Neutron time-of-flight(ToF)measurement is a highly accurate method for obtaining the kinetic energy of a neutron by measuring its velocity,but requires precise acquisition of the neutron signal arrival time.However,th...Neutron time-of-flight(ToF)measurement is a highly accurate method for obtaining the kinetic energy of a neutron by measuring its velocity,but requires precise acquisition of the neutron signal arrival time.However,the high hardware costs and data burden associated with the acquisition of neutron ToF signals pose significant challenges.Higher sampling rates increase the data volume,data processing,and storage hardware costs.Compressed sampling can address these challenges,but it faces issues regarding optimal sampling efficiency and high-quality reconstructed signals.This paper proposes a revolutionary deep learning-based compressed sampling(DL-CS)algorithm for reconstructing neutron ToF signals that outperform traditional compressed sampling methods.This approach comprises four modules:random projection,rising dimensions,initial reconstruction,and final reconstruction.Initially,the technique adaptively compresses neutron ToF signals sequentially using three convolutional layers,replacing random measurement matrices in traditional compressed sampling theory.Subsequently,the signals are reconstructed using a modified inception module,long short-term memory,and self-attention.The performance of this deep compressed sampling method was quantified using the percentage root-mean-square difference,correlation coefficient,and reconstruction time.Experimental results showed that our proposed DL-CS approach can significantly enhance signal quality compared with other compressed sampling methods.This is evidenced by a percentage root-mean-square difference,correlation coefficient,and reconstruction time results of 5%,0.9988,and 0.0108 s,respectively,obtained for sampling rates below 10%for the neutron ToF signal generated using an electron-beam-driven photoneutron source.The results showed that the proposed DL-CS approach significantly improves the signal quality compared with other compressed sampling methods,exhibiting excellent reconstruction accuracy and speed.展开更多
In order to solve the cross-channel signal problem caused by the uniform channelized wideband digital receiver when processing wideband signal and the problem that the sensitivity of the system greatly decreases when ...In order to solve the cross-channel signal problem caused by the uniform channelized wideband digital receiver when processing wideband signal and the problem that the sensitivity of the system greatly decreases when the bandwidth of wideband digital receiver increases,which both decrease the wideband radar signal detection performance,a new wideband digital receiver based on the modulated wideband converter(MWC)discrete compressed sampling structure and an energy detection method based on the new receiver are proposed.Firstly,the proposed receiver utilizes periodic pseudo-random sequences to mix wideband signals with baseband and other sub-bands.Then the mixed signals are low-pass filtered and downsampled to obtain the baseband compressed sampling data,which can increase the sensitivity of the system.Meanwhile,the cross-channel signal will all appear in any subbands,so the cross-channel signal problem can be solved easily by processing the baseband compressed sampling data.Secondly,we establish the signal detection model and formulate the criterion of the energy detection method.And we directly utilize the baseband compressed sampling data to carry out signal detection without signal reconstruction,which decreases the complexity of the algorithm and reduces the computational burden.Finally,simulation experiments demonstrate the effectiveness of the proposed receiver and show that the proposed signal detection method is effective in low signal-to-noise ratio(SNR)compared with the conventional energy detection and the probability of detection increases significantly when SNR increases.展开更多
Blade Tip-Timing(BTT)has been regarded as a promising way of on-line blade vibration monitoring.But blind multi-band BTT vibration reconstruction is a big challenge under variable speeds and under-sampling.In order to...Blade Tip-Timing(BTT)has been regarded as a promising way of on-line blade vibration monitoring.But blind multi-band BTT vibration reconstruction is a big challenge under variable speeds and under-sampling.In order to deal with it,a novel Compressed Sensing(CS)method is proposed based on Multi-Coset Angular Sampling(MCAS)in this paper.First,multi-coset sampling scheme of BTT vibration signals is presented.Then the CS model of BTT vibration signals is derived in order domain.A sufficient condition of the number of BTT sensors is derived for perfect reconstruction and optimal placement of BTT sensors is determined by minimizing the condition number.In the end,numerical simulations are done to validate the proposed method and the performances of four reconstruction algorithms are compared,i.e.,Orthogonal Matching Pursuit(OMP),Multiple Signal Classification(MUSIC),Basis Pursuit Denoising(BPDN)and Modified Focal Underdetermined System Solver(MFOCUSS).Influences of the sensor placement,the number of BTT sensors and measurement noises on the reconstruction performances are also testified.The results demonstrate that the proposed method is feasible and the overall performance of the BPDN algorithm is the best among the four algorithms.Also the reconstruction performance decreases with the accelerations of rotating speed.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
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
A 2 m class robotic drill was sent to the Moon and successfully collected and returned regolith samples in late 2020 by China.It was a typical thick wall spiral drill(TWSD)with a hollow auger containing a complex cori...A 2 m class robotic drill was sent to the Moon and successfully collected and returned regolith samples in late 2020 by China.It was a typical thick wall spiral drill(TWSD)with a hollow auger containing a complex coring system to retain subsurface regolith samples.Before the robotic drill was launched,a series of laboratory tests were carried out to investigate and predict the possible drilling loads it may encounter in the lunar environment.This work presents how the sampling performance of the TWSD is affected by the regolith compressibility.Experiments and analysis during the drilling and sampling process in a simulated lunar regolith environment were conducted.The compressibility of a typical lunar regolith simulant(LRS)was measured through unidirectional compression tests to study the relationship between its inner regolith stress and bulk density.A theoretical model was established to elucidate the cutting discharge behavior by auger flights based on the aforementioned relationship.Experiments were conducted with the LRS,and the results show that the sampling performance is greatly affected by the flux of the drilled cuttings into the spiral flight channels.This work helped in scheduling reasonable drilling parameters to promote the sampling performance of the robotic drill in the Chinese Chang’E 5 mission.展开更多
This paper addresses the issue of the direction of arrival （DOA） estimation under the compressive sampling （CS） framework. A novel approach, modified multiple signal classification （MMUSIC） based on the CS array...This paper addresses the issue of the direction of arrival （DOA） estimation under the compressive sampling （CS） framework. A novel approach, modified multiple signal classification （MMUSIC） based on the CS array （CSA-MMUSIC）, is proposed to resolve the DOA estimation of correlated signals and two closely adjacent signals. By using two random CS matrices, a large size array is compressed into a small size array, which effectively reduces the number of the front end circuit. The theoretical analysis demonstrates that the proposed approach has the advantages of low computational complexity and hardware structure compared to other MMUSIC approaches. Simulation results show that CSAMMUSIC can possess similar angular resolution as MMUSIC.展开更多
Nonperiodic interrupted sampling repeater jamming(ISRJ)against inverse synthetic aperture radar(ISAR)can obtain two-dimensional blanket jamming performance by joint fast and slow time domain interrupted modulation,whi...Nonperiodic interrupted sampling repeater jamming(ISRJ)against inverse synthetic aperture radar(ISAR)can obtain two-dimensional blanket jamming performance by joint fast and slow time domain interrupted modulation,which is obviously dif-ferent from the conventional multi-false-target deception jam-ming.In this paper,a suppression method against this kind of novel jamming is proposed based on inter-pulse energy function and compressed sensing theory.By utilizing the discontinuous property of the jamming in slow time domain,the unjammed pulse is separated using the intra-pulse energy function diffe-rence.Based on this,the two-dimensional orthogonal matching pursuit(2D-OMP)algorithm is proposed.Further,it is proposed to reconstruct the ISAR image with the obtained unjammed pulse sequence.The validity of the proposed method is demon-strated via the Yake-42 plane data simulations.展开更多
Based on compressive sampling transmission model, we demonstrate here a method of quality evaluation for the reconstruction images, which is promising for the transmission of unstructured signal with reduced dimension...Based on compressive sampling transmission model, we demonstrate here a method of quality evaluation for the reconstruction images, which is promising for the transmission of unstructured signal with reduced dimension. By this method, the auxiliary information of the recovery image quality is obtained as a feedback to control number of measurements from compressive sampling video stream. Therefore, the number of measurements can be easily derived at the condition of the absence of information sparsity, and the recovery image quality is effectively improved. Theoretical and experimental results show that this algorithm can estimate the quality of images effectively and is in well consistency with the traditional objective evaluation algorithm.展开更多
Compressive sensing is a revolutionary idea proposed recently to achieve much lower sampling rate for signals.In the image application with limited resources the camera data can be stored and processed in compressed f...Compressive sensing is a revolutionary idea proposed recently to achieve much lower sampling rate for signals.In the image application with limited resources the camera data can be stored and processed in compressed form.An algorithm for moving object and region detection in video using a compressive sampling is developed.The algorithm estimates motion information of the moving object and regions in the video from the compressive measurements of the current image and background scene.The algorithm does not perform inverse compressive operation to obtain the actual pixels of the current image nor the estimated background.This leads to a computationally efficient method and a system compared with the existing motion estimation methods.The experimental results show that the sampling rate can reduce to 25%without sacrificing performance.展开更多
Single-shot ultrafast compressed imaging(UCI)is an effective tool for studying ultrafast dynamics in physics,chemistry,or material science because of its excellent high frame rate and large frame number.However,the ra...Single-shot ultrafast compressed imaging(UCI)is an effective tool for studying ultrafast dynamics in physics,chemistry,or material science because of its excellent high frame rate and large frame number.However,the random code(Rcode)used in traditional UCI will lead to low-frequency noise covering high-frequency information due to its uneven sampling interval,which is a great challenge in the fidelity of large-frame reconstruction.Here,a high-frequency enhanced compressed active photography(H-CAP)is proposed.By uniformizing the sampling interval of R-code,H-CAP capture the ultrafast process with a random uniform sampling mode.This sampling mode makes the high-frequency sampling energy dominant,which greatly suppresses the low-frequency noise blurring caused by R-code and achieves high-frequency information of image enhanced.The superior dynamic performance and large-frame reconstruction ability of H-CAP are verified by imaging optical self-focusing effect and static object,respectively.We applied H-CAP to the spatial-temporal characterization of double-pulse induced silicon surface ablation dynamics,which is performed within 220 frames in a single-shot of 300 ps.H-CAP provides a high-fidelity imaging method for observing ultrafast unrepeatable dynamic processes with large frames.展开更多
Time-delay and Doppler shift estimation is a basic task for pulse-Doppler radar processing. For low-rate sampling of echo signals, several kinds of compressive sampling(CS) pulse-Doppler(CSPD) radar are developed with...Time-delay and Doppler shift estimation is a basic task for pulse-Doppler radar processing. For low-rate sampling of echo signals, several kinds of compressive sampling(CS) pulse-Doppler(CSPD) radar are developed with different analog-to-information conversion(AIC) systems. However, a unified metric is absent to evaluate their parameter estimation performance. Towards this end, this paper derives the deterministic Cramer-Rao bound(CRB)for the joint delay-Doppler estimation of CSPD radar to quantitatively analyze the estimate performance. Theoretical results reveal that the CRBs of both time-delays and Doppler shifts are inversely proportional to the received target signal-to-noise ratio(SNR), the number of transmitted pulses and the sampling rate of AIC systems. The main difference is that the CRB of Doppler shifts also lies on the coherent processing interval. Numerical experiments validate these theoretical results. They also show that the structure of the AIC systems has weak influence on the CRBs, which implies that the AIC structures can be flexibly selected for the implementation of CSPD radar.展开更多
In this paper,we propose a compressive sampling and reconstruction system based on the shift-invariant space associated with the fractional Gabor transform.With this system,we aim to achieve the subNyquist sampling an...In this paper,we propose a compressive sampling and reconstruction system based on the shift-invariant space associated with the fractional Gabor transform.With this system,we aim to achieve the subNyquist sampling and accurate reconstruction for chirp-like signals containing time-varying characteristics.Under the proposed scheme,we introduce the fractional Gabor transform to make a stable expansion for signals in the joint time-fractional-frequency domain.Then the compressive sampling and reconstruction system is constructed under the compressive sensing and shift-invariant space theory.We establish the reconstruction model and propose a block multiple response extension of sparse Bayesian learning algorithm to improve the reconstruction effect.The reconstruction error for the proposed system is analyzed.We show that,with considerations of noises and mismatches,the total error is bounded.The effectiveness of the proposed system is verified by numerical experiments.It is shown that our proposed system outperforms the other systems state-of-the-art.展开更多
Video reconstruction quality largely depends on the ability of employed sparse domain to adequately represent the underlying video in Distributed Compressed Video Sensing (DCVS). In this paper, we propose a novel dyna...Video reconstruction quality largely depends on the ability of employed sparse domain to adequately represent the underlying video in Distributed Compressed Video Sensing (DCVS). In this paper, we propose a novel dynamic global-Principal Component Analysis (PCA) sparse representation algorithm for video based on the sparse-land model and nonlocal similarity. First, grouping by matching is realized at the decoder from key frames that are previously recovered. Second, we apply PCA to each group (sub-dataset) to compute the principle components from which the sub-dictionary is constructed. Finally, the non-key frames are reconstructed from random measurement data using a Compressed Sensing (CS) reconstruction algorithm with sparse regularization. Experimental results show that our algorithm has a better performance compared with the DCT and K-SVD dictionaries.展开更多
Quantitatively correcting the unconfined compressive strength for sample disturbance is an important research project in the practice of ocean engineering and geotechnical engineering. In this study, the specimens of ...Quantitatively correcting the unconfined compressive strength for sample disturbance is an important research project in the practice of ocean engineering and geotechnical engineering. In this study, the specimens of undisturbed natural marine clay obtained from the same depth at the same site were deliberately disturbed to different levels. Then, the specimens with different extents of sample disturbance were trimmed for both oedometer tests and unconfined compression tests. The degree of sample disturbance SD is obtained from the oedometer test data. The relationship between the unconfined compressive strength q u and SD is studied for investigating the effect of sample disturbance on q u. It is found that the value of q u decreases linearly with the increase in SD. Then, a simple method of correcting q u for sample disturbance is proposed. Its validity is also verified through analysis of the existing published data.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
Characterizing spatial distribution of soil liquefaction potential is critical for assessing liquefactionrelated hazards(e.g.building damages caused by liquefaction-induced differential settlement).However,in engineer...Characterizing spatial distribution of soil liquefaction potential is critical for assessing liquefactionrelated hazards(e.g.building damages caused by liquefaction-induced differential settlement).However,in engineering practice,soil liquefaction potential is usually measured at limited locations in a specific site using in situ tests,e.g.cone penetration tests(CPTs),due to the restrictions of time,cost and access to subsurface space.In these cases,liquefaction potential of soil at untested locations requires to be interpreted from limited measured data points using proper interpolation method,leading to remarkable statistical uncertainty in liquefaction assessment.This underlines an important question of how to optimize the locations of CPT soundings and determine the minimum number of CPTs for achieving a target reliability level of liquefaction assessment.To tackle this issue,this study proposes a smart sampling strategy for determining the minimum number of CPTs and their optimal locations in a selfadaptive and data-driven manner.The proposed sampling strategy leverages on information entropy and Bayesian compressive sampling(BCS).Both simulated and real CPT data are used to demonstrate the proposed method.Illustrative examples indicate that the proposed method can adaptively and sequentially select the required number and optimal locations of CPTs.展开更多
Spectrum sensing is a core function at cognitive radio systems to have spectrum awareness. This could be achieved by collecting samples from the frequency band under observation to make a conclusion whether the band i...Spectrum sensing is a core function at cognitive radio systems to have spectrum awareness. This could be achieved by collecting samples from the frequency band under observation to make a conclusion whether the band is occupied, or it is a spectrum hole. The task of sensing is becoming more challenging especially at wideband spectrum scenario. The difficulty is due to conventional sampling rate theory which makes it infeasible to sample such very wide range of frequencies and the technical requirements are very costly. Recently, compressive sensing introduced itself as a pioneer solution that relaxed the wideband sampling rate requirements. It showed the ability to sample a signal below the Nyquist sampling rate and reconstructed it using very few measurements. In this paper, we discuss the approaches used for solving compressed spectrum sensing problem for wideband cognitive radio networks and how the problem is formulated and rendered to improve the detection performance.展开更多
In order to achieve the acoustic signal distributed acquisition of stored grain pests, a novel acoustic signal acquisition system was presented based on the wireless sensor networks. And the system architecture, hardw...In order to achieve the acoustic signal distributed acquisition of stored grain pests, a novel acoustic signal acquisition system was presented based on the wireless sensor networks. And the system architecture, hardware configuration, and software were introduced in detail. Considering bandwidth limitation of wireless sensor networks, random sampling algorithm based on the compressed sensing theory was proposed. The developed acoustic signal acquisition system was applied in sampling the crawl acoustic signal of Tribolinm castaneum Herbst adults in granary. Preliminary experimentation indicated the rationality and practicability of the developed system and the proposed algorithm. They can implement the remote, real-time, and reliable wireless transmission for the acoustic signal sampled data of multiple points stored grain pests effectively.展开更多
This study is to compare three-dimensional(3D)isotropic T2-weighted magnetic resonance imaging(MRI)with compressed sensing-sampling perfection with application optimized contrast(CS-SPACE)and the conventional image(3D...This study is to compare three-dimensional(3D)isotropic T2-weighted magnetic resonance imaging(MRI)with compressed sensing-sampling perfection with application optimized contrast(CS-SPACE)and the conventional image(3D-SPACE)sequence in terms of image quality,estimated signal-to-noise ratio(SNR),relative contrast-to-noise ratio(CNR),and the lesions’conspicuous of the female pelvis.Thirty-six females(age:51,28-73)with cervical carcinoma(n=20),rectal carcinoma(n=7),or uterine fibroid(n=9)were included.Patients underwent magnetic resonance(MR)imaging at a 3T scanner with the sequences of 3D-SPACE,CS-SPACE,and twodimensional(2D)T2-weighted turbo-spin echo(TSE).Quantitative analyses of estimated SNR and relative CNR between tumors and other tissues,image quality,and tissue conspicuity were performed.Two radiologists assessed the difference in diagnostic findings for carcinoma.Quantitative values and qualitative scores were analyzed,respectively.The estimated SNR and the relative CNR of tumor-to-muscle obturator internus,tumor-to-myometrium,and myometrium-to-muscle obturator internus was comparable between 3D-SPACE and CS-SPACE.The overall image quality and the conspicuity of the lesion scores of the CS-SPACE were higher than that of the 3D-SPACE(P<0.01).The CS-SPACE sequence offers shorter scan time,fewer artifacts,and comparable SNR and CNR to conventional 3D-SPACE,and has the potential to improve the performance of T2-weighted images.展开更多
基金supported by the National Defense Technology Foundation Program of China(No.JSJT2022209A001-3)Sichuan Science and Technology Program(No.2021JDRC0011)+1 种基金Nuclear Energy Development Research Program of China(Research on High Energy X-ray Imaging of Nuclear Fuel)Scientific Research and Innovation Team Program of Sichuan University of Science and Engineering(No.SUSE652A001).
文摘Neutron time-of-flight(ToF)measurement is a highly accurate method for obtaining the kinetic energy of a neutron by measuring its velocity,but requires precise acquisition of the neutron signal arrival time.However,the high hardware costs and data burden associated with the acquisition of neutron ToF signals pose significant challenges.Higher sampling rates increase the data volume,data processing,and storage hardware costs.Compressed sampling can address these challenges,but it faces issues regarding optimal sampling efficiency and high-quality reconstructed signals.This paper proposes a revolutionary deep learning-based compressed sampling(DL-CS)algorithm for reconstructing neutron ToF signals that outperform traditional compressed sampling methods.This approach comprises four modules:random projection,rising dimensions,initial reconstruction,and final reconstruction.Initially,the technique adaptively compresses neutron ToF signals sequentially using three convolutional layers,replacing random measurement matrices in traditional compressed sampling theory.Subsequently,the signals are reconstructed using a modified inception module,long short-term memory,and self-attention.The performance of this deep compressed sampling method was quantified using the percentage root-mean-square difference,correlation coefficient,and reconstruction time.Experimental results showed that our proposed DL-CS approach can significantly enhance signal quality compared with other compressed sampling methods.This is evidenced by a percentage root-mean-square difference,correlation coefficient,and reconstruction time results of 5%,0.9988,and 0.0108 s,respectively,obtained for sampling rates below 10%for the neutron ToF signal generated using an electron-beam-driven photoneutron source.The results showed that the proposed DL-CS approach significantly improves the signal quality compared with other compressed sampling methods,exhibiting excellent reconstruction accuracy and speed.
基金supported by the National Natural Science Foundation of China(No.61571146)the Fundamental Research Funds for the Central Universities(HEUCF1608)
文摘In order to solve the cross-channel signal problem caused by the uniform channelized wideband digital receiver when processing wideband signal and the problem that the sensitivity of the system greatly decreases when the bandwidth of wideband digital receiver increases,which both decrease the wideband radar signal detection performance,a new wideband digital receiver based on the modulated wideband converter(MWC)discrete compressed sampling structure and an energy detection method based on the new receiver are proposed.Firstly,the proposed receiver utilizes periodic pseudo-random sequences to mix wideband signals with baseband and other sub-bands.Then the mixed signals are low-pass filtered and downsampled to obtain the baseband compressed sampling data,which can increase the sensitivity of the system.Meanwhile,the cross-channel signal will all appear in any subbands,so the cross-channel signal problem can be solved easily by processing the baseband compressed sampling data.Secondly,we establish the signal detection model and formulate the criterion of the energy detection method.And we directly utilize the baseband compressed sampling data to carry out signal detection without signal reconstruction,which decreases the complexity of the algorithm and reduces the computational burden.Finally,simulation experiments demonstrate the effectiveness of the proposed receiver and show that the proposed signal detection method is effective in low signal-to-noise ratio(SNR)compared with the conventional energy detection and the probability of detection increases significantly when SNR increases.
基金supported by the National Natural Science Foundation of China(No.51975206)。
文摘Blade Tip-Timing(BTT)has been regarded as a promising way of on-line blade vibration monitoring.But blind multi-band BTT vibration reconstruction is a big challenge under variable speeds and under-sampling.In order to deal with it,a novel Compressed Sensing(CS)method is proposed based on Multi-Coset Angular Sampling(MCAS)in this paper.First,multi-coset sampling scheme of BTT vibration signals is presented.Then the CS model of BTT vibration signals is derived in order domain.A sufficient condition of the number of BTT sensors is derived for perfect reconstruction and optimal placement of BTT sensors is determined by minimizing the condition number.In the end,numerical simulations are done to validate the proposed method and the performances of four reconstruction algorithms are compared,i.e.,Orthogonal Matching Pursuit(OMP),Multiple Signal Classification(MUSIC),Basis Pursuit Denoising(BPDN)and Modified Focal Underdetermined System Solver(MFOCUSS).Influences of the sensor placement,the number of BTT sensors and measurement noises on the reconstruction performances are also testified.The results demonstrate that the proposed method is feasible and the overall performance of the BPDN algorithm is the best among the four algorithms.Also the reconstruction performance decreases with the accelerations of rotating speed.
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 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financially supported in-part by the Pre-research project on Civil Aerospace Technologies by CNSA(No.D020201)the National Natural Science Foundation of China(No.51905105,51775011,11932001,51635002,and U2013603)+2 种基金the Natural Science Foundation of Guangdong Province(No.2020A1515011262)the State Key Laboratory of Robotics and Systems(HIT)(No.SKLRS-2020-KF12)the Technology Innovation Strategic Special Funds of Guangdong Province(No.2019A050503011)。
文摘A 2 m class robotic drill was sent to the Moon and successfully collected and returned regolith samples in late 2020 by China.It was a typical thick wall spiral drill(TWSD)with a hollow auger containing a complex coring system to retain subsurface regolith samples.Before the robotic drill was launched,a series of laboratory tests were carried out to investigate and predict the possible drilling loads it may encounter in the lunar environment.This work presents how the sampling performance of the TWSD is affected by the regolith compressibility.Experiments and analysis during the drilling and sampling process in a simulated lunar regolith environment were conducted.The compressibility of a typical lunar regolith simulant(LRS)was measured through unidirectional compression tests to study the relationship between its inner regolith stress and bulk density.A theoretical model was established to elucidate the cutting discharge behavior by auger flights based on the aforementioned relationship.Experiments were conducted with the LRS,and the results show that the sampling performance is greatly affected by the flux of the drilled cuttings into the spiral flight channels.This work helped in scheduling reasonable drilling parameters to promote the sampling performance of the robotic drill in the Chinese Chang’E 5 mission.
基金supported by the National Natural Science Foundation of China(6117119761371045+2 种基金61201307)the Shandong Provincial Natural Science Foundation(ZR2011FM005)the Shandong Provincial Promotive Research Fund for Excellent Young and Middle-aged Scientists(BS2010DX001)
文摘This paper addresses the issue of the direction of arrival （DOA） estimation under the compressive sampling （CS） framework. A novel approach, modified multiple signal classification （MMUSIC） based on the CS array （CSA-MMUSIC）, is proposed to resolve the DOA estimation of correlated signals and two closely adjacent signals. By using two random CS matrices, a large size array is compressed into a small size array, which effectively reduces the number of the front end circuit. The theoretical analysis demonstrates that the proposed approach has the advantages of low computational complexity and hardware structure compared to other MMUSIC approaches. Simulation results show that CSAMMUSIC can possess similar angular resolution as MMUSIC.
基金supported by the National Natural Science Foundation of China(62001481,61890542,62071475)the Natural Science Foundation of Hunan Province(2022JJ40561)the Research Program of National University of Defense Technology(ZK22-46).
文摘Nonperiodic interrupted sampling repeater jamming(ISRJ)against inverse synthetic aperture radar(ISAR)can obtain two-dimensional blanket jamming performance by joint fast and slow time domain interrupted modulation,which is obviously dif-ferent from the conventional multi-false-target deception jam-ming.In this paper,a suppression method against this kind of novel jamming is proposed based on inter-pulse energy function and compressed sensing theory.By utilizing the discontinuous property of the jamming in slow time domain,the unjammed pulse is separated using the intra-pulse energy function diffe-rence.Based on this,the two-dimensional orthogonal matching pursuit(2D-OMP)algorithm is proposed.Further,it is proposed to reconstruct the ISAR image with the obtained unjammed pulse sequence.The validity of the proposed method is demon-strated via the Yake-42 plane data simulations.
基金Supported by the National Natural Science Foundation of China (No. 60972039)Jiangsu Province Natural Science Fund Project (BK2010077)Innovation Project of SCI & Tech for College Graduates of Jiangsu Province(CXLX12 _0475)
文摘Based on compressive sampling transmission model, we demonstrate here a method of quality evaluation for the reconstruction images, which is promising for the transmission of unstructured signal with reduced dimension. By this method, the auxiliary information of the recovery image quality is obtained as a feedback to control number of measurements from compressive sampling video stream. Therefore, the number of measurements can be easily derived at the condition of the absence of information sparsity, and the recovery image quality is effectively improved. Theoretical and experimental results show that this algorithm can estimate the quality of images effectively and is in well consistency with the traditional objective evaluation algorithm.
文摘Compressive sensing is a revolutionary idea proposed recently to achieve much lower sampling rate for signals.In the image application with limited resources the camera data can be stored and processed in compressed form.An algorithm for moving object and region detection in video using a compressive sampling is developed.The algorithm estimates motion information of the moving object and regions in the video from the compressive measurements of the current image and background scene.The algorithm does not perform inverse compressive operation to obtain the actual pixels of the current image nor the estimated background.This leads to a computationally efficient method and a system compared with the existing motion estimation methods.The experimental results show that the sampling rate can reduce to 25%without sacrificing performance.
基金supported by the National Science Foundation of China(No.12127806,No.62175195 and No.12304382)the International Joint Research Laboratory for Micro/Nano Manufacturing and Measurement Technologies.
文摘Single-shot ultrafast compressed imaging(UCI)is an effective tool for studying ultrafast dynamics in physics,chemistry,or material science because of its excellent high frame rate and large frame number.However,the random code(Rcode)used in traditional UCI will lead to low-frequency noise covering high-frequency information due to its uneven sampling interval,which is a great challenge in the fidelity of large-frame reconstruction.Here,a high-frequency enhanced compressed active photography(H-CAP)is proposed.By uniformizing the sampling interval of R-code,H-CAP capture the ultrafast process with a random uniform sampling mode.This sampling mode makes the high-frequency sampling energy dominant,which greatly suppresses the low-frequency noise blurring caused by R-code and achieves high-frequency information of image enhanced.The superior dynamic performance and large-frame reconstruction ability of H-CAP are verified by imaging optical self-focusing effect and static object,respectively.We applied H-CAP to the spatial-temporal characterization of double-pulse induced silicon surface ablation dynamics,which is performed within 220 frames in a single-shot of 300 ps.H-CAP provides a high-fidelity imaging method for observing ultrafast unrepeatable dynamic processes with large frames.
基金supported by the National Natural Science Foundation of China(6140121061571228)
文摘Time-delay and Doppler shift estimation is a basic task for pulse-Doppler radar processing. For low-rate sampling of echo signals, several kinds of compressive sampling(CS) pulse-Doppler(CSPD) radar are developed with different analog-to-information conversion(AIC) systems. However, a unified metric is absent to evaluate their parameter estimation performance. Towards this end, this paper derives the deterministic Cramer-Rao bound(CRB)for the joint delay-Doppler estimation of CSPD radar to quantitatively analyze the estimate performance. Theoretical results reveal that the CRBs of both time-delays and Doppler shifts are inversely proportional to the received target signal-to-noise ratio(SNR), the number of transmitted pulses and the sampling rate of AIC systems. The main difference is that the CRB of Doppler shifts also lies on the coherent processing interval. Numerical experiments validate these theoretical results. They also show that the structure of the AIC systems has weak influence on the CRBs, which implies that the AIC structures can be flexibly selected for the implementation of CSPD radar.
基金supported by National Natural Science Foundation of China(Grant No.61501493)。
文摘In this paper,we propose a compressive sampling and reconstruction system based on the shift-invariant space associated with the fractional Gabor transform.With this system,we aim to achieve the subNyquist sampling and accurate reconstruction for chirp-like signals containing time-varying characteristics.Under the proposed scheme,we introduce the fractional Gabor transform to make a stable expansion for signals in the joint time-fractional-frequency domain.Then the compressive sampling and reconstruction system is constructed under the compressive sensing and shift-invariant space theory.We establish the reconstruction model and propose a block multiple response extension of sparse Bayesian learning algorithm to improve the reconstruction effect.The reconstruction error for the proposed system is analyzed.We show that,with considerations of noises and mismatches,the total error is bounded.The effectiveness of the proposed system is verified by numerical experiments.It is shown that our proposed system outperforms the other systems state-of-the-art.
基金supported by the Innovation Project of Graduate Students of Jiangsu Province, China under Grants No. CXZZ12_0466, No. CXZZ11_0390the National Natural Science Foundation of China under Grants No. 61071091, No. 61271240, No. 61201160, No. 61172118+2 种基金the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province, China under Grant No. 12KJB510019the Science and Technology Research Program of Hubei Provincial Department of Education under Grants No. D20121408, No. D20121402the Program for Research Innovation of Nanjing Institute of Technology Project under Grant No. CKJ20110006
文摘Video reconstruction quality largely depends on the ability of employed sparse domain to adequately represent the underlying video in Distributed Compressed Video Sensing (DCVS). In this paper, we propose a novel dynamic global-Principal Component Analysis (PCA) sparse representation algorithm for video based on the sparse-land model and nonlocal similarity. First, grouping by matching is realized at the decoder from key frames that are previously recovered. Second, we apply PCA to each group (sub-dataset) to compute the principle components from which the sub-dictionary is constructed. Finally, the non-key frames are reconstructed from random measurement data using a Compressed Sensing (CS) reconstruction algorithm with sparse regularization. Experimental results show that our algorithm has a better performance compared with the DCT and K-SVD dictionaries.
文摘Quantitatively correcting the unconfined compressive strength for sample disturbance is an important research project in the practice of ocean engineering and geotechnical engineering. In this study, the specimens of undisturbed natural marine clay obtained from the same depth at the same site were deliberately disturbed to different levels. Then, the specimens with different extents of sample disturbance were trimmed for both oedometer tests and unconfined compression tests. The degree of sample disturbance SD is obtained from the oedometer test data. The relationship between the unconfined compressive strength q u and SD is studied for investigating the effect of sample disturbance on q u. It is found that the value of q u decreases linearly with the increase in SD. Then, a simple method of correcting q u for sample disturbance is proposed. Its validity is also verified through analysis of the existing published data.
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supported by grants from the Research Grant Council of Hong Kong Special Administrative Region,China(Project Nos.CityU 11202121 and CityU 11213119).
文摘Characterizing spatial distribution of soil liquefaction potential is critical for assessing liquefactionrelated hazards(e.g.building damages caused by liquefaction-induced differential settlement).However,in engineering practice,soil liquefaction potential is usually measured at limited locations in a specific site using in situ tests,e.g.cone penetration tests(CPTs),due to the restrictions of time,cost and access to subsurface space.In these cases,liquefaction potential of soil at untested locations requires to be interpreted from limited measured data points using proper interpolation method,leading to remarkable statistical uncertainty in liquefaction assessment.This underlines an important question of how to optimize the locations of CPT soundings and determine the minimum number of CPTs for achieving a target reliability level of liquefaction assessment.To tackle this issue,this study proposes a smart sampling strategy for determining the minimum number of CPTs and their optimal locations in a selfadaptive and data-driven manner.The proposed sampling strategy leverages on information entropy and Bayesian compressive sampling(BCS).Both simulated and real CPT data are used to demonstrate the proposed method.Illustrative examples indicate that the proposed method can adaptively and sequentially select the required number and optimal locations of CPTs.
文摘Spectrum sensing is a core function at cognitive radio systems to have spectrum awareness. This could be achieved by collecting samples from the frequency band under observation to make a conclusion whether the band is occupied, or it is a spectrum hole. The task of sensing is becoming more challenging especially at wideband spectrum scenario. The difficulty is due to conventional sampling rate theory which makes it infeasible to sample such very wide range of frequencies and the technical requirements are very costly. Recently, compressive sensing introduced itself as a pioneer solution that relaxed the wideband sampling rate requirements. It showed the ability to sample a signal below the Nyquist sampling rate and reconstructed it using very few measurements. In this paper, we discuss the approaches used for solving compressed spectrum sensing problem for wideband cognitive radio networks and how the problem is formulated and rendered to improve the detection performance.
文摘In order to achieve the acoustic signal distributed acquisition of stored grain pests, a novel acoustic signal acquisition system was presented based on the wireless sensor networks. And the system architecture, hardware configuration, and software were introduced in detail. Considering bandwidth limitation of wireless sensor networks, random sampling algorithm based on the compressed sensing theory was proposed. The developed acoustic signal acquisition system was applied in sampling the crawl acoustic signal of Tribolinm castaneum Herbst adults in granary. Preliminary experimentation indicated the rationality and practicability of the developed system and the proposed algorithm. They can implement the remote, real-time, and reliable wireless transmission for the acoustic signal sampled data of multiple points stored grain pests effectively.
文摘This study is to compare three-dimensional(3D)isotropic T2-weighted magnetic resonance imaging(MRI)with compressed sensing-sampling perfection with application optimized contrast(CS-SPACE)and the conventional image(3D-SPACE)sequence in terms of image quality,estimated signal-to-noise ratio(SNR),relative contrast-to-noise ratio(CNR),and the lesions’conspicuous of the female pelvis.Thirty-six females(age:51,28-73)with cervical carcinoma(n=20),rectal carcinoma(n=7),or uterine fibroid(n=9)were included.Patients underwent magnetic resonance(MR)imaging at a 3T scanner with the sequences of 3D-SPACE,CS-SPACE,and twodimensional(2D)T2-weighted turbo-spin echo(TSE).Quantitative analyses of estimated SNR and relative CNR between tumors and other tissues,image quality,and tissue conspicuity were performed.Two radiologists assessed the difference in diagnostic findings for carcinoma.Quantitative values and qualitative scores were analyzed,respectively.The estimated SNR and the relative CNR of tumor-to-muscle obturator internus,tumor-to-myometrium,and myometrium-to-muscle obturator internus was comparable between 3D-SPACE and CS-SPACE.The overall image quality and the conspicuity of the lesion scores of the CS-SPACE were higher than that of the 3D-SPACE(P<0.01).The CS-SPACE sequence offers shorter scan time,fewer artifacts,and comparable SNR and CNR to conventional 3D-SPACE,and has the potential to improve the performance of T2-weighted images.
