Indoor Human Fall Detection Algorithm Based on Wireless Sensing

As the main health threat to the elderly living alone and performing indoor activities, falls have attracted great attention from institutions and society. Currently, fall detection systems are mainly based on wear sensors, environmental sensors, and computer vision, which need to be worn or require complex equipment construction. However, they have limitations and will interfere with the daily life of the elderly. On the basis of the indoor propagation theory of wireless signals, this paper proposes a conceptual verification module using Wi-Fi signals to identify human fall behavior. The module can detect falls without invading privacy and affecting human comfort and has the advantages of noninvasive, robustness, universality, and low price. The module combines digital signal processing technology and machine learning technology. This paper analyzes and processes the channel state information (CSI) data of wireless signals, and the local outlier factor algorithm is used to find the abnormal CSI sequence. The support vector machine and extreme gradient boosting algorithms are used for classification, recognition, and comparative research. Experimental results show that the average accuracy of fall detection based on wireless sensing is more than 90%. This work has important social significance in ensuring the safety of the elderly.Temple University. College of Science and TechnologyComputer and Information Science

Trust Index Based Fault Tolerant Multiple Event Localization Algorithm for WSNs

This paper investigates the use of wireless sensor networks for multiple event source localization using binary information from the sensor nodes. The events could continually emit signals whose strength is attenuated inversely proportional to the distance from the source. In this context, faults occur due to various reasons and are manifested when a node reports a wrong decision. In order to reduce the impact of node faults on the accuracy of multiple event localization, we introduce a trust index model to evaluate the fidelity of information which the nodes report and use in the event detection process, and propose the Trust Index based Subtract on Negative Add on Positive (TISNAP) localization algorithm, which reduces the impact of faulty nodes on the event localization by decreasing their trust index, to improve the accuracy of event localization and performance of fault tolerance for multiple event source localization. The algorithm includes three phases: first, the sink identifies the cluster nodes to determine the number of events occurred in the entire region by analyzing the binary data reported by all nodes; then, it constructs the likelihood matrix related to the cluster nodes and estimates the location of all events according to the alarmed status and trust index of the nodes around the cluster nodes. Finally, the sink updates the trust index of all nodes according to the fidelity of their information in the previous reporting cycle. The algorithm improves the accuracy of localization and performance of fault tolerance in multiple event source localization. The experiment results show that when the probability of node fault is close to 50%, the algorithm can still accurately determine the number of the events and have better accuracy of localization compared with other algorithms

Silver nanoparticle-induced enhancement of light extraction in two-dimensional light-emitting diodes

Monolayer transition metal dichalcogenides (TMDCs) with direct bandgaps are considered promising candidates for building light-emitting diodes (LEDs). One crucial indicator of their performance is the brightness of electroluminescence (EL). In this study, we fabricate WS2-based LEDs that make full use of the assistance of effective transient-mode charge injection. By introducing self-assembled silver nanoparticles (NPs) on top of the LED, the extraction efficiency is significantly improved, with a 2.9-fold EL enhancement observed in the experiment. Full-wave simulations further confirm that the improvement comes from the scattering capability of silver NPs, with results qualitatively fitting the experiment. This approach, with its compatibility with van der Waals heterostructures, can be further promoted to enhance the brightness of 2D monolayer TMDC-based LEDs.</p

Two ultraviolet radiation datasets that cover China

Ultraviolet (UV) radiation has significant effects on ecosystems, environments, and human health, as well as atmospheric processes and climate change. Two ultraviolet radiation datasets are described in this paper. One contains hourly observations of UV radiation measured at 40 Chinese Ecosystem Research Network stations from 2005 to 2015. CUV3 broadband radiometers were used to observe the UV radiation, with an accuracy of 5%, which meets the World Meteorology Organization's measurement standards. The extremum method was used to control the quality of the measured datasets. The other dataset contains daily cumulative UV radiation estimates that were calculated using an all-sky estimation model combined with a hybrid model. The reconstructed daily UV radiation data span from 1961 to 2014. The mean absolute bias error and root-mean-square error are smaller than 30% at most stations, and most of the mean bias error values are negative, which indicates underestimation of the UV radiation intensity. These datasets can improve our basic knowledge of the spatial and temporal variations in UV radiation. Additionally, these datasets can be used in studies of potential ozone formation and atmospheric oxidation, as well as simulations of ecological processes

Analyses of multi-size particle mixing behavior in an ore pre-reduction rotary kiln by discrete element method

The particle distribution in pre-reduction rotary kiln directly affects the reduction process of iron ore, and in-depth understanding of the mixing behavior is helpful to improve the product quality and productivity. The present work focused on the mixed dynamics of multi-component and multi-size systems in rotary kiln using discrete element method (DEM). We first confirmed that the final particle distribution and mixing degree are independent of the initial particle distribution, and then further discussed the influence of the key operating parameters such as rotating speed, average size ratio and filling degree on mixing behavior. The size segregation pattern of three components shows that the large particles segregated to the outer region, while the small particles were concentrated in the core region, forming an annular distribution with different particle sizes. Furthermore, the results also indicate that the rotational speed and fill degree show strong influence on the mixing time and have little influence on the mixing quality. Conversely, the average size ratio significantly affects on the mixing quality. The particle segregation is suppressed and the coal and iron ore particles are well mixed together for the whole bed when the average size of coal particles is smaller than that of iron ore particle. The findings of this work provide a reference for controlling and optimizing the particle mixing process in pre-reduction rotary kiln.</jats:p