Papers by Author: Wen Ming Cao

Abstract: While moderate loss of coverage can be tolerated by WSN applications, loss of connectivity can be fatal. Moreover, since sensors are subject to unanticipated failures after deployment, it is not sufficient for a wireless sensor network to just be connected, it should be Clifford 3-connected . In this dissertation, we propose optimal deployment patterns to achieve both full coverage and Cliford 3-connectivity, and analyses their optimality for all values of , where is the communication radius and is the sensing radius.

Abstract: An important issue in deploying a WSN is to provide target coverage with high energy efficiency and fault-tolerance. Sensor nodes in a wireless sensor network are resource-constrained, especially in energy supply, and prone to failure. In this paper, we study the problem of constructing energy efficient target coverage based on Cliffford Algebra . More specifically, we propose solutions to forming k-connected coverage of targets with the minimal number of active nodes based on Cliffford Algebra. We propose two heuristic algorithms to solve the problem. We have carried out extensive simulations to study the performance of the proposed algorithms. The evaluation results have demonstrated their desirable efficiency.

Abstract: We describe a new pattern recognition method which is based on the concept of geometry theory. The method identifies subsets of the data which are embedded in arbitrary oriented lower dimensional space. We definite a kind of mapping, and study its property. Covering subsets of points are repeatedly sampled to construct trial geometry space of various dimensions. The sampling corresponding to the feature space having the best cognition ability between a mode near zero and the rest is selected and the data points are partitioned on the basis of the best cognition ability. The repeated sampling then continues recursively on each block of the data. We propose this algorithm based on cognition models. The experimental results for face recognition demonstrate that the correct rejection rate of the test samples excluded in the classes of training samples is very high and effective.

Abstract: Biometric Pattern Recognition aim at finding the best coverage of per kind of sample’s distribution in the feature space. This paper employed geometric algebra to determine local continuum (connected) direction and connected path of same kind of target of SAR images of the complex geometrical body in high dimensional space. We researched the property of the GA Neuron of the coverage body in high dimensional space and studied a kind of SAR ATR(SAR automatic target recognition) technique which works with small data amount and result to high recognizing rate. Finally, we verified our algorithm with MSTAR (Moving and Stationary Target Acquisition and Recognition) [1] data set.