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Titlebook: Computer Science and Convergence; CSA 2011 & WCC 2011 James J. (Jong Hyuk) Park,Han-Chieh Chao,Jongsung Conference proceedings 2012 Sprin

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Extended NIST Prime Family for Efficient Modular Reductionenough. In this paper, we further extend the idea of NIST primes. Then we find more primes can provide fast modular reduction computation that NIST prime family does not support. Our method provides more efficient modular arithmetic than Montgomery algorithm in prime fields that NIST primes does not support.
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Self-Healing in Smart Distribution Networks Using Data Exchange Model and Reliable Multicast Protocorevent network operators from making a mistake. The approach described in this paper shows that, by enabling data exchange between different substations, and by exploiting substations unused resources, it is possible to obtain a self-healing distribution network in which risks of manual interventions are reduced to minimum.
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https://doi.org/10.1057/9781137027665ation within acceptable risk. Adherence to a DeSPoT policy should ensure that the target fulfills the organizational level requirements to the trust behavior, and that the target is not exposed to unacceptable risk. The paper gives an example-driven presentation of the method.
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International Political Economy Serieshing the vehicles. In this paper, the combination of edges (initial candidate) and bag-of-features (final verification) has improved detection rate significantly as compared to other well known methods. Our method has 96% detection rate on roads inside a city and 98% detection on highways.
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DeSPoT: A Method for the Development and Specification of Policies for Trust Negotiationation within acceptable risk. Adherence to a DeSPoT policy should ensure that the target fulfills the organizational level requirements to the trust behavior, and that the target is not exposed to unacceptable risk. The paper gives an example-driven presentation of the method.
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Single Camera Vehicle Detection Using Edges and Bag-of-Featureshing the vehicles. In this paper, the combination of edges (initial candidate) and bag-of-features (final verification) has improved detection rate significantly as compared to other well known methods. Our method has 96% detection rate on roads inside a city and 98% detection on highways.
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https://doi.org/10.1057/9780230595736 from real life images, while generating well localized edges. We have compared our method using images form Berkely’s segmentation data set. Experimental results show the robustness of our method to noise in real life images.
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