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Recently, most background modeling approaches represent distributions of background changes by using parametric models such as Gaussian mixture models. Because of significant illumination changes and dynamic moving backgrounds with time, variations of background changes are hard to be modeled by parametric background models. Moreover, how to efficiently and effectively update parameters of parametric...
This work tries to bring a marriage between two areas of computer science, social network analysis and machine learning, by exploiting ranking-based learning models for preference prediction on social networks. In the field of social network analysis, the diffusion of information on social networks has been studied for decades. This paper proposes the study of diffusion of preference on social networks...
In this paper, we present a novel tunable phase shifter at 2.4GHz, based on composited right/left-handed (CRLH) transmission line. The phase shifter is designed by adopting LC loaded lines with varactor diodes. The corresponding DC bias circuit is also designed. By changing the bias voltage of the varactor diodes, the phase shift of five cascaded phase shifter units can be changed continuously within...
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