Riccardo Rovatti – författare

At the code level, discrete-time chaotic systems can be used to generate spreading codes for DS-SS systems. At the signal level, continuous-time chaotic systems can be used to generate wideband carriers for digital modulation schemes.The potential of chaos engineering is now recognized worldwide, with research groups actively pursuing the exploitation of chaotic phenomena in cryptography, spread spectrum communications, electromagnetic interference reduction, and many other applications. Although some noteworthy results have already been achieved, until now, the field has lacked both a systematic treatment of these developments and a careful, quantitative comparison of chaos-based and conventional techniques.Chaotic Electronics in Telecommunications fills both of those needs. It addresses the use of chaos in digital communications applications, from the coding level to circuit design. Each chapter offers a formal exposition of the theoretical and engineering tools needed to apply chaos, followed by discussion of the algorithms and circuits needed to apply the theory to real-world communications systems.

This book describes algorithmic methods and hardware implementations that aim to help realize the promise of Compressed Sensing (CS), namely the ability to reconstruct high-dimensional signals from a properly chosen low-dimensional “portrait”. The authors describe a design flow and some low-resource physical realizations of sensing systems based on CS. They highlight the pros and cons of several design choices from a pragmatic point of view, and show how a lightweight and mild but effective form of adaptation to the target signals can be the key to consistent resource saving. The basic principle of the devised design flow can be applied to almost any CS-based sensing system, including analog-to-information converters, and has been proven to fit an extremely diverse set of applications. Many practical aspects required to put a CS-based sensing system to work are also addressed, including saturation, quantization, and leakage phenomena.

This book describes algorithmic methods and hardware implementations that aim to help realize the promise of Compressed Sensing (CS), namely the ability to reconstruct high-dimensional signals from a properly chosen low-dimensional “portrait”.