distributions and for a certain class of random variables that are positively dependent by association or by mixture; and inequalities obtainable through the mathematical tool of majorization and weak majorization. The book also describes some distribution-free inequalities before concluding with an overview of their applications in simultaneous confidence regions, hypothesis testing, multiple decision problems, and reliability and life testing. This monograph is intended for mathematicians, statisticians, students, and those who are primarily interested in inequalities.

    • The Expected-Outcome Model of Two-Player Games

      • 1st Edition
      • Bruce Abramson
      • English
      The Expected-Outcome Model of Two-Player Games deals with the expected-outcome model of two-player games, in which the relative merit of game-tree nodes, rather than board positions, is considered. The ambiguity of static evaluation and the problems it generates in the search system are examined and the development of a domain-independent static evaluator is described. Comprised of eight chapters, this book begins with an overview of the rationale for the mathematical study of games, followed by a discussion on some previous artificial intelligence (AI) research efforts on game-trees. The next section opens with the definition of a node's expected-outcome value as the expected value of the leaves beneath it. The expected-outcome model is outlined, paying particular attention to the expected-outcome value of a game-tree node. This model was implemented on some small versions of tic-tac-toe and Othello. The book also presents results that offer strong support for both the validity of the expected-outcome model and the rationality of its underlying assumptions. This monograph is intended for specialists in AI and computer science.

    • Multivariate Statistical Inference

      • 1st Edition
      • Narayan C. Giri
      • Z. W. Birnbaum + 1 more
      • English
      Multivariate Statistical Inference is a 10-chapter text that covers the theoretical and applied aspects of multivariate analysis, specifically the multivariate normal distribution using the invariance approach. Chapter I contains some special results regarding characteristic roots and vectors, and partitioned submatrices of real and complex matrices, as well as some special theorems on real and complex matrices useful in multivariate analysis. Chapter II deals with the theory of groups and related results that are useful for the development of invariant statistical test procedures, including the Jacobians of some specific transformations that are useful for deriving multivariate sampling distributions. Chapter III is devoted to basic notions of multivariate distributions and the principle of invariance in statistical testing of hypotheses. Chapters IV and V deal with the study of the real multivariate normal distribution through the probability density function and through a simple characterization and the maximum likelihood estimators of the parameters of the multivariate normal distribution and their optimum properties. Chapter VI tackles a systematic derivation of basic multivariate sampling distributions for the real case, while Chapter VII explores the tests and confidence regions of mean vectors of multivariate normal populations with known and unknown covariance matrices and their optimum properties. Chapter VIII is devoted to a systematic derivation of tests concerning covariance matrices and mean vectors of multivariate normal populations and to the study of their optimum properties. Chapters IX and X look into a treatment of discriminant analysis and the different covariance models and their analysis for the multivariate normal distribution. These chapters also deal with the principal components, factor models, canonical correlations, and time series. This book will prove useful to statisticians, mathematicians, and advance mathematics students.

    • Space Power Systems Engineering

      • 1st Edition
      • George C. Szego + 1 more
      • English
      Space Power Systems Engineering is a collection of papers dealing with the requirements for space power systems, system design, component research, the problems of application to spacecraft, and the development of a variety of space electric power systems. Some papers discuss nuclear power systems development, including nuclear reactors, nuclear dynamic systems, nuclear thermoelectric systems, and nuclear thermionic systems. Several papers tackle solar systems development, including solar collectors, solar dynamic systems, solar thermoelectric systems, chemical fuel cell systems, and chemical primary battery systems. A magnetohydrodynamic power system can be utilized for space electric generation. Power conversion or conditioning involves the interface between raw electric power and the on-board consumption of that electric power. One paper cites an application of a potential power system: particularly the engine development in a power package which includes a single-cylinder engine, generator, gas compressor, and recuperator. Some design considerations for the engine include an operation with an 0-H mixture of 2 to 1 obtained either from supercritical tankage or in the form of helium-diluted boil-off gases; a power level of 2-kw average, 3-kw maximum; and an uninterrupted life of 350 hr. The collection can prove immensely beneficial for nuclear engineers, aeronautical engineers, chemists, researchers, or technical designers whose works are related with energy conversion and space power systems.

    • Stochastic Calculus and Stochastic Models

      • 1st Edition
      • E. J. McShane
      • Z. W. Birnbaum + 1 more
      • English
      Probability and Mathematical Statistics: A Series of Monographs and Textbooks: Stochastic Calculus and Stochastic Models focuses on the properties, functions, and applications of stochastic integrals. The publication first ponders on stochastic integrals, existence of stochastic integrals, and continuity, chain rule, and substitution. Discussions focus on differentiation of a composite function, continuity of sample functions, existence and vanishing of stochastic integrals, canonical form, elementary properties of integrals, and the Itô-belated integral. The book then examines stochastic differential equations, including existence of solutions of stochastic differential equations, linear differential equations and their adjoints, approximation lemma, and the Cauchy-Maruyama approximation. The manuscript takes a look at equations in canonical form, as well as justification of the canonical extension in stochastic modeling; rate of convergence of approximations to solutions; comparison of ordinary and stochastic differential equations; and invariance under change of coordinates. The publication is a dependable reference for mathematicians and researchers interested in stochastic integrals.

    • Foundations of Stochastic Analysis

      • 1st Edition
      • M. M. Rao
      • Z. W. Birnbaum + 1 more
      • English
      Foundations of Stochastic Analysis deals with the foundations of the theory of Kolmogorov and Bochner and its impact on the growth of stochastic analysis. Topics covered range from conditional expectations and probabilities to projective and direct limits, as well as martingales and likelihood ratios. Abstract martingales and their applications are also discussed. Comprised of five chapters, this volume begins with an overview of the basic Kolmogorov-Bochner theorem, followed by a discussion on conditional expectations and probabilities containing several characterizations of operators and measures. The applications of these conditional expectations and probabilities to Reynolds operators are also considered. The reader is then introduced to projective limits, direct limits, and a generalized Kolmogorov existence theorem, along with infinite product conditional probability measures. The book also considers martingales and their applications to likelihood ratios before concluding with a description of abstract martingales and their applications to convergence and harmonic analysis, as well as their relation to ergodic theory. This monograph should be of considerable interest to researchers and graduate students working in stochastic analysis.

    • VLSI Electronics: Microstructure

      • 1st Edition
      • Volume 22
      • Anant G. Sabnis
      • English
      As integrated cicuits become more complex, with smaller and smaller geometries, much more care must be taken to avoid reliability problems. This practical volume covers a broad spectrum of reliability issues in integrated circuits, from basic concepts to packaging.

    • Machine Vision

      Theory, Algorithms, Practicalities
      • 1st Edition
      • E. R. Davies
      • P. G. Farrell + 1 more
      • English
      Machine Vision: Theory, Algorithms, Practicalities covers the limitations, constraints, and tradeoffs of vision algorithms. This book is organized into four parts encompassing 21 chapters that tackle general topics, such as noise suppression, edge detection, principles of illumination, feature recognition, Bayes’ theory, and Hough transforms. Part 1 provides research ideas on imaging and image filtering operations, thresholding techniques, edge detection, and binary shape and boundary pattern analyses. Part 2 deals with the area of intermediate-level vision, the nature of the Hough transform, shape detection, and corner location. Part 3 demonstrates some of the practical applications of the basic work previously covered in the book. This part also discusses some of the principles underlying implementation, including on lighting and hardware systems. Part 4 highlights the limitations and constraints of vision algorithms and their corresponding solutions. This book will prove useful to students with undergraduate course on vision for electronic engineering or computer science.

    • Strong Approximations in Probability and Statistics

      • 1st Edition
      • M. Csörgo + 1 more
      • Z. W. Birnbaum + 1 more
      • English
      Strong Approximations in Probability and Statistics presents strong invariance type results for partial sums and empirical processes of independent and identically distributed random variables (IIDRV). This seven-chapter text emphasizes the applicability of strong approximation methodology to a variety of problems of probability and statistics. Chapter 1 evaluates the theorems for Wiener and Gaussian processes that can be extended to partial sums and empirical processes of IIDRV through strong approximation methods, while Chapter 2 addresses the problem of best possible strong approximations of partial sums of IIDRV by a Wiener process. Chapters 3 and 4 contain theorems concerning the one-time parameter Wiener process and strong approximation for the empirical and quantile processes based on IIDRV. Chapter 5 demonstrate the validity of previously discussed theorems, including Brownian bridges and Kiefer process, for empirical and quantile processes. Chapter 6 illustrate the approximation of defined sequences of empirical density, regression, and characteristic functions by appropriate Gaussian processes. Chapter 7 deal with the application of strong approximation methodology to study weak and strong convergence properties of random size partial sum and empirical processes. This book will prove useful to mathematicians and advance mathematics students.

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