02844 am 2200517 n 450 991049598850332120240104030716.02-7132-2590-610.4000/books.editionsehess.1184(CKB)4340000000012989(FrMaCLE)OB-editionsehess-1184(PPN)188823719(EXLCZ)99434000000001298920150903j|||||||| ||| 0freuu||||||m||||Faire des sciences sociales. GénéraliserEmmanuel Désveaux, Michel de FornelParisÉditions de l’École des hautes études en sciences sociales20141 online resource (326 p.) 2-7132-2363-6 À défaut de pouvoir expérimenter, le chercheur en sciences sociales construit ses objets : il les collecte, les classe et les compare, comme l’adepte des sciences de la nature, et s’efforce ainsi de transcender la singularité historique et psychologique de ses observations initiales. Mais dans quelle mesure peut-il généraliser à partir d’un ou de plusieurs faits, et en quoi cette généralisation, qui revient à énoncer une loi, équivaut-elle à une règle universelle ? Et s’il cède à la tentation de l’universalisation, ne risque-t-il pas d’oublier le stade du spécifique ? Les textes rassemblés ici reflètent des positionnements radicalement différents, allant du pessimisme à l’optimisme, quant à la possibilité même de généraliser. Or, dans un monde dont le mouvement vers l’entropie semble s’accélérer toujours davantage et dont les archives sont chaque jour plus ouvertes, généraliser demeure plus nécessaire que jamais, fût-ce au risque de l’erreur ou, plutôt, au prix du dépassement perpétuel.Social Sciences, Interdisciplinaryméthodologie en sciences socialesrecherche scientifiquesciences socialesSocial Sciences, Interdisciplinaryméthodologie en sciences socialesrecherche scientifiquesciences socialesAudoin-Rouzeau Stéphane280712Barry Laurent1458642Cefaï Daniel527804Dakhlia Jocelyne545677Désveaux Emmanuel1314950Dokic Jérôme1304576Fornel Michel de527492Hautcœur Pierre-Cyrille1292186Lechevalier Sébastien1288291Santos Catarina Madeira1458643Schaub Jean-Frédéric386593Urfalino Philippe1352938FR-FrMaCLEBOOK9910495988503321Faire des sciences sociales. Généraliser3658334UNINA04266nam 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