
To address the selfishness issue in Data-driven overlay network (DONet), a selfishness-aware DONet (SA-DONet) is proposed in this paper. SA-DONet allows each node associative with an altruism value for its contributions to peers. Based on the altruism value, segment requesting and sending algorithms are designed to ensure the more altruistic nodes will have more chances to be served. The primary characteristic of our mechanism lies in three aspects. Firstly, SA-DONet can discover the selfish nodes in a decentralized manner and adjust the segment sending and requesting strategy dynamically. Secondly, selfish assessment (altruism value) comes from the node’s history and doesn’t require any extra probe and measuring packets. Lastly, our algorithms remain comparable computing complexity to DONet. Simulation results show that compared with DONet, even with a significant portion of nodes being selfish, SA-DONet can improve the streaming quality of global multicast session with low control overhead.
It is computationally intensive for high frequency algorithms to predict wide band radar signal of large complex targets. A fast novel method combining SBR and EEC techniques with radar signal processing is presented. The fast method gives an order of magnitude speedup in computation time. With the wide radar signal results obtained by this method are presented, to validate the accuracy and efficiency of the fast method.
Based on the architecture of a popular parallel platform and properties of extremely large targets, a high-performance parallel multilevel fast multipole algorithm (MLFMA) is proposed and implemented. A detailed description of the proposed parallel MLFMA is presented by a style of multilevel development from a coarser to finer consideration. Along with numerical experiments to analyze numerical performance of each part of the algorithm, the efficiency, accuracy and capability of the parallel MLFMA are well demonstrated. This high-performance parallel MLFMA has successfully computed scatterings by targets with over 310 millions unknowns and more than 2000 wavelengths in sizes.
Abstract: A novel skin color detection method in JPEG compressed domain has been proposed. Color and texture features of the image blocks are extracted from the entropy decoded DCT coefficients firstly. Then, data mining method is applied to set up the skin color model to describe the relationship between the image block features and the skin detection results, and the initial skin image blocks are detected based on the model. The skin color regions are finally segmented using region growing method. Experimental results show that, compared with the SPM (Skin Probability Map) skin color detection algorithm in pixel domain, the proposed method can achieve higher detection accuracy and faster detection speed.
A particle swarm optimization algorithm with fully communicated information is proposed. To begin with, an information-shared matrix, which provides a platform of information exchange among particles, is built. Then, normal distribution as a tool is used to synthesize information from the information-shared matrix. A method is used to modify particle position aiming to improve the information-shared level. Lastly, by using the method of position disturbance, the ability of utilizing information is enhanced. The obtained results on the benchmark functions show the effectiveness of the proposed algorithm.
It is known that detecting a high-speed and accelerating target is affected by the range migration and Doppler frequency migration in the linear frequency modulation pulse compression radar. In order to solve this problem, the model of received signal is established and the characteristics of the received signal are analyzed firstly. Then a target detection algorithm based on the scaling processing and the fractional Fourier transform is proposed. With the proposed algorithm, the range migration is compensated via using the scaling processing, and the Doppler frequency migration is compensated by using the fractional Fourier transform. Therefore it is able to improve the detection performance of a high-speed and accelerating target.
A strong blind quantum signature protocol was presented based on secret sharing. In this protocol, the three photons of each GHZ (Greenberger-Horne-Zeilinger) triplet are delivered to the message owner Alice, signatory Trent and verifier Bob respectively. Alice measures her photon to blind her message while Trent measures his photon to sign the blinded message, and then Bob verifies the signature according to the measuring results of his photon under the relationship of GHZ states. However, Bob’s actions are restricted by the quantum fingerprints and the audit program. Our protocol ensures that the signature is blind and the message owner is untraceable. Moreover, its security was not influenced by the computational resource of attackers.