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Titlebook: Orthogonal Transforms for Digital Signal Processing; Nasir Ahmed,Kamisetty Ramamohan Rao Book 1975 Springer-Verlag Berlin · Heidelberg 197

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书目名称Orthogonal Transforms for Digital Signal Processing
编辑Nasir Ahmed,Kamisetty Ramamohan Rao
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图书封面Titlebook: Orthogonal Transforms for Digital Signal Processing;  Nasir Ahmed,Kamisetty Ramamohan Rao Book 1975 Springer-Verlag Berlin · Heidelberg 197
描述This book is intended for those wishing to acquire a working knowledge of orthogonal transforms in the area of digital signal processing. The authors hope that their introduction will enhance the opportunities for interdiscipli­ nary work in this field. The book consists of ten chapters. The first seven chapters are devoted to the study of the background, motivation and development of orthogonal transforms, the prerequisites for which are a basic knowledge of Fourier series transform (e.g., via a course in differential equations) and matrix al­ gebra. The last three chapters are relatively specialized in that they are di­ rected toward certain applications of orthogonal transforms in digital signal processing. As such, a knowlegde of discrete probability theory is an essential additional prerequisite. A basic knowledge of communication theory would be helpful, although not essential. Much of the material presented here has evolved from graduate level courses offered by the Departments of Electrical Engineering at Kansas State University and the University of Texas at Arlington, during the past five years. With advanced graduate students, all the material was covered in one semester
出版日期Book 1975
关键词Signal; Transforms; communication; digital signal processing; electrical engineering; signal processing
版次1
doihttps://doi.org/10.1007/978-3-642-45450-9
isbn_softcover978-3-642-45452-3
isbn_ebook978-3-642-45450-9
copyrightSpringer-Verlag Berlin · Heidelberg 1975
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Feature Selection in Pattern Recognition,The subject of pattern recognition can be divided into two main areas of study: (1) feature selection and (2) classifier design, as summarized in Fig. 10.1. . is a signal that belongs to . classes denoted by .., ..,..., ...
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