An important strategy for lightweighting a 3D model is to use the mesh simplification algorithm to reduce the number of triangular meshes on the model surface. The widely used edge collapse algorithm is more efficient and has better simplification effect than other mesh simplification algorithms， but some detailed geometric features may be damaged or lost during the simplification process of this algorithm. Therefore， the approximate curvature of curve and the average area of the first-order neighborhood triangle of the edge to be collapsed were added as penalty factors to optimize the edge collapse cost of the original algorithm. First， according to the definition of curve curvature in geometry， the calculation formula of the approximate curvature of curve was proposed. Then， in the calculation process of vertex normal vector， two stages - area weighting and interior angle weighting were used to modify the initial normal vector， thereby considering more abundant geometric information of the model. The performance of the optimized algorithm was verified by experiments. Compared with the classical Quadratic Error Metric （QEM） algorithm and the mesh simplification algorithm considering the angle error， the optimized algorithm has the maximum error reduced by 73.96% and 49.77% at least and respectively. Compared with the QEM algorithm， the optimized algorithm has the Hausdorff distance reduced by 17.69% at least. It can be seen that in the process of model lightweighting， the optimized algorithm can reduce the deformation of the model and better maintain its own detailed geometric features.

The available security mechanisms for the low-cost Radio Frequency IDentification (RFID) systems are either defective or high-cost. Therefore, this paper proposed an efficient security authentication protocol for low-cost RFID system based on Physical Unclonable Function (PUF) and Linear Feedback Shift Register (LFSR). The protocol provides strong security and can resist physical attack and tag clone with strong privacy.

In a Base Station (BS) cooperation system, the capacity gains of two BSs by cooperating with each other are different owing to the channel asymmetry. Thus, in the process of selecting cooperative BS, when one BS wants to coordinate but another does not want to cooperate with it and prefers others, it will be difficult to judge whether to group the two into a cluster or not and the whole system is capacity-limited. To demonstrate this, the authors proposed an overlapped clustering scheme and then designed a joint precoding algorithm called Zero Force-Tomlinson-Harashima Precoding (ZF-THP) scheme. In this scheme, several BSs were adjusted to be overlapped and THP technique was used to eliminate the interference caused by overlapped BSs. The simulation results show that the proposed scheme solves the clustering contradiction well, efficiently increases the system capacity and enhances the system fairness.