Wireless smart sensors impose severe power constraints that call for power budget optimization at all levels in the design hierarchy. We elucidate a connection between statistical learning theory and rate distortion theory that allows to operate a wireless sensor array at fundamental limits of power dissipation. GiniSVM, a support vector machine kernel-based classifier based on quadratic entropy, is shown to encode the sensor data with maximum fidelity for a given constraint on transmission budget. The transmission power is minimized by GiniSVM in the form of a quadratic cost function under linear constraints. A classifier architecture that implements these principles is presented.
|Journal||Proceedings - IEEE International Symposium on Circuits and Systems|
|State||Published - 2003|
|Event||Proceedings of the 2003 IEEE International Symposium on Circuits and Systems - Bangkok, Thailand|
Duration: May 25 2003 → May 28 2003