通过移动的最小二乘法改变和自定义的控制点操作图片。Image Deformation Using Moving Least Squares 移动最小二乘法 图像变形（matlab实现）

Exploratory Data Analysis Using R provides a classroom-tested introduction to exploratory data analysis (EDA) and introduces the range of "interesting" – good, bad, and ugly – features that can be found in data, and why it is important to find them. It also introduces the mechanics of using R to explore and explain data. The book begins with a detailed overview of data, exploratory analysis, and R, as well as graphics in R. It then explores working with external data, linear regression models, and crafting data stories. The second part of the book focuses on developing R programs, including good programming practices and examples, working with text data, and general predictive models. The book ends with a chapter on "keeping it all together" that includes managing the R installation, managing files, documenting, and an introduction to reproducible computing. The book is designed for both advanced undergraduate, entry-level graduate students, and working professionals with little to no prior exposure to data analysis, modeling, statistics, or programming. it keeps the treatment relatively non-mathematical, even though data analysis is an inherently mathematical subject. Exercises are included at the end of most chapters, and an instructor's solution manual is available. About the Author: Ronald K. Pearson holds the position of Senior Data Scientist with GeoVera, a property insurance company in Fairfield, California, and he has previously held similar positions in a variety of application areas, including software development, drug safety data analysis, and the analysis of industrial process data. He holds a PhD in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology and has published conference and journal papers on topics ranging from nonlinear dynamic model structure selection to the problems of disguised missing data in predictive modeling. Dr. Pearson has authored or co-authored books including Exploring Data in Engineeri

Many textbooks today treat the basic topics in analog and digital communication systems, including coding and decoding algorithms and modulation and demodulation techniques. Most of these textbooks focus, by necessity, on the theory that underlies the design and performance analysis of the various building blocks, such as coders, decoders, modulators, and demodulators, that constitute the basic elements of a communications system. Relatively few of the textbooks, especially those written for undergraduates, include applications that motivate students. SCOPE OF THE BOOK The objective of this book is to serve as a companion or supplement to any of the comprehensive textbooks in communication systems. The book provides a variety of exercises that may be solved on a computer (generally, a personal computer is sufficient) using the popular student edition of MATLAB. We intend the book to be used primarily by senior-level undergraduate students and graduate students in electrical engineering, computer engineering, and computer science. This book will also prove useful to practicing engineers who wish to learn specific MATLAB applications for communication systems. We assume that the reader is familiar with the fundamentals of MATLAB. We do not cover those topics because several tutorial books and manuals on MATLAB are available. By design, the treatment of the communications theory topics is brief. We provide the motivation and a short introduction to each topic, establish the necessary notation, and then illustrate the basic notions through an example. The primary text and the instructor are expected to provide the required depth for the topics treated. For example, we introduce the matched filter and the correlator and assert that these devices result in the optimum demodulation of signals corrupted by additive white Gaussian noise (AWGN), but we do not provide a proof of this assertion. Such a proof generally is given in most core textbooks on communication systems.

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