Action Recognition via Local Descriptors and Holistic Features

In this paper we propose a unified action recognition framework fusing local descriptors and holistic features. The motivation is that the local descriptors and holistic features emphasize different aspects of actions and are suitable for the different types of action databases. The proposed unified framework is based on frame differencing, bag-of-words and feature fusion. We extract two kinds of local descriptors, i.e. 2D and 3D SIFT feature descriptors, both based on 2D SIFT interest points. We apply Zernike moments to extract two kinds of holistic features, one is based on single frames and the other is based on motion energy image. We perform action recognition experiments on the KTH and Weizmann databases, using Support Vector Machines. We apply the leave-one-out and pseudo leave-N-out setups, and compare our proposed approach with state-of-the-art results. Experiments show that our proposed approach is effective. Compared with other approaches our approach is more robust, more versatile, easier to compute and simpler to understand

Plasmonic Cavity for Self-Powered Chemical Detection and Performance Boosted Surface-Enhanced Raman Scattering Detection

With the popularization of the Internet of Things, the application of chemical sensors has become more and more extensive. However, it is difficult for a single functional sensor to meet multiple needs at the same time. For the next generation of chemical sensors, in addition to rapid qualitative and quantitative detection, it is also necessary to solve the problem of a distributed sensor power supply. Triboelectric nanogenerator (TENG) and surface-enhanced Raman scattering (SERS) are two emerging technologies that can be used for chemical testing. The combination of TENG and SERS technology is proposed to be an attractive research strategy to implement qualitative and quantitative analysis, as well as self-powered detection in one device. Herein, the Ag nanoparticle (NP)@polydimethylsiloxane (PDMS) plasmonic cavity is demonstrated, which can be exploited not only as a SERS substrate for qualitative analysis of the target molecules but also as a TENG based self-powered chemical sensor for rapid quantitative analysis. More importantly, the as-designed plasmonic cavity enables prolonged triboelectric field generated by the phenomena of triboelectricity, which in turn enhances the “hot spot” intensities from Ag NPs in the cavity and boosts the SERS signals. In this way, the device can have good feasibility and versatility for chemical detection. Specifically, the measurement of the concentration of many analytes can be successfully realized, including ions and small molecules. The results verify that the proposed sensor system has the potential for self-powered chemical sensors for environmental monitoring and analytical chemistry