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dc.contributor.advisorChakrabarty, Amitabha
dc.contributor.advisorIslam, Md. Saiful
dc.contributor.authorNipu, Md. Nafiul Alam
dc.contributor.authorTalukder, Souvik
dc.date.accessioned2018-02-19T04:27:11Z
dc.date.available2018-02-19T04:27:11Z
dc.date.copyright2017
dc.date.issued12/26/2017
dc.identifier.otherID 13201006
dc.identifier.otherID 13201061
dc.identifier.urihttp://hdl.handle.net/10361/9505
dc.descriptionCataloged from PDF version of thesis report.
dc.descriptionIncludes bibliographical references (pages 45-49).
dc.descriptionThis thesis report is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017.en_US
dc.description.abstractThere have been a large number of methods already exists to identify human(e.g.,face recognition, gait recognition, fingerprint identification, etc.). Channel State Information(CSI) obtained from Wifi chipsets already has proven to be a efficient for detecting humans uniquely. We are presenting a system which can identify human uniquely and we are showing that Wifi signal can be used for identifying humans. We are working on the channel properties of a communication link which describes how a signal propagates from the transmitter to receiver and represents the combined effect. Each of the individuals have unique gait and also it is proven. Therefore, for that every human would have distract signal uniquely in the same Wifi spectrum. Our system will analysis the Channel State Information(CSI) to acquire unique features of an individual which will allow us to identify a human precisely. We have used two separate algorithms with an accuracy of 95% to 84% in Decision Tree and 97.5% to 78% in Random Forest between a group of 2 to 5 people. We propose that this technology can be used in office or in smart homes for security reasons as it is allowing us to identify humans.en_US
dc.description.statementofresponsibilityNipu, Md. Nafiul Alam
dc.description.statementofresponsibilityTalukder, Souvik
dc.format.extent49 pages
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBRAC University thesis reports are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subjectFingerprint identificationen_US
dc.subjectFace recognitionen_US
dc.subjectWifi signalen_US
dc.subjectHuman identificationen_US
dc.titleHuman identification using wifi signalen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, BRAC University
dc.description.degreeB. Computer Science and Engineering 


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