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Recognizing human action in non-instrumented video is a challenging task not only because of the variability produced by general scene factors like illumination, background , occlusion or intra-class variability, but also because of subtle behavioral patterns among interacting people or between people and objects in images. To improve recognition, a system(More)
Neural prosthetics and personal healthcare have increasing need of high channel density low noise low power neural sensor interfaces. The input referred noise and quantization resolution are two essential factors which prevent conventional neural sensor interfaces from simultaneously achieving a good noise efficiency factor and low power consumption. In(More)
In this paper, we put forward a matching algorithm SMA between cloud computing services of multiple input/output parameters, which considers the semantic similarity of concepts in parameters based on WordNet. Moreover, a highly efficacious service composition algorithm Fast-EP and the improved FastB+-EP are presented. Then QoS information is utilized to(More)
—In this paper, we present a fine-grained localization algorithm for wireless sensor networks using a mobile beacon node. The algorithm is based on distance measurement using RSSI. The beacon node is equipped with a GPS sender and RF (radio frequency) transmitter. Each stationary sensor node is equipped with a RF. The beacon node periodically broadcasts its(More)
In this paper, we propose a model-free method for human action recognition via sparse spatiotemporal representation. Similar to the template matching method, the philosophy of the proposed method is to decompose each video sample containing one kind of human actions as a 1 sparse linear combination of several video samples containing multiple kinds of human(More)