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Processing Random Data
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Book Synopsis Processing Random Data by : Robert V. Edwards
Download or read book Processing Random Data written by Robert V. Edwards and published by World Scientific. This book was released on 2006 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: Two features of Processing Random Data differentiate it from other similar books: the focus on computing the reproducibility error for statistical measurements, and its comprehensive coverage of Maximum Likelihood parameter estimation techniques. The book is useful for dealing with situations where there is a model relating to the input and output of a process, but with a random component, which could be noise in the system or the process itself could be random, like turbulence. Parameter estimation techniques are shown for many different types of statistical models, including joint Gaussian. The Cramer-Rao bounds are described as useful estimates of reproducibility errors.Finally, using an example with a random sampling of turbulent flows that can occur when using laser anemometry the book also explains the use of conditional probabilities.
Book Synopsis Processing Random Data: Statistics For Engineers And Scientists by : Robert V Edwards
Download or read book Processing Random Data: Statistics For Engineers And Scientists written by Robert V Edwards and published by World Scientific Publishing Company. This book was released on 2006-07-03 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: Two features of Processing Random Data differentiate it from other similar books: the focus on computing the reproducibility error for statistical measurements, and its comprehensive coverage of Maximum Likelihood parameter estimation techniques. The book is useful for dealing with situations where there is a model relating to the input and output of a process, but with a random component, which could be noise in the system or the process itself could be random, like turbulence. Parameter estimation techniques are shown for many different types of statistical models, including joint Gaussian. The Cramer-Rao bounds are described as useful estimates of reproducibility errors.Finally, using an example with a random sampling of turbulent flows that can occur when using laser anemometry the book also explains the use of conditional probabilities.
Book Synopsis Digital Processing of Random Signals by : Boaz Porat
Download or read book Digital Processing of Random Signals written by Boaz Porat and published by Courier Dover Publications. This book was released on 2008-02-29 with total page 468 pages. Available in PDF, EPUB and Kindle. Book excerpt: This excellent advanced text rigorously covers several topics. Geared toward students of electrical engineering, its material is sufficiently general to be applicable to other engineering fields. 1994 edition.
Book Synopsis Processing Random Data by : Robert Valentino Edwards
Download or read book Processing Random Data written by Robert Valentino Edwards and published by . This book was released on 2006 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Learning Processing by : Daniel Shiffman
Download or read book Learning Processing written by Daniel Shiffman and published by Newnes. This book was released on 2015-09-09 with total page 566 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning Processing, Second Edition, is a friendly start-up guide to Processing, a free, open-source alternative to expensive software and daunting programming languages. Requiring no previous experience, this book is for the true programming beginner. It teaches the basic building blocks of programming needed to create cutting-edge graphics applications including interactive art, live video processing, and data visualization. Step-by-step examples, thorough explanations, hands-on exercises, and sample code, supports your learning curve.A unique lab-style manual, the book gives graphic and web designers, artists, and illustrators of all stripes a jumpstart on working with the Processing programming environment by providing instruction on the basic principles of the language, followed by careful explanations of select advanced techniques. The book has been developed with a supportive learning experience at its core. From algorithms and data mining to rendering and debugging, it teaches object-oriented programming from the ground up within the fascinating context of interactive visual media.This book is ideal for graphic designers and visual artists without programming background who want to learn programming. It will also appeal to students taking college and graduate courses in interactive media or visual computing, and for self-study. - A friendly start-up guide to Processing, a free, open-source alternative to expensive software and daunting programming languages - No previous experience required—this book is for the true programming beginner! - Step-by-step examples, thorough explanations, hands-on exercises, and sample code supports your learning curve
Download or read book Random Data written by Julius S. Bendat and published by John Wiley & Sons. This book was released on 2011-09-20 with total page 555 pages. Available in PDF, EPUB and Kindle. Book excerpt: RANDOM DATA A TIMELY UPDATE OF THE CLASSIC BOOK ON THE THEORY AND APPLICATION OF RANDOM DATA ANALYSIS First published in 1971, Random Data served as an authoritative book on the analysis of experimental physical data for engineering and scientific applications. This Fourth Edition features coverage of new developments in random data management and analysis procedures that are applicable to a broad range of applied fields, from the aerospace and automotive industries to oceanographic and biomedical research. This new edition continues to maintain a balance of classic theory and novel techniques. The authors expand on the treatment of random data analysis theory, including derivations of key relationships in probability and random process theory. The book remains unique in its practical treatment of nonstationary data analysis and nonlinear system analysis, presenting the latest techniques on modern data acquisition, storage, conversion, and qualification of random data prior to its digital analysis. The Fourth Edition also includes: A new chapter on frequency domain techniques to model and identify nonlinear systems from measured input/output random data New material on the analysis of multiple-input/single-output linear models The latest recommended methods for data acquisition and processing of random data Important mathematical formulas to design experiments and evaluate results of random data analysis and measurement procedures Answers to the problem in each chapter Comprehensive and self-contained, Random Data, Fourth Edition is an indispensible book for courses on random data analysis theory and applications at the upper-under-graduate and graduate level. It is also an insightful reference for engineers and scientists who use statistical methods to investigate and solve problems with dynamic data.
Book Synopsis Aerospace Instrumentation by : M. A. Perry
Download or read book Aerospace Instrumentation written by M. A. Perry and published by Elsevier. This book was released on 2015-05-18 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: Aerospace Instrumentation, Volume 4 is a collection of papers presented at the Fourth International Aerospace Instrumentation Symposium, held at the College of Aeronautics, Cranfield. Co-sponsored by the Instrument Society of America, the symposium covers most aspects of aerospace instrumentation. This book is composed of 14 chapters and begins with a description of strain gauge transducers, an introduction to noise, filtering, and random function, as well as the data analysis facility designed to satisfy the needs in the fields of fundamental research and major power plant design and commissioning. A chapter examines equipment for the analysis of random processes for low frequence purposes. Other chapters explore the measurement and analysis of rotor blade airloads, the application of digital computer to instrumentation systems, the features of an altitude test facility, and the trade-offs existing between analogue and digital filtering techniques. The last chapters are devoted to test methods for aircraft performance, stability, and control characteristics determination in non-steady flight. These chapters also treat the operational experience of the B-70 flight test data system. This book will prove useful to aerospace scientists, engineers and research workers.
Book Synopsis Markov Random Fields for Vision and Image Processing by : Andrew Blake
Download or read book Markov Random Fields for Vision and Image Processing written by Andrew Blake and published by MIT Press. This book was released on 2011-07-22 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: State-of-the-art research on MRFs, successful MRF applications, and advanced topics for future study. This volume demonstrates the power of the Markov random field (MRF) in vision, treating the MRF both as a tool for modeling image data and, utilizing recently developed algorithms, as a means of making inferences about images. These inferences concern underlying image and scene structure as well as solutions to such problems as image reconstruction, image segmentation, 3D vision, and object labeling. It offers key findings and state-of-the-art research on both algorithms and applications. After an introduction to the fundamental concepts used in MRFs, the book reviews some of the main algorithms for performing inference with MRFs; presents successful applications of MRFs, including segmentation, super-resolution, and image restoration, along with a comparison of various optimization methods; discusses advanced algorithmic topics; addresses limitations of the strong locality assumptions in the MRFs discussed in earlier chapters; and showcases applications that use MRFs in more complex ways, as components in bigger systems or with multiterm energy functions. The book will be an essential guide to current research on these powerful mathematical tools.
Book Synopsis Probability, Random Variables, and Random Processes by : John J. Shynk
Download or read book Probability, Random Variables, and Random Processes written by John J. Shynk and published by John Wiley & Sons. This book was released on 2012-10-15 with total page 850 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probability, Random Variables, and Random Processes is a comprehensive textbook on probability theory for engineers that provides a more rigorous mathematical framework than is usually encountered in undergraduate courses. It is intended for first-year graduate students who have some familiarity with probability and random variables, though not necessarily of random processes and systems that operate on random signals. It is also appropriate for advanced undergraduate students who have a strong mathematical background. The book has the following features: Several appendices include related material on integration, important inequalities and identities, frequency-domain transforms, and linear algebra. These topics have been included so that the book is relatively self-contained. One appendix contains an extensive summary of 33 random variables and their properties such as moments, characteristic functions, and entropy. Unlike most books on probability, numerous figures have been included to clarify and expand upon important points. Over 600 illustrations and MATLAB plots have been designed to reinforce the material and illustrate the various characterizations and properties of random quantities. Sufficient statistics are covered in detail, as is their connection to parameter estimation techniques. These include classical Bayesian estimation and several optimality criteria: mean-square error, mean-absolute error, maximum likelihood, method of moments, and least squares. The last four chapters provide an introduction to several topics usually studied in subsequent engineering courses: communication systems and information theory; optimal filtering (Wiener and Kalman); adaptive filtering (FIR and IIR); and antenna beamforming, channel equalization, and direction finding. This material is available electronically at the companion website. Probability, Random Variables, and Random Processes is the only textbook on probability for engineers that includes relevant background material, provides extensive summaries of key results, and extends various statistical techniques to a range of applications in signal processing.
Download or read book Random Data written by Julius S. Bendat and published by Wiley-Interscience. This book was released on 2000-02-14 with total page 634 pages. Available in PDF, EPUB and Kindle. Book excerpt: The classic reference on the theory and application of random data analysis-now expanded and revised. This eagerly awaited new edition of the bestselling random data analysis book continues to provide first-rate, practical tools for scientists and engineers who investigate dynamic data as well as those who use statistical methods to solve engineering problems. It is fully updated, covering new procedures developed since 1986 and extending the discussion to a remarkably broad range of applied fields, from aerospace and automotive industries to biomedical research. Comprehensive and self-contained, this new edition also greatly expands coverage of the theory, including derivations of key relationships in probability and random process theory not usually found in books of this kind. Special features of Random Data: Analysis and Measurement Procedures, Third Edition include: * Basic probability functions for level crossings and peak values of random data * Complete derivations of both old and new practical formulas for statistical error analysis of computed estimates * The latest methods for data acquisition and processing as well as nonstationary data analysis * Additional techniques on digital data analysis procedures * New material on the analysis of multiple-input/multiple-output linear systems * Numerous new examples and problem sets * Hundreds of updated illustrations and references *An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.
Book Synopsis Getting Started with Processing.py by : Allison Parrish
Download or read book Getting Started with Processing.py written by Allison Parrish and published by Maker Media, Inc.. This book was released on 2016-05-11 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: Processing opened up the world of programming to artists, designers, educators, and beginners. The Processing.py Python implementation of Processing reinterprets it for today's web. This short book gently introduces the core concepts of computer programming and working with Processing. Written by the co-founders of the Processing project, Reas and Fry, along with co-author Allison Parrish, Getting Started with Processing.py is your fast track to using Python's Processing mode.
Book Synopsis Probability and Random Processes by : Scott Miller
Download or read book Probability and Random Processes written by Scott Miller and published by Academic Press. This book was released on 2012-01-11 with total page 625 pages. Available in PDF, EPUB and Kindle. Book excerpt: Miller and Childers have focused on creating a clear presentation of foundational concepts with specific applications to signal processing and communications, clearly the two areas of most interest to students and instructors in this course. It is aimed at graduate students as well as practicing engineers, and includes unique chapters on narrowband random processes and simulation techniques. The appendices provide a refresher in such areas as linear algebra, set theory, random variables, and more. Probability and Random Processes also includes applications in digital communications, information theory, coding theory, image processing, speech analysis, synthesis and recognition, and other fields. * Exceptional exposition and numerous worked out problems make the book extremely readable and accessible * The authors connect the applications discussed in class to the textbook * The new edition contains more real world signal processing and communications applications * Includes an entire chapter devoted to simulation techniques.
Book Synopsis Parallel and Distributed Processing and Applications by : Ivan Stojmenovic
Download or read book Parallel and Distributed Processing and Applications written by Ivan Stojmenovic and published by Springer Science & Business Media. This book was released on 2007-08-22 with total page 1013 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 5th International Symposium on Parallel and Distributed Processing and Applications, ISPA 2007, held in Niagara Falls, Canada, in August 2007. The 83 revised full papers presented together with three keynote are cover algorithms and applications, architectures and systems, datamining and databases, fault tolerance and security, middleware and cooperative computing, networks, as well as software and languages.
Book Synopsis Machine Learning for Signal Processing by : Max A. Little
Download or read book Machine Learning for Signal Processing written by Max A. Little and published by Oxford University Press, USA. This book was released on 2019 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: Describes in detail the fundamental mathematics and algorithms of machine learning (an example of artificial intelligence) and signal processing, two of the most important and exciting technologies in the modern information economy. Builds up concepts gradually so that the ideas and algorithms can be implemented in practical software applications.
Book Synopsis Random Field Modelling and Its Application in Stochastic Data Processing by : George Christakos
Download or read book Random Field Modelling and Its Application in Stochastic Data Processing written by George Christakos and published by . This book was released on 1990 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Statistical Signal Processing of Complex-Valued Data by : Peter J. Schreier
Download or read book Statistical Signal Processing of Complex-Valued Data written by Peter J. Schreier and published by Cambridge University Press. This book was released on 2010-02-04 with total page 331 pages. Available in PDF, EPUB and Kindle. Book excerpt: Complex-valued random signals are embedded in the very fabric of science and engineering, yet the usual assumptions made about their statistical behavior are often a poor representation of the underlying physics. This book deals with improper and noncircular complex signals, which do not conform to classical assumptions, and it demonstrates how correct treatment of these signals can have significant payoffs. The book begins with detailed coverage of the fundamental theory and presents a variety of tools and algorithms for dealing with improper and noncircular signals. It provides a comprehensive account of the main applications, covering detection, estimation, and signal analysis of stationary, nonstationary, and cyclostationary processes. Providing a systematic development from the origin of complex signals to their probabilistic description makes the theory accessible to newcomers. This book is ideal for graduate students and researchers working with complex data in a range of research areas from communications to oceanography.
Book Synopsis Digital Processing of Random Oscillations by : Viacheslav Karmalita
Download or read book Digital Processing of Random Oscillations written by Viacheslav Karmalita and published by Walter de Gruyter GmbH & Co KG. This book was released on 2019-06-17 with total page 97 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book deals with the autoregressive method for digital processing of random oscillations. The method is based on a one-to-one transformation of the numeric factors of the Yule series model to linear elastic system characteristics. This parametric approach allowed to develop a formal processing procedure from the experimental data to obtain estimates of logarithmic decrement and natural frequency of random oscillations. A straightforward mathematical description of the procedure makes it possible to optimize a discretization of oscillation realizations providing efficient estimates. The derived analytical expressions for confidence intervals of estimates enable a priori evaluation of their accuracy. Experimental validation of the method is also provided. Statistical applications for the analysis of mechanical systems arise from the fact that the loads experienced by machineries and various structures often cannot be described by deterministic vibration theory. Therefore, a sufficient description of real oscillatory processes (vibrations) calls for the use of random functions. In engineering practice, the linear vibration theory (modeling phenomena by common linear differential equations) is generally used. This theory’s fundamental concepts such as natural frequency, oscillation decrement, resonance, etc. are credited for its wide use in different technical tasks. In technical applications two types of research tasks exist: direct and inverse. The former allows to determine stochastic characteristics of the system output X(t) resulting from a random process E(t) when the object model is considered known. The direct task enables to evaluate the effect of an operational environment on the designed object and to predict its operation under various loads. The inverse task is aimed at evaluating the object model on known processes E(t) and X(t), i.e. finding model (equations) factors. This task is usually met at the tests of prototypes to identify (or verify) its model experimentally. To characterize random processes a notion of "shaping dynamic system" is commonly used. This concept allows to consider the observing process as the output of a hypothetical system with the input being stationary Gauss-distributed ("white") noise. Therefore, the process may be exhaustively described in terms of parameters of that system. In the case of random oscillations, the "shaping system" is an elastic system described by the common differential equation of the second order: X ̈(t)+2hX ̇(t)+ ω_0^2 X(t)=E(t), where ω0 = 2π/Т0 is the natural frequency, T0 is the oscillation period, and h is a damping factor. As a result, the process X(t) can be characterized in terms of the system parameters – natural frequency and logarithmic oscillations decrement δ = hT0 as well as the process variance. Evaluation of these parameters is subjected to experimental data processing based on frequency or time-domain representations of oscillations. It must be noted that a concept of these parameters evaluation did not change much during the last century. For instance, in case of the spectral density utilization, evaluation of the decrement values is linked with bandwidth measurements at the points of half-power of the observed oscillations. For a time-domain presentation, evaluation of the decrement requires measuring covariance values delayed by a time interval divisible by T0. Both estimation procedures are derived from a continuous description of research phenomena, so the accuracy of estimates is linked directly to the adequacy of discrete representation of random oscillations. This approach is similar a concept of transforming differential equations to difference ones with derivative approximation by corresponding finite differences. The resulting discrete model, being an approximation, features a methodical error which can be decreased but never eliminated. To render such a presentation more accurate it is imperative to decrease the discretization interval and to increase realization size growing requirements for computing power. The spectral density and covariance function estimates comprise a non-parametric (non-formal) approach. In principle, any non-formal approach is a kind of art i.e. the results depend on the performer’s skills. Due to interference of subjective factors in spectral or covariance estimates of random signals, accuracy of results cannot be properly determined or justified. To avoid the abovementioned difficulties, the application of linear time-series models with well-developed procedures for parameter estimates is more advantageous. A method for the analysis of random oscillations using a parametric model corresponding discretely (no approximation error) with a linear elastic system is developed and presented in this book. As a result, a one-to-one transformation of the model’s numerical factors to logarithmic decrement and natural frequency of random oscillations is established. It allowed to develop a formal processing procedure from experimental data to obtain the estimates of δ and ω0. The proposed approach allows researchers to replace traditional subjective techniques by a formal processing procedure providing efficient estimates with analytically defined statistical uncertainties.
