2026-09-18T19:30:01Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/367642022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Samuel Madden.
Farry, Michael P. (Michael Patrick)
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2007-03-12T17:51:36Z
2007-03-12T17:51:36Z
2006
2006
http://hdl.handle.net/1721.1/36764
78624571
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.
Includes bibliographical references (p. 51-55).
This thesis outlines the development of software that makes use of Bayesian belief networks and signal processing techniques to make meaningful inferences about real-world phenomena using data obtained from sensor networks. The effectiveness of the software is validated by applying it to the problem of detecting face-to-face social interactions between groups of people, given data readings from sensors that record light, temperature, acceleration, sound, and proximity. This application represents a novel method for social network construction which is potentially more accurate and less intrusive than traditional methods, but also more meaningful than newer methods that analyze digitally mediated communication.
by Michael P. Farry.
M.Eng.
55 p.
eng
Massachusetts Institute of Technology
M.I.T. 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. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Sensor networks for social networks
Thesis
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