Wheat ear counting is a prerequisite for the evaluation of wheat yield.A wheat ear counting method based on frequency domain decomposition is proposed in this study to improve the accuracy of wheat yield estimation.Th...Wheat ear counting is a prerequisite for the evaluation of wheat yield.A wheat ear counting method based on frequency domain decomposition is proposed in this study to improve the accuracy of wheat yield estimation.The frequency domain decomposition of wheat ear image is completed by multiscale support value filter(MSVF)combined with improved sampled contourlet transform(ISCT).Support Vector Machine(SVM)is the classic classification and regression algorithm of machine learning.MSVF based on this has strong frequency domain filtering and generalization ability,which can effectively remove the complex background,while the multi-direction characteristics of ISCT enable it to represent the contour and texture information of wheat ears.In order to improve the level of wheat yield prediction,MSVF-ISCT method is used to decompose the ear image in multiscale and multi direction in frequency domain,reduce the interference of irrelevant information,and generate the sub-band image with more abundant information components of ear feature information.Then,the ear feature is extracted by morphological operation and maximum entropy threshold segmentation,and the skeleton thinning and corner detection algorithms are used to count the results.The number of wheat ears in the image can be accurately counted.Experiments show that compared with the traditional algorithms based on spatial domain,this method significantly improves the accuracy of wheat ear counting,which can provide guidance and application for the field of agricultural precision yield estimation.展开更多
This paper presents a voice conversion technique based on bilinear models and introduces the concept of contextual modeling. The bilinear approach reformulates the spectral envelope representation from line spectral f...This paper presents a voice conversion technique based on bilinear models and introduces the concept of contextual modeling. The bilinear approach reformulates the spectral envelope representation from line spectral frequencies feature to a two-factor parameterization corresponding to speaker identity and phonetic information, the so-called style and content factors. This decomposition offers a flexible representation suitable for voice conversion and facilitates the use of efficient training algorithms based on singular value decomposition. In a contextual approach (bilinear) models are trained on subsets of the training data selected on the fly at conversion time depending on the characteristics of the feature vector to be converted. The performance of bilinear models and context modeling is evaluated in objective and perceptual tests by comparison with the popular GMM-based voice conversion method for several sizes and different types of training data.展开更多
A modified matrix enhancement and matrix pencil (MMEMP) method is presented for the scattering centers measurements in step-frequency radar. The method estimates the signal parameter pairs directly unlike the matrix e...A modified matrix enhancement and matrix pencil (MMEMP) method is presented for the scattering centers measurements in step-frequency radar. The method estimates the signal parameter pairs directly unlike the matrix enhancement and matrix pencil (MEMP) method which contains an additional step to pair the parameters related to each dimension. The downrange and crossrange expressions of the scattering centers are deduced, as well as the range ambiguities, from the point of view of MMEMP method. Compared with the Fourier transform method, the numerical simulation shows that both the resolution and precision of the MMEMP method are higher than those of the Fourier method. The processing results of the real measured data for three cylinders prove the above conclusions further.展开更多
With the development of wireless communication technology, the electromagnetic environment has become more and more complex. Conventional signal identification methods are difficult to accurately identify illegal devi...With the development of wireless communication technology, the electromagnetic environment has become more and more complex. Conventional signal identification methods are difficult to accurately identify illegal devices. However, electromagnetic signals have an unavoidable device-specific characteristic unintentionally generated by a transmitter, appearing in the form of an Un Intentional Modulation(UIM), namely Radio Frequency Fingerprint(RFF). RFFs can be used to uniquely identify an emitter to match a received signal with its source. In this paper, the authors propose a novel RFF scheme to separate UIM part from the original signals from the time and frequency domain, and then utilize non-Gaussian measuring tools to extract a set of dimensionreduced secondary features. Additionally, Singular Value Reconstruction(SVR) is developed to extract UIM in the frequency spectrum. In time domain, a curve-fitting residual method is proposed to extract the UIM on the estimated instantaneous phase based on Maximum Likelihood Estimator(MLE). Various aspects of the proposed method are evaluated, including identification accuracy under various Signal-to-Noise Ratio(SNR) conditions, energy relationships between the UIM and the whole signal, and sensitivity to training set size. Compared with other methods, experimental results based on real-world signals prove that the proposed method has remarkable performance and high practicability.展开更多
针对在奇异值分解(Singular Value Decomposition,SVD)中,随机噪声对各阶的贡献几乎相等,导致单一SVD降噪效果不理想的问题,提出了基于SVD和频带熵(Frequency Band Entropy,FBE)相结合的轴承故障特征提取方法。针对基于FBE的带通滤波器...针对在奇异值分解(Singular Value Decomposition,SVD)中,随机噪声对各阶的贡献几乎相等,导致单一SVD降噪效果不理想的问题,提出了基于SVD和频带熵(Frequency Band Entropy,FBE)相结合的轴承故障特征提取方法。针对基于FBE的带通滤波器的阶数和带宽需经验确定的问题,提出了基于信息熵最小值原则的参数优化方法。首先,对原始振动信号在相空间重构Hankel矩阵并利用SVD进行降噪处理,采用奇异值相对变化率来确定模型的阶次;然后,对降噪后的信号进行基于FBE的带通滤波,并采用基于信息熵最小值原则的优化方法确定带通滤波器的阶数和带宽。最后,对滤波信号进行包络谱分析,提取轴承故障特征频率,并用峭度指标证明了带通滤波器的有效性。通过数值仿真和实际轴承故障数据分析,证明了该方法提取轴承故障特征频率的有效性。展开更多
基金National Natural Science Foundation of China(61672032)National Key Research and Development Program of China(2016YFD0800904)+1 种基金Anhui Provincial Science and Technology Project(16030701091)The Open Research Fund of National Engineering Research Center for Agro-Ecological Big Data Analysis&Application,Anhui University(AE2018009).
文摘Wheat ear counting is a prerequisite for the evaluation of wheat yield.A wheat ear counting method based on frequency domain decomposition is proposed in this study to improve the accuracy of wheat yield estimation.The frequency domain decomposition of wheat ear image is completed by multiscale support value filter(MSVF)combined with improved sampled contourlet transform(ISCT).Support Vector Machine(SVM)is the classic classification and regression algorithm of machine learning.MSVF based on this has strong frequency domain filtering and generalization ability,which can effectively remove the complex background,while the multi-direction characteristics of ISCT enable it to represent the contour and texture information of wheat ears.In order to improve the level of wheat yield prediction,MSVF-ISCT method is used to decompose the ear image in multiscale and multi direction in frequency domain,reduce the interference of irrelevant information,and generate the sub-band image with more abundant information components of ear feature information.Then,the ear feature is extracted by morphological operation and maximum entropy threshold segmentation,and the skeleton thinning and corner detection algorithms are used to count the results.The number of wheat ears in the image can be accurately counted.Experiments show that compared with the traditional algorithms based on spatial domain,this method significantly improves the accuracy of wheat ear counting,which can provide guidance and application for the field of agricultural precision yield estimation.
文摘This paper presents a voice conversion technique based on bilinear models and introduces the concept of contextual modeling. The bilinear approach reformulates the spectral envelope representation from line spectral frequencies feature to a two-factor parameterization corresponding to speaker identity and phonetic information, the so-called style and content factors. This decomposition offers a flexible representation suitable for voice conversion and facilitates the use of efficient training algorithms based on singular value decomposition. In a contextual approach (bilinear) models are trained on subsets of the training data selected on the fly at conversion time depending on the characteristics of the feature vector to be converted. The performance of bilinear models and context modeling is evaluated in objective and perceptual tests by comparison with the popular GMM-based voice conversion method for several sizes and different types of training data.
文摘A modified matrix enhancement and matrix pencil (MMEMP) method is presented for the scattering centers measurements in step-frequency radar. The method estimates the signal parameter pairs directly unlike the matrix enhancement and matrix pencil (MEMP) method which contains an additional step to pair the parameters related to each dimension. The downrange and crossrange expressions of the scattering centers are deduced, as well as the range ambiguities, from the point of view of MMEMP method. Compared with the Fourier transform method, the numerical simulation shows that both the resolution and precision of the MMEMP method are higher than those of the Fourier method. The processing results of the real measured data for three cylinders prove the above conclusions further.
基金supported by the Program for Innovative Research Groups of the Hunan Provincial Natural Science Foundation of China(No.2019JJ10004)。
文摘With the development of wireless communication technology, the electromagnetic environment has become more and more complex. Conventional signal identification methods are difficult to accurately identify illegal devices. However, electromagnetic signals have an unavoidable device-specific characteristic unintentionally generated by a transmitter, appearing in the form of an Un Intentional Modulation(UIM), namely Radio Frequency Fingerprint(RFF). RFFs can be used to uniquely identify an emitter to match a received signal with its source. In this paper, the authors propose a novel RFF scheme to separate UIM part from the original signals from the time and frequency domain, and then utilize non-Gaussian measuring tools to extract a set of dimensionreduced secondary features. Additionally, Singular Value Reconstruction(SVR) is developed to extract UIM in the frequency spectrum. In time domain, a curve-fitting residual method is proposed to extract the UIM on the estimated instantaneous phase based on Maximum Likelihood Estimator(MLE). Various aspects of the proposed method are evaluated, including identification accuracy under various Signal-to-Noise Ratio(SNR) conditions, energy relationships between the UIM and the whole signal, and sensitivity to training set size. Compared with other methods, experimental results based on real-world signals prove that the proposed method has remarkable performance and high practicability.
文摘针对在奇异值分解(Singular Value Decomposition,SVD)中,随机噪声对各阶的贡献几乎相等,导致单一SVD降噪效果不理想的问题,提出了基于SVD和频带熵(Frequency Band Entropy,FBE)相结合的轴承故障特征提取方法。针对基于FBE的带通滤波器的阶数和带宽需经验确定的问题,提出了基于信息熵最小值原则的参数优化方法。首先,对原始振动信号在相空间重构Hankel矩阵并利用SVD进行降噪处理,采用奇异值相对变化率来确定模型的阶次;然后,对降噪后的信号进行基于FBE的带通滤波,并采用基于信息熵最小值原则的优化方法确定带通滤波器的阶数和带宽。最后,对滤波信号进行包络谱分析,提取轴承故障特征频率,并用峭度指标证明了带通滤波器的有效性。通过数值仿真和实际轴承故障数据分析,证明了该方法提取轴承故障特征频率的有效性。
