The main problems in three-dimensional gravity inversion are the non-uniqueness of the solutions and the high computational cost of large data sets. To minimize the high computational cost, we propose a new sorting me...The main problems in three-dimensional gravity inversion are the non-uniqueness of the solutions and the high computational cost of large data sets. To minimize the high computational cost, we propose a new sorting method to reduce fluctuations and the high frequency of the sensitivity matrix prior to applying the wavelet transform. Consequently, the sparsity and compression ratio of the sensitivity matrix are improved as well as the accuracy of the forward modeling. Furthermore, memory storage requirements are reduced and the forward modeling is accelerated compared with uncompressed forward modeling. The forward modeling results suggest that the compression ratio of the sensitivity matrix can be more than 300. Furthermore, multiscale inversion based on the wavelet transform is applied to gravity inversion. By decomposing the gravity inversion into subproblems of different scales, the non-uniqueness and stability of the gravity inversion are improved as multiscale data are considered. Finally, we applied conventional focusing inversion and multiscale inversion on simulated and measured data to demonstrate the effectiveness of the proposed gravity inversion method.展开更多
Compressive sensing（CS） has emerged as a novel sampling framework which enables sparse signal acquisition and reconstruction with fewer measurements below the Nyquist rate.An important issue for CS is the constructi...Compressive sensing（CS） has emerged as a novel sampling framework which enables sparse signal acquisition and reconstruction with fewer measurements below the Nyquist rate.An important issue for CS is the construction of measurement matrix or sensing matrix.A new deterministic sensing matrix,named as OOC-B,is proposed by exploiting optical orthogonal codes（OOCs）,Bernoulli matrix and Singer structure,which has the entries of 0,＋1 and-1 before normalization.We have proven that the designed deterministic matrix is asymptotically optimal.In addition,the proposed deterministic sensing matrix is applied to direction of arrival（DOA） estimation of narrowband signals by CS arrays（CSA）processing and CS recovery.Theoretical analysis and simulation results show that the proposed sensing matrix has good performance for DOA estimation.It is very effective for simplifying hardware structure and decreasing computational complexity in DOA estimation by CSA processing.Besides,lower root mean square error（RMSE） and bias are obtained in DOA estimation by CS recovery.展开更多
Aiming at the problem that some existing traffic flow prediction models are only for a single road segment and the model input data are not pre-processed,a heuristic threshold algorithm is used to de-noise the origina...Aiming at the problem that some existing traffic flow prediction models are only for a single road segment and the model input data are not pre-processed,a heuristic threshold algorithm is used to de-noise the original traffic flow data after wavelet decomposition.The correlation coefficients of road traffic flow data are calculated and the data compression matrix of road traffic flow is constructed.Data de-noising minimizes the interference of data to the model,while the correlation analysis of road network data realizes the prediction at the road network level.Utilizing the advantages of long short term memory(LSTM)network in time series data processing,the compression matrix is input into the constructed LSTM model for short-term traffic flow prediction.The LSTM-1 and LSTM-2 models were respectively trained by de-noising processed data and original data.Through simulation experiments,different prediction times were set,and the prediction results of the prediction model proposed in this paper were compared with those of other methods.It is found that the accuracy of the LSTM-2 model proposed in this paper increases by 10.278%on average compared with other prediction methods,and the prediction accuracy reaches 95.58%,which proves that the short-term traffic flow prediction method proposed in this paper is efficient.展开更多
It is understood that the sparse signal recovery with a standard compressive sensing（CS） strategy requires the measurement matrix known as a priori. The measurement matrix is, however, often perturbed in a practical...It is understood that the sparse signal recovery with a standard compressive sensing（CS） strategy requires the measurement matrix known as a priori. The measurement matrix is, however, often perturbed in a practical application.In order to handle such a case, an optimization problem by exploiting the sparsity characteristics of both the perturbations and signals is formulated. An algorithm named as the sparse perturbation signal recovery algorithm（SPSRA） is then proposed to solve the formulated optimization problem. The analytical results show that our SPSRA can simultaneously recover the signal and perturbation vectors by an alternative iteration way, while the convergence of the SPSRA is also analytically given and guaranteed. Moreover, the support patterns of the sparse signal and structured perturbation shown are the same and can be exploited to improve the estimation accuracy and reduce the computation complexity of the algorithm. The numerical simulation results verify the effectiveness of analytical ones.展开更多
In order to solve the problem of high-speed sampling in OFDM based ultra wide band(UWB) systems, this paper first gives analysis on the applicability of existing compressed sampling methods. Then, on the basis of an e...In order to solve the problem of high-speed sampling in OFDM based ultra wide band(UWB) systems, this paper first gives analysis on the applicability of existing compressed sampling methods. Then, on the basis of an established segmented observation model, it presents an optimized parallel segmented compressed sampling(OPSCS) scheme based on Hadamard matrix. The orthogonal Hadamard matrix is adopted to construct the segmented measurement matrix with any dimensions, thus orthogonal or quasi-orthogonal multiplex observation sequences are obtained, and the restricted isometry property is improved. The optimized orthogonal matching pursuit algorithm is also used for the known sparsity avoiding iterative operation. Researches show that the proposed method can effectively reduce the sampling rate in OFDM-UWB systems, and also has a good ability of noise resisting that it achieves a high system performance better than the existing schemes of compressed sampling and even Nyquist rate sampling.展开更多
In order to apply compressive sensing in wireless sensor network, inside the nodes cluster classified by the spatial correlation, we propose that a cluster head adopts free space optical communication with space divis...In order to apply compressive sensing in wireless sensor network, inside the nodes cluster classified by the spatial correlation, we propose that a cluster head adopts free space optical communication with space division multiple access, and a sensor node uses a modulating retro-reflector for communication. Thus while a random sampling matrix is used to guide the establishment of links between head cluster and sensor nodes, the random linear projection is accomplished. To establish multiple links at the same time, an optical space division multiple access antenna is designed. It works in fixed beams switching mode and consists of optic lens with a large field of view（FOV）, fiber array on the focal plane which is used to realize virtual channels segmentation, direction of arrival sensor, optical matrix switch and controller. Based on the angles of nodes＇ laser beams, by dynamically changing the route, optical matrix switch actualizes the multi-beam full duplex tracking receiving and transmission. Due to the structure of fiber array, there will be several fade zones both in the focal plane and in lens＇ FOV. In order to lower the impact of fade zones and harmonize multibeam, a fiber array adjustment is designed. By theoretical, simulated and experimental study, the antenna＇s qualitative feasibility is validated.展开更多
The compressibility of shale matrix reflects the effects of reservoir lithology, material composition, pore structure and tectonic deformation. It is important to understand the factors that influence shale matrix com...The compressibility of shale matrix reflects the effects of reservoir lithology, material composition, pore structure and tectonic deformation. It is important to understand the factors that influence shale matrix compressibility(SMC) and their effects on pore size distribution(PSD) heterogeneity in order to evaluate the properties of unconventional reservoirs.In this study, the volumes of pores whose diameters were in the range 6–100 nm were corrected for SMC for 17 shale samples from basins in China using high-pressure mercury intrusion and low-temperature nitrogen gas adsorption analyses,in order to investigate the factors influencing the SMC values. In addition, the variations in fractal dimensions before and after pore volume correction were determined, using single and multifractal models to explain the effects of SMC on PSD heterogeneity. In this process, the applicability of each fractal model for characterizing PSD heterogeneity was determined using statistical analyses. The Menger and Sierpinski single fractal models, the thermodynamic fractal model and a multifractal model were all used in this study. The results showed the following. The matrix compression restricts the segmentation of the fractal dimension curves for the single fractal Menger and Sierpinski models, which leads to a uniformity of PSD heterogeneity for different pore diameters. However, matrix compression has only a weak influence on the results calculated using a thermodynamic model. The SMC clearly affects the multifractal value variations, showing that the fractal dimension values of shale samples under matrix compression are small. Overall PSD heterogeneity becomes small for pores with diameters below 100 nm and the SMC primarily affects the PSD heterogeneity of higher pore volume areas. The comparison of fractal curves before and after correction and the variance analysis indicate that the thermodynamic model is applicable to quantitatively characterize PSD heterogeneity of shale collected from this sampling area. The results show that PSD heterogeneity increases gradually as micro-pore volumes increase.展开更多
This paper proposes a verifiable secret image sharing scheme based on compressive sensing, secret sharing, and image hashing. In this scheme, Toeplitz matrix generated by two chaotic maps is employed as measurement ma...This paper proposes a verifiable secret image sharing scheme based on compressive sensing, secret sharing, and image hashing. In this scheme, Toeplitz matrix generated by two chaotic maps is employed as measurement matrix. With the help of Shamir threshold scheme and image hashing, the receivers can obtain the stored values and the hash value of image. In the verifying stage and restoring stage, there must be at least t legal receivers to get the effective information. By comparing the hash value of the restored image with the hash value of original image, the scheme can effectively prevent the attacker from tampering or forging the shared images. Experimental results show that the proposed scheme has good recovery performance, can effectively reduce space, and is suitable for real-time transmission, storage, and verification.展开更多
Compressed sensing（CS） provides a new approach to acquire data as a sampling technique and makes it sure that a sparse signal can be reconstructed from few measurements. The construction of compressed matrixes is a ...Compressed sensing（CS） provides a new approach to acquire data as a sampling technique and makes it sure that a sparse signal can be reconstructed from few measurements. The construction of compressed matrixes is a central problem in compressed sensing. This paper provides a construction of deterministic CS matrixes, which are also disjunct and inclusive matrixes, from singular pseudo-symplectic space over finite fields of characteristic 2. Our construction is superior to De Vore＇s construction under some conditions and can be used to reconstruct sparse signals through an efficient algorithm.展开更多
基金This work was supported by the Key National Research Project of China （Nos. 2017YFC0601900 and 2016YFC0303100） and the Key Program of National Natural Science Foundation of China （Nos. 41530320 and 41774125）.
文摘The main problems in three-dimensional gravity inversion are the non-uniqueness of the solutions and the high computational cost of large data sets. To minimize the high computational cost, we propose a new sorting method to reduce fluctuations and the high frequency of the sensitivity matrix prior to applying the wavelet transform. Consequently, the sparsity and compression ratio of the sensitivity matrix are improved as well as the accuracy of the forward modeling. Furthermore, memory storage requirements are reduced and the forward modeling is accelerated compared with uncompressed forward modeling. The forward modeling results suggest that the compression ratio of the sensitivity matrix can be more than 300. Furthermore, multiscale inversion based on the wavelet transform is applied to gravity inversion. By decomposing the gravity inversion into subproblems of different scales, the non-uniqueness and stability of the gravity inversion are improved as multiscale data are considered. Finally, we applied conventional focusing inversion and multiscale inversion on simulated and measured data to demonstrate the effectiveness of the proposed gravity inversion method.
基金supported by the National Natural Science Foundation of China(6117119761371045+2 种基金61201307)the Shandong Provincial Promotive Research Fund for Excellent Young and Middle-aged Scientists(BS2010DX001)the Shandong Provincial Natural Science Foundation (ZR2011FM005)
文摘Compressive sensing（CS） has emerged as a novel sampling framework which enables sparse signal acquisition and reconstruction with fewer measurements below the Nyquist rate.An important issue for CS is the construction of measurement matrix or sensing matrix.A new deterministic sensing matrix,named as OOC-B,is proposed by exploiting optical orthogonal codes（OOCs）,Bernoulli matrix and Singer structure,which has the entries of 0,＋1 and-1 before normalization.We have proven that the designed deterministic matrix is asymptotically optimal.In addition,the proposed deterministic sensing matrix is applied to direction of arrival（DOA） estimation of narrowband signals by CS arrays（CSA）processing and CS recovery.Theoretical analysis and simulation results show that the proposed sensing matrix has good performance for DOA estimation.It is very effective for simplifying hardware structure and decreasing computational complexity in DOA estimation by CSA processing.Besides,lower root mean square error（RMSE） and bias are obtained in DOA estimation by CS recovery.
基金National Natural Science Foundation of China(No.71961016)Planning Fund for the Humanities and Social Sciences of the Ministry of Education(Nos.15XJAZH002,18YJAZH148)Natural Science Foundation of Gansu Province(No.18JR3RA125)。
文摘Aiming at the problem that some existing traffic flow prediction models are only for a single road segment and the model input data are not pre-processed,a heuristic threshold algorithm is used to de-noise the original traffic flow data after wavelet decomposition.The correlation coefficients of road traffic flow data are calculated and the data compression matrix of road traffic flow is constructed.Data de-noising minimizes the interference of data to the model,while the correlation analysis of road network data realizes the prediction at the road network level.Utilizing the advantages of long short term memory(LSTM)network in time series data processing,the compression matrix is input into the constructed LSTM model for short-term traffic flow prediction.The LSTM-1 and LSTM-2 models were respectively trained by de-noising processed data and original data.Through simulation experiments,different prediction times were set,and the prediction results of the prediction model proposed in this paper were compared with those of other methods.It is found that the accuracy of the LSTM-2 model proposed in this paper increases by 10.278%on average compared with other prediction methods,and the prediction accuracy reaches 95.58%,which proves that the short-term traffic flow prediction method proposed in this paper is efficient.
基金supported by the National Natural Science Foundation of China(61171127)
文摘It is understood that the sparse signal recovery with a standard compressive sensing（CS） strategy requires the measurement matrix known as a priori. The measurement matrix is, however, often perturbed in a practical application.In order to handle such a case, an optimization problem by exploiting the sparsity characteristics of both the perturbations and signals is formulated. An algorithm named as the sparse perturbation signal recovery algorithm（SPSRA） is then proposed to solve the formulated optimization problem. The analytical results show that our SPSRA can simultaneously recover the signal and perturbation vectors by an alternative iteration way, while the convergence of the SPSRA is also analytically given and guaranteed. Moreover, the support patterns of the sparse signal and structured perturbation shown are the same and can be exploited to improve the estimation accuracy and reduce the computation complexity of the algorithm. The numerical simulation results verify the effectiveness of analytical ones.
基金supported by the National Natural Science Foundation of China (No.61302062)the National Natural Science Foundation of China (No.61571244)the Natural Science Foundation of Tianjin for Young Scientist (No.13JCQNJC00900)
文摘In order to solve the problem of high-speed sampling in OFDM based ultra wide band(UWB) systems, this paper first gives analysis on the applicability of existing compressed sampling methods. Then, on the basis of an established segmented observation model, it presents an optimized parallel segmented compressed sampling(OPSCS) scheme based on Hadamard matrix. The orthogonal Hadamard matrix is adopted to construct the segmented measurement matrix with any dimensions, thus orthogonal or quasi-orthogonal multiplex observation sequences are obtained, and the restricted isometry property is improved. The optimized orthogonal matching pursuit algorithm is also used for the known sparsity avoiding iterative operation. Researches show that the proposed method can effectively reduce the sampling rate in OFDM-UWB systems, and also has a good ability of noise resisting that it achieves a high system performance better than the existing schemes of compressed sampling and even Nyquist rate sampling.
基金supported by the National Natural Science Foundation of China(61372069)and the"111"Project(B08038)
文摘In order to apply compressive sensing in wireless sensor network, inside the nodes cluster classified by the spatial correlation, we propose that a cluster head adopts free space optical communication with space division multiple access, and a sensor node uses a modulating retro-reflector for communication. Thus while a random sampling matrix is used to guide the establishment of links between head cluster and sensor nodes, the random linear projection is accomplished. To establish multiple links at the same time, an optical space division multiple access antenna is designed. It works in fixed beams switching mode and consists of optic lens with a large field of view（FOV）, fiber array on the focal plane which is used to realize virtual channels segmentation, direction of arrival sensor, optical matrix switch and controller. Based on the angles of nodes＇ laser beams, by dynamically changing the route, optical matrix switch actualizes the multi-beam full duplex tracking receiving and transmission. Due to the structure of fiber array, there will be several fade zones both in the focal plane and in lens＇ FOV. In order to lower the impact of fade zones and harmonize multibeam, a fiber array adjustment is designed. By theoretical, simulated and experimental study, the antenna＇s qualitative feasibility is validated.
基金funded by grants from the Natural Science Foundation of Shandong Province, China (Nos. ZR2021QD072 and ZR2020QD040)。
文摘The compressibility of shale matrix reflects the effects of reservoir lithology, material composition, pore structure and tectonic deformation. It is important to understand the factors that influence shale matrix compressibility(SMC) and their effects on pore size distribution(PSD) heterogeneity in order to evaluate the properties of unconventional reservoirs.In this study, the volumes of pores whose diameters were in the range 6–100 nm were corrected for SMC for 17 shale samples from basins in China using high-pressure mercury intrusion and low-temperature nitrogen gas adsorption analyses,in order to investigate the factors influencing the SMC values. In addition, the variations in fractal dimensions before and after pore volume correction were determined, using single and multifractal models to explain the effects of SMC on PSD heterogeneity. In this process, the applicability of each fractal model for characterizing PSD heterogeneity was determined using statistical analyses. The Menger and Sierpinski single fractal models, the thermodynamic fractal model and a multifractal model were all used in this study. The results showed the following. The matrix compression restricts the segmentation of the fractal dimension curves for the single fractal Menger and Sierpinski models, which leads to a uniformity of PSD heterogeneity for different pore diameters. However, matrix compression has only a weak influence on the results calculated using a thermodynamic model. The SMC clearly affects the multifractal value variations, showing that the fractal dimension values of shale samples under matrix compression are small. Overall PSD heterogeneity becomes small for pores with diameters below 100 nm and the SMC primarily affects the PSD heterogeneity of higher pore volume areas. The comparison of fractal curves before and after correction and the variance analysis indicate that the thermodynamic model is applicable to quantitatively characterize PSD heterogeneity of shale collected from this sampling area. The results show that PSD heterogeneity increases gradually as micro-pore volumes increase.
基金Supported by the National Natural Science Foundation of China(61572089,61502399)the Natural Science Foundation of Chongqing Science and Technology Commission(cstc2017jcyj BX0008,cstc2015jcyj A40039)+2 种基金the Fundamental Research Funds for the Central Universities(106112017 CDJQJ188830,106112017CDJXY180005,106112014CDJZR185501)the Research Program of Chongqing Education Commission(JK15012027,JK1601225)the Scientific Research Project of Yangtze Normal University(2015XJXM39,2015XJXM31)
文摘This paper proposes a verifiable secret image sharing scheme based on compressive sensing, secret sharing, and image hashing. In this scheme, Toeplitz matrix generated by two chaotic maps is employed as measurement matrix. With the help of Shamir threshold scheme and image hashing, the receivers can obtain the stored values and the hash value of image. In the verifying stage and restoring stage, there must be at least t legal receivers to get the effective information. By comparing the hash value of the restored image with the hash value of original image, the scheme can effectively prevent the attacker from tampering or forging the shared images. Experimental results show that the proposed scheme has good recovery performance, can effectively reduce space, and is suitable for real-time transmission, storage, and verification.
基金supported by the National Natural Science Foundation of China (61179026)
文摘Compressed sensing（CS） provides a new approach to acquire data as a sampling technique and makes it sure that a sparse signal can be reconstructed from few measurements. The construction of compressed matrixes is a central problem in compressed sensing. This paper provides a construction of deterministic CS matrixes, which are also disjunct and inclusive matrixes, from singular pseudo-symplectic space over finite fields of characteristic 2. Our construction is superior to De Vore＇s construction under some conditions and can be used to reconstruct sparse signals through an efficient algorithm.
