04133nam 22007935 450 99646558570331620201107091105.01-282-33216-397866123321663-642-02611-710.1007/978-3-642-02611-9(CKB)1000000000761255(SSID)ssj0000299309(PQKBManifestationID)11204755(PQKBTitleCode)TC0000299309(PQKBWorkID)10241665(PQKB)11196966(DE-He213)978-3-642-02611-9(MiAaPQ)EBC3068758(PPN)136310435(EXLCZ)99100000000076125520100301d2009 u| 0engurnn#008mamaatxtccrImage Analysis and Recognition[electronic resource] 6th International Conference, ICIAR 2009, Halifax, Canada, July 6-8, 2009, Proceedings /edited by Mohamed Kamel, Aurelio Campilho1st ed. 2009.Berlin, Heidelberg :Springer Berlin Heidelberg :Imprint: Springer,2009.1 online resource (XX, 957 p.)Image Processing, Computer Vision, Pattern Recognition, and Graphics ;5627Bibliographic Level Mode of Issuance: Monograph3-642-02610-9 Includes bibliographical references and index.Image and Video Processing and Analysis -- Image Segmentation -- Image and Video Retrieval and Indexing -- Pattern Analysis and Recognition -- Biometrics -- Face Recognition -- Shape Analysis -- Motion Analysis and Tracking -- 3D Image Analysis -- Biomedical Image Analysis -- Document Analysis -- Applications.This book constitutes the refereed proceedings of the 6th International Conference on Image Analysis and Recognition, ICIAR 2009, held in Halifax, Canada, in July 2009. The 93 revised full papers presented were carefully reviewed and selected from 164 submissions. The papers are organized in topical sections on image and video processing and analysis; image segmentation; image and video retrieval and indexing; pattern analysis and recognition; biometrics face recognition; shape analysis; motion analysis and tracking; 3D image analysis; biomedical image analysis; document analysis and applications.Image Processing, Computer Vision, Pattern Recognition, and Graphics ;5627Optical data processingPattern recognitionBiometrics (Biology)Artificial intelligenceComputer graphicsAlgorithmsImage Processing and Computer Visionhttps://scigraph.springernature.com/ontologies/product-market-codes/I22021Pattern Recognitionhttps://scigraph.springernature.com/ontologies/product-market-codes/I2203XBiometricshttps://scigraph.springernature.com/ontologies/product-market-codes/I22040Artificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Computer Graphicshttps://scigraph.springernature.com/ontologies/product-market-codes/I22013Algorithm Analysis and Problem Complexityhttps://scigraph.springernature.com/ontologies/product-market-codes/I16021Optical data processing.Pattern recognition.Biometrics (Biology).Artificial intelligence.Computer graphics.Algorithms.Image Processing and Computer Vision.Pattern Recognition.Biometrics.Artificial Intelligence.Computer Graphics.Algorithm Analysis and Problem Complexity.006.4Kamel Mohamededthttp://id.loc.gov/vocabulary/relators/edtCampilho Aurelioedthttp://id.loc.gov/vocabulary/relators/edtBOOK996465585703316Image Analysis and Recognition772474UNISA05320nam 2201117z- 450 991055777940332120210501(CKB)5400000000045595(oapen)https://directory.doabooks.org/handle/20.500.12854/68302(oapen)doab68302(EXLCZ)99540000000004559520202105d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierSmart Wireless Acoustic Sensor Network Design for Noise Monitoring in Smart CitiesBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20211 online resource (240 p.)3-03943-280-X 3-03943-281-8 The Environmental Noise Directive (END) requires that a five-year updating of noise maps is carried out to check and report on the changes that have occurred during the reference period. The updating process is usually achieved using a standardized approach consisting of collecting and processing information through acoustic models to produce the updated noise maps. This procedure is time consuming and costly, and has a significant impact on the financial statement of the authorities responsible for providing the maps. Furthermore, the END requires that easy-to-read noise maps are made available to the public to provide information on noise levels and the subsequent actions to be undertaken by local and central authorities to reduce noise impacts. In order to update the noise maps more easily and in a more effective way, it is convenient to design an integrated system incorporating real-time noise measurement and signal processing to identify and analyze the noise sources present in the mapping area (e.g., road traffic noise, leisure noise, etc.) as well as to automatically generate and present the corresponding noise maps. This wireless acoustic sensor network design requires transversal knowledge, from accurate hardware design for acoustic sensors to network structure design and management of the information with signal processing to identify the origin of the measured noise and graphical user interface application design to present the results to end users. This book is collection in which several views of methodology and technologies required for the development of an efficient wireless acoustic sensor network from the first stages of its design to the tests conducted during deployment, its final performance, and possible subsequent implications for authorities in terms of the definition of policies. Contributions include several LIFE and H2020 projects aimed at the design and implementation of intelligent acoustic sensor networks with a focus on the publication of good practices for the design and deployment of intelligent networks in other locations.History of engineering and technologybicsscacoustic event detectionacoustic impedanceacoustic sensor designacousticsAdrienneaggregate impactanomalous noise eventsbearingCNOSSOS-EUcontribution analysisdampingdeep learningdetectiondigital signal processingdrillDYNAMAP projectdynamic modeldynamic noise mapsENDfanindividual impactintermittency ratiolong short-term memorylow-cost sensorsmap generationmechanical faultmotormultirate filtersnetworksnoisenoise controlnoise eventsnoise mappingnoise mitigationnoise monitoringnoise sourcesoutdoors noisep-p sensorp-u sensorpatternpublic informationreal-time noise mappingregression analysisRMSroad surfacesroad traffic noiseroad traffic noise modelsafetysensor conceptsensor nodesshaftsmart citiessoundsound level meterstabilizationtemporal forecasturban and suburban environmentsurban sites classificationvehicle interior noiseWASNwireless sensor networksHistory of engineering and technologyAlsina-Pagès Rosa Maedt1279926Bellucci PatriziaedtZambon GiovanniedtAlsina-Pagès Rosa MaothBellucci PatriziaothZambon GiovanniothBOOK9910557779403321Smart Wireless Acoustic Sensor Network Design for Noise Monitoring in Smart Cities3016206UNINA