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An end-to-end network traffic matrix is significantly helpful for network management and for Software Defined Networks (SDN). However, the end-to-end network traffic matrix's inferences and estimations are a challenging problem. Moreover, attaining the traffic matrix in high-speed networks for SDN is a prohibitive challenge. This paper investigates how to estimate and recover the end-to-end network traffic matrix in fine time granularity from the sampled traffic traces, which is a hard inverse problem. Different from previous methods, the fractal interpolation is used to reconstruct the finer-granularity network traffic. Then, the cubic spline interpolation method is used to obtain the smooth reconstruction values. To attain an accurate the end-to-end network traffic in fine time granularity, we perform a weighted-geometric-average process for two interpolation results that are obtained. The simulation results show that our approaches are feasible and effective. [ABSTRACT FROM AUTHOR]

Anonymity, which is more and more important to multi-receiver schemes, has been taken into consideration by many researchers recently. To protect the receiver anonymity, in 2010, the first multi-receiver scheme based on the Lagrange interpolating polynomial was proposed. To ensure the sender’s anonymity, the concept of the ring signature was proposed in 2005, but afterwards, this scheme was proven to has some weakness and at the same time, a completely anonymous multi-receiver signcryption scheme is proposed. In this completely anonymous scheme, the sender anonymity is achieved by improving the ring signature, and the receiver anonymity is achieved by also using the Lagrange interpolating polynomial. Unfortunately, the Lagrange interpolation method was proven a failure to protect the anonymity of receivers, because each authorized receiver could judge whether anyone else is authorized or not. Therefore, the completely anonymous multi-receiver signcryption mentioned above can only protect the sender anonymity. In this paper, we propose a new completely anonymous multi-receiver signcryption scheme with a new polynomial technology used to replace the Lagrange interpolating polynomial, which can mix the identity information of receivers to save it as a ciphertext element and prevent the authorized receivers from verifying others. With the receiver anonymity, the proposed scheme also owns the anonymity of the sender at the same time. Meanwhile, the decryption fairness and public verification are also provided. [ABSTRACT FROM AUTHOR]

Reconstruction of freeform surfaces from scattered coordinate data is a difficult problem encountered in many surface fitting and geometric modeling applications. Conventional tessellation and parametric surface fitting techniques are limited because they require prior knowledge about the connectivity between the sampled points. The method of surface reconstruction described in this paper exploits the learning capability of a self-organizing feature map (SOFM) to adaptively fit a deformable sphere to the unorganized 3D coordinate data. The learning algorithm automatically establishes the connectivity between the measured points by iteratively changing the topological relationships within the map. By incorporating additional constraints during the learning process it is possible to have the deformable map follow the shape of objects with surface holes and cavities. Several examples of freeform surfaces with varying levels of complexity are discussed in order to demonstrate the performance of the algorithm. [ABSTRACT FROM AUTHOR]