time frequency feature analysis

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François Auger Signal Processing with Free Software. Practical Experiments François Auger Signal Processing with Free Software. Practical Experiments
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François Auger Signal Processing with Free Software. Practical Experiments


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4900.84 руб.

An ideal resource for students, industrial engineers, and researchers, Signal Processing with Free Software Practical Experiments presents practical experiments in signal processing using free software. The text introduces elementary signals through elementary waveform, signal storage files and elementary operations on signals and then presents the first tools to signal analysis such as temporal and frequency characteristics leading to Time-frequency analysis. Non-parametric spectral analysis is also discussed as well as signal processing through sampling, resampling, quantification, and analog and digital filtering. Table of Contents: 1. Generation of Elementary Signals. Generation of Elementary Waveform. – Elementary Operations on the Signals. – Format of Signal Storage Files. 2. First tools of Signal Analysis. Measurement of Temporal and Frequency Characteristics of a Signal. Time-Frequency Analysis of a Signal. 3. Non-parametric Spectral Analysis. 4. Signal Processing. Sampling. – Resampling. – Quantification. – “Analog” Filtering. Digital Filtering

William Wei W.S. Multivariate Time Series Analysis and Applications William Wei W.S. Multivariate Time Series Analysis and Applications
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William Wei W.S. Multivariate Time Series Analysis and Applications


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9334.93 руб.

An essential guide on high dimensional multivariate time series including all the latest topics from one of the leading experts in the field Following the highly successful and much lauded book, Time Series Analysis—Univariate and Multivariate Methods, this new work by William W.S. Wei focuses on high dimensional multivariate time series, and is illustrated with numerous high dimensional empirical time series. Beginning with the fundamentalconcepts and issues of multivariate time series analysis,this book covers many topics that are not found in general multivariate time series books. Some of these are repeated measurements, space-time series modelling, and dimension reduction. The book also looks at vector time series models, multivariate time series regression models, and principle component analysis of multivariate time series. Additionally, it provides readers with information on factor analysis of multivariate time series, multivariate GARCH models, and multivariate spectral analysis of time series. With the development of computers and the internet, we have increased potential for data exploration. In the next few years, dimension will become a more serious problem. Multivariate Time Series Analysis and its Applications provides some initial solutions, which may encourage the development of related software needed for the high dimensional multivariate time series analysis. Written by bestselling author and leading expert in the field Covers topics not yet explored in current multivariate books Features classroom tested material Written specifically for time series courses Multivariate Time Series Analysis and its Applications is designed for an advanced time series analysis course. It is a must-have for anyone studying time series analysis and is also relevant for students in economics, biostatistics, and engineering.

Rong Chen Nonlinear Time Series Analysis Rong Chen Nonlinear Time Series Analysis
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Rong Chen Nonlinear Time Series Analysis


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9075.63 руб.

A comprehensive resource that draws a balance between theory and applications of nonlinear time series analysis Nonlinear Time Series Analysis offers an important guide to both parametric and nonparametric methods, nonlinear state-space models, and Bayesian as well as classical approaches to nonlinear time series analysis. The authors—noted experts in the field—explore the advantages and limitations of the nonlinear models and methods and review the improvements upon linear time series models. The need for this book is based on the recent developments in nonlinear time series analysis, statistical learning, dynamic systems and advanced computational methods. Parametric and nonparametric methods and nonlinear and non-Gaussian state space models provide a much wider range of tools for time series analysis. In addition, advances in computing and data collection have made available large data sets and high-frequency data. These new data make it not only feasible, but also necessary to take into consideration the nonlinearity embedded in most real-world time series. This vital guide: • Offers research developed by leading scholars of time series analysis • Presents R commands making it possible to reproduce all the analyses included in the text • Contains real-world examples throughout the book • Recommends exercises to test understanding of material presented • Includes an instructor solutions manual and companion website Written for students, researchers, and practitioners who are interested in exploring nonlinearity in time series, Nonlinear Time Series Analysis offers a comprehensive text that explores the advantages and limitations of the nonlinear models and methods and demonstrates the improvements upon linear time series models.

Uwe Hassler Time Series Analysis with Long Memory in View Uwe Hassler Time Series Analysis with Long Memory in View
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Uwe Hassler Time Series Analysis with Long Memory in View


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8751.5 руб.

Provides a simple exposition of the basic time series material, and insights into underlying technical aspects and methods of proof Long memory time series are characterized by a strong dependence between distant events. This book introduces readers to the theory and foundations of univariate time series analysis with a focus on long memory and fractional integration, which are embedded into the general framework. It presents the general theory of time series, including some issues that are not treated in other books on time series, such as ergodicity, persistence versus memory, asymptotic properties of the periodogram, and Whittle estimation. Further chapters address the general functional central limit theory, parametric and semiparametric estimation of the long memory parameter, and locally optimal tests. Intuitive and easy to read, Time Series Analysis with Long Memory in View offers chapters that cover: Stationary Processes; Moving Averages and Linear Processes; Frequency Domain Analysis; Differencing and Integration; Fractionally Integrated Processes; Sample Means; Parametric Estimators; Semiparametric Estimators; and Testing. It also discusses further topics. This book: Offers beginning-of-chapter examples as well as end-of-chapter technical arguments and proofs Contains many new results on long memory processes which have not appeared in previous and existing textbooks Takes a basic mathematics (Calculus) approach to the topic of time series analysis with long memory Contains 25 illustrative figures as well as lists of notations and acronyms Time Series Analysis with Long Memory in View is an ideal text for first year PhD students, researchers, and practitioners in statistics, econometrics, and any application area that uses time series over a long period. It would also benefit researchers, undergraduates, and practitioners in those areas who require a rigorous introduction to time series analysis.

Murat Kulahci Introduction to Time Series Analysis and Forecasting Murat Kulahci Introduction to Time Series Analysis and Forecasting
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Murat Kulahci Introduction to Time Series Analysis and Forecasting


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10124.13 руб.

Praise for the First Edition «…[t]he book is great for readers who need to apply the methods and models presented but have little background in mathematics and statistics.» -MAA Reviews Thoroughly updated throughout, Introduction to Time Series Analysis and Forecasting, Second Edition presents the underlying theories of time series analysis that are needed to analyze time-oriented data and construct real-world short- to medium-term statistical forecasts. Authored by highly-experienced academics and professionals in engineering statistics, the Second Edition features discussions on both popular and modern time series methodologies as well as an introduction to Bayesian methods in forecasting. Introduction to Time Series Analysis and Forecasting, Second Edition also includes: Over 300 exercises from diverse disciplines including health care, environmental studies, engineering, and finance More than 50 programming algorithms using JMP®, SAS®, and R that illustrate the theory and practicality of forecasting techniques in the context of time-oriented data New material on frequency domain and spatial temporal data analysis Expanded coverage of the variogram and spectrum with applications as well as transfer and intervention model functions A supplementary website featuring PowerPoint® slides, data sets, and select solutions to the problems Introduction to Time Series Analysis and Forecasting, Second Edition is an ideal textbook upper-undergraduate and graduate-levels courses in forecasting and time series. The book is also an excellent reference for practitioners and researchers who need to model and analyze time series data to generate forecasts.

Wilfredo Palma Time Series Analysis Wilfredo Palma Time Series Analysis
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Wilfredo Palma Time Series Analysis


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10124.13 руб.

A modern and accessible guide to the analysis of introductory time series data Featuring an organized and self-contained guide, Time Series Analysis provides a broad introduction to the most fundamental methodologies and techniques of time series analysis. The book focuses on the treatment of univariate time series by illustrating a number of well-known models such as ARMA and ARIMA. Providing contemporary coverage, the book features several useful and newlydeveloped techniques such as weak and strong dependence, Bayesian methods, non-Gaussian data, local stationarity, missing values and outliers, and threshold models. Time Series Analysis includes practical applications of time series methods throughout, as well as: Real-world examples and exercise sets that allow readers to practice the presented methods and techniques Numerous detailed analyses of computational aspects related to the implementation of methodologies including algorithm efficiency, arithmetic complexity, and process time End-of-chapter proposed problems and bibliographical notes to deepen readers’ knowledge of the presented material Appendices that contain details on fundamental concepts and select solutions of the problems implemented throughout A companion website with additional data fi les and computer codes Time Series Analysis is an excellent textbook for undergraduate and beginning graduate-level courses in time series as well as a supplement for students in advanced statistics, mathematics, economics, finance, engineering, and physics. The book is also a useful reference for researchers and practitioners in time series analysis, econometrics, and finance. Wilfredo Palma, PhD, is Professor of Statistics in the Department of Statistics at Pontificia Universidad Católica de Chile. He has published several refereed articles and has received over a dozen academic honors and awards. His research interests include time series analysis, prediction theory, state space systems, linear models, and econometrics. He is the author of Long-Memory Time Series: Theory and Methods, also published by Wiley.

DeWayne Derryberry R. Basic Data Analysis for Time Series with R DeWayne Derryberry R. Basic Data Analysis for Time Series with R
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DeWayne Derryberry R. Basic Data Analysis for Time Series with R


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8923.18 руб.

Written at a readily accessible level, Basic Data Analysis for Time Series with R emphasizes the mathematical importance of collaborative analysis of data used to collect increments of time or space. Balancing a theoretical and practical approach to analyzing data within the context of serial correlation, the book presents a coherent and systematic regression-based approach to model selection. The book illustrates these principles of model selection and model building through the use of information criteria, cross validation, hypothesis tests, and confidence intervals. Focusing on frequency- and time-domain and trigonometric regression as the primary themes, the book also includes modern topical coverage on Fourier series and Akaike's Information Criterion (AIC). In addition, Basic Data Analysis for Time Series with R also features: Real-world examples to provide readers with practical hands-on experience Multiple R software subroutines employed with graphical displays Numerous exercise sets intended to support readers understanding of the core concepts Specific chapters devoted to the analysis of the Wolf sunspot number data and the Vostok ice core data sets

Prasanna Kalansuriya Chipless Radio Frequency Identification Reader Signal Processing Prasanna Kalansuriya Chipless Radio Frequency Identification Reader Signal Processing
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Prasanna Kalansuriya Chipless Radio Frequency Identification Reader Signal Processing


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9373.54 руб.

Presents a comprehensive overview and analysis of the recent developments in signal processing for Chipless Radio Frequency Identification Systems This book presents the recent research results on Radio Frequency Identification (RFID) and provides smart signal processing methods for detection, signal integrity, multiple-access and localization, tracking, and collision avoidance in Chipless RFID systems. The book is divided into two sections: The first section discusses techniques for detection and denoising in Chipless RFID systems. These techniques include signal space representation, detection of frequency signatures using UWB impulse radio interrogation, time domain analysis, singularity expansion method for data extraction, and noise reduction and filtering techniques. The second section covers collision and error correction protocols, multi-tag identification through time-frequency analysis, FMCW radar based collision detection and multi-access for Chipless RFID tags as we as localization and tag tracking. Describes the use of UWB impulse radio interrogation to remotely estimate the frequency signature of Chipless RFID tags using the backscatter principle Reviews the collision problem in both chipped and Chipless RFID systems and summarizes the prevailing anti-collision algorithms to address the problem Proposes state-of-the-art multi-access and signal integrity protocols to improve the efficacy of the system in multiple tag reading scenarios Features an industry approach to the integration of various systems of the Chipless RFID reader-integration of physical layers, middleware, and enterprise software Chipless Radio Frequency Identification Reader Signal Processing is primarily written for researchers in the field of RF sensors but can serve as supplementary reading for graduate students and professors in electrical engineering and wireless communications.

Yu-Ping Tian Frequency-Domain Analysis and Design of Distributed Control Systems Yu-Ping Tian Frequency-Domain Analysis and Design of Distributed Control Systems
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Yu-Ping Tian Frequency-Domain Analysis and Design of Distributed Control Systems


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11279.71 руб.

This book presents a unified frequency-domain method for the analysis of distributed control systems. The following important topics are discussed by using the proposed frequency-domain method: (1) Scalable stability criteria of networks of distributed control systems; (2) Effect of heterogeneous delays on the stability of a network of distributed control system; (3) Stability of Internet congestion control algorithms; and (4) Consensus in multi-agent systems. This book is ideal for graduate students in control, networking and robotics, as well as researchers in the fields of control theory and networking who are interested in learning and applying distributed control algorithms or frequency-domain analysis methods.

Ali Moukadem Time-Frequency Domain for Segmentation and Classification of Non-stationary Signals. The Stockwell Transform Applied on Bio-signals and Electric Signals Ali Moukadem Time-Frequency Domain for Segmentation and Classification of Non-stationary Signals. The Stockwell Transform Applied on Bio-signals and Electric Signals
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Ali Moukadem Time-Frequency Domain for Segmentation and Classification of Non-stationary Signals. The Stockwell Transform Applied on Bio-signals and Electric Signals


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5698.83 руб.

This book focuses on signal processing algorithms based on the timefrequency domain. Original methods and algorithms are presented which are able to extract information from non-stationary signals such as heart sounds and power electric signals. The methods proposed focus on the time-frequency domain, and most notably the Stockwell Transform for the feature extraction process and to identify signatures. For the classification method, the Adaline Neural Network is used and compared with other common classifiers. Theory enhancement, original applications and concrete implementation on FPGA for real-time processing are also covered in this book.

Heather Dyke A Companion to the Philosophy of Time Heather Dyke A Companion to the Philosophy of Time
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Heather Dyke A Companion to the Philosophy of Time


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16043.51 руб.

A Companion to the Philosophy of Time presents the broadest treatment of this subject yet; 32 specially commissioned articles – written by an international line-up of experts – provide an unparalleled reference work for students and specialists alike in this exciting field. The most comprehensive reference work on the philosophy of time currently available The first collection to tackle the historical development of the philosophy of time in addition to covering contemporary work Provides a tripartite approach in its organization, covering history of the philosophy of time, time as a feature of the physical world, and time as a feature of experience Includes contributions from both distinguished, well-established scholars and rising stars in the field

Christian Lalanne Mechanical Vibration and Shock Analysis, Random Vibration Christian Lalanne Mechanical Vibration and Shock Analysis, Random Vibration
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Christian Lalanne Mechanical Vibration and Shock Analysis, Random Vibration


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15518.45 руб.

The vast majority of vibrations encountered in the real environment are random in nature. Such vibrations are intrinsically complicated and this volume describes the process that enables us to simplify the required analysis, along with the analysis of the signal in the frequency domain. The power spectrum density is also defined, together with the requisite precautions to be taken in its calculations as well as the processes (windowing, overlapping) necessary to obtain improved results. An additional complementary method – the analysis of statistical properties of the time signal – is also described. This enables the distribution law of the maxima of a random Gaussian signal to be determined and simplifies the calculation of fatigue damage by avoiding direct peak counting.

Wells Herbert George The Time Machine Wells Herbert George The Time Machine
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Wells Herbert George The Time Machine


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340 руб.

The Time Machine is a science fiction novel (1895) about time travel by way of using a vehicle that allowed its operator to travel forwards or backwards in time. The novel has since been adapted into three feature films, two television versions, and a large number of comic book adaptations.

Wells, Herbert George The Time Machine Wells, Herbert George The Time Machine
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Wells, Herbert George The Time Machine


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220 руб.

The Time Machine is a science fiction novel (1895) about time travel by way of using a vehicle that allowed its operator to travel forwards or backwards in time. The novel has since been adapted into three feature films, two television versions, and a large number of comic book adaptations.

Liming Xiu Nanometer Frequency Synthesis Beyond the Phase-Locked Loop Liming Xiu Nanometer Frequency Synthesis Beyond the Phase-Locked Loop
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Liming Xiu Nanometer Frequency Synthesis Beyond the Phase-Locked Loop


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10657.38 руб.

Introducing a new, pioneering approach to integrated circuit design Nanometer Frequency Synthesis Beyond Phase-Locked Loop introduces an innovative new way of looking at frequency that promises to open new frontiers in modern integrated circuit (IC) design. While most books on frequency synthesis deal with the phase-locked loop (PLL), this book focuses on the clock signal. It revisits the concept of frequency, solves longstanding problems in on-chip clock generation, and presents a new time-based information processing approach for future chip design. Beginning with the basics, the book explains how clock signal is used in electronic applications and outlines the shortcomings of conventional frequency synthesis techniques for dealing with clock generation problems. It introduces the breakthrough concept of Time-Average-Frequency, presents the Flying-Adder circuit architecture for the implementation of this approach, and reveals a new circuit device, the Digital-to-Frequency Converter (DFC). Lastly, it builds upon these three key components to explain the use of time rather than level to represent information in signal processing. Provocative, inspiring, and chock-full of ideas for future innovations, the book features: A new way of thinking about the fundamental concept of clock frequency A new circuit architecture for frequency synthesis: the Flying-Adder direct period synthesis A new electronic component: the Digital-to-Frequency Converter A new information processing approach: time-based vs. level-based Examples demonstrating the power of this technology to build better, cheaper, and faster systems Written with the intent of showing readers how to think outside the box, Nanometer Frequency Synthesis Beyond the Phase-Locked Loop is a must-have resource for IC design engineers and researchers as well as anyone who would like to be at the forefront of modern circuit design.

Yuichi Motai Data-Variant Kernel Analysis Yuichi Motai Data-Variant Kernel Analysis
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Yuichi Motai Data-Variant Kernel Analysis


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9749.19 руб.

Describes and discusses the variants of kernel analysis methods for data types that have been intensely studied in recent years This book covers kernel analysis topics ranging from the fundamental theory of kernel functions to its applications. The book surveys the current status, popular trends, and developments in kernel analysis studies. The author discusses multiple kernel learning algorithms and how to choose the appropriate kernels during the learning phase. Data-Variant Kernel Analysis is a new pattern analysis framework for different types of data configurations. The chapters include data formations of offline, distributed, online, cloud, and longitudinal data, used for kernel analysis to classify and predict future state. Data-Variant Kernel Analysis: Surveys the kernel analysis in the traditionally developed machine learning techniques, such as Neural Networks (NN), Support Vector Machines (SVM), and Principal Component Analysis (PCA) Develops group kernel analysis with the distributed databases to compare speed and memory usages Explores the possibility of real-time processes by synthesizing offline and online databases Applies the assembled databases to compare cloud computing environments Examines the prediction of longitudinal data with time-sequential configurations Data-Variant Kernel Analysis is a detailed reference for graduate students as well as electrical and computer engineers interested in pattern analysis and its application in colon cancer detection.

Отсутствует Advanced Time Series Data Analysis. Forecasting Using EViews Отсутствует Advanced Time Series Data Analysis. Forecasting Using EViews
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Отсутствует Advanced Time Series Data Analysis. Forecasting Using EViews


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10268.42 руб.

Introduces the latest developments in forecasting in advanced quantitative data analysis This book presents advanced univariate multiple regressions, which can directly be used to forecast their dependent variables, evaluate their in-sample forecast values, and compute forecast values beyond the sample period. Various alternative multiple regressions models are presented based on a single time series, bivariate, and triple time-series, which are developed by taking into account specific growth patterns of each dependent variables, starting with the simplest model up to the most advanced model. Graphs of the observed scores and the forecast evaluation of each of the models are offered to show the worst and the best forecast models among each set of the models of a specific independent variable. Advanced Time Series Data Analysis: Forecasting Using EViews provides readers with a number of modern, advanced forecast models not featured in any other book. They include various interaction models, models with alternative trends (including the models with heterogeneous trends), and complete heterogeneous models for monthly time series, quarterly time series, and annually time series. Each of the models can be applied by all quantitative researchers. Presents models that are all classroom tested Contains real-life data samples Contains over 350 equation specifications of various time series models Contains over 200 illustrative examples with special notes and comments Applicable for time series data of all quantitative studies Advanced Time Series Data Analysis: Forecasting Using EViews will appeal to researchers and practitioners in forecasting models, as well as those studying quantitative data analysis. It is suitable for those wishing to obtain a better knowledge and understanding on forecasting, specifically the uncertainty of forecast values.

T. Datta K. Seismic Analysis of Structures T. Datta K. Seismic Analysis of Structures
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T. Datta K. Seismic Analysis of Structures


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13224.48 руб.

While numerous books have been written on earthquakes, earthquake resistance design, and seismic analysis and design of structures, none have been tailored for advanced students and practitioners, and those who would like to have most of the important aspects of seismic analysis in one place. With this book, readers will gain proficiencies in the following: fundamentals of seismology that all structural engineers must know; various forms of seismic inputs; different types of seismic analysis like, time and frequency domain analyses, spectral analysis of structures for random ground motion, response spectrum method of analysis; equivalent lateral load analysis as given in earthquake codes; inelastic response analysis and the concept of ductility; ground response analysis and seismic soil structure interaction; seismic reliability analysis of structures; and control of seismic response of structures. Provides comprehensive coverage, from seismology to seismic control Contains useful empirical equations often required in the seismic analysis of structures Outlines explicit steps for seismic analysis of MDOF systems with multi support excitations Works through solved problems to illustrate different concepts Makes use of MATLAB, SAP2000 and ABAQUAS in solving example problems of the book Provides numerous exercise problems to aid understanding of the subject As one of the first books to present such a comprehensive treatment of the topic, Seismic Analysis of Structures is ideal for postgraduates and researchers in Earthquake Engineering, Structural Dynamics, and Geotechnical Earthquake Engineering. Developed for classroom use, the book can also be used for advanced undergraduate students planning for a career or further study in the subject area. The book will also better equip structural engineering consultants and practicing engineers in the use of standard software for seismic analysis of buildings, bridges, dams, and towers. Lecture materials for instructors available at www.wiley.com/go/dattaseismic

Almudena Suarez Analysis and Design of Autonomous Microwave Circuits Almudena Suarez Analysis and Design of Autonomous Microwave Circuits
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Almudena Suarez Analysis and Design of Autonomous Microwave Circuits


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15169.26 руб.

Presents simulation techniques that substantially increase designers' control over the oscillationin autonomous circuits This book facilitates a sound understanding of the free-running oscillation mechanism, the start-up from the noise level, and the establishment of the steady-state oscillation. It deals with the operation principles and main characteristics of free-running and injection-locked oscillators, coupled oscillators, and parametric frequency dividers. Analysis and Design of Autonomous Microwave Circuits provides: An exploration of the main nonlinear-analysis methods, with emphasis on harmonic balance and envelope transient methods Techniques for the efficient simulation of the most common autonomous regimes A presentation and comparison of the main stability-analysis methods in the frequency domain A detailed examination of the instabilization mechanisms that delimit the operation bands of autonomous circuits Coverage of techniques used to eliminate common types of undesired behavior, such as spurious oscillations, hysteresis, and chaos A thorough presentation of the oscillator phase noise A comparison of the main methodologies of phase-noise analysis Techniques for autonomous circuit optimization, based on harmonic balance A consideration of different design objectives: presetting the oscillation frequency and output power, increasing efficiency, modifying the transient duration, and imposing operation bands Analysis and Design of Autonomous Microwave Circuits is a valuable resource for microwave designers, oscillator designers, and graduate students in RF microwave design.

Francis Castanié Digital Spectral Analysis. Parametric, Non-Parametric and Advanced Methods Francis Castanié Digital Spectral Analysis. Parametric, Non-Parametric and Advanced Methods
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Francis Castanié Digital Spectral Analysis. Parametric, Non-Parametric and Advanced Methods


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12835.53 руб.

Digital Spectral Analysis provides a single source that offers complete coverage of the spectral analysis domain. This self-contained work includes details on advanced topics that are usually presented in scattered sources throughout the literature. The theoretical principles necessary for the understanding of spectral analysis are discussed in the first four chapters: fundamentals, digital signal processing, estimation in spectral analysis, and time-series models. An entire chapter is devoted to the non-parametric methods most widely used in industry. High resolution methods are detailed in a further four chapters: spectral analysis by stationary time series modeling, minimum variance, and subspace-based estimators. Finally, advanced concepts are the core of the last four chapters: spectral analysis of non-stationary random signals, space time adaptive processing: irregularly sampled data processing, particle filtering and tracking of varying sinusoids. Suitable for students, engineers working in industry, and academics at any level, this book provides a rare complete overview of the spectral analysis domain.

Antonio Napolitano Generalizations of Cyclostationary Signal Processing. Spectral Analysis and Applications Antonio Napolitano Generalizations of Cyclostationary Signal Processing. Spectral Analysis and Applications
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Antonio Napolitano Generalizations of Cyclostationary Signal Processing. Spectral Analysis and Applications


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11668.66 руб.

The relative motion between the transmitter and the receiver modifies the nonstationarity properties of the transmitted signal. In particular, the almost-cyclostationarity property exhibited by almost all modulated signals adopted in communications, radar, sonar, and telemetry can be transformed into more general kinds of nonstationarity. A proper statistical characterization of the received signal allows for the design of signal processing algorithms for detection, estimation, and classification that significantly outperform algorithms based on classical descriptions of signals.Generalizations of Cyclostationary Signal Processing addresses these issues and includes the following key features: Presents the underlying theoretical framework, accompanied by details of their practical application, for the mathematical models of generalized almost-cyclostationary processes and spectrally correlated processes; two classes of signals finding growing importance in areas such as mobile communications, radar and sonar. Explains second- and higher-order characterization of nonstationary stochastic processes in time and frequency domains. Discusses continuous- and discrete-time estimators of statistical functions of generalized almost-cyclostationary processes and spectrally correlated processes. Provides analysis of mean-square consistency and asymptotic Normality of statistical function estimators. Offers extensive analysis of Doppler channels owing to the relative motion between transmitter and receiver and/or surrounding scatterers. Performs signal analysis using both the classical stochastic-process approach and the functional approach, where statistical functions are built starting from a single function of time.

Yiyang Dong Direct Analysis in Real Time Mass Spectrometry. Principles and Practices of DART-MS Yiyang Dong Direct Analysis in Real Time Mass Spectrometry. Principles and Practices of DART-MS
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Yiyang Dong Direct Analysis in Real Time Mass Spectrometry. Principles and Practices of DART-MS


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11998.84 руб.

DART-MS is a relatively new, but very fast evolving technology. Due to its versatility, it addresses fields of crucial importance to people and community, e.g. food or agricultural, forensic, industrial, environmental, medicinal and clinical analysis.

G. Antille Descriptive Analysis of Matrix-Valued Time-Series G. Antille Descriptive Analysis of Matrix-Valued Time-Series
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G. Antille Descriptive Analysis of Matrix-Valued Time-Series


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79.9 руб.

In this article we present a technique of data analysis applied to three-dimensional tables as, for instance, matrix-valued time-series. The main goal of the method is to describe the evolution of the statistical units with respect to time in a space summarizing the set of matrices. Moreover, our technique points out similar statistical units provided by a classification of their trajectories.

Jian-Ming Jin Theory and Computation of Electromagnetic Fields Jian-Ming Jin Theory and Computation of Electromagnetic Fields
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Jian-Ming Jin Theory and Computation of Electromagnetic Fields


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10873.3 руб.

Reviews the fundamental concepts behind the theory and computation of electromagnetic fields The book is divided in two parts. The first part covers both fundamental theories (such as vector analysis, Maxwell’s equations, boundary condition, and transmission line theory) and advanced topics (such as wave transformation, addition theorems, and fields in layered media) in order to benefit students at all levels. The second part of the book covers the major computational methods for numerical analysis of electromagnetic fields for engineering applications. These methods include the three fundamental approaches for numerical analysis of electromagnetic fields: the finite difference method (the finite difference time-domain method in particular), the finite element method, and the integral equation-based moment method. The second part also examines fast algorithms for solving integral equations and hybrid techniques that combine different numerical methods to seek more efficient solutions of complicated electromagnetic problems. Theory and Computation of Electromagnetic Fields, Second Edition: Provides the foundation necessary for graduate students to learn and understand more advanced topics Discusses electromagnetic analysis in rectangular, cylindrical and spherical coordinates Covers computational electromagnetics in both frequency and time domains Includes new and updated homework problems and examples Theory and Computation of Electromagnetic Fields, Second Edition is written for advanced undergraduate and graduate level electrical engineering students. This book can also be used as a reference for professional engineers interested in learning about analysis and computation skills.


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A modern and accessible guide to the analysis of introductory time series data Featuring an organized and self-contained guide, Time Series Analysis provides a broad introduction to the most fundamental methodologies and techniques of time series analysis. The book focuses on the treatment of univariate time series by illustrating a number of well-known models such as ARMA and ARIMA. Providing contemporary coverage, the book features several useful and newlydeveloped techniques such as weak and strong dependence, Bayesian methods, non-Gaussian data, local stationarity, missing values and outliers, and threshold models. Time Series Analysis includes practical applications of time series methods throughout, as well as: Real-world examples and exercise sets that allow readers to practice the presented methods and techniques Numerous detailed analyses of computational aspects related to the implementation of methodologies including algorithm efficiency, arithmetic complexity, and process time End-of-chapter proposed problems and bibliographical notes to deepen readers’ knowledge of the presented material Appendices that contain details on fundamental concepts and select solutions of the problems implemented throughout A companion website with additional data fi les and computer codes Time Series Analysis is an excellent textbook for undergraduate and beginning graduate-level courses in time series as well as a supplement for students in advanced statistics, mathematics, economics, finance, engineering, and physics. The book is also a useful reference for researchers and practitioners in time series analysis, econometrics, and finance. Wilfredo Palma, PhD, is Professor of Statistics in the Department of Statistics at Pontificia Universidad Católica de Chile. He has published several refereed articles and has received over a dozen academic honors and awards. His research interests include time series analysis, prediction theory, state space systems, linear models, and econometrics. He is the author of Long-Memory Time Series: Theory and Methods, also published by Wiley.
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