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Target Classification in Wireless Distributed Sensor Networks (WSDN) Using AI Techniques Can Komar

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Presentation on theme: "Target Classification in Wireless Distributed Sensor Networks (WSDN) Using AI Techniques Can Komar"— Presentation transcript:

1 Target Classification in Wireless Distributed Sensor Networks (WSDN) Using AI Techniques Can Komar komarcan@boun.edu.tr

2 01/12/2003CMPE 5302 Contents What is WDSN? Surveillance Networks Our Goal Conclusions

3 01/12/2003CMPE 5303 What is WDSN? Collection of sensor devices distributed (generally random) over an area Sensor devices  Small (a few mm or cm)  Low battery power  Low processing power & memory Used for surveillance, geographical observation etc.

4 01/12/2003CMPE 5304 Surveillance Networks Security sensitive regions need to be monitored (border zones, war fields etc.) Wireless distributed sensor networks (WSDN) are useful for surveillance Different types available Acoustic Seismic

5 01/12/2003CMPE 5305 Target Classification Goal  To classify the type of the vehicle detected in the field Data sets  The data collected by the Sensor Networks group in University of Wisconsin in 29 Palms Army Ground Test Area

6 01/12/2003CMPE 5306 Our Goal To obtain a suitable set of classification methods for this task  Low computation  Unreliable/incomplete data sets  Information fusion Comparison of our results with those obtained in SensIT group

7 01/12/2003CMPE 5307 Our Goal Feature extraction  Extraction of events from time series data  Filter out the noise in the data Classifiers used  K-Nearest Neighbor  Maximum Likelihood

8 01/12/2003CMPE 5308 Conclusions K-nearest neighbor seems to have more accurate results but requires high-computation power The noise in the data affects the results drastically

9 Thanks… Any questions?


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