Recommendations based on "Physical Kinetics"

¿Intended for use in one/two-semester introductory courses in vibration for undergraduates in Mechanical Engineering, Civil Engineering, Aerospace Engineering and Mechanics. This text is also suitable for readers with an interest in Mechanical Engineering, Civil Engineering, Aerospace Engineering and Mechanics.Serving as both a text and reference manual, Engineering Vibration, 4e, connects traditional design-oriented topics, the introduction of modal analysis, and the use of MATLAB, Mathcad, or Mathematica. The author provides an unequaled combination of the study of conventional vibration with the use of vibration design, computation, analysis and testing in various engineering applications.

Limnology is the study of the structural and functional interrelationships of organisms of inland waters as they are affected by their dynamic physical, chemical, and biotic environments. Limnology: Lake and River Ecosystems, Third Edition, is a new edition of this established classic text. The coverage remains rigorous and uncompromising and has been thoroughly reviewed and updated with evolving recent research results and theoretical understanding. In addition, the author has expanded coverage of lakes to reservoir and river ecosystems in comparative functional analyses.

The third edition of the book widely recognized as providing the most readable and clearly articulated introduction to Cognitive Linguistics is fully revised and updated to include the considerable developments in Cognitive Linguistics since 1987. It covers recent research on polysemy, meaning relatedness and metaphors, as well as expanding the discussion of syntactic categories and the relevance of computer simulations.

Learn How Coupling Impacts Every Software Design Decision You Make--and How to Control It If you want to build modular, evolvable, and resilient software systems, you have to get coupling right. Every design decision you make influences coupling, which in turn shapes the design options available to you. Despite its importance, coupling often doesn't receive the attention it deserves--until now. Since the dawn of software engineering, it's been clear that proper management of coupling is essential for architecting modular software systems. This topic has been extensively researched over the years, but some of that knowledge has been forgotten, and some of it is challenging to apply in this day and age. In Balancing Coupling in Software Design, author Vlad Khononov has built a model that not only draws on this accumulated knowledge, but also adapts it to modern software engineering practices, offering a fresh perspective on modular software design. With principles grounded in practice, Vlad teaches you not only to navigate the multi-dimensional nature of coupling but also to use coupling as a tool for managing complexity and increasing modularity. And who knows, maybe this book will change the way you think about software design as whole. Defines the concept of coupling and the role it plays in system design and architecture Explains how coupling can both increase complexity and foster modularity of a system Introduces a holistic model that turns coupling into a tool for designing modular software Shows how to evolve design decisions to support continuous growth of software systems Illustrates the discussed principles with detailed examples based on real-life case studies "Coupling is one of those words that is used a lot, but little understood. Vlad propels us from simplistic slogans like 'always decouple components' to a nuanced discussion of coupling in the context of complexity and software evolution. If you build modern software, read this book!" --Gregor Hohpe, author of The Software Architect Elevator Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Now in its second edition, Brain Architecture is the continued exploration of how the brain works. At the very core of our existence, the brain generates our thoughts and feelings, directs our voluntary interactions with the environment, and coordinates all of the vital functions within the body itself. This long-overdue new edition explains this oftentimes daunting intricacy and exquisite detail. The first half of the book discusses the basic parts and how they work, presenting an overview of the nervous system at both the microscopic and macroscopic levels. The approach follows three classic lines of thought that proceed from simple to complex: the history of neuroscience research, the evolution of the nervous system, and the embryological development of the vertebrate central and peripheral nervous systems. The second half of the book outlines the basic wiring diagram of the brain and nervous system-how the parts are interconnected and how they control behavior and the internal state of the body. This is done within the framework of a new four-system network model that greatly simplifies understanding the structure-function organization of the nervous system. Written in clear and sparkling prose, beautifully illustrated, and thoroughly updated, Brain Architecture, Second Edition is must-read for anyone interested in the science of how the brain works.

A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions.Most tasks require a person or an automated system to reason—to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality. Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research.Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction.Olivier Chapelle and Alexander Zien are Research Scientists and Bernhard Schölkopf is Professor and Director at the Max Planck Institute for Biological Cybernetics in Tübingen. Schölkopf is coauthor of Learning with Kernels (MIT Press, 2002) and is a coeditor of Advances in Kernel Methods: Support Vector Learning (1998), Advances in Large-Margin Classifiers (2000), and Kernel Methods in Computational Biology (2004), all published by The MIT Press.

A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines.Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.

Noted for its highly readable style, the new edition of this bestseller provides an updated overview of aeronautical and aerospace engineering. Introduction to Flight blends history and biography with discussion of engineering concepts, and shows the development of flight through this perspective. Anderson covers new developments in flight, including unmanned aerial vehicles, uninhabited combat aerial vehicles, and applications of CFD in aircraft design. Many new and revised problems have been added in this edition. Chapter learning features help readers follow the text discussion while highlighting key engineering and industry applications.