We propose a fast,adaptive multiscale resolution spectral measurement method based on compressed sensing.The method can apply variable measurement resolution over the entire spectral range to reduce the measurement ti...We propose a fast,adaptive multiscale resolution spectral measurement method based on compressed sensing.The method can apply variable measurement resolution over the entire spectral range to reduce the measurement time by over 75%compared to a global high-resolution measurement.Mimicking the characteristics of the human retina system,the resolution distribution follows the principle of gradually decreasing.The system allows the spectral peaks of interest to be captured dynamically or to be specified a priori by a user.The system was tested by measuring single and dual spectral peaks,and the results of spectral peaks are consistent with those of global high-resolution measurements.展开更多
With the advent of the information security era,it is necessary to guarantee the privacy,accuracy,and dependable transfer of pictures.This study presents a new approach to the encryption and compression of color image...With the advent of the information security era,it is necessary to guarantee the privacy,accuracy,and dependable transfer of pictures.This study presents a new approach to the encryption and compression of color images.It is predicated on 2D compressed sensing(CS)and the hyperchaotic system.First,an optimized Arnold scrambling algorithm is applied to the initial color images to ensure strong security.Then,the processed images are con-currently encrypted and compressed using 2D CS.Among them,chaotic sequences replace traditional random measurement matrices to increase the system’s security.Third,the processed images are re-encrypted using a combination of permutation and diffusion algorithms.In addition,the 2D projected gradient with an embedding decryption(2DPG-ED)algorithm is used to reconstruct images.Compared with the traditional reconstruction algorithm,the 2DPG-ED algorithm can improve security and reduce computational complexity.Furthermore,it has better robustness.The experimental outcome and the performance analysis indicate that this algorithm can withstand malicious attacks and prove the method is effective.展开更多
The estimation of sparse underwater acoustic channels with a large time delay spread is investigated under the framework of compressed sensing. For these types of channels, the excessively long impulse response will s...The estimation of sparse underwater acoustic channels with a large time delay spread is investigated under the framework of compressed sensing. For these types of channels, the excessively long impulse response will significantly degrade the convergence rate and tracking capability of the traditional estimation algorithms such as least squares （LS）, while excluding the use of the delay-Doppler spread function due to huge computational complexity. By constructing a Toeplitz matrix with a training sequence as the measurement matrix, the estimation problem of long sparse acoustic channels is formulated into a compressed sensing problem to facilitate the efficient exploitation of sparsity. Furthermore, unlike the traditional l1 norm or exponent-based approximation l0 norm sparse recovery strategy, a novel variant of approximate l0 norm called AL0 is proposed, minimization of which leads to the derivation of a hybrid approach by iteratively projecting the steepest descent solution to the feasible set. Numerical simulations as well as sea trial experiments are compared and analyzed to demonstrate the superior performance of the proposed algorithm.展开更多
To improve spectral X-ray CT reconstructed image quality, the energy-weighted reconstructed image xbins^W and the separable paraboloidal surrogates（SPS） algorithm are proposed for the prior image constrained compres...To improve spectral X-ray CT reconstructed image quality, the energy-weighted reconstructed image xbins^W and the separable paraboloidal surrogates（SPS） algorithm are proposed for the prior image constrained compressed sensing（PICCS）-based spectral X-ray CT image reconstruction. The PICCS-based image reconstruction takes advantage of the compressed sensing theory, a prior image and an optimization algorithm to improve the image quality of CT reconstructions.To evaluate the performance of the proposed method, three optimization algorithms and three prior images are employed and compared in terms of reconstruction accuracy and noise characteristics of the reconstructed images in each energy bin.The experimental simulation results show that the image xbins^W is the best as the prior image in general with respect to the three optimization algorithms; and the SPS algorithm offers the best performance for the simulated phantom with respect to the three prior images. Compared with filtered back-projection（FBP）, the PICCS via the SPS algorithm and xbins^W as the prior image can offer the noise reduction in the reconstructed images up to 80. 46%, 82. 51%, 88. 08% in each energy bin,respectively. M eanwhile, the root-mean-squared error in each energy bin is decreased by 15. 02%, 18. 15%, 34. 11% and the correlation coefficient is increased by 9. 98%, 11. 38%,15. 94%, respectively.展开更多
For Wireless Sensor Networks （WSN） is responsible for sensing, collecting, processing and monitoring of environmental data, but it might be limited in resources. This paper describes in detail the compressed sensing...For Wireless Sensor Networks （WSN） is responsible for sensing, collecting, processing and monitoring of environmental data, but it might be limited in resources. This paper describes in detail the compressed sensing theory, study the wireless sensor network data conventional compression and network coding method. The linear network coding scheme based on sparse random projection theory of compressed sensing. Simulation results show that this system satisfies the requirements of the reconstruction error of packets needed to reduce the number of nodes to the total number of 30%, improves the efficiency of data communications in wireless sensor network, reduce the energy consumption of the system. With other wireless sensor network data compression algorithm, the proposed algorithm has the advantages of simple realization, the compression effect is good, especially suitable for resource limited, and the accuracy requirements are not particularly stringent in wireless sensor networks.展开更多
A new method for reconstructing the compressed sensing color image by solving an optimization problem based on total variation in the quaternion field is proposed, which can effectively improve the reconstructing abil...A new method for reconstructing the compressed sensing color image by solving an optimization problem based on total variation in the quaternion field is proposed, which can effectively improve the reconstructing ability of the color image. First, the color image is converted from RGB （red, green, blue） space to CMYK （cyan, magenta, yellow, black） space, which is assigned to a quaternion matrix. Meanwhile, the quaternion matrix is converted into the information of the phase and amplitude by the Euler form of the quatemion. Secondly, the phase and amplitude of the quatemion matrix are used as the smoothness constraints for the compressed sensing （CS） problem to make the reconstructing results more accurate. Finally, an iterative method based on gradient is used to solve the CS problem. Experimental results show that by considering the information of the phase and amplitude, the proposed method can achieve better performance than the existing method that treats the three components of the color image as independent parts.展开更多
High resolution range imaging with correlation processing suffers from high sidelobe pedestal in random frequency-hopping wideband radar. After the factors which affect the sidelobe pedestal being analyzed, a compress...High resolution range imaging with correlation processing suffers from high sidelobe pedestal in random frequency-hopping wideband radar. After the factors which affect the sidelobe pedestal being analyzed, a compressed sensing based algorithm for high resolution range imaging and a new minimized ll-norm criterion for motion compensation are proposed. The random hopping of the transmitted carrier frequency is converted to restricted isometry property of the observing matrix. Then practical problems of imaging model solution and signal parameter design are resolved. Due to the particularity of the proposed algorithm, two new indicators of range profile, i.e., average signal to sidelobe ratio and local similarity, are defined. The chamber measured data are adopted to testify the validity of the proposed algorithm, and simulations are performed to analyze the precision of velocity measurement as well as the performance of motion compensation. The simulation results show that the proposed algorithm has such advantages as high precision velocity measurement, low sidelobe and short period imaging, which ensure robust imaging for moving targets when signal-to-noise ratio is above 10 dB.展开更多
The traditional compressed sensing method for improving resolution is realized in the frequency domain.This method is aff ected by noise,which limits the signal-to-noise ratio and resolution,resulting in poor inversio...The traditional compressed sensing method for improving resolution is realized in the frequency domain.This method is aff ected by noise,which limits the signal-to-noise ratio and resolution,resulting in poor inversion.To solve this problem,we improved the objective function that extends the frequency domain to the Gaussian frequency domain having denoising and smoothing characteristics.Moreover,the reconstruction of the sparse refl ection coeffi cient is implemented by the mixed L1_L2 norm algorithm,which converts the L0 norm problem into an L1 norm problem.Additionally,a fast threshold iterative algorithm is introduced to speed up convergence and the conjugate gradient algorithm is used to achieve debiasing for eliminating the threshold constraint and amplitude error.The model test indicates that the proposed method is superior to the conventional OMP and BPDN methods.It not only has better denoising and smoothing eff ects but also improves the recognition accuracy of thin interbeds.The actual data application also shows that the new method can eff ectively expand the seismic frequency band and improve seismic data resolution,so the method is conducive to the identifi cation of thin interbeds for beach-bar sand reservoirs.展开更多
The theory of compressed sensing （CS） provides a new chance to reduce the data acquisition time and improve the data usage factor of the stepped frequency radar system. In light of the sparsity of radar target refle...The theory of compressed sensing （CS） provides a new chance to reduce the data acquisition time and improve the data usage factor of the stepped frequency radar system. In light of the sparsity of radar target reflectivity, two imaging methods based on CS, termed the CS-based 2D joint imaging algorithm and the CS-based 2D decoupled imaging algorithm, are proposed. These methods incorporate the coherent mixing operation into the sparse dictionary, and take random measurements in both range and azimuth directions to get high resolution radar images, thus can remarkably reduce the data rate and simplify the hardware design of the radar system while maintaining imaging quality. Ex- periments from both simulated data and measured data in the anechoic chamber show that the proposed imaging methods can get more focused images than the traditional fast Fourier trans- form method. Wherein the joint algorithm has stronger robustness and can provide clearer inverse synthetic aperture radar images, while the decoupled algorithm is computationally more efficient but has slightly degraded imaging quality, which can be improved by increasing measurements or using a robuster recovery algorithm nevertheless.展开更多
In high intensity focused ultrasound(HIFU)treatment,it is crucial to accurately identify denatured and normal biological tissues.In this paper,a novel method based on compressed sensing(CS)and refined composite multi-...In high intensity focused ultrasound(HIFU)treatment,it is crucial to accurately identify denatured and normal biological tissues.In this paper,a novel method based on compressed sensing(CS)and refined composite multi-scale fuzzy entropy(RCMFE)is proposed.First,CS is used to denoise the HIFU echo signals.Then the multi-scale fuzzy entropy(MFE)and RCMFE of the denoised HIFU echo signals are calculated.This study analyzed 90 cases of HIFU echo signals,including 45 cases in normal status and 45 cases in denatured status,and the results show that although both MFE and RCMFE can be used to identify denatured tissues,the intra-class distance of RCMFE on each scale factor is smaller than MFE,and the inter-class distance is larger than MFE.Compared with MFE,RCMFE can calculate the complexity of the signal more accurately and improve the stability,compactness,and separability.When RCMFE is selected as the characteristic parameter,the RCMFE difference between denatured and normal biological tissues is more evident than that of MFE,which helps doctors evaluate the treatment effect more accurately.When the scale factor is selected as 16,the best distinguishing effect can be obtained.展开更多
In the multi-target localization based on Compressed Sensing(CS),the sensing matrix's characteristic is significant to the localization accuracy.To improve the CS-based localization approach's performance,we p...In the multi-target localization based on Compressed Sensing(CS),the sensing matrix's characteristic is significant to the localization accuracy.To improve the CS-based localization approach's performance,we propose a sensing matrix optimization method in this paper,which considers the optimization under the guidance of the t%-averaged mutual coherence.First,we study sensing matrix optimization and model it as a constrained combinatorial optimization problem.Second,the t%-averaged mutual coherence is adopted as the optimality index to evaluate the quality of different sensing matrixes,where the threshold t is derived through the K-means clustering.With the settled optimality index,a hybrid metaheuristic algorithm named Genetic Algorithm-Tabu Local Search(GA-TLS)is proposed to address the combinatorial optimization problem to obtain the final optimized sensing matrix.Extensive simulation results reveal that the CS localization approaches using different recovery algorithms benefit from the proposed sensing matrix optimization method,with much less localization error compared to the traditional sensing matrix optimization methods.展开更多
Wireless Sensor Networks(WSN) are mainly characterized by a potentially large number of distributed sensor nodes which collectively transmit information about sensed events to the sink.In this paper,we present a Distr...Wireless Sensor Networks(WSN) are mainly characterized by a potentially large number of distributed sensor nodes which collectively transmit information about sensed events to the sink.In this paper,we present a Distributed Wavelet Basis Generation(DWBG) algorithm performing at the sink to obtain the distributed wavelet basis in WSN.And on this basis,a Wavelet Transform-based Distributed Compressed Sensing(WTDCS) algorithm is proposed to compress and reconstruct the sensed data with spatial correlation.Finally,we make a detailed analysis of relationship between reconstruction performance and WTDCS algorithm parameters such as the compression ratio,the channel Signal-to-Noise Ratio(SNR),the observation noise power and the correlation decay parameter by simulation.The simulation results show that WTDCS can achieve high performance in terms of energy and reconstruction accuracy,as compared to the conventional distributed wavelet transform algorithm.展开更多
Compressed Sensing （CS） offers a method to solve the channel estimation problems for an underwater acoustic system, based on the existence of a sparse representation of the treated signal and an overcomplete diction...Compressed Sensing （CS） offers a method to solve the channel estimation problems for an underwater acoustic system, based on the existence of a sparse representation of the treated signal and an overcomplete dictionary with a set of non-orthogonal bases. In this paper, we proposed a new approach to optimize dictionaries by decreasing the average measure of the mutual coherence of the effective dictionary. A fixed link between the average mutual coherence and the CS perforrmnce is indicated by designing three factors： operating bandwidth, the number of pilot subcarriers, and coherence bandwidth. Both the Orthogonal Matching Pursuit （OMP） and the Basis Pursuit De-Noising （BPDN） are compared to the Dantzig Selector （DS） for different Signal Noise Ratio （SNR） and shown to benefit from the newly designed dictionary. Nurnerical sinmlations and experimental data of an OFDM receiver are used to evaluate the proposed method in comparison with the conventional LeastSquare （LS） estirmtor. The results show that the dictionary with a better condition considerably improves the perforrmnce of the channel estimation.展开更多
Plug-and-play priors are popular for solving illposed imaging inverse problems. Recent efforts indicate that the convergence guarantee of the imaging algorithms using plug-andplay priors relies on the assumption of bo...Plug-and-play priors are popular for solving illposed imaging inverse problems. Recent efforts indicate that the convergence guarantee of the imaging algorithms using plug-andplay priors relies on the assumption of bounded denoisers. However, the bounded properties of existing plugged Gaussian denoisers have not been proven explicitly. To bridge this gap, we detail a novel provable bounded denoiser termed as BMDual,which combines a trainable denoiser using dual tight frames and the well-known block-matching and 3D filtering(BM3D)denoiser. We incorporate multiple dual frames utilized by BMDual into a novel regularization model induced by a solver. The proposed regularization model is utilized for compressed sensing magnetic resonance imaging(CSMRI). We theoretically show the bound of the BMDual denoiser, the bounded gradient of the CSMRI data-fidelity function, and further demonstrate that the proposed CSMRI algorithm converges. Experimental results also demonstrate that the proposed algorithm has a good convergence behavior, and show the effectiveness of the proposed algorithm.展开更多
A compressed sensing（CS） based channel estimation algorithm is proposed by using the delay-Doppler sparsity of the fast fading channel.A compressive basis expansion channel model with sparsity in both time and frequ...A compressed sensing（CS） based channel estimation algorithm is proposed by using the delay-Doppler sparsity of the fast fading channel.A compressive basis expansion channel model with sparsity in both time and frequency domains is given.The pilots in accordance with a novel random pilot matrix in both time and frequency domains are sent to measure the delay-Doppler sparsity channel.The relatively nonzero channel coefficients are tracked by random pilots at a sampling rate significantly below the Nyquist rate.The sparsity channels are estimated from a very limited number of channel measurements by the basis pursuit algorithm.The proposed algorithm can effectively improve the channel estimation performance when the number of pilot symbols is reduced with improvement of throughput efficiency.展开更多
In airborne array synthetic aperture radar(SAR), the three-dimensional(3D) imaging performance and cross-track resolution depends on the length of the equivalent array. In this paper, Barker sequence criterion is used...In airborne array synthetic aperture radar(SAR), the three-dimensional(3D) imaging performance and cross-track resolution depends on the length of the equivalent array. In this paper, Barker sequence criterion is used for sparse flight sampling of airborne array SAR, in order to obtain high cross-track resolution in as few times of flights as possible. Under each flight, the imaging algorithm of back projection(BP) and the data extraction method based on modified uniformly redundant arrays(MURAs) are utilized to obtain complex 3D image pairs. To solve the side-lobe noise in images, the interferometry between each image pair is implemented, and compressed sensing(CS) reconstruction is adopted in the frequency domain. Furthermore, to restore the geometrical relationship between each flight, the phase information corresponding to negative MURA is compensated on each single-pass image reconstructed by CS. Finally,by coherent accumulation of each complex image, the high resolution in cross-track direction is obtained. Simulations and experiments in X-band verify the availability.展开更多
Compressed sensing offers a new wideband spectrum sensing scheme in Cognitive Radio (CR). A major challenge of this scheme is how to determinate the required measurements while the signal sparsity is not known a prior...Compressed sensing offers a new wideband spectrum sensing scheme in Cognitive Radio (CR). A major challenge of this scheme is how to determinate the required measurements while the signal sparsity is not known a priori. This paper presents a cooperative sensing scheme based on se-quential compressed sensing where sequential measurements are collected from the analog-to-information converters. A novel cooperative compressed sensing recovery algorithm named Simul-taneous Sparsity Adaptive Matching Pursuit (SSAMP) is utilized for sequential compressed sensing in order to estimate the reconstruction errors and determinate the minimal number of required meas-urements. Once the fusion center obtains enough measurements, the reconstruction spectrum sparse vectors are then used to make a decision on spectrum occupancy. Simulations corroborate the effec-tiveness of the estimation and sensing performance of our cooperative scheme. Meanwhile, the per-formance of SSAMP and Simultaneous Orthogonal Matching Pursuit (SOMP) is evaluated by Mean-Square estimation Errors (MSE) and sensing time.展开更多
Wideband acoustic imaging,which combines compressed sensing(CS)and microphone arrays,is widely used for locating acoustic sources.However,the location results of this method are unstable,and the computational efficien...Wideband acoustic imaging,which combines compressed sensing(CS)and microphone arrays,is widely used for locating acoustic sources.However,the location results of this method are unstable,and the computational efficiency is low.In this work,in order to improve the robustness and reduce the computational cost,a DCS-SOMP-SVD compressed sensing method,which combines the distributed compressed sensing using simultaneously orthogonal matching pursuit(DCS-SOMP)and singular value decomposition(SVD)is proposed.The performance of the DCS-SOMP-SVD is studied through both simulation and experiment.In the simulation,the locating results of the DCS-SOMP-SVD method are compared with the wideband BP method and the DCS-SOMP method.In terms of computational efficiency,the proposed method is as efficient as the DCS-SOMP method and more efficient than the wideband BP method.In terms of locating accuracy,the proposed method can still locate all sources when the signal to noise ratio(SNR)is−20 dB,while the wideband BP method and the DCS-SOMP method can only locate all sources when the SNR is higher than 0 dB.The performance of the proposed method can be improved by expanding the frequency range.Moreover,there is no extra source in the maps of the proposed method,even though the target sparsity is overestimated.Finally,a gas leak experiment is conducted to verify the feasibility of the DCS-SOMP-SVD method in the practical engineering environment.The experimental results show that the proposed method can locate both two leak sources in different frequency ranges.This research proposes a DCS-SOMP-SVD method which has sufficient robustness and low computational cost for wideband acoustic imaging.展开更多
Electrical capacitance tomography(ECT)has great application potential inmultiphase processmonitoring,and its visualization results are of great significance for studying the changes in two-phase flow in closed environ...Electrical capacitance tomography(ECT)has great application potential inmultiphase processmonitoring,and its visualization results are of great significance for studying the changes in two-phase flow in closed environments.In this paper,compressed sensing(CS)theory based on dictionary learning is introduced to the inverse problem of ECT,and the K-SVD algorithm is used to learn the overcomplete dictionary to establish a nonlinear mapping between observed capacitance and sparse space.Because the trained overcomplete dictionary has the property to match few features of interest in the reconstructed image of ECT,it is not necessary to rely on the sparsity of coefficient vector to solve the nonlinear mapping as most algorithms based on CS theory.Two-phase flow distribution in a cylindrical pipe was modeled and simulated,and three variations without sparse constraint based on Landweber,Tikhonov,and Newton-Raphson algorithms were used to rapidly reconstruct a 2-D image.展开更多
In marine wireless sensor networks(MWSNs),an appropriate routing protocol is the key to the collaborative collection and efficient transmission of massive data.However,designing an appropriate routing protocol under t...In marine wireless sensor networks(MWSNs),an appropriate routing protocol is the key to the collaborative collection and efficient transmission of massive data.However,designing an appropriate routing protocol under the condition of sparse marine node deployment,highly dynamic network topology,and limited node energy is complicated.Moreover,the absence of continuous endto-end connection introduces further difficulties in the design of routing protocols.In this case,we present a novel energy-efficient opportunistic routing(Novel Energy-Efficient Opportunistic Routing,NEOR)protocol for MWSNs that is based on compressed sensing and power control.First,a lightweight time-series prediction method-weighted moving average method is proposed to predict the packet advancement value such that the number of location information that is exchanged among a node and its neighbor nodes can be minimized.Second,an adaptive power control mechanism is presented to determine the optimal transmitting power and candidate nodeset on the basis of node mobility,packet advancement,communication link quality,and remaining node energy.Subsequently,a timer-based scheduling algorithm is utilized to coordinate packet forwarding to avoid packet conflict.Furthermore,we introduce the compressed sensing theory to compress perceptual data at source nodes and reconstruct the original data at sink nodes.Therefore,energy consumption in the MWSNs is greatly reduced due to the decrease in the amount of data perception and transmission.Numerical simulation experiments are carried out in a wide range of marine scenarios to verify the superiority of our approach over selected benchmark algorithms.展开更多
基金Project supported by the Natural Science Foundation of Shandong Province,China(Grant Nos.ZR2020MF119 and ZR2020MA082)the National Natural Science Foundation of China(Grant No.62002208)the National Key Research and Development Program of China(Grant No.2018YFB0504302).
文摘We propose a fast,adaptive multiscale resolution spectral measurement method based on compressed sensing.The method can apply variable measurement resolution over the entire spectral range to reduce the measurement time by over 75%compared to a global high-resolution measurement.Mimicking the characteristics of the human retina system,the resolution distribution follows the principle of gradually decreasing.The system allows the spectral peaks of interest to be captured dynamically or to be specified a priori by a user.The system was tested by measuring single and dual spectral peaks,and the results of spectral peaks are consistent with those of global high-resolution measurements.
基金This work was supported in part by the National Natural Science Foundation of China under Grants 71571091,71771112the State Key Laboratory of Synthetical Automation for Process Industries Fundamental Research Funds under Grant PAL-N201801the Excellent Talent Training Project of University of Science and Technology Liaoning under Grant 2019RC05.
文摘With the advent of the information security era,it is necessary to guarantee the privacy,accuracy,and dependable transfer of pictures.This study presents a new approach to the encryption and compression of color images.It is predicated on 2D compressed sensing(CS)and the hyperchaotic system.First,an optimized Arnold scrambling algorithm is applied to the initial color images to ensure strong security.Then,the processed images are con-currently encrypted and compressed using 2D CS.Among them,chaotic sequences replace traditional random measurement matrices to increase the system’s security.Third,the processed images are re-encrypted using a combination of permutation and diffusion algorithms.In addition,the 2D projected gradient with an embedding decryption(2DPG-ED)algorithm is used to reconstruct images.Compared with the traditional reconstruction algorithm,the 2DPG-ED algorithm can improve security and reduce computational complexity.Furthermore,it has better robustness.The experimental outcome and the performance analysis indicate that this algorithm can withstand malicious attacks and prove the method is effective.
基金The National Natural Science Foundation of China(No.11274259)the Open Project Program of the Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education(No.UASP1305)
文摘The estimation of sparse underwater acoustic channels with a large time delay spread is investigated under the framework of compressed sensing. For these types of channels, the excessively long impulse response will significantly degrade the convergence rate and tracking capability of the traditional estimation algorithms such as least squares （LS）, while excluding the use of the delay-Doppler spread function due to huge computational complexity. By constructing a Toeplitz matrix with a training sequence as the measurement matrix, the estimation problem of long sparse acoustic channels is formulated into a compressed sensing problem to facilitate the efficient exploitation of sparsity. Furthermore, unlike the traditional l1 norm or exponent-based approximation l0 norm sparse recovery strategy, a novel variant of approximate l0 norm called AL0 is proposed, minimization of which leads to the derivation of a hybrid approach by iteratively projecting the steepest descent solution to the feasible set. Numerical simulations as well as sea trial experiments are compared and analyzed to demonstrate the superior performance of the proposed algorithm.
基金The National Natural Science Foundation of China(No.51575256)the Fundamental Research Funds for the Central Universities(No.NP2015101,XZA16003)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)
文摘To improve spectral X-ray CT reconstructed image quality, the energy-weighted reconstructed image xbins^W and the separable paraboloidal surrogates（SPS） algorithm are proposed for the prior image constrained compressed sensing（PICCS）-based spectral X-ray CT image reconstruction. The PICCS-based image reconstruction takes advantage of the compressed sensing theory, a prior image and an optimization algorithm to improve the image quality of CT reconstructions.To evaluate the performance of the proposed method, three optimization algorithms and three prior images are employed and compared in terms of reconstruction accuracy and noise characteristics of the reconstructed images in each energy bin.The experimental simulation results show that the image xbins^W is the best as the prior image in general with respect to the three optimization algorithms; and the SPS algorithm offers the best performance for the simulated phantom with respect to the three prior images. Compared with filtered back-projection（FBP）, the PICCS via the SPS algorithm and xbins^W as the prior image can offer the noise reduction in the reconstructed images up to 80. 46%, 82. 51%, 88. 08% in each energy bin,respectively. M eanwhile, the root-mean-squared error in each energy bin is decreased by 15. 02%, 18. 15%, 34. 11% and the correlation coefficient is increased by 9. 98%, 11. 38%,15. 94%, respectively.
文摘For Wireless Sensor Networks （WSN） is responsible for sensing, collecting, processing and monitoring of environmental data, but it might be limited in resources. This paper describes in detail the compressed sensing theory, study the wireless sensor network data conventional compression and network coding method. The linear network coding scheme based on sparse random projection theory of compressed sensing. Simulation results show that this system satisfies the requirements of the reconstruction error of packets needed to reduce the number of nodes to the total number of 30%, improves the efficiency of data communications in wireless sensor network, reduce the energy consumption of the system. With other wireless sensor network data compression algorithm, the proposed algorithm has the advantages of simple realization, the compression effect is good, especially suitable for resource limited, and the accuracy requirements are not particularly stringent in wireless sensor networks.
基金The National Basic Research Program of China(973Program)(No.2011CB707904)the National Natural Science Foundation of China(No.61201344,61271312,61073138)+1 种基金the Specialized Research Fund for the Doctoral Program of Higher Education(No.20110092110023,20120092120036)the Natural Science Foundation of Jiangsu Province(No.BK2012329)
文摘A new method for reconstructing the compressed sensing color image by solving an optimization problem based on total variation in the quaternion field is proposed, which can effectively improve the reconstructing ability of the color image. First, the color image is converted from RGB （red, green, blue） space to CMYK （cyan, magenta, yellow, black） space, which is assigned to a quaternion matrix. Meanwhile, the quaternion matrix is converted into the information of the phase and amplitude by the Euler form of the quatemion. Secondly, the phase and amplitude of the quatemion matrix are used as the smoothness constraints for the compressed sensing （CS） problem to make the reconstructing results more accurate. Finally, an iterative method based on gradient is used to solve the CS problem. Experimental results show that by considering the information of the phase and amplitude, the proposed method can achieve better performance than the existing method that treats the three components of the color image as independent parts.
基金Project(61171133) supported by the National Natural Science Foundation of ChinaProject(CX2011B019) supported by Hunan Provincial Innovation Foundation for Postgraduate,ChinaProject(B110404) supported by Innovation Foundation for Outstanding Postgraduates of National University of Defense Technology,China
文摘High resolution range imaging with correlation processing suffers from high sidelobe pedestal in random frequency-hopping wideband radar. After the factors which affect the sidelobe pedestal being analyzed, a compressed sensing based algorithm for high resolution range imaging and a new minimized ll-norm criterion for motion compensation are proposed. The random hopping of the transmitted carrier frequency is converted to restricted isometry property of the observing matrix. Then practical problems of imaging model solution and signal parameter design are resolved. Due to the particularity of the proposed algorithm, two new indicators of range profile, i.e., average signal to sidelobe ratio and local similarity, are defined. The chamber measured data are adopted to testify the validity of the proposed algorithm, and simulations are performed to analyze the precision of velocity measurement as well as the performance of motion compensation. The simulation results show that the proposed algorithm has such advantages as high precision velocity measurement, low sidelobe and short period imaging, which ensure robust imaging for moving targets when signal-to-noise ratio is above 10 dB.
基金National Science and Technology Major Project(No.2016ZX05006-002 and 2017ZX05072-001).
文摘The traditional compressed sensing method for improving resolution is realized in the frequency domain.This method is aff ected by noise,which limits the signal-to-noise ratio and resolution,resulting in poor inversion.To solve this problem,we improved the objective function that extends the frequency domain to the Gaussian frequency domain having denoising and smoothing characteristics.Moreover,the reconstruction of the sparse refl ection coeffi cient is implemented by the mixed L1_L2 norm algorithm,which converts the L0 norm problem into an L1 norm problem.Additionally,a fast threshold iterative algorithm is introduced to speed up convergence and the conjugate gradient algorithm is used to achieve debiasing for eliminating the threshold constraint and amplitude error.The model test indicates that the proposed method is superior to the conventional OMP and BPDN methods.It not only has better denoising and smoothing eff ects but also improves the recognition accuracy of thin interbeds.The actual data application also shows that the new method can eff ectively expand the seismic frequency band and improve seismic data resolution,so the method is conducive to the identifi cation of thin interbeds for beach-bar sand reservoirs.
基金supported by the Prominent Youth Fund of the National Natural Science Foundation of China (61025006)
文摘The theory of compressed sensing （CS） provides a new chance to reduce the data acquisition time and improve the data usage factor of the stepped frequency radar system. In light of the sparsity of radar target reflectivity, two imaging methods based on CS, termed the CS-based 2D joint imaging algorithm and the CS-based 2D decoupled imaging algorithm, are proposed. These methods incorporate the coherent mixing operation into the sparse dictionary, and take random measurements in both range and azimuth directions to get high resolution radar images, thus can remarkably reduce the data rate and simplify the hardware design of the radar system while maintaining imaging quality. Ex- periments from both simulated data and measured data in the anechoic chamber show that the proposed imaging methods can get more focused images than the traditional fast Fourier trans- form method. Wherein the joint algorithm has stronger robustness and can provide clearer inverse synthetic aperture radar images, while the decoupled algorithm is computationally more efficient but has slightly degraded imaging quality, which can be improved by increasing measurements or using a robuster recovery algorithm nevertheless.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11774088 and 11474090)。
文摘In high intensity focused ultrasound(HIFU)treatment,it is crucial to accurately identify denatured and normal biological tissues.In this paper,a novel method based on compressed sensing(CS)and refined composite multi-scale fuzzy entropy(RCMFE)is proposed.First,CS is used to denoise the HIFU echo signals.Then the multi-scale fuzzy entropy(MFE)and RCMFE of the denoised HIFU echo signals are calculated.This study analyzed 90 cases of HIFU echo signals,including 45 cases in normal status and 45 cases in denatured status,and the results show that although both MFE and RCMFE can be used to identify denatured tissues,the intra-class distance of RCMFE on each scale factor is smaller than MFE,and the inter-class distance is larger than MFE.Compared with MFE,RCMFE can calculate the complexity of the signal more accurately and improve the stability,compactness,and separability.When RCMFE is selected as the characteristic parameter,the RCMFE difference between denatured and normal biological tissues is more evident than that of MFE,which helps doctors evaluate the treatment effect more accurately.When the scale factor is selected as 16,the best distinguishing effect can be obtained.
文摘In the multi-target localization based on Compressed Sensing(CS),the sensing matrix's characteristic is significant to the localization accuracy.To improve the CS-based localization approach's performance,we propose a sensing matrix optimization method in this paper,which considers the optimization under the guidance of the t%-averaged mutual coherence.First,we study sensing matrix optimization and model it as a constrained combinatorial optimization problem.Second,the t%-averaged mutual coherence is adopted as the optimality index to evaluate the quality of different sensing matrixes,where the threshold t is derived through the K-means clustering.With the settled optimality index,a hybrid metaheuristic algorithm named Genetic Algorithm-Tabu Local Search(GA-TLS)is proposed to address the combinatorial optimization problem to obtain the final optimized sensing matrix.Extensive simulation results reveal that the CS localization approaches using different recovery algorithms benefit from the proposed sensing matrix optimization method,with much less localization error compared to the traditional sensing matrix optimization methods.
基金the National Basic Research Program of China，the National Natural Science Foundation of China，the open research fund of National Mobile Communications Research Laboratory,Southeast University，the Postdoctoral Science Foundation of Jiangsu Province，the University Natural Science Research Program of Jiangsu Province，the Basic Research Program of Jiangsu Province (Natural Science Foundation)
文摘Wireless Sensor Networks(WSN) are mainly characterized by a potentially large number of distributed sensor nodes which collectively transmit information about sensed events to the sink.In this paper,we present a Distributed Wavelet Basis Generation(DWBG) algorithm performing at the sink to obtain the distributed wavelet basis in WSN.And on this basis,a Wavelet Transform-based Distributed Compressed Sensing(WTDCS) algorithm is proposed to compress and reconstruct the sensed data with spatial correlation.Finally,we make a detailed analysis of relationship between reconstruction performance and WTDCS algorithm parameters such as the compression ratio,the channel Signal-to-Noise Ratio(SNR),the observation noise power and the correlation decay parameter by simulation.The simulation results show that WTDCS can achieve high performance in terms of energy and reconstruction accuracy,as compared to the conventional distributed wavelet transform algorithm.
基金Acknowledgements This work was supported by the National Science Foundation of China under Grant No. 60976065. The authors would like to thank the anonymous reviewers for comments that helped improve the paper.
文摘Compressed Sensing （CS） offers a method to solve the channel estimation problems for an underwater acoustic system, based on the existence of a sparse representation of the treated signal and an overcomplete dictionary with a set of non-orthogonal bases. In this paper, we proposed a new approach to optimize dictionaries by decreasing the average measure of the mutual coherence of the effective dictionary. A fixed link between the average mutual coherence and the CS perforrmnce is indicated by designing three factors： operating bandwidth, the number of pilot subcarriers, and coherence bandwidth. Both the Orthogonal Matching Pursuit （OMP） and the Basis Pursuit De-Noising （BPDN） are compared to the Dantzig Selector （DS） for different Signal Noise Ratio （SNR） and shown to benefit from the newly designed dictionary. Nurnerical sinmlations and experimental data of an OFDM receiver are used to evaluate the proposed method in comparison with the conventional LeastSquare （LS） estirmtor. The results show that the dictionary with a better condition considerably improves the perforrmnce of the channel estimation.
基金supported in part by the National Natural Science Foundation of China (62371414,61901406)the Hebei Natural Science Foundation (F2020203025)+2 种基金the Young Talent Program of Universities and Colleges in Hebei Province (BJ2021044)the Hebei Key Laboratory Project (202250701010046)the Central Government Guides Local Science and Technology Development Fund Projects(216Z1602G)。
文摘Plug-and-play priors are popular for solving illposed imaging inverse problems. Recent efforts indicate that the convergence guarantee of the imaging algorithms using plug-andplay priors relies on the assumption of bounded denoisers. However, the bounded properties of existing plugged Gaussian denoisers have not been proven explicitly. To bridge this gap, we detail a novel provable bounded denoiser termed as BMDual,which combines a trainable denoiser using dual tight frames and the well-known block-matching and 3D filtering(BM3D)denoiser. We incorporate multiple dual frames utilized by BMDual into a novel regularization model induced by a solver. The proposed regularization model is utilized for compressed sensing magnetic resonance imaging(CSMRI). We theoretically show the bound of the BMDual denoiser, the bounded gradient of the CSMRI data-fidelity function, and further demonstrate that the proposed CSMRI algorithm converges. Experimental results also demonstrate that the proposed algorithm has a good convergence behavior, and show the effectiveness of the proposed algorithm.
基金supported by the National Natural Science Foundation of China(60972056)the Innovation Foundation of Shanghai Education Committee(09ZZ89)Shanghai Leading Academic Discipline Project and STCSM(S30108and08DZ2231100)
文摘A compressed sensing（CS） based channel estimation algorithm is proposed by using the delay-Doppler sparsity of the fast fading channel.A compressive basis expansion channel model with sparsity in both time and frequency domains is given.The pilots in accordance with a novel random pilot matrix in both time and frequency domains are sent to measure the delay-Doppler sparsity channel.The relatively nonzero channel coefficients are tracked by random pilots at a sampling rate significantly below the Nyquist rate.The sparsity channels are estimated from a very limited number of channel measurements by the basis pursuit algorithm.The proposed algorithm can effectively improve the channel estimation performance when the number of pilot symbols is reduced with improvement of throughput efficiency.
文摘In airborne array synthetic aperture radar(SAR), the three-dimensional(3D) imaging performance and cross-track resolution depends on the length of the equivalent array. In this paper, Barker sequence criterion is used for sparse flight sampling of airborne array SAR, in order to obtain high cross-track resolution in as few times of flights as possible. Under each flight, the imaging algorithm of back projection(BP) and the data extraction method based on modified uniformly redundant arrays(MURAs) are utilized to obtain complex 3D image pairs. To solve the side-lobe noise in images, the interferometry between each image pair is implemented, and compressed sensing(CS) reconstruction is adopted in the frequency domain. Furthermore, to restore the geometrical relationship between each flight, the phase information corresponding to negative MURA is compensated on each single-pass image reconstructed by CS. Finally,by coherent accumulation of each complex image, the high resolution in cross-track direction is obtained. Simulations and experiments in X-band verify the availability.
基金Supported by the National High Technology Research and Development Program(No.2009AA01Z241)the National Natural Science Foundation(No.60971129,No.61071092)
文摘Compressed sensing offers a new wideband spectrum sensing scheme in Cognitive Radio (CR). A major challenge of this scheme is how to determinate the required measurements while the signal sparsity is not known a priori. This paper presents a cooperative sensing scheme based on se-quential compressed sensing where sequential measurements are collected from the analog-to-information converters. A novel cooperative compressed sensing recovery algorithm named Simul-taneous Sparsity Adaptive Matching Pursuit (SSAMP) is utilized for sequential compressed sensing in order to estimate the reconstruction errors and determinate the minimal number of required meas-urements. Once the fusion center obtains enough measurements, the reconstruction spectrum sparse vectors are then used to make a decision on spectrum occupancy. Simulations corroborate the effec-tiveness of the estimation and sensing performance of our cooperative scheme. Meanwhile, the per-formance of SSAMP and Simultaneous Orthogonal Matching Pursuit (SOMP) is evaluated by Mean-Square estimation Errors (MSE) and sensing time.
基金Supported by National Natural Science Foundation of China(Grant Nos.51675425,52075441)Shaanxi Provincial Key Research Program Project of China(Grant No.2020ZDLGY06-09)+1 种基金Dongguan Municipal Social Science and Technology Development(key)Project of China(Grant No.20185071021600)Science and Technology on Micro-system Laboratory Foundation of China(Grant No.6142804200405).
文摘Wideband acoustic imaging,which combines compressed sensing(CS)and microphone arrays,is widely used for locating acoustic sources.However,the location results of this method are unstable,and the computational efficiency is low.In this work,in order to improve the robustness and reduce the computational cost,a DCS-SOMP-SVD compressed sensing method,which combines the distributed compressed sensing using simultaneously orthogonal matching pursuit(DCS-SOMP)and singular value decomposition(SVD)is proposed.The performance of the DCS-SOMP-SVD is studied through both simulation and experiment.In the simulation,the locating results of the DCS-SOMP-SVD method are compared with the wideband BP method and the DCS-SOMP method.In terms of computational efficiency,the proposed method is as efficient as the DCS-SOMP method and more efficient than the wideband BP method.In terms of locating accuracy,the proposed method can still locate all sources when the signal to noise ratio(SNR)is−20 dB,while the wideband BP method and the DCS-SOMP method can only locate all sources when the SNR is higher than 0 dB.The performance of the proposed method can be improved by expanding the frequency range.Moreover,there is no extra source in the maps of the proposed method,even though the target sparsity is overestimated.Finally,a gas leak experiment is conducted to verify the feasibility of the DCS-SOMP-SVD method in the practical engineering environment.The experimental results show that the proposed method can locate both two leak sources in different frequency ranges.This research proposes a DCS-SOMP-SVD method which has sufficient robustness and low computational cost for wideband acoustic imaging.
基金This research was supported by the National Natural Science Foundation of China(No.51704229)Outstanding Youth Science Fund of Xi’an University of Science and Technology(No.2018YQ2-01).
文摘Electrical capacitance tomography(ECT)has great application potential inmultiphase processmonitoring,and its visualization results are of great significance for studying the changes in two-phase flow in closed environments.In this paper,compressed sensing(CS)theory based on dictionary learning is introduced to the inverse problem of ECT,and the K-SVD algorithm is used to learn the overcomplete dictionary to establish a nonlinear mapping between observed capacitance and sparse space.Because the trained overcomplete dictionary has the property to match few features of interest in the reconstructed image of ECT,it is not necessary to rely on the sparsity of coefficient vector to solve the nonlinear mapping as most algorithms based on CS theory.Two-phase flow distribution in a cylindrical pipe was modeled and simulated,and three variations without sparse constraint based on Landweber,Tikhonov,and Newton-Raphson algorithms were used to rapidly reconstruct a 2-D image.
基金supported by the National Natural Science Foundation of China(Nos.52201403,52201401,52071200,52102397,61701299,51709167)the National Key Research and Development Program(No.2021YFC2801002)+4 种基金the China Postdoctoral Science Foundation(Nos.2021M 700790,2022M712027)the Fund of National Engineering Research Center for Water Transport Safety(No.A2022003)the Foundation for Jiangsu Key Laboratory of Traffic and Transportation Security(No.TTS2021-05)the Fund of Hubei Key Laboratory of Inland Shipping Technology(No.NHHY2021002)the Top-Notch Innovative Program for Postgraduates of Shanghai Maritime University(Nos.2019YBR006,2019YBR002).
文摘In marine wireless sensor networks(MWSNs),an appropriate routing protocol is the key to the collaborative collection and efficient transmission of massive data.However,designing an appropriate routing protocol under the condition of sparse marine node deployment,highly dynamic network topology,and limited node energy is complicated.Moreover,the absence of continuous endto-end connection introduces further difficulties in the design of routing protocols.In this case,we present a novel energy-efficient opportunistic routing(Novel Energy-Efficient Opportunistic Routing,NEOR)protocol for MWSNs that is based on compressed sensing and power control.First,a lightweight time-series prediction method-weighted moving average method is proposed to predict the packet advancement value such that the number of location information that is exchanged among a node and its neighbor nodes can be minimized.Second,an adaptive power control mechanism is presented to determine the optimal transmitting power and candidate nodeset on the basis of node mobility,packet advancement,communication link quality,and remaining node energy.Subsequently,a timer-based scheduling algorithm is utilized to coordinate packet forwarding to avoid packet conflict.Furthermore,we introduce the compressed sensing theory to compress perceptual data at source nodes and reconstruct the original data at sink nodes.Therefore,energy consumption in the MWSNs is greatly reduced due to the decrease in the amount of data perception and transmission.Numerical simulation experiments are carried out in a wide range of marine scenarios to verify the superiority of our approach over selected benchmark algorithms.
