The approach of estimating the number of signals based on information theoretic criteria has good performance in the assumption of white noise, but it always leads to false estimation of the coherent sources in colore...The approach of estimating the number of signals based on information theoretic criteria has good performance in the assumption of white noise, but it always leads to false estimation of the coherent sources in colored noise. An approach combining the combined information theoretic criteria and eigen- value correction, is presented to determine number of signals. The method uses maximum likelihood （ML） and information theoretic criteria to estimate coherent signals alternately, then eliminate the inequality of the eigenvalues caused by colored noise by correcting the noise eigenvalues. The computer simulation results prove the effective performance of the method.展开更多
In array signal processing,number of signals is often a premise of estimating other parameters.For the sake of determining signal number in the condition of strong additive noise or a little sample data,an algorithm f...In array signal processing,number of signals is often a premise of estimating other parameters.For the sake of determining signal number in the condition of strong additive noise or a little sample data,an algorithm for detecting number of wideband signals is provided.First,technique of focusing is used for transforming signals into a same focusing subspace.Then the support vector machine(SVM)can be deduced by the information of eigenvalues and corresponding eigenvectors.At last,the signal number can be determined with the obtained decision function.Several simulations have been carried on verifying the proposed algorithm.展开更多
Blind source separation and estimation of the number of sources usually demand that the number of sensors should be greater than or equal to that of the sources, which, however, is very difficult to satisfy for the co...Blind source separation and estimation of the number of sources usually demand that the number of sensors should be greater than or equal to that of the sources, which, however, is very difficult to satisfy for the complex systems. A new estimating method based on power spectral density （PSD） is presented. When the relation between the number of sensors and that of sources is unknown, the PSD matrix is first obtained by the ratio of PSD of the observation signals, and then the bound of the number of correlated sources with common frequencies can be estimated by comparing every column vector of PSD matrix. The effectiveness of the proposed method is verified by theoretical analysis and experiments, and the influence of noise on the estimation of number of source is simulated.展开更多
逐次逼近寄存器模数转换器(SAR ADC)在逐次逼近的过程中,电容的切换会使参考电压上出现参考纹波噪声,该噪声会影响比较器的判定,进而输出错误的比较结果。针对该问题,基于CMOS 0.5μm工艺,设计了一种具有纹波消除技术的10 bit SAR ADC...逐次逼近寄存器模数转换器(SAR ADC)在逐次逼近的过程中,电容的切换会使参考电压上出现参考纹波噪声,该噪声会影响比较器的判定,进而输出错误的比较结果。针对该问题,基于CMOS 0.5μm工艺,设计了一种具有纹波消除技术的10 bit SAR ADC。通过增加纹波至比较器输入端的额外路径,将参考纹波满摆幅输入至比较器中;同时设计了消除数模转换器(DAC)模块,对参考纹波进行采样和输入,通过反转纹波噪声的极性,消除参考纹波对ADC输出的影响。该设计将信噪比(SNR)提高到56.75 dB,将有效位数(ENOB)提升到9.14 bit,将积分非线性(INL)从-1~5 LSB降低到-0.2~0.3 LSB,将微分非线性(DNL)从-3~4 LSB降低到-0.5~0.5 LSB。展开更多
The failure of rotating machinery applications has major time and cost effects on the industry.Condition monitoring helps to ensure safe operation and also avoids losses.The signal processing method is essential for e...The failure of rotating machinery applications has major time and cost effects on the industry.Condition monitoring helps to ensure safe operation and also avoids losses.The signal processing method is essential for ensuring both the efficiency and accuracy of the monitoring process.Variational mode decomposition(VMD)is a signal processing method which decomposes a non-stationary signal into sets of variational mode functions(VMFs)adaptively and non-recursively.The VMD method offers improved performance for the condition monitoring of rotating machinery applications.However,determining an accurate number of modes for the VMD method is still considered an open research problem.Therefore,a selection method for determining the number of modes for VMD is proposed by taking advantage of the similarities in concept between the original signal and VMF.Simulated signal and online gearbox vibration signals have been used to validate the performance of the proposed method.The statistical parameters of the signals are extracted from the original signals,VMFs and intrinsic mode functions(IMFs)and have been fed into machine learning algorithms to validate the performance of the VMD method.The results show that the features extracted from VMD are both superior and accurate for the monitoring of rotating machinery.Hence the proposed method offers a new approach for the condition monitoring of rotating machinery applications.展开更多
In order to estimate the number of coherent sources, a Hankel matrix with the size of half the number of the received arrays is constructed using snapshot data of observed vectors. And the rank of the Hankel matrix is...In order to estimate the number of coherent sources, a Hankel matrix with the size of half the number of the received arrays is constructed using snapshot data of observed vectors. And the rank of the Hankel matrix is only related with the number of signal sources, no matter the signals are uncorrelated or coherent. We can get the signal and noise eigenvalues by conducting the singular value decomposition （SVD） to the Hankel matrix, the source number can be obtained by calculating the maximum ratio of each eigenvalue pair. The complexity of the algorithm is reduced greatly as only part of the observed data （single snapshot） is used. The Monte-Carlo simulation results demonstrate the feasibility of the algorithm.展开更多
文摘The approach of estimating the number of signals based on information theoretic criteria has good performance in the assumption of white noise, but it always leads to false estimation of the coherent sources in colored noise. An approach combining the combined information theoretic criteria and eigen- value correction, is presented to determine number of signals. The method uses maximum likelihood （ML） and information theoretic criteria to estimate coherent signals alternately, then eliminate the inequality of the eigenvalues caused by colored noise by correcting the noise eigenvalues. The computer simulation results prove the effective performance of the method.
基金This work was supported by the National Natural Science Foundation of China under Grant 61501176Natural Science Foundation of Heilongjiang Province F2018025+1 种基金University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province UNPYSCT-2016017the postdoctoral scientific research developmental fund of Heilongjiang Province in 2017 LBH-Q17149.
文摘In array signal processing,number of signals is often a premise of estimating other parameters.For the sake of determining signal number in the condition of strong additive noise or a little sample data,an algorithm for detecting number of wideband signals is provided.First,technique of focusing is used for transforming signals into a same focusing subspace.Then the support vector machine(SVM)can be deduced by the information of eigenvalues and corresponding eigenvectors.At last,the signal number can be determined with the obtained decision function.Several simulations have been carried on verifying the proposed algorithm.
基金This project is supported by National Natural Science Foundation of China(No.50675076).
文摘Blind source separation and estimation of the number of sources usually demand that the number of sensors should be greater than or equal to that of the sources, which, however, is very difficult to satisfy for the complex systems. A new estimating method based on power spectral density （PSD） is presented. When the relation between the number of sensors and that of sources is unknown, the PSD matrix is first obtained by the ratio of PSD of the observation signals, and then the bound of the number of correlated sources with common frequencies can be estimated by comparing every column vector of PSD matrix. The effectiveness of the proposed method is verified by theoretical analysis and experiments, and the influence of noise on the estimation of number of source is simulated.
基金the Institute of Noise and Vibration UTM for funding the study under the Higher Institution Centre of Excellence(HICoE)Grant Scheme (No.R.K130000.7809. 4J226)Additional funding for this research also comes from the UTM Research University Grant (No.Q. K130000.2543.11H36)Fundamental Research Grant Scheme(No.R.K130000.7840.4F653)by the Ministry of Higher Education Malaysia
文摘The failure of rotating machinery applications has major time and cost effects on the industry.Condition monitoring helps to ensure safe operation and also avoids losses.The signal processing method is essential for ensuring both the efficiency and accuracy of the monitoring process.Variational mode decomposition(VMD)is a signal processing method which decomposes a non-stationary signal into sets of variational mode functions(VMFs)adaptively and non-recursively.The VMD method offers improved performance for the condition monitoring of rotating machinery applications.However,determining an accurate number of modes for the VMD method is still considered an open research problem.Therefore,a selection method for determining the number of modes for VMD is proposed by taking advantage of the similarities in concept between the original signal and VMF.Simulated signal and online gearbox vibration signals have been used to validate the performance of the proposed method.The statistical parameters of the signals are extracted from the original signals,VMFs and intrinsic mode functions(IMFs)and have been fed into machine learning algorithms to validate the performance of the VMD method.The results show that the features extracted from VMD are both superior and accurate for the monitoring of rotating machinery.Hence the proposed method offers a new approach for the condition monitoring of rotating machinery applications.
基金Project supported by the Research and Innovation Project of Education Commission of Shanghai Municipality (Grant No.11YZ14)the Science and Technology Commission of Shanghai Municipality (Grant No.08DZ2231100)the Shanghai Leading Academic Discipline Project (Grant No.S30108)
文摘In order to estimate the number of coherent sources, a Hankel matrix with the size of half the number of the received arrays is constructed using snapshot data of observed vectors. And the rank of the Hankel matrix is only related with the number of signal sources, no matter the signals are uncorrelated or coherent. We can get the signal and noise eigenvalues by conducting the singular value decomposition （SVD） to the Hankel matrix, the source number can be obtained by calculating the maximum ratio of each eigenvalue pair. The complexity of the algorithm is reduced greatly as only part of the observed data （single snapshot） is used. The Monte-Carlo simulation results demonstrate the feasibility of the algorithm.
