Up-to-date, technically accurate coverage of essential topics in image and video processing This is the first book to combine image and video processing with a practical MATLAB®-oriented approach in order to demonstrate the most important image and video techniques and algorithms. Utilizing minimal math, the contents are presented in a clear, objective manner, emphasizing and encouraging experimentation. The book has been organized into two parts. Part I: Image Processing begins with an overview of the field, then introduces the fundamental concepts, notation, and terminology associated with image representation and basic image processing operations. Next, it discusses MATLAB® and its Image Processing Toolbox with the start of a series of chapters with hands-on activities and step-by-step tutorials. These chapters cover image acquisition and digitization; arithmetic, logic, and geometric operations; point-based, histogram-based, and neighborhood-based image enhancement techniques; the Fourier Transform and relevant frequency-domain image filtering techniques; image restoration; mathematical morphology; edge detection techniques; image segmentation; image compression and coding; and feature extraction and representation. Part II: Video Processing presents the main concepts and terminology associated with analog video signals and systems, as well as digital video formats and standards. It then describes the technically involved problem of standards conversion, discusses motion estimation and compensation techniques, shows how video sequences can be filtered, and concludes with an example of a solution to object detection and tracking in video sequences using MATLAB®. Extra features of this book include: More than 30 MATLAB® tutorials, which consist of step-by-step guides to exploring image and video processing techniques using MATLAB® Chapters supported by figures, examples, illustrative problems, and exercises Useful websites and an extensive list of bibliographical references This accessible text is ideal for upper-level undergraduate and graduate students in digital image and video processing courses, as well as for engineers, researchers, software developers, practitioners, and anyone who wishes to learn about these increasingly popular topics on their own.

Data visualization is an efficient and effective medium for communicating large amounts of information, but the design process can often seem like an unexplainable creative endeavor. This concise book aims to demystify the design process by showing you how to use a linear decision-making process to encode your information visually. Delve into different kinds of visualization, including infographics and visual art, and explore the influences at work in each one. Then learn how to apply these concepts to your design process. Learn data visualization classifications, including explanatory, exploratory, and hybrid Discover how three fundamental influences—the designer, the reader, and the data—shape what you create Learn how to describe the specific goal of your visualization and identify the supporting data Decide the spatial position of your visual entities with axes Encode the various dimensions of your data with appropriate visual properties, such as shape and color See visualization best practices and suggestions for encoding various specific data types

As data science evolves to become a business necessity, the importance of assembling a strong and innovative data teams grows. In this in-depth report, data scientist DJ Patil explains the skills,perspectives, tools and processes that position data science teams for success.

All statistical concepts are supported by a large number of examples using data encountered in real life situations; and the text illustrates how the statistical packages MINITAB®, Microsoft Excel ®, and JMP® may be used to aid in the analysis of various data sets. The text also covers an appropriate and understandable level of the design of experiments. This includes randomized block designs, one and two-way designs, Latin square designs, factorial designs, response surface designs, and others. This text is suitable for a one- or two-semester calculus-based undergraduate statistics course for engineers and scientists, and the presentation of material gives instructors flexibility to pick and choose topics for their particular courses.

Practical Biomedical Signal Analysis Using MATLAB presents a coherent treatment of various signal processing methods and applications. The book not only covers the current techniques of biomedical signal processing, but it also offers guidance on which methods are appropriate for a given task and different types of data.The first several chapters o

This book bridges the latest software applications with the benefits of modern resampling techniques Resampling helps students understand the meaning of sampling distributions, sampling variability, P-values, hypothesis tests, and confidence intervals. This groundbreaking book shows how to apply modern resampling techniques to mathematical statistics. Extensively class-tested to ensure an accessible presentation, Mathematical Statistics with Resampling and R utilizes the powerful and flexible computer language R to underscore the significance and benefits of modern resampling techniques. The book begins by introducing permutation tests and bootstrap methods, motivating classical inference methods. Striking a balance between theory, computing, and applications, the authors explore additional topics such as: Exploratory data analysis Calculation of sampling distributions The Central Limit Theorem Monte Carlo sampling Maximum likelihood estimation and properties of estimators Confidence intervals and hypothesis tests Regression Bayesian methods Throughout the book, case studies on diverse subjects such as flight delays, birth weights of babies, and telephone company repair times illustrate the relevance of the real-world applications of the discussed material. Key definitions and theorems of important probability distributions are collected at the end of the book, and a related website is also available, featuring additional material including data sets, R scripts, and helpful teaching hints. Mathematical Statistics with Resampling and R is an excellent book for courses on mathematical statistics at the upper-undergraduate and graduate levels. It also serves as a valuable reference for applied statisticians working in the areas of business, economics, biostatistics, and public health who utilize resampling methods in their everyday work.

Quickly Engages in Applying Algorithmic Techniques to Solve Practical Signal Processing Problems With its active, hands-on learning approach, this text enables readers to master the underlying principles of digital signal processing and its many applications in industries such as digital television, mobile and broadband communications, and medical/scientific devices. Carefully developed MATLAB® examples throughout the text illustrate the mathematical concepts and use of digital signal processing algorithms. Readers will develop a deeper understanding of how to apply the algorithms by manipulating the codes in the examples to see their effect. Moreover, plenty of exercises help to put knowledge into practice solving real-world signal processing challenges. Following an introductory chapter, the text explores: Sampled signals and digital processing Random signals Representing signals and systems Temporal and spatial signal processing Frequency analysis of signals Discrete-time filters and recursive filters Each chapter begins with chapter objectives and an introduction. A summary at the end of each chapter ensures that one has mastered all the key concepts and techniques before progressing in the text. Lastly, appendices listing selected web resources, research papers, and related textbooks enable the investigation of individual topics in greater depth. Upon completion of this text, readers will understand how to apply key algorithmic techniques to address practical signal processing problems as well as develop their own signal processing algorithms. Moreover, the text provides a solid foundation for evaluating and applying new digital processing signal techniques as they are developed.

In SAS Statistics by Example, Ron Cody offers up a cookbook approach for doing statistics with SAS. Structured specifically around the most commonly used statistical tasks or techniques--for example, comparing two means, ANOVA, and regression--this book provides an easy-to-follow, how-to approach to statistical analysis not found in other books.

For each statistical task, Cody includes heavily annotated examples using ODS Statistical Graphics procedures such as SGPLOT, SGSCATTER, and SGPANEL that show how SAS can produce the required statistics. Also, you will learn how to test the assumptions for all relevant statistical tests. Major topics featured include descriptive statistics, one- and two-sample tests, ANOVA, correlation, linear and multiple regression, analysis of categorical data, logistic regression, nonparametric techniques, and power and sample size.

This is not a book that teaches statistics. Rather, SAS Statistics by Example is perfect for intermediate to advanced statistical programmers who know their statistics and want to use SAS to do their analyses.

This book is part of the SAS Press program.

Assuming no knowledge of programming, this book presents both programming concepts and MATLAB’s built-in functions, providing a perfect platform for exploiting MATLAB’s extensive capabilities for tackling engineering problems. It starts with programming concepts such as variables, assignments, input/output, and selection statements, moves onto loops and then solves problems using both the ‘programming concept’ and the ‘power of MATLAB’ side-by-side. In-depth coverage is given to input/output, a topic that is fundamental to many engineering applications. Ancillaries available with the text: Instructor solution manual (available Aug. 1st) electronic images from the text (available Aug 16th) m-files (available Aug 1st) * Presents programming concepts and MATLAB built-in functions side-by-side, giving students the ability to program efficiently and exploit the power of MATLAB to solve problems. * In depth coverage of file input/output, a topic essential for many engineering applications * Systematic, step-by-step approach, building on concepts throughout the book, facilitating easier learning * Sections on ‘common pitfalls’ and ‘programming guidelines’ direct students towards best practice * New to this edition: More engineering applications help the reader learn Matlab in the context of solving technical problems New and revised end of chapter problems Stronger coverage of loops and vectorizing in a new chapter, chapter 5 Updated to reflect current features and functions of the current release of Matlab

SAS 9.3 ODS Graphics: Getting Started with Business and Statistical Graphics provides an overview and quick-start examples of ODS Graphics functionality, which is an extension of the SAS Output Delivery System (ODS). Many SAS procedures use ODS Graphics functionality to produce graphs as automatically as they produce tables. In addition, SAS provides a full suite of ODS Graphics software that facilitates the creation of custom, stand-alone graphs. This software includes the Graph Template Language, ODS Graphics procedures, ODS Graphics Designer, and ODS Graphics Editor. This ebook also explains how the ODS Graphics software components complement each other, and how they can be used together.

This is an introduction to stochastic integration and stochastic differential equations written in an understandable way for a wide audience, from students of mathematics to practitioners in biology, chemistry, physics, and finances. The presentation is based on the naïve stochastic integration, rather than on abstract theories of measure and stochastic processes. The proofs are rather simple for practitioners and, at the same time, rather rigorous for mathematicians. Detailed application examples in natural sciences and finance are presented. Much attention is paid to simulation diffusion processes. The topics covered include Brownian motion; motivation of stochastic models with Brownian motion; Itô and Stratonovich stochastic integrals, Itô's formula; stochastic differential equations (SDEs); solutions of SDEs as Markov processes; application examples in physical sciences and finance; simulation of solutions of SDEs (strong and weak approximations). Exercises with hints and/or solutions are also provided.

R in Action is the first book to present both the R system and the use cases that make it such a compelling package for business developers. The book begins by introducing the R language, including the development environment. Focusing on practical solutions, the book also offers a crash course in practical statistics and covers elegant methods for dealing with messy and incomplete data using features of R. About the Technology R is a powerful language for statistical computing and graphics that can handle virtually any data-crunching task. It runs on all important platforms and provides thousands of useful specialized modules and utilities. This makes R a great way to get meaningful information from mountains of raw data. About the Book R in Action is a language tutorial focused on practical problems. It presents useful statistics examples and includes elegant methods for handling messy, incomplete, and nonnormal data that are difficult to analyze using traditional methods. And statistical analysis is only part of the story. You'll also master R's extensive graphical capabilities for exploring and presenting data visually. What's Inside Practical data analysis, step by step Interfacing R with other software Using R to visualize data Over 130 graphs Eight reference appendixes About the Reader About the Author Dr. Rob Kabacoff is a seasoned researcher who specializes in data analysis. He has taught graduate courses in statistical programming and manages the Quick-R website at statmethods.net. Quotes Lucid and engaging...and fun way to learn R! - Amos A. Folarin, University College London Finally, a book that brings R to the real world. - Charles Malpas, University of Melbourne R from a programmer's point of view. - Philipp K. Janert, Principal Value, LLC A great balance of targeted tutorials and in-depth examples. - Landon Cox, 360VL, Inc. An excellent introduction and reference from the author of the best R website. - Christopher Williams, University of Idaho

The world-wide developer community has downloaded over ten million copies of BIRT (Business Intelligence and Reporting Tools). Built on the open-source Eclipse platform, BIRT is a powerful reporting system that provides an end-to-end solution from creating and deploying reports to integrating report capabilities in enterprise applications. introduces programmers to BIRT architecture and the reporting framework. BIRT technology makes it possible for programmers to build customized reports using scripting and BIRT APIs. This book also includes extensive examples of how to use the Eclipse Plug-in Development Environment to build plug-ins to extend the features of the BIRT framework. The source code for these examples is available for download at www.eclipse.org/birt. Integrating and Extending BIRT, Third Edition, Key topics covered include Installing and deploying BIRT Deploying a BIRT report to an application server Understanding BIRT architecture Scripting in a BIRT report design Integrating BIRT functionality into applications Working with the BIRT extension framework This revised and expanded third edition features the following new content Updated architectural diagrams Expanded scripting examples Debugging event handlers Developing an advanced report item with data binding Developing a data extraction extension Developing a charting extension Readers may also be interested in this book’s companion volume. BIRT: A Field Guide, Third Edition, is the authoritative guide to using BIRT Report Designer, the graphical tool that enables users of all levels to build reports, from simple to complex, without programming.

For the engineering and scientific professional, A Physicist’s Guide to Mathematica, Second Edition provides an updated reference guide based on the 2007 new 6.0 release, providing an organized and integrated desk reference with step-by-step instructions for the most commonly used features of the software as it applies to research in physics. For professors teaching physics and other science courses using the Mathematica software, A Physicist’s Guide to Mathematica, Second Edition is the only fully compatible (new software release) Mathematica text that engages students by providing complete topic coverage, new applications, exercises and examples that enable the user to solve a wide range of physics problems. Does not require prior knowledge of Mathematica or computer programming Can be used as either a primary or supplemental text for upper-division physics majors Provides over 450 end-of-section exercises and end-of-chapter problems Serves as a reference suitable for chemists, physical scientists, and engineers Compatible with Mathematica Version 6, a recent major release

Praise for the Second Edition "As a comprehensive statistics reference book for quality improvement, it certainly is one of the best books available." — Technometrics This new edition continues to provide the most current, proven statistical methods for quality control and quality improvement The use of quantitative methods offers numerous benefits in the fields of industry and business, both through identifying existing trouble spots and alerting management and technical personnel to potential problems. Statistical Methods for Quality Improvement, Third Edition guides readers through a broad range of tools and techniques that make it possible to quickly identify and resolve both current and potential trouble spots within almost any manufacturing or nonmanufacturing process. The book provides detailed coverage of the application of control charts, while also exploring critical topics such as regression, design of experiments, and Taguchi methods. In this new edition, the author continues to explain how to combine the many statistical methods explored in the book in order to optimize quality control and improvement. The book has been thoroughly revised and updated to reflect the latest research and practices in statistical methods and quality control, and new features include: Updated coverage of control charts, with newly added tools The latest research on the monitoring of linear profiles and other types of profiles Sections on generalized likelihood ratio charts and the effects of parameter estimation on the properties of CUSUM and EWMA procedures New discussions on design of experiments that include conditional effects and fraction of design space plots New material on Lean Six Sigma and Six Sigma programs and training Incorporating the latest software applications, the author has added coverage on how to use Minitab software to obtain probability limits for attribute charts. new exercises have been added throughout the book, allowing readers to put the latest statistical methods into practice. Updated references are also provided, shedding light on the current literature and providing resources for further study of the topic. Statistical Methods for Quality Improvement, Third Edition is an excellent book for courses on quality control and design of experiments at the upper-undergraduate and graduate levels. the book also serves as a valuable reference for practicing statisticians, engineers, and physical scientists interested in statistical quality improvement.

A complete and thorough introduction to business statistics using JMP. While the book is designed for introductory business statistics courses at the undergraduate or MBA level, industry professionals wanting to brush up on their knowledge of statistics and those wanting an introduction to using JMP for statistical analysis will also find it useful.

Building on the unique features that made the first edition a bestseller, this second edition includes additional solved problems and web access to the large collection of MATLAB scripts that are highlighted throughout the text. The book offers expanded coverage of audio engineering, transducers, and sensor networking technology. It also includes new chapters on digital audio processing, as well as acoustics and vibrations transducers. The text addresses the use of meta-data architectures using XML and agent-based automated data mining and control. The numerous algorithms presented can be applied locally or network-based to solve complex detection problems.

Practical data design tips from a data visualization expert of the modern age Data doesn't decrease; it is ever-increasing and can be overwhelming to organize in a way that makes sense to its intended audience. Wouldn't it be wonderful if we could actually visualize data in such a way that we could maximize its potential and tell a story in a clear, concise manner? Thanks to the creative genius of Nathan Yau, we can. With this full-color book, data visualization guru and author Nathan Yau uses step-by-step tutorials to show you how to visualize and tell stories with data. He explains how to gather, parse, and format data and then design high quality graphics that help you explore and present patterns, outliers, and relationships. Presents a unique approach to visualizing and telling stories with data, from a data visualization expert and the creator of flowingdata.com, Nathan Yau Offers step-by-step tutorials and practical design tips for creating statistical graphics, geographical maps, and information design to find meaning in the numbers Details tools that can be used to visualize data-native graphics for the Web, such as ActionScript, Flash libraries, PHP, and JavaScript and tools to design graphics for print, such as R and Illustrator Contains numerous examples and descriptions of patterns and outliers and explains how to show them Visualize This demonstrates how to explain data visually so that you can present your information in a way that is easy to understand and appealing.

What does a histogram tell you about contrast, dynamic range, underexposure, and overexposure? Why does exposure differ for digital photography versus film photography? What happens when the exposure is wrong? The exposure triangle: ISO, aperture, and shutter speed Differences between exposing for raw files and JPEG files Exposing to the right to obtain an optimum raw file Shooting in automatic mode–when to do so and how to use exposure compensation Shooting in manual mode–when to do so and how to adjust exposure

Think Stats: Probability and Statistics for Programmers is a textbook for a new kind of introductory prob-stat class. It emphasizes the use of statistics to explore large datasets. It takes a computation approach: students write programs in Python as a way of developing and testing their understanding.