The GPS multipath signal model is presented, which indicates that the coherent DLL outputs in multipath environment are the convolution between the ideal DLL outputs and the channel responses. So the channel responses...The GPS multipath signal model is presented, which indicates that the coherent DLL outputs in multipath environment are the convolution between the ideal DLL outputs and the channel responses. So the channel responses can be estimated by a least square method using the observed curve of the DLL discriminator. In terms of the estimated multipath channels, two multipath mitigation methods are discussed, which are equalization filtering and multipath subtracting, respectively. It is shown, by computer simulation, that the least square method has a good performance in channels estimation and the multipath errors can be mitigated almost completely by either of the methods. However, the multipath subtracting method has relative small remnant errors than equalization filtering.展开更多
For physical ozone absorption without reaction,two parametric estimation methods,i.e.the common linear least square fitting and non-linear Simplex search methods,were applied,respectively,to determine the ozone mass t...For physical ozone absorption without reaction,two parametric estimation methods,i.e.the common linear least square fitting and non-linear Simplex search methods,were applied,respectively,to determine the ozone mass transfer coefficient during absorption and both methods give almost the same mass transfer coefficient.While for chemical absorption with ozone decomposition reaction,the common linear least square fitting method is not applicable for the evaluation of ozone mass transfer coefficient due to the difficulty of model linearization for describing ozone concentration dissolved in water.The nonlinear Simplex method obtains the mass transfer coefficient by minimizing the sum of the differences between the simulated and experimental ozone concentration during the whole absorption process,without the limitation of linear relationship between the dissolved ozone concentration and absorption time during the initial stage of absorption.Comparison of the ozone concentration profiles between the simulation and experimental data demonstrates that Simplex method may determine ozone mass transfer coefficient during absorption in an accurate and high efficiency way with wide applicability.展开更多
Linear minimum mean square error(MMSE)detection has been shown to achieve near-optimal performance for massive multiple-input multiple-output(MIMO)systems but inevitably involves complicated matrix inversion,which ent...Linear minimum mean square error(MMSE)detection has been shown to achieve near-optimal performance for massive multiple-input multiple-output(MIMO)systems but inevitably involves complicated matrix inversion,which entails high complexity.To avoid the exact matrix inversion,a considerable number of implicit and explicit approximate matrix inversion based detection methods is proposed.By combining the advantages of both the explicit and the implicit matrix inversion,this paper introduces a new low-complexity signal detection algorithm.Firstly,the relationship between implicit and explicit techniques is analyzed.Then,an enhanced Newton iteration method is introduced to realize an approximate MMSE detection for massive MIMO uplink systems.The proposed improved Newton iteration significantly reduces the complexity of conventional Newton iteration.However,its complexity is still high for higher iterations.Thus,it is applied only for first two iterations.For subsequent iterations,we propose a novel trace iterative method(TIM)based low-complexity algorithm,which has significantly lower complexity than higher Newton iterations.Convergence guarantees of the proposed detector are also provided.Numerical simulations verify that the proposed detector exhibits significant performance enhancement over recently reported iterative detectors and achieves close-to-MMSE performance while retaining the low-complexity advantage for systems with hundreds of antennas.展开更多
[Objective]Crop line extraction is critical for improving the efficiency of autonomous agricultural machines in the field.However,traditional detection methods struggle to maintain high accuracy and efficiency under c...[Objective]Crop line extraction is critical for improving the efficiency of autonomous agricultural machines in the field.However,traditional detection methods struggle to maintain high accuracy and efficiency under challenging conditions,such as strong light exposure and weed interference.The aims are to develop an effective crop line extraction method by combining YOLOv8-G,Affinity Propagation,and the Least Squares method to enhance detection accuracy and performance in complex field environments.[Methods]The proposed method employs machine vision techniques to address common field challenges.YOLOv8-G,an improved object detection algorithm that combines YOLOv8 and Ghost‐NetV2 for lightweight,high-speed performance,was used to detect the central points of crops.These points were then clustered using the Affinity Propagation algorithm,followed by the application of the Least Squares method to extract the crop lines.Comparative tests were conducted to evaluate multiple backbone networks within the YOLOv8 framework,and ablation studies were performed to validate the enhancements made in YOLOv8-G.[Results and Discussions]The performance of the proposed method was compared with classical object detection and clustering algorithms.The YOLOv8-G algorithm achieved average precision(AP)values of 98.22%,98.15%,and 97.32%for corn detection at 7,14,and 21 days after emergence,respectively.Additionally,the crop line extraction accuracy across all stages was 96.52%.These results demonstrate the model's ability to maintain high detection accuracy despite challenging conditions in the field.[Conclusions]The proposed crop line extraction method effectively addresses field challenges such as lighting and weed interference,enabling rapid and accurate crop identification.This approach supports the automatic navigation of agricultural machinery,offering significant improvements in the precision and efficiency of field operations.展开更多
为解决电池模组极柱焊接缺陷检测精度低、效率低的问题,提出了一种基于机器视觉的焊接缺陷检测算法。首先,对采集图像进行预处理操作;其次,通过组件筛选结合改进的Canny算法获取目标区域的无干扰边缘轮廓,为了改善拟合干扰现象,利用基...为解决电池模组极柱焊接缺陷检测精度低、效率低的问题,提出了一种基于机器视觉的焊接缺陷检测算法。首先,对采集图像进行预处理操作;其次,通过组件筛选结合改进的Canny算法获取目标区域的无干扰边缘轮廓,为了改善拟合干扰现象,利用基于密度的聚类(density-based spatial clustering of applications with noise,DBSCAN)算法对焊接区域的内外圆边缘点集进行分离;然后,采用改进的最小二乘法对内外圆点集分别进行拟合得到精准的焊接区域;最后,以焊接区域内外圆的面积差和同心度来检测焊接面积缺陷和焊偏,通过双向扫面检测法进行焊接区域灰度值遍历,根据对应的灰度值范围和区域大小来检测焊穿和炸点缺陷。实验表明,该算法能够精确拟合焊接区域并准确识别出焊接缺陷,具有较高的检测精度和效率,能够满足工业生产需求。展开更多
文摘The GPS multipath signal model is presented, which indicates that the coherent DLL outputs in multipath environment are the convolution between the ideal DLL outputs and the channel responses. So the channel responses can be estimated by a least square method using the observed curve of the DLL discriminator. In terms of the estimated multipath channels, two multipath mitigation methods are discussed, which are equalization filtering and multipath subtracting, respectively. It is shown, by computer simulation, that the least square method has a good performance in channels estimation and the multipath errors can be mitigated almost completely by either of the methods. However, the multipath subtracting method has relative small remnant errors than equalization filtering.
基金Project(2011467001)supported by the Ministry of Environment Protection of ChinaProject(2010DFB94130)supported by the Ministry of Science and Technology of China
文摘For physical ozone absorption without reaction,two parametric estimation methods,i.e.the common linear least square fitting and non-linear Simplex search methods,were applied,respectively,to determine the ozone mass transfer coefficient during absorption and both methods give almost the same mass transfer coefficient.While for chemical absorption with ozone decomposition reaction,the common linear least square fitting method is not applicable for the evaluation of ozone mass transfer coefficient due to the difficulty of model linearization for describing ozone concentration dissolved in water.The nonlinear Simplex method obtains the mass transfer coefficient by minimizing the sum of the differences between the simulated and experimental ozone concentration during the whole absorption process,without the limitation of linear relationship between the dissolved ozone concentration and absorption time during the initial stage of absorption.Comparison of the ozone concentration profiles between the simulation and experimental data demonstrates that Simplex method may determine ozone mass transfer coefficient during absorption in an accurate and high efficiency way with wide applicability.
基金Supported by the National Natural Science Foundation of China （10231060）, the Special Research Found of Doctoral Program of Higher Education of China（200d0319003 ）, the Research Project of Xuzhou Institute of Technology（ XKY200622）.
基金supported by National Natural Science Foundation of China(62371225,62371227)。
文摘Linear minimum mean square error(MMSE)detection has been shown to achieve near-optimal performance for massive multiple-input multiple-output(MIMO)systems but inevitably involves complicated matrix inversion,which entails high complexity.To avoid the exact matrix inversion,a considerable number of implicit and explicit approximate matrix inversion based detection methods is proposed.By combining the advantages of both the explicit and the implicit matrix inversion,this paper introduces a new low-complexity signal detection algorithm.Firstly,the relationship between implicit and explicit techniques is analyzed.Then,an enhanced Newton iteration method is introduced to realize an approximate MMSE detection for massive MIMO uplink systems.The proposed improved Newton iteration significantly reduces the complexity of conventional Newton iteration.However,its complexity is still high for higher iterations.Thus,it is applied only for first two iterations.For subsequent iterations,we propose a novel trace iterative method(TIM)based low-complexity algorithm,which has significantly lower complexity than higher Newton iterations.Convergence guarantees of the proposed detector are also provided.Numerical simulations verify that the proposed detector exhibits significant performance enhancement over recently reported iterative detectors and achieves close-to-MMSE performance while retaining the low-complexity advantage for systems with hundreds of antennas.
文摘[Objective]Crop line extraction is critical for improving the efficiency of autonomous agricultural machines in the field.However,traditional detection methods struggle to maintain high accuracy and efficiency under challenging conditions,such as strong light exposure and weed interference.The aims are to develop an effective crop line extraction method by combining YOLOv8-G,Affinity Propagation,and the Least Squares method to enhance detection accuracy and performance in complex field environments.[Methods]The proposed method employs machine vision techniques to address common field challenges.YOLOv8-G,an improved object detection algorithm that combines YOLOv8 and Ghost‐NetV2 for lightweight,high-speed performance,was used to detect the central points of crops.These points were then clustered using the Affinity Propagation algorithm,followed by the application of the Least Squares method to extract the crop lines.Comparative tests were conducted to evaluate multiple backbone networks within the YOLOv8 framework,and ablation studies were performed to validate the enhancements made in YOLOv8-G.[Results and Discussions]The performance of the proposed method was compared with classical object detection and clustering algorithms.The YOLOv8-G algorithm achieved average precision(AP)values of 98.22%,98.15%,and 97.32%for corn detection at 7,14,and 21 days after emergence,respectively.Additionally,the crop line extraction accuracy across all stages was 96.52%.These results demonstrate the model's ability to maintain high detection accuracy despite challenging conditions in the field.[Conclusions]The proposed crop line extraction method effectively addresses field challenges such as lighting and weed interference,enabling rapid and accurate crop identification.This approach supports the automatic navigation of agricultural machinery,offering significant improvements in the precision and efficiency of field operations.
文摘为解决电池模组极柱焊接缺陷检测精度低、效率低的问题,提出了一种基于机器视觉的焊接缺陷检测算法。首先,对采集图像进行预处理操作;其次,通过组件筛选结合改进的Canny算法获取目标区域的无干扰边缘轮廓,为了改善拟合干扰现象,利用基于密度的聚类(density-based spatial clustering of applications with noise,DBSCAN)算法对焊接区域的内外圆边缘点集进行分离;然后,采用改进的最小二乘法对内外圆点集分别进行拟合得到精准的焊接区域;最后,以焊接区域内外圆的面积差和同心度来检测焊接面积缺陷和焊偏,通过双向扫面检测法进行焊接区域灰度值遍历,根据对应的灰度值范围和区域大小来检测焊穿和炸点缺陷。实验表明,该算法能够精确拟合焊接区域并准确识别出焊接缺陷,具有较高的检测精度和效率,能够满足工业生产需求。
