Clustering analysis is a basic and essential method for mining heterogeneous information networks, which consist of multiple types of objects and rich semantic relations among different object types. Heterogeneous information networks are ubiquitous in the real-world applications, such as bibliographic networks and social media networks. Unfortunately, most existing approaches, such as spectral clustering, are designed to analyze homogeneous information networks, which are composed of only one type of objects and links. Some recent studies focused on heterogeneous information networks and yielded some research fruits, such as RankClus and NetClus. However, they often assumed that the heterogeneous information networks usually follow some simple schemas, such as bityped network schema or star network schema. To overcome the above limitations, we model the heterogeneous information network as a tensor without the restriction of network schema. Then, a tensor CP decomposition method is adapted to formulate the clustering problem in heterogeneous information networks. Further, we develop two stochastic gradient descent algorithms, namely, SGDClus and SOSClus, which lead to effective clustering multityped objects simultaneously. The experimental results on both synthetic datasets and real-world dataset have demonstrated that our proposed clustering framework can model heterogeneous information networks efficiently and outperform state-of-the-art clustering methods.
With the advantages of high positioning accuracy and low cost, visible light positioning (VLP) is becoming a promising solution for practical indoor positioning system. However, most of the VLP systems require at least two VLP LED lamps for accurate position calculation. Therefore, the application of VLP in practical scenarios may be restricted due to this limitation. In this paper, we propose a fast and high-accuracy single-LED based VLP system. Firstly, an unbalanced single-LED VLP algorithm is proposed to increase the positioning accuracy and reduce the computational complexity. Secondly, a fast beacon searching algorithm is proposed to further reduce the processing time for each captured image. Finally, since the proposed algorithms have the advantages of high accuracy and low complexity, the proposed system can also be implemented on a low-end hardware platform. Experimental results show that the average positioning error of the proposed system is decreased to 2.26 cm at the height of 3 m, and the average positioning time is reduced to 6.3 ms on a laptop and 60ms on a low-end embedded platform.
We report a new and low cost method to synthesize colloidal CdSe and coated CdSe/CuSe nanocrystals in aqueous solution. The results of structural characterization and optical measurements indicated that the good size control had been achieved. The photoluminescence study confirmed the surface passivation of CdSe core with CuSe outlayer and revealed strong enhancement of band-edge luminescence due to the quantum confinement in coated CdSe/CuSe nanocrystals.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.