It has been over a decade since the first coded aperture video compressive sensing(CS)system was reported.The underlying principle of this technology is to employ a high-frequency modulator in the optical path to modu...It has been over a decade since the first coded aperture video compressive sensing(CS)system was reported.The underlying principle of this technology is to employ a high-frequency modulator in the optical path to modulate a recorded high-speed scene within one integration time.The superimposed image captured in this manner is modulated and compressed,since multiple modulation patterns are imposed.Following this,reconstruction algorithms are utilized to recover the desired high-speed scene.One leading advantage of video CS is that a single captured measurement can be used to reconstruct a multi-frame video,thereby enabling a low-speed camera to capture high-speed scenes.Inspired by this,a number of variants of video CS systems have been built,mainly using different modulation devices.Meanwhile,in order to obtain high-quality reconstruction videos,many algorithms have been developed,from optimization-based iterative algorithms to deep-learning-based ones.Recently,emerging deep learning methods have been dominant due to their high-speed inference and high-quality reconstruction,highlighting the possibility of deploying video CS in practical applications.Toward this end,this paper reviews the progress that has been achieved in video CS during the past decade.We further analyze the efforts that need to be made—in terms of both hardware and algorithms—to enable real applications.Research gaps are put forward and future directions are summarized to help researchers and engineers working on this topic.展开更多
Images are the most important carrier of human information. Moreover, how to safely transmit digital imagesthrough public channels has become an urgent problem. In this paper, we propose a novel image encryptionalgori...Images are the most important carrier of human information. Moreover, how to safely transmit digital imagesthrough public channels has become an urgent problem. In this paper, we propose a novel image encryptionalgorithm, called chaotic compressive sensing (CS) encryption (CCSE), which can not only improve the efficiencyof image transmission but also introduce the high security of the chaotic system. Specifically, the proposed CCSEcan fully leverage the advantages of the Chebyshev chaotic system and CS, enabling it to withstand various attacks,such as differential attacks, and exhibit robustness. First, we use a sparse trans-form to sparse the plaintext imageand then use theArnold transformto perturb the image pixels. After that,we elaborate aChebyshev Toeplitz chaoticsensing matrix for CCSE. By using this Toeplitz matrix, the perturbed image is compressed and sampled to reducethe transmission bandwidth and the amount of data. Finally, a bilateral diffusion operator and a chaotic encryptionoperator are used to perturb and expand the image pixels to change the pixel position and value of the compressedimage, and ultimately obtain an encrypted image. Experimental results show that our method can be resistant tovarious attacks, such as the statistical attack and noise attack, and can outperform its current competitors.展开更多
A novel image encryption scheme based on parallel compressive sensing and edge detection embedding technology is proposed to improve visual security. Firstly, the plain image is sparsely represented using the discrete...A novel image encryption scheme based on parallel compressive sensing and edge detection embedding technology is proposed to improve visual security. Firstly, the plain image is sparsely represented using the discrete wavelet transform.Then, the coefficient matrix is scrambled and compressed to obtain a size-reduced image using the Fisher–Yates shuffle and parallel compressive sensing. Subsequently, to increase the security of the proposed algorithm, the compressed image is re-encrypted through permutation and diffusion to obtain a noise-like secret image. Finally, an adaptive embedding method based on edge detection for different carrier images is proposed to generate a visually meaningful cipher image. To improve the plaintext sensitivity of the algorithm, the counter mode is combined with the hash function to generate keys for chaotic systems. Additionally, an effective permutation method is designed to scramble the pixels of the compressed image in the re-encryption stage. The simulation results and analyses demonstrate that the proposed algorithm performs well in terms of visual security and decryption quality.展开更多
This paper explores the recovery of block sparse signals in frame-based settings using the l_(2)/l_(q)-synthesis technique(0<q≤1).We propose a new null space property,referred to as block D-NSP_(q),which is based ...This paper explores the recovery of block sparse signals in frame-based settings using the l_(2)/l_(q)-synthesis technique(0<q≤1).We propose a new null space property,referred to as block D-NSP_(q),which is based on the dictionary D.We establish that matrices adhering to the block D-NSP_(q)condition are both necessary and sufficient for the exact recovery of block sparse signals via l_(2)/l_(q)-synthesis.Additionally,this condition is essential for the stable recovery of signals that are block-compressible with respect to D.This D-NSP_(q)property is identified as the first complete condition for successful signal recovery using l_(2)/l_(q)-synthesis.Furthermore,we assess the theoretical efficacy of the l2/lq-synthesis method under conditions of measurement noise.展开更多
To overcome the low efficiency of conventional confocal Raman spectroscopy,many efforts have been devoted to parallelizing the Raman excitation and acquisition,in which the scattering from multiple foci is projected o...To overcome the low efficiency of conventional confocal Raman spectroscopy,many efforts have been devoted to parallelizing the Raman excitation and acquisition,in which the scattering from multiple foci is projected onto different locations on a spectrometer's CCD,along either its vertical,horizontal dimension,or even both.While the latter projection scheme relieves the limitation on the row numbers of the CCD,the spectra of multiple foci are recorded in one spectral channel,resulting in spectral overlapping.Here,we developed a method under a com-pressive sensing framework to demultiplex the superimposed spectra of multiple cells during their dynamic processes.Unlike the previous methods which ignore the information connection be-tween the spectra of the cells recorded at different time,the proposed method utilizes a prior that a cell's spectra acquired at different time have the same sparsity structure in their principal components.Rather than independently demultiplexing the mixed spectra at the individual time intervals,the method demultiplexes the whole spectral sequence acquired continuously during the dynamic process.By penalizing the sparsity combined from all time intervals,the collaborative optimization of the inversion problem gave more accurate recovery results.The performances of the method were substantiated by a 1D Raman tweezers array,which monitored the germination of multiple bacterial spores.The method can be extended to the monitoring of many living cells randomly scattering on a coverslip,and has a potential to improve the throughput by a few orders.展开更多
This paper considers the fundamental channel estimation problem for the multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM)system in the presence of multi-cell interference.Specificall...This paper considers the fundamental channel estimation problem for the multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM)system in the presence of multi-cell interference.Specifically,this paper focuses on both channel modelling and receiver design for interference estimation and mitigation.We propose a delay-calibrated block-wise linear model,which extracts the delay of the dominant tap of each interference as a key parameter and approximates the residual channel coefficients by the recently developed blockwise linear model.Based on the delay-calibrated block-wise linear model and the angle-domain channel sparsity,we further conceive a message passing algorithm to solve the channel estimation problem.Numerical results demonstrate the superior performance of the proposed algorithm over the state-of-the-art algorithms.展开更多
BACKGROUND A key cardiac magnetic resonance(CMR)challenge is breath-holding duration,difficult for cardiac patients.AIM To evaluate whether artificial intelligence-assisted compressed sensing CINE(AICS-CINE)reduces im...BACKGROUND A key cardiac magnetic resonance(CMR)challenge is breath-holding duration,difficult for cardiac patients.AIM To evaluate whether artificial intelligence-assisted compressed sensing CINE(AICS-CINE)reduces image acquisition time of CMR compared to conventional CINE(C-CINE).METHODS Cardio-oncology patients(n=60)and healthy volunteers(n=29)underwent sequential C-CINE and AI-CS-CINE with a 1.5-T scanner.Acquisition time,visual image quality assessment,and biventricular metrics(end-diastolic volume,endsystolic volume,stroke volume,ejection fraction,left ventricular mass,and wall thickness)were analyzed and compared between C-CINE and AI-CS-CINE with Bland–Altman analysis,and calculation of intraclass coefficient(ICC).RESULTS In 89 participants(58.5±16.8 years,42 males,47 females),total AI-CS-CINE acquisition and reconstruction time(37 seconds)was 84%faster than C-CINE(238 seconds).C-CINE required repeats in 23%(20/89)of cases(approximately 8 minutes lost),while AI-CS-CINE only needed one repeat(1%;2 seconds lost).AICS-CINE had slightly lower contrast but preserved structural clarity.Bland-Altman plots and ICC(0.73≤r≤0.98)showed strong agreement for left ventricle(LV)and right ventricle(RV)metrics,including those in the cardiac amyloidosis subgroup(n=31).AI-CS-CINE enabled faster,easier imaging in patients with claustrophobia,dyspnea,arrhythmias,or restlessness.Motion-artifacted C-CINE images were reliably interpreted from AI-CS-CINE.CONCLUSION AI-CS-CINE accelerated CMR image acquisition and reconstruction,preserved anatomical detail,and diminished impact of patient-related motion.Quantitative AI-CS-CINE metrics agreed closely with C-CINE in cardio-oncology patients,including the cardiac amyloidosis cohort,as well as healthy volunteers regardless of left and right ventricular size and function.AI-CS-CINE significantly enhanced CMR workflow,particularly in challenging cases.The strong analytical concordance underscores reliability and robustness of AI-CS-CINE as a valuable tool.展开更多
Face hallucination or super-resolution is an inverse problem which is underdetermined,and the compressive sensing(CS)theory provides an effective way of seeking inverse problem solutions.In this paper,a novel compress...Face hallucination or super-resolution is an inverse problem which is underdetermined,and the compressive sensing(CS)theory provides an effective way of seeking inverse problem solutions.In this paper,a novel compressive sensing based face hallucination method is presented,which is comprised of three steps:dictionary learning、sparse coding and solving maximum a posteriori(MAP)formulation.In the first step,the K-SVD dictionary learning algorithm is adopted to obtain a dictionary which can sparsely represent high resolution(HR)face image patches.In the second step,we seek the sparsest representation for each low-resolution(LR)face image paches input using the learned dictionary,super resolution image blocks are obtained from the sparsest coefficients and dictionaries,which then are assembled into super-resolution(SR)image.Finally,MAP formulation is introduced to satisfy the consistency restrictive condition and obtain the higher quality HR images.The experimental results demonstrate that our approach can achieve better super-resolution faces compared with other state-of-the-art method.展开更多
In order to reduce the pilot number and improve spectral efficiency, recently emerged compressive sensing （CS） is applied to the digital broadcast channel estimation. According to the six channel profiles of the Eur...In order to reduce the pilot number and improve spectral efficiency, recently emerged compressive sensing （CS） is applied to the digital broadcast channel estimation. According to the six channel profiles of the European Telecommunication Standards Institute（ETSI） digital radio mondiale （DRM） standard, the subspace pursuit （SP） algorithm is employed for delay spread and attenuation estimation of each path in the case where the channel profile is identified and the multipath number is known. The stop condition for SP is that the sparsity of the estimation equals the multipath number. For the case where the multipath number is unknown, the orthogonal matching pursuit （OMP） algorithm is employed for channel estimation, while the stop condition is that the estimation achieves the noise variance. Simulation results show that with the same number of pilots, CS algorithms outperform the traditional cubic-spline-interpolation-based least squares （LS） channel estimation. SP is also demonstrated to be better than OMP when the multipath number is known as a priori.展开更多
A joint two-dimensional(2D)direction-of-arrival(DOA)and radial Doppler frequency estimation method for the L-shaped array is proposed in this paper based on the compressive sensing(CS)framework.Revised from the conven...A joint two-dimensional(2D)direction-of-arrival(DOA)and radial Doppler frequency estimation method for the L-shaped array is proposed in this paper based on the compressive sensing(CS)framework.Revised from the conventional CS-based methods where the joint spatial-temporal parameters are characterized in one large scale matrix,three smaller scale matrices with independent azimuth,elevation and Doppler frequency are introduced adopting a separable observation model.Afterwards,the estimation is achieved by L1-norm minimization and the Bayesian CS algorithm.In addition,under the L-shaped array topology,the azimuth and elevation are separated yet coupled to the same radial Doppler frequency.Hence,the pair matching problem is solved with the aid of the radial Doppler frequency.Finally,numerical simulations corroborate the feasibility and validity of the proposed algorithm.展开更多
Media based modulation(MBM)is expected to be a prominent modulation scheme,which has access to the high data rate by using radio frequency(RF)mirrors and fewer transmit antennas.Associated with multiuser multiple inpu...Media based modulation(MBM)is expected to be a prominent modulation scheme,which has access to the high data rate by using radio frequency(RF)mirrors and fewer transmit antennas.Associated with multiuser multiple input multiple output(MIMO),the MBM scheme achieves better performance than other conventional multiuser MIMO schemes.In this paper,the massive MIMO uplink is considered and a conjunctive MBM transmission scheme for each user is employed.This conjunctive MBM transmission scheme gathers aggregate MBM signals in multiple continuous time slots,which exploits the structured sparsity of these aggregate MBM signals.Under this kind of scenario,a multiuser detector with low complexity based on the compressive sensing(CS)theory to gain better detection performance is proposed.This detector is developed from the greedy sparse recovery technique compressive sampling matching pursuit(CoSaMP)and exploits not only the inherently distributed sparsity of MBM signals but also the structured sparsity of multiple aggregate MBM signals.By exploiting these sparsity,the proposed CoSaMP based multiuser detector achieves reliable detection with low complexity.Simulation results demonstrate that the proposed CoSaMP based multiuser detector achieves better detection performance compared with the conventional methods.展开更多
Ultra-wide-band (UWB) signals are suitable for localization, since their high time resolution can provide precise time of arrival (TOA) estimation. However, one major challenge in UWB signal processing is the requirem...Ultra-wide-band (UWB) signals are suitable for localization, since their high time resolution can provide precise time of arrival (TOA) estimation. However, one major challenge in UWB signal processing is the requirement of high sampling rate which leads to complicated signal processing and expensive hardware. In this paper, we present a novel UWB signal sampling method called UWB signal sampling via temporal sparsity (USSTS). Its sampling rate is much lower than Nyquist rate. Moreover, it is implemented in one step and no extra processing unit is needed. Simulation results show that USSTS can not recover the signal precisely, but for the use in localization, the accuracy of TOA estimation is the same as that in traditional methods. Therefore, USSTS gives a novel and effective solution for the use of UWB signals in localization.展开更多
In deep mineral exploration, the acquisition of audio magnetotelluric （AMT） data is severely affected by ambient noise near the observation sites; This near-field noise restricts investigation depths. Mathematical m...In deep mineral exploration, the acquisition of audio magnetotelluric （AMT） data is severely affected by ambient noise near the observation sites; This near-field noise restricts investigation depths. Mathematical morphological filtering （MMF） proved effective in suppressing large-scale strong and variably shaped noise, typically low-frequency noise, but can not deal with pulse noise of AMT data. We combine compressive sensing and MMF. First we use MMF to suppress the large-scale strong ambient noise; second, we use the improved orthogonal match pursuit （IOMP） algorithm to remove the residual pulse noise. To remove the noise and protect the useful AMT signal, a redundant dictionary that matches with spikes and is insensitive to the useful signal is designed. Synthetic and field data from the Luzong field suggest that the proposed method suppresses the near-source noise and preserves the signal well; thus, better results are obtained that improve the output of either MMF or IOMP.展开更多
基金supported by the National Natural Science Foundation of China(61931012,62171258,62088102,and 62271414)the Zhejiang Provincial Outstanding Youth Science Foundation(LR23F010001)the Key Project of Westlake Institute for Optoelectronics(2023GD007).
文摘It has been over a decade since the first coded aperture video compressive sensing(CS)system was reported.The underlying principle of this technology is to employ a high-frequency modulator in the optical path to modulate a recorded high-speed scene within one integration time.The superimposed image captured in this manner is modulated and compressed,since multiple modulation patterns are imposed.Following this,reconstruction algorithms are utilized to recover the desired high-speed scene.One leading advantage of video CS is that a single captured measurement can be used to reconstruct a multi-frame video,thereby enabling a low-speed camera to capture high-speed scenes.Inspired by this,a number of variants of video CS systems have been built,mainly using different modulation devices.Meanwhile,in order to obtain high-quality reconstruction videos,many algorithms have been developed,from optimization-based iterative algorithms to deep-learning-based ones.Recently,emerging deep learning methods have been dominant due to their high-speed inference and high-quality reconstruction,highlighting the possibility of deploying video CS in practical applications.Toward this end,this paper reviews the progress that has been achieved in video CS during the past decade.We further analyze the efforts that need to be made—in terms of both hardware and algorithms—to enable real applications.Research gaps are put forward and future directions are summarized to help researchers and engineers working on this topic.
基金the National Natural Science Foundation of China(Nos.62002028,62102040 and 62202066).
文摘Images are the most important carrier of human information. Moreover, how to safely transmit digital imagesthrough public channels has become an urgent problem. In this paper, we propose a novel image encryptionalgorithm, called chaotic compressive sensing (CS) encryption (CCSE), which can not only improve the efficiencyof image transmission but also introduce the high security of the chaotic system. Specifically, the proposed CCSEcan fully leverage the advantages of the Chebyshev chaotic system and CS, enabling it to withstand various attacks,such as differential attacks, and exhibit robustness. First, we use a sparse trans-form to sparse the plaintext imageand then use theArnold transformto perturb the image pixels. After that,we elaborate aChebyshev Toeplitz chaoticsensing matrix for CCSE. By using this Toeplitz matrix, the perturbed image is compressed and sampled to reducethe transmission bandwidth and the amount of data. Finally, a bilateral diffusion operator and a chaotic encryptionoperator are used to perturb and expand the image pixels to change the pixel position and value of the compressedimage, and ultimately obtain an encrypted image. Experimental results show that our method can be resistant tovarious attacks, such as the statistical attack and noise attack, and can outperform its current competitors.
基金supported by the Key Area R&D Program of Guangdong Province (Grant No.2022B0701180001)the National Natural Science Foundation of China (Grant No.61801127)+1 种基金the Science Technology Planning Project of Guangdong Province,China (Grant Nos.2019B010140002 and 2020B111110002)the Guangdong-Hong Kong-Macao Joint Innovation Field Project (Grant No.2021A0505080006)。
文摘A novel image encryption scheme based on parallel compressive sensing and edge detection embedding technology is proposed to improve visual security. Firstly, the plain image is sparsely represented using the discrete wavelet transform.Then, the coefficient matrix is scrambled and compressed to obtain a size-reduced image using the Fisher–Yates shuffle and parallel compressive sensing. Subsequently, to increase the security of the proposed algorithm, the compressed image is re-encrypted through permutation and diffusion to obtain a noise-like secret image. Finally, an adaptive embedding method based on edge detection for different carrier images is proposed to generate a visually meaningful cipher image. To improve the plaintext sensitivity of the algorithm, the counter mode is combined with the hash function to generate keys for chaotic systems. Additionally, an effective permutation method is designed to scramble the pixels of the compressed image in the re-encryption stage. The simulation results and analyses demonstrate that the proposed algorithm performs well in terms of visual security and decryption quality.
基金Supported by The Featured Innovation Projects of the General University of Guangdong Province(2023KTSCX096)The Special Projects in Key Areas of Guangdong Province(ZDZX1088)Research Team Project of Guangdong University of Education(2024KYCXTD018)。
文摘This paper explores the recovery of block sparse signals in frame-based settings using the l_(2)/l_(q)-synthesis technique(0<q≤1).We propose a new null space property,referred to as block D-NSP_(q),which is based on the dictionary D.We establish that matrices adhering to the block D-NSP_(q)condition are both necessary and sufficient for the exact recovery of block sparse signals via l_(2)/l_(q)-synthesis.Additionally,this condition is essential for the stable recovery of signals that are block-compressible with respect to D.This D-NSP_(q)property is identified as the first complete condition for successful signal recovery using l_(2)/l_(q)-synthesis.Furthermore,we assess the theoretical efficacy of the l2/lq-synthesis method under conditions of measurement noise.
基金This work was supported by the National Key R&D Program of China(2019YFC1605500,2018YFF01011700)the National Natural Science Foundation of China(21973111)+1 种基金Guangxi Natural Science Foundation(2017GXNSFAA198029)Scientific Development Fund of Guangxi Academy of Sciences(2018YFJ 403).
文摘To overcome the low efficiency of conventional confocal Raman spectroscopy,many efforts have been devoted to parallelizing the Raman excitation and acquisition,in which the scattering from multiple foci is projected onto different locations on a spectrometer's CCD,along either its vertical,horizontal dimension,or even both.While the latter projection scheme relieves the limitation on the row numbers of the CCD,the spectra of multiple foci are recorded in one spectral channel,resulting in spectral overlapping.Here,we developed a method under a com-pressive sensing framework to demultiplex the superimposed spectra of multiple cells during their dynamic processes.Unlike the previous methods which ignore the information connection be-tween the spectra of the cells recorded at different time,the proposed method utilizes a prior that a cell's spectra acquired at different time have the same sparsity structure in their principal components.Rather than independently demultiplexing the mixed spectra at the individual time intervals,the method demultiplexes the whole spectral sequence acquired continuously during the dynamic process.By penalizing the sparsity combined from all time intervals,the collaborative optimization of the inversion problem gave more accurate recovery results.The performances of the method were substantiated by a 1D Raman tweezers array,which monitored the germination of multiple bacterial spores.The method can be extended to the monitoring of many living cells randomly scattering on a coverslip,and has a potential to improve the throughput by a few orders.
基金supported in part by the National Key Research and Development Program of China under Grant 2020YFB1804800。
文摘This paper considers the fundamental channel estimation problem for the multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM)system in the presence of multi-cell interference.Specifically,this paper focuses on both channel modelling and receiver design for interference estimation and mitigation.We propose a delay-calibrated block-wise linear model,which extracts the delay of the dominant tap of each interference as a key parameter and approximates the residual channel coefficients by the recently developed blockwise linear model.Based on the delay-calibrated block-wise linear model and the angle-domain channel sparsity,we further conceive a message passing algorithm to solve the channel estimation problem.Numerical results demonstrate the superior performance of the proposed algorithm over the state-of-the-art algorithms.
基金Supported by James Russell Hornsby and Jun Xiong Fund and United Imaging Healthcare.
文摘BACKGROUND A key cardiac magnetic resonance(CMR)challenge is breath-holding duration,difficult for cardiac patients.AIM To evaluate whether artificial intelligence-assisted compressed sensing CINE(AICS-CINE)reduces image acquisition time of CMR compared to conventional CINE(C-CINE).METHODS Cardio-oncology patients(n=60)and healthy volunteers(n=29)underwent sequential C-CINE and AI-CS-CINE with a 1.5-T scanner.Acquisition time,visual image quality assessment,and biventricular metrics(end-diastolic volume,endsystolic volume,stroke volume,ejection fraction,left ventricular mass,and wall thickness)were analyzed and compared between C-CINE and AI-CS-CINE with Bland–Altman analysis,and calculation of intraclass coefficient(ICC).RESULTS In 89 participants(58.5±16.8 years,42 males,47 females),total AI-CS-CINE acquisition and reconstruction time(37 seconds)was 84%faster than C-CINE(238 seconds).C-CINE required repeats in 23%(20/89)of cases(approximately 8 minutes lost),while AI-CS-CINE only needed one repeat(1%;2 seconds lost).AICS-CINE had slightly lower contrast but preserved structural clarity.Bland-Altman plots and ICC(0.73≤r≤0.98)showed strong agreement for left ventricle(LV)and right ventricle(RV)metrics,including those in the cardiac amyloidosis subgroup(n=31).AI-CS-CINE enabled faster,easier imaging in patients with claustrophobia,dyspnea,arrhythmias,or restlessness.Motion-artifacted C-CINE images were reliably interpreted from AI-CS-CINE.CONCLUSION AI-CS-CINE accelerated CMR image acquisition and reconstruction,preserved anatomical detail,and diminished impact of patient-related motion.Quantitative AI-CS-CINE metrics agreed closely with C-CINE in cardio-oncology patients,including the cardiac amyloidosis cohort,as well as healthy volunteers regardless of left and right ventricular size and function.AI-CS-CINE significantly enhanced CMR workflow,particularly in challenging cases.The strong analytical concordance underscores reliability and robustness of AI-CS-CINE as a valuable tool.
文摘Face hallucination or super-resolution is an inverse problem which is underdetermined,and the compressive sensing(CS)theory provides an effective way of seeking inverse problem solutions.In this paper,a novel compressive sensing based face hallucination method is presented,which is comprised of three steps:dictionary learning、sparse coding and solving maximum a posteriori(MAP)formulation.In the first step,the K-SVD dictionary learning algorithm is adopted to obtain a dictionary which can sparsely represent high resolution(HR)face image patches.In the second step,we seek the sparsest representation for each low-resolution(LR)face image paches input using the learned dictionary,super resolution image blocks are obtained from the sparsest coefficients and dictionaries,which then are assembled into super-resolution(SR)image.Finally,MAP formulation is introduced to satisfy the consistency restrictive condition and obtain the higher quality HR images.The experimental results demonstrate that our approach can achieve better super-resolution faces compared with other state-of-the-art method.
基金The National Natural Science Foundation of China (No.60872075)the National High Technology Research and Development Program of China (863 Program) (No.2008AA01Z227)
文摘In order to reduce the pilot number and improve spectral efficiency, recently emerged compressive sensing （CS） is applied to the digital broadcast channel estimation. According to the six channel profiles of the European Telecommunication Standards Institute（ETSI） digital radio mondiale （DRM） standard, the subspace pursuit （SP） algorithm is employed for delay spread and attenuation estimation of each path in the case where the channel profile is identified and the multipath number is known. The stop condition for SP is that the sparsity of the estimation equals the multipath number. For the case where the multipath number is unknown, the orthogonal matching pursuit （OMP） algorithm is employed for channel estimation, while the stop condition is that the estimation achieves the noise variance. Simulation results show that with the same number of pilots, CS algorithms outperform the traditional cubic-spline-interpolation-based least squares （LS） channel estimation. SP is also demonstrated to be better than OMP when the multipath number is known as a priori.
文摘A joint two-dimensional(2D)direction-of-arrival(DOA)and radial Doppler frequency estimation method for the L-shaped array is proposed in this paper based on the compressive sensing(CS)framework.Revised from the conventional CS-based methods where the joint spatial-temporal parameters are characterized in one large scale matrix,three smaller scale matrices with independent azimuth,elevation and Doppler frequency are introduced adopting a separable observation model.Afterwards,the estimation is achieved by L1-norm minimization and the Bayesian CS algorithm.In addition,under the L-shaped array topology,the azimuth and elevation are separated yet coupled to the same radial Doppler frequency.Hence,the pair matching problem is solved with the aid of the radial Doppler frequency.Finally,numerical simulations corroborate the feasibility and validity of the proposed algorithm.
文摘Media based modulation(MBM)is expected to be a prominent modulation scheme,which has access to the high data rate by using radio frequency(RF)mirrors and fewer transmit antennas.Associated with multiuser multiple input multiple output(MIMO),the MBM scheme achieves better performance than other conventional multiuser MIMO schemes.In this paper,the massive MIMO uplink is considered and a conjunctive MBM transmission scheme for each user is employed.This conjunctive MBM transmission scheme gathers aggregate MBM signals in multiple continuous time slots,which exploits the structured sparsity of these aggregate MBM signals.Under this kind of scenario,a multiuser detector with low complexity based on the compressive sensing(CS)theory to gain better detection performance is proposed.This detector is developed from the greedy sparse recovery technique compressive sampling matching pursuit(CoSaMP)and exploits not only the inherently distributed sparsity of MBM signals but also the structured sparsity of multiple aggregate MBM signals.By exploiting these sparsity,the proposed CoSaMP based multiuser detector achieves reliable detection with low complexity.Simulation results demonstrate that the proposed CoSaMP based multiuser detector achieves better detection performance compared with the conventional methods.
基金supported by National science foundation(No. 60772035): Key technique study on heterogeneous network convergenceDoctoral grant(No.20070004010)s: Study on cross layer design for heterogeneous network convergence+1 种基金National 863 Hi-Tech Projects(No.2007AA01Z277): Pa-rameter design based electromagnetic compatibility study in cognitive radio communication systemNational science foundation(No. 60830001): Wireless communication fundamentals and key techniuqes for high speed rail way control and safety data transmission
文摘Ultra-wide-band (UWB) signals are suitable for localization, since their high time resolution can provide precise time of arrival (TOA) estimation. However, one major challenge in UWB signal processing is the requirement of high sampling rate which leads to complicated signal processing and expensive hardware. In this paper, we present a novel UWB signal sampling method called UWB signal sampling via temporal sparsity (USSTS). Its sampling rate is much lower than Nyquist rate. Moreover, it is implemented in one step and no extra processing unit is needed. Simulation results show that USSTS can not recover the signal precisely, but for the use in localization, the accuracy of TOA estimation is the same as that in traditional methods. Therefore, USSTS gives a novel and effective solution for the use of UWB signals in localization.
基金supported by the National High Technology Research and Development Program of China(No.2014AA06A602)National Natural Science Foundation of China(No.41404111)Natural Science Foundation of Hunan Province(No.2015JJ3088)
文摘In deep mineral exploration, the acquisition of audio magnetotelluric （AMT） data is severely affected by ambient noise near the observation sites; This near-field noise restricts investigation depths. Mathematical morphological filtering （MMF） proved effective in suppressing large-scale strong and variably shaped noise, typically low-frequency noise, but can not deal with pulse noise of AMT data. We combine compressive sensing and MMF. First we use MMF to suppress the large-scale strong ambient noise; second, we use the improved orthogonal match pursuit （IOMP） algorithm to remove the residual pulse noise. To remove the noise and protect the useful AMT signal, a redundant dictionary that matches with spikes and is insensitive to the useful signal is designed. Synthetic and field data from the Luzong field suggest that the proposed method suppresses the near-source noise and preserves the signal well; thus, better results are obtained that improve the output of either MMF or IOMP.
