01230nam 2200409 450 991015039570332120230808200540.03-8452-6607-4(CKB)3710000000943514(MiAaPQ)EBC4742353(EXLCZ)99371000000094351420170817h20162016 uy 0gerurcnu||||||||rdacontentrdamediardacarrierAuslandserwerb von Transportnetzen im energierechtlichen Rechtsrahmen /Simon KohmBaden-Baden, [Germany] :Nomos,2016.©20161 online resource (343 pages) illustrationsKartellund Regulierungsrecht ;Band 143-8487-2448-0 Includes bibliographical references.Law enforcementLaw enforcementGermanyLaw enforcement.Law enforcement363.2Kohm Simon 1375438MiAaPQMiAaPQMiAaPQBOOK9910150395703321Auslandserwerb von Transportnetzen im energierechtlichen Rechtsrahmen3409868UNINA04266nam 22006375 450 991029990160332120200706010332.03-319-61373-110.1007/978-3-319-61373-4(CKB)4340000000062783(DE-He213)978-3-319-61373-4(MiAaPQ)EBC4915508(PPN)203671767(EXLCZ)99434000000006278320170714d2018 u| 0engurnn#008mamaatxtrdacontentcrdamediacrrdacarrierAdapted Compressed Sensing for Effective Hardware Implementations A Design Flow for Signal-Level Optimization of Compressed Sensing Stages /by Mauro Mangia, Fabio Pareschi, Valerio Cambareri, Riccardo Rovatti, Gianluca Setti1st ed. 2018.Cham :Springer International Publishing :Imprint: Springer,2018.1 online resource (XIV, 319 p. 180 illus., 142 illus. in color.)3-319-61372-3 Includes bibliographical references at the end of each chapters.Chapter 1. Introduction to Compressed Sensing: Fundamentals and Guarantees -- Chapter 2.How (Well) Compressed Sensing Works in Practice -- Chapter 3. From Universal to Adapted Acquisition: Rake that Signal! -- Chapter 4.The Rakeness Problem with Implementation and Complexity Constraints -- Chapter 5.Generating Raking Matrices: a Fascinating Second-Order Problem -- Chapter 6.Architectures for Compressed Sensing -- Chapter 7.Analog-to-information Conversion -- Chapter 8.Low-complexity Biosignal Compression using Compressed Sensing -- Chapter 9.Security at the analog-to-information interface using Compressed Sensing.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.Electronic circuitsSignal processingImage processingSpeech processing systemsElectronicsMicroelectronicsCircuits and Systemshttps://scigraph.springernature.com/ontologies/product-market-codes/T24068Signal, Image and Speech Processinghttps://scigraph.springernature.com/ontologies/product-market-codes/T24051Electronics and Microelectronics, Instrumentationhttps://scigraph.springernature.com/ontologies/product-market-codes/T24027Electronic circuits.Signal processing.Image processing.Speech processing systems.Electronics.Microelectronics.Circuits and Systems.Signal, Image and Speech Processing.Electronics and Microelectronics, Instrumentation.621.3815Mangia Mauroauthttp://id.loc.gov/vocabulary/relators/aut1062650Pareschi Fabioauthttp://id.loc.gov/vocabulary/relators/autCambareri Valerioauthttp://id.loc.gov/vocabulary/relators/autRovatti Riccardoauthttp://id.loc.gov/vocabulary/relators/autSetti Gianlucaauthttp://id.loc.gov/vocabulary/relators/autBOOK9910299901603321Adapted Compressed Sensing for Effective Hardware Implementations2527395UNINA