Fault and Calibration

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  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.

Code for robust blind calibration is here and original code for our 2007 paper is here.

Ganti, Ravi Sastry, Laura Balzano, and Rebecca Willett. 2015. “Matrix Completion Under Monotonic Single Index Models.” In Proceedings of the Conference for Advances in Neural Information Processing Systems, 1864–72. http://papers.nips.cc/paper/5916-matrix-completion-under-monotonic-single-index-models.
Lipor, John, and Laura Balzano. 2014. “Robust Blind Calibration via Total Least Squares.” In 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 4244–48. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6854402&tag=1.
Ni, Kevin, Nithya Ramanathan, Mohamed Nabil Hajj Chehade, Laura Balzano, Sheela Nair, Sadaf Zahedi, Eddie Kohler, Greg Pottie, Mark Hansen, and Mani Srivastava. 2009. “Sensor Network Data Fault Types.” ACM Transactions on Sensor Networks (TOSN) 5 (3): 25. http://dl.acm.org/citation.cfm?id=1525863.
Ganeriwal, Saurabh, Laura K. Balzano, and Mani B. Srivastava. 2008. “Reputation-Based Framework for High Integrity Sensor Networks.” ACM Transactions on Sensor Networks (TOSN) 4 (3): 15. http://dl.acm.org/citation.cfm?id=1362546.
Balzano, Laura, and Robert Nowak. 2007. “Blind Calibration of Sensor Networks.” In Proceedings of the 6th International Conference on Information Processing in Sensor Networks, 79–88. http://dl.acm.org/citation.cfm?id=1236372.