- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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GB-SAR Interferometry Based on Dimension-Reduced Compressive Sensing and Multiple Measurement Vectors Model
摘要： To reduce the data acquisition time and the high-level sidelobes produced by conventional focusing methods for ground-based synthetic aperture radar interferometry, we present a new method to provide accurate displacement maps based on the dimension-reduced compressive sensing (CS) method combined with the multiple measurement vectors (MMVs) model. The proposed CS method consists in selecting the supported area of targets, estimated by the fast conventional method with undersampled data. The following sparse reconstruction is applied only to the selected areas. The MMV-based approach allows increasing the coherence and the precision of displacement estimates. Two experiments are carried out to assess the performance of the proposed method.
关键词： multiple measurement vectors (MMVs) model,SAR interferometry,Compressive sensing (CS),ground-based synthetic aperture radar (GB-SAR),SAR
更新于2025-09-23 15:22:29
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Spectral Zooming and Resolution Limits of Spatial Spectral Compressive Spectral Imagers
摘要： The recently introduced Spatial Spectral Compressive Spectral Imager (SSCSI) has been proposed as an alternative to carry out spatial and spectral coding using a binary on-off coded aperture. In SSCSI, the pixel pitch size of the coded aperture, as well as its location with respect to the detector array, play a critical role in the quality of image reconstruction. In this paper, a rigorous discretization model for this architecture is developed, based on a light propagation analysis across the imager. The attainable spatial and spectral resolution, and the various parameters affecting them, is derived through this process. Much like the displacement of zoom lens components leads to higher spatial resolution of a scene, a shift of the coded aperture in the SSCSI in reference to the detector leads to higher spectral resolution. This allows the recovery of spectrally detailed datacubes by physically displacing the mask towards the spectral plane. To prove the underlying concepts, computer simulations and experimental data are presented in this paper.
关键词： discretization model,spatial resolution,coded aperture,Spectral imaging,compressive sensing,spectral resolution
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Hyperspectral Compressive Sensing on Low Energy Consumption Board
摘要： Hyperspectral imaging instruments allow remote Earth exploration by measuring hundreds of spectral bands (at different wavelength channels) for the same area of the Earth surface. The acquired data cube comprises several GBs per flight, which have attracted attention to onboard compression techniques. Typically these compression techniques are expensive from the computational point of view. This paper presents a compressive sensing method implementation on a low power consumption Graphic Processing Unit. The experiments are conducted on a Jetson TX1 board, which is well suited to perform vector operations such as dot products. These experiments have been performed to demonstrate the applicability, in terms of accuracy and time consuming, of these methods for onboard processing. The results show that by using this low power consumption GPU it is possible to obtain real-time performance with a very limited power requirements.
关键词： GPU,Low-Power Consumption,Hyperspectral Imagery,Compressive Sensing
更新于2025-09-23 15:21:21
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A Prediction-Based Spatial-Spectral Adaptive Hyperspectral Compressive Sensing Algorithm
摘要： In order to improve the performance of storage and transmission of massive hyperspectral data, a prediction-based spatial-spectral adaptive hyperspectral compressive sensing (PSSAHCS) algorithm is proposed. Firstly, the spatial block size of hyperspectral images is adaptively obtained according to the spatial self-correlation coefficient. Secondly, a k-means clustering algorithm is used to group the hyperspectral images. Thirdly, we use a local means and local standard deviations (LMLSD) algorithm to find the optimal image in the group as the key band, and the non-key bands in the group can be smoothed by linear prediction. Fourthly, the random Gaussian measurement matrix is used as the sampling matrix, and the discrete cosine transform (DCT) matrix serves as the sparse basis. Finally, the stagewise orthogonal matching pursuit (StOMP) is used to reconstruct the hyperspectral images. The experimental results show that the proposed PSSAHCS algorithm can achieve better evaluation results—the subjective evaluation, the peak signal-to-noise ratio, and the spatial autocorrelation coefficient in the spatial domain, and spectral curve comparison and correlation between spectra-reconstructed performance in the spectral domain—than those of single spectral compression sensing (SSCS), block hyperspectral compressive sensing (BHCS), and adaptive grouping distributed compressive sensing (AGDCS). PSSAHCS can not only compress and reconstruct hyperspectral images effectively, but also has strong denoise performance.
关键词： interspectral prediction,compressive sensing,spatial-spectral adaptation,hyperspectral images
更新于2025-09-23 15:21:21
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A Novel Recovery Method of Soft X-ray Spectrum Unfolding Based on Compressive Sensing
摘要： In the experiment of inertial con?nement fusion, soft X-ray spectrum unfolding can provide important information to optimize the design of the laser and target. As the laser beams increase, there are limited locations for installing detection channels to obtain measurements, and the soft X-ray spectrum can be dif?cult to recover. In this paper, a novel recovery method of soft X-ray spectrum unfolding based on compressive sensing is proposed, in which (1) the spectrum recovery is formulated as a problem of accurate signal recovery from very few measurements (i.e., compressive sensing), and (2) the proper basis atoms are selected adaptively over a Legendre orthogonal basis dictionary with a large size and Lasso regression in the sense of (cid:96)1 norm, which enables the spectrum to be accurately recovered with little measured data from the limited detection channels. Finally, the presented approach is validated with experimental data. The results show that it can still achieve comparable accuracy from only 8 spectrometer detection channels as it has previously done from 14 detection channels. This means that the presented approach is capable of recovering spectrum from the data of limited detection channels, and it can be used to save more space for other detectors.
关键词： spectral measurement,spectrum unfolding,sparse representation,soft X-ray spectrometer,compressive sensing,lasso regression
更新于2025-09-23 15:21:21
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Multibeam for Joint Communication and Sensing Using Steerable Analog Antenna Arrays
摘要： Beamforming has great potential for joint communication and radar sensing (JCAS), which is becoming a demanding feature on many emerging platforms such as unmanned aerial vehicles and smart cars. Although beamforming has been extensively studied for communication and radar sensing respectively, its application in the joint system is not straightforward due to different beamforming requirements by communication and sensing. In this paper, we propose a novel multibeam framework using steerable analog antenna arrays, which allows seamless integration of communication and sensing. Different to conventional JCAS schemes that support JCAS using a single beam, our framework is based on the key innovation of multibeam technology: providing fixed subbeam for communication and packet-varying scanning subbeam for sensing, simultaneously from a single transmitting array. We provide a system architecture and protocols for the proposed framework, complying well with modern packet communication systems with multicarrier modulation. We also propose low-complexity and effective multibeam design and generation methods, which offer great flexibility in meeting different communication and sensing requirements. We further develop sensing parameter estimation algorithms using conventional digital Fourier transform and 1D compressive sensing techniques, matching well with the multibeam framework. Simulation results are provided and validate the effectiveness of our proposed framework, beamforming design methods and the sensing algorithms.
关键词： Compressive Sensing,Beamforming,Multibeam,Joint Communication and radar Sensing
更新于2025-09-23 15:21:01
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[IEEE 2019 International Conference on Communication and Electronics Systems (ICCES) - Coimbatore, India (2019.7.17-2019.7.19)] 2019 International Conference on Communication and Electronics Systems (ICCES) - Synchronization of Distributed Photovoltaic Generation with an Active network using Phase Locked Loop technique
摘要： Optical tomographic imaging requires an accurate forward model as well as regularization to mitigate missing-data artifacts and to suppress noise. Nonlinear forward models can provide more accurate interpretation of the measured data than their linear counterparts, but they generally result in computationally prohibitive reconstruction algorithms. Although sparsity-driven regularizers significantly improve the quality of reconstructed image, they further increase the computational burden of imaging. In this paper, we present a novel iterative imaging method for optical tomography that combines a nonlinear forward model based on the beam propagation method (BPM) with an edge-preserving three-dimensional (3-D) total variation (TV) regularizer. The central element of our approach is a time-reversal scheme, which allows for an efficient computation of the derivative of the transmitted wave-field with respect to the distribution of the refractive index. This time-reversal scheme together with our stochastic proximal-gradient algorithm makes it possible to optimize under a nonlinear forward model in a computationally tractable way, thus enabling a high-quality imaging of the refractive index throughout the object. We demonstrate the effectiveness of our method through several experiments on simulated and experimentally measured data.
关键词： Optical phase tomography,sparse reconstruction,beam propagation method,total variation regularization,compressive sensing,stochastic proximal-gradient
更新于2025-09-23 15:21:01
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AIP Conference Proceedings [AIP Publishing LLC 41ST ANNUAL REVIEW OF PROGRESS IN QUANTITATIVE NONDESTRUCTIVE EVALUATION: Volume 34 - Boise, Idaho (20–25 July 2014)] - Fast and low-dose computed laminography using compressive sensing based technique
摘要： Computed laminography (CL) is well known for inspecting microstructures in the materials, weldments and soldering defects in high density packed components or multilayer printed circuit boards. The overload problem on x-ray tube and gross failure of the radio-sensitive electronics devices during a scan are among important issues in CL which needs to be addressed. The sparse-view CL can be one of the viable option to overcome such issues. In this work a numerical aluminum welding phantom was simulated to collect sparsely sampled projection data at only 40 views using a conventional CL scanning scheme i.e. oblique scan. A compressive-sensing inspired total-variation (TV) minimization algorithm was utilized to reconstruct the images. It is found that the images reconstructed using sparse view data are visually comparable with the images reconstructed using full scan data set i.e. at 360 views on regular interval. We have quantitatively confirmed that tiny structures such as copper and tungsten slags, and copper flakes in the reconstructed images from sparsely sampled data are comparable with the corresponding structure present in the fully sampled data case. A blurring effect can be seen near the edges of few pores at the bottom of the reconstructed images from sparsely sampled data, despite the overall image quality is reasonable for fast and low-dose NDT.
关键词： Computed laminography,total-variation minimization,compressive sensing,non-destructive testing,sparse-view
更新于2025-09-23 15:21:01
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ADMM-CSNet: A Deep Learning Approach for Image Compressive Sensing
摘要： Compressive sensing (CS) is an effective technique for reconstructing image from a small amount of sampled data. It has been widely applied in medical imaging, remote sensing, image compression, etc. In this paper, we propose two versions of a novel deep learning architecture, dubbed as ADMM-CSNet, by combining the traditional model-based CS method and data-driven deep learning method for image reconstruction from sparsely sampled measurements. We ?rst consider a generalized CS model for image reconstruction with undetermined regularizations in undetermined transform domains, and then two ef?cient solvers using Alternating Direction Method of Multipliers (ADMM) algorithm for optimizing the model are proposed. We further unroll and generalize the ADMM algorithm to be two deep architectures, in which all parameters of the CS model and the ADMM algorithm are discriminatively learned by end-to-end training. For both applications of fast CS complex-valued MR imaging and CS imaging of real-valued natural images, the proposed ADMM-CSNet achieved favorable reconstruction accuracy in fast computational speed compared with the traditional and the other deep learning methods.
关键词： Compressive sensing,MR Imaging,deep learning,ADMM-CSNet,ADMM
更新于2025-09-23 15:21:01
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[IEEE 2019 International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON) - Novosibirsk, Russia (2019.10.21-2019.10.27)] 2019 International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON) - Numerical Calculation Current Form of Led Lamp PWM Stabilizer
摘要： A traf?c matrix is generally used by several network management tasks in a data center network, such as traf?c engineering and anomaly detection. It gives a ?ow-level view of the network traf?c volume. Despite the explicit importance of the traf?c matrix, it is signi?cantly dif?cult to implement a large-scale measurement to build an absolute traf?c matrix. Generally, the traf?c matrix obtained by the operators is imperfect, i.e., some traf?c data may be lost. Hence, we focus on the problems of recovering these missing traf?c data in this paper. To recover these missing traf?c data, we propose the spatio-temporal Kronecker compressive sensing method, which draws on Kronecker compressive sensing. In our method, we account for the spatial and temporal properties of the traf?c matrix to construct a sparsifying basis that can sparsely represent the traf?c matrix. Simultaneously, we consider the low-rank property of the traf?c matrix and propose a novel recovery model. We ?nally assess the estimation error of the proposed method by recovering real traf?c.
关键词： network management,Kronecker compressive sensing,network measurement,matrix completion,Traf?c matrix recovery
更新于2025-09-23 15:19:57