{"id":183,"date":"2013-05-18T22:34:54","date_gmt":"2013-05-18T22:34:54","guid":{"rendered":"http:\/\/web.eecs.umich.edu\/~girasole\/wordpress\/?page_id=183"},"modified":"2018-03-12T15:05:36","modified_gmt":"2018-03-12T15:05:36","slug":"fault-and-calibration","status":"publish","type":"page","link":"https:\/\/web.eecs.umich.edu\/~girasole\/?page_id=183","title":{"rendered":"Fault and Calibration"},"content":{"rendered":"

Sensor data has been a focus of signal processing for decades. In modern sensor applications, engineers are deploying hundreds or thousands of sensors to monitor\u00a0 the environment, computer networks, vehicle traffic, and many other phenomena of interest. Signal processing for such large datasets can no longer rely on careful maintenance and calibration nor on expert-based outlier detection (i.e. a person looking at the data). We are developing machine learning algorithms to ensure that these data are still useful and enlightening, as well as the supporting theory to give guarantees that we may use data from large deployments with confidence.<\/p>\n

Code for robust blind calibration is here<\/a> and original code for our 2007 paper is here<\/a>.<\/p>\n

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Ganti, Ravi Sastry, Laura Balzano, and Rebecca Willett. 2015. \u201cMatrix Completion Under Monotonic Single Index Models.\u201d Proceedings of the Conference for Advances in Neural Information Processing Systems<\/i>, 1864\u201372. http:\/\/papers.nips.cc\/paper\/5916-matrix-completion-under-monotonic-single-index-models<\/a>.<\/div>\n<\/div>\n\t\t\t\t<\/div>\t\t\t\t
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Lipor, John, and Laura Balzano. 2014. \u201cRobust Blind Calibration via Total Least Squares.\u201d 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)<\/i>, May, 4244\u201348. http:\/\/ieeexplore.ieee.org\/xpls\/abs_all.jsp?arnumber=6854402&tag=1<\/a>.<\/div>\n<\/div>\n\t\t\t\t<\/div>\t\t\t\t
\n
\n
Ni, Kevin, Nithya Ramanathan, Mohamed Nabil Hajj Chehade, et al. 2009. \u201cSensor Network Data Fault Types.\u201d ACM Transactions on Sensor Networks (TOSN)<\/i> 5 (3): 25. http:\/\/dl.acm.org\/citation.cfm?id=1525863<\/a>.<\/div>\n<\/div>\n\t\t\t\t<\/div>\t\t\t\t
\n
\n
Ganeriwal, Saurabh, Laura K. Balzano, and Mani B. Srivastava. 2008. \u201cReputation-Based Framework for High Integrity Sensor Networks.\u201d ACM Transactions on Sensor Networks (TOSN)<\/i> 4 (3): 15. http:\/\/dl.acm.org\/citation.cfm?id=1362546<\/a>.<\/div>\n<\/div>\n\t\t\t\t<\/div>\t\t\t\t
\n
\n
Balzano, Laura, and Robert Nowak. 2007. \u201cBlind Calibration of Sensor Networks.\u201d Proceedings of the 6th International Conference on Information Processing in Sensor Networks<\/i>, 79\u201388. http:\/\/dl.acm.org\/citation.cfm?id=1236372<\/a>.<\/div>\n<\/div>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/div>\n\t<\/div>\n\n\n","protected":false},"excerpt":{"rendered":"

Sensor data has been a focus of signal processing for decades. In modern sensor applications, engineers are deploying hundreds or thousands of sensors to monitor\u00a0 the environment, computer networks, vehicle traffic, and many other phenomena of interest. Signal processing for such large datasets can no longer rely on careful maintenance and calibration nor on expert-based […]<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":182,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","meta":[],"_links":{"self":[{"href":"https:\/\/web.eecs.umich.edu\/~girasole\/index.php?rest_route=\/wp\/v2\/pages\/183"}],"collection":[{"href":"https:\/\/web.eecs.umich.edu\/~girasole\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/web.eecs.umich.edu\/~girasole\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/web.eecs.umich.edu\/~girasole\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/web.eecs.umich.edu\/~girasole\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=183"}],"version-history":[{"count":7,"href":"https:\/\/web.eecs.umich.edu\/~girasole\/index.php?rest_route=\/wp\/v2\/pages\/183\/revisions"}],"predecessor-version":[{"id":522,"href":"https:\/\/web.eecs.umich.edu\/~girasole\/index.php?rest_route=\/wp\/v2\/pages\/183\/revisions\/522"}],"up":[{"embeddable":true,"href":"https:\/\/web.eecs.umich.edu\/~girasole\/index.php?rest_route=\/wp\/v2\/pages\/182"}],"wp:attachment":[{"href":"https:\/\/web.eecs.umich.edu\/~girasole\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=183"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}