This paper deals with the blind separation of nonstation-ary sources and direction-of-arrival （DOA） estimation in the under-determined case, when there are more sources than sensors. We assume the sources to be time...This paper deals with the blind separation of nonstation-ary sources and direction-of-arrival （DOA） estimation in the under-determined case, when there are more sources than sensors. We assume the sources to be time-frequency （TF） disjoint to a certain extent. In particular, the number of sources presented at any TF neighborhood is strictly less than that of sensors. We can identify the real number of active sources and achieve separation in any TF neighborhood by the sparse representation method. Compared with the subspace-based algorithm under the same sparseness assumption, which suffers from the extra noise effect since it can-not estimate the true number of active sources, the proposed algorithm can estimate the number of active sources and their cor-responding TF values in any TF neighborhood simultaneously. An-other contribution of this paper is a new estimation procedure for the DOA of sources in the underdetermined case, which combines the TF sparseness of sources and the clustering technique. Sim-ulation results demonstrate the validity and high performance of the proposed algorithm in both blind source separation （BSS） and DOA estimation.展开更多
为解决采用频域反射技术进行长电缆缺陷检测时,其结果易受低频段数据缺失占比的影响,而难以判断电缆缺陷极性的问题,该文提出一种基于频域反射技术的电缆阻抗失配点时频脉冲转换算法以实现缺陷的定位和极性判别。首先,采用2阶Nuttall自...为解决采用频域反射技术进行长电缆缺陷检测时,其结果易受低频段数据缺失占比的影响,而难以判断电缆缺陷极性的问题,该文提出一种基于频域反射技术的电缆阻抗失配点时频脉冲转换算法以实现缺陷的定位和极性判别。首先,采用2阶Nuttall自卷积窗的快速傅里叶计算方法对不同类型的电缆阻抗失配点进行定位。其次,提出电缆阻抗失配点时频脉冲转换算法,利用高斯窄带包络信号良好的频段调节能力和时频特性,详细阐述了时频脉冲的参数设计和极性判断方法。之后,运用仿真,对不同类型的阻抗失配点进行时频脉冲转换,验证了该算法的有效性。最后,采用该算法对实验室含接头长1500m的10k V XLPE电缆进行实验验证。仿真和实验结果表明:时频脉冲转换算法可以有效地定位长电缆阻抗失配点并且实现不同类型阻抗失配点的极性识别;同时,不同长度电缆阻抗失配点的时频脉冲转换结果不受低频段数据缺失占比的影响,有效提高了长电缆阻抗失配点的极性识别率。展开更多
The enhanced definition of Mechatronics involves the four underlying characteristics of integrated,unified,unique,and systematic approaches.In this realm,Mechatronics is not limited to electro-mechanical systems,in th...The enhanced definition of Mechatronics involves the four underlying characteristics of integrated,unified,unique,and systematic approaches.In this realm,Mechatronics is not limited to electro-mechanical systems,in the multi-physics sense,but involves other physical domains such as fluid and thermal.This paper summarizes the mechatronic approach to modeling.Linear graphs facilitate the development of state-space models of mechatronic systems,through this approach.The use of linear graphs in mechatronic modeling is outlined and an illustrative example of sound system modeling is given.Both time-domain and frequency-domain approaches are presented for the use of linear graphs.A mechatronic model of a multi-physics system may be simplified by converting all the physical domains into an equivalent single-domain system that is entirely in the output domain of the system.This approach of converting(transforming)physical domains is presented.An illustrative example of a pressure-controlled hydraulic actuator system that operates a mechanical load is given.展开更多
现代语音合成和音色转换系统产生的虚假语音对自动说话人识别系统构成了严重威胁。大多数现有的虚假语音检测系统对在训练中已知的攻击类型表现良好,但对实际应用中的未知攻击类型检测效果显著降低。因此,结合最近提出的双路径Res2Net(D...现代语音合成和音色转换系统产生的虚假语音对自动说话人识别系统构成了严重威胁。大多数现有的虚假语音检测系统对在训练中已知的攻击类型表现良好,但对实际应用中的未知攻击类型检测效果显著降低。因此,结合最近提出的双路径Res2Net(DP-Res2Net),提出一种基于时域波形的半监督端到端虚假语音检测方法。首先,为了解决训练数据集和测试数据集两者数据分布差异较大的问题,采用半监督学习进行领域迁移;然后,对于特征工程,直接将时域采样点输入DP-Res2Net中,增加局部的多尺度信息,并充分利用音频片段之间的依赖性;最后,输入特征经过浅层卷积模块、特征融合模块、全局平均池化模块得到嵌入张量,用来判别自然语音与虚假伪造语音。在公开可用的ASVspoof 2021 Speech Deep Fake评估集和VCC数据集上评估了所提出方法的性能,实验结果表明它的等错误率(EER)为19.97%,与官方最优基线系统相比降低了10.8%。基于时域波形的半监督端到端检测虚假语音检测方法面对未知攻击时是有效的,且具有更高的泛化能力。展开更多
Around one in eight women will be diagnosed with breast cancer at some time.Improved patient outcomes necessitate both early detection and an accurate diagnosis.Histological images are routinely utilized in the proces...Around one in eight women will be diagnosed with breast cancer at some time.Improved patient outcomes necessitate both early detection and an accurate diagnosis.Histological images are routinely utilized in the process of diagnosing breast cancer.Methods proposed in recent research only focus on classifying breast cancer on specific magnification levels.No study has focused on using a combined dataset with multiple magnification levels to classify breast cancer.A strategy for detecting breast cancer is provided in the context of this investigation.Histopathology image texture data is used with the wavelet transform in this technique.The proposed method comprises converting histopathological images from Red Green Blue(RGB)to Chrominance of Blue and Chrominance of Red(YCBCR),utilizing a wavelet transform to extract texture information,and classifying the images with Extreme Gradient Boosting(XGBOOST).Furthermore,SMOTE has been used for resampling as the dataset has imbalanced samples.The suggested method is evaluated using 10-fold cross-validation and achieves an accuracy of 99.27%on the BreakHis 1.040X dataset,98.95%on the BreakHis 1.0100X dataset,98.92%on the BreakHis 1.0200X dataset,98.78%on the BreakHis 1.0400X dataset,and 98.80%on the combined dataset.The findings of this study imply that improved breast cancer detection rates and patient outcomes can be achieved by combining wavelet transformation with textural signals to detect breast cancer in histopathology images.展开更多
An intelligent sound-based early fault detection system has been proposed for vehicles using machine learning.The system is designed to detect faults in vehicles at an early stage by analyzing the sound emitted by the...An intelligent sound-based early fault detection system has been proposed for vehicles using machine learning.The system is designed to detect faults in vehicles at an early stage by analyzing the sound emitted by the car.Early detection and correction of defects can improve the efficiency and life of the engine and other mechanical parts.The system uses a microphone to capture the sound emitted by the vehicle and a machine-learning algorithm to analyze the sound and detect faults.A possible fault is determined in the vehicle based on this processed sound.Binary classification is done at the first stage to differentiate between faulty and healthy cars.We collected noisy and normal sound samples of the car engine under normal and different abnormal conditions from multiple workshops and verified the data from experts.We used the time domain,frequency domain,and time-frequency domain features to detect the normal and abnormal conditions of the vehicle correctly.We used abnormal car data to classify it into fifteen other classical vehicle problems.We experimented with various signal processing techniques and presented the comparison results.In the detection and further problem classification,random forest showed the highest results of 97%and 92%with time-frequency features.展开更多
基金supported by the National Natural Science Foundation of China(61072120)
文摘This paper deals with the blind separation of nonstation-ary sources and direction-of-arrival （DOA） estimation in the under-determined case, when there are more sources than sensors. We assume the sources to be time-frequency （TF） disjoint to a certain extent. In particular, the number of sources presented at any TF neighborhood is strictly less than that of sensors. We can identify the real number of active sources and achieve separation in any TF neighborhood by the sparse representation method. Compared with the subspace-based algorithm under the same sparseness assumption, which suffers from the extra noise effect since it can-not estimate the true number of active sources, the proposed algorithm can estimate the number of active sources and their cor-responding TF values in any TF neighborhood simultaneously. An-other contribution of this paper is a new estimation procedure for the DOA of sources in the underdetermined case, which combines the TF sparseness of sources and the clustering technique. Sim-ulation results demonstrate the validity and high performance of the proposed algorithm in both blind source separation （BSS） and DOA estimation.
文摘为解决采用频域反射技术进行长电缆缺陷检测时,其结果易受低频段数据缺失占比的影响,而难以判断电缆缺陷极性的问题,该文提出一种基于频域反射技术的电缆阻抗失配点时频脉冲转换算法以实现缺陷的定位和极性判别。首先,采用2阶Nuttall自卷积窗的快速傅里叶计算方法对不同类型的电缆阻抗失配点进行定位。其次,提出电缆阻抗失配点时频脉冲转换算法,利用高斯窄带包络信号良好的频段调节能力和时频特性,详细阐述了时频脉冲的参数设计和极性判断方法。之后,运用仿真,对不同类型的阻抗失配点进行时频脉冲转换,验证了该算法的有效性。最后,采用该算法对实验室含接头长1500m的10k V XLPE电缆进行实验验证。仿真和实验结果表明:时频脉冲转换算法可以有效地定位长电缆阻抗失配点并且实现不同类型阻抗失配点的极性识别;同时,不同长度电缆阻抗失配点的时频脉冲转换结果不受低频段数据缺失占比的影响,有效提高了长电缆阻抗失配点的极性识别率。
基金supported by research grants from the Natural Sciences and Engineering Research Council(NSERC)of Canada
文摘The enhanced definition of Mechatronics involves the four underlying characteristics of integrated,unified,unique,and systematic approaches.In this realm,Mechatronics is not limited to electro-mechanical systems,in the multi-physics sense,but involves other physical domains such as fluid and thermal.This paper summarizes the mechatronic approach to modeling.Linear graphs facilitate the development of state-space models of mechatronic systems,through this approach.The use of linear graphs in mechatronic modeling is outlined and an illustrative example of sound system modeling is given.Both time-domain and frequency-domain approaches are presented for the use of linear graphs.A mechatronic model of a multi-physics system may be simplified by converting all the physical domains into an equivalent single-domain system that is entirely in the output domain of the system.This approach of converting(transforming)physical domains is presented.An illustrative example of a pressure-controlled hydraulic actuator system that operates a mechanical load is given.
文摘现代语音合成和音色转换系统产生的虚假语音对自动说话人识别系统构成了严重威胁。大多数现有的虚假语音检测系统对在训练中已知的攻击类型表现良好,但对实际应用中的未知攻击类型检测效果显著降低。因此,结合最近提出的双路径Res2Net(DP-Res2Net),提出一种基于时域波形的半监督端到端虚假语音检测方法。首先,为了解决训练数据集和测试数据集两者数据分布差异较大的问题,采用半监督学习进行领域迁移;然后,对于特征工程,直接将时域采样点输入DP-Res2Net中,增加局部的多尺度信息,并充分利用音频片段之间的依赖性;最后,输入特征经过浅层卷积模块、特征融合模块、全局平均池化模块得到嵌入张量,用来判别自然语音与虚假伪造语音。在公开可用的ASVspoof 2021 Speech Deep Fake评估集和VCC数据集上评估了所提出方法的性能,实验结果表明它的等错误率(EER)为19.97%,与官方最优基线系统相比降低了10.8%。基于时域波形的半监督端到端检测虚假语音检测方法面对未知攻击时是有效的,且具有更高的泛化能力。
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2023R236),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Around one in eight women will be diagnosed with breast cancer at some time.Improved patient outcomes necessitate both early detection and an accurate diagnosis.Histological images are routinely utilized in the process of diagnosing breast cancer.Methods proposed in recent research only focus on classifying breast cancer on specific magnification levels.No study has focused on using a combined dataset with multiple magnification levels to classify breast cancer.A strategy for detecting breast cancer is provided in the context of this investigation.Histopathology image texture data is used with the wavelet transform in this technique.The proposed method comprises converting histopathological images from Red Green Blue(RGB)to Chrominance of Blue and Chrominance of Red(YCBCR),utilizing a wavelet transform to extract texture information,and classifying the images with Extreme Gradient Boosting(XGBOOST).Furthermore,SMOTE has been used for resampling as the dataset has imbalanced samples.The suggested method is evaluated using 10-fold cross-validation and achieves an accuracy of 99.27%on the BreakHis 1.040X dataset,98.95%on the BreakHis 1.0100X dataset,98.92%on the BreakHis 1.0200X dataset,98.78%on the BreakHis 1.0400X dataset,and 98.80%on the combined dataset.The findings of this study imply that improved breast cancer detection rates and patient outcomes can be achieved by combining wavelet transformation with textural signals to detect breast cancer in histopathology images.
基金The authors are pleased to announce that The Superior University,Lahore,sponsors this research.
文摘An intelligent sound-based early fault detection system has been proposed for vehicles using machine learning.The system is designed to detect faults in vehicles at an early stage by analyzing the sound emitted by the car.Early detection and correction of defects can improve the efficiency and life of the engine and other mechanical parts.The system uses a microphone to capture the sound emitted by the vehicle and a machine-learning algorithm to analyze the sound and detect faults.A possible fault is determined in the vehicle based on this processed sound.Binary classification is done at the first stage to differentiate between faulty and healthy cars.We collected noisy and normal sound samples of the car engine under normal and different abnormal conditions from multiple workshops and verified the data from experts.We used the time domain,frequency domain,and time-frequency domain features to detect the normal and abnormal conditions of the vehicle correctly.We used abnormal car data to classify it into fifteen other classical vehicle problems.We experimented with various signal processing techniques and presented the comparison results.In the detection and further problem classification,random forest showed the highest results of 97%and 92%with time-frequency features.
