In this paper, a fast algorithm to reconstruct the spectrum of non-uniformly sampled signals is proposed. Compared with the original algorithm, the fast algorithm has a higher computational efficiency, especially when...In this paper, a fast algorithm to reconstruct the spectrum of non-uniformly sampled signals is proposed. Compared with the original algorithm, the fast algorithm has a higher computational efficiency, especially when sampling sequence is long. Particularly, a transformation matrix is built, and the reconstructed spectrum is perfectly synthesized from the spectrum of every sampling channel. The fast algorithm has solved efficiency issues of spectrum reconstruction algorithm, and making it possible for the actual application of spectrum reconstruction algorithm in multi-channel Synthetic Aperture Radar (SAR).展开更多
The nonuniform distribution of interference spectrum in wavenumber k-space is a key issue to limit the imaging quality of Fourier-domain optical coherence tomography(FD-OCT).At present,the reconstruction quality at di...The nonuniform distribution of interference spectrum in wavenumber k-space is a key issue to limit the imaging quality of Fourier-domain optical coherence tomography(FD-OCT).At present,the reconstruction quality at different depths among a variety of processing methods in k-space is still uncertain.Using simulated and experimental interference spectra at different depths,the effects of common six processing methods including uniform resampling(linear interpolation(LI),cubic spline interpolation(CSI),time-domain interpolation(TDI),and K-B window convolution)and nonuniform sampling direct-reconstruction(Lomb periodogram(LP)and nonuniform discrete Fourier transform(NDFT))on the reconstruction quality of FD-OCT were quantitatively analyzed and compared in this work.The results obtained by using simulated and experimental data were coincident.From the experimental results,the averaged peak intensity,axial resolution,and signal-to-noise ratio(SNR)of NDFT at depth from 0.5 to 3.0mm were improved by about 1.9 dB,1.4 times,and 11.8 dB,respectively,compared to the averaged indices of all the uniform resampling methods at all depths.Similarly,the improvements of the above three indices of LP were 2.0 dB,1.4 times,and 11.7 dB,respectively.The analysis method and the results obtained in this work are helpful to select an appropriate processing method in k-space,so as to improve the imaging quality of FD-OCT.展开更多
The Unmanned Aerial Vehicle(UAV)-assisted sensing-transmission--computing integrated system plays a vital role in emergency rescue scenarios involving damaged infrastructure.To tackle the challenges of data transmissi...The Unmanned Aerial Vehicle(UAV)-assisted sensing-transmission--computing integrated system plays a vital role in emergency rescue scenarios involving damaged infrastructure.To tackle the challenges of data transmission and enable timely rescue decision-making,we propose DWT-3DRec-an efficient wireless transmission model for 3D scene reconstruction.This model leverages MobileNetV2 to extract image and pose features,which are transmitted through a Dual-path Adaptive Noise Modulation network(DANM).Moreover,we introduce the Gumbel Channel Masking Module(GCMM),which enhances feature extraction and improves reconstruction reliability by mitigating the effects of dynamic noise.At the ground receiver,the Multi-scale Deep Source-Channel Coding for 3D Reconstruction(MDS-3DRecon)framework integrates Deep Joint Source-Channel Coding(DeepJSCC)with Cityscale Neural Radiance Fields(CityNeRF).It adopts a progressive close-view training strategy and incorporates an Adaptive Fusion Module(AFM)to achieve high-precision scene reconstruction.Experimental results demonstrate that DWT-3DRec significantly outperforms the Joint Photographic Experts Group(JPEG)standard in transmitting image and pose data,achieving an average loss as low as 0.0323 and exhibiting strong robustness across a Signal-to-Noise Ratio(SNR)range of 5--20 dB.In large-scale 3D scene reconstruction tasks,MDS-3DRecon surpasses Multum in Parvo Neural Radiance Fields(Mip-NeRF)and Bungee Neural Radiance Field(BungeeNeRF),achieving a Peak Signal-to-Noise Ratio(PSNR)of 24.921 dB and a reconstruction loss of 0.188.Ablation studies further confirm the essential roles of GCMM,DANM,and AFM in enabling highfidelity 3D reconstruction.展开更多
It is well known that nonuniform sampling is usually needed in special signals processing. In this paper, a general method to reconstruct Nth-order periodically nonuniform sampled signals is presented which is also de...It is well known that nonuniform sampling is usually needed in special signals processing. In this paper, a general method to reconstruct Nth-order periodically nonuniform sampled signals is presented which is also developed to digital domain, and the designs of the digital filters and the synthesis system are given. This paper extends the studies of Kohlenberg, whose work concentrate on the periodically nonuniform sampling of second order, as well as the studies of A.J.Coulson, J.L.Brown, whose work deal with the problems of two-band signals’ Nth-order sampling and reconstruction.展开更多
A method that attempts to recover signal using generalized inverse theory is presented to obtain a good approximation of the signal in reconstruction space from its generalized samples. The proposed approaches differ ...A method that attempts to recover signal using generalized inverse theory is presented to obtain a good approximation of the signal in reconstruction space from its generalized samples. The proposed approaches differ with the assumptions on reconstruction space. If the reconstruction space satisfies one-to-one relationship between the samples and the reconstruction model, then we propose a method, which achieves consistent signal reconstruction. At the same time, when the number of samples is more than the number of reconstruction functions, the minimal-norm reconstruction signal can be obtained. Finally, it is demonstrated that the minimal-norm reconstruction can outperform consistent signal reconstruction in both theory and simulations for the problem.展开更多
In this paper,we propose a compressive sampling and reconstruction system based on the shift-invariant space associated with the fractional Gabor transform.With this system,we aim to achieve the subNyquist sampling an...In this paper,we propose a compressive sampling and reconstruction system based on the shift-invariant space associated with the fractional Gabor transform.With this system,we aim to achieve the subNyquist sampling and accurate reconstruction for chirp-like signals containing time-varying characteristics.Under the proposed scheme,we introduce the fractional Gabor transform to make a stable expansion for signals in the joint time-fractional-frequency domain.Then the compressive sampling and reconstruction system is constructed under the compressive sensing and shift-invariant space theory.We establish the reconstruction model and propose a block multiple response extension of sparse Bayesian learning algorithm to improve the reconstruction effect.The reconstruction error for the proposed system is analyzed.We show that,with considerations of noises and mismatches,the total error is bounded.The effectiveness of the proposed system is verified by numerical experiments.It is shown that our proposed system outperforms the other systems state-of-the-art.展开更多
Finite rate of innovation sampling is a novel sub-Nyquist sampling method that can reconstruct a signal from sparse sampling data.The application of this method in ultrasonic testing greatly reduces the signal samplin...Finite rate of innovation sampling is a novel sub-Nyquist sampling method that can reconstruct a signal from sparse sampling data.The application of this method in ultrasonic testing greatly reduces the signal sampling rate and the quantity of sampling data.However,the pulse number of the signal must be known beforehand for the signal reconstruction procedure.The accuracy of this prior information directly affects the accuracy of the estimated parameters of the signal and influences the assessment of flaws,leading to a lower defect detection ratio.Although the pulse number can be pre-given by theoretical analysis,the process is still unable to assess actual complex random orientation defects.Therefore,this paper proposes a new method that uses singular value decomposition(SVD) for estimating the pulse number from sparse sampling data and avoids the shortcoming of providing the pulse number in advance for signal reconstruction.When the sparse sampling data have been acquired from the ultrasonic signal,these data are transformed to discrete Fourier coefficients.A Hankel matrix is then constructed from these coefficients,and SVD is performed on the matrix.The decomposition coefficients reserve the information of the pulse number.When the decomposition coefficients generated by noise according to noise level are removed,the number of the remaining decomposition coefficients is the signal pulse number.The feasibility of the proposed method was verified through simulation experiments.The applicability was tested in ultrasonic experiments by using sample flawed pipelines.Results from simulations and real experiments demonstrated the efficiency of this method.展开更多
The microscopic characteristics of skeletal particles in rock and soil media have important effects on macroscopic mechanical properties.A mathematical procedure called spherical harmonic function analysis was here de...The microscopic characteristics of skeletal particles in rock and soil media have important effects on macroscopic mechanical properties.A mathematical procedure called spherical harmonic function analysis was here developed to characterize micromorphology of particles and determine the meso effects in a discrete manner.This method has strong mathematical properties with respect to orthogonality and rotating invariance.It was used here to characterize and reconstruct particle micromorphology in three-dimensional space.The applicability and accuracy of the method were assessed through comparison of basic geometric properties such as volume and surface area.The results show that the micromorphological characteristics of reproduced particles become more and more readily distinguishable as the reproduced order number of spherical harmonic function increases,and the error can be brought below 5%when the order number reaches 10.This level of precision is sharp enough to distinguish the characteristics of real particles.Reconstructed particles of the same size but different reconstructed orders were used to form cylindrical samples,and the stress-strain curves of these samples filled with different-order particles which have their mutual morphological features were compared using PFC3D.Results show that the higher the spherical harmonic order of reconstructed particles,the lower the initial compression modulus and the larger the strain at peak intensity.However,peak strength shows only a random relationship to spherical harmonic order.Microstructure reconstruction was here shown to be an efficient means of numerically simulating of multi-scale rock and soil media and studying the mechanical properties of soil samples.展开更多
The 3D reconstruction using deep learning-based intelligent systems can provide great help for measuring an individual’s height and shape quickly and accurately through 2D motion-blurred images.Generally,during the a...The 3D reconstruction using deep learning-based intelligent systems can provide great help for measuring an individual’s height and shape quickly and accurately through 2D motion-blurred images.Generally,during the acquisition of images in real-time,motion blur,caused by camera shaking or human motion,appears.Deep learning-based intelligent control applied in vision can help us solve the problem.To this end,we propose a 3D reconstruction method for motion-blurred images using deep learning.First,we develop a BF-WGAN algorithm that combines the bilateral filtering(BF)denoising theory with a Wasserstein generative adversarial network(WGAN)to remove motion blur.The bilateral filter denoising algorithm is used to remove the noise and to retain the details of the blurred image.Then,the blurred image and the corresponding sharp image are input into the WGAN.This algorithm distinguishes the motion-blurred image from the corresponding sharp image according to the WGAN loss and perceptual loss functions.Next,we use the deblurred images generated by the BFWGAN algorithm for 3D reconstruction.We propose a threshold optimization random sample consensus(TO-RANSAC)algorithm that can remove the wrong relationship between two views in the 3D reconstructed model relatively accurately.Compared with the traditional RANSAC algorithm,the TO-RANSAC algorithm can adjust the threshold adaptively,which improves the accuracy of the 3D reconstruction results.The experimental results show that our BF-WGAN algorithm has a better deblurring effect and higher efficiency than do other representative algorithms.In addition,the TO-RANSAC algorithm yields a calculation accuracy considerably higher than that of the traditional RANSAC algorithm.展开更多
Various electromagnetic signals are excited by the beam in the acceleration and beam-diagnostic elements of a particle accelerator.It is important to obtain time-domain waveforms of these signals with high temporal re...Various electromagnetic signals are excited by the beam in the acceleration and beam-diagnostic elements of a particle accelerator.It is important to obtain time-domain waveforms of these signals with high temporal resolution for research,such as the study of beam–cavity interactions and bunch-by-bunch parameter measurements.Therefore,a signal reconstruction algorithm with ultrahigh spatiotemporal resolution and bunch phase compensation based on equivalent sampling is proposed in this paper.Compared with traditional equivalent sampling,the use of phase compensation and setting the bunch signal zero-crossing point as the time reference can construct a more accurate reconstructed signal.The basic principles of the method,simulation,and experimental comparison are also introduced.Based on the beam test platform of the Shanghai Synchrotron Radiation Facility(SSRF)and the method of experimental verification,the factors that affect the reconstructed signal quality are analyzed and discussed,including the depth of the sampled data,quantization noise of analog-to-digital converter,beam transverse oscillation,and longitudinal oscillation.The results of the beam experiments show that under the user operation conditions of the SSRF,a beam excitation signal with an amplitude uncertainty of 2%can be reconstructed.展开更多
The relationships between soil total nitrogen(STN)and influencing factors are scale-dependent.The objective of this study was to identify the multi-scale spatial relationships of STN with selected environmental factor...The relationships between soil total nitrogen(STN)and influencing factors are scale-dependent.The objective of this study was to identify the multi-scale spatial relationships of STN with selected environmental factors(elevation,slope and topographic wetness index),intrinsic soil factors(soil bulk density,sand content,silt content,and clay content)and combined environmental factors(including the first two principal components(PC1 and PC2)of the Vis-NIR soil spectra)along three sampling transects located at the upstream,midstream and downstream of Taiyuan Basin on the Chinese Loess Plateau.We separated the multivariate data series of STN and influencing factors at each transect into six intrinsic mode functions(IMFs)and one residue by multivariate empirical mode decomposition(MEMD).Meanwhile,we obtained the predicted equations of STN based on MEMD by stepwise multiple linear regression(SMLR).The results indicated that the dominant scales of explained variance in STN were at scale 995 m for transect 1,at scales 956 and 8852 m for transect 2,and at scales 972,5716 and 12,317 m for transect 3.Multi-scale correlation coefficients between STN and influencing factors were less significant in transect 3 than in transects 1 and 2.The goodness of fit root mean square error(RMSE),normalized root mean square error(NRMSE),and coefficient of determination(R2)indicated that the prediction of STN at the sampling scale by summing all of the predicted IMFs and residue was more accurate than that by SMLR directly.Therefore,the multi-scale method of MEMD has a good potential in characterizing the multi-scale spatial relationships between STN and influencing factors at the basin landscape scale.展开更多
To improve the prediction accuracy of micro-electromechanical systems(MEMS)gyroscope random drift series,a multi-scale prediction model based on empirical mode decomposition(EMD)and support vector regression(SVR)is pr...To improve the prediction accuracy of micro-electromechanical systems(MEMS)gyroscope random drift series,a multi-scale prediction model based on empirical mode decomposition(EMD)and support vector regression(SVR)is proposed.Firstly,EMD is employed to decompose the raw drift series into a finite number of intrinsic mode functions(IMFs)with the frequency descending successively.Secondly,according to the time-frequency characteristic of each IMF,the corresponding SVR prediction model is established based on phase space reconstruction.Finally,the prediction results are obtained by adding up the prediction results of all IMFs with equal weight.The experimental results demonstrate the validity of the proposed model in random drift prediction of MEMS gyroscope.Compared with a single SVR model,the proposed model has higher prediction precision,which can provide the basis for drift error compensation of MEMS gyroscope.展开更多
In this paper, we mainly pay attention to the weighted sampling and reconstruction algorithm in lattice-invariant signal spaces. We give the reconstruction formula in lattice-invariant signal spaces, which is a genera...In this paper, we mainly pay attention to the weighted sampling and reconstruction algorithm in lattice-invariant signal spaces. We give the reconstruction formula in lattice-invariant signal spaces, which is a generalization of former results in shift-invariant signal spaces. That is, we generalize and improve Aldroubi, Groechenig and Chen＇s results, respectively. So we obtain a general reconstruction algorithm in lattice-invariant signal spaces, which the signal spaces is sufficiently large to accommodate a large number of possible models. They are maybe useful for signal processing and communication theory.展开更多
In this advanced exploration, we focus on multiple parameters estimation in bistatic Multiple-Input Multiple-Output(MIMO) radar systems, a crucial technique for target localization and imaging. Our research innovative...In this advanced exploration, we focus on multiple parameters estimation in bistatic Multiple-Input Multiple-Output(MIMO) radar systems, a crucial technique for target localization and imaging. Our research innovatively addresses the joint estimation of the Direction of Departure(DOD), Direction of Arrival(DOA), and Doppler frequency for incoherent targets. We propose a novel approach that significantly reduces computational complexity by utilizing the TemporalSpatial Nested Sampling Model(TSNSM). Our methodology begins with a multi-linear mapping mechanism to efficiently eliminate unnecessary virtual Degrees of Freedom(DOFs) and reorganize the remaining ones. We then employ the Toeplitz matrix triple iteration reconstruction method, surpassing the traditional Temporal-Spatial Smoothing Window(TSSW) approach, to mitigate the single snapshot effect and reduce computational demands. We further refine the highdimensional ESPRIT algorithm for joint estimation of DOD, DOA, and Doppler frequency, eliminating the need for additional parameter pairing. Moreover, we meticulously derive the Cramér-Rao Bound(CRB) for the TSNSM. This signal model allows for a second expansion of DOFs in time and space domains, achieving high precision in target angle and Doppler frequency estimation with low computational complexity. Our adaptable algorithm is validated through simulations and is suitable for sparse array MIMO radars with various structures, ensuring higher precision in parameter estimation with less complexity burden.展开更多
Seismic data reconstruction can provide high-density sampling and regular input data for inversion and imaging,playing a crucial role in seismic data processing.In seismic data reconstruction,a common scenario involve...Seismic data reconstruction can provide high-density sampling and regular input data for inversion and imaging,playing a crucial role in seismic data processing.In seismic data reconstruction,a common scenario involves a significant distance between the source and the first receiver,which makes it unattainable to acquire near-offset data.A new workflow for seismic data extrapolation is proposed to address this issue,which is based on a multi-scale dynamic time warping(MS-DTW)algorithm.MS-DTW can accurately calculate the time-shift between two time series and is a robust method for predicting time-offset(t-x)domain data.Using the time-shift calculated by the MS-DTW as the basic input,predict the two-way traveltime(TWT)of other traces based on the TWT of the reference trace.Perform autoregressive polynomial fitting on TWT and extrapolate TWT based on the fitted polynomial coefficients.Extract amplitude information from the TWT curve,fit the amplitude curve,and extrapolate the amplitude using polynomial coefficients.The proposed workflow does not necessitate data conversion to other domains and does not require prior knowledge of underground geological information.It applies to both isotropic and anisotropic media.The effectiveness of the workflow was verified through synthetic data and field data.The results show that compared with the method of predictive painting based on local slope,this approach can accurately predict missing near-offset seismic signals and demonstrates good robustness to noise.展开更多
Video emotion recognition is widely used due to its alignment with the temporal characteristics of human emotional expression,but existingmodels have significant shortcomings.On the one hand,Transformermultihead self-...Video emotion recognition is widely used due to its alignment with the temporal characteristics of human emotional expression,but existingmodels have significant shortcomings.On the one hand,Transformermultihead self-attention modeling of global temporal dependency has problems of high computational overhead and feature similarity.On the other hand,fixed-size convolution kernels are often used,which have weak perception ability for emotional regions of different scales.Therefore,this paper proposes a video emotion recognition model that combines multi-scale region-aware convolution with temporal interactive sampling.In terms of space,multi-branch large-kernel stripe convolution is used to perceive emotional region features at different scales,and attention weights are generated for each scale feature.In terms of time,multi-layer odd-even down-sampling is performed on the time series,and oddeven sub-sequence interaction is performed to solve the problem of feature similarity,while reducing computational costs due to the linear relationship between sampling and convolution overhead.This paper was tested on CMU-MOSI,CMU-MOSEI,and Hume Reaction.The Acc-2 reached 83.4%,85.2%,and 81.2%,respectively.The experimental results show that the model can significantly improve the accuracy of emotion recognition.展开更多
文摘In this paper, a fast algorithm to reconstruct the spectrum of non-uniformly sampled signals is proposed. Compared with the original algorithm, the fast algorithm has a higher computational efficiency, especially when sampling sequence is long. Particularly, a transformation matrix is built, and the reconstructed spectrum is perfectly synthesized from the spectrum of every sampling channel. The fast algorithm has solved efficiency issues of spectrum reconstruction algorithm, and making it possible for the actual application of spectrum reconstruction algorithm in multi-channel Synthetic Aperture Radar (SAR).
基金supported by the National Natural Science Foundation of China(Grant Nos.61575205 and 62175022)Sichuan Natural Science Foundation(2022NSFSC0803)Sichuan Science and Technology Program(2021JDRC0035).
文摘The nonuniform distribution of interference spectrum in wavenumber k-space is a key issue to limit the imaging quality of Fourier-domain optical coherence tomography(FD-OCT).At present,the reconstruction quality at different depths among a variety of processing methods in k-space is still uncertain.Using simulated and experimental interference spectra at different depths,the effects of common six processing methods including uniform resampling(linear interpolation(LI),cubic spline interpolation(CSI),time-domain interpolation(TDI),and K-B window convolution)and nonuniform sampling direct-reconstruction(Lomb periodogram(LP)and nonuniform discrete Fourier transform(NDFT))on the reconstruction quality of FD-OCT were quantitatively analyzed and compared in this work.The results obtained by using simulated and experimental data were coincident.From the experimental results,the averaged peak intensity,axial resolution,and signal-to-noise ratio(SNR)of NDFT at depth from 0.5 to 3.0mm were improved by about 1.9 dB,1.4 times,and 11.8 dB,respectively,compared to the averaged indices of all the uniform resampling methods at all depths.Similarly,the improvements of the above three indices of LP were 2.0 dB,1.4 times,and 11.7 dB,respectively.The analysis method and the results obtained in this work are helpful to select an appropriate processing method in k-space,so as to improve the imaging quality of FD-OCT.
基金supported by the National Key Research and Development Program of China(2022YFB4500800)the Applied Basic Research Program Project of Liaoning Province(2023JH2/101300192)+2 种基金the National Natural Science Foundation of China(62032013,62072094)the Fundamental Research Funds for the Central Universities(N2416006,N2416016)Shenyang Science and Technology Plan Project(ZX20250050).
文摘The Unmanned Aerial Vehicle(UAV)-assisted sensing-transmission--computing integrated system plays a vital role in emergency rescue scenarios involving damaged infrastructure.To tackle the challenges of data transmission and enable timely rescue decision-making,we propose DWT-3DRec-an efficient wireless transmission model for 3D scene reconstruction.This model leverages MobileNetV2 to extract image and pose features,which are transmitted through a Dual-path Adaptive Noise Modulation network(DANM).Moreover,we introduce the Gumbel Channel Masking Module(GCMM),which enhances feature extraction and improves reconstruction reliability by mitigating the effects of dynamic noise.At the ground receiver,the Multi-scale Deep Source-Channel Coding for 3D Reconstruction(MDS-3DRecon)framework integrates Deep Joint Source-Channel Coding(DeepJSCC)with Cityscale Neural Radiance Fields(CityNeRF).It adopts a progressive close-view training strategy and incorporates an Adaptive Fusion Module(AFM)to achieve high-precision scene reconstruction.Experimental results demonstrate that DWT-3DRec significantly outperforms the Joint Photographic Experts Group(JPEG)standard in transmitting image and pose data,achieving an average loss as low as 0.0323 and exhibiting strong robustness across a Signal-to-Noise Ratio(SNR)range of 5--20 dB.In large-scale 3D scene reconstruction tasks,MDS-3DRecon surpasses Multum in Parvo Neural Radiance Fields(Mip-NeRF)and Bungee Neural Radiance Field(BungeeNeRF),achieving a Peak Signal-to-Noise Ratio(PSNR)of 24.921 dB and a reconstruction loss of 0.188.Ablation studies further confirm the essential roles of GCMM,DANM,and AFM in enabling highfidelity 3D reconstruction.
文摘It is well known that nonuniform sampling is usually needed in special signals processing. In this paper, a general method to reconstruct Nth-order periodically nonuniform sampled signals is presented which is also developed to digital domain, and the designs of the digital filters and the synthesis system are given. This paper extends the studies of Kohlenberg, whose work concentrate on the periodically nonuniform sampling of second order, as well as the studies of A.J.Coulson, J.L.Brown, whose work deal with the problems of two-band signals’ Nth-order sampling and reconstruction.
文摘A method that attempts to recover signal using generalized inverse theory is presented to obtain a good approximation of the signal in reconstruction space from its generalized samples. The proposed approaches differ with the assumptions on reconstruction space. If the reconstruction space satisfies one-to-one relationship between the samples and the reconstruction model, then we propose a method, which achieves consistent signal reconstruction. At the same time, when the number of samples is more than the number of reconstruction functions, the minimal-norm reconstruction signal can be obtained. Finally, it is demonstrated that the minimal-norm reconstruction can outperform consistent signal reconstruction in both theory and simulations for the problem.
基金supported by National Natural Science Foundation of China(Grant No.61501493)。
文摘In this paper,we propose a compressive sampling and reconstruction system based on the shift-invariant space associated with the fractional Gabor transform.With this system,we aim to achieve the subNyquist sampling and accurate reconstruction for chirp-like signals containing time-varying characteristics.Under the proposed scheme,we introduce the fractional Gabor transform to make a stable expansion for signals in the joint time-fractional-frequency domain.Then the compressive sampling and reconstruction system is constructed under the compressive sensing and shift-invariant space theory.We establish the reconstruction model and propose a block multiple response extension of sparse Bayesian learning algorithm to improve the reconstruction effect.The reconstruction error for the proposed system is analyzed.We show that,with considerations of noises and mismatches,the total error is bounded.The effectiveness of the proposed system is verified by numerical experiments.It is shown that our proposed system outperforms the other systems state-of-the-art.
基金Supported by the National Natural Science Foundation of China(Grant No.51375217)
文摘Finite rate of innovation sampling is a novel sub-Nyquist sampling method that can reconstruct a signal from sparse sampling data.The application of this method in ultrasonic testing greatly reduces the signal sampling rate and the quantity of sampling data.However,the pulse number of the signal must be known beforehand for the signal reconstruction procedure.The accuracy of this prior information directly affects the accuracy of the estimated parameters of the signal and influences the assessment of flaws,leading to a lower defect detection ratio.Although the pulse number can be pre-given by theoretical analysis,the process is still unable to assess actual complex random orientation defects.Therefore,this paper proposes a new method that uses singular value decomposition(SVD) for estimating the pulse number from sparse sampling data and avoids the shortcoming of providing the pulse number in advance for signal reconstruction.When the sparse sampling data have been acquired from the ultrasonic signal,these data are transformed to discrete Fourier coefficients.A Hankel matrix is then constructed from these coefficients,and SVD is performed on the matrix.The decomposition coefficients reserve the information of the pulse number.When the decomposition coefficients generated by noise according to noise level are removed,the number of the remaining decomposition coefficients is the signal pulse number.The feasibility of the proposed method was verified through simulation experiments.The applicability was tested in ultrasonic experiments by using sample flawed pipelines.Results from simulations and real experiments demonstrated the efficiency of this method.
基金Project(2015CB057903)supported by the National Basic Research Program of ChinaProjects(51679071,51309089)supported by the National Natural Science Foundation of China+2 种基金Project(BK20130846)supported by the Natural Science Foundation of Jiangsu Province,ChinaProject(2013BAB06B00)supported by the National Key Technology R&D Program,ChinaProject(2015B06014)supported by the Fundamental Research Funds for the Central Universities,China
文摘The microscopic characteristics of skeletal particles in rock and soil media have important effects on macroscopic mechanical properties.A mathematical procedure called spherical harmonic function analysis was here developed to characterize micromorphology of particles and determine the meso effects in a discrete manner.This method has strong mathematical properties with respect to orthogonality and rotating invariance.It was used here to characterize and reconstruct particle micromorphology in three-dimensional space.The applicability and accuracy of the method were assessed through comparison of basic geometric properties such as volume and surface area.The results show that the micromorphological characteristics of reproduced particles become more and more readily distinguishable as the reproduced order number of spherical harmonic function increases,and the error can be brought below 5%when the order number reaches 10.This level of precision is sharp enough to distinguish the characteristics of real particles.Reconstructed particles of the same size but different reconstructed orders were used to form cylindrical samples,and the stress-strain curves of these samples filled with different-order particles which have their mutual morphological features were compared using PFC3D.Results show that the higher the spherical harmonic order of reconstructed particles,the lower the initial compression modulus and the larger the strain at peak intensity.However,peak strength shows only a random relationship to spherical harmonic order.Microstructure reconstruction was here shown to be an efficient means of numerically simulating of multi-scale rock and soil media and studying the mechanical properties of soil samples.
基金the National Natural Science Foundation of China under Grant 61902311in part by the Japan Society for the Promotion of Science(JSPS)Grants-in-Aid for Scientific Research(KAKENHI)under Grant JP18K18044.
文摘The 3D reconstruction using deep learning-based intelligent systems can provide great help for measuring an individual’s height and shape quickly and accurately through 2D motion-blurred images.Generally,during the acquisition of images in real-time,motion blur,caused by camera shaking or human motion,appears.Deep learning-based intelligent control applied in vision can help us solve the problem.To this end,we propose a 3D reconstruction method for motion-blurred images using deep learning.First,we develop a BF-WGAN algorithm that combines the bilateral filtering(BF)denoising theory with a Wasserstein generative adversarial network(WGAN)to remove motion blur.The bilateral filter denoising algorithm is used to remove the noise and to retain the details of the blurred image.Then,the blurred image and the corresponding sharp image are input into the WGAN.This algorithm distinguishes the motion-blurred image from the corresponding sharp image according to the WGAN loss and perceptual loss functions.Next,we use the deblurred images generated by the BFWGAN algorithm for 3D reconstruction.We propose a threshold optimization random sample consensus(TO-RANSAC)algorithm that can remove the wrong relationship between two views in the 3D reconstructed model relatively accurately.Compared with the traditional RANSAC algorithm,the TO-RANSAC algorithm can adjust the threshold adaptively,which improves the accuracy of the 3D reconstruction results.The experimental results show that our BF-WGAN algorithm has a better deblurring effect and higher efficiency than do other representative algorithms.In addition,the TO-RANSAC algorithm yields a calculation accuracy considerably higher than that of the traditional RANSAC algorithm.
基金supported by the National Key R&D Program of China(No.2022YFA1602201)the international partnership program of the Chinese Academy of Sciences(No.211134KYSB20200057).
文摘Various electromagnetic signals are excited by the beam in the acceleration and beam-diagnostic elements of a particle accelerator.It is important to obtain time-domain waveforms of these signals with high temporal resolution for research,such as the study of beam–cavity interactions and bunch-by-bunch parameter measurements.Therefore,a signal reconstruction algorithm with ultrahigh spatiotemporal resolution and bunch phase compensation based on equivalent sampling is proposed in this paper.Compared with traditional equivalent sampling,the use of phase compensation and setting the bunch signal zero-crossing point as the time reference can construct a more accurate reconstructed signal.The basic principles of the method,simulation,and experimental comparison are also introduced.Based on the beam test platform of the Shanghai Synchrotron Radiation Facility(SSRF)and the method of experimental verification,the factors that affect the reconstructed signal quality are analyzed and discussed,including the depth of the sampled data,quantization noise of analog-to-digital converter,beam transverse oscillation,and longitudinal oscillation.The results of the beam experiments show that under the user operation conditions of the SSRF,a beam excitation signal with an amplitude uncertainty of 2%can be reconstructed.
基金financially supported by the Research Project of Shanxi Scholarship Council of China (2017– 075)the Natural Science foundation for Young Scientists of Shanxi Province (201801D221103)the Innovation Grant of Shanxi Agricultural University (2017ZZ07)
文摘The relationships between soil total nitrogen(STN)and influencing factors are scale-dependent.The objective of this study was to identify the multi-scale spatial relationships of STN with selected environmental factors(elevation,slope and topographic wetness index),intrinsic soil factors(soil bulk density,sand content,silt content,and clay content)and combined environmental factors(including the first two principal components(PC1 and PC2)of the Vis-NIR soil spectra)along three sampling transects located at the upstream,midstream and downstream of Taiyuan Basin on the Chinese Loess Plateau.We separated the multivariate data series of STN and influencing factors at each transect into six intrinsic mode functions(IMFs)and one residue by multivariate empirical mode decomposition(MEMD).Meanwhile,we obtained the predicted equations of STN based on MEMD by stepwise multiple linear regression(SMLR).The results indicated that the dominant scales of explained variance in STN were at scale 995 m for transect 1,at scales 956 and 8852 m for transect 2,and at scales 972,5716 and 12,317 m for transect 3.Multi-scale correlation coefficients between STN and influencing factors were less significant in transect 3 than in transects 1 and 2.The goodness of fit root mean square error(RMSE),normalized root mean square error(NRMSE),and coefficient of determination(R2)indicated that the prediction of STN at the sampling scale by summing all of the predicted IMFs and residue was more accurate than that by SMLR directly.Therefore,the multi-scale method of MEMD has a good potential in characterizing the multi-scale spatial relationships between STN and influencing factors at the basin landscape scale.
基金National Natural Science Foundation of China(No.61427810)。
文摘To improve the prediction accuracy of micro-electromechanical systems(MEMS)gyroscope random drift series,a multi-scale prediction model based on empirical mode decomposition(EMD)and support vector regression(SVR)is proposed.Firstly,EMD is employed to decompose the raw drift series into a finite number of intrinsic mode functions(IMFs)with the frequency descending successively.Secondly,according to the time-frequency characteristic of each IMF,the corresponding SVR prediction model is established based on phase space reconstruction.Finally,the prediction results are obtained by adding up the prediction results of all IMFs with equal weight.The experimental results demonstrate the validity of the proposed model in random drift prediction of MEMS gyroscope.Compared with a single SVR model,the proposed model has higher prediction precision,which can provide the basis for drift error compensation of MEMS gyroscope.
文摘In this paper, we mainly pay attention to the weighted sampling and reconstruction algorithm in lattice-invariant signal spaces. We give the reconstruction formula in lattice-invariant signal spaces, which is a generalization of former results in shift-invariant signal spaces. That is, we generalize and improve Aldroubi, Groechenig and Chen＇s results, respectively. So we obtain a general reconstruction algorithm in lattice-invariant signal spaces, which the signal spaces is sufficiently large to accommodate a large number of possible models. They are maybe useful for signal processing and communication theory.
基金supported in part by the National Natural Science Foundation of China(No.62071476)in part by China Postdoctoral Science Foundation(No.2022M723879)in part by the Science and Technology Innovation Program of Hunan Province,China(No.2021RC3080)。
文摘In this advanced exploration, we focus on multiple parameters estimation in bistatic Multiple-Input Multiple-Output(MIMO) radar systems, a crucial technique for target localization and imaging. Our research innovatively addresses the joint estimation of the Direction of Departure(DOD), Direction of Arrival(DOA), and Doppler frequency for incoherent targets. We propose a novel approach that significantly reduces computational complexity by utilizing the TemporalSpatial Nested Sampling Model(TSNSM). Our methodology begins with a multi-linear mapping mechanism to efficiently eliminate unnecessary virtual Degrees of Freedom(DOFs) and reorganize the remaining ones. We then employ the Toeplitz matrix triple iteration reconstruction method, surpassing the traditional Temporal-Spatial Smoothing Window(TSSW) approach, to mitigate the single snapshot effect and reduce computational demands. We further refine the highdimensional ESPRIT algorithm for joint estimation of DOD, DOA, and Doppler frequency, eliminating the need for additional parameter pairing. Moreover, we meticulously derive the Cramér-Rao Bound(CRB) for the TSNSM. This signal model allows for a second expansion of DOFs in time and space domains, achieving high precision in target angle and Doppler frequency estimation with low computational complexity. Our adaptable algorithm is validated through simulations and is suitable for sparse array MIMO radars with various structures, ensuring higher precision in parameter estimation with less complexity burden.
基金the National Natural Science Foundation of China(42374133)the Beijing Nova Program(2022056)for their funding of this research。
文摘Seismic data reconstruction can provide high-density sampling and regular input data for inversion and imaging,playing a crucial role in seismic data processing.In seismic data reconstruction,a common scenario involves a significant distance between the source and the first receiver,which makes it unattainable to acquire near-offset data.A new workflow for seismic data extrapolation is proposed to address this issue,which is based on a multi-scale dynamic time warping(MS-DTW)algorithm.MS-DTW can accurately calculate the time-shift between two time series and is a robust method for predicting time-offset(t-x)domain data.Using the time-shift calculated by the MS-DTW as the basic input,predict the two-way traveltime(TWT)of other traces based on the TWT of the reference trace.Perform autoregressive polynomial fitting on TWT and extrapolate TWT based on the fitted polynomial coefficients.Extract amplitude information from the TWT curve,fit the amplitude curve,and extrapolate the amplitude using polynomial coefficients.The proposed workflow does not necessitate data conversion to other domains and does not require prior knowledge of underground geological information.It applies to both isotropic and anisotropic media.The effectiveness of the workflow was verified through synthetic data and field data.The results show that compared with the method of predictive painting based on local slope,this approach can accurately predict missing near-offset seismic signals and demonstrates good robustness to noise.
基金supported,in part,by the National Nature Science Foundation of China under Grant 62272236,62376128in part,by the Natural Science Foundation of Jiangsu Province under Grant BK20201136,BK20191401.
文摘Video emotion recognition is widely used due to its alignment with the temporal characteristics of human emotional expression,but existingmodels have significant shortcomings.On the one hand,Transformermultihead self-attention modeling of global temporal dependency has problems of high computational overhead and feature similarity.On the other hand,fixed-size convolution kernels are often used,which have weak perception ability for emotional regions of different scales.Therefore,this paper proposes a video emotion recognition model that combines multi-scale region-aware convolution with temporal interactive sampling.In terms of space,multi-branch large-kernel stripe convolution is used to perceive emotional region features at different scales,and attention weights are generated for each scale feature.In terms of time,multi-layer odd-even down-sampling is performed on the time series,and oddeven sub-sequence interaction is performed to solve the problem of feature similarity,while reducing computational costs due to the linear relationship between sampling and convolution overhead.This paper was tested on CMU-MOSI,CMU-MOSEI,and Hume Reaction.The Acc-2 reached 83.4%,85.2%,and 81.2%,respectively.The experimental results show that the model can significantly improve the accuracy of emotion recognition.
