Clustering algorithm in initialization of multi-hop wireless sensor networks

In most application scenarios of wireless sensor networks (WSN), sensor nodes are usually deployed randomly and do not have any knowledge about the network environment or even their ID's at the initial stage of their operations. In this paper, we address the clustering problems with a newly deployed multi-hop WSN where most existing clustering algorithms can hardly be used due to the absence of MAC link connections among the nodes. We propose an effective clustering algorithm based on a random contention model without the prior knowledge of the network and the ID's of nodes. Computer simulations have been used to show the effectiveness of the algorithm with a relatively low complexity if compared with existing schemes

Segment Anything is A Good Pseudo-label Generator for Weakly Supervised Semantic Segmentation

Weakly supervised semantic segmentation with weak labels is a long-lived ill-posed problem. Mainstream methods mainly focus on improving the quality of pseudo labels. In this report, we attempt to explore the potential of 'prompt to masks' from the powerful class-agnostic large segmentation model, segment-anything. Specifically, different weak labels are used as prompts to the segment-anything model, generating precise class masks. The class masks are utilized to generate pseudo labels to train the segmentation networks. We have conducted extensive experiments on PASCAL VOC 2012 dataset. Experiments demonstrate that segment-anything can serve as a good pseudo-label generator. The code will be made publicly available.Comment: Technical repor

Second-Harmonic Generation and Spectrum Modulation by Active Nonlinear Metamaterial

The nonlinear properties of a metamaterial sample composed of double-layer metallic patterns and voltage controllable diodes are experimentally investigated. Second harmonics and spectrum modulations are clearly observed in a wide band of microwave frequencies, showing that this kind of metamaterial is not only tunable by low DC bias voltage, but also behaves strong nonlinear property under a small power incidence. These properties are difficult to be found in normal, naturally occurring materials.Comment: 14 pages, 4 figure

Students' Intention of Visiting Urban Green Spaces after the COVID-19 Lockdown in China.

This study addresses students' perceptions of using urban green spaces (UGSs) after the easing of COVID-19 lockdown in China. We questioned whether they are still mindful of the risks from the outdoor gathering, or conversely, starting to learn the restoration benefits from the green spaces. Online self-reported surveys were distributed to the Chinese students aging from 14 to 30 who study in Hunan and Jiangsu Provinces, China. We finally obtained 608 complete and valid questionnaire forms from all participants. Their intentions of visiting UGSs were investigated based on the extended theory of planned behavior model. Structural equation modeling was employed to test the hypothesized psychological model. The results have shown good estimation performance on risk perception and perceived knowledge to explain the variances in their attitudes, social norms, and perceived behavior control. Among these three endogenous variables, the perceived behavior control owns the greatest and positive influence on the behavioral intention, inferring that controllability is crucial for students to make decisions of visiting green spaces in a post-pandemic context