Passive seismic data contain large amounts of low-frequency information. To effectively extract and compensate active seismic data that lack low frequencies, we propose a multitaper spectral reconstruction method base...Passive seismic data contain large amounts of low-frequency information. To effectively extract and compensate active seismic data that lack low frequencies, we propose a multitaper spectral reconstruction method based on multiple sinusoidal tapers and derive equations for multisource and multitrace conditions. Compared to conventional cross correlation and deconvolution reconstruction methods, the proposed method can more accurately reconstruct the relative amplitude of recordings. Multidomain iterative denoising improves the SNR of retrieved data. By analyzing the spectral characteristics of passive data before and after reconstruction, we found that the data are expressed more clearly after reconstruction and denoising. To compensate for the low-frequency information in active data using passive seismic data, we match the power spectrum, supplement it, and then smooth it in the frequency domain. Finally, we use numerical simulation to verify the proposed method and conduct prestack depth migration using data after low-frequency compensation. The proposed power-matching method adds the losing low frequency information in the active seismic data using the low-frequency information of passive- source seismic data. The imaging of compensated data gives a more detailed information of deep structures.展开更多
Wideband spectrum sensing has drawn much attention in recent years since it provides more opportunities to the secondary users. However, wideband spectrum sensing requires a long time and a complex mechanism at the se...Wideband spectrum sensing has drawn much attention in recent years since it provides more opportunities to the secondary users. However, wideband spectrum sensing requires a long time and a complex mechanism at the sensing terminal. A two-stage wideband spectrum sensing scheme is considered to proceed spectrum sensing with low time consumption and high performance to tackle this predicament. In this scheme, a novel multitaper spectrum sensing （MSS） method is proposed to mitigate the poor performance of energy detection （ED） in the low signal-to-noise ratio （SNR） region. The closed-form expression of the decision threshold is derived based on the Neyman-Pearson criterion and the probability of detection in the Rayleigh fading channel is analyzed. An optimization problem is formulated to maximize the probability of detection of the proposed two-stage scheme and the average sensing time of the two-stage scheme is analyzed. Numerical results validate the efficiency of MSS and show that the two-stage spectrum sensing scheme enjoys higher performance in the low SNR region and lower time cost in the high SNR region than the single-stage scheme.展开更多
In order to attain good quality transfer function estimates from magnetotelluric field data(i.e.,smooth behavior and small uncertainties across all frequencies),we compare time series data processing with and without ...In order to attain good quality transfer function estimates from magnetotelluric field data(i.e.,smooth behavior and small uncertainties across all frequencies),we compare time series data processing with and without a multitaper approach for spectral estimation.There are several common ways to increase the reliability of the Fourier spectral estimation from experimental(noisy)data;for example to subdivide the experimental time series into segments,taper these segments(using single taper),perform the Fourier transform of the individual segments,and average the resulting spectra.展开更多
Two gain forms of spectral amplitude subtraction are derived theoretically without neglecting the correlation of speech and noise spectrum during the period of a fralne. In the implementation, the constrained gain is ...Two gain forms of spectral amplitude subtraction are derived theoretically without neglecting the correlation of speech and noise spectrum during the period of a fralne. In the implementation, the constrained gain is expressed as a function of noncausal a priori SNR （Signal-to-Noise Ratio）. Noise and noncausal a priori SNR are estimated from the multitaper spectrum of the noisy signal with algorithms modified to be suitable for the multitaper spectruln. Objective evaluations show that in case of white Gaussian noise the proposed method outperforms some methods based on LSA （Log Spectral Amplitude） in terms of MBSD （Modified Bark Spectral Distortion）, segmental SNR and overall SNR, and informal listening tests show that speech reconstructed in this way has little speech distortion and musical noise is nearly inaudible even at low SNR.展开更多
A novel data processing procedure for fMRI was suggested in this paper, by which spatial and temporal characteristics of stimuli-induced signal dynamic responses can be investigated simultaneously. First the multitape...A novel data processing procedure for fMRI was suggested in this paper, by which spatial and temporal characteristics of stimuli-induced signal dynamic responses can be investigated simultaneously. First the multitaper spectral estimation was utilized to estimate the spectrum of each voxel; the significance of the line frequency components at the interested frequency was tested to detect the task-related cortex areas; the temporal independent component analysis (tICA) was then applied to the activated voxels to obtain stimuli-induced signal dynamic responses. The advantages of this procedure are: few assumptions are needed for the cerebral hemodynamics and spatial distribution of task-related areas, problems which often appear in tICA analysis of fMRI data, such as the lack of stability, reliability and robustness, are overcome by the suggested method.展开更多
The authors aim to interpret human and AI interactions from the decision perspective.The authors decompose the interaction analysis into the following main components in the context of interactions:Individual behavior...The authors aim to interpret human and AI interactions from the decision perspective.The authors decompose the interaction analysis into the following main components in the context of interactions:Individual behavior patterns,interaction relationships,and comprehensive analysis.The authors interpret intertemporal decisions from a physical perspective and employ cross-discipline concepts and methodologies to extract the behavior characteristics of players in the empirical case study.About the individual behavior patterns,the authors find that human players prefer short-term periods to AI in decision-making.The interaction relationship analysis reveals a dynamic relationship between possible short-term co-movement and nearly counter-movement in the long run.The authors apply principal component analysis to descriptive indicators and discover a regular decision hierarchy.The main behavior pattern of players in the game of Go is switching between careful and daring behaviors.The differences in the decision hierarchies imply a discrepancy of patience between humans and AI.展开更多
基金sponsored by the Natural Science Foundation of China(No.41374115)National High Technology Research and Development Program of China(863 project)(No.2014AA06A605)
文摘Passive seismic data contain large amounts of low-frequency information. To effectively extract and compensate active seismic data that lack low frequencies, we propose a multitaper spectral reconstruction method based on multiple sinusoidal tapers and derive equations for multisource and multitrace conditions. Compared to conventional cross correlation and deconvolution reconstruction methods, the proposed method can more accurately reconstruct the relative amplitude of recordings. Multidomain iterative denoising improves the SNR of retrieved data. By analyzing the spectral characteristics of passive data before and after reconstruction, we found that the data are expressed more clearly after reconstruction and denoising. To compensate for the low-frequency information in active data using passive seismic data, we match the power spectrum, supplement it, and then smooth it in the frequency domain. Finally, we use numerical simulation to verify the proposed method and conduct prestack depth migration using data after low-frequency compensation. The proposed power-matching method adds the losing low frequency information in the active seismic data using the low-frequency information of passive- source seismic data. The imaging of compensated data gives a more detailed information of deep structures.
基金Project supported by the National Natural Science Foundation of China(Grant No.61301179)the China Postdoctoral Science Foundation(Grant No.2014M550479)the Doctorial Programs Foundation of the Ministry of Education,China(Grant No.20110203110011)
文摘Wideband spectrum sensing has drawn much attention in recent years since it provides more opportunities to the secondary users. However, wideband spectrum sensing requires a long time and a complex mechanism at the sensing terminal. A two-stage wideband spectrum sensing scheme is considered to proceed spectrum sensing with low time consumption and high performance to tackle this predicament. In this scheme, a novel multitaper spectrum sensing （MSS） method is proposed to mitigate the poor performance of energy detection （ED） in the low signal-to-noise ratio （SNR） region. The closed-form expression of the decision threshold is derived based on the Neyman-Pearson criterion and the probability of detection in the Rayleigh fading channel is analyzed. An optimization problem is formulated to maximize the probability of detection of the proposed two-stage scheme and the average sensing time of the two-stage scheme is analyzed. Numerical results validate the efficiency of MSS and show that the two-stage spectrum sensing scheme enjoys higher performance in the low SNR region and lower time cost in the high SNR region than the single-stage scheme.
文摘In order to attain good quality transfer function estimates from magnetotelluric field data(i.e.,smooth behavior and small uncertainties across all frequencies),we compare time series data processing with and without a multitaper approach for spectral estimation.There are several common ways to increase the reliability of the Fourier spectral estimation from experimental(noisy)data;for example to subdivide the experimental time series into segments,taper these segments(using single taper),perform the Fourier transform of the individual segments,and average the resulting spectra.
基金Supported by 973 Project of China (No.2002 CB312102)and the National Natural Science Foundation of China (No.60272044).
文摘Two gain forms of spectral amplitude subtraction are derived theoretically without neglecting the correlation of speech and noise spectrum during the period of a fralne. In the implementation, the constrained gain is expressed as a function of noncausal a priori SNR （Signal-to-Noise Ratio）. Noise and noncausal a priori SNR are estimated from the multitaper spectrum of the noisy signal with algorithms modified to be suitable for the multitaper spectruln. Objective evaluations show that in case of white Gaussian noise the proposed method outperforms some methods based on LSA （Log Spectral Amplitude） in terms of MBSD （Modified Bark Spectral Distortion）, segmental SNR and overall SNR, and informal listening tests show that speech reconstructed in this way has little speech distortion and musical noise is nearly inaudible even at low SNR.
基金This work was supported by the National Science Fund for Distinguished Young Scholars(Grant No.60225015)the National Natural Science Foundation of China(Grant Nos.30370416.30100054)+1 种基金the Ministry of Science and Technology of China(Grant No.2001CCA04 100)the Ministry of Education of China(TRAPOYT).
文摘A novel data processing procedure for fMRI was suggested in this paper, by which spatial and temporal characteristics of stimuli-induced signal dynamic responses can be investigated simultaneously. First the multitaper spectral estimation was utilized to estimate the spectrum of each voxel; the significance of the line frequency components at the interested frequency was tested to detect the task-related cortex areas; the temporal independent component analysis (tICA) was then applied to the activated voxels to obtain stimuli-induced signal dynamic responses. The advantages of this procedure are: few assumptions are needed for the cerebral hemodynamics and spatial distribution of task-related areas, problems which often appear in tICA analysis of fMRI data, such as the lack of stability, reliability and robustness, are overcome by the suggested method.
基金supported by the National Natural Science Foundation of China under Grant No.71988101.
文摘The authors aim to interpret human and AI interactions from the decision perspective.The authors decompose the interaction analysis into the following main components in the context of interactions:Individual behavior patterns,interaction relationships,and comprehensive analysis.The authors interpret intertemporal decisions from a physical perspective and employ cross-discipline concepts and methodologies to extract the behavior characteristics of players in the empirical case study.About the individual behavior patterns,the authors find that human players prefer short-term periods to AI in decision-making.The interaction relationship analysis reveals a dynamic relationship between possible short-term co-movement and nearly counter-movement in the long run.The authors apply principal component analysis to descriptive indicators and discover a regular decision hierarchy.The main behavior pattern of players in the game of Go is switching between careful and daring behaviors.The differences in the decision hierarchies imply a discrepancy of patience between humans and AI.
