On the Schur Decomposition of a Matrix for Parallel Computation
IEEE Transactions on Computers
Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
Decision estimation and classification: an introduction to pattern recognition and related topics
Decision estimation and classification: an introduction to pattern recognition and related topics
Efficient implementation of Jacobi algorithms and Jacobi sets on distributed memory architectures
Journal of Parallel and Distributed Computing - Special issue: algorithms for hypercube computers
Low Communication Overhead Jacobi Algorithms for Eigenvalues Computation on Hypercubes
The Journal of Supercomputing
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Proceedings of the 1st international conference on Embedded networked sensor systems
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SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Journal of Parallel and Distributed Computing
Vehicle classification in distributed sensor networks
Journal of Parallel and Distributed Computing
A line in the sand: a wireless sensor network for target detection, classification, and tracking
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Military communications systems and technologies
Timing Analysis for Sensor Network Nodes of the Atmega Processor Family
RTAS '05 Proceedings of the 11th IEEE Real Time on Embedded Technology and Applications Symposium
Lightweight detection and classification for wireless sensor networks in realistic environments
Proceedings of the 3rd international conference on Embedded networked sensor systems
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AICCSA '06 Proceedings of the IEEE International Conference on Computer Systems and Applications
Collaborative signal processing for distributed classification in sensor networks
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Distance based decision fusion in a distributed wireless sensor network
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
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The classification phase is computationally intensive and frequently recurs in tracking applications in sensor networks. Most related work uses traditional signal processing classifiers, such as Maximum A Posterior (MAP) classifier. Naïve formulations of MAP are not feasible for resource constraint sensornet nodes. In this paper, we study computationally efficient methods for classification. We propose to use one-sided Jacobi iterations for eigen value decomposition of the covariance matrices, the inverse of which are needed in MAP classifier. We show that this technique greatly simplifies the execution of MAP classifier and makes it a feasible and efficient choice for sensornet applications.