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Results from 92 different test configurations were analyzed in terms of detection and classification. Hit and false alarm rates of classification algorithm were formed, and detection ranges were determined based on these results. Amplification values of low-intensity seismic data were also taken into account in the analysis. Algorithm-dependent constants such as adaptive thresholds sample sizes were examined for performance. Detection and classification of seismic signals due to footstep, rain, or vehicle were successfully performed. <\/jats:p>","DOI":"10.1155\/2013\/120386","type":"journal-article","created":{"date-parts":[[2013,10,27]],"date-time":"2013-10-27T21:02:05Z","timestamp":1382907725000},"page":"120386","update-policy":"http:\/\/dx.doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["Footstep and Vehicle Detection Using Seismic Sensors in Wireless Sensor Network: Field Tests"],"prefix":"10.1177","volume":"9","author":[{"given":"G\u00f6khan","family":"Ko\u00e7","sequence":"first","affiliation":[{"name":"Ko\u00e7Sistem Information Communication Services Inc., Istanbul 34700, Turkey"},{"name":"Department of Electrical and Electronics Engineering, Yeditepe University, Istanbul 34755, Turkey"}]},{"given":"Korkut","family":"Yegin","sequence":"additional","affiliation":[{"name":"Ko\u00e7Sistem Information Communication Services Inc., Istanbul 34700, Turkey"},{"name":"Department of Electrical and Electronics Engineering, Yeditepe University, Istanbul 34755, Turkey"}]}],"member":"179","published-online":{"date-parts":[[2013,10,27]]},"reference":[{"key":"B1-2013-120386","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-17289-2_47"},{"key":"B3-2013-120386","first-page":"14","volume":"4393","author":"Richman M. 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