This study used Topological Weighted Centroid (TWC) to analyze the Coronavirus outbreak in Brazil. This analysis only uses latitude and longitude in formation of the capitals with the confirmed cases on May 24, 2020 t...This study used Topological Weighted Centroid (TWC) to analyze the Coronavirus outbreak in Brazil. This analysis only uses latitude and longitude in formation of the capitals with the confirmed cases on May 24, 2020 to illustrate the usefulness of TWC though any date could have been used. There are three types of TWC analyses, each type having five associated algorithms that produce fifteen maps, TWC-Original, TWC-Frequency and TWC-Windowing. We focus on TWC-Original to illustrate our approach. The TWC method without using the transportation information predicts the network for COVID-19 outbreak that matches very well with the main radial transportation routes network in Brazil.展开更多
Many sensor network applications require location awareness,but it is often too expensive to equip a global positioning system(GPS) receiver for each network node.Hence,localization schemes for sensor networks typical...Many sensor network applications require location awareness,but it is often too expensive to equip a global positioning system(GPS) receiver for each network node.Hence,localization schemes for sensor networks typically use a small number of seed nodes that know their locations and protocols whereby other nodes estimate their locations from the messages they receive.For the inherent shortcomings of general particle filter(the sequential Monte Carlo method) this paper introduces particle swarm optimization and weighted centroid algorithm to optimize it.Based on improvement a distributed localization algorithm named WC-IPF(weighted centroid algorithm improved particle filter) has been proposed for localization.In this localization scheme the initial estimate position can be acquired by weighted centroid algorithm.Then the accurate position can be gotten via improved particle filter recursively.The extend simulation results show that the proposed algorithm is efficient for most condition.展开更多
To improve the accuracy of indoor localization methods with channel state information(CSI)images,a localization method that used CSI images from selected multiple access points(APs)was proposed.The method had an off-l...To improve the accuracy of indoor localization methods with channel state information(CSI)images,a localization method that used CSI images from selected multiple access points(APs)was proposed.The method had an off-line phase and an on-line phase.In the off-line phase,three APs were selected from the four APs in the localization area based on the received signal strength indication(RSSI).Next,CSI data was collected from the three selected APs using a commercial Intel 5300 network interface card.A single-channel subimage was constructed for each selected AP by combining the amplitude information from different antennas and the phase difference information between neighboring antennas.These sub-images were then merged to form a three-channel RGB image,which was subsequently fed into the convolutional neural network(CNN)for training.The CNN model was saved upon completion of training.In the on-line phase,the CSI data from the target device was collected,converted into images using the same process as in the off-line phase,and fed into the well-trained CNN model.Finally,the real position of the target device was estimated using a weighted centroid algorithm based on the model’s output probabilities.The proposed method was validated in indoor environments using two datasets,achieving good localization accuracy.展开更多
文摘This study used Topological Weighted Centroid (TWC) to analyze the Coronavirus outbreak in Brazil. This analysis only uses latitude and longitude in formation of the capitals with the confirmed cases on May 24, 2020 to illustrate the usefulness of TWC though any date could have been used. There are three types of TWC analyses, each type having five associated algorithms that produce fifteen maps, TWC-Original, TWC-Frequency and TWC-Windowing. We focus on TWC-Original to illustrate our approach. The TWC method without using the transportation information predicts the network for COVID-19 outbreak that matches very well with the main radial transportation routes network in Brazil.
文摘Many sensor network applications require location awareness,but it is often too expensive to equip a global positioning system(GPS) receiver for each network node.Hence,localization schemes for sensor networks typically use a small number of seed nodes that know their locations and protocols whereby other nodes estimate their locations from the messages they receive.For the inherent shortcomings of general particle filter(the sequential Monte Carlo method) this paper introduces particle swarm optimization and weighted centroid algorithm to optimize it.Based on improvement a distributed localization algorithm named WC-IPF(weighted centroid algorithm improved particle filter) has been proposed for localization.In this localization scheme the initial estimate position can be acquired by weighted centroid algorithm.Then the accurate position can be gotten via improved particle filter recursively.The extend simulation results show that the proposed algorithm is efficient for most condition.
基金supported by Lanzhou Science and Technology Plan Project(No.2023-3-104)Gansu Province Higher Education Industry Support Plan Project(No.2023CYZC-40)Gansu Province Excellent Graduate“Innovation Star”Program(No.2023CXZX-546)。
文摘To improve the accuracy of indoor localization methods with channel state information(CSI)images,a localization method that used CSI images from selected multiple access points(APs)was proposed.The method had an off-line phase and an on-line phase.In the off-line phase,three APs were selected from the four APs in the localization area based on the received signal strength indication(RSSI).Next,CSI data was collected from the three selected APs using a commercial Intel 5300 network interface card.A single-channel subimage was constructed for each selected AP by combining the amplitude information from different antennas and the phase difference information between neighboring antennas.These sub-images were then merged to form a three-channel RGB image,which was subsequently fed into the convolutional neural network(CNN)for training.The CNN model was saved upon completion of training.In the on-line phase,the CSI data from the target device was collected,converted into images using the same process as in the off-line phase,and fed into the well-trained CNN model.Finally,the real position of the target device was estimated using a weighted centroid algorithm based on the model’s output probabilities.The proposed method was validated in indoor environments using two datasets,achieving good localization accuracy.
