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    Linear Predictor-Based Lossless Compression of Vibration Sensor Data: Systems Approach

    Source: Journal of Engineering Mechanics:;2007:;Volume ( 133 ):;issue: 004
    Author:
    Yunfeng Zhang
    ,
    Jian Li
    DOI: 10.1061/(ASCE)0733-9399(2007)133:4(431)
    Publisher: American Society of Civil Engineers
    Abstract: This paper presents a novel systems approach to compressing sensor network data. Unlike previous data compression methods, the proposed lossless linear predictor-based sensor data compression method utilizes structural system information to minimize the signal correlation in sensor network data. In the proposed method, linear predictor is derived in a system identification framework in which auto-regressive (AR) model is used as its model structure and the instrumental variables (IV) method is used to calculate the predictor parameters. A parametric study was carried out to study the effects of changes in system property, number of sensors, and sensor noise level on the compression performance of the proposed method. Both numerical simulation and experimental results show that the proposed sensor data compression method has a better compression performance than conventional linear predictor-based data compression method for single sensor.
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      Linear Predictor-Based Lossless Compression of Vibration Sensor Data: Systems Approach

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    http://yetl.yabesh.ir/yetl1/handle/yetl/86408
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    contributor authorYunfeng Zhang
    contributor authorJian Li
    date accessioned2017-05-08T22:41:10Z
    date available2017-05-08T22:41:10Z
    date copyrightApril 2007
    date issued2007
    identifier other%28asce%290733-9399%282007%29133%3A4%28431%29.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/86408
    description abstractThis paper presents a novel systems approach to compressing sensor network data. Unlike previous data compression methods, the proposed lossless linear predictor-based sensor data compression method utilizes structural system information to minimize the signal correlation in sensor network data. In the proposed method, linear predictor is derived in a system identification framework in which auto-regressive (AR) model is used as its model structure and the instrumental variables (IV) method is used to calculate the predictor parameters. A parametric study was carried out to study the effects of changes in system property, number of sensors, and sensor noise level on the compression performance of the proposed method. Both numerical simulation and experimental results show that the proposed sensor data compression method has a better compression performance than conventional linear predictor-based data compression method for single sensor.
    publisherAmerican Society of Civil Engineers
    titleLinear Predictor-Based Lossless Compression of Vibration Sensor Data: Systems Approach
    typeJournal Paper
    journal volume133
    journal issue4
    journal titleJournal of Engineering Mechanics
    identifier doi10.1061/(ASCE)0733-9399(2007)133:4(431)
    treeJournal of Engineering Mechanics:;2007:;Volume ( 133 ):;issue: 004
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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