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检索条件"丛书名=Synthesis Lectures on Signal Processing"

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Linear Algebra for Pattern processing: Projection, Singular Value Decomposition, and Pseudoinverse

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丛书名： synthesis lectures on signal processing

2021年

作者： Kenichi Kanatani

ISBN: (数字)9781636391083

ISBN: (纸本)9781636391076;9781636391090

Linear algebra is one of the most basic foundations of a wide range of scientific domains, and most textbooks of linear algebra are written by mathematicians. However, this book is specifically intended to students and researchers of pattern information processing, analyzing signals such as images and exploring computer vision and computer graphics applications. The author himself is a researcher of this domain. Such pattern information processing deals with a large amount of data, which are represented by high-dimensional vectors and matrices. There, the role of linear algebra is not merely numerical computation of large-scale vectors and matrices. In fact, data processing is usually accompanied with "geometric interpretation." For example, we can think of one data set being "orthogonal" to another and define a "distance" between them or invoke geometric relationships such as "projecting" some data onto some space. Such geometric concepts not only help us mentally visualize abstract high-dimensional spaces in intuitive terms but also lead us to find what kind of processing is appropriate for what kind of goals. First, we take up the concept of "projection" of linear spaces and describe "spectral decomposition," "singular value decomposition," and "pseudoinverse" in terms of projection. As their applications, we discuss least-squares solutions of simultaneous linear equations and covariance matrices of probability distributions of vector random variables that are not necessarily positive definite. We also discuss fitting subspaces to point data and factorizing matrices in high dimensions in relation to motion image analysis. Finally, we introduce a computer vision application of reconstructing the 3D location of a point from three camera views to illustrate the role of linear algebra in dealing with data with noise. This book is expected to help students and researchers of pattern information processing deepen the geometric understanding of linear algebra.

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signals and Systems: A One Semester Modular Course

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丛书名： synthesis lectures on signal processing

2021年

作者： Khalid Sayood

ISBN: (数字)9781636391021

ISBN: (纸本)9781636391038;9781636391014

This book is designed for use as a textbook for a one semester signals and Systems class. It is sufficiently user friendly to be used for self study as well. It begins with a gentle introduction to the idea of abstraction by looking at numbers—the one highly abstract concept we use all the time. It then introduces some special functions that are useful for analyzing signals and systems. It then spends some time discussing some of the properties of systems; the goal being to introduce the idea of a linear time-invariant system which is the focus of the rest of the book. Fourier series, discrete and continuous time Fourier transforms are introduced as tools for the analysis of signals. The concepts of sampling and modulation which are very much a part of everyday life are discussed as applications of the these tools. Laplace transform and Z transform are then introduced as tools to analyze systems. The notions of stability of systems and feedback are analyzed using these tools. The book is divided into thirty bite-sized modules. Each module also links up with a video lecture through a QR code in each module. The video lectures are approximately thirty minutes long. There are a set of self study questions at the end of each module along with answers to help the reader reinforce the concepts in the module.

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Machine and Deep Learning Algorithms and Applications

Machine and Deep Learning Algorithms and Applications

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丛书名： synthesis lectures on signal processing

2021年

作者： Uday Shankar Shanthamallu Andreas Spanias

ISBN: (数字)9781636392660

ISBN: (纸本)9781636392653;9781636392677

This book introduces basic machine learning concepts and applications for a broad audience that includes students, faculty, and industry practitioners. We begin by describing how machine learning provides capabilities to computers and embedded systems to learn from data. A typical machine learning algorithm involves training, and generally the performance of a machine learning model improves with more training data. Deep learning is a sub-area of machine learning that involves extensive use of layers of artificial neural networks typically trained on massive amounts of data. Machine and deep learning methods are often used in contemporary data science tasks to address the growing data sets and detect, cluster, and classify data patterns. Although machine learning commercial interest has grown relatively recently, the roots of machine learning go back to decades ago. We note that nearly all organizations, including industry, government, defense, and health, are using machine learning to address a variety of needs and *** machine learning paradigms presented can be broadly divided into the following three categories: supervised learning, unsupervised learning, and semi-supervised learning. Supervised learning algorithms focus on learning a mapping function, and they are trained with supervision on labeled data. Supervised learning is further sub-divided into classification and regression algorithms. Unsupervised learning typically does not have access to ground truth, and often the goal is to learn or uncover the hidden pattern in the data. Through semi-supervised learning, one can effectively utilize a large volume of unlabeled data and a limited amount of labeled data to improve machine learning model performances. Deep learning and neural networks are also covered in this book. Deep neural networks have attracted a lot of interest during the last ten years due to the availability of graphics processing units (GPU) computational power, big data, and new

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Smartphone-Based Real-Time Digital signal processing Third

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丛书名： synthesis lectures on signal processing,

2020年

作者： Nasser Kehtarnavaz Abhishek Sehgal Shane Parris Arian Azarang

ISBN: (数字)9781681739465

ISBN: (纸本)9781681739472;9781681739458

Real-time or applied digital signal processing courses are offered as follow-ups to conventional or theory-oriented digital signal processing courses in many engineering programs for the purpose of teaching students the technical know-how for putting signal processing 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 to use smartphones as hardware boards for real-time implementation of signal processing algorithms, thus providing an alternative to the hardware boards that are used in signal processing laboratory courses. The fact that mobile devices, in particular smartphones, have become powerful processing platforms led to the development of this book to enable students to use their own smartphones to run signal processing 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 signal processing courses to smartphones creates a truly flexible laboratory experience or environment for students. In addition, it relieves the cost burden associated with using dedicated signal processing boards noting that the software development tools for smartphones are free of charge and are well-maintained by smartphone manufacturers. This book is written in such a way that it can be used as a textbook for real-time or applied digital signal processing courses offered at many universities. Ten lab experiments that are commonly encountered in such courses are covered in the book. It is written primarily for those who are already familiar with signal processing concepts and are interested in their real-time and practical aspects. Similar to existing real-time courses, knowl

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The Art of Teaching Physics with Ancient Chinese Science and Technology

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丛书名： synthesis lectures on signal processing

2020年

作者： Matt Marone

ISBN: (数字)9781681739052

ISBN: (纸本)9781681739045;9781681739069

Blending physics with the study of ancient Chinese science, technology, and culture is a unique and highly effective way to present the fundamentals of physics to non-science majors. Based on the author’s course at Mercer University (Georgia, U.S.), The Art of Teaching Physics with Ancient Chinese Science and Technology exposes a wide range of students to the scientific method and techniques of experimental analysis through the eyes and discoveries of ancient Chinese “polymaths” long before the European concept of the scientific method was even considered. No other book so deftly makes the connections from ancient China to Ben Franklin to Michael Faraday while teaching physics at the same time. A distinctive characteristic of this book is the detailed hands-on laboratory experiments. This first includes making a simple magnetic compass and magnetometer. Students then use the compass/magnetometer to measure the strength of the magnetic field produced by a long straight wire. The second experiment covers two different methods of mining copper to introduce students to simple chemical principles such as displacement reactions, oxidation, reduction, and electronegativity. Originally developed for non-science students in an Asian studies environment, this book provides a valuable resource for science teachers who wish to explore the historical connections largely ignored in traditional texts. When paired with Teaching Physics through Ancient Chinese Science and Technology (Marone, 2019), these two texts provide a unique means of studying selected topics traditionally found in a two-semester Physics course.

关键词： bisac category: science \/ history \/ history \/ asia \/ china \/ science \/ physics \/ general

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Anywhere-Anytime signals and Systems Laboratory: From MATLAB to Smartphones, Third Edition

Anywhere-Anytime Signals and Systems Laboratory: From MATLAB...

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丛书名： synthesis lectures on signal processing,

2020年

作者： Nasser Kehtarnavaz Fatemeh Saki Adrian Duran Arian Azarang

ISBN: (数字)9781681738864

ISBN: (纸本)9781681738857;9781681738871

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.

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Anywhere-Anytime signals and Systems Laboratory: From MATLAB to Smartphones

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丛书名： synthesis lectures on signal processing 14

2016年

作者： Nasser Kehtarnavaz Fatemeh Saki

ISBN: (纸本)9781627059015

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 most students already possess. This smartphone-based approach enables an anywhere-anytime platform for students to conduct signals and systems experiments. This book covers the laboratory experiments that are normally covered in signals and systems courses and discusses how to run MATLAB codes for these experiments on smartphones, thus enabling a truly mobile laboratory environment for students to learn the implementation aspects of signals and systems concepts. A zipped file of the codes discussed in the book can be acquired via the website http://***/bookcodes/product/signalsSystemsBookcodes.

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Sequential Monte Carlo Methods for Nonlinear Discrete-Time Filtering

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丛书名： synthesis lectures on signal processing

2013年

作者： Bruno, Marcelo G. S.

ISBN: (数字)9781627051200

ISBN: (纸本)9781627051194

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

关键词： Communications Engineering \/ Telecommunications

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An Introduction to Kalman Filtering with MATLAB Examples

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丛书名： synthesis lectures on signal processing

2013年

作者： Kovvali, Narayan Banavar, Mahesh Spanias, Andreas

ISBN: (数字)9781627051408

ISBN: (纸本)9781627051392

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.

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processing of Seismic Reflection Data Using MATLAB

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丛书名： synthesis lectures on signal processing

2011年

作者： Mousa, Wail Al-Shuhail, Abdullatif A.

ISBN: (数字)9781608457939

ISBN: (纸本)9781608457922

This short book is for students, professors and professionals interested in signal processing 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 signal processing 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

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