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020 _a9780387795829
_99780387795829
024 7 _a10.1007/9780387795829
_2doi
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039 9 _a201509030800
_bVLOAD
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040 _aMX-SnUAN
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050 4 _aTK5102.9
100 1 _aTakeda, Kazuya.
_eeditor.
_9300436
245 1 0 _aIn-Vehicle Corpus and Signal Processing for Driver Behavior /
_cedited by Kazuya Takeda, John H. L. Hansen, Hakan Erdo?an, Hüseyin Abut.
264 1 _aBoston, MA :
_bSpringer US,
_c2009.
300 _brecurso en línea.
336 _atexto
_btxt
_2rdacontent
337 _acomputadora
_bc
_2rdamedia
338 _arecurso en línea
_bcr
_2rdacarrier
347 _aarchivo de texto
_bPDF
_2rda
500 _aSpringer eBooks
505 0 _aImproved Vehicle Safety and How Technology Will Get Us There, Hopefully -- New Concepts on Safe Driver-Assistance Systems -- Real-World Data Collection with “UYANIK” -- On-Going Data Collection of Driving Behavior Signals -- UTDrive: The Smart Vehicle Project -- Wireless Lan-Based Vehicular Location Information Processing -- Perceptually Optimized Packet Scheduling for Robust Real-Time Intervehicle Video Communications -- Machine Learning Systems for Detecting Driver Drowsiness -- Extraction of Pedestrian Regions Using Histogram and Locally Estimated Feature Distribution -- EEG Emotion Recognition System -- Three-Dimensional Ultrasound Imaging in Air for Parking and Pedestrian Protection -- A New Method for Evaluating Mental Work Load In n-Back Tasks -- Estimation of Acoustic Microphone Vocal Tract Parameters from Throat Microphone Recordings -- Cross-Probability Model Based on Gmm for Feature Vector Normalization -- Robust Feature Combination for Speech Recognition Using Linear Microphone Array in a Car -- Prediction of Driving Actions from Driving Signals -- Design of Audio-Visual Interface for Aiding Driver’s Voice Commands in Automotive Environment -- Estimation of High-Variance Vehicular Noise -- Feature Compensation Employing Model Combination for Robust In-Vehicle Speech Recognition.
520 _aIn-Vehicle Corpus and Signal Processing for Driver Behavior is comprised of expanded papers from the third biennial DSPinCARS held in Istanbul in June 2007. The goal is to bring together scholars working on the latest techniques, standards, and emerging deployment on this central field of living at the age of wireless communications, smart vehicles, and human-machine-assisted safer and comfortable driving. Topics covered in this book include: improved vehicle safety; safe driver assistance systems; smart vehicles; wireless LAN-based vehicular location information processing; EEG emotion recognition systems; and new methods for predicting driving actions using driving signals. In-Vehicle Corpus and Signal Processing for Driver Behavior is appropriate for researchers, engineers, and professionals working in signal processing technologies, next generation vehicle design, and networks for mobile platforms.
590 _aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700 1 _aHansen, John H. L.
_eeditor.
_9304004
700 1 _aErdo?an, Hakan.
_eeditor.
_9304005
710 2 _aSpringerLink (Servicio en línea)
_9299170
776 0 8 _iEdición impresa:
_z9780387795812
700 1 _9300434
_aAbut, Hüseyin.
_eeditor.
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-0-387-79582-9
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
942 _c14
999 _c279592
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