Human recognition using wireless router signal
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.
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| التنسيق: | أطروحة |
| اللغة: | English |
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Brac University
2019
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://hdl.handle.net/10361/12775 |
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10361-12775 |
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10361-127752022-01-26T10:19:59Z Human recognition using wireless router signal Chowdhury, S. M. A. Muksit Shawon, Hasinur Are n Patwary, Tanvir Wazy Ullah Rahman, Rukshanda Chakrabarty, Amitabha Department of Computer Science and Engineering, Brac University Human identi fication Channel state information Sub-carrier information Channel frequency response K-nearest neighbors Support vector machine Multilayer perceptrons Biometric identification This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019. Cataloged from PDF version of thesis. Includes bibliographical references (pages 34-36). Human identi cation technology can revolutionize numerous sectors in human life and a large number of methods already exist to identify humans such as voice recognition, ngerprint identi cation, face recognition and so on. As WiFi devices have become an inseparable commodity in our daily life, we are presenting a system which can identify human uniquely using WiFi signals and Channel State Information( CSI). Every person has some unique moving features and gestures which can be predicted by WiFi spectrum sensing. When a person walks through a region that is emitting WiFi transmission he or she can be easily identi ed by our model. Every person moves in a unique manner and therefore causes unique disturbances in the WiFi signals. Using Channel State Information (CSI)of the Wi-Fi signal, we have extracted 10 uncommon characteristics that separate one human being from another. We have analyzed channel state properties of a communication link from the transmitter to receiver and their combined e ects. In our database, we stored the trajectory of di erent people and matched them against measured trace. Our system has showcased 93% to 83% accuracy for K-NN, 94.09% to 88.15% for SVM and 96.05% to 89.84% for MLP for a group of 10 to 50 people. Our system has also shown an accuracy of 96% for K-NN, 97% for MLP in detecting gender for males from the 50 people and an accuracy of 86% for K-NN, 92% for MLP in detecting gender for female from 50 people consisting of 39 male and 11 female. However, the gender identi cation accuracy for both male and female were an equal 94% for KNN and 97% for MLP when the dataset consisted of 11 male and 11 female. Our proposition is that we can implement our system in residential homes and medium size o ces as smart security system for identifying humans. S. M. A. Muksit Chowdhury Hasinur Are n Shawon Tanvir Wazy Ullah Patwary Rukshanda Rahman B. Computer Science 2019-10-02T05:11:28Z 2019-10-02T05:11:28Z 2019 2019-08 Thesis ID 15301029 ID 15301067 ID 15301065 ID 15301016 http://hdl.handle.net/10361/12775 en Brac University theses 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. 36 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Human identi fication Channel state information Sub-carrier information Channel frequency response K-nearest neighbors Support vector machine Multilayer perceptrons Biometric identification |
| spellingShingle |
Human identi fication Channel state information Sub-carrier information Channel frequency response K-nearest neighbors Support vector machine Multilayer perceptrons Biometric identification Chowdhury, S. M. A. Muksit Shawon, Hasinur Are n Patwary, Tanvir Wazy Ullah Rahman, Rukshanda Human recognition using wireless router signal |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019. |
| author2 |
Chakrabarty, Amitabha |
| author_facet |
Chakrabarty, Amitabha Chowdhury, S. M. A. Muksit Shawon, Hasinur Are n Patwary, Tanvir Wazy Ullah Rahman, Rukshanda |
| format |
Thesis |
| author |
Chowdhury, S. M. A. Muksit Shawon, Hasinur Are n Patwary, Tanvir Wazy Ullah Rahman, Rukshanda |
| author_sort |
Chowdhury, S. M. A. Muksit |
| title |
Human recognition using wireless router signal |
| title_short |
Human recognition using wireless router signal |
| title_full |
Human recognition using wireless router signal |
| title_fullStr |
Human recognition using wireless router signal |
| title_full_unstemmed |
Human recognition using wireless router signal |
| title_sort |
human recognition using wireless router signal |
| publisher |
Brac University |
| publishDate |
2019 |
| url |
http://hdl.handle.net/10361/12775 |
| work_keys_str_mv |
AT chowdhurysmamuksit humanrecognitionusingwirelessroutersignal AT shawonhasinuraren humanrecognitionusingwirelessroutersignal AT patwarytanvirwazyullah humanrecognitionusingwirelessroutersignal AT rahmanrukshanda humanrecognitionusingwirelessroutersignal |
| _version_ |
1814309041574248448 |