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New SAR Imaging Algorithm via the Optimal Time-Frequency Transform Domain 被引量:1
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作者 Zhenli Wang Qun Wang +2 位作者 Jiayin Liu Zheng Liang Jingsong Xu 《Computers, Materials & Continua》 SCIE EI 2020年第12期2351-2363,共13页
To address the low-resolution imaging problem in relation to traditional Range Doppler(RD)algorithm,this paper intends to propose a new algorithm based on Fractional Fourier Transform(FrFT),which proves highly advanta... To address the low-resolution imaging problem in relation to traditional Range Doppler(RD)algorithm,this paper intends to propose a new algorithm based on Fractional Fourier Transform(FrFT),which proves highly advantageous in the acquisition of high-resolution Synthetic Aperture Radar(SAR)images.The expression of the optimal order of SAR range signals using FrFT is deduced in detail,and the corresponding expression of the azimuth signal is also given.Theoretical analysis shows that,the optimal order in range(azimuth)direction,which turns out to be very unique,depends on the known imaging parameters of SAR,therefore the engineering practicability of FrFT-RD algorithm can be greatly improved without the need of order iteration.The FrFT-RD algorithm is established after an analysis of the optimal time-frequency transform.Experimental results demonstrate that,compared with traditional RD algorithm,the main-lobe width of the peak-point target of FrFT-RD algorithm is narrow in both range and azimuth directions.While the peak amplitude of the first side-lobe is reduced significantly,those of other side-lobes also drop in various degrees.In this way,the imaging resolution of range and azimuth can be increased considerably. 展开更多
关键词 Fourier transform fractional Fourier transform synthetic aperture radar range doppler algorithm
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The W transform and its improved methods for time-frequency analysis of seismic data
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作者 WANG Yanghua RAO Ying ZHAO Zhencong 《Petroleum Exploration and Development》 SCIE 2024年第4期886-896,共11页
The conventional linear time-frequency analysis method cannot achieve high resolution and energy focusing in the time and frequency dimensions at the same time,especially in the low frequency region.In order to improv... The conventional linear time-frequency analysis method cannot achieve high resolution and energy focusing in the time and frequency dimensions at the same time,especially in the low frequency region.In order to improve the resolution of the linear time-frequency analysis method in the low-frequency region,we have proposed a W transform method,in which the instantaneous frequency is introduced as a parameter into the linear transformation,and the analysis time window is constructed which matches the instantaneous frequency of the seismic data.In this paper,the W transform method is compared with the Wigner-Ville distribution(WVD),a typical nonlinear time-frequency analysis method.The WVD method that shows the energy distribution in the time-frequency domain clearly indicates the gravitational center of time and the gravitational center of frequency of a wavelet,while the time-frequency spectrum of the W transform also has a clear gravitational center of energy focusing,because the instantaneous frequency corresponding to any time position is introduced as the transformation parameter.Therefore,the W transform can be benchmarked directly by the WVD method.We summarize the development of the W transform and three improved methods in recent years,and elaborate on the evolution of the standard W transform,the chirp-modulated W transform,the fractional-order W transform,and the linear canonical W transform.Through three application examples of W transform in fluvial sand body identification and reservoir prediction,it is verified that W transform can improve the resolution and energy focusing of time-frequency spectra. 展开更多
关键词 time-frequency analysis W transform Wigner-Ville distribution matching pursuit energy focusing RESOLUTION
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Ground roll attenuation using a time-frequency dependent polarization filter based on the S transform 被引量:6
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作者 谭玉阳 何川 +1 位作者 王艳冬 赵忠 《Applied Geophysics》 SCIE CSCD 2013年第3期279-294,358,共17页
The ground roll and body wave usually show significant differences in arrival time, frequency content, and polarization characteristics, and conventional polarization filters that operate in either the time or frequen... The ground roll and body wave usually show significant differences in arrival time, frequency content, and polarization characteristics, and conventional polarization filters that operate in either the time or frequency domain cannot consider all these elements. Therefore, we have developed a time-frequency dependent polarization filter based on the S transform to attenuate the ground roll in seismic records. Our approach adopts the complex coefficients of the S transform of the multi-component seismic data to estimate the local polarization attributes and utilizes the estimated attributes to construct the filter function. In this study, we select the S transform to design this polarization filter because its scalable window length can ensure the same number of cycles of a Fourier sinusoid, thereby rendering more precise estimation of local polarization attributes. The results of applying our approach in synthetic and real data examples demonstrate that the proposed polarization filter can effectively attenuate the ground roll and successfully preserve the body wave. 展开更多
关键词 Ground roll S transform spectral matrix polarization attributes polarization filter
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Effects of Gabor transform parameters on signa time-frequency resolution
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作者 尹陈 贺振华 黄德济 《Applied Geophysics》 SCIE CSCD 2006年第3期169-173,共5页
In this paper, it is described that the time-frequency resolution of geophysical signals is affected by the time window function attenuation coefficient and sampling interval and how such effects are eliminated effect... In this paper, it is described that the time-frequency resolution of geophysical signals is affected by the time window function attenuation coefficient and sampling interval and how such effects are eliminated effectively. Improving the signal resolution is the key to signal time-frequency analysis processing and has wide use in geophysical data processing and extraction of attribute parameters. In this paper, authors research the effects of the attenuation coefficient choice of the Gabor transform window function and sampling interval on signal resolution. Unsuitable parameters not only decrease the signal resolution on the frequency spectrum but also miss the signals. It is essential to first give the optimum window and range of parameters through time-frequency analysis simulation using the Gabor transform. In the paper, the suggestions about the range and choice of the optimum sampling interval and processing methods of general seismic signals are given. 展开更多
关键词 Gabor transform time-frequency analysis RESOLUTION Gaussion window sampling interval.
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Working condition recognition of sucker rod pumping system based on 4-segment time-frequency signature matrix and deep learning
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作者 Yun-Peng He Hai-Bo Cheng +4 位作者 Peng Zeng Chuan-Zhi Zang Qing-Wei Dong Guang-Xi Wan Xiao-Ting Dong 《Petroleum Science》 SCIE EI CAS CSCD 2024年第1期641-653,共13页
High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an eff... High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS. 展开更多
关键词 Sucker-rod pumping system Dynamometer card Working condition recognition Deep learning time-frequency signature time-frequency signature matrix
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Quasi-LFM radar waveform recognition based on fractional Fourier transform and time-frequency analysis 被引量:3
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作者 XIE Cunxiang ZHANG Limin ZHONG Zhaogen 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2021年第5期1130-1142,共13页
Recent advances in electronics have increased the complexity of radar signal modulation.The quasi-linear frequency modulation(quasi-LFM)radar waveforms(LFM,Frank code,P1−P4 code)have similar time-frequency distributio... Recent advances in electronics have increased the complexity of radar signal modulation.The quasi-linear frequency modulation(quasi-LFM)radar waveforms(LFM,Frank code,P1−P4 code)have similar time-frequency distributions,and it is difficult to identify such signals using traditional time-frequency analysis methods.To solve this problem,this paper proposes an algorithm for automatic recognition of quasi-LFM radar waveforms based on fractional Fourier transform and time-frequency analysis.First of all,fractional Fourier transform and the Wigner-Ville distribution(WVD)are used to determine the number of main ridgelines and the tilt angle of the target component in WVD.Next,the standard deviation of the target component's width in the signal's WVD is calculated.Finally,an assembled classifier using neural network is built to recognize different waveforms by automatically combining the three features.Simulation results show that the overall recognition rate of the proposed algorithm reaches 94.17%under 0 dB.When the training data set and the test data set are mixed with noise,the recognition rate reaches 89.93%.The best recognition accuracy is achieved when the size of the training set is taken as 400.The algorithm complexity can meet the requirements of real-time recognition. 展开更多
关键词 quasi-linear frequency modulation(quasi-LFM)radar waveform time-frequency distribution fractional Fourier transform(FrFT) assembled classifier
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Localization method of subsynchronous oscillation source based on high-resolution time-frequency distribution image and CNN
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作者 Hui Liu Yundan Cheng +3 位作者 Yanhui Xu Guanqun Sun Rusi Chen Xiaodong Yu 《Global Energy Interconnection》 EI CSCD 2024年第1期1-13,共13页
The penetration of new energy sources such as wind power is increasing,which consequently increases the occurrence rate of subsynchronous oscillation events.However,existing subsynchronous oscillation source-identific... The penetration of new energy sources such as wind power is increasing,which consequently increases the occurrence rate of subsynchronous oscillation events.However,existing subsynchronous oscillation source-identification methods primarily analyze fixed-mode oscillations and rarely consider time-varying features,such as frequency drift,caused by the random volatility of wind farms when oscillations occur.This paper proposes a subsynchronous oscillation sourcelocalization method that involves an enhanced short-time Fourier transform and a convolutional neural network(CNN).First,an enhanced STFT is performed to secure high-resolution time-frequency distribution(TFD)images from the measured data of the generation unit ports.Next,these TFD images are amalgamated to form a subsynchronous oscillation feature map that serves as input to the CNN to train the localization model.Ultimately,the trained CNN model realizes the online localization of subsynchronous oscillation sources.The effectiveness and accuracy of the proposed method are validated via multimachine system models simulating forced and natural oscillation events using the Power Systems Computer Aided Design platform.Test results show that the proposed method can localize subsynchronous oscillation sources online while considering unpredictable fluctuations in wind farms,thus providing a foundation for oscillation suppression in practical engineering scenarios. 展开更多
关键词 Subsynchronous oscillation source localization Synchronous squeezing transform Enhanced short-time Fourier transform Convolutional neural networks
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LOCALIZED RADON-WIGNER TRANSFORM AND GENERALIZED-MARGINAL TIME-FREQUENCY DISTRIBUTIONS
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作者 Xu Chunguang Gao Xinbo Xie Weixin (School of Electronic Engineering, Xidian University, Xi’an, 71007l) 《Journal of Electronics(China)》 2000年第2期116-122,共7页
This paper introduces the localized Radon transform (LRT) into time-frequency distributions and presents the localized Radon-Wigner transform (LRWT). The definition of LRWT and a fast algorithm is derived, the propert... This paper introduces the localized Radon transform (LRT) into time-frequency distributions and presents the localized Radon-Wigner transform (LRWT). The definition of LRWT and a fast algorithm is derived, the properties of LRWT and its relationship with Radon-Wigner transform, Wigner distribution (WD), ambiguity function (AF), and generalized-marginal time-frequency distributions are analyzed. 展开更多
关键词 time-frequency DISTRIBUTIONS LOCALIZED Radon-Wigner transform Generalized-marginal time-frequency DISTRIBUTIONS
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High-resolution seismic inversion method based on joint data-driven in the time-frequency domain
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作者 Yu Liu Sisi Miao 《Artificial Intelligence in Geosciences》 2024年第1期189-201,共13页
Seismic inversion can be divided into time-domain inversion and frequency-domain inversion based on different transform domains.Time-domain inversion has stronger stability and noise resistance compared to frequencydo... Seismic inversion can be divided into time-domain inversion and frequency-domain inversion based on different transform domains.Time-domain inversion has stronger stability and noise resistance compared to frequencydomain inversion.Frequency domain inversion has stronger ability to identify small-scale bodies and higher inversion resolution.Therefore,the research on the joint inversion method in the time-frequency domain is of great significance for improving the inversion resolution,stability,and noise resistance.The introduction of prior information constraints can effectively reduce ambiguity in the inversion process.However,the existing modeldriven time-frequency joint inversion assumes a specific prior distribution of the reservoir.These methods do not consider the original features of the data and are difficult to describe the relationship between time-domain features and frequency-domain features.Therefore,this paper proposes a high-resolution seismic inversion method based on joint data-driven in the time-frequency domain.The method is based on the impedance and reflectivity samples from logging,using joint dictionary learning to obtain adaptive feature information of the reservoir,and using sparse coefficients to capture the intrinsic relationship between impedance and reflectivity.The optimization result of the inversion is achieved through the regularization term of the joint dictionary sparse representation.We have finally achieved an inversion method that combines constraints on time-domain features and frequency features.By testing the model data and field data,the method has higher resolution in the inversion results and good noise resistance. 展开更多
关键词 time-frequency domain Joint dictionary learning DATA-DRIVEN High-resolution inversion
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Stage IV malignant transformation of mature cystic teratoma palliatively treated with concurrent chemoradiotherapy:A case report
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作者 Saori Kondo Takashi Suzuki +4 位作者 Kanato Yoshiike Sakura Yamanaka Kenta Sonehara Hiroshi Nabeshima Osamu Oguchi 《World Journal of Clinical Cases》 SCIE 2025年第1期56-61,共6页
BACKGROUND Malignant transformation(MT)of mature cystic teratoma(MCT)has a poor prognosis,especially in advanced cases.Concurrent chemoradiotherapy(CCRT)has an inhibitory effect on MT.CASE SUMMARY Herein,we present a ... BACKGROUND Malignant transformation(MT)of mature cystic teratoma(MCT)has a poor prognosis,especially in advanced cases.Concurrent chemoradiotherapy(CCRT)has an inhibitory effect on MT.CASE SUMMARY Herein,we present a case in which CCRT had a reduction effect preoperatively.A 73-year-old woman with pyelonephritis was referred to our hospital.Computed tomography revealed right hydronephrosis and a 6-cm pelvic mass.Endoscopic ultrasound-guided fine-needle biopsy(EUS-FNB)revealed squamous cell carci-noma.The patient was diagnosed with MT of MCT.Due to her poor general con-dition and renal malfunction,we selected CCRT,expecting fewer adverse effects.After CCRT,her performance status improved,and the tumor size was reduced;surgery was performed.Five months postoperatively,the patient developed dis-semination and lymph node metastases.Palliative chemotherapy was ineffective.She died 18 months after treatment initiation.CONCLUSION EUS-FNB was useful in the diagnosis of MT of MCT;CCRT suppressed the disea-se and improved quality of life. 展开更多
关键词 Mature cystic teratoma Malignant transformation Squamous cell carcinoma Concurrent chemoradiotherapy Endoscopic ultrasound-guided fine-needle biopsy Case report
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Transforming growth factor-beta 1 enhances discharge activity of cortical neurons
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作者 Zhihui Ren Tian Li +5 位作者 Xueer Liu Zelin Zhang Xiaoxuan Chen Weiqiang Chen Kangsheng Li Jiangtao Sheng 《Neural Regeneration Research》 SCIE CAS 2025年第2期548-556,共9页
Transforming growth factor-beta 1(TGF-β1)has been extensively studied for its pleiotropic effects on central nervous system diseases.The neuroprotective or neurotoxic effects of TGF-β1 in specific brain areas may de... Transforming growth factor-beta 1(TGF-β1)has been extensively studied for its pleiotropic effects on central nervous system diseases.The neuroprotective or neurotoxic effects of TGF-β1 in specific brain areas may depend on the pathological process and cell types involved.Voltage-gated sodium channels(VGSCs)are essential ion channels for the generation of action potentials in neurons,and are involved in various neuroexcitation-related diseases.However,the effects of TGF-β1 on the functional properties of VGSCs and firing properties in cortical neurons remain unclear.In this study,we investigated the effects of TGF-β1 on VGSC function and firing properties in primary cortical neurons from mice.We found that TGF-β1 increased VGSC current density in a dose-and time-dependent manner,which was attributable to the upregulation of Nav1.3 expression.Increased VGSC current density and Nav1.3 expression were significantly abolished by preincubation with inhibitors of mitogen-activated protein kinase kinase(PD98059),p38 mitogen-activated protein kinase(SB203580),and Jun NH2-terminal kinase 1/2 inhibitor(SP600125).Interestingly,TGF-β1 significantly increased the firing threshold of action potentials but did not change their firing rate in cortical neurons.These findings suggest that TGF-β1 can increase Nav1.3 expression through activation of the ERK1/2-JNK-MAPK pathway,which leads to a decrease in the firing threshold of action potentials in cortical neurons under pathological conditions.Thus,this contributes to the occurrence and progression of neuroexcitatory-related diseases of the central nervous system. 展开更多
关键词 central nervous system cortical neurons ERK firing properties JNK Nav1.3 p38 transforming growth factor-beta 1 traumatic brain injury voltage-gated sodium currents
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Generator Unit Fault Diagnosis Using the Frequency Slice Wavelet Transform Time-frequency Analysis Method 被引量:9
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作者 DUAN Chendong GAO Qiang XU Xianfeng 《中国电机工程学报》 EI CSCD 北大核心 2013年第32期I0014-I0014,16,共1页
为了提取有效的故障特征,提出了基于频率切片小波变换时频分解的故障特征分离提取方法。先对信号进行频率切片小波变换获取其时频分布,然后根据信号的能量分布特点选择时频区域,再以较高的时频分辨率对选择的时频区域进一步细化分析... 为了提取有效的故障特征,提出了基于频率切片小波变换时频分解的故障特征分离提取方法。先对信号进行频率切片小波变换获取其时频分布,然后根据信号的能量分布特点选择时频区域,再以较高的时频分辨率对选择的时频区域进一步细化分析,以突出隐含在信号中的时频特征,在此基础上分割出含有故障特征时频区域,再通过滤波和逆变换重构分离出有效的故障特征。仿真实验和工程应用表明,这种方法可从噪声信号中分离出有效的特征分量,在发电机组故障特征提取时取得了较好的效果。 展开更多
关键词 频率分析 小波变换 时频分析方法 故障诊断 发电机组 切片 振动信号 非平稳
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The Time-Frequency Energy Attenuation Factor and Its Application on the Basis of Gauss Linear Frequency-Modulated Continuous Wavelet Transform
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作者 LiuXiqiang ShenPing +4 位作者 LiHong ShanChanglun JiAidong ZhangPing CaiMingjun 《Earthquake Research in China》 2004年第1期42-53,共12页
Based on the Gauss linear frequency modulated wavelet transform, a new characteristic index is presented, namely time frequency energy attenuation factor which can reflect the difference features of waveform in earthq... Based on the Gauss linear frequency modulated wavelet transform, a new characteristic index is presented, namely time frequency energy attenuation factor which can reflect the difference features of waveform in earthquake focus mechanism, wave traveling path and its attenuation characteristics in focal area or near field. In order to test its validity, we select the natural earthquakes and explosion or collapse events whose focus mechanisms vary obviously,and some natural earthquakes located at the same site or in a very small area. The study indicates that the time frequency energy attenuation factors of the natural earthquakes are obviously different with that of explosion or collapse events, and the change of the time frequency energy attenuation factors is relatively stable for the earthquakes under the normal seismicity background. Using the above mentioned method, it is expected to offer a useful criterion for strong earthquake prediction by continuous earthquake observation. 展开更多
关键词 Continuous wavelet transform Time frequency energy attenuation factor The space difference characteristics The time change characteristics
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Intelligibility evaluation of enhanced whisper in joint time-frequency domain 被引量:1
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作者 周健 魏昕 +1 位作者 梁瑞宇 赵力 《Journal of Southeast University(English Edition)》 EI CAS 2014年第3期261-266,共6页
Some factors influencing the intelligibility of the enhanced whisper in the joint time-frequency domain are evaluated. Specifically, both the spectrum density and different regions of the enhanced spectrum are analyze... Some factors influencing the intelligibility of the enhanced whisper in the joint time-frequency domain are evaluated. Specifically, both the spectrum density and different regions of the enhanced spectrum are analyzed. Experimental results show that for a spectrum of some density, the joint time-frequency gain-modification based speech enhancement algorithm achieves significant improvement in intelligibility. Additionally, the spectrum region where the estimated spectrum is smaller than the clean spectrum, is the most important region contributing to intelligibility improvement for the enhanced whisper. The spectrum region where the estimated spectrum is larger than twice the size of the clean spectrum is detrimental to speech intelligibility perception within the whisper context. 展开更多
关键词 whispered speech enhancement intelligibilityevaluation real-valued discrete Gabor transform joint time-frequency analysis
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Application of sparse time-frequency decomposition to seismic data 被引量:3
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作者 王雄文 王华忠 《Applied Geophysics》 SCIE CSCD 2014年第4期447-458,510,共13页
The Gabor and S transforms are frequently used in time-frequency decomposition methods. Constrained by the uncertainty principle, both transforms produce low-resolution time-frequency decomposition results in the time... The Gabor and S transforms are frequently used in time-frequency decomposition methods. Constrained by the uncertainty principle, both transforms produce low-resolution time-frequency decomposition results in the time and frequency domains. To improve the resolution of the time-frequency decomposition results, we use the instantaneous frequency distribution function(IFDF) to express the seismic signal. When the instantaneous frequencies of the nonstationary signal satisfy the requirements of the uncertainty principle, the support of IFDF is just the support of the amplitude ridges in the signal obtained using the short-time Fourier transform. Based on this feature, we propose a new iteration algorithm to achieve the sparse time-frequency decomposition of the signal. The iteration algorithm uses the support of the amplitude ridges of the residual signal obtained with the short-time Fourier transform to update the time-frequency components of the signal. The summation of the updated time-frequency components in each iteration is the result of the sparse timefrequency decomposition. Numerical examples show that the proposed method improves the resolution of the time-frequency decomposition results and the accuracy of the analysis of the nonstationary signal. We also use the proposed method to attenuate the ground roll of field seismic data with good results. 展开更多
关键词 time-frequency analysis sparse time-frequency decomposition nonstationary signal RESOLUTION
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TVAR Time-frequency Analysis for Non-stationary Vibration Signals of Spacecraft 被引量:7
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作者 杨海 程伟 朱虹 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2008年第5期423-432,共10页
Predicting the time-varying auto-spectral density of a spacecraft in high-altitude orbits requires an accurate model for the non-stationary random vibration signals with densely spaced modal frequency. The traditional... Predicting the time-varying auto-spectral density of a spacecraft in high-altitude orbits requires an accurate model for the non-stationary random vibration signals with densely spaced modal frequency. The traditional time-varying algorithm limits prediction accuracy, thus affecting a number of operational decisions. To solve this problem, a time-varying auto regressive (TVAR) model based on the process neural network (PNN) and the empirical mode decomposition (EMD) is proposed. The time-varying system is tracked on-line by establishing a time-varying parameter model, and then the relevant parameter spectrum is obtained. Firstly, the EMD method is utilized to decompose the signal into several intrinsic mode functions (IMFs). Then for each IMF, the PNN is established and the time-varying auto-spectral density is obtained. Finally, the time-frequency distribution of the signals can be reconstructed by linear superposition. The simulation and the analytical results from an example demonstrate that this approach possesses simplicity, effectiveness, and feasibility, as well as higher frequency resolution. 展开更多
关键词 non-stationary random vibration time-frequency distribution process neural network empirical mode decomposition
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Parameter estimation of maneuvering targets in OTHR based on sparse time-frequency representation 被引量:2
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作者 Jinfeng Hu Xuan He +3 位作者 Wange Li Hui Ai Huiyong Li Julan Xie 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2016年第3期574-580,共7页
This paper proposes a new method for estimating the parameter of maneuvering targets based on sparse time-frequency transform in over-the-horizon radar(OTHR). In this method, the sparse time-frequency distribution o... This paper proposes a new method for estimating the parameter of maneuvering targets based on sparse time-frequency transform in over-the-horizon radar(OTHR). In this method, the sparse time-frequency distribution of the radar echo is obtained by solving a sparse optimization problem based on the short-time Fourier transform. Then Hough transform is employed to estimate the parameter of the targets. The proposed algorithm has the following advantages: Compared with the Wigner-Hough transform method, the computational complexity of the sparse optimization is low due to the application of fast Fourier transform(FFT). And the computational cost of Hough transform is also greatly reduced because of the sparsity of the time-frequency distribution. Compared with the high order ambiguity function(HAF) method, the proposed method improves in terms of precision and robustness to noise. Simulation results show that compared with the HAF method, the required SNR and relative mean square error are 8 dB lower and 50 dB lower respectively in the proposed method. While processing the field experiment data, the execution time of Hough transform in the proposed method is only 4% of the Wigner-Hough transform method. 展开更多
关键词 over-the-horizon radar(OTHR) maneuvering tar-get parameter estimation sparse time-frequency transform Hough transform
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Seismic data denoising based on mixed time-frequency methods 被引量:3
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作者 蔡涵鹏 贺振华 黄德济 《Applied Geophysics》 SCIE CSCD 2011年第4期319-327,371,共10页
Deconvolution denoising in the f-x domain has some defects when facing situations like complicated geology structure, coherent noise of steep dip angles, and uneven spatial sampling. To solve these problems, a new fil... Deconvolution denoising in the f-x domain has some defects when facing situations like complicated geology structure, coherent noise of steep dip angles, and uneven spatial sampling. To solve these problems, a new filtering method is proposed, which uses the generalized S transform which has good time-frequency concentration criterion to transform seismic data from the time-space to time-frequency-space domain (t-f-x). Then in the t-f-x domain apply Empirical Mode Decomposition (EMD) on each frequency slice and clear the Intrinsic Mode Functions (IMFs) that noise dominates to suppress coherent and random noise. The model study shows that the high frequency component in the first IMF represents mainly noise, so clearing the first IMF can suppress noise. The EMD filtering method in the t-f-x domain after generalized S transform is equivalent to self-adaptive f-k filtering that depends on position, frequency, and truncation characteristics of high wave numbers. This filtering method takes local data time-frequency characteristic into consideration and is easy to perform. Compared with AR predictive filtering, the component that this method filters is highly localized and contains relatively fewer low wave numbers and the filter result does not show over-smoothing effects. Real data processing proves that the EMD filtering method in the t-f-x domain after generalized S transform can effectively suppress random and coherent noise of steep dips. 展开更多
关键词 Empirical Mode Decomposition generalized S transform coherent noise random noise noise suppression
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CNN-Transformer特征融合多目标跟踪算法 被引量:4
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作者 张英俊 白小辉 谢斌红 《计算机工程与应用》 CSCD 北大核心 2024年第2期180-190,共11页
在卷积神经网络(CNN)中,卷积运算能高效地提取目标的局部特征,却难以捕获全局表示;而在视觉Transformer中,注意力机制可以捕获长距离的特征依赖,但会忽略局部特征细节。针对以上问题,提出一种基于CNN-Transformer双分支主干网络进行特... 在卷积神经网络(CNN)中,卷积运算能高效地提取目标的局部特征,却难以捕获全局表示;而在视觉Transformer中,注意力机制可以捕获长距离的特征依赖,但会忽略局部特征细节。针对以上问题,提出一种基于CNN-Transformer双分支主干网络进行特征提取和融合的多目标跟踪算法CTMOT(CNN-transformer multi-object tracking)。使用基于CNN和Transformer双分支并行的主干网络分别提取图像的局部和全局特征。使用双向桥接模块(two-way braidge module,TBM)对两种特征进行充分融合。将融合后的特征输入两组并行的解码器进行处理。将解码器输出的检测框和跟踪框进行匹配,完成多目标跟踪任务。在多目标跟踪数据集MOT17、MOT20、KITTI以及UADETRAC上进行评估,CTMOT算法的MOTP和IDs指标在四个数据集上均达到了SOTA效果,MOTA指标分别达到了76.4%、66.3%、92.36%和88.57%,在MOT数据集上与SOTA方法效果相当,在KITTI数据集上达到SOTA效果。由于同时完成目标检测和关联,能够端到端进行目标跟踪,跟踪速度可达35 FPS,表明CTMOT算法在跟踪的实时性和准确性上达到了较好的平衡,具有较大潜力。 展开更多
关键词 多目标跟踪 transformER 特征融合
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基于Depth-wise卷积和视觉Transformer的图像分类模型 被引量:3
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作者 张峰 黄仕鑫 +1 位作者 花强 董春茹 《计算机科学》 CSCD 北大核心 2024年第2期196-204,共9页
图像分类作为一种常见的视觉识别任务,有着广阔的应用场景。在处理图像分类问题时,传统的方法通常使用卷积神经网络,然而,卷积网络的感受野有限,难以建模图像的全局关系表示,导致分类精度低,难以处理复杂多样的图像数据。为了对全局关... 图像分类作为一种常见的视觉识别任务,有着广阔的应用场景。在处理图像分类问题时,传统的方法通常使用卷积神经网络,然而,卷积网络的感受野有限,难以建模图像的全局关系表示,导致分类精度低,难以处理复杂多样的图像数据。为了对全局关系进行建模,一些研究者将Transformer应用于图像分类任务,但为了满足Transformer的序列化和并行化要求,需要将图像分割成大小相等、互不重叠的图像块,破坏了相邻图像数据块之间的局部信息。此外,由于Transformer具有较少的先验知识,模型往往需要在大规模数据集上进行预训练,因此计算复杂度较高。为了同时建模图像相邻块之间的局部信息并充分利用图像的全局信息,提出了一种基于Depth-wise卷积的视觉Transformer(Efficient Pyramid Vision Transformer,EPVT)模型。EPVT模型可以实现以较低的计算成本提取相邻图像块之间的局部和全局信息。EPVT模型主要包含3个关键组件:局部感知模块(Local Perceptron Module,LPM)、空间信息融合模块(Spatial Information Fusion,SIF)和“+卷积前馈神经网络(Convolution Feed-forward Network,CFFN)。LPM模块用于捕获图像的局部相关性;SIF模块用于融合相邻图像块之间的局部信息,并利用不同图像块之间的远距离依赖关系,提升模型的特征表达能力,使模型学习到输出特征在不同维度下的语义信息;CFFN模块用于编码位置信息和重塑张量。在图像分类数据集ImageNet-1K上,所提模型优于现有的同等规模的视觉Transformer分类模型,取得了82.6%的分类准确度,证明了该模型在大规模数据集上具有竞争力。 展开更多
关键词 深度学习 图像分类 Depth-wise卷积 视觉transformer 注意力机制
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