Pseudo Random Signal Processing

In recent years, pseudo random signal processing has proven to be a critical enabler of modern communication, information, security and measurement systems. The signal’s pseudo random, noise-like properties make it vitally important as a tool for protecting against interference, alleviating multipath propagation and allowing the potential of sharing bandwidth with other users. Taking a practical approach to the topic, this text provides a comprehensive and systematic guide to understanding and using pseudo random signals. Covering theoretical principles, design methodologies and applications, Pseudo Random Signal Processing: Theory and Application: sets out the mathematical foundations needed to implement powerful pseudo random signal processing techniques; presents information about binary and nonbinary pseudo random sequence generation and design objectives; examines the creation of system architectures, including those with microprocessors, digital signal processors, memory circuits and software suits; gives a detailed discussion of sophisticated applications such as spread spectrum communications, ranging and satellite navigation systems, scrambling, system verification, and sensor and optical fibre systems. Pseudo Random Signal Processing: Theory and Applicationis an essential introduction to the subject for practising Electronics Engineers and researchers in the fields of mobile communications, satellite navigation, signal analysis, circuit testing, cryptology, watermarking, and measurement. It is also a useful reference for graduate students taking courses in Electronics, Communications and Computer Engineering.

This book brings together papers presented at the 2017 International Conference on Communications, Signal Processing, and Systems (ICCSP 2017), which was held on July 14–17, 2017 in Harbin, China. Presenting the latest developments and discussing the interactions and links between these multidisciplinary fields, the book spans topics ranging from communications, signal processing and systems. It is aimed at undergraduate and graduate electrical engineering, computer science and mathematics students, researchers and engineers from academia and industry as well as government employees.

As demand for applications working in extended frequency ranges increases, classical Digital signal processing (DSP) techniques, not protected against aliasing, are becoming less effective. Digital alias-free signal processing (DASP) is a technique for overcoming the problems of aliasing at extended frequency ranges. Based on non-uniform or randomised sampling techniques and the development of novel algorithms, it creates the capacity to suppress potential aliasing crucial for high frequency applications and to reduce the complexity of designs. This book provides practical and comprehensive coverage of the theory and techniques behind alias-free digital signal processing. Key features: Analyses issues of sampling, randomised and pseudo-randomised quantisation and direct and indirectly randomised sampling. Examines periodic and hybrid sampling, including information on processing algorithms and potential limitations imposed by signal dynamics. Sets out leading methods and techniques for complexity reduced designs, in particular designs of large aperture sensor arrays, massive data acquisition and compression from a number of signal sources and complexity-reduced processing of non-uniform data. Presents examples of engineering applications using these techniques including spectrum analysis, waveform reconstruction and the estimation of various parameters, emphasising the importance of the technique for developing new technologies. Links DASP and traditional technologies by mapping them into embedded systems with standard inputs and outputs. Digital Alias-free Signal Processing is ideal for practising engineers and researchers working on the development of digital signal processing applications at extended frequencies. It is also a valuable reference for electrical and computer engineering graduates taking courses in signal processing or digital signal processing.

Digital signal processing plays a central role in the development of modern communication and information processing systems. The theory and application of signal processing is concerned with the identification, modelling and utilisation of patterns and structures in a signal process. The observation signals are often distorted, incomplete and noisy and therefore noise reduction, the removal of channel distortion, and replacement of lost samples are important parts of a signal processing system. The fourth edition of Advanced Digital Signal Processing and Noise Reduction updates and extends the chapters in the previous edition and includes two new chapters on MIMO systems, Correlation and Eigen analysis and independent component analysis. The wide range of topics covered in this book include Wiener filters, echo cancellation, channel equalisation, spectral estimation, detection and removal of impulsive and transient noise, interpolation of missing data segments, speech enhancement and noise/interference in mobile communication environments. This book provides a coherent and structured presentation of the theory and applications of statistical signal processing and noise reduction methods. Two new chapters on MIMO systems, correlation and Eigen analysis and independent component analysis Comprehensive coverage of advanced digital signal processing and noise reduction methods for communication and information processing systems Examples and applications in signal and information extraction from noisy data Comprehensive but accessible coverage of signal processing theory including probability models, Bayesian inference, hidden Markov models, adaptive filters and Linear prediction models Advanced Digital Signal Processing and Noise Reduction is an invaluable text for postgraduates, senior undergraduates and researchers in the fields of digital signal processing, telecommunications and statistical data analysis. It will also be of interest to professional engineers in telecommunications and audio and signal processing industries and network planners and implementers in mobile and wireless communication communities.