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"Digital signal processing, Second Edition enables electrical engineers and technicians in the fields of biomedical, computer, and electronics engineering to master the essential fundamentals of DSP principles and practice. Many instructive worked examples are used to illustrate the material, and the use of mathematics is minimized for easier grasp of concepts. As such, this title is also useful to undergraduates in electrical engineering, and as a reference for science students and practicing engineers.The book goes beyond DSP theory, to show implementation of algorithms in hardware and software. Additional topics covered include adaptive filtering with noise reduction and echo cancellations, speech compression, signal sampling, digital filter realizations, filter design, multimedia applications, over-sampling, etc. More advanced topics are also covered, such as adaptive filters, speech compression such as PCM, u-law, ADPCM, and multi-rate DSP and over-sampling ADC.New to this edition :- MATLAB projects dealing with practical applications added throughout the book- New chapter (chapter 13) covering sub-band coding and wavelet transforms, methods that have become popular in the DSP field- New applications included in many chapters, including applications of DFT to seismic signals, electrocardiography data, and vibration signals- All real-time C programs revised for the TMS320C6713 DSK"

"This timely text/​reference presents a broad overview of advanced deep learning architectures for learning effective feature representation for perceptual and biometrics-related tasks. The text offers a showcase of cutting-edge research on the use of convolutional neural networks (CNN) in face, iris, fingerprint, and vascular biometric systems, in addition to surveillance systems that use soft biometrics. Issues of biometrics security are also examined. Topics and features: Addresses the application of deep learning to enhance the performance of biometrics identification across a wide range of different biometrics modalities Revisits deep learning for face biometrics, offering insights from neuroimaging, and provides comparison with popular CNN-based architectures for face recognition Examines deep learning for state-of-the-art latent fingerprint and finger-vein recognition, as well as iris recognition Discusses deep learning for soft biometrics, including approaches for gesture-based identification, gender classification, and tattoo recognition Investigates deep learning for biometrics security, covering biometrics template protection methods, and liveness detection to protect against fake biometrics samples Presents contributions from a global selection of pre-eminent experts in the field representing academia, industry and government laboratories Providing both an accessible introduction to the practical applications of deep learning in biometrics, and a comprehensive coverage of the entire spectrum of biometric modalities, this authoritative volume will be of great interest to all researchers, practitioners and students involved in related areas of computer vision, pattern recognition and machine learning. Dr. Bir Bhanu is Bourns Presidential Chair, Distinguished Professor of Electrical and Computer Engineering and the Director of the Center for Research in Intelligent Systems at the University of California at Riverside, USA. Some of his other Springer publications include the titles Video Bioinformatics, Distributed Video Sensor Networks, and Human Recognition at a Distance in Video. Dr. Ajay Kumar is an Associate Professor in the Department of Computing at the Hong Kong Polytechnic University."

"The main aim of this book is to introduce different signal processing models which have been used in analyzing periodic data, and different statistical and computational issues. This book discuss in detail the sinusoidal frequency model which has been used extensively in analyzing periodic data occuring in various fields. This book have tried to introduce different associated models and higher dimensional statistical signal processing models which have been further discussed in the literature. Different real data sets have been analyzed to illustrate how different models can be used in practice. Several open problems have been indicated for future research."

"Providing a comprehensive look at the current technologies, procedures, and architectures, this book presents state-of-the-art research together with the current and future challenges in building new smart applications in an efficient, scalable, and sustainable way. --"

"This text uses broad coverage and clear exposition to present a complete picture of the dynamic computer science field. Accessible to students from all backgrounds, Glenn Brookshear uses a language-independent context to encourage the development of a practical, realistic understanding of the field. An overview of each of the important areas of Computer Science provides students with a general level of proficiency for future courses"