In secure multicast, one of the challenging problems is the authentication of multicast packets. This paper presents a novel scheme to address this problem, which combines ideas in both the hash tree schemes and the h...In secure multicast, one of the challenging problems is the authentication of multicast packets. This paper presents a novel scheme to address this problem, which combines ideas in both the hash tree schemes and the hash chain schemes. In this scheme, a group of packets is partitioned into equal-sized subgroups. Then a Merkle hash tree is built for each subgroup of packets, and the hash value of every root is appended to preceding packets to form hash chains. Its performance is analyzed and simulated using Biased Coin Toss loss model and 2-state Markov Chain loss model, respectively. Compared with the original hash chain schemes, results show that this scheme is much more efficient in term of communication overhead.展开更多
These problems of nonlinearity, fuzziness and few labeled data were rarely considered in traditional remote sensing image classification. A semi-supervised kernel fuzzy C-means （SSKFCM） algorithm is proposed to over...These problems of nonlinearity, fuzziness and few labeled data were rarely considered in traditional remote sensing image classification. A semi-supervised kernel fuzzy C-means （SSKFCM） algorithm is proposed to overcome these disadvantages of remote sensing image classification in this paper. The SSKFCM algorithm is achieved by introducing a kernel method and semi-supervised learning technique into the standard fuzzy C-means （FCM） algorithm. A set of Beijing-1 micro-satellite＇s multispectral images are adopted to be classified by several algorithms, such as FCM, kernel FCM （KFCM）, semi-supervised FCM （SSFCM） and SSKFCM. The classification results are estimated by corresponding indexes. The results indicate that the SSKFCM algorithm significantly improves the classification accuracy of remote sensing images compared with the others.展开更多
基金Supported by the Natural Science Foundation of China (No. 60173066)
文摘In secure multicast, one of the challenging problems is the authentication of multicast packets. This paper presents a novel scheme to address this problem, which combines ideas in both the hash tree schemes and the hash chain schemes. In this scheme, a group of packets is partitioned into equal-sized subgroups. Then a Merkle hash tree is built for each subgroup of packets, and the hash value of every root is appended to preceding packets to form hash chains. Its performance is analyzed and simulated using Biased Coin Toss loss model and 2-state Markov Chain loss model, respectively. Compared with the original hash chain schemes, results show that this scheme is much more efficient in term of communication overhead.
基金Supported by the National High Technology Research and Development Programme （No.2007AA12Z227） and the National Natural Science Foundation of China （No.40701146）.
文摘These problems of nonlinearity, fuzziness and few labeled data were rarely considered in traditional remote sensing image classification. A semi-supervised kernel fuzzy C-means （SSKFCM） algorithm is proposed to overcome these disadvantages of remote sensing image classification in this paper. The SSKFCM algorithm is achieved by introducing a kernel method and semi-supervised learning technique into the standard fuzzy C-means （FCM） algorithm. A set of Beijing-1 micro-satellite＇s multispectral images are adopted to be classified by several algorithms, such as FCM, kernel FCM （KFCM）, semi-supervised FCM （SSFCM） and SSKFCM. The classification results are estimated by corresponding indexes. The results indicate that the SSKFCM algorithm significantly improves the classification accuracy of remote sensing images compared with the others.
