Signal Processing and Inference

Cyber Physical Systems combine a plethora of data from multiple sensors, robots and people. Different sources of noise in the sensor data make the tasks of signal processing, inference and learning particularly challenging. This activity encompasses a number of algorithms for denoising, sensor fusion, cross modality training and pattern recognition in sensor data. Applications range from activity detection and wellness monitoring to identification and authentication systems.

Selected Publications

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• The Cougar Project: A Work−In−Progress Report

  Alan Demers‚ Johannes Gehrke‚ Rajmohan Rajaraman‚ Niki Trigoni and Yong Yao.

  In ACM SIGMOD Record. Vol. 34. No. 4. December, 2003.

  Details about The Cougar Project: A Work−In−Progress Report | BibTeX data for The Cougar Project: A Work−In−Progress Report | Download of The Cougar Project: A Work−In−Progress Report

• Energy−Efficient Data Management for Sensor Networks: A Work−In−Progress Report

  Alan Demers‚ Johannes Gehrke‚ Rajmohan Rajaraman‚ Niki Trigoni and Yong Yao

  In 2nd IEEE Upstate New York Workshop on Sensor Networks. 2003.

  Details about Energy−Efficient Data Management for Sensor Networks: A Work−In−Progress Report | BibTeX data for Energy−Efficient Data Management for Sensor Networks: A Work−In−Progress Report | Download of Energy−Efficient Data Management for Sensor Networks: A Work−In−Progress Report

• Hybrid Push−Pull Query Processing for Sensor Networks

  Niki Trigoni‚ Yong Yao‚ Alan Demers‚ Johannes Gehrke and Rajmohan Rajaraman

  In GI Workshop on Sensor Networks (WSN). 2004.

  Details about Hybrid Push−Pull Query Processing for Sensor Networks | BibTeX data for Hybrid Push−Pull Query Processing for Sensor Networks | Download of Hybrid Push−Pull Query Processing for Sensor Networks