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In this paper, we propose a multiobjective optimization framework for the sensor selection problem in uncertain Wireless Sensor Networks (WSNs). The uncertainties of the WSNs result in a set of sensor observations with insufficient information about the target. We propose a novel mutual information upper bound (MIUB)-based sensor selection scheme, which has a low computational complexity, same as...
In this paper, we propose a portfolio theory based sensor selection framework in Wireless Sensor Networks (WSNs) with unreliable sensor observations for target localization. Fisher information (FI) is used as the sensor selection metric in our work. Our objective is to find a sensor selection scheme that considers both the expected FI gain and the reliability of the sensors, where we observe that...
In this paper, we propose a sensor selection strategy for target tracking in Wireless Sensor Networks by formulating it as a multiobjective optimization problem (MOP). At each time step of tracking, we obtain tradeoff solutions between two conflicting objectives: minimization of the number of selected sensors and minimization of the information gap between the Fisher Information when all the sensors...
Image enhancement plays a fundamentally important role in nearly all of the vision and image processing systems. This paper presents a novel scheme for the enhancement of images using stochastic resonance (SR) noise. In this scheme, a suitable dose of noise is added to the lower quality images such that the performance of a suboptimal image enhancer is improved without altering its parameters. In...
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