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The wireless sensor network (WSN) is an interesting area for modern day research groups. Tiny sensor nodes are deployed in a diversity of environments but with limited resources. Scarce resources compel researchers to employ an operating system that requires limited memory and minimum power. Tiny operating system (TinyOS) is a widely used operating system for sensor nodes, which provides concurrency...
This paper addresses the problem of outlier detection in the power grid. A sparse online least squares one-class support vector machine classification algorithm is presented to detect outliers in a data stream. An approximate linear dependence criteria is used to obtain a sparse solution by sequentially processing each data point only once, keeping with the requirement of data processing over a data...
This paper addresses the problem of bad data detection in the power grid. An online probability density based technique is presented to identify bad measurements within a sensor data stream in a decentralized manner using only the data from the neighboring buses and a one-hop communication system. Analyzing the spatial and temporal dependency between the measurements, the proposed algorithm identifies...
Composite filters based on Mathematical Morphological (MM) operators are getting considerable attraction in denoising Magnetic Resonance (MR) images. However, most of the approaches depend on pre-fixed combination of MM operators. In this paper, we propose a genetic programming (GP) based approach for denoising MR images. An Optimal Composite Morphological Supervised Filter FOCMSF is developed through...
This paper describes an illumination normalization technique which works at the pre-processing stage where the face image is first divided into equal sub-regions. Each sub-region is then processed separately for illumination normalization. Then the segments are joined back followed by further processing like noise removal and contrast enhancement. The proposed technique is tested on Yale dataset and...
This paper addresses one of the primary problems of visual information processing known as image restoration. Image restoration is a challenging task because of its ill-posed inverse nature. A modified Hopfield neural network with fuzzy adaptive regularization is proposed that shows potential to minimize constraint mean square error in order to guarantee the optimized results. Adaptive regularization...
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