ABSTRACT
As a toxic substance, mercury can easily cause harm to organisms and humans. The development of methods that allow rapid detection of low concentrations of mercury ions has a positive effect on the natural environment and human health. The fluorescent probe RBSH reported in this paper has a detection limit as low as 5.9 nM, and a fast response time and allows naked eye detection. We characterized its structure by nuclear magnetic resonance and mass spectrometry, and explored the response mechanism of the probe using Job's plot, and 1H NMR and mass spectrometry. UV-vis spectrophotometry and fluorescence spectroscopy show the excellent optical properties of the probe RBSH. The low toxicity and high cell penetration capacity demonstrated by the cellular assay open up the possibility of biological experiments. By selecting hosts (natural water samples, soybean plants and zebrafish) where mercury ions are likely to be present in the biological chain for low concentration Hg2+ detection, the results all demonstrated the excellent performance of the probe RBSH.
Subject(s)
Mercury , Animals , Fluorescent Dyes , Humans , Ions , Mercury/toxicity , Spectrometry, Fluorescence , ZebrafishABSTRACT
The existing pedestrian detection algorithms cannot effectively extract features of heavily occluded targets which results in lower detection accuracy. To solve the heavy occlusion in crowds, we propose a multi-scale feature pyramid network based on ResNet (MFPN) to enhance the features of occluded targets and improve the detection accuracy. MFPN includes two modules, namely double feature pyramid network (FPN) integrated with ResNet (DFR) and repulsion loss of minimum (RLM). We propose the double FPN which improves the architecture to further enhance the semantic information and contours of occluded pedestrians, and provide a new way for feature extraction of occluded targets. The features extracted by our network can be more separated and clearer, especially those heavily occluded pedestrians. Repulsion loss is introduced to improve the loss function which can keep predicted boxes away from the ground truths of the unrelated targets. Experiments carried out on the public CrowdHuman dataset, we obtain 90.96% AP which yields the best performance, 5.16% AP gains compared to the FPN-ResNet50 baseline. Compared with the state-of-the-art works, the performance of the pedestrian detection system has been boosted with our method.
ABSTRACT
In this paper, a wireless sensor network (WSN) technology adapted to underground channel conditions is developed, which has important theoretical and practical value for safety monitoring in underground coal mines. According to the characteristics that the space, time and frequency resources of underground tunnel are open, it is proposed to constitute wireless sensor nodes based on multicarrier code division multiple access (MC-CDMA) to make full use of these resources. To improve the wireless transmission performance of source sensor nodes, it is also proposed to utilize cooperative sensors with good channel conditions from the sink node to assist source sensors with poor channel conditions. Moreover, the total power of the source sensor and its cooperative sensors is allocated on the basis of their channel conditions to increase the energy efficiency of the WSN. To solve the problem that multiple access interference (MAI) arises when multiple source sensors transmit monitoring information simultaneously, a kind of multi-sensor detection (MSD) algorithm with particle swarm optimization (PSO), namely D-PSO, is proposed for the time-frequency coded cooperative MC-CDMA WSN. Simulation results show that the average bit error rate (BER) performance of the proposed WSN in an underground coal mine is improved significantly by using wireless sensor nodes based on MC-CDMA, adopting time-frequency coded cooperative transmission and D-PSO algorithm with particle swarm optimization.
