This book, "Orthogonal Transforms for Digital Signal Processing," authored by N. Ahmed and K. R. Rao, is intended to provide a comprehensive introduction to orthogonal transforms in the field of digital signal processing. The book is divided into ten chapters, with the first seven chapters focusing on the background, motivation, and development of orthogonal transforms, requiring a basic understanding of Fourier series transforms and matrix algebra. The last three chapters delve into specialized applications of these transforms, necessitating additional knowledge of discrete probability theory. The authors, who are professors at Kansas State University and the University of Texas at Arlington, have drawn from their graduate-level courses to develop the content. The book covers topics such as Fourier representation of signals and sequences, fast Fourier transforms, a class of orthogonal functions, the Walsh-Hadamard transform, miscellaneous orthogonal transforms, generalized Wiener filtering, data compression, and feature selection in pattern recognition. Each chapter includes references, problems, and appendices to enhance understanding and practical application. The preface acknowledges the contributions of graduate students, faculty members, and administrative support, as well as the authors' families for their support. The book is copyrighted by Springer-Verlag Berlin - Heidelberg and is available in both hardcover and softcover formats.This book, "Orthogonal Transforms for Digital Signal Processing," authored by N. Ahmed and K. R. Rao, is intended to provide a comprehensive introduction to orthogonal transforms in the field of digital signal processing. The book is divided into ten chapters, with the first seven chapters focusing on the background, motivation, and development of orthogonal transforms, requiring a basic understanding of Fourier series transforms and matrix algebra. The last three chapters delve into specialized applications of these transforms, necessitating additional knowledge of discrete probability theory. The authors, who are professors at Kansas State University and the University of Texas at Arlington, have drawn from their graduate-level courses to develop the content. The book covers topics such as Fourier representation of signals and sequences, fast Fourier transforms, a class of orthogonal functions, the Walsh-Hadamard transform, miscellaneous orthogonal transforms, generalized Wiener filtering, data compression, and feature selection in pattern recognition. Each chapter includes references, problems, and appendices to enhance understanding and practical application. The preface acknowledges the contributions of graduate students, faculty members, and administrative support, as well as the authors' families for their support. The book is copyrighted by Springer-Verlag Berlin - Heidelberg and is available in both hardcover and softcover formats.