In many applications of Wireless Sensor Networks(WSNs),event detection is the main purpose of users.Moreover,determining where and when that event occurs is crucial;thus,the positions of nodes must be identified.Subse...In many applications of Wireless Sensor Networks(WSNs),event detection is the main purpose of users.Moreover,determining where and when that event occurs is crucial;thus,the positions of nodes must be identified.Subsequently,in a range-free case,the Distance Vector-Hop(DV-Hop)heuristic is the commonly used localization algorithm because of its simplicity and low cost.The DV-Hop algorithm consists of a set of reference nodes,namely,anchors,to periodically broadcast their current positions and assist nearby unknown nodes during localization.Another potential solution includes the use of only one mobile anchor instead of these sets of anchors.This solution presents a new challenge in the localization of rang-free WSNs because of its favorable results and reduced cost.In this paper,we propose an analytical probabilistic model for multi-hop distance estimation between mobile anchor nodes and unknown nodes.We derive a non-linear analytic function that provides the relation between the hop counts and distance estimation.Moreover,based on the recursive least square algorithm,we present a new formulation of the original DV-Hop localization algorithm,namely,online DV-Hop localization,in WSNs.Finally,different scenarios of path planning and simulation results are conducted.展开更多
In precision agriculture,the accurate segmentation of crops and weeds in agronomic images has always been the center of attention.Many methods have been proposed but still the clean and sharp segmentation of crops and...In precision agriculture,the accurate segmentation of crops and weeds in agronomic images has always been the center of attention.Many methods have been proposed but still the clean and sharp segmentation of crops and weeds is a challenging issue for the images with a high presence of weeds.This work proposes a segmentation method based on the combination of semantic segmentation and K-means algorithms for the segmenta-tion of crops and weeds in color images.Agronomic images of two different databases were used for the segmentation algorithms.Using the thresholding technique,everything except plants was removed from the images.Afterward,semantic segmentation was applied using U-net followed by the segmentation of crops and weeds using the K-means subtractive algorithm.The comparison of segmentation performance was made for the proposed method and K-Means clustering and superpixels algorithms.The proposed algorithm pro-vided more accurate segmentation in comparison to other methods with the maximum accuracy of equivalent to 99.19%.Based on the confusion matrix,the true-positive and true-negative values were 0.9952 and 0.8985 representing the true classification rate of crops and weeds,respectively.The results indicated that the proposed method successfully provided accurate and convincing results for the segmentation of crops and weeds in the images with a complex presence of weeds.展开更多
文摘In many applications of Wireless Sensor Networks(WSNs),event detection is the main purpose of users.Moreover,determining where and when that event occurs is crucial;thus,the positions of nodes must be identified.Subsequently,in a range-free case,the Distance Vector-Hop(DV-Hop)heuristic is the commonly used localization algorithm because of its simplicity and low cost.The DV-Hop algorithm consists of a set of reference nodes,namely,anchors,to periodically broadcast their current positions and assist nearby unknown nodes during localization.Another potential solution includes the use of only one mobile anchor instead of these sets of anchors.This solution presents a new challenge in the localization of rang-free WSNs because of its favorable results and reduced cost.In this paper,we propose an analytical probabilistic model for multi-hop distance estimation between mobile anchor nodes and unknown nodes.We derive a non-linear analytic function that provides the relation between the hop counts and distance estimation.Moreover,based on the recursive least square algorithm,we present a new formulation of the original DV-Hop localization algorithm,namely,online DV-Hop localization,in WSNs.Finally,different scenarios of path planning and simulation results are conducted.
文摘In precision agriculture,the accurate segmentation of crops and weeds in agronomic images has always been the center of attention.Many methods have been proposed but still the clean and sharp segmentation of crops and weeds is a challenging issue for the images with a high presence of weeds.This work proposes a segmentation method based on the combination of semantic segmentation and K-means algorithms for the segmenta-tion of crops and weeds in color images.Agronomic images of two different databases were used for the segmentation algorithms.Using the thresholding technique,everything except plants was removed from the images.Afterward,semantic segmentation was applied using U-net followed by the segmentation of crops and weeds using the K-means subtractive algorithm.The comparison of segmentation performance was made for the proposed method and K-Means clustering and superpixels algorithms.The proposed algorithm pro-vided more accurate segmentation in comparison to other methods with the maximum accuracy of equivalent to 99.19%.Based on the confusion matrix,the true-positive and true-negative values were 0.9952 and 0.8985 representing the true classification rate of crops and weeds,respectively.The results indicated that the proposed method successfully provided accurate and convincing results for the segmentation of crops and weeds in the images with a complex presence of weeds.
