Real-time or applied digital signalprocessing courses are offered as follow-ups to conventional or theory-oriented digital signalprocessing courses in many engineering programs for the purpose of teaching students t...
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ISBN:
(数字)9783031025372
Real-time or applied digital signalprocessing courses are offered as follow-ups to conventional or theory-oriented digital signalprocessing courses in many engineering programs for the purpose of teaching students the technical know-how for putting signalprocessing algorithms or theory into practical use. These courses normally involve access to a teaching laboratory that is equipped with hardware boards, in particular DSP boards, together with their supporting software. A number of textbooks have been written discussing how to achieve real-time implementation on these hardware boards. This book discusses how smartphones can be used as hardware boards for real-time implementation of signalprocessing algorithms as an alternative to the hardware boards that are currently being used in signalprocessing teaching laboratories. The fact that mobile devices, in particular smartphones, have now become powerful processing platforms has led to the development of this book, thus enabling students to use their own smartphones to run signalprocessing algorithms in real-time considering that these days nearly all students possess smartphones. Changing the hardware platforms that are currently used in applied or real-time signalprocessing courses to smartphones creates a truly mobile laboratory experience or environment for students. In addition, it relieves the cost burden associated with using a dedicated signalprocessing board noting that the software development tools for smartphones are free of charge and are well-developed. This book is written in such a way that it can be used as a textbook for applied or real time digital signalprocessing courses offered at many universities. Ten lab experiments that are commonly encountered in such courses are covered in the book. This book is written primarily for those who are already familiar with signalprocessing concepts and are interested in their real-time and practical aspects. Similar to existing real-time courses, know
This short book is for students, professors and professionals interested in signalprocessing of seismic data using MATLAB™. The step-by-step demo of the full reflection seismic data processing workflow using a comple...
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ISBN:
(数字)9783031025341
ISBN:
(纸本)9783031014062
This short book is for students, professors and professionals interested in signalprocessing of seismic data using MATLAB™. The step-by-step demo of the full reflection seismic data processing workflow using a complete real seismic data set places itself as a very useful feature of the book. This is especially true when students are performing their projects, and when professors and researchers are testing their new developed algorithms in MATLAB™ for processing seismic data. The book provides the basic seismic and signalprocessing theory required for each chapter and shows how to process the data from raw field records to a final image of the subsurface all using MATLAB™. The MATLAB™ codes and seismic data can be downloaded here.
Table of Contents: Seismic Data processing: A Quick Overview / Examination of A Real Seismic Data Set / Quality Control of Real Seismic Data / Seismic Noise Attenuation / Seismic Deconvolution / Carrying the processing Forward / Static Corrections / Seismic Migration / Concluding Remarks
The Kalman filter is the Bayesian optimum solution to the problem of sequentially estimating the states of a dynamical system in which the state evolution and measurement processes are both linear and Gaussian. Given ...
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ISBN:
(数字)9783031025365
ISBN:
(纸本)9783031014086
The Kalman filter is the Bayesian optimum solution to the problem of sequentially estimating the states of a dynamical system in which the state evolution and measurement processes are both linear and Gaussian. Given the ubiquity of such systems, the Kalman filter finds use in a variety of applications, e.g., target tracking, guidance and navigation, and communications systems. The purpose of this book is to present a brief introduction to Kalman filtering. The theoretical framework of the Kalman filter is first presented, followed by examples showing its use in practical applications. Extensions of the method to nonlinear problems and distributed applications are discussed. A software implementation of the algorithm in the MATLAB programming language is provided, as well as MATLAB code for several example applications discussed in the manuscript.
A typical undergraduate electrical engineering curriculum incorporates a signals and systems course. The widely used approach for the laboratory component of such courses involves the utilization of MATLAB to implemen...
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ISBN:
(数字)9783031025426
ISBN:
(纸本)9783031014147
A typical undergraduate electrical engineering curriculum incorporates a signals and systems course. The widely used approach for the laboratory component of such courses involves the utilization of MATLAB to implement signals and systems concepts. This book presents a newly developed laboratory paradigm where MATLAB codes are made to run on smartphones which are possessed by nearly all students. As a result, this laboratory paradigm provides an anywhere-anytime hardware platform or processing board for students to learn implementation aspects of signals and systems concepts. The book covers the laboratory experiments that are normally covered in signals and systems courses and discusses how to run MATLAB codes for these experiments as apps on both Android and iOS smartphones, thus enabling a truly mobile laboratory paradigm. A zipped file of the codes discussed in the book can be acquired via the website http://***/bookcodes/product/signalsSystemsBookcodesThirdEdition
This book is Volume I of the series DSP for MATLAB™ and LabVIEW™. The entire series consists of four volumes that collectively cover basic digital signalprocessing in a practical and accessible manner, but which none...
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ISBN:
(数字)9783031025280
ISBN:
(纸本)9783031014000
This book is Volume I of the series DSP for MATLAB™ and LabVIEW™. The entire series consists of four volumes that collectively cover basic digital signalprocessing in a practical and accessible manner, but which nonetheless include all essential foundation mathematics. As the series title implies, the scripts (of which there are more than 200) described in the text and supplied in code form here will run on both MATLAB and LabVIEW. Volume I consists of four chapters. The first chapter gives a brief overview of the field of digital signalprocessing. This is followed by a chapter detailing many useful signals and concepts, including convolution, recursion, difference equations, LTI systems, etc. The third chapter covers conversion from the continuous to discrete domain and back (i.e., analog-to-digital and digital-to-analog conversion), aliasing, the Nyquist rate, normalized frequency, conversion from one sample rate to another, waveform generation at various sample rates from stored wave data, and Mu-law compression. The fourth and final chapter of the present volume introduces the reader to many important principles of signalprocessing, including correlation, the correlation sequence, the Real DFT, correlation by convolution, matched filtering, simple FIR filters, and simple IIR filters. Chapter 4, in particular, provides an intuitive or "first principle" understanding of how digital filtering and frequency transforms work, preparing the reader for Volumes II and III, which provide, respectively, detailed coverage of discrete frequency transforms (including the Discrete Time Fourier Transform, the Discrete Fourier Transform, and the z-Transform) and digital filter design (FIR design using Windowing, Frequency Sampling, and Optimum Equiripple techniques, and Classical IIR design). Volume IV, the culmination of the series, is an introductory treatment of LMS Adaptive Filtering and applications. The text for all volumes contains many examples, and many useful comput
Linear prediction theory has had a profound impact in the field of digital signalprocessing. Although the theory dates back to the early 1940s, its influence can still be seen in applications today. The theory is bas...
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ISBN:
(数字)9783031025273
ISBN:
(纸本)9783031013997
Linear prediction theory has had a profound impact in the field of digital signalprocessing. Although the theory dates back to the early 1940s, its influence can still be seen in applications today. The theory is based on very elegant mathematics and leads to many beautiful insights into statistical signalprocessing. Although prediction is only a part of the more general topics of linear estimation, filtering, and smoothing, this book focuses on linear prediction. This has enabled detailed discussion of a number of issues that are normally not found in texts. For example, the theory of vector linear prediction is explained in considerable detail and so is the theory of line spectral processes. This focus and its small size make the book different from many excellent texts which cover the topic, including a few that are actually dedicated to linear prediction. There are several examples and computer-based demonstrations of the theory. Applications are mentioned wherever appropriate, but the focus is not on the detailed development of these applications. The writing style is meant to be suitable for self-study as well as for classroom use at the senior and first-year graduate levels. The text is self-contained for readers with introductory exposure to signalprocessing, random processes, and the theory of matrices, and a historical perspective and detailed outline are given in the first chapter.
Table of Contents: Introduction / The Optimal Linear Prediction Problem / Levinson's Recursion / Lattice Structures for Linear Prediction / Autoregressive Modeling / Prediction Error Bound and Spectral Flatness / Line Spectral Processes / Linear Prediction Theory for Vector Processes / Appendix A: Linear Estimation of Random Variables / B: Proof of a Property of Autocorrelations / C: Stability of the Inverse Filter / Recursion Satisfied by AR Autocorrelations
In these notes, we introduce particle filtering as a recursive importance sampling method that approximates the minimum-mean-square-error (MMSE) estimate of a sequence of hidden state vectors in scenarios where the jo...
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ISBN:
(数字)9783031025358
ISBN:
(纸本)9783031014079
In these notes, we introduce particle filtering as a recursive importance sampling method that approximates the minimum-mean-square-error (MMSE) estimate of a sequence of hidden state vectors in scenarios where the joint probability distribution of the states and the observations is non-Gaussian and, therefore, closed-form analytical expressions for the MMSE estimate are generally unavailable.
We begin the notes with a review of Bayesian approaches to static (i.e., time-invariant) parameter estimation. In the sequel, we describe the solution to the problem of sequential state estimation in linear, Gaussian dynamic models, which corresponds to the well-known Kalman (or Kalman-Bucy) filter. Finally, we move to the general nonlinear, non-Gaussian stochastic filtering problem and present particle filtering as a sequential Monte Carlo approach to solve that problem in a statistically optimal way.
We review several techniques to improve the performance of particle filters, including importance function optimization, particle resampling, Markov Chain Monte Carlo move steps, auxiliary particle filtering, and regularized particle filtering. We also discuss Rao-Blackwellized particle filtering as a technique that is particularly well-suited for many relevant applications such as fault detection and inertial navigation. Finally, we conclude the notes with a discussion on the emerging topic of distributed particle filtering using multiple processors located at remote nodes in a sensor network.
Throughout the notes, we often assume a more general framework than in most introductory textbooks by allowing either the observation model or the hidden state dynamic model to include unknown parameters. In a fully Bayesian fashion, we treat those unknown parameters also as random variables. Using suitable dynamic conjugate priors, that approach can be applied then to perform joint state and parameter estimation.
Table of Contents: Introduction / Bayesian Estimation of Static Vectors
Compressed sensing (CS) allows signals and images to be reliably inferred from undersampled measurements. Exploiting CS allows the creation of new types of high-performance sensors including infrared cameras and magne...
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ISBN:
(数字)9783031025419
ISBN:
(纸本)9783031014130
Compressed sensing (CS) allows signals and images to be reliably inferred from undersampled measurements. Exploiting CS allows the creation of new types of high-performance sensors including infrared cameras and magnetic resonance imaging systems. Advances in computer vision and deep learning have enabled new applications of automated systems. In this book, we introduce reconstruction-free compressive vision, where image processing and computer vision algorithms are embedded directly in the compressive domain, without the need for first reconstructing the measurements into images or video. Reconstruction of CS images is computationally expensive and adds to system complexity. Therefore, reconstruction-free compressive vision is an appealing alternative particularly for power-aware systems and bandwidth-limited applications that do not have on-board post-processing computational capabilities. Engineers must balance maintaining algorithm performance while minimizing both the number of measurements needed and the computational requirements of the algorithms. Our study explores the intersection of compressed sensing and computer vision, with the focus on applications in surveillance and autonomous navigation. Other applications are also discussed at the end and a comprehensive list of references including survey papers are given for further reading.
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