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003 AR-LpUFI
005 20220927105900.0
007 cr nn 008mamaa
008 140308s2014 gw | s |||| 0|eng d
020 _a9783319052786
024 7 _a10.1007/978-3-319-05278-6
_2doi
050 4 _aTK7800-8360
050 4 _aTK7874-7874.9
072 7 _aTJF
_2bicssc
072 7 _aTEC008000
_2bisacsh
072 7 _aTEC008070
_2bisacsh
100 1 _aAlippi, Cesare.
_9261131
245 1 0 _aIntelligence for Embedded Systems
_h[libro electrónico] : ;
_bA Methodological Approach /
_cby Cesare Alippi.
260 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2014.
300 _axix, 283 p. :
_bil.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aIntroduction -- From Metrology to Digital Data -- Uncertainty, Informaiton and Learning Mechanisms -- Randomized Algorithms -- Robustness Analysis -- Emotional Cognitive Mechanisms for Embedded Systems -- Performance Estimation and Probably Approximately Correct Computation -- Intelligent Mechanisms in Embedded Systems -- Learning in Nonstationary and Evolving Environments -- Fault Diagnosis Systems.
520 _aAddressing current issues of which any engineer or computer scientist should be aware, this monograph is a response to the need to adopt a new computational paradigm as the methodological basis for designing pervasive embedded systems with sensor capabilities. The requirements of this paradigm are to control complexity, to limit cost and energy consumption, and to provide adaptation and cognition abilities allowing the embedded system to interact proactively with the real world. The quest for such intelligence requires the formalization of a new generation of intelligent systems able to exploit advances in digital architectures and in sensing technologies. The book sheds light on the theory behind intelligence for embedded systems with specific focus on: ·        robustness (the robustness of a computational flow and its evaluation); ·        intelligence (how to mimic the adaptation and cognition abilities of the human brain), ·        the capacity to learn in non-stationary and evolving environments by detecting changes and reacting accordingly; and ·        a new paradigm that, by accepting results that are correct in probability, allows the complexity of the embedded application the be kept under control. Theories, concepts and methods are provided to motivate researchers in this exciting and timely interdisciplinary area. Applications such as porting a neural network from a high-precision platform to a digital embedded system and evaluat ing its robustness level are described. Examples show how the methodology introduced can be adopted in the case of cyber-physical systems to manage the interaction between embedded devices and physical world.. Researchers and graduate students in computer science and various engineering-related disciplines will find the methods and approaches propounded in Intelligence for Embedded Systems of great interest. The book will also be an important resource for practitioners working on embedded systems and applications.
650 0 _aEngineering.
_9259622
650 0 _aSpecial purpose computers.
_9261132
650 0 _aComputational intelligence.
_9259845
650 0 _aElectronics.
_9259648
650 0 _aMicroelectronics.
_9259649
650 2 4 _aInstrumentation.
_9259652
776 0 8 _iPrinted edition:
_z9783319052779
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-05278-6
912 _aZDB-2-ENG
929 _aCOM
942 _cEBK
999 _aSKV
_c27887
_d27887