Invariants of the Space Point Element Structure and Their Applications

In this paper, a new geometric structure of projective invariants is proposed. Compared with the traditional invariant calculation method based on 3D reconstruction, this method is comparable in the reliability of invariant calculation. According to this method, the only thing needed to find out is the geometric relationship between 3D points and 2D points, and the invariant can be obtained by using a single frame image. In the method based on 3D reconstruction, the basic matrix of two images is estimated first, and then, the 3D projective invariants are calculated according to the basic matrix. Therefore, in terms of algorithm complexity, the method proposed in this paper is superior to the traditional method. In this paper, we also study the projection transformation from a 3D point to a 2D point in space. According to this relationship, the geometric invariant relationships of other point structures can be easily derived, which have important applications in model-based object recognition. At the same time, the experimental results show that the eight-point structure invariants proposed in this paper can effectively describe the essential characteristics of the 3D structure of the target, without the influence of view, scaling, lighting, and other link factors, and have good stability and reliability.

Experimental investigations on graphene oxide/rubber composite thermal conductivity

Graphene oxide/rubber composites were experimentally investigated for obtaining their thermal properties. Three kinds of the composite matrix material have been used: NBR, HNBR and FKM. The reduced graphene oxide in the form of crumped flakes has been applied as the filler influencing on thermal conductivity of the composites. Two values of graphene oxide weight concentration have been taken into account in the investigation. Thermal conductivity of the composites and basic matrix has been measured by the professional apparatus with the use of the guarded heat plate method. Before measurements the preliminary tests using the simplified comparative method have been performed. The results obtained, both from preliminary tests and using the guarded heat plate method, show an increase in thermal conductivity with increasing the reduced graphene oxide content in the composite. The experimental investigation allowed to determine not only the increase in thermal properties of graphene oxide/rubber composites compared to the basic matrix, but also the absolute values of thermal conductivities. Additionally, the SEM analysis showed that the tested composite samples contain agglomerates of the rGO nanoparticles. The occurrence of agglomerates could affect the composite thermal properties. This was noticed in the comparatively measurements of the temperature of different composites during the heating of samples tested. The maximum enhancement of thermal conductivity obtained was about 11% compared to the basis matrix of the composites tested.

A Novel Pixel Merging-Based Lossless Recovery Algorithm for Basic Matrix VSS

Lossless recovery in visual secret share (VSS) is very meaningful. In this paper, a novel lossless recovery algorithm for the basic matrix VSS is proposed. The secret image is reconstructed losslessly by using simple exclusive XOR operation and merging pixel. The algorithm not only can apply to the VSS without pixel expansion but also can apply to VSS with pixel expansion. The condition of lossless recovery of a VSS is given by analyzing the XOR all columns of basic matrixes. Simulations are conducted to evaluate the efficiency of the proposed scheme.