Sensing and compressing 3-D models
Krumm, J.
1998-02-01
The goal of this research project was to create a passive and robust computer vision system for producing 3-D computer models of arbitrary scenes. Although the authors were unsuccessful in achieving the overall goal, several components of this research have shown significant potential. Of particular interest is the application of parametric eigenspace methods for planar pose measurement of partially occluded objects in gray-level images. The techniques presented provide a simple, accurate, and robust solution to the planar pose measurement problem. In addition, the representational efficiency of eigenspace methods used with gray-level features were successfully extended to binary features, which are less sensitive to illumination changes. The results of this research are presented in two papers that were written during the course of this project. The papers are included in sections 2 and 3. The first section of this report summarizes the 3-D modeling efforts.
Modeling 3D faces from samplings via compressive sensing
NASA Astrophysics Data System (ADS)
Sun, Qi; Tang, Yanlong; Hu, Ping
2013-07-01
3D data is easier to acquire for family entertainment purpose today because of the mass-production, cheapness and portability of domestic RGBD sensors, e.g., Microsoft Kinect. However, the accuracy of facial modeling is affected by the roughness and instability of the raw input data from such sensors. To overcome this problem, we introduce compressive sensing (CS) method to build a novel 3D super-resolution scheme to reconstruct high-resolution facial models from rough samples captured by Kinect. Unlike the simple frame fusion super-resolution method, this approach aims to acquire compressed samples for storage before a high-resolution image is produced. In this scheme, depth frames are firstly captured and then each of them is measured into compressed samples using sparse coding. Next, the samples are fused to produce an optimal one and finally a high-resolution image is recovered from the fused sample. This framework is able to recover 3D facial model of a given user from compressed simples and this can reducing storage space as well as measurement cost in future devices e.g., single-pixel depth cameras. Hence, this work can potentially be applied into future applications, such as access control system using face recognition, and smart phones with depth cameras, which need high resolution and little measure time.
3D multifocus astigmatism and compressed sensing (3D MACS) based superresolution reconstruction
Huang, Jiaqing; Sun, Mingzhai; Gumpper, Kristyn; Chi, Yuejie; Ma, Jianjie
2015-01-01
Single molecule based superresolution techniques (STORM/PALM) achieve nanometer spatial resolution by integrating the temporal information of the switching dynamics of fluorophores (emitters). When emitter density is low for each frame, they are located to the nanometer resolution. However, when the emitter density rises, causing significant overlapping, it becomes increasingly difficult to accurately locate individual emitters. This is particularly apparent in three dimensional (3D) localization because of the large effective volume of the 3D point spread function (PSF). The inability to precisely locate the emitters at a high density causes poor temporal resolution of localization-based superresolution technique and significantly limits its application in 3D live cell imaging. To address this problem, we developed a 3D high-density superresolution imaging platform that allows us to precisely locate the positions of emitters, even when they are significantly overlapped in three dimensional space. Our platform involves a multi-focus system in combination with astigmatic optics and an ℓ1-Homotopy optimization procedure. To reduce the intrinsic bias introduced by the discrete formulation of compressed sensing, we introduced a debiasing step followed by a 3D weighted centroid procedure, which not only increases the localization accuracy, but also increases the computation speed of image reconstruction. We implemented our algorithms on a graphic processing unit (GPU), which speeds up processing 10 times compared with central processing unit (CPU) implementation. We tested our method with both simulated data and experimental data of fluorescently labeled microtubules and were able to reconstruct a 3D microtubule image with 1000 frames (512×512) acquired within 20 seconds. PMID:25798314
High-performance 3D compressive sensing MRI reconstruction.
Kim, Daehyun; Trzasko, Joshua D; Smelyanskiy, Mikhail; Haider, Clifton R; Manduca, Armando; Dubey, Pradeep
2010-01-01
Compressive Sensing (CS) is a nascent sampling and reconstruction paradigm that describes how sparse or compressible signals can be accurately approximated using many fewer samples than traditionally believed. In magnetic resonance imaging (MRI), where scan duration is directly proportional to the number of acquired samples, CS has the potential to dramatically decrease scan time. However, the computationally expensive nature of CS reconstructions has so far precluded their use in routine clinical practice - instead, more-easily generated but lower-quality images continue to be used. We investigate the development and optimization of a proven inexact quasi-Newton CS reconstruction algorithm on several modern parallel architectures, including CPUs, GPUs, and Intel's Many Integrated Core (MIC) architecture. Our (optimized) baseline implementation on a quad-core Core i7 is able to reconstruct a 256 × 160×80 volume of the neurovasculature from an 8-channel, 10 × undersampled data set within 56 seconds, which is already a significant improvement over existing implementations. The latest six-core Core i7 reduces the reconstruction time further to 32 seconds. Moreover, we show that the CS algorithm benefits from modern throughput-oriented architectures. Specifically, our CUDA-base implementation on NVIDIA GTX480 reconstructs the same dataset in 16 seconds, while Intel's Knights Ferry (KNF) of the MIC architecture even reduces the time to 12 seconds. Such level of performance allows the neurovascular dataset to be reconstructed within a clinically viable time.
Wang, Qingzhu; Chen, Xiaoming; Wei, Mengying; Miao, Zhuang
2016-11-04
The existing techniques for simultaneous encryption and compression of images refer lossy compression. Their reconstruction performances did not meet the accuracy of medical images because most of them have not been applicable to three-dimensional (3D) medical image volumes intrinsically represented by tensors. We propose a tensor-based algorithm using tensor compressive sensing (TCS) to address these issues. Alternating least squares is further used to optimize the TCS with measurement matrices encrypted by discrete 3D Lorenz. The proposed method preserves the intrinsic structure of tensor-based 3D images and achieves a better balance of compression ratio, decryption accuracy, and security. Furthermore, the characteristic of the tensor product can be used as additional keys to make unauthorized decryption harder. Numerical simulation results verify the validity and the reliability of this scheme.
Compressed sensing MRI reconstruction from 3D multichannel data using GPUs.
Chang, Ching-Hua; Yu, Xiangdong; Ji, Jim X
2017-02-15
To accelerate iterative reconstructions of compressed sensing (CS) MRI from 3D multichannel data using graphics processing units (GPUs). The sparsity of MRI signals and parallel array receivers can reduce the data acquisition requirements. However, iterative CS reconstructions from data acquired using an array system may take a significantly long time, especially for a large number of parallel channels. This paper presents an efficient method for CS-MRI reconstruction from 3D multichannel data using GPUs. In this method, CS reconstructions were simultaneously processed in a channel-by-channel fashion on the GPU, in which the computations of multiple-channel 3D-CS reconstructions are highly parallelized. The final image was then produced by a sum-of-squares method on the central processing unit. Implementation details including algorithm, data/memory management, and parallelization schemes are reported in the paper. Both simulated data and in vivo MRI array data were tested. The results showed that the proposed method can significantly improve the image reconstruction efficiency, typically shortening the runtime by a factor of 30. Using low-cost GPUs and an efficient algorithm allowed the 3D multislice compressive-sensing reconstruction to be performed in less than 1 s. The rapid reconstructions are expected to help bring high-dimensional, multichannel parallel CS MRI closer to clinical applications. Magn Reson Med, 2017. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.
Compressed Sensing for Resolution Enhancement of Hyperpolarized 13C Flyback 3D-MRSI
Hu, Simon; Lustig, Michael; Chen, Albert P.; Crane, Jason; Kerr, Adam; Kelley, Douglas A.C.; Hurd, Ralph; Kurhanewicz, John; Nelson, Sarah J.; Pauly, John M.; Vigneron, Daniel B.
2008-01-01
High polarization of nuclear spins in liquid state through dynamic nuclear polarization has enabled the direct monitoring of 13C metabolites in vivo at very high signal to noise, allowing for rapid assessment of tissue metabolism. The abundant SNR afforded by this hyperpolarization technique makes high resolution 13C 3D-MRSI feasible. However, the number of phase encodes that can be fit into the short acquisition time for hyperpolarized imaging limits spatial coverage and resolution. To take advantage of the high SNR available from hyperpolarization, we have applied compressed sensing to achieve a factor of 2 enhancement in spatial resolution without increasing acquisition time or decreasing coverage. In this paper, the design and testing of compressed sensing suited for a flyback 13C 3D-MRSI sequence are presented. The key to this design was the undersampling of spectral k-space using a novel blipped scheme, thus taking advantage of the considerable sparsity in typical hyperpolarized 13C spectra. Phantom tests validated the accuracy of the compressed sensing approach and initial mouse experiments demonstrated in vivo feasibility. PMID:18367420
Depth map resolution enhancement for 2D/3D imaging system via compressive sensing
NASA Astrophysics Data System (ADS)
Han, Juanjuan; Loffeld, Otmar; Hartmann, Klaus
2011-08-01
This paper introduces a novel approach for post-processing of depth map which enhances the depth map resolution in order to achieve visually pleasing 3D models from a new monocular 2D/3D imaging system consists of a Photonic mixer device (PMD) range camera and a standard color camera. The proposed method adopts the revolutionary inversion theory framework called Compressive Sensing (CS). The depth map of low resolution is considered as the result of applying blurring and down-sampling techniques to that of high-resolution. Based on the underlying assumption that the high-resolution depth map is compressible in frequency domain and recent theoretical work on CS, the high-resolution version can be estimated and furthermore reconstructed via solving non-linear optimization problem. And therefore the improved depth map reconstruction provides a useful help to build an improved 3D model of a scene. The experimental results on the real data are presented. In the meanwhile the proposed scheme opens new possibilities to apply CS to a multitude of potential applications on various multimodal data analysis and processing.
Rapid compressed sensing reconstruction of 3D non-Cartesian MRI.
Baron, Corey A; Dwork, Nicholas; Pauly, John M; Nishimura, Dwight G
2017-09-23
Conventional non-Cartesian compressed sensing requires multiple nonuniform Fourier transforms every iteration, which is computationally expensive. Accordingly, time-consuming reconstructions have slowed the adoption of undersampled 3D non-Cartesian acquisitions into clinical protocols. In this work we investigate several approaches to minimize reconstruction times without sacrificing accuracy. The reconstruction problem can be reformatted to exploit the Toeplitz structure of matrices that are evaluated every iteration, but it requires larger oversampling than what is strictly required by nonuniform Fourier transforms. Accordingly, we investigate relative speeds of the two approaches for various nonuniform Fourier transform kernel sizes and oversampling for both GPU and CPU implementations. Second, we introduce a method to minimize matrix sizes by estimating the image support. Finally, density compensation weights have been used as a preconditioning matrix to improve convergence, but this increases noise. We propose a more general approach to preconditioning that allows a trade-off between accuracy and convergence speed. When using a GPU, the Toeplitz approach was faster for all practical parameters. Second, it was found that properly accounting for image support can prevent aliasing errors with minimal impact on reconstruction time. Third, the proposed preconditioning scheme improved convergence rates by an order of magnitude with negligible impact on noise. With the proposed methods, 3D non-Cartesian compressed sensing with clinically relevant reconstruction times (<2 min) is feasible using practical computer resources. Magn Reson Med, 2017. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.
Comparison of Compressed Sensing Algorithms for Inversion of 3-D Electrical Resistivity Tomography.
NASA Astrophysics Data System (ADS)
Peddinti, S. R.; Ranjan, S.; Kbvn, D. P.
2016-12-01
Image reconstruction algorithms derived from electrical resistivity tomography (ERT) are highly non-linear, sparse, and ill-posed. The inverse problem is much severe, when dealing with 3-D datasets that result in large sized matrices. Conventional gradient based techniques using L2 norm minimization with some sort of regularization can impose smoothness constraint on the solution. Compressed sensing (CS) is relatively new technique that takes the advantage of inherent sparsity in parameter space in one or the other form. If favorable conditions are met, CS was proven to be an efficient image reconstruction technique that uses limited observations without losing edge sharpness. This paper deals with the development of an open source 3-D resistivity inversion tool using CS framework. The forward model was adopted from RESINVM3D (Pidlisecky et al., 2007) with CS as the inverse code. Discrete cosine transformation (DCT) function was used to induce model sparsity in orthogonal form. Two CS based algorithms viz., interior point method and two-step IST were evaluated on a synthetic layered model with surface electrode observations. The algorithms were tested (in terms of quality and convergence) under varying degrees of parameter heterogeneity, model refinement, and reduced observation data space. In comparison to conventional gradient algorithms, CS was proven to effectively reconstruct the sub-surface image with less computational cost. This was observed by a general increase in NRMSE from 0.5 in 10 iterations using gradient algorithm to 0.8 in 5 iterations using CS algorithms.
A new combined prior based reconstruction method for compressed sensing in 3D ultrasound imaging
NASA Astrophysics Data System (ADS)
Uddin, Muhammad S.; Islam, Rafiqul; Tahtali, Murat; Lambert, Andrew J.; Pickering, Mark R.
2015-03-01
Ultrasound (US) imaging is one of the most popular medical imaging modalities, with 3D US imaging gaining popularity recently due to its considerable advantages over 2D US imaging. However, as it is limited by long acquisition times and the huge amount of data processing it requires, methods for reducing these factors have attracted considerable research interest. Compressed sensing (CS) is one of the best candidates for accelerating the acquisition rate and reducing the data processing time without degrading image quality. However, CS is prone to introduce noise-like artefacts due to random under-sampling. To address this issue, we propose a combined prior-based reconstruction method for 3D US imaging. A Laplacian mixture model (LMM) constraint in the wavelet domain is combined with a total variation (TV) constraint to create a new regularization regularization prior. An experimental evaluation conducted to validate our method using synthetic 3D US images shows that it performs better than other approaches in terms of both qualitative and quantitative measures.
High-Performance 3D Compressive Sensing MRI Reconstruction Using Many-Core Architectures
Kim, Daehyun; Trzasko, Joshua; Smelyanskiy, Mikhail; Haider, Clifton; Dubey, Pradeep; Manduca, Armando
2011-01-01
Compressive sensing (CS) describes how sparse signals can be accurately reconstructed from many fewer samples than required by the Nyquist criterion. Since MRI scan duration is proportional to the number of acquired samples, CS has been gaining significant attention in MRI. However, the computationally intensive nature of CS reconstructions has precluded their use in routine clinical practice. In this work, we investigate how different throughput-oriented architectures can benefit one CS algorithm and what levels of acceleration are feasible on different modern platforms. We demonstrate that a CUDA-based code running on an NVIDIA Tesla C2050 GPU can reconstruct a 256 × 160 × 80 volume from an 8-channel acquisition in 19 seconds, which is in itself a significant improvement over the state of the art. We then show that Intel's Knights Ferry can perform the same 3D MRI reconstruction in only 12 seconds, bringing CS methods even closer to clinical viability. PMID:21922017
Fast imaging of laboratory core floods using 3D compressed sensing RARE MRI.
Ramskill, N P; Bush, I; Sederman, A J; Mantle, M D; Benning, M; Anger, B C; Appel, M; Gladden, L F
2016-09-01
Three-dimensional (3D) imaging of the fluid distributions within the rock is essential to enable the unambiguous interpretation of core flooding data. Magnetic resonance imaging (MRI) has been widely used to image fluid saturation in rock cores; however, conventional acquisition strategies are typically too slow to capture the dynamic nature of the displacement processes that are of interest. Using Compressed Sensing (CS), it is possible to reconstruct a near-perfect image from significantly fewer measurements than was previously thought necessary, and this can result in a significant reduction in the image acquisition times. In the present study, a method using the Rapid Acquisition with Relaxation Enhancement (RARE) pulse sequence with CS to provide 3D images of the fluid saturation in rock core samples during laboratory core floods is demonstrated. An objective method using image quality metrics for the determination of the most suitable regularisation functional to be used in the CS reconstructions is reported. It is shown that for the present application, Total Variation outperforms the Haar and Daubechies3 wavelet families in terms of the agreement of their respective CS reconstructions with a fully-sampled reference image. Using the CS-RARE approach, 3D images of the fluid saturation in the rock core have been acquired in 16min. The CS-RARE technique has been applied to image the residual water saturation in the rock during a water-water displacement core flood. With a flow rate corresponding to an interstitial velocity of vi=1.89±0.03ftday(-1), 0.1 pore volumes were injected over the course of each image acquisition, a four-fold reduction when compared to a fully-sampled RARE acquisition. Finally, the 3D CS-RARE technique has been used to image the drainage of dodecane into the water-saturated rock in which the dynamics of the coalescence of discrete clusters of the non-wetting phase are clearly observed. The enhancement in the temporal resolution that has
Whole lung morphometry with 3D multiple b-value hyperpolarized gas MRI and compressed sensing.
Chan, Ho-Fung; Stewart, Neil J; Parra-Robles, Juan; Collier, Guilhem J; Wild, Jim M
2017-05-01
To demonstrate three-dimensional (3D) multiple b-value diffusion-weighted (DW) MRI of hyperpolarized (3) He gas for whole lung morphometry with compressed sensing (CS). A fully-sampled, two b-value, 3D hyperpolarized (3) He DW-MRI dataset was acquired from the lungs of a healthy volunteer and retrospectively undersampled in the ky and kz phase-encoding directions for CS simulations. Optimal k-space undersampling patterns were determined by minimizing the mean absolute error between reconstructed and fully-sampled (3) He apparent diffusion coefficient (ADC) maps. Prospective three-fold, undersampled, 3D multiple b-value (3) He DW-MRI datasets were acquired from five healthy volunteers and one chronic obstructive pulmonary disease (COPD) patient, and the mean values of maps of ADC and mean alveolar dimension (LmD ) were validated against two-dimensional (2D) and 3D fully-sampled (3) He DW-MRI experiments. Reconstructed undersampled datasets showed no visual artifacts and good preservation of the main image features and quantitative information. A good agreement between fully-sampled and prospective undersampled datasets was found, with a mean difference of +3.4% and +5.1% observed in mean global ADC and LmD values, respectively. These differences were within the standard deviation range and consistent with values reported from healthy and COPD lungs. Accelerated CS acquisition has facilitated 3D multiple b-value (3) He DW-MRI scans in a single breath-hold, enabling whole lung morphometry mapping. Magn Reson Med 77:1916-1925, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals
Lorintiu, Oana; Liebgott, Hervé; Alessandrini, Martino; Bernard, Olivier; Friboulet, Denis
2015-12-01
In this paper we present a compressed sensing (CS) method adapted to 3D ultrasound imaging (US). In contrast to previous work, we propose a new approach based on the use of learned overcomplete dictionaries that allow for much sparser representations of the signals since they are optimized for a particular class of images such as US images. In this study, the dictionary was learned using the K-SVD algorithm and CS reconstruction was performed on the non-log envelope data by removing 20% to 80% of the original data. Using numerically simulated images, we evaluate the influence of the training parameters and of the sampling strategy. The latter is done by comparing the two most common sampling patterns, i.e., point-wise and line-wise random patterns. The results show in particular that line-wise sampling yields an accuracy comparable to the conventional point-wise sampling. This indicates that CS acquisition of 3D data is feasible in a relatively simple setting, and thus offers the perspective of increasing the frame rate by skipping the acquisition of RF lines. Next, we evaluated this approach on US volumes of several ex vivo and in vivo organs. We first show that the learned dictionary approach yields better performances than conventional fixed transforms such as Fourier or discrete cosine. Finally, we investigate the generality of the learned dictionary approach and show that it is possible to build a general dictionary allowing to reliably reconstruct different volumes of different ex vivo or in vivo organs.
Real-time 3D video utilizing a compressed sensing time-of-flight single-pixel camera
NASA Astrophysics Data System (ADS)
Edgar, Matthew P.; Sun, Ming-Jie; Gibson, Graham M.; Spalding, Gabriel C.; Phillips, David B.; Padgett, Miles J.
2016-09-01
Time-of-flight 3D imaging is an important tool for applications such as remote sensing, machine vision and autonomous navigation. Conventional time-of-flight three-dimensional imaging systems that utilize a raster scanned laser to measure the range of each pixel in the scene sequentially, inherently have acquisition times that scale directly with the resolution. Here we show a modified time-of-flight 3D camera employing structured illumination, which uses a visible camera to enable a novel compressed sensing technique, minimising the acquisition time as well as providing a high-resolution reflectivity map for image overlay. Furthermore, a quantitative assessment of the 3D imaging performance is provided.
Nam, Seunghoon; Akçakaya, Mehmet; Basha, Tamer; Stehning, Christian; Manning, Warren J; Tarokh, Vahid; Nezafat, Reza
2013-01-01
A disadvantage of three-dimensional (3D) isotropic acquisition in whole-heart coronary MRI is the prolonged data acquisition time. Isotropic 3D radial trajectories allow undersampling of k-space data in all three spatial dimensions, enabling accelerated acquisition of the volumetric data. Compressed sensing (CS) reconstruction can provide further acceleration in the acquisition by removing the incoherent artifacts due to undersampling and improving the image quality. However, the heavy computational overhead of the CS reconstruction has been a limiting factor for its application. In this article, a parallelized implementation of an iterative CS reconstruction method for 3D radial acquisitions using a commercial graphics processing unit is presented. The execution time of the graphics processing unit-implemented CS reconstruction was compared with that of the C++ implementation, and the efficacy of the undersampled 3D radial acquisition with CS reconstruction was investigated in both phantom and whole-heart coronary data sets. Subsequently, the efficacy of CS in suppressing streaking artifacts in 3D whole-heart coronary MRI with 3D radial imaging and its convergence properties were studied. The CS reconstruction provides improved image quality (in terms of vessel sharpness and suppression of noise-like artifacts) compared with the conventional 3D gridding algorithm, and the graphics processing unit implementation greatly reduces the execution time of CS reconstruction yielding 34-54 times speed-up compared with C++ implementation. Copyright © 2012 Wiley Periodicals, Inc.
Bevacqua, Martina T; Scapaticci, Rosa
2016-02-01
In microwave breast cancer imaging magnetic nanoparticles have been recently proposed as contrast agent. Due to the non-magnetic nature of human tissues, magnetic nanoparticles make possible the overcoming of some limitations of conventional microwave imaging techniques, thus providing reliable and specific diagnosis of breast cancer. In this paper, a Compressive Sensing inspired inversion technique is introduced for the reconstruction of the magnetic contrast induced within the tumor. The applicability of Compressive Sensing theory is guaranteed by the fact that the underlying inverse scattering problem is linear and the searched magnetic perturbation is sparse. From the numerical analysis, performed in realistic conditions in 3D geometry, it has been pointed out that the adoption of this new tool allows improving resolution and accuracy of the reconstructions, as well as reducing the number of required measurements.
An L1-norm phase constraint for half-Fourier compressed sensing in 3D MR imaging.
Li, Guobin; Hennig, Jürgen; Raithel, Esther; Büchert, Martin; Paul, Dominik; Korvink, Jan G; Zaitsev, Maxim
2015-10-01
In most half-Fourier imaging methods, explicit phase replacement is used. In combination with parallel imaging, or compressed sensing, half-Fourier reconstruction is usually performed in a separate step. The purpose of this paper is to report that integration of half-Fourier reconstruction into iterative reconstruction minimizes reconstruction errors. The L1-norm phase constraint for half-Fourier imaging proposed in this work is compared with the L2-norm variant of the same algorithm, with several typical half-Fourier reconstruction methods. Half-Fourier imaging with the proposed phase constraint can be seamlessly combined with parallel imaging and compressed sensing to achieve high acceleration factors. In simulations and in in-vivo experiments half-Fourier imaging with the proposed L1-norm phase constraint enables superior performance both reconstruction of image details and with regard to robustness against phase estimation errors. The performance and feasibility of half-Fourier imaging with the proposed L1-norm phase constraint is reported. Its seamless combination with parallel imaging and compressed sensing enables use of greater acceleration in 3D MR imaging.
High speed 3D overhauser-enhanced MRI using combined b-SSFP and compressed sensing.
Sarracanie, Mathieu; Armstrong, Brandon D; Stockmann, Jason; Rosen, Matthew S
2014-02-01
Overhauser-enhanced MRI is a promising technique for imaging the distribution and dynamics of free radicals. A key challenge for Overhauser-enhanced MRI is attaining high spatial and temporal resolution while simultaneously limiting resonator and sample heating due to the long, high power radio-frequency pulses needed to saturate the electron resonance. The approach presented here embeds EPR pulses within a balanced steady state free precession sequence. Unlike other Overhauser-enhanced MRI methods, no separate Overhauser prepolarization step is required. This steady-state approach also eliminates the problem of time-varying Overhauser-enhanced signal and provides constant polarization in the sample during the acquisition. A further increase in temporal resolution was achieved by incorporating undersampled k-space strategies and compressed sensing reconstruction. We demonstrate 1 × 2 × 3.5 mm(3) resolution at 6.5 mT across a 54 × 54 × 110 mm(3) sample in 33 s while sampling 30% of k-space. The work presented here overcomes the main limitations of Overhauser enhanced MRI as previously described in the literature, drastically improving speed and resolution, and enabling new opportunities for the measurement of free radicals in living organisms, and for the study of dynamic processes such as metabolism and flow. Copyright © 2013 Wiley Periodicals, Inc.
Geraghty, Benjamin J; Lau, Justin Y C; Chen, Albert P; Cunningham, Charles H
2017-02-01
To enable large field-of-view, time-resolved volumetric coverage in hyperpolarized (13) C metabolic imaging by implementing a novel data acquisition and image reconstruction method based on the compressed sensing framework. A spectral-spatial pulse for single-resonance excitation followed by a symmetric echo-planar imaging (EPI) readout was implemented for encoding a 72 × 18 cm(2) field of view at 5 × 5 mm(2) resolution. Random undersampling was achieved with blipped z-gradients during the ramp portion of the echo-planar imaging readout. The sequence and reconstruction were tested with phantom studies and consecutive in vivo hyperpolarized (13) C scans in rats. Retrospectively and prospectively undersampled data were compared on the basis of structural similarity in the reconstructed images and the quantification of the lactate-to-pyruvate ratio in rat kidneys. No artifacts or loss of resolution are evident in the compressed sensing reconstructed images acquired with the proposed sequence. Structural similarity analysis indicate that compressed sensing reconstructions can accurately recover spatial features in the metabolic images evaluated. A novel z-blip acquisition sequence for compressed sensing accelerated hyperpolarized (13) C 3D echo-planar imaging was developed and demonstrated. The close agreement in lactate-to-pyruvate ratios from both retrospectively and prospectively undersampled data from rats shows that metabolic information is preserved with acceleration factors up to 3-fold with the developed method. Magn Reson Med 77:538-546, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.
Larson, Peder E. Z.; Hu, Simon; Lustig, Michael; Kerr, Adam B.; Nelson, Sarah J.; Kurhanewicz, John; Pauly, John M.; Vigneron, Daniel B.
2010-01-01
Hyperpolarized 13C MRSI can detect not only the uptake of the pre-polarized molecule but also its metabolic products in vivo, thus providing a powerful new method to study cellular metabolism. Imaging the dynamic perfusion and conversion of these metabolites provides additional tissue information but requires methods for efficient hyperpolarization usage and rapid acquisitions. In this work, we have developed a time-resolved 3D MRSI method for acquiring hyperpolarized 13C data by combining compressed sensing methods for acceleration and multiband excitation pulses to efficiently use the magnetization. This method achieved a 2 sec temporal resolution with full volumetric coverage of a mouse, and metabolites were observed for up to 60 sec following injection of hyperpolarized [1-13C]-pyruvate. The compressed sensing acquisition used random phase encode gradient blips to create a novel random undersampling pattern tailored to dynamic MRSI with sampling incoherency in four (time, frequency and two spatial) dimensions. The reconstruction was also tailored to dynamic MRSI by applying a temporal wavelet sparsifying transform in order to exploit the inherent temporal sparsity. Customized multiband excitation pulses were designed with a lower flip angle for the [1-13C]-pyruvate substrate given its higher concentration than its metabolic products ([1-13C]-lactate and [1-13C]-alanine), thus using less hyperpolarization per excitation. This approach has enabled the monitoring of perfusion and uptake of the pyruvate, and the conversion dynamics to lactate and alanine throughout a volume with high spatial and temporal resolution. PMID:20939089
Park, Ilwoo; Hu, Simon; Bok, Robert; Ozawa, Tomoko; Ito, Motokazu; Mukherjee, Joydeep; Phillips, Joanna J.; James, C. David; Pieper, Russell O.; Ronen, Sabrina M.; Vigneron, Daniel B.; Nelson, Sarah J.
2013-01-01
High resolution compressed sensing hyperpolarized 13C magnetic resonance spectroscopic imaging was applied in orthotopic human glioblastoma xenografts for quantitative assessment of spatial variations in 13C metabolic profiles and comparison with histopathology. A new compressed sensing sampling design with a factor of 3.72 acceleration was implemented to enable a factor of 4 increase in spatial resolution. Compressed sensing 3D 13C magnetic resonance spectroscopic imaging data were acquired from a phantom and 10 tumor-bearing rats following injection of hyperpolarized [1-13C]-pyruvate using a 3T scanner. The 13C metabolic profiles were compared with hematoxylin and eosin staining and carbonic anhydrase 9 staining. The high-resolution compressed sensing 13C magnetic resonance spectroscopic imaging data enabled the differentiation of distinct 13C metabolite patterns within abnormal tissues with high specificity in similar scan times compared to the fully sampled method. The results from pathology confirmed the different characteristics of 13C metabolic profiles between viable, non-necrotic, nonhypoxic tumor, and necrotic, hypoxic tissue. PMID:22851374
ICER-3D Hyperspectral Image Compression Software
NASA Technical Reports Server (NTRS)
Xie, Hua; Kiely, Aaron; Klimesh, matthew; Aranki, Nazeeh
2010-01-01
Software has been developed to implement the ICER-3D algorithm. ICER-3D effects progressive, three-dimensional (3D), wavelet-based compression of hyperspectral images. If a compressed data stream is truncated, the progressive nature of the algorithm enables reconstruction of hyperspectral data at fidelity commensurate with the given data volume. The ICER-3D software is capable of providing either lossless or lossy compression, and incorporates an error-containment scheme to limit the effects of data loss during transmission. The compression algorithm, which was derived from the ICER image compression algorithm, includes wavelet-transform, context-modeling, and entropy coding subalgorithms. The 3D wavelet decomposition structure used by ICER-3D exploits correlations in all three dimensions of sets of hyperspectral image data, while facilitating elimination of spectral ringing artifacts, using a technique summarized in "Improving 3D Wavelet-Based Compression of Spectral Images" (NPO-41381), NASA Tech Briefs, Vol. 33, No. 3 (March 2009), page 7a. Correlation is further exploited by a context-modeling subalgorithm, which exploits spectral dependencies in the wavelet-transformed hyperspectral data, using an algorithm that is summarized in "Context Modeler for Wavelet Compression of Hyperspectral Images" (NPO-43239), which follows this article. An important feature of ICER-3D is a scheme for limiting the adverse effects of loss of data during transmission. In this scheme, as in the similar scheme used by ICER, the spatial-frequency domain is partitioned into rectangular error-containment regions. In ICER-3D, the partitions extend through all the wavelength bands. The data in each partition are compressed independently of those in the other partitions, so that loss or corruption of data from any partition does not affect the other partitions. Furthermore, because compression is progressive within each partition, when data are lost, any data from that partition received
Qing, Kun; Altes, Talissa A.; Tustison, Nicholas J.; Feng, Xue; Chen, Xiao; Mata, Jaime F.; Miller, G. Wilson; de Lange, Eduard E.; Tobias, William A.; Cates, Gordon D.; Brookeman, James R.; Mugler, John P.
2015-01-01
Purpose To develop and validate a method for acquiring helium-3 (3He) and proton (1H) three-dimensional (3D) image sets of the human lung with isotropic spatial resolution within a 10-s breath-hold by using compressed sensing (CS) acceleration, and to assess the fidelity of undersampled images compared to fully-sampled images. Methods The undersampling scheme for CS acceleration was optimized and tested using 3He ventilation data. Rapid 3D acquisition of both 3He and 1H data during one breath-hold was then implemented, based on a balanced steady-state free-precession pulse sequence, by random undersampling of k space with reconstruction via minimizing the L1 norm and total variance. CS-reconstruction fidelity was evaluated quantitatively by comparing fully-sampled and retrospectively-undersampled image sets. Results Helium-3 and 1H 3D image sets of the lung with isotropic 3.9-mm resolution were acquired during a single breath-hold in 12 s and 8 s using acceleration factors of 2 and 3, respectively. Comparison of fully-sampled and retrospectively-undersampled 3He and 1H images yielded mean absolute errors <10% and structural similarity indices >0.9. Conclusion By randomly undersampling k space and using CS reconstruction, high-quality 3He and 1H 3D image sets with isotropic 3.9-mm resolution can be acquired within an 8-s breath-hold. PMID:25335080
Levine, Evan; Daniel, Bruce; Vasanawala, Shreyas; Hargreaves, Brian; Saranathan, Manojkumar
2017-05-01
To enable robust, high spatio-temporal-resolution three-dimensional Cartesian MRI using a scheme incorporating a novel variable density random k-space sampling trajectory allowing flexible and retrospective selection of the temporal footprint with compressed sensing (CS). A complementary Poisson-disc k-space sampling trajectory was designed to allow view sharing and varying combinations of reduced view sharing with CS from the same prospective acquisition. These schemes were used for two-point Dixon-based dynamic contrast-enhanced MRI (DCE-MRI) of the breast and abdomen. Results were validated in vivo with a novel approach using variable-flip-angle data, which was retrospectively accelerated using the same methods but offered a ground truth. In breast DCE-MRI, the temporal footprint could be reduced 2.3-fold retrospectively without introducing noticeable artifacts, improving depiction of rapidly enhancing lesions. Further, experiments with variable-flip-angle data showed that reducing view sharing improved accuracy in reconstruction and T1 mapping. In abdominal MRI, 2.3-fold and 3.6-fold reductions in temporal footprint allowed reduced motion artifacts. The complementary-Poisson-disc k-space sampling trajectory allowed a retrospective spatiotemporal resolution tradeoff using CS and view sharing, imparting robustness to motion and contrast enhancement. The technique was also validated using a novel approach of fully acquired variable-flip-angle acquisition. Magn Reson Med 77:1774-1785, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.
Bannas, Peter; Li, Yinsheng; Motosugi, Utaroh; Li, Ke; Lubner, Meghan; Chen, Guang-Hong; Pickhardt, Perry J.
2015-01-01
Purpose To assess the effect of the prior-image-constrained-compressed-sensing based metal-artefactreduction (PICCS-MAR) algorithm on streak artefact reduction and 2D and 3D-image quality improvement in patients with total hip arthroplasty (THA) undergoing CT colonography (CTC). Material and Methods PICCS-MAR was applied to filtered-back-projection (FBP)-reconstructed DICOM CTC-images in 52 patients with THA (unilateral, n=30; bilateral, n=22). For FBP and PICCS-MAR series, ROI-measurements of CT-numbers were obtained at predefined levels for fat, muscle, air, and the most severe artefact. Two radiologists independently reviewed 2D and 3D CTC-images and graded artefacts and image quality using a five-point-scale (1=severe streak/no-diagnostic confidence, 5=no streak/excellent image-quality, high-confidence). Results were compared using paired and unpaired t-tests, Wilcoxon signed-ranks and Mann-Whitney-tests. Results Streak artefacts and image quality scores for FBP versus PICCS-MAR 2D-images (median: 1 vs. 3 and 2 vs. 3, respectively) and 3D images (median: 2 vs. 4 and 3 vs. 4, respectively) showed significant improvement after PICCS-MAR (all P<.001). PICCS-MAR significantly improved the accuracy of mean CT numbers for fat, muscle and the area with the most severe artefact (all P<.001). Conclusion PICCS-MAR substantially reduces streak artefacts related to THA on DICOM images, thereby enhancing visualization of anatomy on 2D and 3D CTC images and increasing diagnostic confidence. PMID:26521266
Bannas, Peter; Li, Yinsheng; Motosugi, Utaroh; Li, Ke; Lubner, Meghan; Chen, Guang-Hong; Pickhardt, Perry J
2016-07-01
To assess the effect of the prior-image-constrained-compressed-sensing-based metal-artefact-reduction (PICCS-MAR) algorithm on streak artefact reduction and 2D and 3D-image quality improvement in patients with total hip arthroplasty (THA) undergoing CT colonography (CTC). PICCS-MAR was applied to filtered-back-projection (FBP)-reconstructed DICOM CTC-images in 52 patients with THA (unilateral, n = 30; bilateral, n = 22). For FBP and PICCS-MAR series, ROI-measurements of CT-numbers were obtained at predefined levels for fat, muscle, air, and the most severe artefact. Two radiologists independently reviewed 2D and 3D CTC-images and graded artefacts and image quality using a five-point-scale (1 = severe streak/no-diagnostic confidence, 5 = no streak/excellent image-quality, high-confidence). Results were compared using paired and unpaired t-tests and Wilcoxon signed-rank and Mann-Whitney-tests. Streak artefacts and image quality scores for FBP versus PICCS-MAR 2D-images (median: 1 vs. 3 and 2 vs. 3, respectively) and 3D images (median: 2 vs. 4 and 3 vs. 4, respectively) showed significant improvement after PICCS-MAR (all P < 0.001). PICCS-MAR significantly improved the accuracy of mean CT numbers for fat, muscle and the area with the most severe artefact (all P < 0.001). PICCS-MAR substantially reduces streak artefacts related to THA on DICOM images, thereby enhancing visualization of anatomy on 2D and 3D CTC images and increasing diagnostic confidence. • PICCS-MAR significantly reduces streak artefacts associated with total hip arthroplasty on 2D and 3D CTC. • PICCS-MAR significantly improves 2D and 3D CTC image quality and diagnostic confidence. • PICCS-MAR can be applied retrospectively to DICOM images from single-kVp CT.
Wu, Juan; Lerotic, Mirna; Collins, Sean; Leary, Rowan; Saghi, Zineb; Midgley, Paul; Berejnov, Slava; Susac, Darija; Stumper, Juergen; Singh, Gurvinder; Hitchcock, Adam P
2017-09-12
Soft X-ray spectro-tomography provides three-dimensional (3D) chemical mapping based on natural X-ray absorption properties. Since radiation damage is intrinsic to X-ray absorption, it is important to find ways to maximize signal within a given dose. For tomography, using the smallest number of tilt series images that gives a faithful reconstruction is one such method. Compressed sensing (CS) methods have relatively recently been applied to tomographic reconstruction algorithms, providing faithful 3D reconstructions with a much smaller number of projection images than when conventional reconstruction methods are used. Here, CS is applied in the context of scanning transmission X-ray microscopy tomography. Reconstructions by weighted back-projection, the simultaneous iterative reconstruction technique, and CS are compared. The effects of varying tilt angle increment and angular range for the tomographic reconstructions are examined. Optimization of the regularization parameter in the CS reconstruction is explored and discussed. The comparisons show that CS can provide improved reconstruction fidelity relative to weighted back-projection and simultaneous iterative reconstruction techniques, with increasingly pronounced advantages as the angular sampling is reduced. In particular, missing wedge artifacts are significantly reduced and there is enhanced recovery of sharp edges. Examples of using CS for low-dose scanning transmission X-ray microscopy spectroscopic tomography are presented.
Seo, Nieun; Park, Mi-Suk; Han, Kyunghwa; Kim, Dongeun; King, Kevin F; Choi, Jin-Young; Kim, Honsoul; Kim, Hye Jin; Lee, Minsu; Bae, Heejin; Kim, Myeong-Jin
2017-03-11
To assess the feasibility of 3D navigator-triggered magnetic resonance cholangiopancreatography (MRCP) with combined parallel imaging (PI) and compressed sensing (CS). With Institutional Review Board approval, 30 consecutive patients who underwent MRCP for suspected pancreaticobiliary disease were prospectively recruited. All patients underwent 3D navigator-triggered MRCP with conventional PI alone, and with combined PI and CS using a 3T machine. The acquisition time and relative duct-to-periductal contrast ratios (RCs) at three biliary segments were quantitatively compared between the two MRCP methods. Qualitative image parameters were independently evaluated by two blinded radiologists, and were compared between two methods using the Wilcoxon signed-rank test. The mean acquisition time of MRCP with combined PI and CS (131.87 ± 33.60 sec) was significantly shorter compared with that of MRCP with PI (253.63 ± 56.08 sec; P < 0.001). The RC obtained using MRCP with combined PI and CS at two segments was slightly lower compared to that obtained using MRCP with PI (P = 0.007 and 0.002). Both reviewers found no significant differences in duct visualization, overall image quality, and degree of artifacts between the two methods (P ≥ 0.063; P = 0.637; and P = 0.752, respectively). Lesion conspicuity and confidence in duct abnormalities were comparable between two MRCP methods in both readers (P = 0.564 and P > 0.999). Combined PI and CS reconstruction is feasible for 3D navigator-triggered MRCP, providing image quality comparable to that of MRCP with PI alone, in about half the acquisition time. 2 J. Magn. Reson. Imaging 2017. © 2017 International Society for Magnetic Resonance in Medicine.
Ono, Ayako; Arizono, Shigeki; Fujimoto, Koji; Akasaka, Thai; Yamashita, Rikiya; Furuta, Akihiro; Isoda, Hiroyoshi; Togashi, Kaori
2017-07-05
To evaluate images of non-contrast-enhanced 3D MR portography within a breath-hold (BH) using compressed sensing (CS) compared to standard respiratory-triggered (RT) sequences. Fifty-nine healthy volunteers underwent MR portography using two sequences of balanced steady-state free-precession (bSSFP) with time-spatial labeling inversion pulses (Time-SLIP): BH bSSFP-CS and RT bSSFP. Two radiologists independently scored the diagnostic acceptability to delineate the portal branches (MPV: main portal vein; RPV: right portal vein; LPV: left portal vein; RPPV: right posterior portal vein; and P4 and P8: portal branch of segment 4 and segment 8, respectively) and the overall image quality on a four-point scale. We assessed noninferiority of BH bSSFP-CS to RT bSSFP. For quantitative analysis, vessel-to-liver contrast (Cv-l) was calculated in MPV, RPV and LPV. BH bSSFP sequence was successfully performed with a 30-second acquisition time. The diagnostic acceptability scores of BH bSSFP-CS compared with RT bSSFP were statistically noninferior: MPV (95% CI for score difference of Reader 1 and Reader 2, respectively: [-0.16, 0.06], [-0.05, 0.02]), RPV ([-0.00, 0.11], [-0.01, 0.08]), LPV ([-0.03, 0.10], [-0.10, 0.03]), RPPV ([-0.03, 0.10], [-0.05, 0.05]), P4 ([-0.13, 0.34], [-0.28, 0.21]) and P8 ([-0.21, 0.11], [-0.25, -0.02]). However, the overall image quality of BH bSSFP-CS did not show noninferiority ([-0.61, -0.24], [-0.54, -0.17]). Cv-l values were significantly lower in BH bSSFP-CS (P<0.001). CS enabled non-contrast-enhanced 3D bSSFP MR portography to be performed within a BH while maintaining noninferior diagnostic acceptability compared to standard RT bSSFP MR portography. Copyright © 2017 Elsevier Inc. All rights reserved.
Hu, Simon; Lustig, Michael; Balakrishnan, Asha; Larson, Peder E. Z.; Bok, Robert; Kurhanewicz, John; Nelson, Sarah J.; Goga, Andrei; Pauly, John M.; Vigneron, Daniel B.
2010-01-01
High polarization of nuclear spins in liquid state through hyperpolarized technology utilizing dynamic nuclear polarization has enabled the direct monitoring of 13C metabolites in vivo at a high signal-to-noise ratio. Acquisition time limitations due to T1 decay of the hyperpolarized signal require accelerated imaging methods, such as compressed sensing, for optimal speed and spatial coverage. In this paper, the design and testing of a new echo-planar 13C three-dimensional magnetic resonance spectroscopic imaging (MRSI) compressed sensing sequence is presented. The sequence provides up to a factor of 7.53 in acceleration with minimal reconstruction artifacts. The key to the design is employing x and y gradient blips during a fly-back readout to pseudorandomly undersample kf-kx-ky space. The design was validated in simulations and phantom experiments where the limits of undersampling and the effects of noise on the compressed sensing nonlinear reconstruction were tested. Finally, this new pulse sequence was applied in vivo in preclinical studies involving transgenic prostate cancer and transgenic liver cancer murine models to obtain much higher spatial and temporal resolution than possible with conventional echo-planar spectroscopic imaging methods. PMID:20017160
3D microstructure modeling of compressed fiber-based materials
NASA Astrophysics Data System (ADS)
Gaiselmann, Gerd; Tötzke, Christian; Manke, Ingo; Lehnert, Werner; Schmidt, Volker
2014-07-01
A novel parametrized model that describes the 3D microstructure of compressed fiber-based materials is introduced. It allows to virtually generate the microstructure of realistically compressed gas-diffusion layers (GDL). Given the input of a 3D microstructure of some fiber-based material, the model compresses the system of fibers in a uniaxial direction for arbitrary compression rates. The basic idea is to translate the fibers in the direction of compression according to a vector field which depends on the rate of compression and on the locations of fibers within the material. In order to apply the model to experimental 3D image data of fiber-based materials given for several compression states, an optimal vector field is estimated by simulated annealing. The model is applied to 3D image data of non-woven GDL in PEMFC gained by synchrotron tomography for different compression rates. The compression model is validated by comparing structural characteristics computed for experimentally compressed and virtually compressed microstructures, where two kinds of compression - using a flat stamp and a stamp with a flow-field profile - are applied. For both stamps types, a good agreement is found. Furthermore, the compression model is combined with a stochastic 3D microstructure model for uncompressed fiber-based materials. This allows to efficiently generate compressed fiber-based microstructures in arbitrary volumes.
Wech, T; Pickl, W; Tran-Gia, J; Ritter, C; Beer, M; Hahn, D; Köstler, H
2014-01-01
The aim of this study was to perform functional MR imaging of the whole heart in a single breath-hold using an undersampled 3 D trajectory for data acquisition in combination with compressed sensing for image reconstruction. Measurements were performed using an SSFP sequence on a 3 T whole-body system equipped with a 32-channel body array coil. A 3 D radial stack-of-stars sampling scheme was utilized enabling efficient undersampling of the k-space and thereby accelerating data acquisition. Compressed sensing was applied for the reconstruction of the missing data. A validation study was performed based on a fully sampled dataset acquired by standard Cartesian cine imaging of 2 D slices on a healthy volunteer. The results were investigated with regard to systematic errors and resolution losses possibly introduced by the developed reconstruction. Subsequently, the proposed technique was applied for in-vivo functional cardiac imaging of the whole heart in a single breath-hold of 27 s. The developed technique was tested on three healthy volunteers to examine its reproducibility. By means of the results of the simulation (temporal resolution: 47 ms, spatial resolution: 1.4 × 1.4 × 8 mm, 3 D image matrix: 208 × 208 × 10), an overall acceleration factor of 10 has been found where the compressed sensing reconstructed image series shows only very low systematic errors and a slight in-plane resolution loss of 15 %. The results of the in-vivo study (temporal resolution: 40.5 ms, spatial resolution: 2.1 × 2.1 × 8 mm, 3 D image matrix: 224 × 224 × 12) performed with an acceleration factor of 10.7 confirm the overall good image quality of the presented technique for undersampled acquisitions. The combination of 3 D radial data acquisition and model-based compressed sensing reconstruction allows high acceleration factors enabling cardiac functional imaging of the whole heart within only one breath-hold. The
An efficient and robust 3D mesh compression based on 3D watermarking and wavelet transform
NASA Astrophysics Data System (ADS)
Zagrouba, Ezzeddine; Ben Jabra, Saoussen; Didi, Yosra
2011-06-01
The compression and watermarking of 3D meshes are very important in many areas of activity including digital cinematography, virtual reality as well as CAD design. However, most studies on 3D watermarking and 3D compression are done independently. To verify a good trade-off between protection and a fast transfer of 3D meshes, this paper proposes a new approach which combines 3D mesh compression with mesh watermarking. This combination is based on a wavelet transformation. In fact, the used compression method is decomposed to two stages: geometric encoding and topologic encoding. The proposed approach consists to insert a signature between these two stages. First, the wavelet transformation is applied to the original mesh to obtain two components: wavelets coefficients and a coarse mesh. Then, the geometric encoding is done on these two components. The obtained coarse mesh will be marked using a robust mesh watermarking scheme. This insertion into coarse mesh allows obtaining high robustness to several attacks. Finally, the topologic encoding is applied to the marked coarse mesh to obtain the compressed mesh. The combination of compression and watermarking permits to detect the presence of signature after a compression of the marked mesh. In plus, it allows transferring protected 3D meshes with the minimum size. The experiments and evaluations show that the proposed approach presents efficient results in terms of compression gain, invisibility and robustness of the signature against of many attacks.
Lossless Compression of Medical Images Using 3D Predictors.
Lucas, Luis; Rodrigues, Nuno; Cruz, Luis; Faria, Sergio
2017-06-09
This paper describes a highly efficient method for lossless compression of volumetric sets of medical images, such as CTs or MRIs. The proposed method, referred to as 3D-MRP, is based on the principle of minimum rate predictors (MRP), which is one of the state-of-the-art lossless compression technologies, presented in the data compression literature. The main features of the proposed method include the use of 3D predictors, 3D-block octree partitioning and classification, volume-based optimisation and support for 16 bit-depth images. Experimental results demonstrate the efficiency of the 3D-MRP algorithm for the compression of volumetric sets of medical images, achieving gains above 15% and 12% for 8 bit and 16 bit-depth contents, respectively, when compared to JPEG-LS, JPEG2000, CALIC, HEVC, as well as other proposals based on MRP algorithm.
Compression of M-FISH images using 3D SPIHT
NASA Astrophysics Data System (ADS)
Wu, Qiang; Xiong, Zixiang; Castleman, Kenneth R.
2001-12-01
With the recent development of the use of digital media for cytogenetic imaging applications, efficient compression techniques are highly desirable to accommodate the rapid growth of image data. This paper introduces a lossy to lossless coding technique for compression of multiplex fluorescence in situ hybridization (M-FISH) images, based on 3-D set partitioning in hierarchical trees (3-D SPIHT). Using a lifting-based integer wavelet decomposition, the 3-D SPIHT achieves both embedded coding and substantial improvement in lossless compression over the Lempel-Ziv (WinZip) coding which is the current method for archiving M-FISH images. The lossy compression performance of the 3-D SPIHT is also significantly better than that of the 2-D based JPEG-2000.
DCT and DST Based Image Compression for 3D Reconstruction
NASA Astrophysics Data System (ADS)
Siddeq, Mohammed M.; Rodrigues, Marcos A.
2017-03-01
This paper introduces a new method for 2D image compression whose quality is demonstrated through accurate 3D reconstruction using structured light techniques and 3D reconstruction from multiple viewpoints. The method is based on two discrete transforms: (1) A one-dimensional Discrete Cosine Transform (DCT) is applied to each row of the image. (2) The output from the previous step is transformed again by a one-dimensional Discrete Sine Transform (DST), which is applied to each column of data generating new sets of high-frequency components followed by quantization of the higher frequencies. The output is then divided into two parts where the low-frequency components are compressed by arithmetic coding and the high frequency ones by an efficient minimization encoding algorithm. At decompression stage, a binary search algorithm is used to recover the original high frequency components. The technique is demonstrated by compressing 2D images up to 99% compression ratio. The decompressed images, which include images with structured light patterns for 3D reconstruction and from multiple viewpoints, are of high perceptual quality yielding accurate 3D reconstruction. Perceptual assessment and objective quality of compression are compared with JPEG and JPEG2000 through 2D and 3D RMSE. Results show that the proposed compression method is superior to both JPEG and JPEG2000 concerning 3D reconstruction, and with equivalent perceptual quality to JPEG2000.
Li, Fangmin; Liu, Guo; Liu, Jian; Chen, Xiaochuang; Ma, Xiaolin
2016-01-01
Most location-based services are based on a global positioning system (GPS), which only works well in outdoor environments. Compared to outdoor environments, indoor localization has created more buzz in recent years as people spent most of their time indoors working at offices and shopping at malls, etc. Existing solutions mainly rely on inertial sensors (i.e., accelerometer and gyroscope) embedded in mobile devices, which are usually not accurate enough to be useful due to the mobile devices’ random movements while people are walking. In this paper, we propose the use of shoe sensing (i.e., sensors attached to shoes) to achieve 3D indoor positioning. Specifically, a short-time energy-based approach is used to extract the gait pattern. Moreover, in order to improve the accuracy of vertical distance estimation while the person is climbing upstairs, a state classification is designed to distinguish the walking status including plane motion (i.e., normal walking and jogging horizontally), walking upstairs, and walking downstairs. Furthermore, we also provide a mechanism to reduce the vertical distance accumulation error. Experimental results show that we can achieve nearly 100% accuracy when extracting gait patterns from walking/jogging with a low-cost shoe sensor, and can also achieve 3D indoor real-time positioning with high accuracy. PMID:27801839
Real-time 3D video compression for tele-immersive environments
NASA Astrophysics Data System (ADS)
Yang, Zhenyu; Cui, Yi; Anwar, Zahid; Bocchino, Robert; Kiyanclar, Nadir; Nahrstedt, Klara; Campbell, Roy H.; Yurcik, William
2006-01-01
Tele-immersive systems can improve productivity and aid communication by allowing distributed parties to exchange information via a shared immersive experience. The TEEVE research project at the University of Illinois at Urbana-Champaign and the University of California at Berkeley seeks to foster the development and use of tele-immersive environments by a holistic integration of existing components that capture, transmit, and render three-dimensional (3D) scenes in real time to convey a sense of immersive space. However, the transmission of 3D video poses significant challenges. First, it is bandwidth-intensive, as it requires the transmission of multiple large-volume 3D video streams. Second, existing schemes for 2D color video compression such as MPEG, JPEG, and H.263 cannot be applied directly because the 3D video data contains depth as well as color information. Our goal is to explore from a different angle of the 3D compression space with factors including complexity, compression ratio, quality, and real-time performance. To investigate these trade-offs, we present and evaluate two simple 3D compression schemes. For the first scheme, we use color reduction to compress the color information, which we then compress along with the depth information using zlib. For the second scheme, we use motion JPEG to compress the color information and run-length encoding followed by Huffman coding to compress the depth information. We apply both schemes to 3D videos captured from a real tele-immersive environment. Our experimental results show that: (1) the compressed data preserves enough information to communicate the 3D images effectively (min. PSNR > 40) and (2) even without inter-frame motion estimation, very high compression ratios (avg. > 15) are achievable at speeds sufficient to allow real-time communication (avg. ~ 13 ms per 3D video frame).
A comparative study on 3D range data compression methods
NASA Astrophysics Data System (ADS)
Bell, Tyler; Zhang, Song
2016-05-01
As high-quality 3D range scanners become increasingly adopted, a common issue emerges that is how best to properly store captured 3D data as it inherently contains a large amount of information per each frame. One approach that has proved successful is to convert 3D range data to 2D regular color images that can be further compressed using traditional image compression techniques (e.g., JPEG). In literature, there are three major conversion methods: (1) virtual fringe projection; (2) direct depth encoding; and (3) multiwavelength depth en- coding. This paper compares the effectiveness and limitations of all three major compression methods, especially when the resultant 2D images are stored with low-quality lossy (i.e., JPEG) image formats. Experimental data found that multiwavelength depth encoding outperforms both other methods, especially under various levels of lossy JPEG compression. Principles of each method will be explained, and experimental data will be presented to evaluate each method.
Novel Approaches in 3D Sensing, Imaging, and Visualization
NASA Astrophysics Data System (ADS)
Schulein, Robert; Daneshpanah, M.; Cho, M.; Javidi, B.
Three-dimensional (3D) imaging systems are being researched extensively for purposes of sensing and visualization in fields as diverse as defense, medical, art, and entertainment. When compared to traditional 2D imaging techniques, 3D imaging offers advantages in ranging, robustness to scene occlusion, and target recognition performance. Amongst the myriad 3D imaging techniques, 3D multiperspective imaging technologies have received recent attention due to the technologies' relatively low cost, scalability, and passive sensing capabilities. Multiperspective 3D imagers collect 3D scene information by recording 2D intensity information from multiple perspectives, thus retaining both ray intensity and angle information. Three novel developments in 3D sensing, imaging, and visualization systems are presented: 3D imaging with axially distributed sensing, 3D optical profilometry, and occluded 3D object tracking.
Novel 3D Compression Methods for Geometry, Connectivity and Texture
NASA Astrophysics Data System (ADS)
Siddeq, M. M.; Rodrigues, M. A.
2016-06-01
A large number of applications in medical visualization, games, engineering design, entertainment, heritage, e-commerce and so on require the transmission of 3D models over the Internet or over local networks. 3D data compression is an important requirement for fast data storage, access and transmission within bandwidth limitations. The Wavefront OBJ (object) file format is commonly used to share models due to its clear simple design. Normally each OBJ file contains a large amount of data (e.g. vertices and triangulated faces, normals, texture coordinates and other parameters) describing the mesh surface. In this paper we introduce a new method to compress geometry, connectivity and texture coordinates by a novel Geometry Minimization Algorithm (GM-Algorithm) in connection with arithmetic coding. First, each vertex ( x, y, z) coordinates are encoded to a single value by the GM-Algorithm. Second, triangle faces are encoded by computing the differences between two adjacent vertex locations, which are compressed by arithmetic coding together with texture coordinates. We demonstrate the method on large data sets achieving compression ratios between 87 and 99 % without reduction in the number of reconstructed vertices and triangle faces. The decompression step is based on a Parallel Fast Matching Search Algorithm (Parallel-FMS) to recover the structure of the 3D mesh. A comparative analysis of compression ratios is provided with a number of commonly used 3D file formats such as VRML, OpenCTM and STL highlighting the performance and effectiveness of the proposed method.
Highly compressible 3D periodic graphene aerogel microlattices
Zhu, Cheng; Han, T. Yong-Jin; Duoss, Eric B.; Golobic, Alexandra M.; Kuntz, Joshua D.; Spadaccini, Christopher M.; Worsley, Marcus A.
2015-04-22
Graphene is a two-dimensional material that offers a unique combination of low density, exceptional mechanical properties, large surface area and excellent electrical conductivity. Recent progress has produced bulk 3D assemblies of graphene, such as graphene aerogels, but they possess purely stochastic porous networks, which limit their performance compared with the potential of an engineered architecture. Here we report the fabrication of periodic graphene aerogel microlattices, possessing an engineered architecture via a 3D printing technique known as direct ink writing. The 3D printed graphene aerogels are lightweight, highly conductive and exhibit supercompressibility (up to 90% compressive strain). Moreover, the Young’s moduli of the 3D printed graphene aerogels show an order of magnitude improvement over bulk graphene materials with comparable geometric density and possess large surface areas. Ultimately, adapting the 3D printing technique to graphene aerogels realizes the possibility of fabricating a myriad of complex aerogel architectures for a broad range of applications.
Highly compressible 3D periodic graphene aerogel microlattices
Zhu, Cheng; Han, T. Yong-Jin; Duoss, Eric B.; Golobic, Alexandra M.; Kuntz, Joshua D.; Spadaccini, Christopher M.; Worsley, Marcus A.
2015-01-01
Graphene is a two-dimensional material that offers a unique combination of low density, exceptional mechanical properties, large surface area and excellent electrical conductivity. Recent progress has produced bulk 3D assemblies of graphene, such as graphene aerogels, but they possess purely stochastic porous networks, which limit their performance compared with the potential of an engineered architecture. Here we report the fabrication of periodic graphene aerogel microlattices, possessing an engineered architecture via a 3D printing technique known as direct ink writing. The 3D printed graphene aerogels are lightweight, highly conductive and exhibit supercompressibility (up to 90% compressive strain). Moreover, the Young's moduli of the 3D printed graphene aerogels show an order of magnitude improvement over bulk graphene materials with comparable geometric density and possess large surface areas. Adapting the 3D printing technique to graphene aerogels realizes the possibility of fabricating a myriad of complex aerogel architectures for a broad range of applications. PMID:25902277
Highly compressible 3D periodic graphene aerogel microlattices
NASA Astrophysics Data System (ADS)
Zhu, Cheng; Han, T. Yong-Jin; Duoss, Eric B.; Golobic, Alexandra M.; Kuntz, Joshua D.; Spadaccini, Christopher M.; Worsley, Marcus A.
2015-04-01
Graphene is a two-dimensional material that offers a unique combination of low density, exceptional mechanical properties, large surface area and excellent electrical conductivity. Recent progress has produced bulk 3D assemblies of graphene, such as graphene aerogels, but they possess purely stochastic porous networks, which limit their performance compared with the potential of an engineered architecture. Here we report the fabrication of periodic graphene aerogel microlattices, possessing an engineered architecture via a 3D printing technique known as direct ink writing. The 3D printed graphene aerogels are lightweight, highly conductive and exhibit supercompressibility (up to 90% compressive strain). Moreover, the Young's moduli of the 3D printed graphene aerogels show an order of magnitude improvement over bulk graphene materials with comparable geometric density and possess large surface areas. Adapting the 3D printing technique to graphene aerogels realizes the possibility of fabricating a myriad of complex aerogel architectures for a broad range of applications.
Lossless compression of 3D seismic data using a horizon displacement compensated 3D lifting scheme
NASA Astrophysics Data System (ADS)
Meftah, Anis; Antonini, Marc; Ben Amar, Chokri
2010-01-01
In this paper we present a method to optimize the computation of the wavelet transform for the 3D seismic data while reducing the energy of coefficients to the minimum. This allow us to reduce the entropy of the signal and so increase the compression ratios. The proposed method exploits the geometrical information contained in the seismic 3D data to optimize the computation of the wavelet transform. Indeed, the classic filtering is replaced by a filtering following the horizons contained in the 3D seismic images. Applying this approach in two dimensions permits us to obtain wavelets coefficients with lowest energy. The experiments show that our method permits to save extra 8% of the size of the object compared to the classic wavelet transform.
A skinning prediction scheme for dynamic 3D mesh compression
NASA Astrophysics Data System (ADS)
Mamou, Khaled; Zaharia, Titus; Prêteux, Françoise
2006-08-01
This paper presents a new prediction-based compression technique for dynamic 3D meshes with constant connectivity and time-varying geometry. The core of the proposed algorithm is a skinning model used for motion compensation. The mesh is first partitioned within vertex clusters that can be described by a single affine motion model. The proposed segmentation technique automatically determines the number of clusters and relays on a decimation strategy privileging the simplification of vertices exhibiting the same affine motion over the whole animation sequence. The residual prediction errors are finally compressed using a temporal-DCT representation. The performances of our encoder are objectively evaluated on a data set of eight animation sequences with various sizes, geometries and topologies, and exhibiting both rigid and elastic motions. The experimental evaluation shows that the proposed compression scheme outperforms state of the art techniques such as MPEG-4/AFX, Dynapack, RT, GV, MCGV, TDCT, PCA and RT compression schemes.
Postprocessing of compressed 3D graphic data by using subdivision
NASA Astrophysics Data System (ADS)
Cheang, Ka Man; Li, Jiankun; Kuo, C.-C. Jay
1998-10-01
In this work, we present a postprocessing technique applied to a 3D graphic model of a lower resolution to obtain a visually more pleasant representation. Our method is an improved version of the Butterfly subdivision scheme developed by Zorin et al. Our main contribution is to exploit the flatness information of local areas of a 3D graphic model for adaptive refinement. Consequently, we can avoid unnecessary subdivision in regions which are relatively flat. The proposed new algorithm not only reduces the computational complexity but also saves the storage space. With the hierarchical mesh compression method developed by Li and Kuo as the baseline coding method, we show that the postprocessing technique can greatly improve the visual quality of the decoded 3D graphic model.
Highly compressible 3D periodic graphene aerogel microlattices
Zhu, Cheng; Han, T. Yong-Jin; Duoss, Eric B.; ...
2015-04-22
Graphene is a two-dimensional material that offers a unique combination of low density, exceptional mechanical properties, large surface area and excellent electrical conductivity. Recent progress has produced bulk 3D assemblies of graphene, such as graphene aerogels, but they possess purely stochastic porous networks, which limit their performance compared with the potential of an engineered architecture. Here we report the fabrication of periodic graphene aerogel microlattices, possessing an engineered architecture via a 3D printing technique known as direct ink writing. The 3D printed graphene aerogels are lightweight, highly conductive and exhibit supercompressibility (up to 90% compressive strain). Moreover, the Young’s modulimore » of the 3D printed graphene aerogels show an order of magnitude improvement over bulk graphene materials with comparable geometric density and possess large surface areas. Ultimately, adapting the 3D printing technique to graphene aerogels realizes the possibility of fabricating a myriad of complex aerogel architectures for a broad range of applications.« less
NASA Astrophysics Data System (ADS)
Je, U. K.; Lee, M. S.; Cho, H. S.; Hong, D. K.; Park, Y. O.; Park, C. K.; Cho, H. M.; Choi, S. I.; Woo, T. H.
2015-06-01
In practical applications of three-dimensional (3D) tomographic imaging, there are often challenges for image reconstruction from insufficient sampling data. In computed tomography (CT), for example, image reconstruction from sparse views and/or limited-angle (<360°) views would enable fast scanning with reduced imaging doses to the patient. In this study, we investigated and implemented a reconstruction algorithm based on the compressed-sensing (CS) theory, which exploits the sparseness of the gradient image with substantially high accuracy, for potential applications to low-dose, high-accurate dental cone-beam CT (CBCT). We performed systematic simulation works to investigate the image characteristics and also performed experimental works by applying the algorithm to a commercially-available dental CBCT system to demonstrate its effectiveness for image reconstruction in insufficient sampling problems. We successfully reconstructed CBCT images of superior accuracy from insufficient sampling data and evaluated the reconstruction quality quantitatively. Both simulation and experimental demonstrations of the CS-based reconstruction from insufficient data indicate that the CS-based algorithm can be applied directly to current dental CBCT systems for reducing the imaging doses and further improving the image quality.
Improving 3D Wavelet-Based Compression of Hyperspectral Images
NASA Technical Reports Server (NTRS)
Klimesh, Matthew; Kiely, Aaron; Xie, Hua; Aranki, Nazeeh
2009-01-01
Two methods of increasing the effectiveness of three-dimensional (3D) wavelet-based compression of hyperspectral images have been developed. (As used here, images signifies both images and digital data representing images.) The methods are oriented toward reducing or eliminating detrimental effects of a phenomenon, referred to as spectral ringing, that is described below. In 3D wavelet-based compression, an image is represented by a multiresolution wavelet decomposition consisting of several subbands obtained by applying wavelet transforms in the two spatial dimensions corresponding to the two spatial coordinate axes of the image plane, and by applying wavelet transforms in the spectral dimension. Spectral ringing is named after the more familiar spatial ringing (spurious spatial oscillations) that can be seen parallel to and near edges in ordinary images reconstructed from compressed data. These ringing phenomena are attributable to effects of quantization. In hyperspectral data, the individual spectral bands play the role of edges, causing spurious oscillations to occur in the spectral dimension. In the absence of such corrective measures as the present two methods, spectral ringing can manifest itself as systematic biases in some reconstructed spectral bands and can reduce the effectiveness of compression of spatially-low-pass subbands. One of the two methods is denoted mean subtraction. The basic idea of this method is to subtract mean values from spatial planes of spatially low-pass subbands prior to encoding, because (a) such spatial planes often have mean values that are far from zero and (b) zero-mean data are better suited for compression by methods that are effective for subbands of two-dimensional (2D) images. In this method, after the 3D wavelet decomposition is performed, mean values are computed for and subtracted from each spatial plane of each spatially-low-pass subband. The resulting data are converted to sign-magnitude form and compressed in a
Nonconvex compressive video sensing
NASA Astrophysics Data System (ADS)
Chen, Liangliang; Yan, Ming; Qian, Chunqi; Xi, Ning; Zhou, Zhanxin; Yang, Yongliang; Song, Bo; Dong, Lixin
2016-11-01
High-speed cameras explore more details than normal cameras in the time sequence, while the conventional video sampling suffers from the trade-off between temporal and spatial resolutions due to the sensor's physical limitation. Compressive sensing overcomes this obstacle by combining the sampling and compression procedures together. A single-pixel-based real-time video acquisition is proposed to record dynamic scenes, and a fast nonconvex algorithm for the nonconvex sorted ℓ1 regularization is applied to reconstruct frame differences using few numbers of measurements. Then, an edge-detection-based denoising method is employed to reduce the error in the frame difference image. The experimental results show that the proposed algorithm together with the single-pixel imaging system makes compressive video cameras available.
Beamforming Using Compressive Sensing
2011-10-01
Am. 130 (4), October 2011 VC 2011 Acoustical Society of America G. F. Edelmann and C. F. Gaumond: JASA Express Letters [DOI: 10.1121/1.3632046...arbitrarily spaced array, the rank of A may be insufficient, G. F. Edelmann and C. F. Gaumond: JASA Express Letters [DOI: 10.1121/1.3632046] Published Online...09 September 2011 J. Acoust. Soc. Am. 130 (4), October 2011 G. F. Edelmann and C. F. Gaumond: Beamforming using compressive sensing EL233 Downloaded
Stevens, Andrew J.; Kovarik, Libor; Abellan, Patricia; Yuan, Xin; Carin, Lawrence; Browning, Nigel D.
2015-08-02
One of the main limitations of imaging at high spatial and temporal resolution during in-situ TEM experiments is the frame rate of the camera being used to image the dynamic process. While the recent development of direct detectors has provided the hardware to achieve frame rates approaching 0.1ms, the cameras are expensive and must replace existing detectors. In this paper, we examine the use of coded aperture compressive sensing methods [1, 2, 3, 4] to increase the framerate of any camera with simple, low-cost hardware modifications. The coded aperture approach allows multiple sub-frames to be coded and integrated into a single camera frame during the acquisition process, and then extracted upon readout using statistical compressive sensing inversion. Our simulations show that it should be possible to increase the speed of any camera by at least an order of magnitude. Compressive Sensing (CS) combines sensing and compression in one operation, and thus provides an approach that could further improve the temporal resolution while correspondingly reducing the electron dose rate. Because the signal is measured in a compressive manner, fewer total measurements are required. When applied to TEM video capture, compressive imaging couled improve acquisition speed and reduce the electron dose rate. CS is a recent concept, and has come to the forefront due the seminal work of Candès [5]. Since the publication of Candès, there has been enormous growth in the application of CS and development of CS variants. For electron microscopy applications, the concept of CS has also been recently applied to electron tomography [6], and reduction of electron dose in scanning transmission electron microscopy (STEM) imaging [7]. To demonstrate the applicability of coded aperture CS video reconstruction for atomic level imaging, we simulate compressive sensing on observations of Pd nanoparticles and Ag nanoparticles during exposure to high temperatures and other environmental
Finite element solver for 3-D compressible viscous flows
NASA Technical Reports Server (NTRS)
Reddy, K. C.; Reddy, J. N.
1986-01-01
The space shuttle main engine (SSME) has extremely complex internal flow structure. The geometry of the flow domain is three-dimensional with complicated topology. The flow is compressible, viscous, and turbulent with large gradients in flow quantities and regions of recirculations. The analysis of the flow field in SSME involves several tedious steps. One is the geometrical modeling of the particular zone of the SSME being studied. Accessing the geometry definition, digitalizing it, and developing surface interpolations suitable for an interior grid generator require considerable amount of manual labor. There are several types of grid generators available with some general-purpose finite element programs. An efficient and robust computational scheme for solving 3D Navier-Stokes equations has to be implemented. Post processing software has to be adapted to visualize and analyze the computed 3D flow field. The progress made in a project to develop software for the analysis of the flow is discussed. The technical approach to the development of the finite element scheme and the relaxation procedure are discussed. The three dimensional finite element code for the compressible Navier-Stokes equations is listed.
High-speed optical 3D sensing and its applications
NASA Astrophysics Data System (ADS)
Watanabe, Yoshihiro
2016-12-01
This paper reviews high-speed optical 3D sensing technologies for obtaining the 3D shape of a target using a camera. The focusing speed is from 100 to 1000 fps, exceeding normal camera frame rates, which are typically 30 fps. In particular, contactless, active, and real-time systems are introduced. Also, three example applications of this type of sensing technology are introduced, including surface reconstruction from time-sequential depth images, high-speed 3D user interaction, and high-speed digital archiving.
Beamforming using compressive sensing.
Edelmann, Geoffrey F; Gaumond, Charles F
2011-10-01
Compressive sensing (CS) is compared with conventional beamforming using horizontal beamforming of at-sea, towed-array data. They are compared qualitatively using bearing time records and quantitatively using signal-to-interference ratio. Qualitatively, CS exhibits lower levels of background interference than conventional beamforming. Furthermore, bearing time records show increasing, but tolerable, levels of background interference when the number of elements is decreased. For the full array, CS generates signal-to-interference ratio of 12 dB, but conventional beamforming only 8 dB. The superiority of CS over conventional beamforming is much more pronounced with undersampling.
Compressed sensing for STEM tomography.
Donati, Laurène; Nilchian, Masih; Trépout, Sylvain; Messaoudi, Cédric; Marco, Sergio; Unser, Michael
2017-08-01
A central challenge in scanning transmission electron microscopy (STEM) is to reduce the electron radiation dosage required for accurate imaging of 3D biological nano-structures. Methods that permit tomographic reconstruction from a reduced number of STEM acquisitions without introducing significant degradation in the final volume are thus of particular importance. In random-beam STEM (RB-STEM), the projection measurements are acquired by randomly scanning a subset of pixels at every tilt view. In this work, we present a tailored RB-STEM acquisition-reconstruction framework that fully exploits the compressed sensing principles. We first demonstrate that RB-STEM acquisition fulfills the "incoherence" condition when the image is expressed in terms of wavelets. We then propose a regularized tomographic reconstruction framework to recover volumes from RB-STEM measurements. We demonstrate through simulations on synthetic and real projection measurements that the proposed framework reconstructs high-quality volumes from strongly downsampled RB-STEM data and outperforms existing techniques at doing so. This application of compressed sensing principles to STEM paves the way for a practical implementation of RB-STEM and opens new perspectives for high-quality reconstructions in STEM tomography. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.
A finite element solver for 3-D compressible viscous flows
NASA Technical Reports Server (NTRS)
Reddy, K. C.; Reddy, J. N.; Nayani, S.
1990-01-01
Computation of the flow field inside a space shuttle main engine (SSME) requires the application of state of the art computational fluid dynamic (CFD) technology. Several computer codes are under development to solve 3-D flow through the hot gas manifold. Some algorithms were designed to solve the unsteady compressible Navier-Stokes equations, either by implicit or explicit factorization methods, using several hundred or thousands of time steps to reach a steady state solution. A new iterative algorithm is being developed for the solution of the implicit finite element equations without assembling global matrices. It is an efficient iteration scheme based on a modified nonlinear Gauss-Seidel iteration with symmetric sweeps. The algorithm is analyzed for a model equation and is shown to be unconditionally stable. Results from a series of test problems are presented. The finite element code was tested for couette flow, which is flow under a pressure gradient between two parallel plates in relative motion. Another problem that was solved is viscous laminar flow over a flat plate. The general 3-D finite element code was used to compute the flow in an axisymmetric turnaround duct at low Mach numbers.
Compressive sensing in medical imaging
Graff, Christian G.; Sidky, Emil Y.
2015-01-01
The promise of compressive sensing, exploitation of compressibility to achieve high quality image reconstructions with less data, has attracted a great deal of attention in the medical imaging community. At the Compressed Sensing Incubator meeting held in April 2014 at OSA Headquarters in Washington, DC, presentations were given summarizing some of the research efforts ongoing in compressive sensing for x-ray computed tomography and magnetic resonance imaging systems. This article provides an expanded version of these presentations. Sparsity-exploiting reconstruction algorithms that have gained popularity in the medical imaging community are studied, and examples of clinical applications that could benefit from compressive sensing ideas are provided. The current and potential future impact of compressive sensing on the medical imaging field is discussed. PMID:25968400
Compressive sensing in medical imaging.
Graff, Christian G; Sidky, Emil Y
2015-03-10
The promise of compressive sensing, exploitation of compressibility to achieve high quality image reconstructions with less data, has attracted a great deal of attention in the medical imaging community. At the Compressed Sensing Incubator meeting held in April 2014 at OSA Headquarters in Washington, DC, presentations were given summarizing some of the research efforts ongoing in compressive sensing for x-ray computed tomography and magnetic resonance imaging systems. This article provides an expanded version of these presentations. Sparsity-exploiting reconstruction algorithms that have gained popularity in the medical imaging community are studied, and examples of clinical applications that could benefit from compressive sensing ideas are provided. The current and potential future impact of compressive sensing on the medical imaging field is discussed.
Compressively sensed complex networks.
Dunlavy, Daniel M.; Ray, Jaideep; Pinar, Ali
2010-07-01
The aim of this project is to develop low dimension parametric (deterministic) models of complex networks, to use compressive sensing (CS) and multiscale analysis to do so and to exploit the structure of complex networks (some are self-similar under coarsening). CS provides a new way of sampling and reconstructing networks. The approach is based on multiresolution decomposition of the adjacency matrix and its efficient sampling. It requires preprocessing of the adjacency matrix to make it 'blocky' which is the biggest (combinatorial) algorithm challenge. Current CS reconstruction algorithm makes no use of the structure of a graph, its very general (and so not very efficient/customized). Other model-based CS techniques exist, but not yet adapted to networks. Obvious starting point for future work is to increase the efficiency of reconstruction.
MSV3d: database of human MisSense Variants mapped to 3D protein structure.
Luu, Tien-Dao; Rusu, Alin-Mihai; Walter, Vincent; Ripp, Raymond; Moulinier, Luc; Muller, Jean; Toursel, Thierry; Thompson, Julie D; Poch, Olivier; Nguyen, Hoan
2012-01-01
The elucidation of the complex relationships linking genotypic and phenotypic variations to protein structure is a major challenge in the post-genomic era. We present MSV3d (Database of human MisSense Variants mapped to 3D protein structure), a new database that contains detailed annotation of missense variants of all human proteins (20 199 proteins). The multi-level characterization includes details of the physico-chemical changes induced by amino acid modification, as well as information related to the conservation of the mutated residue and its position relative to functional features in the available or predicted 3D model. Major releases of the database are automatically generated and updated regularly in line with the dbSNP (database of Single Nucleotide Polymorphism) and SwissVar releases, by exploiting the extensive Décrypthon computational grid resources. The database (http://decrypthon.igbmc.fr/msv3d) is easily accessible through a simple web interface coupled to a powerful query engine and a standard web service. The content is completely or partially downloadable in XML or flat file formats. Database URL: http://decrypthon.igbmc.fr/msv3d.
Advanced 3D Sensing and Visualization System for Unattended Monitoring
Carlson, J.J.; Little, C.Q.; Nelson, C.L.
1999-01-01
The purpose of this project was to create a reliable, 3D sensing and visualization system for unattended monitoring. The system provides benefits for several of Sandia's initiatives including nonproliferation, treaty verification, national security and critical infrastructure surety. The robust qualities of the system make it suitable for both interior and exterior monitoring applications. The 3D sensing system combines two existing sensor technologies in a new way to continuously maintain accurate 3D models of both static and dynamic components of monitored areas (e.g., portions of buildings, roads, and secured perimeters in addition to real-time estimates of the shape, location, and motion of humans and moving objects). A key strength of this system is the ability to monitor simultaneous activities on a continuous basis, such as several humans working independently within a controlled workspace, while also detecting unauthorized entry into the workspace. Data from the sensing system is used to identi~ activities or conditions that can signi~ potential surety (safety, security, and reliability) threats. The system could alert a security operator of potential threats or could be used to cue other detection, inspection or warning systems. An interactive, Web-based, 3D visualization capability was also developed using the Virtual Reality Modeling Language (VRML). The intex%ace allows remote, interactive inspection of a monitored area (via the Internet or Satellite Links) using a 3D computer model of the area that is rendered from actual sensor data.
Compressed sensing for body MRI.
Feng, Li; Benkert, Thomas; Block, Kai Tobias; Sodickson, Daniel K; Otazo, Ricardo; Chandarana, Hersh
2017-04-01
The introduction of compressed sensing for increasing imaging speed in magnetic resonance imaging (MRI) has raised significant interest among researchers and clinicians, and has initiated a large body of research across multiple clinical applications over the last decade. Compressed sensing aims to reconstruct unaliased images from fewer measurements than are traditionally required in MRI by exploiting image compressibility or sparsity. Moreover, appropriate combinations of compressed sensing with previously introduced fast imaging approaches, such as parallel imaging, have demonstrated further improved performance. The advent of compressed sensing marks the prelude to a new era of rapid MRI, where the focus of data acquisition has changed from sampling based on the nominal number of voxels and/or frames to sampling based on the desired information content. This article presents a brief overview of the application of compressed sensing techniques in body MRI, where imaging speed is crucial due to the presence of respiratory motion along with stringent constraints on spatial and temporal resolution. The first section provides an overview of the basic compressed sensing methodology, including the notion of sparsity, incoherence, and nonlinear reconstruction. The second section reviews state-of-the-art compressed sensing techniques that have been demonstrated for various clinical body MRI applications. In the final section, the article discusses current challenges and future opportunities. 5 J. Magn. Reson. Imaging 2017;45:966-987. © 2016 International Society for Magnetic Resonance in Medicine.
3-D Adaptive Sparsity Based Image Compression with Applications to Optical Coherence Tomography
Fang, Leyuan; Li, Shutao; Kang, Xudong; Izatt, Joseph A.; Farsiu, Sina
2015-01-01
We present a novel general-purpose compression method for tomographic images, termed 3D adaptive sparse representation based compression (3D-ASRC). In this paper, we focus on applications of 3D-ASRC for the compression of ophthalmic 3D optical coherence tomography (OCT) images. The 3D-ASRC algorithm exploits correlations among adjacent OCT images to improve compression performance, yet is sensitive to preserving their differences. Due to the inherent denoising mechanism of the sparsity based 3D-ASRC, the quality of the compressed images are often better than the raw images they are based on. Experiments on clinical-grade retinal OCT images demonstrate the superiority of the proposed 3D-ASRC over other well-known compression methods. PMID:25561591
Designing experiments through compressed sensing.
Young, Joseph G.; Ridzal, Denis
2013-06-01
In the following paper, we discuss how to design an ensemble of experiments through the use of compressed sensing. Specifically, we show how to conduct a small number of physical experiments and then use compressed sensing to reconstruct a larger set of data. In order to accomplish this, we organize our results into four sections. We begin by extending the theory of compressed sensing to a finite product of Hilbert spaces. Then, we show how these results apply to experiment design. Next, we develop an efficient reconstruction algorithm that allows us to reconstruct experimental data projected onto a finite element basis. Finally, we verify our approach with two computational experiments.
Hyperspectral image compression: adapting SPIHT and EZW to anisotropic 3-D wavelet coding.
Christophe, Emmanuel; Mailhes, Corinne; Duhamel, Pierre
2008-12-01
Hyperspectral images present some specific characteristics that should be used by an efficient compression system. In compression, wavelets have shown a good adaptability to a wide range of data, while being of reasonable complexity. Some wavelet-based compression algorithms have been successfully used for some hyperspectral space missions. This paper focuses on the optimization of a full wavelet compression system for hyperspectral images. Each step of the compression algorithm is studied and optimized. First, an algorithm to find the optimal 3-D wavelet decomposition in a rate-distortion sense is defined. Then, it is shown that a specific fixed decomposition has almost the same performance, while being more useful in terms of complexity issues. It is shown that this decomposition significantly improves the classical isotropic decomposition. One of the most useful properties of this fixed decomposition is that it allows the use of zero tree algorithms. Various tree structures, creating a relationship between coefficients, are compared. Two efficient compression methods based on zerotree coding (EZW and SPIHT) are adapted on this near-optimal decomposition with the best tree structure found. Performances are compared with the adaptation of JPEG 2000 for hyperspectral images on six different areas presenting different statistical properties.
Compressive Sensing for Quantum Imaging
NASA Astrophysics Data System (ADS)
Howland, Gregory A.
This thesis describes the application of compressive sensing to several challenging problems in quantum imaging with practical and fundamental implications. Compressive sensing is a measurement technique that compresses a signal during measurement such that it can be dramatically undersampled. Compressive sensing has been shown to be an extremely efficient measurement technique for imaging, particularly when detector arrays are not available. The thesis first reviews compressive sensing through the lens of quantum imaging and quantum measurement. Four important applications and their corresponding experiments are then described in detail. The first application is a compressive sensing, photon-counting lidar system. A novel depth mapping technique that uses standard, linear compressive sensing is described. Depth maps up to 256 x 256 pixel transverse resolution are recovered with depth resolution less than 2.54 cm. The first three-dimensional, photon counting video is recorded at 32 x 32 pixel resolution and 14 frames-per-second. The second application is the use of compressive sensing for complementary imaging---simultaneously imaging the transverse-position and transverse-momentum distributions of optical photons. This is accomplished by taking random, partial projections of position followed by imaging the momentum distribution on a cooled CCD camera. The projections are shown to not significantly perturb the photons' momenta while allowing high resolution position images to be reconstructed using compressive sensing. A variety of objects and their diffraction patterns are imaged including the double slit, triple slit, alphanumeric characters, and the University of Rochester logo. The third application is the use of compressive sensing to characterize spatial entanglement of photon pairs produced by spontaneous parametric downconversion. The technique gives a theoretical speedup N2/log N for N-dimensional entanglement over the standard raster scanning technique
NASA Astrophysics Data System (ADS)
Mekuria, Rufael; Cesar, Pablo; Doumanis, Ioannis; Frisiello, Antonella
2015-09-01
Compression of 3D object based video is relevant for 3D Immersive applications. Nevertheless, the perceptual aspects of the degradation introduced by codecs for meshes and point clouds are not well understood. In this paper we evaluate the subjective and objective degradations introduced by such codecs in a state of art 3D immersive virtual room. In the 3D immersive virtual room, users are captured with multiple cameras, and their surfaces are reconstructed as photorealistic colored/textured 3D meshes or point clouds. To test the perceptual effect of compression and transmission, we render degraded versions with different frame rates in different contexts (near/far) in the scene. A quantitative subjective study with 16 users shows that negligible distortion of decoded surfaces compared to the original reconstructions can be achieved in the 3D virtual room. In addition, a qualitative task based analysis in a full prototype field trial shows increased presence, emotion, user and state recognition of the reconstructed 3D Human representation compared to animated computer avatars.
3D temperature field reconstruction using ultrasound sensing system
NASA Astrophysics Data System (ADS)
Liu, Yuqian; Ma, Tong; Cao, Chengyu; Wang, Xingwei
2016-04-01
3D temperature field reconstruction is of practical interest to the power, transportation and aviation industries and it also opens up opportunities for real time control or optimization of high temperature fluid or combustion process. In our paper, a new distributed optical fiber sensing system consisting of a series of elements will be used to generate and receive acoustic signals. This system is the first active temperature field sensing system that features the advantages of the optical fiber sensors (distributed sensing capability) and the acoustic sensors (non-contact measurement). Signals along multiple paths will be measured simultaneously enabled by a code division multiple access (CDMA) technique. Then a proposed Gaussian Radial Basis Functions (GRBF)-based approach can approximate the temperature field as a finite summation of space-dependent basis functions and time-dependent coefficients. The travel time of the acoustic signals depends on the temperature of the media. On this basis, the Gaussian functions are integrated along a number of paths which are determined by the number and distribution of sensors. The inversion problem to estimate the unknown parameters of the Gaussian functions can be solved with the measured times-of-flight (ToF) of acoustic waves and the length of propagation paths using the recursive least square method (RLS). The simulation results show an approximation error less than 2% in 2D and 5% in 3D respectively. It demonstrates the availability and efficiency of our proposed 3D temperature field reconstruction mechanism.
Lustig, Michael; Alley, Marcus T.; Murphy, Mark J.; Vasanawala, Shreyas S.
2012-01-01
Purpose: To assess the potential of compressed-sensing parallel-imaging four-dimensional (4D) phase-contrast magnetic resonance (MR) imaging and specialized imaging software in the evaluation of valvular insufficiency and intracardiac shunts in patients with congenital heart disease. Materials and Methods: Institutional review board approval was obtained for this HIPAA-compliant study. Thirty-four consecutive retrospectively identified patients in whom a compressed-sensing parallel-imaging 4D phase-contrast sequence was performed as part of routine clinical cardiac MR imaging between March 2010 and August 2011 and who had undergone echocardiography were included. Multiplanar, volume-rendered, and stereoscopic three-dimensional velocity-fusion visualization algorithms were developed and implemented in Java and OpenGL. Two radiologists independently reviewed 4D phase-contrast studies for each of 34 patients (mean age, 6 years; age range, 10 months to 21 years) and tabulated visible shunts and valvular regurgitation. These results were compared with color Doppler echocardiographic and cardiac MR imaging reports, which were generated without 4D phase-contrast visualization. Cohen κ statistics were computed to assess interobserver agreement and agreement with echocardiographic results. Results: The 4D phase-contrast acquisitions were performed, on average, in less than 10 minutes. Among 123 valves seen in 34 4D phase-contrast studies, 29 regurgitant valves were identified, with good agreement between observers (k = 0.85). There was also good agreement with the presence of at least mild regurgitation at echocardiography (observer 1, κ = 0.76; observer 2, κ = 0.77) with high sensitivity (observer 1, 75%; observer 2, 82%) and specificity (observer 1, 97%; observer 2, 95%) relative to the reference standard. Eight intracardiac shunts were identified, four of which were not visible with conventional cardiac MR imaging but were detected with echocardiography. No
3-D wavelet compression and progressive inverse wavelet synthesis rendering of concentric mosaic.
Luo, Lin; Wu, Yunnan; Li, Jin; Zhang, Ya-Qin
2002-01-01
Using an array of photo shots, the concentric mosaic offers a quick way to capture and model a realistic three-dimensional (3-D) environment. We compress the concentric mosaic image array with a 3-D wavelet transform and coding scheme. Our compression algorithm and bitstream syntax are designed to ensure that a local view rendering of the environment requires only a partial bitstream, thereby eliminating the need to decompress the entire compressed bitstream before rendering. By exploiting the ladder-like structure of the wavelet lifting scheme, the progressive inverse wavelet synthesis (PIWS) algorithm is proposed to maximally reduce the computational cost of selective data accesses on such wavelet compressed datasets. Experimental results show that the 3-D wavelet coder achieves high-compression performance. With the PIWS algorithm, a 3-D environment can be rendered in real time from a compressed dataset.
Conceptual compression for pattern recognition in 3D model output
NASA Astrophysics Data System (ADS)
Prudden, Rachel; Robinson, Niall; Arribas, Alberto
2017-04-01
The problem of data compression is closely related to the idea of comprehension. If you understand a scene at a qualitative level, this should enable you to make reasonable predictions about its contents, meaning that less extra information is needed to encode it precisely. These ideas have already been applied in the field of image compression; see for example the work on conceptual compression by Google DeepMind. Applying similar methods to multidimensional atmospheric data could have significant benefits. Beyond reducing storage demands, the ability to recognise complex features would make it far easier to interpret and search large volumes of meteorological data. Our poster will present some early work in this area.
Compressed sensing for phase retrieval.
Newton, Marcus C
2012-05-01
To date there are several iterative techniques that enjoy moderate success when reconstructing phase information, where only intensity measurements are made. There remains, however, a number of cases in which conventional approaches are unsuccessful. In the last decade, the theory of compressed sensing has emerged and provides a route to solving convex optimisation problems exactly via ℓ(1)-norm minimization. Here the application of compressed sensing to phase retrieval in a nonconvex setting is reported. An algorithm is presented that applies reweighted ℓ(1)-norm minimization to yield accurate reconstruction where conventional methods fail.
Deterministic Compressed Sensing
2011-11-01
International Mathematical Research Notices, 64:4019–4041, 2005. [218] M. Rudelson and R. Vershynin. On sparse reconstruction from Fourier and Gaussian...Baraniuk. Sudocodes - Fast measurement and reconstruction of sparse signals. In IEEE International Symposium on Information Theory (ISIT), 2006. [223] K...sensing matrices as well as simple and e cient recovery algorithms. We show that by reformulating signal reconstruction as a zero-sum game we can e
Numerical simulations of 3D compressible vortex ring
NASA Astrophysics Data System (ADS)
Dora, C. L.; De, A.; Das, D.
2017-07-01
In this paper, the evolution and instability of compressible vortex rings at the open end of shock tube are investigated. Three-dimensional Navier-stokes simulations are performed to reveal the instability of circular-compressible vortex rings at different shock Mach numbers. Three shock Mach numbers Ms=1.26, 1.5 and 1.61 each corresponding to subsonic, moderately under-expanded, and highly under-expanded jet regimes, respectively are considered for simulations. It is observed that, in all the cases, the initial stable vortex ring becomes unstable to azimuthal disturbances. The azimuthal waves are seen to develop around the circumference of the vortex core and amplify with time, similar to incompressible vortex rings. At Ms=1.61, the results indicate that a counter-rotating vortex ring forms ahead of the primary vortex ring due to the roll-up of the slipstream vortices. With the presence of counter-rotating vortex ring, the primary vortex ring is severely stretched and the azimuthal waves are seen to be amplified. Furthermore, a preliminary analysis on the number of waves (n) formed around the circumference of the vortex ring is presented. The results show that incompressible instability analysis under-predicts the value of n. However, by incorporating the compressibility correction, the value of n closely matches with the present simulation.
Volumetric medical image compression using 3D listless embedded block partitioning.
Senapati, Ranjan K; Prasad, P M K; Swain, Gandharba; Shankar, T N
2016-01-01
This paper presents a listless variant of a modified three-dimensional (3D)-block coding algorithm suitable for medical image compression. A higher degree of correlation is achieved by using a 3D hybrid transform. The 3D hybrid transform is performed by a wavelet transform in the spatial dimension and a Karhunen-Loueve transform in the spectral dimension. The 3D transformed coefficients are arranged in a one-dimensional (1D) fashion, as in the hierarchical nature of the wavelet-coefficient distribution strategy. A novel listless block coding algorithm is applied to the mapped 1D coefficients which encode in an ordered-bit-plane fashion. The algorithm originates from the most significant bit plane and terminates at the least significant bit plane to generate an embedded bit stream, as in 3D-SPIHT. The proposed algorithm is called 3D hierarchical listless block (3D-HLCK), which exhibits better compression performance than that exhibited by 3D-SPIHT. Further, it is highly competitive with some of the state-of-the-art 3D wavelet coders for a wide range of bit rates for magnetic resonance, digital imaging and communication in medicine and angiogram images. 3D-HLCK provides rate and resolution scalability similar to those provided by 3D-SPIHT and 3D-SPECK. In addition, a significant memory reduction is achieved owing to the listless nature of 3D-HLCK.
Compressive Sensing with Optical Chaos
Rontani, D.; Choi, D.; Chang, C.-Y.; Locquet, A.; Citrin, D. S.
2016-01-01
Compressive sensing (CS) is a technique to sample a sparse signal below the Nyquist-Shannon limit, yet still enabling its reconstruction. As such, CS permits an extremely parsimonious way to store and transmit large and important classes of signals and images that would be far more data intensive should they be sampled following the prescription of the Nyquist-Shannon theorem. CS has found applications as diverse as seismology and biomedical imaging. In this work, we use actual optical signals generated from temporal intensity chaos from external-cavity semiconductor lasers (ECSL) to construct the sensing matrix that is employed to compress a sparse signal. The chaotic time series produced having their relevant dynamics on the 100 ps timescale, our results open the way to ultrahigh-speed compression of sparse signals. PMID:27910863
Compressive Sensing with Optical Chaos
NASA Astrophysics Data System (ADS)
Rontani, D.; Choi, D.; Chang, C.-Y.; Locquet, A.; Citrin, D. S.
2016-12-01
Compressive sensing (CS) is a technique to sample a sparse signal below the Nyquist-Shannon limit, yet still enabling its reconstruction. As such, CS permits an extremely parsimonious way to store and transmit large and important classes of signals and images that would be far more data intensive should they be sampled following the prescription of the Nyquist-Shannon theorem. CS has found applications as diverse as seismology and biomedical imaging. In this work, we use actual optical signals generated from temporal intensity chaos from external-cavity semiconductor lasers (ECSL) to construct the sensing matrix that is employed to compress a sparse signal. The chaotic time series produced having their relevant dynamics on the 100 ps timescale, our results open the way to ultrahigh-speed compression of sparse signals.
3D Compressible Melt Transport with Adaptive Mesh Refinement
NASA Astrophysics Data System (ADS)
Dannberg, Juliane; Heister, Timo
2015-04-01
Melt generation and migration have been the subject of numerous investigations, but their typical time and length-scales are vastly different from mantle convection, which makes it difficult to study these processes in a unified framework. The equations that describe coupled Stokes-Darcy flow have been derived a long time ago and they have been successfully implemented and applied in numerical models (Keller et al., 2013). However, modelling magma dynamics poses the challenge of highly non-linear and spatially variable material properties, in particular the viscosity. Applying adaptive mesh refinement to this type of problems is particularly advantageous, as the resolution can be increased in mesh cells where melt is present and viscosity gradients are high, whereas a lower resolution is sufficient in regions without melt. In addition, previous models neglect the compressibility of both the solid and the fluid phase. However, experiments have shown that the melt density change from the depth of melt generation to the surface leads to a volume increase of up to 20%. Considering these volume changes in both phases also ensures self-consistency of models that strive to link melt generation to processes in the deeper mantle, where the compressibility of the solid phase becomes more important. We describe our extension of the finite-element mantle convection code ASPECT (Kronbichler et al., 2012) that allows for solving additional equations describing the behaviour of silicate melt percolating through and interacting with a viscously deforming host rock. We use the original compressible formulation of the McKenzie equations, augmented by an equation for the conservation of energy. This approach includes both melt migration and melt generation with the accompanying latent heat effects. We evaluate the functionality and potential of this method using a series of simple model setups and benchmarks, comparing results of the compressible and incompressible formulation and
Towards an efficient compression of 3D coordinates of macromolecular structures
Valasatava, Yana; Bradley, Anthony R.; Rose, Alexander S.; Duarte, Jose M.; Prlić, Andreas
2017-01-01
The size and complexity of 3D macromolecular structures available in the Protein Data Bank is constantly growing. Current tools and file formats have reached limits of scalability. New compression approaches are required to support the visualization of large molecular complexes and enable new and scalable means for data analysis. We evaluated a series of compression techniques for coordinates of 3D macromolecular structures and identified the best performing approaches. By balancing compression efficiency in terms of the decompression speed and compression ratio, and code complexity, our results provide the foundation for a novel standard to represent macromolecular coordinates in a compact and useful file format. PMID:28362865
Towards an efficient compression of 3D coordinates of macromolecular structures.
Valasatava, Yana; Bradley, Anthony R; Rose, Alexander S; Duarte, Jose M; Prlić, Andreas; Rose, Peter W
2017-01-01
The size and complexity of 3D macromolecular structures available in the Protein Data Bank is constantly growing. Current tools and file formats have reached limits of scalability. New compression approaches are required to support the visualization of large molecular complexes and enable new and scalable means for data analysis. We evaluated a series of compression techniques for coordinates of 3D macromolecular structures and identified the best performing approaches. By balancing compression efficiency in terms of the decompression speed and compression ratio, and code complexity, our results provide the foundation for a novel standard to represent macromolecular coordinates in a compact and useful file format.
Introduction to the special section on 3D representation, compression, and rendering.
Vetro, Anthony; Frossard, Pascal; Lee, Sanghoon; Mueller, Karsten; Ohm, Jens-Rainer; Sullivan, Gary
2013-09-01
A new set of three-dimensional (3D) data formats and associated compression technologies are emerging with the aim to achieve more flexible representation and higher compression of 3D and multiview video content. These new tools will facilitate the generation of multiview output (e.g., as needed for multiview auto-stereoscopic displays), provide richer immersive multimedia experiences, and allow new interactive applications. This special section includes a timely set of papers covering the most recent technical developments in this area with papers covering topics in the different aspects of 3D systems, from representation and compression algorithms to rendering techniques and quality assessment. This special section includes a good balance on topics that are of interest to academic, industrial, and standardization communities. We believe that this collection of papers represent the most recent advances in representation, compression, rendering, and quality assessment of 3D scenes.
Holographic reconstruction by compressive sensing
NASA Astrophysics Data System (ADS)
Leportier, T.; Park, M.-C.
2017-06-01
Techniques based on compressive sensing (CS) have been proposed recently for the optical capture of compressed holographic data. However, even though several remarkable articles have presented mathematical theories and numerical simulations, only a few experimental demonstrations have been reported. In this paper, we investigate the use of different metrics for the estimation of sparsity and show that the Gini index is the most consistent. In addition, we compare the sparsifying bases based on discrete cosine transform, Fourier transform and Fresnelets. We demonstrate that the Fresnelets basis is the best choice for the reconstruction of digital holograms by CS. Finally, we present an experimental set-up for optical acquisition of phase-shifted holograms with an imaging system based on a single-pixel sensor.
Deterministic sensing matrices in compressive sensing: a survey.
Nguyen, Thu L N; Shin, Yoan
2013-01-01
Compressive sensing is a sampling method which provides a new approach to efficient signal compression and recovery by exploiting the fact that a sparse signal can be suitably reconstructed from very few measurements. One of the most concerns in compressive sensing is the construction of the sensing matrices. While random sensing matrices have been widely studied, only a few deterministic sensing matrices have been considered. These matrices are highly desirable on structure which allows fast implementation with reduced storage requirements. In this paper, a survey of deterministic sensing matrices for compressive sensing is presented. We introduce a basic problem in compressive sensing and some disadvantage of the random sensing matrices. Some recent results on construction of the deterministic sensing matrices are discussed.
Faster STORM Using Compressed Sensing
Zhu, Lei; Zhang, Wei; Elnatan, Daniel; Huang, Bo
2012-01-01
In super-resolution microscopy methods based on single-molecule switching, the rate to accumulate single-molecule activation events often limits the time resolution. Here, we developed a sparse-signal recovery technique using compressed sensing to analyze images with highly overlapping fluorescent spots. This method allows an activated fluorophore density an order of magnitude higher than what conventional single-molecule fitting methods can handle. Using this method, we have demonstrated imaging microtubule dynamics in living cells with a time resolution of 3 s. PMID:22522657
Investigation of out of plane compressive strength of 3D printed sandwich composites
NASA Astrophysics Data System (ADS)
Dikshit, V.; Yap, Y. L.; Goh, G. D.; Yang, H.; Lim, J. C.; Qi, X.; Yeong, W. Y.; Wei, J.
2016-07-01
In this study, the 3D printing technique was utilized to manufacture the sandwich composites. Composite filament fabrication based 3D printer was used to print the face-sheet, and inkjet 3D printer was used to print the sandwich core structure. This work aims to study the compressive failure of the sandwich structure manufactured by using these two manufacturing techniques. Two different types of core structures were investigated with the same type of face-sheet configuration. The core structures were printed using photopolymer, while the face-sheet was made using nylon/glass. The out-of-plane compressive strength of the 3D printed sandwich composite structure has been examined in accordance with ASTM standards C365/C365-M and presented in this paper.
Tension and compression fatigue response of unnotched 3D braided composites
NASA Technical Reports Server (NTRS)
Portanova, M. A.
1992-01-01
The unnotched compression and tension fatigue response of a 3-D braided composite was measured. Both gross compressive stress and tensile stress were plotted against cycles to failure to evaluate the fatigue life of these materials. Damage initiation and growth was monitored visually and by tracking compliance change during cycle loading. The intent was to establish by what means the strength of a 3-D architecture will start to degrade, at what point will it degrade beyond an acceptable level, and how this material will typically fail.
First application of the 3D-MHB on dynamic compressive behavior of UHPC
NASA Astrophysics Data System (ADS)
Cadoni, Ezio; Dotta, Matteo; Forni, Daniele; Riganti, Gianmario; Albertini, Carlo
2015-09-01
In order to study the dynamic behaviour of material in confined conditions a new machine was conceived and called 3D-Modified Hopkinson Bar (3D-MHB). It is a Modified Hopkinson Bar apparatus designed to apply dynamic loading in materials having a tri-axial stress state. It consists of a pulse generator system (with pre-tensioned bar and brittle joint), 1 input bar, and 5 output bars. The first results obtained on Ultra High Performance Concrete in compression with three different mono-axial compression states are presented. The results show how the pre-stress states minimize the boundary condition and a more uniform response is obtained.
A Novel Image Compression Algorithm for High Resolution 3D Reconstruction
NASA Astrophysics Data System (ADS)
Siddeq, M. M.; Rodrigues, M. A.
2014-06-01
This research presents a novel algorithm to compress high-resolution images for accurate structured light 3D reconstruction. Structured light images contain a pattern of light and shadows projected on the surface of the object, which are captured by the sensor at very high resolutions. Our algorithm is concerned with compressing such images to a high degree with minimum loss without adversely affecting 3D reconstruction. The Compression Algorithm starts with a single level discrete wavelet transform (DWT) for decomposing an image into four sub-bands. The sub-band LL is transformed by DCT yielding a DC-matrix and an AC-matrix. The Minimize-Matrix-Size Algorithm is used to compress the AC-matrix while a DWT is applied again to the DC-matrix resulting in LL2, HL2, LH2 and HH2 sub-bands. The LL2 sub-band is transformed by DCT, while the Minimize-Matrix-Size Algorithm is applied to the other sub-bands. The proposed algorithm has been tested with images of different sizes within a 3D reconstruction scenario. The algorithm is demonstrated to be more effective than JPEG2000 and JPEG concerning higher compression rates with equivalent perceived quality and the ability to more accurately reconstruct the 3D models.
Compressed Sensing for Metrics Development
NASA Astrophysics Data System (ADS)
McGraw, R. L.; Giangrande, S. E.; Liu, Y.
2012-12-01
Models by their very nature tend to be sparse in the sense that they are designed, with a few optimally selected key parameters, to provide simple yet faithful representations of a complex observational dataset or computer simulation output. This paper seeks to apply methods from compressed sensing (CS), a new area of applied mathematics currently undergoing a very rapid development (see for example Candes et al., 2006), to FASTER needs for new approaches to model evaluation and metrics development. The CS approach will be illustrated for a time series generated using a few-parameter (i.e. sparse) model. A seemingly incomplete set of measurements, taken at a just few random sampling times, is then used to recover the hidden model parameters. Remarkably there is a sharp transition in the number of required measurements, beyond which both the model parameters and time series are recovered exactly. Applications to data compression, data sampling/collection strategies, and to the development of metrics for model evaluation by comparison with observation (e.g. evaluation of model predictions of cloud fraction using cloud radar observations) are presented and discussed in context of the CS approach. Cited reference: Candes, E. J., Romberg, J., and Tao, T. (2006), Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information, IEEE Transactions on Information Theory, 52, 489-509.
Compressive Behavior of 3D Woven Composite Stiffened Panels: Experimental and Numerical Study
NASA Astrophysics Data System (ADS)
Zhou, Guangming; Pan, Ruqin; Li, Chao; Cai, Deng'an; Wang, Xiaopei
2016-10-01
The structural behavior and damage propagation of 3D woven composite stiffened panels with different woven patterns under axial-compression are here investigated. The panel is 2.5D interlock woven composites (2.5DIWC), while the straight-stiffeners are 3D woven orthogonal composites (3DWOC). They are coupled together with the Z-fibers from the stiffener passing straight thought the thickness of the panel. A "T-shape" model, in which the fiber bundle structure and resin matrix are drawn out to simulate the real situation of the connection area, is established to predict elastic constants and strength of the connection region. Based on Hashin failure criterion, a progressive damage model is carried out to simulate the compressive behavior of the stiffened panel. The 3D woven composite stiffened panels are manufactured using RTM process and then tested. A good agreement between experimental results and numerical predicted values for the compressive failure load is obtained. From initial damage to final collapse, the panel and stiffeners will not separate each other in the connection region. The main failure mode of 3D woven composite stiffened panels is compressive failure of fiber near the loading end corner.
Compressive Behavior of 3D Woven Composite Stiffened Panels: Experimental and Numerical Study
NASA Astrophysics Data System (ADS)
Zhou, Guangming; Pan, Ruqin; Li, Chao; Cai, Deng'an; Wang, Xiaopei
2017-08-01
The structural behavior and damage propagation of 3D woven composite stiffened panels with different woven patterns under axial-compression are here investigated. The panel is 2.5D interlock woven composites (2.5DIWC), while the straight-stiffeners are 3D woven orthogonal composites (3DWOC). They are coupled together with the Z-fibers from the stiffener passing straight thought the thickness of the panel. A "T-shape" model, in which the fiber bundle structure and resin matrix are drawn out to simulate the real situation of the connection area, is established to predict elastic constants and strength of the connection region. Based on Hashin failure criterion, a progressive damage model is carried out to simulate the compressive behavior of the stiffened panel. The 3D woven composite stiffened panels are manufactured using RTM process and then tested. A good agreement between experimental results and numerical predicted values for the compressive failure load is obtained. From initial damage to final collapse, the panel and stiffeners will not separate each other in the connection region. The main failure mode of 3D woven composite stiffened panels is compressive failure of fiber near the loading end corner.
JP3D compression of solar data-cubes: Photospheric imaging and spectropolarimetry
NASA Astrophysics Data System (ADS)
Del Moro, Dario; Giovannelli, Luca; Pietropaolo, Ermanno; Berrilli, Francesco
2017-02-01
Hyperspectral imaging is an ubiquitous technique in solar physics observations and the recent advances in solar instrumentation enabled us to acquire and record data at an unprecedented rate. The huge amount of data which will be archived in the upcoming solar observatories press us to compress the data in order to reduce the storage space and transfer times. The correlation present over all dimensions, spatial, temporal and spectral, of solar data-sets suggests the use of a 3D base wavelet decomposition, to achieve higher compression rates. In this work, we evaluate the performance of the recent JPEG2000 Part 10 standard, known as JP3D, for the lossless compression of several types of solar data-cubes. We explore the differences in: a) The compressibility of broad-band or narrow-band time-sequence; I or V Stokes profiles in spectropolarimetric data-sets; b) Compressing data in [x,y, λ] packages at different times or data in [x,y,t] packages of different wavelength; c) Compressing a single large data-cube or several smaller data-cubes; d) Compressing data which is under-sampled or super-sampled with respect to the diffraction cut-off.
Blind compressive sensing dynamic MRI
Lingala, Sajan Goud; Jacob, Mathews
2013-01-01
We propose a novel blind compressive sensing (BCS) frame work to recover dynamic magnetic resonance images from undersampled measurements. This scheme models the dynamic signal as a sparse linear combination of temporal basis functions, chosen from a large dictionary. In contrast to classical compressed sensing, the BCS scheme simultaneously estimates the dictionary and the sparse coefficients from the undersampled measurements. Apart from the sparsity of the coefficients, the key difference of the BCS scheme with current low rank methods is the non-orthogonal nature of the dictionary basis functions. Since the number of degrees of freedom of the BCS model is smaller than that of the low-rank methods, it provides improved reconstructions at high acceleration rates. We formulate the reconstruction as a constrained optimization problem; the objective function is the linear combination of a data consistency term and sparsity promoting ℓ1 prior of the coefficients. The Frobenius norm dictionary constraint is used to avoid scale ambiguity. We introduce a simple and efficient majorize-minimize algorithm, which decouples the original criterion into three simpler sub problems. An alternating minimization strategy is used, where we cycle through the minimization of three simpler problems. This algorithm is seen to be considerably faster than approaches that alternates between sparse coding and dictionary estimation, as well as the extension of K-SVD dictionary learning scheme. The use of the ℓ1 penalty and Frobenius norm dictionary constraint enables the attenuation of insignificant basis functions compared to the ℓ0 norm and column norm constraint assumed in most dictionary learning algorithms; this is especially important since the number of basis functions that can be reliably estimated is restricted by the available measurements. We also observe that the proposed scheme is more robust to local minima compared to K-SVD method, which relies on greedy sparse coding
3D hydrodynamic focusing microfluidics for emerging sensing technologies.
Daniele, Michael A; Boyd, Darryl A; Mott, David R; Ligler, Frances S
2015-05-15
While the physics behind laminar flows has been studied for 200 years, understanding of how to use parallel flows to augment the capabilities of microfluidic systems has been a subject of study primarily over the last decade. The use of one flow to focus another within a microfluidic channel has graduated from a two-dimensional to a three-dimensional process and the design principles are only now becoming established. This review explores the underlying principles for hydrodynamic focusing in three dimensions (3D) using miscible fluids and the application of these principles for creation of biosensors, separation of cells and particles for sample manipulation, and fabrication of materials that could be used for biosensors. Where sufficient information is available, the practicality of devices implementing fluid flows directed in 3D is evaluated and the advantages and limitations of 3D hydrodynamic focusing for the particular application are highlighted.
Accurate compressed look up table method for CGH in 3D holographic display.
Gao, Chuan; Liu, Juan; Li, Xin; Xue, Gaolei; Jia, Jia; Wang, Yongtian
2015-12-28
Computer generated hologram (CGH) should be obtained with high accuracy and high speed in 3D holographic display, and most researches focus on the high speed. In this paper, a simple and effective computation method for CGH is proposed based on Fresnel diffraction theory and look up table. Numerical simulations and optical experiments are performed to demonstrate its feasibility. The proposed method can obtain more accurate reconstructed images with lower memory usage compared with split look up table method and compressed look up table method without sacrificing the computational speed in holograms generation, so it is called accurate compressed look up table method (AC-LUT). It is believed that AC-LUT method is an effective method to calculate the CGH of 3D objects for real-time 3D holographic display where the huge information data is required, and it could provide fast and accurate digital transmission in various dynamic optical fields in the future.
3D Urban Remote Sensing Using Polarimetric SAR Tomography
NASA Astrophysics Data System (ADS)
Sun, Yuan; Wang, Chao; Zhang, Hong; Wu, Fan; Zhang, Bo
2013-08-01
During near decade, space-borne tomographic synthetic aperture radar (TomoSAR) has demonstrated unique capability in the retrieval of height location of reflectors. In this paper, we proposed a tomographic 3D reconstruction method with single bounce and double bounce scattering mechanism on the basis of the jointly sparsity of the two scattering patterns. The method has been applied to RADARSAT-2 Fine Quad Mode data with six baselines. The layover phenomenon within each resolution pixel has been analysed, together with the retrieval of the position of these points. The 3D structure of Suzhou Sports Stadium is also acquired the location of strong scattering points within each cell with layover.
Subjective evaluation of mobile 3D video content: depth range versus compression artifacts
NASA Astrophysics Data System (ADS)
Jumisko-Pyykkö, Satu; Haustola, Tomi; Boev, Atanas; Gotchev, Atanas
2011-02-01
Mobile 3D television is a new form of media experience, which combines the freedom of mobility with the greater realism of presenting visual scenes in 3D. Achieving this combination is a challenging task as greater viewing experience has to be achieved with the limited resources of the mobile delivery channel such as limited bandwidth and power constrained handheld player. This challenge sets need for tight optimization of the overall mobile 3DTV system. Presence of depth and compression artifacts in the played 3D video are two major factors that influence viewer's subjective quality of experience and satisfaction. The primary goal of this study has been to examine the influence of varying depth and compression artifacts on the subjective quality of experience for mobile 3D video content. In addition, the influence of the studied variables on simulator sickness symptoms has been studied and vocabulary-based descriptive quality of experience has been conducted for a sub-set of variables in order to understand the perceptual characteristics in detail. In the experiment, 30 participants have evaluated the overall quality of different 3D video contents with varying depth ranges and compressed with varying quantization parameters. The test video content has been presented on a portable autostereoscopic LCD display with horizontal double density pixel arrangement. The results of the psychometric study indicate that compression artifacts are a dominant factor determining the quality of experience compared to varying depth range. More specifically, contents with strong compression has been rejected by the viewers and deemed unacceptable. The results of descriptive study confirm the dominance of visible spatial artifacts along the added value of depth for artifact-free content. The level of visual discomfort has been determined as not offending.
JP3D compressed-domain watermarking of volumetric medical data sets
NASA Astrophysics Data System (ADS)
Ouled Zaid, Azza; Makhloufi, Achraf; Olivier, Christian
2010-01-01
Increasing transmission of medical data across multiple user systems raises concerns for medical image watermarking. Additionaly, the use of volumetric images triggers the need for efficient compression techniques in picture archiving and communication systems (PACS), or telemedicine applications. This paper describes an hybrid data hiding/compression system, adapted to volumetric medical imaging. The central contribution is to integrate blind watermarking, based on turbo trellis-coded quantization (TCQ), to JP3D encoder. Results of our method applied to Magnetic Resonance (MR) and Computed Tomography (CT) medical images have shown that our watermarking scheme is robust to JP3D compression attacks and can provide relative high data embedding rate whereas keep a relative lower distortion.
Compressed sensing traction force microscopy.
Brask, Jonatan Bohr; Singla-Buxarrais, Guillem; Uroz, Marina; Vincent, Romaric; Trepat, Xavier
2015-10-01
Adherent cells exert traction forces on their substrate, and these forces play important roles in biological functions such as mechanosensing, cell differentiation and cancer invasion. The method of choice to assess these active forces is traction force microscopy (TFM). Despite recent advances, TFM remains highly sensitive to measurement noise and exhibits limited spatial resolution. To improve the resolution and noise robustness of TFM, here we adapt techniques from compressed sensing (CS) to the reconstruction of the traction field from the substrate displacement field. CS enables the recovery of sparse signals at higher resolution from lower resolution data. Focal adhesions (FAs) of adherent cells are spatially sparse implying that traction fields are also sparse. Here we show, by simulation and by experiment, that the CS approach enables circumventing the Nyquist-Shannon sampling theorem to faithfully reconstruct the traction field at a higher resolution than that of the displacement field. This allows reaching state-of-the-art resolution using only a medium magnification objective. We also find that CS improves reconstruction quality in the presence of noise. A great scientific advance of the past decade is the recognition that physical forces determine an increasing list of biological processes. Traction force microscopy which measures the forces that cells exert on their surroundings has seen significant recent improvements, however the technique remains sensitive to measurement noise and severely limited in spatial resolution. We exploit the fact that the force fields are sparse to boost the spatial resolution and noise robustness by applying ideas from compressed sensing. The novel method allows high resolution on a larger field of view. This may in turn allow better understanding of the cell forces at the multicellular level, which are known to be important in wound healing and cancer invasion. Copyright © 2015 Acta Materialia Inc. Published by Elsevier
Photon counting passive 3D image sensing for automatic target recognition.
Yeom, Seokwon; Javidi, Bahram; Watson, Edward
2005-11-14
In this paper, we propose photon counting three-dimensional (3D) passive sensing and object recognition using integral imaging. The application of this approach to 3D automatic target recognition (ATR) is investigated using both linear and nonlinear matched filters. We find there is significant potential of the proposed system for 3D sensing and recognition with a low number of photons. The discrimination capability of the proposed system is quantified in terms of discrimination ratio, Fisher ratio, and receiver operating characteristic (ROC) curves. To the best of our knowledge, this is the first report on photon counting 3D passive sensing and ATR with integral imaging.
Spatial Compressive Sensing for Strain Data Reconstruction from Sparse Sensors
2014-10-01
the novel theory of compressive sensing and principles of continuum mechanics. Compressive sensing , also known as compressed sensing , refers to the...asserts that certain signals or images can be recovered from what was previously believed to be a highly incomplete measurement. Compressed sensing ...matrix completion problem is quite similar to compressive sensing , as a similar heuristic approach , convex relaxation, is used to recover
Compressed Sensing for Multidimensional Spectroscopy Experiments.
Sanders, Jacob N; Saikin, Semion K; Mostame, Sarah; Andrade, Xavier; Widom, Julia R; Marcus, Andrew H; Aspuru-Guzik, Alán
2012-09-20
Compressed sensing is a processing method that significantly reduces the number of measurements needed to accurately resolve signals in many fields of science and engineering. We develop a two-dimensional variant of compressed sensing for multidimensional spectroscopy and apply it to experimental data. For the model system of atomic rubidium vapor, we find that compressed sensing provides an order-of-magnitude (about 10-fold) improvement in spectral resolution along each dimension, as compared to a conventional discrete Fourier transform, using the same data set. More attractive is that compressed sensing allows for random undersampling of the experimental data, down to less than 5% of the experimental data set, with essentially no loss in spectral resolution. We believe that by combining powerful resolution with ease of use, compressed sensing can be a powerful tool for the analysis and interpretation of ultrafast spectroscopy data.
An Encoding Method for Compressing Geographical Coordinates in 3d Space
NASA Astrophysics Data System (ADS)
Qian, C.; Jiang, R.; Li, M.
2017-09-01
This paper proposed an encoding method for compressing geographical coordinates in 3D space. By the way of reducing the length of geographical coordinates, it helps to lessen the storage size of geometry information. In addition, the encoding algorithm subdivides the whole space according to octree rules, which enables progressive transmission and loading. Three main steps are included in this method: (1) subdividing the whole 3D geographic space based on octree structure, (2) resampling all the vertices in 3D models, (3) encoding the coordinates of vertices with a combination of Cube Index Code (CIC) and Geometry Code. A series of geographical 3D models were applied to evaluate the encoding method. The results showed that this method reduced the storage size of most test data by 90 % or even more under the condition of a speed of encoding and decoding. In conclusion, this method achieved a remarkable compression rate in vertex bit size with a steerable precision loss. It shall be of positive meaning to the web 3d map storing and transmission.
Lossy compression of hyperspectral images using shearlet transform and 3D SPECK
NASA Astrophysics Data System (ADS)
Karami, A.
2015-10-01
In this paper, a new lossy compression method for hyperspectral images (HSI) is introduced. HSI are considered as a 3D dataset with two dimensions in the spatial and one dimension in the spectral domain. In the proposed method, first 3D multidirectional anisotropic shearlet transform is applied to the HSI. Because, unlike traditional wavelets, shearlets are theoretically optimal in representing images with edges and other geometrical features. Second, soft thresholding method is applied to the shearlet transform coefficients and finally the modified coefficients are encoded using Three Dimensional- Set Partitioned Embedded bloCK (3D SPECK). Our simulation results show that the proposed method, in comparison with well-known approaches such as 3D SPECK (using 3D wavelet) and combined PCA and JPEG2000 algorithms, provides a higher SNR (signal to noise ratio) for any given compression ratio (CR). It is noteworthy to mention that the superiority of proposed method is distinguishable as the value of CR grows. In addition, the effect of proposed method on the spectral unmixing analysis is also evaluated.
3D Printing-Based Integrated Water Quality Sensing System.
Banna, Muinul; Bera, Kaustav; Sochol, Ryan; Lin, Liwei; Najjaran, Homayoun; Sadiq, Rehan; Hoorfar, Mina
2017-06-08
The online and accurate monitoring of drinking water supply networks is critically in demand to rapidly detect the accidental or deliberate contamination of drinking water. At present, miniaturized water quality monitoring sensors developed in the laboratories are usually tested under ambient pressure and steady-state flow conditions; however, in Water Distribution Systems (WDS), both the pressure and the flowrate fluctuate. In this paper, an interface is designed and fabricated using additive manufacturing or 3D printing technology-material extrusion (Trade Name: fused deposition modeling, FDM) and material jetting-to provide a conduit for miniaturized sensors for continuous online water quality monitoring. The interface is designed to meet two main criteria: low pressure at the inlet of the sensors and a low flowrate to minimize the water bled (i.e., leakage), despite varying pressure from WDS. To meet the above criteria, a two-dimensional computational fluid dynamics model was used to optimize the geometry of the channel. The 3D printed interface, with the embedded miniaturized pH and conductivity sensors, was then tested at different temperatures and flowrates. The results show that the response of the pH sensor is independent of the flowrate and temperature. As for the conductivity sensor, the flowrate and temperature affect only the readings at a very low conductivity (4 µS/cm) and high flowrates (30 mL/min), and a very high conductivity (460 µS/cm), respectively.
3D Printing-Based Integrated Water Quality Sensing System
Banna, Muinul; Bera, Kaustav; Sochol, Ryan; Lin, Liwei; Najjaran, Homayoun; Sadiq, Rehan; Hoorfar, Mina
2017-01-01
The online and accurate monitoring of drinking water supply networks is critically in demand to rapidly detect the accidental or deliberate contamination of drinking water. At present, miniaturized water quality monitoring sensors developed in the laboratories are usually tested under ambient pressure and steady-state flow conditions; however, in Water Distribution Systems (WDS), both the pressure and the flowrate fluctuate. In this paper, an interface is designed and fabricated using additive manufacturing or 3D printing technology—material extrusion (Trade Name: fused deposition modeling, FDM) and material jetting—to provide a conduit for miniaturized sensors for continuous online water quality monitoring. The interface is designed to meet two main criteria: low pressure at the inlet of the sensors and a low flowrate to minimize the water bled (i.e., leakage), despite varying pressure from WDS. To meet the above criteria, a two-dimensional computational fluid dynamics model was used to optimize the geometry of the channel. The 3D printed interface, with the embedded miniaturized pH and conductivity sensors, was then tested at different temperatures and flowrates. The results show that the response of the pH sensor is independent of the flowrate and temperature. As for the conductivity sensor, the flowrate and temperature affect only the readings at a very low conductivity (4 µS/cm) and high flowrates (30 mL/min), and a very high conductivity (460 µS/cm), respectively. PMID:28594387
Sensing and 3D Mapping of Soil Compaction
Tekin, Yücel; Kul, Basri; Okursoy, Rasim
2008-01-01
Soil compaction is an important physical limiting factor for the root growth and plant emergence and is one of the major causes for reduced crop yield worldwide. The objective of this study was to generate 2D/3D soil compaction maps for different depth layers of the soil. To do so, a soil penetrometer was designed, which was mounted on the three-point hitch of an agricultural tractor, consisting of a mechanical system, data acquisition system (DAS), and 2D/3D imaging and analysis software. The system was successfully tested in field conditions, measuring soil penetration resistances as a function of depth from 0 to 40 cm at 1 cm intervals. The software allows user to either tabulate the measured quantities or generate maps as soon as data collection has been terminated. The system may also incorporate GPS data to create geo-referenced soil maps. The software enables the user to graph penetration resistances at a specified coordinate. Alternately, soil compaction maps could be generated using data collected from multiple coordinates. The data could be automatically stratified to determine soil compaction distribution at different layers of 5, 10,.…, 40 cm depths. It was concluded that the system tested in this study could be used to assess the soil compaction at topsoil and the randomly distributed hardpan formations just below the common tillage depths, enabling visualization of spatial variability through the imaging software. PMID:27879888
Mechanical response of 3D Insert(®) PCL to compression.
Brunelli, M; Perrault, C M; Lacroix, D
2017-01-01
3D polymeric scaffolds are increasingly used for in vitro experiments aiming to mimic the environment found in vivo, to support for cellular growth and to induce differentiation through the application of external mechanical cues. In research, experimental results must be shown to be reproducible to be claimed as valid and the first clause to ensure consistency is to provide identical initial experimental conditions between trials. As a matter of fact, 3D structures fabricated in batch are supposed to present a highly reproducible geometry and consequently, to give the same bulk response to mechanical forces. This study aims to measure the overall mechanical response to compression of commercially available 3D Insert PCL scaffolds (3D PCL) fabricated in series by fuse deposition and evaluate how small changes in the architecture of scaffolds affect the mechanical response. The apparent elastic modulus (Ea) was evaluated by performing quasi-static mechanical tests at various temperatures showing a decrease in material stiffness from 5MPa at 25°C to 2.2MPa at 37°C. Then, a variability analysis revealed variations in Ea related to the repositioning of the sample into the testing machine, but also consistent differences comparing different scaffolds. To clarify the source of the differences measured in the mechanical response, the same scaffolds previously undergoing compression, were scanned by micro computed tomography (μCT) to identify any architectural difference. Eventually, to clarify the contribution given by differences in the architecture to the standard deviation of Ea, their mechanical response was qualitatively compared to a compact reference material such as polydimethylsiloxane (PDMS). This study links the geometry, architecture and mechanical response to compression of 3D PCL scaffolds and shows the importance of controlling such parameters in the manufacturing process to obtain scaffolds that can be used in vitro or in vivo under reproducible
Compressive sensing for nuclear security.
Gestner, Brian Joseph
2013-12-01
Special nuclear material (SNM) detection has applications in nuclear material control, treaty verification, and national security. The neutron and gamma-ray radiation signature of SNMs can be indirectly observed in scintillator materials, which fluoresce when exposed to this radiation. A photomultiplier tube (PMT) coupled to the scintillator material is often used to convert this weak fluorescence to an electrical output signal. The fluorescence produced by a neutron interaction event differs from that of a gamma-ray interaction event, leading to a slightly different pulse in the PMT output signal. The ability to distinguish between these pulse types, i.e., pulse shape discrimination (PSD), has enabled applications such as neutron spectroscopy, neutron scatter cameras, and dual-mode neutron/gamma-ray imagers. In this research, we explore the use of compressive sensing to guide the development of novel mixed-signal hardware for PMT output signal acquisition. Effectively, we explore smart digitizers that extract sufficient information for PSD while requiring a considerably lower sample rate than conventional digitizers. Given that we determine the feasibility of realizing these designs in custom low-power analog integrated circuits, this research enables the incorporation of SNM detection into wireless sensor networks.
NASA Astrophysics Data System (ADS)
Yang, L. M.; Shu, C.; Wang, Y.; Sun, Y.
2016-08-01
The sphere function-based gas kinetic scheme (GKS), which was presented by Shu and his coworkers [23] for simulation of inviscid compressible flows, is extended to simulate 3D viscous incompressible and compressible flows in this work. Firstly, we use certain discrete points to represent the spherical surface in the phase velocity space. Then, integrals along the spherical surface for conservation forms of moments, which are needed to recover 3D Navier-Stokes equations, are approximated by integral quadrature. The basic requirement is that these conservation forms of moments can be exactly satisfied by weighted summation of distribution functions at discrete points. It was found that the integral quadrature by eight discrete points on the spherical surface, which forms the D3Q8 discrete velocity model, can exactly match the integral. In this way, the conservative variables and numerical fluxes can be computed by weighted summation of distribution functions at eight discrete points. That is, the application of complicated formulations resultant from integrals can be replaced by a simple solution process. Several numerical examples including laminar flat plate boundary layer, 3D lid-driven cavity flow, steady flow through a 90° bending square duct, transonic flow around DPW-W1 wing and supersonic flow around NACA0012 airfoil are chosen to validate the proposed scheme. Numerical results demonstrate that the present scheme can provide reasonable numerical results for 3D viscous flows.
NASA Astrophysics Data System (ADS)
Huang, Bormin; Huang, Hung-Lung; Chen, Hao; Ahuja, Alok; Baggett, Kevin; Schmit, Timothy J.; Heymann, Roger W.
2004-02-01
The next-generation NOAA/NESDIS GOES-R hyperspectral sounder, now referred to as the HES (Hyperspectral Environmental Suite), will have hyperspectral resolution (over one thousand channels with spectral widths on the order of 0.5 wavenumber) and high spatial resolution (less than 10 km). Hyperspectral sounder data is a particular class of data requiring high accuracy for useful retrieval of atmospheric temperature and moisture profiles, surface characteristics, cloud properties, and trace gas information. Hence compression of these data sets is better to be lossless or near lossless. Given the large volume of three-dimensional hyperspectral sounder data that will be generated by the HES instrument, the use of robust data compression techniques will be beneficial to data transfer and archive. In this paper, we study lossless data compression for the HES using 3D integer wavelet transforms via the lifting schemes. The wavelet coefficients are processed with the 3D set partitioning in hierarchical trees (SPIHT) scheme followed by context-based arithmetic coding. SPIHT provides better coding efficiency than Shapiro's original embedded zerotree wavelet (EZW) algorithm. We extend the 3D SPIHT scheme to take on any size of 3D satellite data, each of whose dimensions need not be divisible by 2N, where N is the levels of the wavelet decomposition being performed. The compression ratios of various kinds of wavelet transforms are presented along with a comparison with the JPEG2000 codec.
NASA Astrophysics Data System (ADS)
Huang, Bormin; Huang, Hung-Lung; Chen, Hao; Ahuja, Alok; Baggett, Kevin; Schmit, Timothy J.; Heymann, Roger W.
2003-09-01
Hyperspectral sounder data is a particular class of data that requires high accuracy for useful retrieval of atmospheric temperature and moisture profiles, surface characteristics, cloud properties, and trace gas information. Therefore compression of these data sets is better to be lossless or near lossless. The next-generation NOAA/NESDIS GOES-R hyperspectral sounder, now referred to as the HES (Hyperspectral Environmental Suite), will have hyperspectral resolution (over one thousand channels with spectral widths on the order of 0.5 wavenumber) and high spatial resolution (less than 10 km). Given the large volume of three-dimensional hyperspectral sounder data that will be generated by the HES instrument, the use of robust data compression techniques will be beneficial to data transfer and archive. In this paper, we study lossless data compression for the HES using 3D integer wavelet transforms via the lifting schemes. The wavelet coefficients are then processed with the 3D embedded zerotree wavelet (EZW) algorithm followed by context-based arithmetic coding. We extend the 3D EZW scheme to take on any size of 3D satellite data, each of whose dimensions need not be divisible by 2N, where N is the levels of the wavelet decomposition being performed. The compression ratios of various kinds of wavelet transforms are presented along with a comparison with the JPEG2000 codec.
ROI-preserving 3D video compression method utilizing depth information
NASA Astrophysics Data System (ADS)
Ti, Chunli; Xu, Guodong; Guan, Yudong; Teng, Yidan
2015-09-01
Efficiently transmitting the extra information of three dimensional (3D) video is becoming a key issue of the development of 3DTV. 2D plus depth format not only occupies the smaller bandwidth and is compatible transmission under the condition of the existing channel, but also can provide technique support for advanced 3D video compression in some extend. This paper proposes an ROI-preserving compression scheme to further improve the visual quality at a limited bit rate. According to the connection between the focus of Human Visual System (HVS) and depth information, region of interest (ROI) can be automatically selected via depth map progressing. The main improvement from common method is that a meanshift based segmentation is executed to the depth map before foreground ROI selection to keep the integrity of scene. Besides, the sensitive areas along the edges are also protected. The Spatio-temporal filtering adapting to H.264 is used to the non-ROI of both 2D video and depth map before compression. Experiments indicate that, the ROI extracted by this method is more undamaged and according with subjective feeling, and the proposed method can keep the key high-frequency information more effectively while the bit rate is reduced.
NASA Astrophysics Data System (ADS)
Wei, Shih-Chieh; Huang, Bormin
2004-10-01
Hyperspectral sounder data is used for retrieval of useful geophysical parameters which promise better weather prediction. It features two characteristics. First it is huge in size with 2D spatial coverage and high spectral resolution in the infrared region. Second it allows low tolerance of noise and error in retrieving the geophysical parameters where a mathematically ill-posed problem is involved. Therefore compression is better to be lossless or near lossless for data transfer and archive. Meanwhile medical data from X-ray computerized tomography (CT) or magnetic resonance imaging (MRI) techniques also possesses similar characteristics. It provides motivation to apply lossless compression schemes for medical data to the hyperspectral sounder data. In this paper, we explore the use of a wavelet-based lossless data compression scheme for the 3D hyperspectral data which uses in sequence a forward difference scheme, an integer wavelet transform, a Burrows-Wheeler transform and an arithmetic coder. Compared to previous work, our approach is shown to outperform the CALIC and 3D EZW schemes.
Line relaxation methods for the solution of 2D and 3D compressible flows
NASA Technical Reports Server (NTRS)
Hassan, O.; Probert, E. J.; Morgan, K.; Peraire, J.
1993-01-01
An implicit finite element based algorithm for the compressible Navier-Stokes equations is outlined, and the solution of the resulting equation by a line relaxation on general meshes of triangles or tetrahedra is described. The problem of generating and adapting unstructured meshes for viscous flows is reexamined, and an approach for both 2D and 3D simulations is proposed. An efficient approach appears to be the use of an implicit/explicit procedure, with the implicit treatment being restricted to those regions of the mesh where viscous effects are known to be dominant. Numerical examples demonstrating the computational performance of the proposed techniques are given.
Free boundary value problem to 3D spherically symmetric compressible Navier-Stokes-Poisson equations
NASA Astrophysics Data System (ADS)
Kong, Huihui; Li, Hai-Liang
2017-02-01
In the paper, we consider the free boundary value problem to 3D spherically symmetric compressible isentropic Navier-Stokes-Poisson equations for self-gravitating gaseous stars with γ -law pressure density function for 6/5 <γ ≤ 4/3. For stress-free boundary condition and zero flow density continuously across the free boundary, the global existence of spherically symmetric weak solutions is shown, and the regularity and long time behavior of global solution are investigated for spherically symmetric initial data with the total mass smaller than a critical mass.
Compressive sensing exploiting wavelet-domain dependencies for ECG compression
NASA Astrophysics Data System (ADS)
Polania, Luisa F.; Carrillo, Rafael E.; Blanco-Velasco, Manuel; Barner, Kenneth E.
2012-06-01
Compressive sensing (CS) is an emerging signal processing paradigm that enables sub-Nyquist sampling of sparse signals. Extensive previous work has exploited the sparse representation of ECG signals in compression applications. In this paper, we propose the use of wavelet domain dependencies to further reduce the number of samples in compressive sensing-based ECG compression while decreasing the computational complexity. R wave events manifest themselves as chains of large coefficients propagating across scales to form a connected subtree of the wavelet coefficient tree. We show that the incorporation of this connectedness as additional prior information into a modified version of the CoSaMP algorithm can significantly reduce the required number of samples to achieve good quality in the reconstruction. This approach also allows more control over the ECG signal reconstruction, in particular, the QRS complex, which is typically distorted when prior information is not included in the recovery. The compression algorithm was tested upon records selected from the MIT-BIH arrhythmia database. Simulation results show that the proposed algorithm leads to high compression ratios associated with low distortion levels relative to state-of-the-art compression algorithms.
Compressible magma/mantle dynamics: 3-D, adaptive simulations in ASPECT
NASA Astrophysics Data System (ADS)
Dannberg, Juliane; Heister, Timo
2016-12-01
Melt generation and migration are an important link between surface processes and the thermal and chemical evolution of the Earth's interior. However, their vastly different timescales make it difficult to study mantle convection and melt migration in a unified framework, especially for 3-D global models. And although experiments suggest an increase in melt volume of up to 20 per cent from the depth of melt generation to the surface, previous computations have neglected the individual compressibilities of the solid and the fluid phase. Here, we describe our extension of the finite element mantle convection code ASPECT that adds melt generation and migration. We use the original compressible formulation of the McKenzie equations, augmented by an equation for the conservation of energy. Applying adaptive mesh refinement to this type of problems is particularly advantageous, as the resolution can be increased in areas where melt is present and viscosity gradients are high, whereas a lower resolution is sufficient in regions without melt. Together with a high-performance, massively parallel implementation, this allows for high-resolution, 3-D, compressible, global mantle convection simulations coupled with melt migration. We evaluate the functionality and potential of this method using a series of benchmarks and model setups, compare results of the compressible and incompressible formulation, and show the effectiveness of adaptive mesh refinement when applied to melt migration. Our model of magma dynamics provides a framework for modelling processes on different scales and investigating links between processes occurring in the deep mantle and melt generation and migration. This approach could prove particularly useful applied to modelling the generation of komatiites or other melts originating in greater depths. The implementation is available in the Open Source ASPECT repository.
Expanding Window Compressed Sensing for Non-Uniform Compressible Signals
Liu, Yu; Zhu, Xuqi; Zhang, Lin; Cho, Sung Ho
2012-01-01
Many practical compressible signals like image signals or the networked data in wireless sensor networks have non-uniform support distribution in their sparse representation domain. Utilizing this prior information, a novel compressed sensing (CS) scheme with unequal protection capability is proposed in this paper by introducing a windowing strategy called expanding window compressed sensing (EW-CS). According to the importance of different parts of the signal, the signal is divided into several nested subsets, i.e., the expanding windows. Each window generates its own measurements using a random sensing matrix. The more significant elements are contained by more windows, so they are captured by more measurements. This design makes the EW-CS scheme have more convenient implementation and better overall recovery quality for non-uniform compressible signals than ordinary CS schemes. These advantages are theoretically analyzed and experimentally confirmed. Moreover, the EW-CS scheme is applied to the compressed acquisition of image signals and networked data where it also has superior performance than ordinary CS and the existing unequal protection CS schemes. PMID:23201984
Compressed sensing for bioelectric signals: a review.
Craven, Darren; McGinley, Brian; Kilmartin, Liam; Glavin, Martin; Jones, Edward
2015-03-01
This paper provides a comprehensive review of compressed sensing or compressive sampling (CS) in bioelectric signal compression applications. The aim is to provide a detailed analysis of the current trends in CS, focusing on the advantages and disadvantages in compressing different biosignals and its suitability for deployment in embedded hardware. Performance metrics such as percent root-mean-squared difference (PRD), signal-to-noise ratio (SNR), and power consumption are used to objectively quantify the capabilities of CS. Furthermore, CS is compared to state-of-the-art compression algorithms in compressing electrocardiogram (ECG) and electroencephalography (EEG) as examples of typical biosignals. The main technical challenges associated with CS are discussed along with the predicted future trends.
Compression of 3D Point Clouds Using a Region-Adaptive Hierarchical Transform.
De Queiroz, Ricardo; Chou, Philip A
2016-06-01
In free-viewpoint video, there is a recent trend to represent scene objects as solids rather than using multiple depth maps. Point clouds have been used in computer graphics for a long time and with the recent possibility of real time capturing and rendering, point clouds have been favored over meshes in order to save computation. Each point in the cloud is associated with its 3D position and its color. We devise a method to compress the colors in point clouds which is based on a hierarchical transform and arithmetic coding. The transform is a hierarchical sub-band transform that resembles an adaptive variation of a Haar wavelet. The arithmetic encoding of the coefficients assumes Laplace distributions, one per sub-band. The Laplace parameter for each distribution is transmitted to the decoder using a custom method. The geometry of the point cloud is encoded using the well-established octtree scanning. Results show that the proposed solution performs comparably to the current state-of-the-art, in many occasions outperforming it, while being much more computationally efficient. We believe this work represents the state-of-the-art in intra-frame compression of point clouds for real-time 3D video.
Atrial fibrillation detection on compressed sensed ECG.
Da Poian, Giulia; Liu, Chengyu; Bernardini, Riccardo; Rinaldo, Roberto; Clifford, Gari D
2017-06-27
Compressive sensing (CS) approaches to electrocardiogram (ECG) analysis provide efficient methods for real time encoding of cardiac activity. In doing so, it is important to assess the downstream effect of the compression on any signal processing and classification algorithms. CS is particularly suitable for low power wearable devices, thanks to its low-complex digital or hardware implementation that directly acquires a compressed version of the signal through random projections. In this work, we evaluate the impact of CS compression on atrial fibrillation (AF) detection accuracy. We compare schemes with data reconstruction based on wavelet and Gaussian models, followed by a P&T-based identification of beat-to-beat (RR) intervals on the MIT-BIH atrial fibrillation database. A state-of-the-art AF detector is applied to the RR time series and the accuracy of the AF detector is then evaluated under different levels of compression. We also consider a new beat detection procedure which operates directly in the compressed domain, avoiding costly signal reconstruction procedures. We demonstrate that for compression ratios up to 30[Formula: see text] the AF detector applied to RR intervals derived from the compressed signal exhibits results comparable to those achieved when employing a standard QRS detector on the raw uncompressed signals, and exhibits only a 2% accuracy drop at a compression ratio of 60%. We also show that the Gaussian-based reconstruction approach is superior in terms of AF detection accuracy, with a negligible drop in performance at a compression ratio ⩽75%, compared to a wavelet approach, which exhibited a significant drop in accuracy at a compression ratio ⩾65%. The results suggest that CS should be considered as a plausible methodology for both efficient real time ECG compression (at moderate compression rates) and for offline analysis (at high compression rates).
Acceleration of multi-dimensional propagator measurements with compressed sensing.
Paulsen, Jeffrey L; Cho, HyungJoon; Cho, Gyunggoo; Song, Yi-Qiao
2011-12-01
NMR can probe the microstructures of anisotropic materials such as liquid crystals, stretched polymers and biological tissues through measurement of the diffusion propagator, where internal structures are indicated by restricted diffusion. Multi-dimensional measurements can probe the microscopic anisotropy, but full sampling can then quickly become prohibitively time consuming. However, for incompletely sampled data, compressed sensing is an effective reconstruction technique to enable accelerated acquisition. We demonstrate that with a compressed sensing scheme, one can greatly reduce the sampling and the experimental time with minimal effect on the reconstruction of the diffusion propagator with an example of anisotropic diffusion. We compare full sampling down to 64× sub-sampling for the 2D propagator measurement and reduce the acquisition time for the 3D experiment by a factor of 32 from ∼80 days to ∼2.5 days. Copyright Â© 2011 Elsevier Inc. All rights reserved.
Tension-Compression Fatigue Behavior of 2D and 3D Polymer Matrix Composites at Elevated Temperature
2015-09-21
TENSION-COMPRESSION FATIGUE BEHAVIOR OF 2D AND 3D POLYMER MATRIX COMPOSITES AT ELEVATED...BEHAVIOR OF 2D AND 3D POLYMER MATRIX COMPOSITES AT ELEVATED TEMPERATURE THESIS Presented to the Faculty Department of Aeronautics and Astronautics...BEHAVIOR OF 2D AND 3D POLYMER MATRIX COMPOSITES AT ELEVATED TEMPERATURE Saleh A. Alnatifat, B.S.M.E Captain, RSAF Committee Membership
Improved Pediatric MR Imaging with Compressed Sensing1
Alley, Marcus T.; Hargreaves, Brian A.; Barth, Richard A.; Pauly, John M.; Lustig, Michael
2010-01-01
Purpose: To develop a method that combines parallel imaging and compressed sensing to enable faster and/or higher spatial resolution magnetic resonance (MR) imaging and show its feasibility in a pediatric clinical setting. Materials and Methods: Institutional review board approval was obtained for this HIPAA-compliant study, and informed consent or assent was given by subjects. A pseudorandom k-space undersampling pattern was incorporated into a three-dimensional (3D) gradient-echo sequence; aliasing then has an incoherent noiselike pattern rather than the usual coherent fold-over wrapping pattern. This k-space–sampling pattern was combined with a compressed sensing nonlinear reconstruction method that exploits the assumption of sparsity of medical images to permit reconstruction from undersampled k-space data and remove the noiselike aliasing. Thirty-four patients (15 female and 19 male patients; mean age, 8.1 years; range, 0–17 years) referred for cardiovascular, abdominal, and knee MR imaging were scanned with this 3D gradient-echo sequence at high acceleration factors. Obtained k-space data were reconstructed with both a traditional parallel imaging algorithm and the nonlinear method. Both sets of images were rated for image quality, radiologist preference, and delineation of specific structures by two radiologists. Wilcoxon and symmetry tests were performed to test the hypothesis that there was no significant difference in ratings for image quality, preference, and delineation of specific structures. Results: Compressed sensing images were preferred more often, had significantly higher image quality ratings, and greater delineation of anatomic structures (P < .001) than did images obtained with the traditional parallel reconstruction method. Conclusion: A combination of parallel imaging and compressed sensing is feasible in a clinical setting and may provide higher resolution and/or faster imaging, addressing the challenge of delineating anatomic
Shale nanopore reconstruction with compressive sensing
NASA Astrophysics Data System (ADS)
Guo, Long; Xiao, Lizhi
2017-03-01
With increasing global demand for energy resources, shale gas has been paid considerable attention in recent years. Nanopore geometry is the basis for all microscopic rock physics and petrophysical numerical experiments for shale. At present, nano digital cores can be acquired via thin section reconstruction, nanometer-scale x-ray computed tomography (nano-CT), and focused ion beam and scanning electron microscopy (FIB-SEM). FIB-SEM detects nanoscale pores in the xy-plane with a resolution of up to 0.8 nm voxel‑1, and it is usually provides higher resolution than nano-CT. The main workload associated with FIB-SEM is the need to recut the sample many times and scan every section, with these then being overlaid to create a three-dimensional (3D) pore model. Each cutting distance can be ascertained, but this cannot be controlled precisely because of the fundamental limits of focused ion beams. Many interpolation methods can be used to fit the anisotropy resolution. However, these methods can also alter the geometry of the pores. Nanopores that are close to the limiting resolution are particularly susceptible to stretching. Linear interpolation is likely to lengthen the pores in the low-resolution direction. The subsequent calculation of sensitive physical attributes will be affected by geometric alterations. Through foundational work in the compressive sensing (CS) method, we present a reconstruction workflow for maintaining the pore shape using prior knowledge and reliable information. The images are reassembled with equal distance, so the nanoscale structures can have a resolution of unity in three dimensions.
Fast electron microscopy via compressive sensing
Larson, Kurt W; Anderson, Hyrum S; Wheeler, Jason W
2014-12-09
Various technologies described herein pertain to compressive sensing electron microscopy. A compressive sensing electron microscope includes a multi-beam generator and a detector. The multi-beam generator emits a sequence of electron patterns over time. Each of the electron patterns can include a plurality of electron beams, where the plurality of electron beams is configured to impart a spatially varying electron density on a sample. Further, the spatially varying electron density varies between each of the electron patterns in the sequence. Moreover, the detector collects signals respectively corresponding to interactions between the sample and each of the electron patterns in the sequence.
High-resolution three-dimensional imaging with compress sensing
NASA Astrophysics Data System (ADS)
Wang, Jingyi; Ke, Jun
2016-10-01
LIDAR three-dimensional imaging technology have been used in many fields, such as military detection. However, LIDAR require extremely fast data acquisition speed. This makes the manufacture of detector array for LIDAR system is very difficult. To solve this problem, we consider using compress sensing which can greatly decrease the data acquisition and relax the requirement of a detection device. To use the compressive sensing idea, a spatial light modulator will be used to modulate the pulsed light source. Then a photodetector is used to receive the reflected light. A convex optimization problem is solved to reconstruct the 2D depth map of the object. To improve the resolution in transversal direction, we use multiframe image restoration technology. For each 2D piecewise-planar scene, we move the SLM half-pixel each time. Then the position where the modulated light illuminates will changed accordingly. We repeat moving the SLM to four different directions. Then we can get four low-resolution depth maps with different details of the same plane scene. If we use all of the measurements obtained by the subpixel movements, we can reconstruct a high-resolution depth map of the sense. A linear minimum-mean-square error algorithm is used for the reconstruction. By combining compress sensing and multiframe image restoration technology, we reduce the burden on data analyze and improve the efficiency of detection. More importantly, we obtain high-resolution depth maps of a 3D scene.
Inductively Driven, 3D Liner Compression of a Magnetized Plasma to Megabar Energy Densities
Slough, John
2015-02-01
modules. The additional energy and switching capability proposed will thus provide for optimal utilization of the liner energy. The following tasks were outlined for the three year effort: (1) Design and assemble the foil liner compression test structure and chamber including the compression bank and test foils [Year 1]. (2) Perform foil liner compression experiments and obtain performance data over a range on liner dimensions and bank parameters [Year 2]. (3) Carry out compression experiments of the FRC plasma to Megagauss fields and measure key fusion parameters [Year 3]. (4) Develop numerical codes and analyze experimental results, and determine the physics and scaling for future work [Year 1-3]. The principle task of the project was to design and assemble the foil liner FRC formation chamber, the full compression test structure and chamber including the compression bank. This task was completed successfully. The second task was to test foils in the test facility constructed in year one and characterize the performance obtained from liner compression. These experimental measurements were then compared with analytical predictions, and numerical code results. The liner testing was completed and compared with both the analytical results as well as the code work performed with the 3D structural dynamics package of ANSYS Metaphysics®. This code is capable of modeling the dynamic behavior of materials well into the non-linear regime (e.g. a bullet hit plate glass). The liner dynamic behavior was found to be remarkably close to that predicted by the 3D structural dynamics results. Incorporating a code that can also include the magnetics and plasma physics has also made significant progress at the UW. The remaining test bed construction and assembly task is was completed, and the FRC formation and merging experiments were carried out as planned. The liner compression of the FRC to Megagauss fields was not performed due to not obtaining a sufficiently long lived FRC during the
Compressed Sensing Meets Wave Chaology
NASA Astrophysics Data System (ADS)
Pinto, Innocenzo M.; Addesso, Paolo; Principe, Maria
2015-03-01
The Wigner distribution is an important tool in the study of high-frequency wave-packet dynamics in ray-chaotic enclosures. Smoothing the Wigner distribution helps improving its readability, by suppressing nonlinear artifacts, but spoils its resolution. Adding a sparsity constraint to smoothing, in the spirit of the compressed coding paradigm, restores resolution while still avoiding artifacts. The result is particularly valuable in the perspective of complexity gauging via Renyi-Wehrl entropy measures. Representative numerical experiments are presented to substantiate such clues.
Yamauchi, Takahiro; Kitai, Ryuhei; Neishi, Hiroyuki; Tsunetoshi, Kenzo; Matsuda, Ken; Arishima, Hidetaka; Kodera, Toshiaki; Arai, Yoshikazu; Takeuchi, Hiroaki; Kikuta, Ken-ichiro
2014-02-01
We report the usefulness of 3D-FIESTA magnetic resonance imaging(MRI)for the detection of oculomotor nerve palsy in a case of pituitary apoplexy. A 69-year-old man with diabetes mellitus presented with complete left-side blepharoptosis. Computed tomography of the brain showed an intrasellar mass with hemorrhage. MRI demonstrated a pituitary adenoma with a cyst toward the left cavernous sinus, which was diagnosed as pituitary apoplexy. 3D-FIESTA revealed that the left oculomotor nerve was compressed by the cyst. He underwent trans-sphenoid tumor resection at 5 days after his hospitalization. Post-operative 3D-FIESTA MRI revealed decrease in compression of the left oculomotor nerve by the cyst. His left oculomotor palsy recovered completely within a few months. Oculomotor nerve palsy can occur due to various diseases, and 3D-FIESTA MRI is useful for detection of oculomotor nerve compression, especially in the field of parasellar lesions.
Guo, Zi-Yi; Chen, Jing; Yang, Guang; Tang, Qian-Yu; Chen, Cai-Xiang; Fu, Shui-Xi; Yu, Dan
2012-12-01
To evaluate the anatomical characteristics and patterns of neurovascular compression in patients suffering trigeminal neuralgia, using 3D high-resolution magnetic resonance imaging methods and fusion technologies. The analysis of the anatomy of the facial nerve, brain stem and the vascular structures related to this nerve was made in 100 consecutive patients for TN. 3D high resolution MRI studies (3D SPGR, T1 enhanced 3D MP-RAGE and T2/T1 3D FIESTA) simultaneous visualization were used to assessed using the software 3D DOCTOR. In 93 patients (93%), there were one or several locals of neurovascular compression (NVC). The superior cerebellar artery was involved in 71 cases (76%), the other vessels including the antero-inferior cerebellar artery, the basilar artery, the vertebral artery, and some venous structures. The mean distance between NVC and nerve origin site in the brainstem was (3.76 ± 2.90) mm). In 39 patients (42%), the vascular compression was located proximally and in 42 (45%) the compression was located distally. Nerve dislocation or distortion by the vessel was observed in 30 cases (32%). This 3D high resolution MRI and image fusion technology could be useful for diagnostic and therapeutic decisions in TN. Copyright © 2012 Hainan Medical College. Published by Elsevier B.V. All rights reserved.
Mechano-sensing and cell migration: a 3D model approach.
Borau, C; Kamm, R D; García-Aznar, J M
2011-12-01
Cell migration is essential for tissue development in different physiological and pathological conditions. It is a complex process orchestrated by chemistry, biological factors, microstructure and surrounding mechanical properties. Focusing on the mechanical interactions, cells do not only exert forces on the matrix that surrounds them, but they also sense and react to mechanical cues in a process called mechano-sensing. Here, we hypothesize the involvement of mechano-sensing in the regulation of directional cell migration through a three-dimensional (3D) matrix. For this purpose, we develop a 3D numerical model of individual cell migration, which incorporates the mechano-sensing process of the cell as the main mechanism regulating its movement. Consistent with this hypothesis, we found that factors, such as substrate stiffness, boundary conditions and external forces, regulate specific and distinct cell movements.
M-OTDR sensing system based on 3D encoded microstructures
Sun, Qizhen; Ai, Fan; Liu, Deming; Cheng, Jianwei; Luo, Hongbo; Peng, Kuan; Luo, Yiyang; Yan, Zhijun; Shum, Perry Ping
2017-01-01
In this work, a quasi-distributed sensing scheme named as microstructured OTDR (M-OTDR) by introducing ultra-weak microstructures along the fiber is proposed. Owing to its relative higher reflectivity compared with the backscattered coefficient in fiber and three dimensional (3D) i.e. wavelength/frequency/time encoded property, the M-OTDR system exhibits the superiorities of high signal to noise ratio (SNR), high spatial resolution of millimeter level and high multiplexing capacity up to several ten thousands theoretically. A proof-of-concept system consisting of 64 sensing units is constructed to demonstrate the feasibility and sensing performance. With the help of the demodulation method based on 3D analysis and spectrum reconstruction of the signal light, quasi-distributed temperature sensing with a spatial resolution of 20 cm as well as a measurement resolution of 0.1 °C is realized. PMID:28106132
3D reconstruction of a compressible flow by synchronized multi-camera BOS
NASA Astrophysics Data System (ADS)
Nicolas, F.; Donjat, D.; Léon, O.; Le Besnerais, G.; Champagnat, F.; Micheli, F.
2017-05-01
This paper investigates the application of a 3D density reconstruction from a limited number of background-oriented schlieren (BOS) images as recently proposed in Nicolas et al. (Exp Fluids 57(1):1-21, 2016), to the case of compressible flows, such as underexpanded jets. First, an optimization of a 2D BOS setup is conducted to mitigate the intense local blurs observed in raw BOS images and caused by strong density gradients present in the jets. It is demonstrated that a careful choice of experimental conditions enables one to obtain sharp deviation fields from 2D BOS images. Second, a 3DBOS experimental bench involving 12 synchronized cameras is specifically designed for the present study. It is shown that the 3DBOS method can provide physically consistent 3D reconstructions of instantaneous and mean density fields for various underexpanded jet flows issued into quiescent air. Finally, an analysis of the density structure of a moderately underexpanded jet is conducted through phase-averaging, highlighting the development of a large-scale coherent structure associated with a jet shear layer instability.
Mackert, B M; Curio, G; Burghoff, M; Trahms, L; Marx, P
1998-08-01
Tibial nerve somatosensory evoked magnetic fields (tSEFs) over the lower back reflect the propagation of compound action currents along fibers of plexus, nerve roots and cauda equina. One clinical perspective for this 'magnetoneurography' is the non-invasive 3D localization of focal slowing or blocks of conduction. Here, first tSEF mappings in 3 consecutive patients with acute unilateral S1 nerve root compression are reported. Right and left tibial nerves were electrostimulated in alternation; tSEF responses were recorded using a multichannel SQUID-detector; additionally, spinal and cortical SEP, F-wave and H-reflex studies were performed. In all patients an intraindividual side-to-side comparison of spinal tSEF mappings was obtained: using a dipolar source model compound action currents could be visualized propagating along plexus, nerve roots and cauda equina on the non-affected side whereas on the affected side normally-propagating dipolar field patterns could be recorded only distal to the spinal transforaminal root entrance; this reflects focal slowing or block of conduction in nerve root fibers as indicated by the SEP, F-wave and H-reflex study results. With a registration time of 15 min a 3D localization of proximal slowing or block of conduction was successfully performed in patients suffering from acute nerve root lesions.
NASA Astrophysics Data System (ADS)
Kimpe, T.; Bruylants, T.; Sneyders, Y.; Deklerck, R.; Schelkens, P.
2007-03-01
The size of medical data has increased significantly over the last few years. This poses severe problems for the rapid transmission of medical data across the hospital network resulting into longer access times of the images. Also longterm storage of data becomes more and more a problem. In an attempt to overcome the increasing data size often lossless or lossy compression algorithms are being used. This paper compares the existing JPEG2000 compression algorithm and the new emerging JP3D standard for compression of volumetric datasets. The main benefit of JP3D is that this algorithm truly is a 3D compression algorithm that exploits correlation not only within but also in between slices of a dataset. We evaluate both lossless and lossy modes of these algorithms. As a first step we perform an objective evaluation. Using RMSE and PSNR metrics we determine which compression algorithm performs best and this for multiple compression ratios and for several clinically relevant medical datasets. It is well known that RMSE and PSNR often do not correlate well with subjectively perceived image quality. Therefore we also perform a psycho visual analysis by means of a numerical observer. With this observer model we analyze how compression artifacts actually are perceived by a human observer. Results show superior performance of the new JP3D algorithm compared to the existing JPEG2000 algorithm.
Experimentally exploring compressed sensing quantum tomography
NASA Astrophysics Data System (ADS)
Steffens, A.; Riofrío, C. A.; McCutcheon, W.; Roth, I.; Bell, B. A.; McMillan, A.; Tame, M. S.; Rarity, J. G.; Eisert, J.
2017-06-01
In the light of the progress in quantum technologies, the task of verifying the correct functioning of processes and obtaining accurate tomographic information about quantum states becomes increasingly important. Compressed sensing, a machinery derived from the theory of signal processing, has emerged as a feasible tool to perform robust and significantly more resource-economical quantum state tomography for intermediate-sized quantum systems. In this work, we provide a comprehensive analysis of compressed sensing tomography in the regime in which tomographically complete data is available with reliable statistics from experimental observations of a multi-mode photonic architecture. Due to the fact that the data is known with high statistical significance, we are in a position to systematically explore the quality of reconstruction depending on the number of employed measurement settings, randomly selected from the complete set of data, and on different model assumptions. We present and test a complete prescription to perform efficient compressed sensing and are able to reliably use notions of model selection and cross validation to account for experimental imperfections and finite counting statistics. Thus, we establish compressed sensing as an effective tool for quantum state tomography, specifically suited for photonic systems.
Data compression in remote sensing applications
NASA Technical Reports Server (NTRS)
Sayood, Khalid
1992-01-01
A survey of current data compression techniques which are being used to reduce the amount of data in remote sensing applications is provided. The survey aspect is far from complete, reflecting the substantial activity in this area. The purpose of the survey is more to exemplify the different approaches being taken rather than to provide an exhaustive list of the various proposed approaches.
Whole brain susceptibility mapping using compressed sensing.
Wu, Bing; Li, Wei; Guidon, Arnaud; Liu, Chunlei
2012-01-01
The derivation of susceptibility from image phase is hampered by the ill-conditioned filter inversion in certain k-space regions. In this article, compressed sensing is used to compensate for the k-space regions where direct filter inversion is unstable. A significantly lower level of streaking artifacts is produced in the resulting susceptibility maps for both simulated and in vivo data sets compared to outcomes obtained using the direct threshold method. It is also demonstrated that the compressed sensing based method outperforms regularization based methods. The key difference between the regularized inversions and compressed sensing compensated inversions is that, in the former case, the entire k-space spectrum estimation is affected by the ill-conditioned filter inversion in certain k-space regions, whereas in the compressed sensing based method only the ill-conditioned k-space regions are estimated. In the susceptibility map calculated from the phase measurement obtained using a 3T scanner, not only are the iron-rich regions well depicted, but good contrast between white and gray matter interfaces that feature a low level of susceptibility variations are also obtained. The correlation between the iron content and the susceptibility levels in iron-rich deep nucleus regions is studied, and strong linear relationships are observed which agree with previous findings.
Chandrasekaran, S.; Liebig, W. V.; Mecklenberg, M.; ...
2015-11-04
Aerographite (AG) is a mechanically robust, lightweight synthetic cellular material, which consists of a 3D interconnected network of tubular carbon [1]. The presence of open channels in AG aids to infiltrate them with polymer matrices, thereby yielding an electrical conducting and lightweight composite. Aerographite produced with densities in the range of 7–15 mg/cm3 was infiltrated with a low viscous epoxy resin by means of vacuum infiltration technique. Detailed morphological and structural investigations on synthesized AG and AG/epoxy composite were performed by scanning electron microscopic techniques. Our present study investigates the fracture and failure of AG/epoxy composites and its energy absorptionmore » capacity under compression. The composites displayed an extended plateau region when uni-axially compressed, which led to an increase in energy absorption of ~133% per unit volume for 1.5 wt% of AG, when compared to pure epoxy. Preliminary results on fracture toughness showed an enhancement of ~19% in KIC for AG/epoxy composites with 0.45 wt% of AG. Furthermore, our observations of fractured surfaces under scanning electron microscope gives evidence of pull-out of arms of AG tetrapod, interface and inter-graphite failure as the dominating mechanism for the toughness improvement in these composites. These observations were consistent with the results obtained from photoelasticity experiments on a thin film AG/epoxy model composite.« less
Chandrasekaran, S.; Liebig, W. V.; Mecklenberg, M.; Fiedler, B.; Smazna, D.; Adelung, R.; Schulte, K.
2015-11-04
Aerographite (AG) is a mechanically robust, lightweight synthetic cellular material, which consists of a 3D interconnected network of tubular carbon [1]. The presence of open channels in AG aids to infiltrate them with polymer matrices, thereby yielding an electrical conducting and lightweight composite. Aerographite produced with densities in the range of 7–15 mg/cm^{3} was infiltrated with a low viscous epoxy resin by means of vacuum infiltration technique. Detailed morphological and structural investigations on synthesized AG and AG/epoxy composite were performed by scanning electron microscopic techniques. Our present study investigates the fracture and failure of AG/epoxy composites and its energy absorption capacity under compression. The composites displayed an extended plateau region when uni-axially compressed, which led to an increase in energy absorption of ~133% per unit volume for 1.5 wt% of AG, when compared to pure epoxy. Preliminary results on fracture toughness showed an enhancement of ~19% in K_{IC} for AG/epoxy composites with 0.45 wt% of AG. Furthermore, our observations of fractured surfaces under scanning electron microscope gives evidence of pull-out of arms of AG tetrapod, interface and inter-graphite failure as the dominating mechanism for the toughness improvement in these composites. These observations were consistent with the results obtained from photoelasticity experiments on a thin film AG/epoxy model composite.
Holographic 3D imaging through diffuse media by compressive sampling of the mutual intensity
NASA Astrophysics Data System (ADS)
Falldorf, Claas; Klein, Thorsten; Agour, Mostafa; Bergmann, Ralf B.
2017-05-01
We present a method for holographic imaging through a volume scattering material, which is based on selfreference and light with good spatial but limited temporal coherence. In contrast to existing techniques, we do not require a separate reference wave, thus our approach provides great advantages towards the flexibility of the measurement system. The main applications are remote sensing and investigation of moving objects through gaseous streams, bubbles or foggy water for example. Furthermore, due to the common path nature, the system is also insensitive to mechanical disturbances. The measurement result is a complex amplitude which is comparable to a phase shifted digital hologramm and therefore allows 3D imaging, numerical refocusing and quantitative phase contrast imaging. As an example of application, we present measurements of the quantitative phase contrast of the epidermis of an onion through a volume scattering material.
Recurrent potential pulse technique for improvement of glucose sensing ability of 3D polypyrrole
NASA Astrophysics Data System (ADS)
Cysewska, Karolina; Karczewski, Jakub; Jasiński, Piotr
2017-07-01
In this work, a new approach for using a 3D polypyrrole (PPy) conducting polymer as a sensing material for glucose detection is proposed. Polypyrrole is electrochemically polymerized on a platinum screen-printed electrode in an aqueous solution of lithium perchlorate and pyrrole. PPy exhibits a high electroactive surface area and high electrochemical stability, which results in it having excellent electrocatalytic properties. The studies show that using the recurrent potential pulse technique results in an increase in PPy sensing stability, compared to the amperometric approach. This is due to the fact that the technique, under certain parameters, allows the PPy redox properties to be fully utilized, whilst preventing its anodic degradation. Because of this, the 3D PPy presented here has become a very good candidate as a sensing material for glucose detection, and can work without any additional dopants, mediators or enzymes.
Coding Strategies and Implementations of Compressive Sensing
NASA Astrophysics Data System (ADS)
Tsai, Tsung-Han
This dissertation studies the coding strategies of computational imaging to overcome the limitation of conventional sensing techniques. The information capacity of conventional sensing is limited by the physical properties of optics, such as aperture size, detector pixels, quantum efficiency, and sampling rate. These parameters determine the spatial, depth, spectral, temporal, and polarization sensitivity of each imager. To increase sensitivity in any dimension can significantly compromise the others. This research implements various coding strategies subject to optical multidimensional imaging and acoustic sensing in order to extend their sensing abilities. The proposed coding strategies combine hardware modification and signal processing to exploiting bandwidth and sensitivity from conventional sensors. We discuss the hardware architecture, compression strategies, sensing process modeling, and reconstruction algorithm of each sensing system. Optical multidimensional imaging measures three or more dimensional information of the optical signal. Traditional multidimensional imagers acquire extra dimensional information at the cost of degrading temporal or spatial resolution. Compressive multidimensional imaging multiplexes the transverse spatial, spectral, temporal, and polarization information on a two-dimensional (2D) detector. The corresponding spectral, temporal and polarization coding strategies adapt optics, electronic devices, and designed modulation techniques for multiplex measurement. This computational imaging technique provides multispectral, temporal super-resolution, and polarization imaging abilities with minimal loss in spatial resolution and noise level while maintaining or gaining higher temporal resolution. The experimental results prove that the appropriate coding strategies may improve hundreds times more sensing capacity. Human auditory system has the astonishing ability in localizing, tracking, and filtering the selected sound sources or
3D-Web-GIS RFID location sensing system for construction objects.
Ko, Chien-Ho
2013-01-01
Construction site managers could benefit from being able to visualize on-site construction objects. Radio frequency identification (RFID) technology has been shown to improve the efficiency of construction object management. The objective of this study is to develop a 3D-Web-GIS RFID location sensing system for construction objects. An RFID 3D location sensing algorithm combining Simulated Annealing (SA) and a gradient descent method is proposed to determine target object location. In the algorithm, SA is used to stabilize the search process and the gradient descent method is used to reduce errors. The locations of the analyzed objects are visualized using the 3D-Web-GIS system. A real construction site is used to validate the applicability of the proposed method, with results indicating that the proposed approach can provide faster, more accurate, and more stable 3D positioning results than other location sensing algorithms. The proposed system allows construction managers to better understand worksite status, thus enhancing managerial efficiency.
Close-range environmental remote sensing with 3D hyperspectral technologies
NASA Astrophysics Data System (ADS)
Nevalainen, O.; Honkavaara, E.; Hakala, T.; Kaasalainen, Sanna; Viljanen, N.; Rosnell, T.; Khoramshahi, E.; Näsi, R.
2016-10-01
Estimation of the essential climate variables (ECVs), such as photosynthetically active radiation (FAPAR) and the leaf area index (LAI), is largely based on satellite-based remote sensing and the subsequent inversion of radiative transfer (RT) models. In order to build models that accurately describe the radiative transfer within and below the canopy, detailed 3D structural (geometrical) and spectral (radiometrical) information of the canopy is needed. Close-range remote sensing, such as terrestrial remote sensing and UAV-based 3D spectral measurements, offers significant opportunity to improve the RT modelling and ECV estimation of forests. Finnish Geospatial Research Institute (FGI) has been developing active and passive high resolution 3D hyperspectral measurement technologies that provide reflectance, anisotropy and 3D structure information of forests (i.e. hyperspectral point clouds). Technologies include hyperspectral imaging from unmanned airborne vehicle (UAV), terrestrial hyperspectral lidar (HSL) and terrestrial hyperspectral stereoscopic imaging. A measurement campaign to demonstrate these technologies in ECV estimation with uncertainty propagation was carried out in the Wytham Woods, Oxford, UK, in June 2015. Our objective is to develop traceable processing procedures for generating hyperspectral point clouds with geometric and radiometric uncertainty propagation using hyperspectral aerial and terrestrial imaging and hyperspectral terrestrial laser scanning. The article and presentation will present the methodology, instrumentation and first results of our study.
3D-Web-GIS RFID Location Sensing System for Construction Objects
2013-01-01
Construction site managers could benefit from being able to visualize on-site construction objects. Radio frequency identification (RFID) technology has been shown to improve the efficiency of construction object management. The objective of this study is to develop a 3D-Web-GIS RFID location sensing system for construction objects. An RFID 3D location sensing algorithm combining Simulated Annealing (SA) and a gradient descent method is proposed to determine target object location. In the algorithm, SA is used to stabilize the search process and the gradient descent method is used to reduce errors. The locations of the analyzed objects are visualized using the 3D-Web-GIS system. A real construction site is used to validate the applicability of the proposed method, with results indicating that the proposed approach can provide faster, more accurate, and more stable 3D positioning results than other location sensing algorithms. The proposed system allows construction managers to better understand worksite status, thus enhancing managerial efficiency. PMID:23864821
Simulation of 3-D viscous compressible flow in multistage turbomachinery by finite element methods
NASA Astrophysics Data System (ADS)
Sleiman, Mohamad
1999-11-01
continuity of fluxes across. This permits the rotor and stator equations to be solved in a fully- coupled manner, allowing larger time steps in attaining a time-periodic solution. This equal pitch approach has been validated on the complex geometry of a centrifugal stage. Finally, for a stage configuration with unequal pitches, the time-inclined method, developed by Giles (1991) for 2-D viscous compressible flow, has been extended to 3-D and formulated in terms of the physical solution vector U, rather than Q, a non-physical one. The method has been evaluated for unsteady flow through a rotor blade passage of the power turbine of a turboprop.
Compressive Sensing Image Sensors-Hardware Implementation
Dadkhah, Mohammadreza; Deen, M. Jamal; Shirani, Shahram
2013-01-01
The compressive sensing (CS) paradigm uses simultaneous sensing and compression to provide an efficient image acquisition technique. The main advantages of the CS method include high resolution imaging using low resolution sensor arrays and faster image acquisition. Since the imaging philosophy in CS imagers is different from conventional imaging systems, new physical structures have been developed for cameras that use the CS technique. In this paper, a review of different hardware implementations of CS encoding in optical and electrical domains is presented. Considering the recent advances in CMOS (complementary metal–oxide–semiconductor) technologies and the feasibility of performing on-chip signal processing, important practical issues in the implementation of CS in CMOS sensors are emphasized. In addition, the CS coding for video capture is discussed. PMID:23584123
Restricted isometry properties and nonconvex compressive sensing
NASA Astrophysics Data System (ADS)
Chartrand, Rick; Staneva, Valentina
2008-06-01
The recently emerged field known as compressive sensing has produced powerful results showing the ability to recover sparse signals from surprisingly few linear measurements, using ell1 minimization. In previous work, numerical experiments showed that ellp minimization with 0 < p < 1 recovers sparse signals from fewer linear measurements than does ell1 minimization. It was also shown that a weaker restricted isometry property is sufficient to guarantee perfect recovery in the ellp case. In this work, we generalize this result to an ellp variant of the restricted isometry property, and then determine how many random, Gaussian measurements are sufficient for the condition to hold with high probability. The resulting sufficient condition is met by fewer measurements for smaller p. This adds to the theoretical justification for the methods already being applied to replacing high-dose CT scans with a small number of x-rays and reducing MRI scanning time. The potential benefits extend to any application of compressive sensing.
Compressive line sensing underwater imaging system
NASA Astrophysics Data System (ADS)
Ouyang, Bing; Dalgleish, Fraser R.; Caimi, Frank M.; Giddings, Thomas E.; Britton, Walter; Vuorenkoski, Anni K.; Nootz, Gero
2014-05-01
Compressive sensing (CS) theory has drawn great interest and led to new imaging techniques in many different fields. Over the last few years, the authors have conducted extensive research on CS-based active electro-optical imaging in a scattering medium, such as the underwater environment. This paper proposes a compressive line sensing underwater imaging system that is more compatible with conventional underwater survey operations. This new imaging system builds on our frame-based CS underwater laser imager concept, which is more advantageous for hover capable platforms. We contrast features of CS underwater imaging with those of traditional underwater electro-optical imaging and highlight some advantages of the CS approach. Simulation and initial underwater validation test results are also presented.
Compressive line sensing underwater imaging system
NASA Astrophysics Data System (ADS)
Ouyang, B.; Dalgleish, F. R.; Vuorenkoski, A. K.; Caimi, F. M.; Britton, W.
2013-05-01
Compressive sensing (CS) theory has drawn great interest and led to new imaging techniques in many different fields. In recent years, the FAU/HBOI OVOL has conducted extensive research to study the CS based active electro-optical imaging system in the scattering medium such as the underwater environment. The unique features of such system in comparison with the traditional underwater electro-optical imaging system are discussed. Building upon the knowledge from the previous work on a frame based CS underwater laser imager concept, more advantageous for hover-capable platforms such as the Hovering Autonomous Underwater Vehicle (HAUV), a compressive line sensing underwater imaging (CLSUI) system that is more compatible with the conventional underwater platforms where images are formed in whiskbroom fashion, is proposed in this paper. Simulation results are discussed.
Compressive sensing and entropy in seismic signals
NASA Astrophysics Data System (ADS)
Marinho, Eberton S.; Rocha, Tiago C.; Corso, Gilberto; Lucena, Liacir S.
2017-09-01
This work analyzes the correlation between the seismic signal entropy and the Compressive Sensing (CS) recovery index. The recovery index measures the quality of a signal reconstructed by the CS method. We analyze the performance of two CS algorithms: the ℓ1-MAGIC and the Fast Bayesian Compressive Sensing (BCS). We have observed a negative correlation between the performance of CS and seismic signal entropy. Signals with low entropy have small recovery index in their reconstruction by CS. The rationale behind our finding is: a sparse signal is easy to recover by CS and, besides, a sparse signal has low entropy. In addition, ℓ1-MAGIC shows a more significant correlation between entropy and CS performance than Fast BCS.
Application of Compressive Sensing to Digital Holography
2015-05-01
April 2015. Report contains color. 14. ABSTRACT Compressive sensing has been used in many imaging domains to facilitate high quality reconstruction...The Full Aperture (FA) Image Plane Architecture in which the Laser Diode (LD) Provides Coherent Illumination to an Object via Some Illumination...Optics (IO). A Small Amount of Light from the LD is Picked-Off by a Beam-Splitter (BS) to Form a Reference Field. The Scattered Object Field is Imaged
Sparsity optimized compressed sensing image recovery
NASA Astrophysics Data System (ADS)
Wang, Sha; Chen, Yueting; Feng, Huajun; Xu, Zhihai; Li, Qi
2014-05-01
Training over-complete dictionaries which facilitate a sparse representation of the image leads to state-of-the-art results in compressed sensing image restoration. The training sparsity should be specified when training, while the recovering sparsity should also be set when image recovery. We find that the recovering sparsity has significant effects on the image reconstruction properties. To further improve the compressed sensing image recover accuracy, in this paper, we proposed a method by optimal estimation of the recovering sparsity according to the training sparsity to control the reconstruction method, and better reconstruction results can be achieved successfully. The method mainly includes three procedures. Firstly, forecasting the possible sparsity range by analyzing a large test data set to obtain a possible sparsity set. We find that the possible sparsity is always 3~5 times the training sparsity. Secondly, to precisely estimate the optimal recovering sparsity, we choose only several samples randomly from the compressed sensing measurements and using the sparsity candidates in the possible sparsity set to reconstruct the original image patches. Thirdly, choosing the sparsity corresponding to the best recovered result as the optimal recovering sparsity to be used in image reconstruction. The estimation computational cost is relatively small and the reconstruction result can be much better than the traditional method. The experimental results show that, the PSNR of the recovered images adopting our estimation method can be higher up to 4dB compared to the traditional method without the sparsity estimation.
3D printed sensing patches with embedded polymer optical fibre Bragg gratings
NASA Astrophysics Data System (ADS)
Zubel, Michal G.; Sugden, Kate; Saez-Rodriguez, D.; Nielsen, K.; Bang, O.
2016-05-01
The first demonstration of a polymer optical fibre Bragg grating (POFBG) embedded in a 3-D printed structure is reported. Its cyclic strain performance and temperature characteristics are examined and discussed. The sensing patch has a repeatable strain sensitivity of 0.38 pm/μepsilon. Its temperature behaviour is unstable, with temperature sensitivity values varying between 30-40 pm/°C.
Designing robust sensing matrix for image compression.
Li, Gang; Li, Xiao; Li, Sheng; Bai, Huang; Jiang, Qianru; He, Xiongxiong
2015-12-01
This paper deals with designing sensing matrix for compressive sensing systems. Traditionally, the optimal sensing matrix is designed so that the Gram of the equivalent dictionary is as close as possible to a target Gram with small mutual coherence. A novel design strategy is proposed, in which, unlike the traditional approaches, the measure considers of mutual coherence behavior of the equivalent dictionary as well as sparse representation errors of the signals. The optimal sensing matrix is defined as the one that minimizes this measure and hence is expected to be more robust against sparse representation errors. A closed-form solution is derived for the optimal sensing matrix with a given target Gram. An alternating minimization-based algorithm is also proposed for addressing the same problem with the target Gram searched within a set of relaxed equiangular tight frame Grams. The experiments are carried out and the results show that the sensing matrix obtained using the proposed approach outperforms those existing ones using a fixed dictionary in terms of signal reconstruction accuracy for synthetic data and peak signal-to-noise ratio for real images.
Biomedical sensor design using analog compressed sensing
NASA Astrophysics Data System (ADS)
Balouchestani, Mohammadreza; Krishnan, Sridhar
2015-05-01
The main drawback of current healthcare systems is the location-specific nature of the system due to the use of fixed/wired biomedical sensors. Since biomedical sensors are usually driven by a battery, power consumption is the most important factor determining the life of a biomedical sensor. They are also restricted by size, cost, and transmission capacity. Therefore, it is important to reduce the load of sampling by merging the sampling and compression steps to reduce the storage usage, transmission times, and power consumption in order to expand the current healthcare systems to Wireless Healthcare Systems (WHSs). In this work, we present an implementation of a low-power biomedical sensor using analog Compressed Sensing (CS) framework for sparse biomedical signals that addresses both the energy and telemetry bandwidth constraints of wearable and wireless Body-Area Networks (BANs). This architecture enables continuous data acquisition and compression of biomedical signals that are suitable for a variety of diagnostic and treatment purposes. At the transmitter side, an analog-CS framework is applied at the sensing step before Analog to Digital Converter (ADC) in order to generate the compressed version of the input analog bio-signal. At the receiver side, a reconstruction algorithm based on Restricted Isometry Property (RIP) condition is applied in order to reconstruct the original bio-signals form the compressed bio-signals with high probability and enough accuracy. We examine the proposed algorithm with healthy and neuropathy surface Electromyography (sEMG) signals. The proposed algorithm achieves a good level for Average Recognition Rate (ARR) at 93% and reconstruction accuracy at 98.9%. In addition, The proposed architecture reduces total computation time from 32 to 11.5 seconds at sampling-rate=29 % of Nyquist rate, Percentage Residual Difference (PRD)=26 %, Root Mean Squared Error (RMSE)=3 %.
NASA Astrophysics Data System (ADS)
Cheng, Kai-jen; Dill, Jeffrey
2013-05-01
In this paper, a lossless to lossy transform based image compression of hyperspectral images based on Integer Karhunen-Loève Transform (IKLT) and Integer Discrete Wavelet Transform (IDWT) is proposed. Integer transforms are used to accomplish reversibility. The IKLT is used as a spectral decorrelator and the 2D-IDWT is used as a spatial decorrelator. The three-dimensional Binary Embedded Zerotree Wavelet (3D-BEZW) algorithm efficiently encodes hyperspectral volumetric image by implementing progressive bitplane coding. The signs and magnitudes of transform coefficients are encoded separately. Lossy and lossless compressions of signs are implemented by conventional EZW algorithm and arithmetic coding respectively. The efficient 3D-BEZW algorithm is applied to code magnitudes. Further compression can be achieved using arithmetic coding. The lossless and lossy compression performance is compared with other state of the art predictive and transform based image compression methods on Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) images. Results show that the 3D-BEZW performance is comparable to predictive algorithms. However, its computational cost is comparable to transform- based algorithms.
Embedding silica and polymer fibre Bragg gratings (FBG) in plastic 3D-printed sensing patches
NASA Astrophysics Data System (ADS)
Zubel, Michal G.; Sugden, Kate; Webb, David J.; Sáez-Rodríguez, David; Nielsen, Kristian; Bang, Ole
2016-04-01
This paper reports the first demonstration of a silica fibre Bragg grating (SOFBG) embedded in an FDM 3-D printed housing to yield a dual grating temperature-compensated strain sensor. We also report the first ever integration of polymer fibre Bragg grating (POFBG) within a 3-D printed sensing patch for strain or temperature sensing. The cyclic strain performance and temperature characteristics of both devices are examined and discussed. The strain sensitivities of the sensing patches were 0.40 and 0.95 pm/μɛ for SOFBG embedded in ABS, 0.38 pm/μɛ for POFBG in PLA, and 0.15 pm/μɛ for POFBG in ABS. The strain response was linear above a threshold and repeatable. The temperature sensitivity of the SOFBG sensing patch was found to be up to 169 pm/°C, which was up to 17 times higher than for an unembedded silica grating. Unstable temperature response POFBG embedded in PLA was reported, with temperature sensitivity values varying between 30 and 40 pm/°C.
Parallel hyperspectral compressive sensing method on GPU
NASA Astrophysics Data System (ADS)
Bernabé, Sergio; Martín, Gabriel; Nascimento, José M. P.
2015-10-01
Remote hyperspectral sensors collect large amounts of data per flight usually with low spatial resolution. It is known that the bandwidth connection between the satellite/airborne platform and the ground station is reduced, thus a compression onboard method is desirable to reduce the amount of data to be transmitted. This paper presents a parallel implementation of an compressive sensing method, called parallel hyperspectral coded aperture (P-HYCA), for graphics processing units (GPU) using the compute unified device architecture (CUDA). This method takes into account two main properties of hyperspectral dataset, namely the high correlation existing among the spectral bands and the generally low number of endmembers needed to explain the data, which largely reduces the number of measurements necessary to correctly reconstruct the original data. Experimental results conducted using synthetic and real hyperspectral datasets on two different GPU architectures by NVIDIA: GeForce GTX 590 and GeForce GTX TITAN, reveal that the use of GPUs can provide real-time compressive sensing performance. The achieved speedup is up to 20 times when compared with the processing time of HYCA running on one core of the Intel i7-2600 CPU (3.4GHz), with 16 Gbyte memory.
3D nitrogen-doped graphene/β-cyclodextrin: host-guest interactions for electrochemical sensing
NASA Astrophysics Data System (ADS)
Liu, Jilun; Leng, Xuanye; Xiao, Yao; Hu, Chengguo; Fu, Lei
2015-07-01
Host-guest interactions, especially those between cyclodextrins (CDs, including α-, β- and γ-CD) and various guest molecules, exhibit a very high supramolecular recognition ability. Thus, they have received considerable attention in different fields. These specific interactions between host and guest molecules are promising for biosensing and clinical detection. However, there is a lack of an ideal electrode substrate for CDs to increase their performance in electrochemical sensing. Herein, we propose a new 3D nitrogen-doped graphene (3D-NG) based electrochemical sensor, taking advantage of the superior sensitivity of host-guest interactions. Our 3D-NG was fabricated by a template-directed chemical vapour deposition (CVD) method, and it showed a large specific surface area, a high capacity for biomolecules and a high electron transfer efficiency. Thus, for the first time, we took 3D-NG as an electrode substrate for β-CD to establish a new type of biosensor. Using dopamine (DA) and acetaminophen (APAP) as representative guest molecules, our 3D-NG/β-CD biosensor shows extremely high sensitivities (5468.6 μA mM-1 cm-2 and 2419.2 μA mM-1 cm-2, respectively), which are significantly higher than those reported in most previous studies. The stable adsorption of β-CD on 3D-NG indicates potential applications in clinical detection and medical testing.Host-guest interactions, especially those between cyclodextrins (CDs, including α-, β- and γ-CD) and various guest molecules, exhibit a very high supramolecular recognition ability. Thus, they have received considerable attention in different fields. These specific interactions between host and guest molecules are promising for biosensing and clinical detection. However, there is a lack of an ideal electrode substrate for CDs to increase their performance in electrochemical sensing. Herein, we propose a new 3D nitrogen-doped graphene (3D-NG) based electrochemical sensor, taking advantage of the superior sensitivity
An optimal sensing strategy for recognition and localization of 3-D natural quadric objects
NASA Technical Reports Server (NTRS)
Lee, Sukhan; Hahn, Hernsoo
1991-01-01
An optimal sensing strategy for an optical proximity sensor system engaged in the recognition and localization of 3-D natural quadric objects is presented. The optimal sensing strategy consists of the selection of an optimal beam orientation and the determination of an optimal probing plane that compose an optimal data collection operation known as an optimal probing. The decision of an optimal probing is based on the measure of discrimination power of a cluster of surfaces on a multiple interpretation image (MII), where the measure of discrimination power is defined in terms of a utility function computing the expected number of interpretations that can be pruned out by a probing. An object representation suitable for active sensing based on a surface description vector (SDV) distribution graph and hierarchical tables is presented. Experimental results are shown.
Applying Mean-Shift - Clustering for 3D object detection in remote sensing data
NASA Astrophysics Data System (ADS)
Simon, Jürgen-Lorenz; Diederich, Malte; Troemel, Silke
2013-04-01
The timely warning and forecasting of high-impact weather events is crucial for life, safety and economy. Therefore, the development and improvement of methods for detection and nowcasting / short-term forecasting of these events is an ongoing research question. A new 3D object detection and tracking algorithm is presented. Within the project "object-based analysis and seamless predictin (OASE)" we address a better understanding and forecasting of convective events based on the synergetic use of remotely sensed data and new methods for detection, nowcasting, validation and assimilation. In order to gain advanced insight into the lifecycle of convective cells, we perform an object-detection on a new high-resolution 3D radar- and satellite based composite and plan to track the detected objects over time, providing us with a model of the lifecycle. The insights in the lifecycle will be used in order to improve prediction of convective events in the nowcasting time scale, as well as a new type of data to be assimilated into numerical weather models, thus seamlessly bridging the gap between nowcasting and NWP.. The object identification (or clustering) is performed using a technique borrowed from computer vision, called mean-shift clustering. Mean-Shift clustering works without many of the parameterizations or rigid threshold schemes employed by many existing schemes (e. g. KONRAD, TITAN, Trace-3D), which limit the tracking to fully matured, convective cells of significant size and/or strength. Mean-Shift performs without such limiting definitions, providing a wider scope for studying larger classes of phenomena and providing a vehicle for research into the object definition itself. Since the mean-shift clustering technique could be applied on many types of remote-sensing and model data for object detection, it is of general interest to the remote sensing and modeling community. The focus of the presentation is the introduction of this technique and the results of its
Speech Enhancement based on Compressive Sensing Algorithm
NASA Astrophysics Data System (ADS)
Sulong, Amart; Gunawan, Teddy S.; Khalifa, Othman O.; Chebil, Jalel
2013-12-01
There are various methods, in performance of speech enhancement, have been proposed over the years. The accurate method for the speech enhancement design mainly focuses on quality and intelligibility. The method proposed with high performance level. A novel speech enhancement by using compressive sensing (CS) is a new paradigm of acquiring signals, fundamentally different from uniform rate digitization followed by compression, often used for transmission or storage. Using CS can reduce the number of degrees of freedom of a sparse/compressible signal by permitting only certain configurations of the large and zero/small coefficients, and structured sparsity models. Therefore, CS is significantly provides a way of reconstructing a compressed version of the speech in the original signal by taking only a small amount of linear and non-adaptive measurement. The performance of overall algorithms will be evaluated based on the speech quality by optimise using informal listening test and Perceptual Evaluation of Speech Quality (PESQ). Experimental results show that the CS algorithm perform very well in a wide range of speech test and being significantly given good performance for speech enhancement method with better noise suppression ability over conventional approaches without obvious degradation of speech quality.
Self-calibration and biconvex compressive sensing
NASA Astrophysics Data System (ADS)
Ling, Shuyang; Strohmer, Thomas
2015-11-01
The design of high-precision sensing devises becomes ever more difficult and expensive. At the same time, the need for precise calibration of these devices (ranging from tiny sensors to space telescopes) manifests itself as a major roadblock in many scientific and technological endeavors. To achieve optimal performance of advanced high-performance sensors one must carefully calibrate them, which is often difficult or even impossible to do in practice. In this work we bring together three seemingly unrelated concepts, namely self-calibration, compressive sensing, and biconvex optimization. The idea behind self-calibration is to equip a hardware device with a smart algorithm that can compensate automatically for the lack of calibration. We show how several self-calibration problems can be treated efficiently within the framework of biconvex compressive sensing via a new method called SparseLift. More specifically, we consider a linear system of equations {\\boldsymbol{y}}={\\boldsymbol{D}}{\\boldsymbol{A}}{\\boldsymbol{x}}, where both {\\boldsymbol{x}} and the diagonal matrix {\\boldsymbol{D}} (which models the calibration error) are unknown. By ‘lifting’ this biconvex inverse problem we arrive at a convex optimization problem. By exploiting sparsity in the signal model, we derive explicit theoretical guarantees under which both {\\boldsymbol{x}} and {\\boldsymbol{D}} can be recovered exactly, robustly, and numerically efficiently via linear programming. Applications in array calibration and wireless communications are discussed and numerical simulations are presented, confirming and complementing our theoretical analysis.
LIBS spectroscopic classification relative to compressive sensing
NASA Astrophysics Data System (ADS)
Griffin, Steven T.; Jacobs, Eddie; Furxhi, Orges
2011-05-01
Laser Induced Breakdown Spectroscopy (LIBS) utilizes a diversity of standard spectroscopic techniques for classification of materials present in the sample. Pre-excitation processing sometimes limits the analyte to a short list of candidates. Prior art demonstrates that sparsity is present in the data. This is sometimes characterized as identification by components. Traditionally, spectroscopic identification has been accomplished by an expert reader in a manner typical for MRI images in the medicine. In an effort to automate this process, more recent art has emphasized the use of customized variations to standard classification algorithms. In addition, formal mathematical proofs for compressive sensing have been advanced. Recently the University of Memphis has been contracted by the Spectroscopic Materials Identification Center to advance and characterize the sensor research and development related to LIBS. Applications include portable standoff sensing for improvised explosive device detection and related law enforcement and military applications. Reduction of the mass, power consumption and other portability parameters is seen as dependent on classification choices for a LIBS system. This paper presents results for the comparison of standard LIBS classification techniques to those implied by Compressive Sensing mathematics. Optimization results and implications for portable LIBS design are presented.
Enablement of scientific remote sensing missions with in-space 3D printing
NASA Astrophysics Data System (ADS)
Hirsch, Michael; McGuire, Thomas; Parsons, Michael; Leake, Skye; Straub, Jeremy
2016-05-01
This paper provides an overview of the capability of a 3D printer to successfully operate in-space to create structures and equipment useful in the field of scientific remote sensing. Applications of this printer involve oceanography, weather tracking, as well as space exploration sensing. The design for the 3D printer includes a parabolic array to collect and focus thermal energy. This thermal energy then be used to heat the extrusion head, allowing for the successful extrusion of the print material. Print material can range from plastics to metals, with the hope of being able to extrude aluminum for its low-mass structural integrity and its conductive properties. The printer will be able to print structures as well as electrical components. The current process of creating and launching a remote sensor into space is constrained by many factors such as gravity on earth, the forces of launch, the size of the launch vehicle, and the number of available launches. The design intent of the in-space 3D printer is to ease or eliminate these constraints, making space-based scientific remote sensors a more readily available resource.
Cochlea-inspired sensing node for compressive sensing
NASA Astrophysics Data System (ADS)
Peckens, Courtney A.; Lynch, Jerome P.
2013-04-01
While sensing technologies for structural monitoring applications have made significant advances over the last several decades, there is still room for improvement in terms of computational efficiency, as well as overall energy consumption. The biological nervous system can offer a potential solution to address these current deficiencies. The nervous system is capable of sensing and aggregating information about the external environment through very crude processing units known as neurons. Neurons effectively communicate in an extremely condensed format by encoding information into binary electrical spike trains, thereby reducing the amount of raw information sent throughout a neural network. Due to its unique signal processing capabilities, the mammalian cochlea and its interaction with the biological nervous system is of particular interest for devising compressive sensing strategies for dynamic engineered systems. The cochlea uses a novel method of place theory and frequency decomposition, thereby allowing for rapid signal processing within the nervous system. In this study, a low-power sensing node is proposed that draws inspiration from the mechanisms employed by the cochlea and the biological nervous system. As such, the sensor is able to perceive and transmit a compressed representation of the external stimulus with minimal distortion. Each sensor represents a basic building block, with function similar to the neuron, and can form a network with other sensors, thus enabling a system that can convey input stimulus in an extremely condensed format. The proposed sensor is validated through a structural monitoring application of a single degree of freedom structure excited by seismic ground motion.
On the Estimation of Forest Resources Using 3D Remote Sensing Techniques and Point Cloud Data
NASA Astrophysics Data System (ADS)
Karjalainen, Mika; Karila, Kirsi; Liang, Xinlian; Yu, Xiaowei; Huang, Guoman; Lu, Lijun
2016-08-01
In recent years, 3D capable remote sensing techniques have shown great potential in forest biomass estimation because of their ability to measure the forest canopy structure, tree height and density. The objective of the Dragon3 forest resources research project (ID 10667) and the supporting ESA young scientist project (ESA contract NO. 4000109483/13/I-BG) was to study the use of satellite based 3D techniques in forest tree height estimation, and consequently in forest biomass and biomass change estimation, by combining satellite data with terrestrial measurements. Results from airborne 3D techniques were also used in the project. Even though, forest tree height can be estimated from 3D satellite SAR data to some extent, there is need for field reference plots. For this reason, we have also been developing automated field plot measurement techniques based on Terrestrial Laser Scanning data, which can be used to train and calibrate satellite based estimation models. In this paper, results of canopy height models created from TerraSAR-X stereo and TanDEM-X INSAR data are shown as well as preliminary results from TLS field plot measurement system. Also, results from the airborne CASMSAR system to measure forest canopy height from P- and X- band INSAR are presented.
Compressive Sensing via Nonlocal Smoothed Rank Function
Fan, Ya-Ru; Liu, Jun; Zhao, Xi-Le
2016-01-01
Compressive sensing (CS) theory asserts that we can reconstruct signals and images with only a small number of samples or measurements. Recent works exploiting the nonlocal similarity have led to better results in various CS studies. To better exploit the nonlocal similarity, in this paper, we propose a non-convex smoothed rank function based model for CS image reconstruction. We also propose an efficient alternating minimization method to solve the proposed model, which reduces a difficult and coupled problem to two tractable subproblems. Experimental results have shown that the proposed method performs better than several existing state-of-the-art CS methods for image reconstruction. PMID:27583683
Compressive Sensing via Nonlocal Smoothed Rank Function.
Fan, Ya-Ru; Huang, Ting-Zhu; Liu, Jun; Zhao, Xi-Le
2016-01-01
Compressive sensing (CS) theory asserts that we can reconstruct signals and images with only a small number of samples or measurements. Recent works exploiting the nonlocal similarity have led to better results in various CS studies. To better exploit the nonlocal similarity, in this paper, we propose a non-convex smoothed rank function based model for CS image reconstruction. We also propose an efficient alternating minimization method to solve the proposed model, which reduces a difficult and coupled problem to two tractable subproblems. Experimental results have shown that the proposed method performs better than several existing state-of-the-art CS methods for image reconstruction.
Symmetric Toeplitz-Structured Compressed Sensing Matrices
NASA Astrophysics Data System (ADS)
Huang, Tao; Fan, Yi-Zheng; Zhu, Ming
2015-11-01
How to construct a suitable measurement matrix is an important topic in compressed sensing. A significant part of the recent work is that the measurement matrices are not completely random on the entries but exhibit some considerable structures. In this paper, we proved that a symmetric Toeplitz matrix and its variant can be used as measurement matrices and recovery signal with high probability. Compared with random matrices (e.g. Gaussian and Bernoulli matrices) and some structured matrices (e.g. Toeplitz and circulant matrices), we need to generate fewer independent entries to obtain the measurement matrix while the effectiveness of the recovery keeps good.
Radar target classification using compressively sensed features
NASA Astrophysics Data System (ADS)
Jouny, Ismail
2017-05-01
The paper focuses on extracting scattering centers of radar targets using compressive sensing and using them as features in a target recognition system. It has been shown that a target's high resolution range profile (HRRP) is sparse in time corresponding to few scatterers that can be associated with target geometry. The recognition system is tested using real radar data of commercial aircraft models. Classification is carried out using distance based and correlation based techniques. Scenarios where the target aspect angle is unknown or known to be within a certain range are also examined.
Compressed sensing for phase contrast CT
Gaass, Thomas; Potdevin, Guillaume; Noeel, Peter B.; Tapfer, Arne; Willner, Marian; Herzen, Julia; Bech, Martin; Pfeiffer, Franz; Haase, Axel
2012-07-31
Modern x-ray techniques opened the possibility to retrieve phase information. Phase-contrast computed tomography (PCCT) has the potential to significantly improve soft tissue contrast. Radiation dose, however, continues to be an issue when moving from bench to bedside. Dose reduction in this work is achieved by sparsely acquiring PCCT data. To compensate for appearing aliasing artifacts we introduce a compressed sensing (CS) reconstruction framework. We present the feasibility of CS on PCCT with numerical as well as measured phantom data. The results proof that CS compensates for under-sampling artifacts and maintains the superior soft tissue contrast and detail visibility in the reconstructed images.
Wang, Lin; Acosta, Miguel A; Leach, Jennie B; Carrier, Rebecca L
2013-04-21
Capability of measuring and monitoring local oxygen concentration at the single cell level (tens of microns scale) is often desirable but difficult to achieve in cell culture. In this study, biocompatible oxygen sensing beads were prepared and tested for their potential for real-time monitoring and mapping of local oxygen concentration in 3D micro-patterned cell culture systems. Each oxygen sensing bead is composed of a silica core loaded with both an oxygen sensitive Ru(Ph2phen3)Cl2 dye and oxygen insensitive Nile blue reference dye, and a poly-dimethylsiloxane (PDMS) shell rendering biocompatibility. Human intestinal epithelial Caco-2 cells were cultivated on a series of PDMS and type I collagen based substrates patterned with micro-well arrays for 3 or 7 days, and then brought into contact with oxygen sensing beads. Using an image analysis algorithm to convert florescence intensity of beads to partial oxygen pressure in the culture system, tens of microns-size oxygen sensing beads enabled the spatial measurement of local oxygen concentration in the microfabricated system. Results generally indicated lower oxygen level inside wells than on top of wells, and local oxygen level dependence on structural features of cell culture surfaces. Interestingly, chemical composition of cell culture substrates also appeared to affect oxygen level, with type-I collagen based cell culture systems having lower oxygen concentration compared to PDMS based cell culture systems. In general, results suggest that oxygen sensing beads can be utilized to achieve real-time and local monitoring of micro-environment oxygen level in 3D microfabricated cell culture systems.
A simple approach for the fabrication of 3D microelectrodes for impedimetric sensing
NASA Astrophysics Data System (ADS)
Tahsin Guler, Mustafa; Bilican, Ismail; Agan, Sedat; Elbuken, Caglar
2015-09-01
In this paper, we present a very simple method to fabricate three-dimensional (3D) microelectrodes integrated with microfluidic devices. We form the electrodes by etching a microwire placed across a microchannel. For precise control of the electrode spacing, we employ a hydrodynamic focusing microfluidic device and control the width of the etching solution stream. The focused widths of the etchant solution and the etching time determine the gap formed between the electrodes. Using the same microfluidic device, we can fabricate integrated 3D electrodes with different electrode gaps. We have demonstrated the functionality of these electrodes using an impedimetric particle counting setup. Using 3D microelectrodes with a diameter of 25 μm, we have detected 6 μm-diameter polystyrene beads in a buffer solution as well as erythrocytes in a PBS solution. We study the effect of electrode spacing on the signal-to-noise ratio of the impedance signal and we demonstrate that the smaller the electrode spacing the higher the signal obtained from a single microparticle. The sample stream is introduced to the system using the same hydrodynamic focusing device, which ensures the alignment of the sample in between the electrodes. Utilising a 3D hydrodynamic focusing approach, we force all the particles to go through the sensing region of the electrodes. This fabrication scheme not only provides a very low-cost and easy method for rapid prototyping, but which can also be used for applications requiring 3D electric field focused through a narrow section of the microchannel.
Region-based compression of remote sensing stereo image pairs
NASA Astrophysics Data System (ADS)
Yan, Ruomei; Li, Yunsong; Wu, Chengke; Wang, Keyan; Li, Shizhong
2009-08-01
According to the data characteristics of remote sensing stereo image pairs, a novel compression algorithm based on the combination of feature-based image matching (FBM), area-based image matching (ABM), and region-based disparity estimation is proposed. First, the Scale Invariant Feature Transform (SIFT) and the Sobel operator are carried out for texture classification. Second, an improved ABM is used in the area with flat terrain (flat area), while the disparity estimation, a combination of quadtree decomposition and FBM, is used in the area with alpine terrain (alpine area). Furthermore, the radiation compensation is applied in every area. Finally, the disparities, the residual image, and the reference image are compressed by JPEG2000 together. The new algorithm provides a reasonable prediction in different areas according to characteristics of image textures, which improves the precision of the sensed image. The experimental results show that the PSNR of the proposed algorithm can obtain up to about 3dB's gain compared with the traditional algorithm at low or medium bitrates, and the subjective quality is obviously enhanced.
Adaptive compression of remote sensing stereo image pairs
NASA Astrophysics Data System (ADS)
Li, Yunsong; Yan, Ruomei; Wu, Chengke; Wang, Keyan; Li, Shizhong; Wang, Yu
2010-09-01
According to the data characteristics of remote sensing stereo image pairs, a novel adaptive compression algorithm based on the combination of feature-based image matching (FBM), area-based image matching (ABM), and region-based disparity estimation is proposed. First, the Scale Invariant Feature Transform (SIFT) and the Sobel operator are carried out for texture classification. Second, an improved ABM is used in the flat area, while the disparity estimation is used in the alpine area. The radiation compensation is applied to further improve the performance. Finally, the residual image and the reference image are compressed by JPEG2000 independently. The new algorithm provides a reasonable prediction in different areas according to the image textures, which improves the precision of the sensed image. The experimental results show that the PSNR of the proposed algorithm can obtain up to about 3dB's gain compared with the traditional algorithm at low or medium bitrates, and the DTM and subjective quality is also obviously enhanced.
Frequency extrapolation by nonconvex compressive sensing
Chartrand, Rick; Sidky, Emil Y; Pan, Xiaochaun
2010-12-03
Tomographic imaging modalities sample subjects with a discrete, finite set of measurements, while the underlying object function is continuous. Because of this, inversion of the imaging model, even under ideal conditions, necessarily entails approximation. The error incurred by this approximation can be important when there is rapid variation in the object function or when the objects of interest are small. In this work, we investigate this issue with the Fourier transform (FT), which can be taken as the imaging model for magnetic resonance imaging (MRl) or some forms of wave imaging. Compressive sensing has been successful for inverting this data model when only a sparse set of samples are available. We apply the compressive sensing principle to a somewhat related problem of frequency extrapolation, where the object function is represented by a super-resolution grid with many more pixels than FT measurements. The image on the super-resolution grid is obtained through nonconvex minimization. The method fully utilizes the available FT samples, while controlling aliasing and ringing. The algorithm is demonstrated with continuous FT samples of the Shepp-Logan phantom with additional small, high-contrast objects.
Distributed Compressive Sensing: A Deep Learning Approach
NASA Astrophysics Data System (ADS)
Palangi, Hamid; Ward, Rabab; Deng, Li
2016-09-01
Various studies that address the compressed sensing problem with Multiple Measurement Vectors (MMVs) have been recently carried. These studies assume the vectors of the different channels to be jointly sparse. In this paper, we relax this condition. Instead we assume that these sparse vectors depend on each other but that this dependency is unknown. We capture this dependency by computing the conditional probability of each entry in each vector being non-zero, given the "residuals" of all previous vectors. To estimate these probabilities, we propose the use of the Long Short-Term Memory (LSTM)[1], a data driven model for sequence modelling that is deep in time. To calculate the model parameters, we minimize a cross entropy cost function. To reconstruct the sparse vectors at the decoder, we propose a greedy solver that uses the above model to estimate the conditional probabilities. By performing extensive experiments on two real world datasets, we show that the proposed method significantly outperforms the general MMV solver (the Simultaneous Orthogonal Matching Pursuit (SOMP)) and a number of the model-based Bayesian methods. The proposed method does not add any complexity to the general compressive sensing encoder. The trained model is used just at the decoder. As the proposed method is a data driven method, it is only applicable when training data is available. In many applications however, training data is indeed available, e.g. in recorded images and videos.
Phase diagram of matrix compressed sensing.
Schülke, Christophe; Schniter, Philip; Zdeborová, Lenka
2016-12-01
In the problem of matrix compressed sensing, we aim to recover a low-rank matrix from a few noisy linear measurements. In this contribution, we analyze the asymptotic performance of a Bayes-optimal inference procedure for a model where the matrix to be recovered is a product of random matrices. The results that we obtain using the replica method describe the state evolution of the Parametric Bilinear Generalized Approximate Message Passing (P-BiG-AMP) algorithm, recently introduced in J. T. Parker and P. Schniter [IEEE J. Select. Top. Signal Process. 10, 795 (2016)1932-455310.1109/JSTSP.2016.2539123]. We show the existence of two different types of phase transition and their implications for the solvability of the problem, and we compare the results of our theoretical analysis to the numerical performance reached by P-BiG-AMP. Remarkably, the asymptotic replica equations for matrix compressed sensing are the same as those for a related but formally different problem of matrix factorization.
Phase diagram of matrix compressed sensing
NASA Astrophysics Data System (ADS)
Schülke, Christophe; Schniter, Philip; Zdeborová, Lenka
2016-12-01
In the problem of matrix compressed sensing, we aim to recover a low-rank matrix from a few noisy linear measurements. In this contribution, we analyze the asymptotic performance of a Bayes-optimal inference procedure for a model where the matrix to be recovered is a product of random matrices. The results that we obtain using the replica method describe the state evolution of the Parametric Bilinear Generalized Approximate Message Passing (P-BiG-AMP) algorithm, recently introduced in J. T. Parker and P. Schniter [IEEE J. Select. Top. Signal Process. 10, 795 (2016), 10.1109/JSTSP.2016.2539123]. We show the existence of two different types of phase transition and their implications for the solvability of the problem, and we compare the results of our theoretical analysis to the numerical performance reached by P-BiG-AMP. Remarkably, the asymptotic replica equations for matrix compressed sensing are the same as those for a related but formally different problem of matrix factorization.
NASA Astrophysics Data System (ADS)
Chen, Luoyang; Liu, Jiansheng; cheng, Jiangtao; Liu, Haitao; Zhou, Hongwen
2017-03-01
3D optical coherence tomography imaging (OCT) combined with compressive sensing (CS) has been proved to be an attractive and effective tool in a variety of fields, such as medicine and biology. To achieve high quality imaging while using as less CS sampling rate as possible is the goal of this approach. Here we present an innovative single step fully 3D CS-OCT volumetric image recovery method, in which 3D OCT volumetric image of the object is compressively sampled via our proposed CS coding strategies in all three dimensions while its sparsity is simultaneously taken into consideration in every direction. The object can be directly recovered as the whole volume reconstruction via our advanced full 3D CS reconstruction algorithm. The numerical simulations of a human retina OCT volumetric image reconstruction by our method demonstrate a PSNR of as high as 38dB at a sampling rate of less than 10%.
Modeling the Impact of Drizzle and 3D Cloud Structure on Remote Sensing of Effective Radius
NASA Technical Reports Server (NTRS)
Platnick, Steven; Zinner, Tobias; Ackerman, S.
2008-01-01
Remote sensing of cloud particle size with passive sensors like MODIS is an important tool for cloud microphysical studies. As a measure of the radiatively relevant droplet size, effective radius can be retrieved with different combinations of visible through shortwave infrared channels. MODIS observations sometimes show significantly larger effective radii in marine boundary layer cloud fields derived from the 1.6 and 2.1 pm channel observations than for 3.7 pm retrievals. Possible explanations range from 3D radiative transport effects and sub-pixel cloud inhomogeneity to the impact of drizzle formation on the droplet distribution. To investigate the potential influence of these factors, we use LES boundary layer cloud simulations in combination with 3D Monte Carlo simulations of MODIS observations. LES simulations of warm cloud spectral microphysics for cases of marine stratus and broken stratocumulus, each for two different values of cloud condensation nuclei density, produce cloud structures comprising droplet size distributions with and without drizzle size drops. In this study, synthetic MODIS observations generated from 3D radiative transport simulations that consider the full droplet size distribution will be generated for each scene. The operational MODIS effective radius retrievals will then be applied to the simulated reflectances and the results compared with the LES microphysics.
Modeling the Impact of Drizzle and 3D Cloud Structure on Remote Sensing of Effective Radius
NASA Technical Reports Server (NTRS)
Platnick, Steven; Zinner, Tobias; Ackerman, S.
2008-01-01
Remote sensing of cloud particle size with passive sensors like MODIS is an important tool for cloud microphysical studies. As a measure of the radiatively relevant droplet size, effective radius can be retrieved with different combinations of visible through shortwave infrared channels. MODIS observations sometimes show significantly larger effective radii in marine boundary layer cloud fields derived from the 1.6 and 2.1 pm channel observations than for 3.7 pm retrievals. Possible explanations range from 3D radiative transport effects and sub-pixel cloud inhomogeneity to the impact of drizzle formation on the droplet distribution. To investigate the potential influence of these factors, we use LES boundary layer cloud simulations in combination with 3D Monte Carlo simulations of MODIS observations. LES simulations of warm cloud spectral microphysics for cases of marine stratus and broken stratocumulus, each for two different values of cloud condensation nuclei density, produce cloud structures comprising droplet size distributions with and without drizzle size drops. In this study, synthetic MODIS observations generated from 3D radiative transport simulations that consider the full droplet size distribution will be generated for each scene. The operational MODIS effective radius retrievals will then be applied to the simulated reflectances and the results compared with the LES microphysics.
ShrinkWrap: 3D model abstraction for remote sensing simulation
Pope, Paul A
2009-01-01
Remote sensing simulations often require the use of 3D models of objects of interest. There are a multitude of these models available from various commercial sources. There are image processing, computational, database storage, and . data access advantages to having a regularized, encapsulating, triangular mesh representing the surface of a 3D object model. However, this is usually not how these models are stored. They can have too much detail in some areas, and not enough detail in others. They can have a mix of planar geometric primitives (triangles, quadrilaterals, n-sided polygons) representing not only the surface of the model, but also interior features. And the exterior mesh is usually not regularized nor encapsulating. This paper presents a method called SHRlNKWRAP which can be used to process 3D object models to achieve output models having the aforementioned desirable traits. The method works by collapsing an encapsulating sphere, which has a regularized triangular mesh on its surface, onto the surface of the model. A GUI has been developed to make it easy to leverage this capability. The SHRlNKWRAP processing chain and use of the GUI are described and illustrated.
NASA Astrophysics Data System (ADS)
Munoz, H.; Taheri, A.; Chanda, E. K.
2016-07-01
A non-contact optical method for strain measurement applying three-dimensional digital image correlation (3D DIC) in uniaxial compression is presented. A series of monotonic uniaxial compression tests under quasi-static loading conditions on Hawkesbury sandstone specimens were conducted. A prescribed constant lateral-strain rate to control the applied axial load in a closed-loop system allowed capturing the complete stress-strain behaviour of the rock, i.e. the pre-peak and post-peak stress-strain regimes. 3D DIC uses two digital cameras to acquire images of the undeformed and deformed shape of an object to perform image analysis and provides deformation and motion measurements. Observations showed that 3D DIC provides strains free from bedding error in contrast to strains from LVDT. Erroneous measurements due to the compliance of the compressive machine are also eliminated. Furthermore, by 3D DIC technique relatively large strains developed in the post-peak regime, in particular within localised zones, difficult to capture by bonded strain gauges, can be measured in a straight forward manner. Field of strains and eventual strain localisation in the rock surface were analysed by 3D DIC method, coupled with the respective stress levels in the rock. Field strain development in the rock samples, both in axial and shear strain domains suggested that strain localisation takes place progressively and develops at a lower rate in pre-peak regime. It is accelerated, otherwise, in post-peak regime associated with the increasing rate of strength degradation. The results show that a major failure plane, due to strain localisation, becomes noticeable only long after the peak stress took place. In addition, post-peak stress-strain behaviour was observed to be either in a form of localised strain in a shearing zone or inelastic unloading outside of the shearing zone.
Lithographic VCSEL array multimode and single mode sources for sensing and 3D imaging
NASA Astrophysics Data System (ADS)
Leshin, J.; Li, M.; Beadsworth, J.; Yang, X.; Zhang, Y.; Tucker, F.; Eifert, L.; Deppe, D. G.
2016-05-01
Sensing applications along with free space data links can benefit from advanced laser sources that produce novel radiation patterns and tight spectral control for optical filtering. Vertical-cavity surface-emitting lasers (VCSELs) are being developed for these applications. While oxide VCSELs are being produced by most companies, a new type of oxide-free VCSEL is demonstrating many advantages in beam pattern, spectral control, and reliability. These lithographic VCSELs offer increased power density from a given aperture size, and enable dense integration of high efficiency and single mode elements that improve beam pattern. In this paper we present results for lithographic VCSELs and describes integration into military systems for very low cost pulsed applications, as well as continuouswave applications in novel sensing applications. The VCSELs are being developed for U.S. Army for soldier weapon engagement simulation training to improve beam pattern and spectral control. Wavelengths in the 904 nm to 990 nm ranges are being developed with the spectral control designed to eliminate unwanted water absorption bands from the data links. Multiple beams and radiation patterns based on highly compact packages are being investigated for improved target sensing and transmission fidelity in free space data links. These novel features based on the new VCSEL sources are also expected to find applications in 3-D imaging, proximity sensing and motion control, as well as single mode sensors such as atomic clocks and high speed data transmission.
Fusing Passive and Active Sensed Images to Gain Infrared-Textured 3d Models
NASA Astrophysics Data System (ADS)
Weinmann, M.; Hoegner, L.; Leitloff, J.; Stilla, U.; Hinz, S.; Jutzi, B.
2012-07-01
Obtaining a 3D description of man-made and natural environments is a basic task in Computer Vision, Photogrammetry and Remote Sensing. New active sensors provide the possibility of capturing range information by images with a single measurement. With this new technique, image-based active ranging is possible which allows for capturing dynamic scenes, e.g. with moving pedestrians or moving vehicles. The currently available range imaging devices usually operate within the close-infrared domain to capture range and furthermore active and passive intensity images. Depending on the application, a 3D description with additional spectral information such as thermal-infrared data can be helpful and offers new opportunities for the detection and interpretation of human subjects and interactions. Therefore, thermal-infrared data combined with range information is promising. In this paper, an approach for mapping thermal-infrared data on range data is proposed. First, a camera calibration is carried out for the range imaging system (PMD[vision] CamCube 2.0) and the thermal-infrared system (InfraTec VarioCAM hr). Subsequently, a registration of close-infrared and thermal infrared intensity images derived from different sensor devices is performed. In this context, wavelength independent properties are selected in order to derive point correspondences between the different spectral domains. Finally, the thermal infrared images are enhanced with information derived from data acquired with the range imaging device and the enhanced IR texture is projected onto the respective 3D point cloud data for gaining appropriate infrared-textured 3D models. The feasibility of the proposed methodology is demonstrated for an experimental setup which is well-suited for investigating these proposed possibilities. Hence, the presented work is a first step towards the development of methods for combined thermal-infrared and range representation.
Evaluation of a combined pre-processing and H.264-compression scheme for 3D integral images
NASA Astrophysics Data System (ADS)
Olsson, Roger; Sjöström, Mårten; Xu, Youzhi
2007-01-01
To provide sufficient 3D-depth fidelity, integral imaging (II) requires an increase in spatial resolution of several orders of magnitude from today's 2D images. We have recently proposed a pre-processing and compression scheme for still II-frames based on forming a pseudo video sequence (PVS) from sub images (SI), which is later coded using the H.264/MPEG-4 AVC video coding standard. The scheme has shown good performance on a set of reference images. In this paper we first investigate and present how five different ways to select the SIs when forming the PVS affect the schemes compression efficiency. We also study how the II-frame structure relates to the performance of a PVS coding scheme. Finally we examine the nature of the coding artifacts which are specific to the evaluated PVS-schemes. We can conclude that for all except the most complex reference image, all evaluated SI selection orders significantly outperforms JPEG 2000 where compression ratios of up to 342:1, while still keeping PSNR > 30 dB, is achieved. We can also confirm that when selecting PVS-scheme, the scheme which results in a higher PVS-picture resolution should be preferred to maximize compression efficiency. Our study of the coded II-frames also indicates that the SI-based PVS, contrary to other PVS schemes, tends to distribute its coding artifacts more homogenously over all 3D-scene depths.
Hyperspectral images lossless compression using the 3D binary EZW algorithm
NASA Astrophysics Data System (ADS)
Cheng, Kai-jen; Dill, Jeffrey
2013-02-01
This paper presents a transform based lossless compression for hyperspectral images which is inspired by Shapiro (1993)'s EZW algorithm. The proposed compression method uses a hybrid transform which includes an integer Karhunrn-Loeve transform (KLT) and integer discrete wavelet transform (DWT). The integer KLT is employed to eliminate the presence of correlations among the bands of the hyperspectral image. The integer 2D discrete wavelet transform (DWT) is applied to eliminate the correlations in the spatial dimensions and produce wavelet coefficients. These coefficients are then coded by a proposed binary EZW algorithm. The binary EZW eliminates the subordinate pass of conventional EZW by coding residual values, and produces binary sequences. The binary EZW algorithm combines the merits of well-known EZW and SPIHT algorithms, and it is computationally simpler for lossless compression. The proposed method was applied to AVIRIS images and compared to other state-of-the-art image compression techniques. The results show that the proposed lossless image compression is more efficient and it also has higher compression ratio than other algorithms.
FPGA Implementation of Optimal 3D-Integer DCT Structure for Video Compression.
Jacob, J Augustin; Kumar, N Senthil
2015-01-01
A novel optimal structure for implementing 3D-integer discrete cosine transform (DCT) is presented by analyzing various integer approximation methods. The integer set with reduced mean squared error (MSE) and high coding efficiency are considered for implementation in FPGA. The proposed method proves that the least resources are utilized for the integer set that has shorter bit values. Optimal 3D-integer DCT structure is determined by analyzing the MSE, power dissipation, coding efficiency, and hardware complexity of different integer sets. The experimental results reveal that direct method of computing the 3D-integer DCT using the integer set [10, 9, 6, 2, 3, 1, 1] performs better when compared to other integer sets in terms of resource utilization and power dissipation.
FPGA Implementation of Optimal 3D-Integer DCT Structure for Video Compression
Jacob, J. Augustin; Kumar, N. Senthil
2015-01-01
A novel optimal structure for implementing 3D-integer discrete cosine transform (DCT) is presented by analyzing various integer approximation methods. The integer set with reduced mean squared error (MSE) and high coding efficiency are considered for implementation in FPGA. The proposed method proves that the least resources are utilized for the integer set that has shorter bit values. Optimal 3D-integer DCT structure is determined by analyzing the MSE, power dissipation, coding efficiency, and hardware complexity of different integer sets. The experimental results reveal that direct method of computing the 3D-integer DCT using the integer set [10, 9, 6, 2, 3, 1, 1] performs better when compared to other integer sets in terms of resource utilization and power dissipation. PMID:26601120
Compressed sensing recovery via nonconvex shrinkage penalties
NASA Astrophysics Data System (ADS)
Woodworth, Joseph; Chartrand, Rick
2016-07-01
The {{\\ell }}0 minimization of compressed sensing is often relaxed to {{\\ell }}1, which yields easy computation using the shrinkage mapping known as soft thresholding, and can be shown to recover the original solution under certain hypotheses. Recent work has derived a general class of shrinkages and associated nonconvex penalties that better approximate the original {{\\ell }}0 penalty and empirically can recover the original solution from fewer measurements. We specifically examine p-shrinkage and firm thresholding. In this work, we prove that given data and a measurement matrix from a broad class of matrices, one can choose parameters for these classes of shrinkages to guarantee exact recovery of the sparsest solution. We further prove convergence of the algorithm iterative p-shrinkage (IPS) for solving one such relaxed problem.
Surface wave tomography with compressive sensing
NASA Astrophysics Data System (ADS)
Zhan, Z.; Li, Q.; Huang, J.
2016-12-01
The development of dense seismic arrays has led to the emergence of new surface wave tomography methods, such as Eikonal/Helmholtz tomography, wavefield gradiometry, and seismic noise gradiometry. All these methods need to calculate spatial derivatives of some attributes of the wavefields, for example travel time, amplitude, or the displacement itself. Usually, the waveforms contain noise and are observed on an irregular grid. Interpolation and smoothing are necessary to mitigate the problems, but also limit the resolution of tomography. Here, we propose a new data pre-processing method based on compressive sensing (CS). The new method uses sparsity promoting inverse techniques and curvelet transform to achieve simultaneous seismic data interpolation and denoising in frequency domain. The resulted wavefields can then be used in any wavefield-based surface wave tomography method. Preliminary tests with finite-difference synthetics show improved tomographic images, compared with images from the same methods applied to the original synthetics.
A compressed sensing perspective of hippocampal function
Petrantonakis, Panagiotis C.; Poirazi, Panayiota
2014-01-01
Hippocampus is one of the most important information processing units in the brain. Input from the cortex passes through convergent axon pathways to the downstream hippocampal subregions and, after being appropriately processed, is fanned out back to the cortex. Here, we review evidence of the hypothesis that information flow and processing in the hippocampus complies with the principles of Compressed Sensing (CS). The CS theory comprises a mathematical framework that describes how and under which conditions, restricted sampling of information (data set) can lead to condensed, yet concise, forms of the initial, subsampled information entity (i.e., of the original data set). In this work, hippocampus related regions and their respective circuitry are presented as a CS-based system whose different components collaborate to realize efficient memory encoding and decoding processes. This proposition introduces a unifying mathematical framework for hippocampal function and opens new avenues for exploring coding and decoding strategies in the brain. PMID:25152718
Multichannel Compressive Sensing MRI Using Noiselet Encoding
Pawar, Kamlesh; Egan, Gary; Zhang, Jingxin
2015-01-01
The incoherence between measurement and sparsifying transform matrices and the restricted isometry property (RIP) of measurement matrix are two of the key factors in determining the performance of compressive sensing (CS). In CS-MRI, the randomly under-sampled Fourier matrix is used as the measurement matrix and the wavelet transform is usually used as sparsifying transform matrix. However, the incoherence between the randomly under-sampled Fourier matrix and the wavelet matrix is not optimal, which can deteriorate the performance of CS-MRI. Using the mathematical result that noiselets are maximally incoherent with wavelets, this paper introduces the noiselet unitary bases as the measurement matrix to improve the incoherence and RIP in CS-MRI. Based on an empirical RIP analysis that compares the multichannel noiselet and multichannel Fourier measurement matrices in CS-MRI, we propose a multichannel compressive sensing (MCS) framework to take the advantage of multichannel data acquisition used in MRI scanners. Simulations are presented in the MCS framework to compare the performance of noiselet encoding reconstructions and Fourier encoding reconstructions at different acceleration factors. The comparisons indicate that multichannel noiselet measurement matrix has better RIP than that of its Fourier counterpart, and that noiselet encoded MCS-MRI outperforms Fourier encoded MCS-MRI in preserving image resolution and can achieve higher acceleration factors. To demonstrate the feasibility of the proposed noiselet encoding scheme, a pulse sequences with tailored spatially selective RF excitation pulses was designed and implemented on a 3T scanner to acquire the data in the noiselet domain from a phantom and a human brain. The results indicate that noislet encoding preserves image resolution better than Fouirer encoding. PMID:25965548
Multichannel compressive sensing MRI using noiselet encoding.
Pawar, Kamlesh; Egan, Gary; Zhang, Jingxin
2015-01-01
The incoherence between measurement and sparsifying transform matrices and the restricted isometry property (RIP) of measurement matrix are two of the key factors in determining the performance of compressive sensing (CS). In CS-MRI, the randomly under-sampled Fourier matrix is used as the measurement matrix and the wavelet transform is usually used as sparsifying transform matrix. However, the incoherence between the randomly under-sampled Fourier matrix and the wavelet matrix is not optimal, which can deteriorate the performance of CS-MRI. Using the mathematical result that noiselets are maximally incoherent with wavelets, this paper introduces the noiselet unitary bases as the measurement matrix to improve the incoherence and RIP in CS-MRI. Based on an empirical RIP analysis that compares the multichannel noiselet and multichannel Fourier measurement matrices in CS-MRI, we propose a multichannel compressive sensing (MCS) framework to take the advantage of multichannel data acquisition used in MRI scanners. Simulations are presented in the MCS framework to compare the performance of noiselet encoding reconstructions and Fourier encoding reconstructions at different acceleration factors. The comparisons indicate that multichannel noiselet measurement matrix has better RIP than that of its Fourier counterpart, and that noiselet encoded MCS-MRI outperforms Fourier encoded MCS-MRI in preserving image resolution and can achieve higher acceleration factors. To demonstrate the feasibility of the proposed noiselet encoding scheme, a pulse sequences with tailored spatially selective RF excitation pulses was designed and implemented on a 3T scanner to acquire the data in the noiselet domain from a phantom and a human brain. The results indicate that noislet encoding preserves image resolution better than Fouirer encoding.
2014-11-01
binder path on the tensile properties and failure of multilayer woven CFRP composites. Composites Science and Technology. 2000;60:149–156. 19. Gu H...Steven G, Ishikawa T. Behavior of 3D orthogonal woven CFRP composites part I: experimental investifation. Composites: Part A. 2000;31:259–271. 21
Compressed Sensing Electron Tomography for Determining Biological Structure
NASA Astrophysics Data System (ADS)
Guay, Matthew D.; Czaja, Wojciech; Aronova, Maria A.; Leapman, Richard D.
2016-06-01
There has been growing interest in applying compressed sensing (CS) theory and practice to reconstruct 3D volumes at the nanoscale from electron tomography datasets of inorganic materials, based on known sparsity in the structure of interest. Here we explore the application of CS for visualizing the 3D structure of biological specimens from tomographic tilt series acquired in the scanning transmission electron microscope (STEM). CS-ET reconstructions match or outperform commonly used alternative methods in full and undersampled tomogram recovery, but with less significant performance gains than observed for the imaging of inorganic materials. We propose that this disparity stems from the increased structural complexity of biological systems, as supported by theoretical CS sampling considerations and numerical results in simulated phantom datasets. A detailed analysis of the efficacy of CS-ET for undersampled recovery is therefore complicated by the structure of the object being imaged. The numerical nonlinear decoding process of CS shares strong connections with popular regularized least-squares methods, and the use of such numerical recovery techniques for mitigating artifacts and denoising in reconstructions of fully sampled datasets remains advantageous. This article provides a link to the software that has been developed for CS-ET reconstruction of electron tomographic data sets.
Compressed Sensing Electron Tomography for Determining Biological Structure
Guay, Matthew D.; Czaja, Wojciech; Aronova, Maria A.; Leapman, Richard D.
2016-01-01
There has been growing interest in applying compressed sensing (CS) theory and practice to reconstruct 3D volumes at the nanoscale from electron tomography datasets of inorganic materials, based on known sparsity in the structure of interest. Here we explore the application of CS for visualizing the 3D structure of biological specimens from tomographic tilt series acquired in the scanning transmission electron microscope (STEM). CS-ET reconstructions match or outperform commonly used alternative methods in full and undersampled tomogram recovery, but with less significant performance gains than observed for the imaging of inorganic materials. We propose that this disparity stems from the increased structural complexity of biological systems, as supported by theoretical CS sampling considerations and numerical results in simulated phantom datasets. A detailed analysis of the efficacy of CS-ET for undersampled recovery is therefore complicated by the structure of the object being imaged. The numerical nonlinear decoding process of CS shares strong connections with popular regularized least-squares methods, and the use of such numerical recovery techniques for mitigating artifacts and denoising in reconstructions of fully sampled datasets remains advantageous. This article provides a link to the software that has been developed for CS-ET reconstruction of electron tomographic data sets. PMID:27291259
Laser speckle reduction based on compressive sensing and edge detection
NASA Astrophysics Data System (ADS)
Wen, Dong-hai; Jiang, Yue-song; Hua, Hou-qiang; Yu, Rong; Gao, Qian; Zhang, Yan-zhong
2013-09-01
Polarization active imager technology obtains images encoded by parameters different than just the reflectivity and therefore provides new information on the image. So polarization active imager systems represent a very powerful observation tool. However, automatic interpretation of the information contained in the reflected intensity of the polarization active image data is extremely difficult because of the speckle phenomenon. An approach for speckle reduction of polarization active image based on the concepts of compressive sensing (CS) theory and edge detection. First, A Canny operator is first utilized to detect and remove edges from the polarization active image. Then, a dictionary learning algorithm which is applied to sparse image representation. The dictionary learning problem is expressed as a box-constrained quadratic program and a fast projected gradient method is introduced to solve it. The Gradient Projection for Square Reconstruction (GPSR) algorithm for solving bound constrained quadratic programming to reduce the speckle noise in the polarization active images. The block-matching 3-D (BM3D) algorithm is used to reduce speckle nosie, it works in two steps: The first one uses hard thresholding to build a relatively clean image for estimating statistics, while the second one performs the actual denoising through empirical Wiener filtering in the transform domain. Finally, the removed edges are added to the reconstructed image. Experimental results show that the visual quality and evaluation indexes outperform the other methods with no edge preservation. The proposed algorithm effectively realizes both despeckling and edge preservation and reaches the state-of-the-art performance.
Polarimetric and Indoor Imaging Fusion Based on Compressive Sensing
2013-04-01
Signal Process., vol. 57, no. 6, pp. 2275-2284, 2009. [20] A. Gurbuz, J. McClellan, and W. Scott, Jr., "Compressive sensing for subsurface imaging using...SciTech Publishing, 2010, pp. 922- 938. [45] A. C. Gurbuz, J. H. McClellan, and W. R. Scott, Jr., "Compressive sensing for subsurface imaging using
3D polypyrrole structures as a sensing material for glucose detection
NASA Astrophysics Data System (ADS)
Cysewska, Karolina; Szymańska, Magdalena; Jasiński, Piotr
2016-11-01
In this work, 3D polypyrrole (PPy) structures as material for glucose detection is proposed. Polypyrrole was electrochemically polymerized on platinum screen-printed electrode from an aqueous solution of lithium perchlorate and pyrrole. The growth mechanism of such PPy structures was studied by ex-situ scanning electron microscopy. Preliminary studies show that studied here PPy film is a good candidate as a sensing material for glucose biosensor. It exhibits very high sensitivity (28.5 mA·mM-1·cm-2) and can work without any additional dopants, mediators or enzymes. It was also shown that glucose detection depends on the PPy morphology. The same PPy material was immobilized with the glucose oxidase enzyme. Such material exhibited higher signal response, however it lost its stability very fast.
3D indoor scene reconstruction and change detection for robotic sensing and navigation
NASA Astrophysics Data System (ADS)
Liu, Ruixu; Asari, Vijayan K.
2017-05-01
A new methodology for 3D change detection which can support effective robot sensing and navigation in a reconstructed indoor environment is presented in this paper. We register the RGB-D images acquired with an untracked camera into a globally consistent and accurate point-cloud model. This paper introduces a robust system that detects camera position for multiple RGB video frames by using both photo-metric error and feature based method. It utilizes the iterative closest point (ICP) algorithm to establish geometric constraints between the point-cloud as they become aligned. For the change detection part, a bag-of-word (DBoW) model is used to match the current frame with the previous key frames based on RGB images with Oriented FAST and Rotated BRIEF (ORB) feature. Then combine the key-frame translation and ICP to align the current point-cloud with reconstructed 3D scene to localize the robot position. Meanwhile, camera position and orientation are used to aid robot navigation. After preprocessing the data, we create an Octomap Model to detect the scene change measurements. The experimental evaluations performed to evaluate the capability of our algorithm show that the robot's location and orientation are accurately determined and provide promising results for change detection indicating all the object changes with very limited false alarm rate.
Application of Compressive Sensing to Gravitational Microlensing Experiments
NASA Technical Reports Server (NTRS)
Korde-Patel, Asmita; Barry, Richard K.; Mohsenin, Tinoosh
2016-01-01
Compressive Sensing is an emerging technology for data compression and simultaneous data acquisition. This is an enabling technique for significant reduction in data bandwidth, and transmission power and hence, can greatly benefit spaceflight instruments. We apply this process to detect exoplanets via gravitational microlensing. We experiment with various impact parameters that describe microlensing curves to determine the effectiveness and uncertainty caused by Compressive Sensing. Finally, we describe implications for spaceflight missions.
Application of Compressive Sensing to Gravitational Microlensing Experiments
NASA Astrophysics Data System (ADS)
Korde-Patel, Asmita; Barry, Richard K.; Mohsenin, Tinoosh
2017-06-01
Compressive Sensing is an emerging technology for data compression and simultaneous data acquisition. This is an enabling technique for significant reduction in data bandwidth, and transmission power and hence, can greatly benefit space-flight instruments. We apply this process to detect exoplanets via gravitational microlensing. We experiment with various impact parameters that describe microlensing curves to determine the effectiveness and uncertainty caused by Compressive Sensing. Finally, we describe implications for space-flight missions.
Yan, Pengli; Brown, Emery; Su, Qing; Li, Jun; Wang, Jian; Xu, Changxue; Zhou, Chi; Lin, Dong
2017-10-01
Metallic aerogels have attracted intense attention due to their superior properties, such as high electrical conductivity, ultralow densities, and large specific surface area. The preparation of metal aerogels with high efficiency and controllability remains challenge. A 3D freeze assembling printing technique integrated with drop-on-demand inkjet printing and freeze casting are proposed for metallic aerogels preparation. This technique enables tailoring both the macrostructure and microstructure of silver nanowire aerogels (SNWAs) by integrating programmable 3D printing and freeze casting, respectively. The density of the printed SNWAs is controllable, which can be down to 1.3 mg cm(-3) . The ultralight SNWAs reach high electrical conductivity of 1.3 S cm(-1) and exhibit excellent compressive resilience under 50% compressive strain. Remarkably, the printing methodology also enables tuning aerogel architectures with designed Poisson's ratio (from negative to positive). Moreover, these aerogel architechtures with tunable Poisson's ratio present highly electromechanical stability under high compressive and tensile strain (both strain up to 20% with fully recovery). © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Real-time sensing of mouth 3-D position and orientation
NASA Astrophysics Data System (ADS)
Burdea, Grigore C.; Dunn, Stanley M.; Mallik, Matsumita; Jun, Heesung
1990-07-01
A key problem in using digital subtraction radiography in dentistry is the ability to reposition the X-ray source and patient so as to reproduce an identical imaging geometry. In this paper we describe an approach to solving this problem based on real time sensing of the 3-D position and orientation of the patient's mouth. The research described here is part of a program which has a long term goal to develop an automated digital subtraction radiography system. This will allow the patient and X-ray source to be accurately repositioned without the mechanical fixtures that are presently used to preserve the imaging geometry. If we can measure the position and orientation of the mouth, then the desired position of the source can be computed as the product of the transformation matrices describing the desired imaging geometry and the position vector of the targeted tooth. Position and orientation of the mouth is measured by a real time sensing device using low-frequency magnetic field technology. We first present the problem of repositioning the patient and source and then outline our analytic solution. Then we describe an experimental setup to measure the accuracy, reproducibility and resolution of the sensor and present results of preliminary experiments.
Pope, Paul A; Ranken, Doug M
2010-01-01
A method for abstracting a 3D model by shrinking a triangular mesh, defined upon a best fitting ellipsoid surrounding the model, onto the model's surface has been previously described. This ''shrinkwrap'' process enables a semi-regular mesh to be defined upon an object's surface. This creates a useful data structure for conducting remote sensing simulations and image processing. However, using a best fitting ellipsoid having a graticule-based tessellation to seed the shrinkwrap process suffers from a mesh which is too dense at the poles. To achieve a more regular mesh, the use of a best fitting, subdivided icosahedron was tested. By subdividing each of the twenty facets of the icosahedron into regular triangles of a predetermined size, arbitrarily dense, highly-regular starting meshes can be created. Comparisons of the meshes resulting from these two seed surfaces are described. Use of a best fitting icosahedron-based mesh as the seed surface in the shrinkwrap process is preferable to using a best fitting ellipsoid. The impacts to remote sensing simulations, specifically generation of synthetic imagery, is illustrated.
Dynamical electron compressibility in the 3D topological insulator Bi2Se3
NASA Astrophysics Data System (ADS)
Inhofer, Andreas; Assaf, Badih; Wilmart, Quentin; Veyrat, Louis; Nowka, Christian; Dufouleur, Joseph; Giraud, Romain; Hampel, Silke; Buechner, Bernd; Fève, Gwendal; Berroir, Jean-Marc; Placais, Bernard
Measurements of the quantum capacitance cq, related to the electron compressibility χ =cq /e2 is a sensitive tool to probe the density of states. In a topological insulator (TI) the situation is enriched by the coexistence and the interplay of topologically protected surface states and massive bulk carriers. We investigate top-gate metal-oxyde-TI capacitors using Bi2Se3 thin crystals at GHz frequencies. These measurements provide insight into the compressibillity of such a two electron-fluid system. Furthermore, the dynamical response yields information about electron scattering properties in TIs. More specifically, in our measurements we track simultaneously the conductivity σ and the compressibility as a function of a DC-gate voltage. Using the Einstein relation σ =cq D , we have access to the gate dependence of the electron diffusion constant D (Vg) , a signature of the peculiar scattering mechanisms in TIs.
NASA Astrophysics Data System (ADS)
Yu, Haibo; Zhao, Junning
2017-01-01
In this paper, we study the global existence for classical solutions to the 3D isentropic compressible Navier-Stokes equations in a cuboid domain. Compared to the Cauchy problem studied in Hoff (1995 J. Differ. Equ. 120 215-54), Hoff (2005 J. Math. Fluid Mech. 7 315-38), Huang et al (2012 Commun. Pure Appl. Math. 65 549-85), some new thoughts are applied to obtain upper bounds for density. Precisely, through piecewise estimation and some time-depending a priori estimates, we establish time-uniform upper bounds for density under the assumption that the initial energy is small. The initial vacuum is allowed.
Xia, Yidong; Luo, Hong; Frisbey, Megan; ...
2014-07-01
A set of implicit methods are proposed for a third-order hierarchical WENO reconstructed discontinuous Galerkin method for compressible flows on 3D hybrid grids. An attractive feature in these methods are the application of the Jacobian matrix based on the P1 element approximation, resulting in a huge reduction of memory requirement compared with DG (P2). Also, three approaches -- analytical derivation, divided differencing, and automatic differentiation (AD) are presented to construct the Jacobian matrix respectively, where the AD approach shows the best robustness. A variety of compressible flow problems are computed to demonstrate the fast convergence property of the implemented flowmore » solver. Furthermore, an SPMD (single program, multiple data) programming paradigm based on MPI is proposed to achieve parallelism. The numerical results on complex geometries indicate that this low-storage implicit method can provide a viable and attractive DG solution for complicated flows of practical importance.« less
NASA Astrophysics Data System (ADS)
Shahriari, D.; Amiri, A.; Sadeghi, M. H.
2010-07-01
In hot forging of Nimonic 115, it is desirable to determine friction coefficients. Changing magnitudes of temperature and type of lubricant at the surface of the workpiece and dies influence friction coefficient. This paper describes an experimental investigation of friction under hot forging conditions using the ring compression test. The 3D FEM simulations were used to derive the friction calibration curves and to evaluate material deformation, geometric changes, and load-displacement results. A series of ring compression tests were carried out to obtain friction coefficients for a number of lubricants including mica plate, glass powder, graphite powder, and dry condition. The experiments show how the variations in temperature at the interface affected frictional behavior. On the basis of these results, mica is recommended for hot forging of Nimonic 115 and its friction coefficient is approximately 0.3.
Xia, Yidong; Luo, Hong; Frisbey, Megan; Nourgaliev, Robert
2014-07-01
A set of implicit methods are proposed for a third-order hierarchical WENO reconstructed discontinuous Galerkin method for compressible flows on 3D hybrid grids. An attractive feature in these methods are the application of the Jacobian matrix based on the P1 element approximation, resulting in a huge reduction of memory requirement compared with DG (P2). Also, three approaches -- analytical derivation, divided differencing, and automatic differentiation (AD) are presented to construct the Jacobian matrix respectively, where the AD approach shows the best robustness. A variety of compressible flow problems are computed to demonstrate the fast convergence property of the implemented flow solver. Furthermore, an SPMD (single program, multiple data) programming paradigm based on MPI is proposed to achieve parallelism. The numerical results on complex geometries indicate that this low-storage implicit method can provide a viable and attractive DG solution for complicated flows of practical importance.
NASA Astrophysics Data System (ADS)
Kim, Min Woo; Choi, Jiyoung; Yu, Liu; Lee, Kyung Eun; Han, Sung-Sik; Ye, Jong Chul
2007-02-01
Sparse object supports are often encountered in many imaging problems. For such sparse objects, recent theory of compressed sensing tells us that accurate reconstruction of objects are possible even from highly limited number of measurements drastically smaller than the Nyquist sampling limit by solving L I minimization problem. This paper employs the compressed sensing theory for cryo-electron microscopy (cryo-EM) single particle reconstruction of virus particles. Cryo-EM single particle reconstruction is a nice application of the compressed sensing theory because of the following reasons: 1) in some cases, due to the difficulty in sample collection, each experiment can obtain micrographs with limited number of virus samples, providing undersampled projection data, and 2) the nucleic acid of a viron is enclosed within capsid composed of a few proteins; hence the support of capsid in 3-D real space is quite sparse. In order to minimize the L I cost function derived from compressed sensing, we develop a novel L I minimization method based on the sliding mode control theory. Experimental results using synthetic and real virus data confirm that the our algorithm provides superior reconstructions of 3-D viral structures compared to the conventional reconstruction algorithms.
Gaul, C; Hastreiter, P; Duncker, A; Naraghi, R
2011-10-01
Glossopharyngeal neuralgia is a rare condition with neuralgic sharp pain in the pharyngeal and auricular region. Classical glossopharyngeal neuralgia is caused by neurovascular compression at the root entry zone of the nerve. Regarding the rare occurrence of glossopharyngeal neuralgia, we report clinical data and magnetic resonance imaging (MRI) findings in a case series of 19 patients, of whom 18 underwent surgery. Two patients additionally suffered from trigeminal neuralgia and three from additional symptomatic vagal nerve compression. In all patients, ipsilateral neurovascular compression syndrome of the IX cranial nerve could be shown by high-resolution MRI and image processing, which was confirmed intraoperatively. Additional neurovascular compression of the V cranial nerve was shown in patients suffering from trigeminal neuralgia. Vagal nerve neurovascular compression could be seen in all patients during surgery. Sixteen patients were completely pain free after surgery without need of anticonvulsant treatment. As a consequence of the operation, two patients suffered from transient cerebrospinal fluid hypersecretion as a reaction to Teflon implants. One patient suffered postoperatively from deep vein thrombosis and pulmonary embolism. Six patients showed transient cranial nerve dysfunctions (difficulties in swallowing, vocal cord paresis), but all recovered within 1 week. One patient complained of a gnawing and burning pain in the cervical area. Microvascular decompression is a second-line treatment after failure of standard medical treatment with high success in glossopharyngeal neuralgia. High-resolution MRI and 3D visualization of the brainstem and accompanying vessels as well as the cranial nerves is helpful in identifying neurovascular compression before microvascular decompression procedure.
Bar-Kochba, Eyal; Scimone, Mark T; Estrada, Jonathan B; Franck, Christian
2016-08-02
In the United States over 1.7 million cases of traumatic brain injury are reported yearly, but predictive correlation of cellular injury to impact tissue strain is still lacking, particularly for neuronal injury resulting from compression. Given the prevalence of compressive deformations in most blunt head trauma, this information is critically important for the development of future mitigation and diagnosis strategies. Using a 3D in vitro neuronal compression model, we investigated the role of impact strain and strain rate on neuronal lifetime, viability, and pathomorphology. We find that strain magnitude and rate have profound, yet distinctively different effects on the injury pathology. While strain magnitude affects the time of neuronal death, strain rate influences the pathomorphology and extent of population injury. Cellular injury is not initiated through localized deformation of the cytoskeleton but rather driven by excess strain on the entire cell. Furthermore we find that, mechanoporation, one of the key pathological trigger mechanisms in stretch and shear neuronal injuries, was not observed under compression.
NASA Astrophysics Data System (ADS)
Bar-Kochba, Eyal; Scimone, Mark T.; Estrada, Jonathan B.; Franck, Christian
2016-08-01
In the United States over 1.7 million cases of traumatic brain injury are reported yearly, but predictive correlation of cellular injury to impact tissue strain is still lacking, particularly for neuronal injury resulting from compression. Given the prevalence of compressive deformations in most blunt head trauma, this information is critically important for the development of future mitigation and diagnosis strategies. Using a 3D in vitro neuronal compression model, we investigated the role of impact strain and strain rate on neuronal lifetime, viability, and pathomorphology. We find that strain magnitude and rate have profound, yet distinctively different effects on the injury pathology. While strain magnitude affects the time of neuronal death, strain rate influences the pathomorphology and extent of population injury. Cellular injury is not initiated through localized deformation of the cytoskeleton but rather driven by excess strain on the entire cell. Furthermore we find that, mechanoporation, one of the key pathological trigger mechanisms in stretch and shear neuronal injuries, was not observed under compression.
Bar-Kochba, Eyal; Scimone, Mark T.; Estrada, Jonathan B.; Franck, Christian
2016-01-01
In the United States over 1.7 million cases of traumatic brain injury are reported yearly, but predictive correlation of cellular injury to impact tissue strain is still lacking, particularly for neuronal injury resulting from compression. Given the prevalence of compressive deformations in most blunt head trauma, this information is critically important for the development of future mitigation and diagnosis strategies. Using a 3D in vitro neuronal compression model, we investigated the role of impact strain and strain rate on neuronal lifetime, viability, and pathomorphology. We find that strain magnitude and rate have profound, yet distinctively different effects on the injury pathology. While strain magnitude affects the time of neuronal death, strain rate influences the pathomorphology and extent of population injury. Cellular injury is not initiated through localized deformation of the cytoskeleton but rather driven by excess strain on the entire cell. Furthermore we find that, mechanoporation, one of the key pathological trigger mechanisms in stretch and shear neuronal injuries, was not observed under compression. PMID:27480807
Multimode waveguide speckle patterns for compressive sensing.
Valley, George C; Sefler, George A; Justin Shaw, T
2016-06-01
Compressive sensing (CS) of sparse gigahertz-band RF signals using microwave photonics may achieve better performances with smaller size, weight, and power than electronic CS or conventional Nyquist rate sampling. The critical element in a CS system is the device that produces the CS measurement matrix (MM). We show that passive speckle patterns in multimode waveguides potentially provide excellent MMs for CS. We measure and calculate the MM for a multimode fiber and perform simulations using this MM in a CS system. We show that the speckle MM exhibits the sharp phase transition and coherence properties needed for CS and that these properties are similar to those of a sub-Gaussian MM with the same mean and standard deviation. We calculate the MM for a multimode planar waveguide and find dimensions of the planar guide that give a speckle MM with a performance similar to that of the multimode fiber. The CS simulations show that all measured and calculated speckle MMs exhibit a robust performance with equal amplitude signals that are sparse in time, in frequency, and in wavelets (Haar wavelet transform). The planar waveguide results indicate a path to a microwave photonic integrated circuit for measuring sparse gigahertz-band RF signals using CS.
Energy preserved sampling for compressed sensing MRI.
Zhang, Yudong; Peterson, Bradley S; Ji, Genlin; Dong, Zhengchao
2014-01-01
The sampling patterns, cost functions, and reconstruction algorithms play important roles in optimizing compressed sensing magnetic resonance imaging (CS-MRI). Simple random sampling patterns did not take into account the energy distribution in k-space and resulted in suboptimal reconstruction of MR images. Therefore, a variety of variable density (VD) based samplings patterns had been developed. To further improve it, we propose a novel energy preserving sampling (ePRESS) method. Besides, we improve the cost function by introducing phase correction and region of support matrix, and we propose iterative thresholding algorithm (ITA) to solve the improved cost function. We evaluate the proposed ePRESS sampling method, improved cost function, and ITA reconstruction algorithm by 2D digital phantom and 2D in vivo MR brains of healthy volunteers. These assessments demonstrate that the proposed ePRESS method performs better than VD, POWER, and BKO; the improved cost function can achieve better reconstruction quality than conventional cost function; and the ITA is faster than SISTA and is competitive with FISTA in terms of computation time.
Visually weighted reconstruction of compressive sensing MRI.
Oh, Heeseok; Lee, Sanghoon
2014-04-01
Compressive sensing (CS) enables the reconstruction of a magnetic resonance (MR) image from undersampled data in k-space with relatively low-quality distortion when compared to the original image. In addition, CS allows the scan time to be significantly reduced. Along with a reduction in the computational overhead, we investigate an effective way to improve visual quality through the use of a weighted optimization algorithm for reconstruction after variable density random undersampling in the phase encoding direction over k-space. In contrast to conventional magnetic resonance imaging (MRI) reconstruction methods, the visual weight, in particular, the region of interest (ROI), is investigated here for quality improvement. In addition, we employ a wavelet transform to analyze the reconstructed image in the space domain and fully utilize data sparsity over the spatial and frequency domains. The visual weight is constructed by reflecting the perceptual characteristics of the human visual system (HVS), and then applied to ℓ1 norm minimization, which gives priority to each coefficient during the reconstruction process. Using objective quality assessment metrics, it was found that an image reconstructed using the visual weight has higher local and global quality than those processed by conventional methods. Copyright © 2014 Elsevier Inc. All rights reserved.
Whole Brain Susceptibility Mapping Using Compressed Sensing
Wu, Bing; Li, Wei; Guidon, Arnaud; Liu, Chunlei
2011-01-01
The derivation of susceptibility from image phase is hampered by the ill-conditioned filter inversion in certain k-space regions. In this paper, compressed sensing (CS) is used to compensate for the k-space regions where direct filter inversion is unstable. A significantly lower level of streaking artifacts is produced in the resulting susceptibility maps for both simulated and in vivo data sets compared to outcomes obtained using the direct threshold method. It is also demonstrated that the CS based method outperforms regularization based methods. The key difference between the regularized inversions and CS compensated inversions is that, in the former case, the entire k-space spectrum estimation is affected by the ill-conditioned filter inversion in certain k-space regions, whereas in the CS based method only the ill-conditioned k-space regions are estimated. In the susceptibility map calculated from the phase measurement obtained using a 3T scanner, not only are the iron-rich regions well depicted, but good contrast between white and gray matter interfaces that feature a low level of susceptibility variations are also obtained. The correlation between the iron content and the susceptibility levels in iron-rich deep nucleus regions is studied, and strong linear relationships are observed which agree with previous findings. PMID:21671269
Bacterial community reconstruction using compressed sensing.
Amir, Amnon; Zuk, Or
2011-11-01
Bacteria are the unseen majority on our planet, with millions of species and comprising most of the living protoplasm. We propose a novel approach for reconstruction of the composition of an unknown mixture of bacteria using a single Sanger-sequencing reaction of the mixture. Our method is based on compressive sensing theory, which deals with reconstruction of a sparse signal using a small number of measurements. Utilizing the fact that in many cases each bacterial community is comprised of a small subset of all known bacterial species, we show the feasibility of this approach for determining the composition of a bacterial mixture. Using simulations, we show that sequencing a few hundred base-pairs of the 16S rRNA gene sequence may provide enough information for reconstruction of mixtures containing tens of species, out of tens of thousands, even in the presence of realistic measurement noise. Finally, we show initial promising results when applying our method for the reconstruction of a toy experimental mixture with five species. Our approach may have a potential for a simple and efficient way for identifying bacterial species compositions in biological samples. All supplementary data and the MATLAB code are available at www.broadinstitute.org/?orzuk/publications/BCS/.
Energy Preserved Sampling for Compressed Sensing MRI
Peterson, Bradley S.; Ji, Genlin; Dong, Zhengchao
2014-01-01
The sampling patterns, cost functions, and reconstruction algorithms play important roles in optimizing compressed sensing magnetic resonance imaging (CS-MRI). Simple random sampling patterns did not take into account the energy distribution in k-space and resulted in suboptimal reconstruction of MR images. Therefore, a variety of variable density (VD) based samplings patterns had been developed. To further improve it, we propose a novel energy preserving sampling (ePRESS) method. Besides, we improve the cost function by introducing phase correction and region of support matrix, and we propose iterative thresholding algorithm (ITA) to solve the improved cost function. We evaluate the proposed ePRESS sampling method, improved cost function, and ITA reconstruction algorithm by 2D digital phantom and 2D in vivo MR brains of healthy volunteers. These assessments demonstrate that the proposed ePRESS method performs better than VD, POWER, and BKO; the improved cost function can achieve better reconstruction quality than conventional cost function; and the ITA is faster than SISTA and is competitive with FISTA in terms of computation time. PMID:24971155
Compressed sensing MRI exploiting complementary dual decomposition.
Park, Suhyung; Park, Jaeseok
2014-04-01
Compressed sensing (CS) MRI exploits the sparsity of an image in a transform domain to reconstruct the image from incoherently under-sampled k-space data. However, it has been shown that CS suffers particularly from loss of low-contrast image features with increasing reduction factors. To retain image details in such degraded experimental conditions, in this work we introduce a novel CS reconstruction method exploiting feature-based complementary dual decomposition with joint estimation of local scale mixture (LSM) model and images. Images are decomposed into dual block sparse components: total variation for piecewise smooth parts and wavelets for residuals. The LSM model parameters of residuals in the wavelet domain are estimated and then employed as a regional constraint in spatially adaptive reconstruction of high frequency subbands to restore image details missing in piecewise smooth parts. Alternating minimization of the dual image components subject to data consistency is performed to extract image details from residuals and add them back to their complementary counterparts while the LSM model parameters and images are jointly estimated in a sequential fashion. Simulations and experiments demonstrate the superior performance of the proposed method in preserving low-contrast image features even at high reduction factors.
Energy-efficient sensing in wireless sensor networks using compressed sensing.
Razzaque, Mohammad Abdur; Dobson, Simon
2014-02-12
Sensing of the application environment is the main purpose of a wireless sensor network. Most existing energy management strategies and compression techniques assume that the sensing operation consumes significantly less energy than radio transmission and reception. This assumption does not hold in a number of practical applications. Sensing energy consumption in these applications may be comparable to, or even greater than, that of the radio. In this work, we support this claim by a quantitative analysis of the main operational energy costs of popular sensors, radios and sensor motes. In light of the importance of sensing level energy costs, especially for power hungry sensors, we consider compressed sensing and distributed compressed sensing as potential approaches to provide energy efficient sensing in wireless sensor networks. Numerical experiments investigating the effectiveness of compressed sensing and distributed compressed sensing using real datasets show their potential for efficient utilization of sensing and overall energy costs in wireless sensor networks. It is shown that, for some applications, compressed sensing and distributed compressed sensing can provide greater energy efficiency than transform coding and model-based adaptive sensing in wireless sensor networks.
Energy-Efficient Sensing in Wireless Sensor Networks Using Compressed Sensing
Razzaque, Mohammad Abdur; Dobson, Simon
2014-01-01
Sensing of the application environment is the main purpose of a wireless sensor network. Most existing energy management strategies and compression techniques assume that the sensing operation consumes significantly less energy than radio transmission and reception. This assumption does not hold in a number of practical applications. Sensing energy consumption in these applications may be comparable to, or even greater than, that of the radio. In this work, we support this claim by a quantitative analysis of the main operational energy costs of popular sensors, radios and sensor motes. In light of the importance of sensing level energy costs, especially for power hungry sensors, we consider compressed sensing and distributed compressed sensing as potential approaches to provide energy efficient sensing in wireless sensor networks. Numerical experiments investigating the effectiveness of compressed sensing and distributed compressed sensing using real datasets show their potential for efficient utilization of sensing and overall energy costs in wireless sensor networks. It is shown that, for some applications, compressed sensing and distributed compressed sensing can provide greater energy efficiency than transform coding and model-based adaptive sensing in wireless sensor networks. PMID:24526302
Application of compressed sensing to the simulation of atomic systems
Andrade, Xavier; Sanders, Jacob N.; Aspuru-Guzik, Alán
2012-01-01
Compressed sensing is a method that allows a significant reduction in the number of samples required for accurate measurements in many applications in experimental sciences and engineering. In this work, we show that compressed sensing can also be used to speed up numerical simulations. We apply compressed sensing to extract information from the real-time simulation of atomic and molecular systems, including electronic and nuclear dynamics. We find that, compared to the standard discrete Fourier transform approach, for the calculation of vibrational and optical spectra the total propagation time, and hence the computational cost, can be reduced by approximately a factor of five. PMID:22891294
NASA Astrophysics Data System (ADS)
Javidi, Bahram; Yeom, Seokwon; Moon, Inkyu; Daneshpanah, Mehdi
2006-05-01
In this paper, we present an overview of three-dimensional (3D) optical imaging techniques for real-time automated sensing, visualization, and recognition of dynamic biological microorganisms. Real time sensing and 3D reconstruction of the dynamic biological microscopic objects can be performed by single-exposure on-line (SEOL) digital holographic microscopy. A coherent 3D microscope-based interferometer is constructed to record digital holograms of dynamic micro biological events. Complex amplitude 3D images of the biological microorganisms are computationally reconstructed at different depths by digital signal processing. Bayesian segmentation algorithms are applied to identify regions of interest for further processing. A number of pattern recognition approaches are addressed to identify and recognize the microorganisms. One uses 3D morphology of the microorganisms by analyzing 3D geometrical shapes which is composed of magnitude and phase. Segmentation, feature extraction, graph matching, feature selection, and training and decision rules are used to recognize the biological microorganisms. In a different approach, 3D technique is used that are tolerant to the varying shapes of the non-rigid biological microorganisms. After segmentation, a number of sampling patches are arbitrarily extracted from the complex amplitudes of the reconstructed 3D biological microorganism. These patches are processed using a number of cost functions and statistical inference theory for the equality of means and equality of variances between the sampling segments. Also, we discuss the possibility of employing computational integral imaging for 3D sensing, visualization, and recognition of biological microorganisms illuminated under incoherent light. Experimental results with several biological microorganisms are presented to illustrate detection, segmentation, and identification of micro biological events.
NASA Technical Reports Server (NTRS)
Agnone, Anthony M.
1987-01-01
The performance of a fixed-geometry, swept, mixed compression hypersonic inlet is presented. The experimental evaluation was conducted for a Mach number of 6.0 and for several angles of attack. The measured surface pressures and pitot pressure surveys at the inlet throat are compared to computations using a three-dimensional Euler code and an integral boundary layer theory. Unique features of the intake design, including the boundary layer control, insure a high inlet performance. The experimental data show the inlet has a high mass averaged total pressure recovery, a high mass capture and nearly uniform flow diffusion. The swept inlet exhibits excellent starting characteristics, and high flow stability at angle of attack.
Finite element methods of analysis for 3D inviscid compressible flows
NASA Technical Reports Server (NTRS)
Peraire, Jaime
1990-01-01
The applicants have developed a finite element based approach for the solution of three-dimensional compressible flows. The procedure enables flow solutions to be obtained on tetrahedral discretizations of computational domains of complex form. A further development was the incorporation of a solution adaptive mesh strategy in which the adaptivity is achieved by complete remeshing of the solution domain. During the previous year, the applicants were working with the Advanced Aerodynamics Concepts Branch at NASA Ames Research Center with an implementation of the basic meshing and solution procedure. The objective of the work to be performed over this twelve month period was the transfer of the adaptive mesh technology and also the undertaking of basic research into alternative flow algorithms for the Euler equations on unstructured meshes.
Johnson, Christopher; Sheshadri, Priyanka; Ketchum, Jessica M; Narayanan, Lokesh K; Weinberger, Paul M; Shirwaiker, Rohan A
2016-06-01
Infection or damage to the trachea, a thin walled and cartilage reinforced conduit that connects the pharynx and larynx to the lungs, leads to serious respiratory medical conditions which can often prove fatal. Current clinical strategies for complex tracheal reconstruction are of limited availability and efficacy, but tissue engineering and regenerative medicine approaches may provide viable alternatives. In this study, we have developed a new "hybrid graft" approach that utilizes decellularized tracheal tissue along with a resorbable polymer scaffold, and holds promise for potential clinical applications. First, we evaluated the effect of our decellularization process on the compression properties of porcine tracheal segments, and noted approximately 63% decrease in resistance to compression following decellularization. Next we developed four C-shape scaffold designs by varying the base geometry and thickness, and fabricated polycaprolactone scaffolds using a combination of 3D-Bioplotting and thermally-assisted forming. All scaffolds designs were evaluated in vitro under three different environmental testing conditions to determine the design that offered the best resistance to compression. These were further studied to determine the effect of gamma radiation sterilization and cyclic compression loading. Finally, hybrid grafts were developed by securing these optimal design scaffolds to decellularized tracheal segments and evaluated in vitro under physiological testing conditions. Results show that the resistance to compression offered by the hybrid grafts created using gamma radiation sterilized scaffolds was comparable to that of fresh tracheal segments. Given that current clinical attempts at tracheal transplantation using decellularized tissue have been fraught with luminal collapse and complications, our data support the possibility that future embodiments using a hybrid graft approach may reduce the need for intraluminal stenting in tracheal transplant
3D change detection in staggered voxels model for robotic sensing and navigation
NASA Astrophysics Data System (ADS)
Liu, Ruixu; Hampshire, Brandon; Asari, Vijayan K.
2016-05-01
3D scene change detection is a challenging problem in robotic sensing and navigation. There are several unpredictable aspects in performing scene change detection. A change detection method which can support various applications in varying environmental conditions is proposed. Point cloud models are acquired from a RGB-D sensor, which provides the required color and depth information. Change detection is performed on robot view point cloud model. A bilateral filter smooths the surface and fills the holes as well as keeps the edge details on depth image. Registration of the point cloud model is implemented by using Random Sample Consensus (RANSAC) algorithm. It uses surface normal as the previous stage for the ground and wall estimate. After preprocessing the data, we create a point voxel model which defines voxel as surface or free space. Then we create a color model which defines each voxel that has a color by the mean of all points' color value in this voxel. The preliminary change detection is detected by XOR subtract on the point voxel model. Next, the eight neighbors for this center voxel are defined. If they are neither all `changed' voxels nor all `no changed' voxels, a histogram of location and hue channel color is estimated. The experimental evaluations performed to evaluate the capability of our algorithm show promising results for novel change detection that indicate all the changing objects with very limited false alarm rate.
Reducing Disk Storage of Full-3D Seismic Waveform Tomography (F3DT) Through Lossy Online Compression
Lindstrom, Peter; Chen, Po; Lee, En-Jui
2016-05-05
Full-3D seismic waveform tomography (F3DT) is the latest seismic tomography technique that can assimilate broadband, multi-component seismic waveform observations into high-resolution 3D subsurface seismic structure models. The main drawback in the current F3DT implementation, in particular the scattering-integral implementation (F3DT-SI), is the high disk storage cost and the associated I/O overhead of archiving the 4D space-time wavefields of the receiver- or source-side strain tensors. The strain tensor fields are needed for computing the data sensitivity kernels, which are used for constructing the Jacobian matrix in the Gauss-Newton optimization algorithm. In this study, we have successfully integrated a lossy compression algorithm into our F3DT SI workflow to significantly reduce the disk space for storing the strain tensor fields. The compressor supports a user-specified tolerance for bounding the error, and can be integrated into our finite-difference wave-propagation simulation code used for computing the strain fields. The decompressor can be integrated into the kernel calculation code that reads the strain fields from the disk and compute the data sensitivity kernels. During the wave-propagation simulations, we compress the strain fields before writing them to the disk. To compute the data sensitivity kernels, we read the compressed strain fields from the disk and decompress them before using them in kernel calculations. Experiments using a realistic dataset in our California statewide F3DT project have shown that we can reduce the strain-field disk storage by at least an order of magnitude with acceptable loss, and also improve the overall I/O performance of the entire F3DT-SI workflow significantly. The integration of the lossy online compressor may potentially open up the possibilities of the wide adoption of F3DT-SI in routine seismic tomography practices in the near future.
Reducing disk storage of full-3D seismic waveform tomography (F3DT) through lossy online compression
NASA Astrophysics Data System (ADS)
Lindstrom, Peter; Chen, Po; Lee, En-Jui
2016-08-01
Full-3D seismic waveform tomography (F3DT) is the latest seismic tomography technique that can assimilate broadband, multi-component seismic waveform observations into high-resolution 3D subsurface seismic structure models. The main drawback in the current F3DT implementation, in particular the scattering-integral implementation (F3DT-SI), is the high disk storage cost and the associated I/O overhead of archiving the 4D space-time wavefields of the receiver- or source-side strain tensors. The strain tensor fields are needed for computing the data sensitivity kernels, which are used for constructing the Jacobian matrix in the Gauss-Newton optimization algorithm. In this study, we have successfully integrated a lossy compression algorithm into our F3DT-SI workflow to significantly reduce the disk space for storing the strain tensor fields. The compressor supports a user-specified tolerance for bounding the error, and can be integrated into our finite-difference wave-propagation simulation code used for computing the strain fields. The decompressor can be integrated into the kernel calculation code that reads the strain fields from the disk and compute the data sensitivity kernels. During the wave-propagation simulations, we compress the strain fields before writing them to the disk. To compute the data sensitivity kernels, we read the compressed strain fields from the disk and decompress them before using them in kernel calculations. Experiments using a realistic dataset in our California statewide F3DT project have shown that we can reduce the strain-field disk storage by at least an order of magnitude with acceptable loss, and also improve the overall I/O performance of the entire F3DT-SI workflow significantly. The integration of the lossy online compressor may potentially open up the possibilities of the wide adoption of F3DT-SI in routine seismic tomography practices in the near future.
Reducing Disk Storage of Full-3D Seismic Waveform Tomography (F3DT) Through Lossy Online Compression
Lindstrom, Peter; Chen, Po; Lee, En-Jui
2016-05-05
Full-3D seismic waveform tomography (F3DT) is the latest seismic tomography technique that can assimilate broadband, multi-component seismic waveform observations into high-resolution 3D subsurface seismic structure models. The main drawback in the current F3DT implementation, in particular the scattering-integral implementation (F3DT-SI), is the high disk storage cost and the associated I/O overhead of archiving the 4D space-time wavefields of the receiver- or source-side strain tensors. The strain tensor fields are needed for computing the data sensitivity kernels, which are used for constructing the Jacobian matrix in the Gauss-Newton optimization algorithm. In this study, we have successfully integrated a lossy compression algorithm into our F3DT SI workflow to significantly reduce the disk space for storing the strain tensor fields. The compressor supports a user-specified tolerance for bounding the error, and can be integrated into our finite-difference wave-propagation simulation code used for computing the strain fields. The decompressor can be integrated into the kernel calculation code that reads the strain fields from the disk and compute the data sensitivity kernels. During the wave-propagation simulations, we compress the strain fields before writing them to the disk. To compute the data sensitivity kernels, we read the compressed strain fields from the disk and decompress them before using them in kernel calculations. Experiments using a realistic dataset in our California statewide F3DT project have shown that we can reduce the strain-field disk storage by at least an order of magnitude with acceptable loss, and also improve the overall I/O performance of the entire F3DT-SI workflow significantly. The integration of the lossy online compressor may potentially open up the possibilities of the wide adoption of F3DT-SI in routine seismic tomography practices in the near future.
Depth-image-based rendering (DIBR), compression, and transmission for a new approach on 3D-TV
NASA Astrophysics Data System (ADS)
Fehn, Christoph
2004-05-01
This paper presents details of a system that allows for an evolutionary introduction of depth perception into the existing 2D digital TV framework. The work is part of the European Information Society Technologies (IST) project "Advanced Three-Dimensional Television System Technologies" (ATTEST), an activity, where industries, research centers and universities have joined forces to design a backwards-compatible, flexible and modular broadcast 3D-TV system. At the very heart of the described new concept is the generation and distribution of a novel data representation format, which consists of monoscopic color video and associated per-pixel depth information. From these data, one or more "virtual" views of a real-world scene can be synthesized in real-time at the receiver side (i.e. a 3D-TV set-top box) by means of so-called depth-image-based rendering (DIBR) techniques. This publication will provide: (1) a detailed description of the fundamentals of this new approach on 3D-TV; (2) a comparison with the classical approach of "stereoscopic" video; (3) a short introduction to DIBR techniques in general; (4) the development of a specific DIBR algorithm that can be used for the efficient generation of high-quality "virtual" stereoscopic views; (5) a number of implementation details that are specific to the current state of the development; (6) research on the backwards-compatible compression and transmission of 3D imagery using state-of-the-art MPEG (Moving Pictures Expert Group) tools.
Accelerated high-resolution photoacoustic tomography via compressed sensing
NASA Astrophysics Data System (ADS)
Arridge, Simon; Beard, Paul; Betcke, Marta; Cox, Ben; Huynh, Nam; Lucka, Felix; Ogunlade, Olumide; Zhang, Edward
2016-12-01
Current 3D photoacoustic tomography (PAT) systems offer either high image quality or high frame rates but are not able to deliver high spatial and temporal resolution simultaneously, which limits their ability to image dynamic processes in living tissue (4D PAT). A particular example is the planar Fabry-Pérot (FP) photoacoustic scanner, which yields high-resolution 3D images but takes several minutes to sequentially map the incident photoacoustic field on the 2D sensor plane, point-by-point. However, as the spatio-temporal complexity of many absorbing tissue structures is rather low, the data recorded in such a conventional, regularly sampled fashion is often highly redundant. We demonstrate that combining model-based, variational image reconstruction methods using spatial sparsity constraints with the development of novel PAT acquisition systems capable of sub-sampling the acoustic wave field can dramatically increase the acquisition speed while maintaining a good spatial resolution: first, we describe and model two general spatial sub-sampling schemes. Then, we discuss how to implement them using the FP interferometer and demonstrate the potential of these novel compressed sensing PAT devices through simulated data from a realistic numerical phantom and through measured data from a dynamic experimental phantom as well as from in vivo experiments. Our results show that images with good spatial resolution and contrast can be obtained from highly sub-sampled PAT data if variational image reconstruction techniques that describe the tissues structures with suitable sparsity-constraints are used. In particular, we examine the use of total variation (TV) regularization enhanced by Bregman iterations. These novel reconstruction strategies offer new opportunities to dramatically increase the acquisition speed of photoacoustic scanners that employ point-by-point sequential scanning as well as reducing the channel count of parallelized schemes that use detector arrays.
NASA Astrophysics Data System (ADS)
Jinesh, Mathew; MacPherson, William N.; Hand, Duncan P.; Maier, Robert R. J.
2016-05-01
A smart metal component having the potential for high temperature strain sensing capability is reported. The stainless steel (SS316) structure is made by selective laser melting (SLM). A fiber Bragg grating (FBG) is embedded in to a 3D printed U-groove by high temperature brazing using a silver based alloy, achieving an axial FBG compression of 13 millistrain at room temperature. Initial results shows that the test component can be used for up to 700°C for sensing applications.
Tan, Yu Jun; Tan, Xipeng; Yeong, Wai Yee; Tor, Shu Beng
2016-01-01
A hybrid 3D bioprinting approach using porous microscaffolds and extrusion-based printing method is presented. Bioink constitutes of cell-laden poly(D,L-lactic-co-glycolic acid) (PLGA) porous microspheres with thin encapsulation of agarose-collagen composite hydrogel (AC hydrogel). Highly porous microspheres enable cells to adhere and proliferate before printing. Meanwhile, AC hydrogel allows a smooth delivery of cell-laden microspheres (CLMs), with immediate gelation of construct upon printing on cold build platform. Collagen fibrils were formed in the AC hydrogel during culture at body temperature, improving the cell affinity and spreading compared to pure agarose hydrogel. Cells were proven to proliferate in the bioink and the bioprinted construct. High cell viability up to 14 days was observed. The compressive strength of the bioink is more than 100 times superior to those of pure AC hydrogel. A potential alternative in tissue engineering of tissue replacements and biological models is made possible by combining the advantages of the conventional solid scaffolds with the new 3D bioprinting technology. PMID:27966623
The 3D Navier-Stokes analysis of a Mach 2.68 bifurcated rectangular mixed-compression inlet
NASA Technical Reports Server (NTRS)
Mizukami, M.; Saunders, J. D.
1995-01-01
The supersonic diffuser of a Mach 2.68 bifurcated, rectangular, mixed-compression inlet was analyzed using a three-dimensional (3D) Navier-Stokes flow solver. A two-equation turbulence model, and a porous bleed model based on unchoked bleed hole discharge coefficients were used. Comparisons were made with experimental data, inviscid theory, and two-dimensional Navier-Stokes analyses. The main objective was to gain insight into the inlet fluid dynamics. Examination of the computational results along with the experimental data suggest that the cowl shock-sidewall boundary layer interaction near the leading edge caused a substantial separation in the wind tunnel inlet model. As a result, the inlet performance may have been compromised by increased spillage and higher bleed mass flow requirements. The internal flow contained substantial waves that were not in the original inviscid design. 3D effects were fairly minor for this inlet at on-design conditions. Navier-Stokes analysis appears to be an useful tool for gaining insight into the inlet fluid dynamics. It provides a higher fidelity simulation of the flowfield than the original inviscid design, by taking into account boundary layers, porous bleed, and their interactions with shock waves.
Parkale, Yuvraj V; Nalbalwar, Sanjay L
2016-01-01
Compressed sensing is a novel signal compression technique in which signal is compressed while sensing. The compressed signal is recovered with the only few numbers of observations compared to conventional Shannon-Nyquist sampling, and thus reduces the storage requirements. In this study, we have proposed the 1-D discrete wavelet transform (DWT) based sensing matrices for speech signal compression. The present study investigates the performance analysis of the different DWT based sensing matrices such as: Daubechies, Coiflets, Symlets, Battle, Beylkin and Vaidyanathan wavelet families. First, we have proposed the Daubechies wavelet family based sensing matrices. The experimental result indicates that the db10 wavelet based sensing matrix exhibits the better performance compared to other Daubechies wavelet based sensing matrices. Second, we have proposed the Coiflets wavelet family based sensing matrices. The result shows that the coif5 wavelet based sensing matrix exhibits the best performance. Third, we have proposed the sensing matrices based on Symlets wavelet family. The result indicates that the sym9 wavelet based sensing matrix demonstrates the less reconstruction time and the less relative error, and thus exhibits the good performance compared to other Symlets wavelet based sensing matrices. Next, we have proposed the DWT based sensing matrices using the Battle, Beylkin and the Vaidyanathan wavelet families. The Beylkin wavelet based sensing matrix demonstrates the less reconstruction time and relative error, and thus exhibits the good performance compared to the Battle and the Vaidyanathan wavelet based sensing matrices. Further, an attempt was made to find out the best-proposed DWT based sensing matrix, and the result reveals that sym9 wavelet based sensing matrix shows the better performance among all other proposed matrices. Subsequently, the study demonstrates the performance analysis of the sym9 wavelet based sensing matrix and state-of-the-art random
Li, Bo; Li, Hao; Li, Jun; Zhang, Yuchen; Wang, Xiaoying; Zhang, Jue; Dong, Li; Fang, Jing
2015-09-01
In this study, we sought to investigate the feasibility of a new technique termed relaxation enhanced compressed sensing three-dimensional motion-sensitizing driven equilibrium prepared 3D rapid gradient echo sequence (RECS-3D MERGE). The RECS-3D MERGE sequence consisted of a 3D MERGE sequence for imaging, a period of delay time (TD) for relaxation enhancement, and a pseudo-centric phase encoding order used for under-sampling acquisition to maintain scan efficiency. Seven healthy volunteers and six patients with 40% to 75% carotid artery stenosis were recruited in this study. Healthy subjects underwent 3D MERGE, RECS-3D MERGE and two-dimensional (2D) T1-weighted double inversion recovery fast spin echo (T1W DIR-FSE) scans. The signal ratio (SR) values of 21 RECS-3D MERGE scans were compared in order to determine the optimal scan parameter set of acceleration factor (AF) and delay time (TD) for RECS-3D MERGE sequence. Patients then underwent 3D MERGE, RECS-3D MERGE using the aforementioned optimal scan parameter set and 2D T1W DIR-FSE scans. Two radiologists, blinded to the imaging technique, qualitatively graded each image on a six-point ordinal scale. The highest value of SR occurred with the scan parameter set of 3-fold AF and 800ms TD. Compared to 3D MERGE, RECS-3D MERGE with the parameter set significantly improved the image quality for both healthy subjects and patients experiments, while the scan efficiency was not sacrificed. And no significant differences were observed in the subjective scores of RECS-3D MERGE and 2D T1W DIR-FSE image qualities. RECS-3D MERGE technique achieved significant improvement in black-blood image quality compared with 3D MERGE. And the image quality of this 3D rapid carotid black-blood imaging technique is comparable to 2D T1W DIR-FSE while it has much higher scan efficiency. Copyright © 2015 Elsevier Inc. All rights reserved.
Reducing Disk Storage of Full-3D Seismic Waveform Tomography (F3DT) Through Lossy Online Compression
Lindstrom, Peter; Chen, Po; Lee, En-Jui
2016-05-05
Full-3D seismic waveform tomography (F3DT) is the latest seismic tomography technique that can assimilate broadband, multi-component seismic waveform observations into high-resolution 3D subsurface seismic structure models. The main drawback in the current F3DT implementation, in particular the scattering-integral implementation (F3DT-SI), is the high disk storage cost and the associated I/O overhead of archiving the 4D space-time wavefields of the receiver- or source-side strain tensors. The strain tensor fields are needed for computing the data sensitivity kernels, which are used for constructing the Jacobian matrix in the Gauss-Newton optimization algorithm. In this study, we have successfully integrated a lossy compression algorithmmore » into our F3DT SI workflow to significantly reduce the disk space for storing the strain tensor fields. The compressor supports a user-specified tolerance for bounding the error, and can be integrated into our finite-difference wave-propagation simulation code used for computing the strain fields. The decompressor can be integrated into the kernel calculation code that reads the strain fields from the disk and compute the data sensitivity kernels. During the wave-propagation simulations, we compress the strain fields before writing them to the disk. To compute the data sensitivity kernels, we read the compressed strain fields from the disk and decompress them before using them in kernel calculations. Experiments using a realistic dataset in our California statewide F3DT project have shown that we can reduce the strain-field disk storage by at least an order of magnitude with acceptable loss, and also improve the overall I/O performance of the entire F3DT-SI workflow significantly. The integration of the lossy online compressor may potentially open up the possibilities of the wide adoption of F3DT-SI in routine seismic tomography practices in the near future.« less
Accelerated whole-brain multi-parameter mapping using blind compressed sensing.
Bhave, Sampada; Lingala, Sajan Goud; Johnson, Casey P; Magnotta, Vincent A; Jacob, Mathews
2016-03-01
To introduce a blind compressed sensing (BCS) framework to accelerate multi-parameter MR mapping, and demonstrate its feasibility in high-resolution, whole-brain T1ρ and T2 mapping. BCS models the evolution of magnetization at every pixel as a sparse linear combination of bases in a dictionary. Unlike compressed sensing, the dictionary and the sparse coefficients are jointly estimated from undersampled data. Large number of non-orthogonal bases in BCS accounts for more complex signals than low rank representations. The low degree of freedom of BCS, attributed to sparse coefficients, translates to fewer artifacts at high acceleration factors (R). From 2D retrospective undersampling experiments, the mean square errors in T1ρ and T2 maps were observed to be within 0.1% up to R = 10. BCS was observed to be more robust to patient-specific motion as compared to other compressed sensing schemes and resulted in minimal degradation of parameter maps in the presence of motion. Our results suggested that BCS can provide an acceleration factor of 8 in prospective 3D imaging with reasonable reconstructions. BCS considerably reduces scan time for multiparameter mapping of the whole brain with minimal artifacts, and is more robust to motion-induced signal changes compared to current compressed sensing and principal component analysis-based techniques. © 2015 Wiley Periodicals, Inc.
Characterization of 3D printing output using an optical sensing system
NASA Astrophysics Data System (ADS)
Straub, Jeremy
2015-05-01
This paper presents the experimental design and initial testing of a system to characterize the progress and performance of a 3D printer. The system is based on five Raspberry Pi single-board computers. It collects images of the 3D printed object, which are compared to an ideal model. The system, while suitable for printers of all sizes, can potentially be produced at a sufficiently low cost to allow its incorporation into consumer-grade printers. The efficacy and accuracy of this system is presented and discussed. The paper concludes with a discussion of the benefits of being able to characterize 3D printer performance.
Study of 3D remote sensing system based on optical scanning holography
NASA Astrophysics Data System (ADS)
Zhao, Shihu; Yan, Lei
2009-06-01
High-precision and real-time remote sensing imaging system is an important part of remote sensing development. Holography is a method of wave front record and recovery which was presented by Dennis Gabor. As a new kind of holography techniques, Optical scanning holography (OSH) and remote sensing imaging are intended to be combined together and applied in acquisition and interference measurement of remote sensing. The key principles and applicability of OSH are studied and the mathematic relation between Fresnel Zone Plate number, numerical aperture and object distance was deduced, which are proved to be feasible for OSH to apply in large scale remote sensing. At last, a new three-dimensional reflected OSH remote sensing imaging system is designed with the combination of scanning technique to record hologram patterns of large scale remote sensing scenes. This scheme is helpful for expanding OSH technique to remote sensing in future.
On the SNR Variability in Noisy Compressed Sensing
NASA Astrophysics Data System (ADS)
Lavrenko, Anastasia; Romer, Florian; Galdo, Giovanni Del; Thoma, Reiner
2017-08-01
Compressed sensing (CS) is a sampling paradigm that allows to simultaneously measure and compress signals that are sparse or compressible in some domain. The choice of a sensing matrix that carries out the measurement has a defining impact on the system performance and it is often advocated to draw its elements randomly. It has been noted that in the presence of input (signal) noise, the application of the sensing matrix causes SNR degradation due to the noise folding effect. In fact, it might also result in the variations of the output SNR in compressive measurements over the support of the input signal, potentially resulting in unexpected non-uniform system performance. In this work, we study the impact of a distribution from which the elements of a sensing matrix are drawn on the spread of the output SNR. We derive analytic expressions for several common types of sensing matrices and show that the SNR spread grows with the decrease of the number of measurements. This makes its negative effect especially pronounced for high compression rates that are often of interest in CS.
Interactive video compression for remote sensing
NASA Astrophysics Data System (ADS)
Maleh, Ray; Boyle, Frank A.; Deignan, Paul B.; Yancey, Jerry W.
2011-05-01
Modern day remote video cameras enjoy the ability of producing quality video streams at extremely high resolutions. Unfortunately, the benefit of such technology cannot be realized when the channel between the sensor and the operator restricts the bit-rate of incoming data. In order to cram more information into the available bandwidth, video technologies typically employ compression schemes (e.g. H.264/MPEG 4 standard) which exploit spatial and temporal redundancies. We present an alternative method utilizing region of interest (ROI) based compression. Each region in the incoming scene is assigned a score measuring importance to the operator. Scores may be determined based on the manual selection of one or more objects which are then automatically tracked by the system; or alternatively, listeners may be pre-assigned to various areas that trigger high scores upon the occurrence of customizable events. A multi-resolution wavelet expansion is then used to optimally transmit important regions at higher resolutions and frame rates than less interesting peripheral background objects subject to bandwidth constraints. We show that our methodology makes it possible to obtain high compression ratios while ensuring no loss in overall situational awareness. If combined with modules from traditional video codecs, compression ratios of 100:1 to 1000:1, depending on ROI size, can easily be achieved.
Less is more: how compressed sensing is transforming metrology in chemistry.
Holland, Daniel J; Gladden, Lynn F
2014-12-01
Mathematics has had a profound impact on science, providing a means to understand the world around us in unprecedented ways. With the advent of the digital age, the subject of information theory has grown hugely in importance. In particular, over the last two decades significant advances in our understanding of sampling and function reconstruction have culminated in the development of an idea known as compressed sensing. What seems like an abstract idea is now having a profound impact throughout the scientific world-from enabling high-resolution imaging of pediatric patients in clinical medicine through to advancing 3D electron tomography images of nanoparticle catalysts and NMR spectroscopy studies of proteins. In this Minireview, we summarize these applications and provide an outlook on how the principles of compressed sensing are leading to entirely new approaches to measurement throughout the physical and life sciences. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
NASA Astrophysics Data System (ADS)
Roberts, M. A.; Graymer, R. W.; McPhee, D.
2015-12-01
During the late Miocene, a small change in the relative motion of the Pacific plate resulted in compressive as well as translational deformation along the central San Andreas Fault (SAF), creating thrust faults and folds throughout this region of California. We constructed a 3D model of an upper crustal volume between Pinnacles National Park and Gold Hill by assembling geologic map data and cross sections, geophysical data, and petroleum well logs in MoveTm, software which has the ability to forward and reverse model movement along faults and folds. For this study, we chose a blind thrust fault west of the SAF near Parkfield to compare deformation produced by MoveTm's forward modeling algorithm with that observed. We chose various synclines east of the SAF to explore the software's ability to unfold (reverse model) units. For the initial round of modeling, strike-slip movement has been omitted as the fault algorithm was designed primarily for extensional or compressional environments. Preliminary forward modeling of originally undeformed strata along the blind thrust produced geometries similar to those in the present-day 3D geologic model. The modeled amount of folding produced in hanging wall strata was less severe, suggesting these units were slightly folded before displacement. Based on these results, the algorithm shows potential in predicting deformation related to blind thrusts. Contraction in the region varies with fold axis location and orientation. MoveTm's unfolding algorithm can allow researchers to measure the amount of contraction a fold represents, and compare that amount across the modeled area as a way of observing regional stress patterns. The unfolding algorithm also allows for passive deformation of strata unconformably underlying the fold; one example reveals a steeper orientation of Cretaceous units prior to late Miocene deformation. Such modeling capabilities can allow for a better understanding of the structural history of the region.
NeuroGlasses: a neural sensing healthcare system for 3-D vision technology.
Gong, Fang; Xu, Wenyao; Lee, Jueh-Yu; He, Lei; Sarrafzadeh, Majid
2012-03-01
3-D vision technologies are extensively used in a wide variety of applications. Particularly glasses-based 3-D technology facilities are increasingly becoming affordable to our daily lives. Considering health issues raised by 3-D video technologies, to the best of our knowledge, most of the pilot studies are practiced in a highly-controlled laboratory environment only. In this paper, we present NeuroGlasses, a nonintrusive wearable physiological signal monitoring system to facilitate health analysis and diagnosis of 3-D video watchers. The NeuroGlasses system acquires health-related signals by physiological sensors and provides feedbacks of health-related features. Moreover, the NeuroGlasses system employs signal-specific reconstruction and feature extraction to compensate the distortion of signals caused by variation of the placement of the sensors. We also propose a server-based NeuroGlasses infrastructure where physiological features can be extracted for real-time response or collected on the server side for long term analysis and diagnosis. Through an on-campus pilot study, the experimental results show that NeuroGlasses system can effectively provide physiological information for healthcare purpose. Furthermore, it approves that 3-D vision technology has a significant impact on the physiological signals, such as EEG, which potentially leads to neural diseases. © 2012 IEEE
Compressed Sensing in On-Grid MIMO Radar
Minner, Michael F.
2015-01-01
The accurate detection of targets is a significant problem in multiple-input multiple-output (MIMO) radar. Recent advances of Compressive Sensing offer a means of efficiently accomplishing this task. The sparsity constraints needed to apply the techniques of Compressive Sensing to problems in radar systems have led to discretizations of the target scene in various domains, such as azimuth, time delay, and Doppler. Building upon recent work, we investigate the feasibility of on-grid Compressive Sensing-based MIMO radar via a threefold azimuth-delay-Doppler discretization for target detection and parameter estimation. We utilize a colocated random sensor array and transmit distinct linear chirps to a small scene with few, slowly moving targets. Relying upon standard far-field and narrowband assumptions, we analyze the efficacy of various recovery algorithms in determining the parameters of the scene through numerical simulations, with particular focus on the ℓ 1-squared Nonnegative Regularization method. PMID:27280124
Compressed Sensing in On-Grid MIMO Radar.
Minner, Michael F
2015-01-01
The accurate detection of targets is a significant problem in multiple-input multiple-output (MIMO) radar. Recent advances of Compressive Sensing offer a means of efficiently accomplishing this task. The sparsity constraints needed to apply the techniques of Compressive Sensing to problems in radar systems have led to discretizations of the target scene in various domains, such as azimuth, time delay, and Doppler. Building upon recent work, we investigate the feasibility of on-grid Compressive Sensing-based MIMO radar via a threefold azimuth-delay-Doppler discretization for target detection and parameter estimation. We utilize a colocated random sensor array and transmit distinct linear chirps to a small scene with few, slowly moving targets. Relying upon standard far-field and narrowband assumptions, we analyze the efficacy of various recovery algorithms in determining the parameters of the scene through numerical simulations, with particular focus on the ℓ 1-squared Nonnegative Regularization method.
Implementation of Compressed Sensing in Telecardiology Sensor Networks
Correia Pinheiro, Eduardo; Postolache, Octavian Adrian; Silva Girão, Pedro
2010-01-01
Mobile solutions for patient cardiac monitoring are viewed with growing interest, and improvements on current implementations are frequently reported, with wireless, and in particular, wearable devices promising to achieve ubiquity. However, due to unavoidable power consumption limitations, the amount of data acquired, processed, and transmitted needs to be diminished, which is counterproductive, regarding the quality of the information produced. Compressed sensing implementation in wireless sensor networks (WSNs) promises to bring gains not only in power savings to the devices, but also with minor impact in signal quality. Several cardiac signals have a sparse representation in some wavelet transformations. The compressed sensing paradigm states that signals can be recovered from a few projections into another basis, incoherent with the first. This paper evaluates the compressed sensing paradigm impact in a cardiac monitoring WSN, discussing the implications in data reliability, energy management, and the improvements accomplished by in-network processing. PMID:20885973
2D imaging and 3D sensing data acquisition and mutual registration for painting conservation
NASA Astrophysics Data System (ADS)
Fontana, Raffaella; Gambino, Maria Chiara; Greco, Marinella; Marras, Luciano; Pampaloni, Enrico M.; Pelagotti, Anna; Pezzati, Luca; Poggi, Pasquale
2004-12-01
We describe the application of 2D and 3D data acquisition and mutual registration to the conservation of paintings. RGB color image acquisition, IR and UV fluorescence imaging, together with the more recent hyperspectral imaging (32 bands) are among the most useful techniques in this field. They generally are meant to provide information on the painting materials, on the employed techniques and on the object state of conservation. However, only when the various images are perfectly registered on each other and on the 3D model, no ambiguity is possible and safe conclusions may be drawn. We present the integration of 2D and 3D measurements carried out on two different paintings: "Madonna of the Yarnwinder" by Leonardo da Vinci, and "Portrait of Lionello d'Este", by Pisanello, both painted in the XV century.
2D imaging and 3D sensing data acquisition and mutual registration for painting conservation
NASA Astrophysics Data System (ADS)
Fontana, Raffaella; Gambino, Maria Chiara; Greco, Marinella; Marras, Luciano; Pampaloni, Enrico M.; Pelagotti, Anna; Pezzati, Luca; Poggi, Pasquale
2005-01-01
We describe the application of 2D and 3D data acquisition and mutual registration to the conservation of paintings. RGB color image acquisition, IR and UV fluorescence imaging, together with the more recent hyperspectral imaging (32 bands) are among the most useful techniques in this field. They generally are meant to provide information on the painting materials, on the employed techniques and on the object state of conservation. However, only when the various images are perfectly registered on each other and on the 3D model, no ambiguity is possible and safe conclusions may be drawn. We present the integration of 2D and 3D measurements carried out on two different paintings: "Madonna of the Yarnwinder" by Leonardo da Vinci, and "Portrait of Lionello d'Este", by Pisanello, both painted in the XV century.
Imaging industry expectations for compressed sensing in MRI
NASA Astrophysics Data System (ADS)
King, Kevin F.; Kanwischer, Adriana; Peters, Rob
2015-09-01
Compressed sensing requires compressible data, incoherent acquisition and a nonlinear reconstruction algorithm to force creation of a compressible image consistent with the acquired data. MRI images are compressible using various transforms (commonly total variation or wavelets). Incoherent acquisition of MRI data by appropriate selection of pseudo-random or non-Cartesian locations in k-space is straightforward. Increasingly, commercial scanners are sold with enough computing power to enable iterative reconstruction in reasonable times. Therefore integration of compressed sensing into commercial MRI products and clinical practice is beginning. MRI frequently requires the tradeoff of spatial resolution, temporal resolution and volume of spatial coverage to obtain reasonable scan times. Compressed sensing improves scan efficiency and reduces the need for this tradeoff. Benefits to the user will include shorter scans, greater patient comfort, better image quality, more contrast types per patient slot, the enabling of previously impractical applications, and higher throughput. Challenges to vendors include deciding which applications to prioritize, guaranteeing diagnostic image quality, maintaining acceptable usability and workflow, and acquisition and reconstruction algorithm details. Application choice depends on which customer needs the vendor wants to address. The changing healthcare environment is putting cost and productivity pressure on healthcare providers. The improved scan efficiency of compressed sensing can help alleviate some of this pressure. Image quality is strongly influenced by image compressibility and acceleration factor, which must be appropriately limited. Usability and workflow concerns include reconstruction time and user interface friendliness and response. Reconstruction times are limited to about one minute for acceptable workflow. The user interface should be designed to optimize workflow and minimize additional customer training. Algorithm
NASA Astrophysics Data System (ADS)
Tu, Jihui; Sui, Haigang; Feng, Wenqing; Song, Zhina
2016-06-01
In this paper, a novel approach of building damaged detection is proposed using high resolution remote sensing images and 3D GIS-Model data. Traditional building damage detection method considers to detect damaged building due to earthquake, but little attention has been paid to analyze various building damaged types(e.g., trivial damaged, severely damaged and totally collapsed.) Therefore, we want to detect the different building damaged type using 2D and 3D feature of scenes because the real world we live in is a 3D space. The proposed method generalizes that the image geometric correction method firstly corrects the post-disasters remote sensing image using the 3D GIS model or RPC parameters, then detects the different building damaged types using the change of the height and area between the pre- and post-disasters and the texture feature of post-disasters. The results, evaluated on a selected study site of the Beichuan earthquake ruins, Sichuan, show that this method is feasible and effective in building damage detection. It has also shown that the proposed method is easily applicable and well suited for rapid damage assessment after natural disasters.
NASA Astrophysics Data System (ADS)
Wason, H.; Herrmann, F. J.; Kumar, R.
2016-12-01
Current efforts towards dense shot (or receiver) sampling and full azimuthal coverage to produce high resolution images have led to the deployment of multiple source vessels (or streamers) across marine survey areas. Densely sampled marine seismic data acquisition, however, is expensive, and hence necessitates the adoption of sampling schemes that save acquisition costs and time. Compressed sensing is a sampling paradigm that aims to reconstruct a signal--that is sparse or compressible in some transform domain--from relatively fewer measurements than required by the Nyquist sampling criteria. Leveraging ideas from the field of compressed sensing, we show how marine seismic acquisition can be setup as a compressed sensing problem. A step ahead from multi-source seismic acquisition is simultaneous source acquisition--an emerging technology that is stimulating both geophysical research and commercial efforts--where multiple source arrays/vessels fire shots simultaneously resulting in better coverage in marine surveys. Following the design principles of compressed sensing, we propose a pragmatic simultaneous time-jittered time-compressed marine acquisition scheme where single or multiple source vessels sail across an ocean-bottom array firing airguns at jittered times and source locations, resulting in better spatial sampling and speedup acquisition. Our acquisition is low cost since our measurements are subsampled. Simultaneous source acquisition generates data with overlapping shot records, which need to be separated for further processing. We can significantly impact the reconstruction quality of conventional seismic data from jittered data and demonstrate successful recovery by sparsity promotion. In contrast to random (sub)sampling, acquisition via jittered (sub)sampling helps in controlling the maximum gap size, which is a practical requirement of wavefield reconstruction with localized sparsifying transforms. We illustrate our results with simulations of
Robust compressive sensing of sparse signals: a review
NASA Astrophysics Data System (ADS)
Carrillo, Rafael E.; Ramirez, Ana B.; Arce, Gonzalo R.; Barner, Kenneth E.; Sadler, Brian M.
2016-12-01
Compressive sensing generally relies on the ℓ 2 norm for data fidelity, whereas in many applications, robust estimators are needed. Among the scenarios in which robust performance is required, applications where the sampling process is performed in the presence of impulsive noise, i.e., measurements are corrupted by outliers, are of particular importance. This article overviews robust nonlinear reconstruction strategies for sparse signals based on replacing the commonly used ℓ 2 norm by M-estimators as data fidelity functions. The derived methods outperform existing compressed sensing techniques in impulsive environments, while achieving good performance in light-tailed environments, thus offering a robust framework for CS.
Optical frequency comb interference profilometry using compressive sensing.
Pham, Quang Duc; Hayasaki, Yoshio
2013-08-12
We describe a new optical system using an ultra-stable mode-locked frequency comb femtosecond laser and compressive sensing to measure an object's surface profile. The ultra-stable frequency comb laser was used to precisely measure an object with a large depth, over a wide dynamic range. The compressive sensing technique was able to obtain the spatial information of the object with two single-pixel fast photo-receivers, with no mechanical scanning and fewer measurements than the number of sampling points. An optical experiment was performed to verify the advantages of the proposed method.
Diffuse optical tomography based on time-resolved compressive sensing
NASA Astrophysics Data System (ADS)
Farina, A.; Betcke, M.; Di Sieno, L.; Bassi, A.; Ducros, N.; Pifferi, A.; Valentini, G.; Arridge, S.; D'Andrea, C.
2017-02-01
Diffuse Optical Tomography (DOT) can be described as a highly multidimensional problem generating a huge data set with long acquisition/computational times. Biological tissue behaves as a low pass filter in the spatial frequency domain, hence compressive sensing approaches, based on both patterned illumination and detection, are useful to reduce the data set while preserving the information content. In this work, a multiple-view time-domain compressed sensing DOT system is presented and experimentally validated on non-planar tissue-mimicking phantoms containing absorbing inclusions.
Compact and robust hyperspectral camera based on compressed sensing
NASA Astrophysics Data System (ADS)
Žídek, K.; Denk, O.; Hlubuček, J.; Václavík, J.
2016-11-01
Spectrum of light which is emitted or reflected by an object carries immense amount of information about the object. A simple piece of evidence is the importance of color sensing for human vision. Combining an image acquisition with efficient measurement of light spectra for each detected pixel is therefore one of the important issues in imaging, referred as hyperspectral imaging. We demonstrate a construction of a compact and robust hyperspectral camera for the visible and near-IR spectral region. The camera was designed vastly based on off-shelf optics, yet an extensive optimization and addition of three customized parts enabled construction of the camera featuring a low f-number (F/3.9) and fully concentric optics. We employ a novel approach of compressed sensing (namely coded aperture snapshot spectral imaging, abbrev. CASSI). The compressed sensing enables to computationally extract an encoded hyperspectral information from a single camera exposition. Owing to the technique the camera lacks any moving or scanning part, while it can record the full image and spectral information in a single snapshot. Moreover, unlike the commonly used compressed sensing table-top apparatuses, the camera represents a portable device able to work outside a lab. We demonstrate the spectro-temporal reconstruction of recorded scenes based on 90×90 random matrix encoding. Finally, we discuss potential of the compressed sensing in hyperspectral camera.
Sparsifying transformations of photoacoustic signals enabling compressed sensing algorithms
NASA Astrophysics Data System (ADS)
Burgholzer, P.; Sandbichler, M.; Krahmer, F.; Berer, T.; Haltmeier, M.
2016-03-01
Compressed sensing allows performing much fewer measurements than advised by the Shannon sampling theory. This is surprising because it requires the solution of a system of equations with much fewer equations than unknowns. This is possible if one can assume sparsity of the solution, which means that only a few components of the solution are significantly different from zero. An important ingredient for compressed sensing is the restricted isometry property (RIP) of the sensing matrix, which is satisfied for certain types of random measurement ensembles. Then a sparse solution can be found by minimizing the ℓ1-norm. Using standard approaches, photoacoustic imaging generally neither satisfies sparsity of the data nor the RIP. Therefore, no theoretical recovery guarantees could be given. Despite ℓ1- minimization has been used for photoacoustic image reconstruction, only marginal improvements in comparison to classical photoacoustic reconstruction have been observed. We propose the application of a sparsifying temporal transformation to the detected pressure signals, which allows obtaining theoretical recovery guarantees for our compressed sensing scheme. Such a sparsifying transform can be found because spatial and temporal evolution of the pressure wave are not independent, but connected by the wave equation. We give an example of a sparsifying transform and apply our compressed sensing scheme to reconstruct images from simulated data.
NASA Astrophysics Data System (ADS)
Yu, Hengyong; Wang, Ge
2009-07-01
The authors would like to add some missing references. On page 2799, lines 15 and 16 from the bottom should read 'Specifically, the algorithm can be summarized in the following pseudo-code (Candes and Romberg 2005, Candes et al 2006, Sidky et al 2006, Chen et al 2008, Sidky and Pan 2008)'. References Candes E J and Romberg J 2005 Signal recovery from random projections Computational Imaging III; Proc. SPIE 5764 76-86 Candes E J, Romberg J and Tao T 2006 Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information IEEE Trans. Inf. Theory 52 489-509 Chen G H, Tang J and Leng S 2008 Prior image constrained compressed sensing (PICCS): a method to accurately reconstruct dynamic CT images from highly undersampled projection data sets Med. Phys. 35 660-3 Sidky E Y, Kao C M and Pan X C 2006 Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT J. X-ray Sci. Technol. 14 119-39 Sidky E Y and Pan X C 2008 Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization Phys. Med. Biol. 53 4777-807
NASA Astrophysics Data System (ADS)
Altschuler, Bruce R.; Oliver, William R.; Altschuler, Martin D.
1996-02-01
We describe a system for rapid and convenient video data acquisition and 3-D numerical coordinate data calculation able to provide precise 3-D topographical maps and 3-D archival data sufficient to reconstruct a 3-D virtual reality display of a crime scene or mass disaster area. Under a joint U.S. army/U.S. Air Force project with collateral U.S. Navy support, to create a 3-D surgical robotic inspection device -- a mobile, multi-sensor robotic surgical assistant to aid the surgeon in diagnosis, continual surveillance of patient condition, and robotic surgical telemedicine of combat casualties -- the technology is being perfected for remote, non-destructive, quantitative 3-D mapping of objects of varied sizes. This technology is being advanced with hyper-speed parallel video technology and compact, very fast laser electro-optics, such that the acquisition of 3-D surface map data will shortly be acquired within the time frame of conventional 2-D video. With simple field-capable calibration, and mobile or portable platforms, the crime scene investigator could set up and survey the entire crime scene, or portions of it at high resolution, with almost the simplicity and speed of video or still photography. The survey apparatus would record relative position, location, and instantly archive thousands of artifacts at the site with 3-D data points capable of creating unbiased virtual reality reconstructions, or actual physical replicas, for the investigators, prosecutors, and jury.
Polymer optical fibers integrated directly into 3D orthogonal woven composites for sensing
NASA Astrophysics Data System (ADS)
Hamouda, Tamer; Seyam, Abdel-Fattah M.; Peters, Kara
2015-02-01
This study demonstrates that standard polymer optical fibers (POF) can be directly integrated into composites from 3D orthogonal woven preforms during the weaving process and then serve as in-situ sensors to detect damage due to bending or impact loads. Different composite samples with embedded POF were fabricated of 3D orthogonal woven composites with different parameters namely number of y-/x-layers and x-yarn density. The signal of POF was not affected significantly by the preform structure. During application of resin using VARTM technique, significant drop in backscattering level was observed due to pressure caused by vacuum on the embedded POF. Measurements of POF signal while in the final composites after resin cure indicated that the backscattering level almost returned to the original level of un-embedded POF. The POF responded to application of bending and impact loads to the composite with a reduction in the backscattering level. The backscattering level almost returned back to its original level after removing the bending load until damage was present in the composite. Similar behavior occurred due to impact events. As the POF itself is used as the sensor and can be integrated throughout the composite, large sections of future 3D woven composite structures could be monitored without the need for specialized sensors or complex instrumentation.
Autonomous Real-Time Interventional Scan Plane Control With a 3-D Shape-Sensing Needle
Plata, Juan Camilo; Holbrook, Andrew B.; Park, Yong-Lae; Pauly, Kim Butts; Daniel, Bruce L.; Cutkosky, Mark R.
2016-01-01
This study demonstrates real-time scan plane control dependent on three-dimensional needle bending, as measured from magnetic resonance imaging (MRI)-compatible optical strain sensors. A biopsy needle with embedded fiber Bragg grating (FBG) sensors to measure surface strains is used to estimate its full 3-D shape and control the imaging plane of an MR scanner in real-time, based on the needle’s estimated profile. The needle and scanner coordinate frames are registered to each other via miniature radio-frequency (RF) tracking coils, and the scan planes autonomously track the needle as it is deflected, keeping its tip in view. A 3-D needle annotation is superimposed over MR-images presented in a 3-D environment with the scanner’s frame of reference. Scan planes calculated based on the FBG sensors successfully follow the tip of the needle. Experiments using the FBG sensors and RF coils to track the needle shape and location in real-time had an average root mean square error of 4.2 mm when comparing the estimated shape to the needle profile as seen in high resolution MR images. This positional variance is less than the image artifact caused by the needle in high resolution SPGR (spoiled gradient recalled) images. Optical fiber strain sensors can estimate a needle’s profile in real-time and be used for MRI scan plane control to potentially enable faster and more accurate physician response. PMID:24968093
Autonomous real-time interventional scan plane control with a 3-D shape-sensing needle.
Elayaperumal, Santhi; Plata, Juan Camilo; Holbrook, Andrew B; Park, Yong-Lae; Pauly, Kim Butts; Daniel, Bruce L; Cutkosky, Mark R
2014-11-01
This study demonstrates real-time scan plane control dependent on three-dimensional needle bending, as measured from magnetic resonance imaging (MRI)-compatible optical strain sensors. A biopsy needle with embedded fiber Bragg grating (FBG) sensors to measure surface strains is used to estimate its full 3-D shape and control the imaging plane of an MR scanner in real-time, based on the needle's estimated profile. The needle and scanner coordinate frames are registered to each other via miniature radio-frequency (RF) tracking coils, and the scan planes autonomously track the needle as it is deflected, keeping its tip in view. A 3-D needle annotation is superimposed over MR-images presented in a 3-D environment with the scanner's frame of reference. Scan planes calculated based on the FBG sensors successfully follow the tip of the needle. Experiments using the FBG sensors and RF coils to track the needle shape and location in real-time had an average root mean square error of 4.2 mm when comparing the estimated shape to the needle profile as seen in high resolution MR images. This positional variance is less than the image artifact caused by the needle in high resolution SPGR (spoiled gradient recalled) images. Optical fiber strain sensors can estimate a needle's profile in real-time and be used for MRI scan plane control to potentially enable faster and more accurate physician response.
3D Analysis of Remote-Sensed Heliospheric Data for Space Weather Forecasting
NASA Astrophysics Data System (ADS)
Yu, H. S.; Jackson, B. V.; Hick, P. P.; Buffington, A.; Bisi, M. M.; Odstrcil, D.; Hong, S.; Kim, J.; Yi, J.; Tokumaru, M.; Gonzalez-Esparza, A.
2015-12-01
The University of California, San Diego (UCSD) time-dependent iterative kinematic reconstruction technique has been used and expanded upon for over two decades. It currently provides some of the most accurate predictions and three-dimensional (3D) analyses of heliospheric solar-wind parameters now available using interplanetary scintillation (IPS) data. The parameters provided include reconstructions of velocity, density, and magnetic fields. Precise time-dependent results are obtained at any solar distance in the inner heliosphere using current Solar-Terrestrial Environment Laboratory (STELab), Nagoya University, Japan IPS data sets, but the reconstruction technique can also incorporate data from other IPS systems from around the world. With access using world IPS data systems, not only can predictions using the reconstruction technique be made without observation dead times due to poor longitude coverage or system outages, but the program can itself be used to standardize observations of IPS. Additionally, these analyses are now being exploited as inner-boundary values to drive an ENLIL 3D-MHD heliospheric model in real time. A major potential of this is that it will use the more realistic physics of 3D-MHD modeling to provide an automatic forecast of CMEs and corotating structures up to several days in advance of the event/features arriving at Earth, with or without involving coronagraph imagery or the necessity of magnetic fields being used to provide the background solar wind speeds.
NASA Astrophysics Data System (ADS)
Iftekhar, Ahmed Tashfin; Ho, Jenny Che-Ting; Mellinger, Axel; Kaya, Tolga
2017-03-01
Sweat-based physiological monitoring has been intensively explored in the last decade with the hopes of developing real-time hydration monitoring devices. Although the content of sweat (electrolytes, lactate, urea, etc.) provides significant information about the physiology, it is also very important to know the rate of sweat at the time of sweat content measurements because the sweat rate is known to alter the concentrations of sweat compounds. We developed a calorimetric based flow rate sensor using PolydimethylSiloxane that is suitable for sweat rate applications. Our simple approach on using temperature-based flow rate detection can easily be adapted to multiple sweat collection and analysis devices. Moreover, we have developed a 3D finite element analysis model of the device using COMSOL Multiphysics™ and verified the flow rate measurements. The experiment investigated flow rate values from 0.3 μl/min up to 2.1 ml/min, which covers the human sweat rate range (0.5 μl/min-10 μl/min). The 3D model simulations and analytical model calculations covered an even wider range in order to understand the main physical mechanisms of the device. With a verified 3D model, different environmental heat conditions could be further studied to shed light on the physiology of the sweat rate.
Li, Bo; Dong, Li; Chen, Bin; Ji, Shuangxi; Cai, Wenchao; Wang, Ye; Zhang, Jue; Zhang, Zhaoqi; Wang, Xiaoying; Fang, Jing
2013-11-01
In this study, we sought to investigate the feasibility of turbo fast three-dimensional (3D) black-blood imaging by combining a 3D motion-sensitizing driven equilibrium rapid gradient echo sequence with compressed sensing. A pseudo-centric phase encoding order was developed for compressed sensing-3D motion-sensitizing driven equilibrium rapid gradient echo to suppress flow signal in undersampled 3D k-space. Nine healthy volunteers were recruited for this study. Signal-to-tissue ratio, contrast-to-tissue ratio (CTR) and CTR efficiency (CTReff ) between fully sampled and undersampled images were calculated and compared in seven subjects. Moreover, isotropic high resolution images using different compressed sensing acceleration factors were evaluated in two other subjects. Wall-lumen signal-to-tissue ratio or CTR were comparable between the undersampled and the fully sampled images, while significant improvement of CTReff was achieved in the undersampled images. At an isotropic high spatial resolution of 0.7 × 0.7 × 0.7 mm(3) , all undersampled images exhibited similar level of the flow suppression efficiency and the capability of delineating outer vessel wall boundary and lumen-wall interface, when compared with the fully sampled images. The proposed turbo fast compressed sensing 3D black-blood imaging technique improves scan efficiency without sacrificing flow suppression efficiency and vessel wall image quality. It could be a valuable tool for rapid 3D vessel wall imaging. Copyright © 2012 Wiley Periodicals, Inc.
Compressive sensing scalp EEG signals: implementations and practical performance.
Abdulghani, Amir M; Casson, Alexander J; Rodriguez-Villegas, Esther
2012-11-01
Highly miniaturised, wearable computing and communication systems allow unobtrusive, convenient and long term monitoring of a range of physiological parameters. For long term operation from the physically smallest batteries, the average power consumption of a wearable device must be very low. It is well known that the overall power consumption of these devices can be reduced by the inclusion of low power consumption, real-time compression of the raw physiological data in the wearable device itself. Compressive sensing is a new paradigm for providing data compression: it has shown significant promise in fields such as MRI; and is potentially suitable for use in wearable computing systems as the compression process required in the wearable device has a low computational complexity. However, the practical performance very much depends on the characteristics of the signal being sensed. As such the utility of the technique cannot be extrapolated from one application to another. Long term electroencephalography (EEG) is a fundamental tool for the investigation of neurological disorders and is increasingly used in many non-medical applications, such as brain-computer interfaces. This article investigates in detail the practical performance of different implementations of the compressive sensing theory when applied to scalp EEG signals.
Accelerated MR diffusion tensor imaging using distributed compressed sensing.
Wu, Yin; Zhu, Yan-Jie; Tang, Qiu-Yang; Zou, Chao; Liu, Wei; Dai, Rui-Bin; Liu, Xin; Wu, Ed X; Ying, Leslie; Liang, Dong
2014-02-01
Diffusion tensor imaging (DTI) is known to suffer from long acquisition time in the orders of several minutes or even hours. Therefore, a feasible way to accelerate DTI data acquisition is highly desirable. In this article, the feasibility and efficacy of distributed compressed sensing to fast DTI is investigated by exploiting the joint sparsity prior in diffusion-weighted images. Fully sampled DTI datasets were obtained from both simulated phantom and experimental heart sample, with diffusion gradient applied in six directions. The k-space data were undersampled retrospectively with acceleration factors from 2 to 6. Diffusion-weighted images were reconstructed by solving an l2-l1 norm minimization problem. Reconstruction performance with varied signal-to-noise ratio and acceleration factors were evaluated by root-mean-square error and maps of reconstructed DTI indices. Superiority of distributed compressed sensing over basic compressed sensing was confirmed with simulation, and the reconstruction accuracy was influenced by signal-to-noise ratio and acceleration factors. Experimental results demonstrate that DTI indices including fractional anisotropy, mean diffusivities, and orientation of primary eigenvector can be obtained with high accuracy at acceleration factors up to 4. Distributed compressed sensing is shown to be able to accelerate DTI and may be used to reduce DTI acquisition time practically. Copyright © 2013 Wiley Periodicals, Inc.
Low power real-time data acquisition using compressive sensing
NASA Astrophysics Data System (ADS)
Powers, Linda S.; Zhang, Yiming; Chen, Kemeng; Pan, Huiqing; Wu, Wo-Tak; Hall, Peter W.; Fairbanks, Jerrie V.; Nasibulin, Radik; Roveda, Janet M.
2017-05-01
New possibilities exist for the development of novel hardware/software platforms havin g fast data acquisition capability with low power requirements. One application is a high speed Adaptive Design for Information (ADI) system that combines the advantages of feature-based data compression, low power nanometer CMOS technology, and stream computing [1]. We have developed a compressive sensing (CS) algorithm which linearly reduces the data at the analog front end, an approach which uses analog designs and computations instead of smaller feature size transistors for higher speed and lower power. A level-crossing sampling approach replaces Nyquist sampling. With an in-memory design, the new compressive sensing based instrumentation performs digitization only when there is enough variation in the input and when the random selection matrix chooses this input.
Experimental quantum compressed sensing for a seven-qubit system.
Riofrío, C A; Gross, D; Flammia, S T; Monz, T; Nigg, D; Blatt, R; Eisert, J
2017-05-17
Well-controlled quantum devices with their increasing system size face a new roadblock hindering further development of quantum technologies. The effort of quantum tomography-the reconstruction of states and processes of a quantum device-scales unfavourably: state-of-the-art systems can no longer be characterized. Quantum compressed sensing mitigates this problem by reconstructing states from incomplete data. Here we present an experimental implementation of compressed tomography of a seven-qubit system-a topological colour code prepared in a trapped ion architecture. We are in the highly incomplete-127 Pauli basis measurement settings-and highly noisy-100 repetitions each-regime. Originally, compressed sensing was advocated for states with few non-zero eigenvalues. We argue that low-rank estimates are appropriate in general since statistical noise enables reliable reconstruction of only the leading eigenvectors. The remaining eigenvectors behave consistently with a random-matrix model that carries no information about the true state.
Experimental quantum compressed sensing for a seven-qubit system
NASA Astrophysics Data System (ADS)
Riofrío, C. A.; Gross, D.; Flammia, S. T.; Monz, T.; Nigg, D.; Blatt, R.; Eisert, J.
2017-05-01
Well-controlled quantum devices with their increasing system size face a new roadblock hindering further development of quantum technologies. The effort of quantum tomography--the reconstruction of states and processes of a quantum device--scales unfavourably: state-of-the-art systems can no longer be characterized. Quantum compressed sensing mitigates this problem by reconstructing states from incomplete data. Here we present an experimental implementation of compressed tomography of a seven-qubit system--a topological colour code prepared in a trapped ion architecture. We are in the highly incomplete--127 Pauli basis measurement settings--and highly noisy--100 repetitions each--regime. Originally, compressed sensing was advocated for states with few non-zero eigenvalues. We argue that low-rank estimates are appropriate in general since statistical noise enables reliable reconstruction of only the leading eigenvectors. The remaining eigenvectors behave consistently with a random-matrix model that carries no information about the true state.
Canali, C; Mazzoni, C; Larsen, L B; Heiskanen, A; Martinsen, Ø G; Wolff, A; Dufva, M; Emnéus, J
2015-09-07
We present the characterisation and validation of multiplexed 4-terminal (4T) impedance measurements as a method for sensing the spatial location of cell aggregates within large three-dimensional (3D) gelatin scaffolds. The measurements were performed using an array of four rectangular chambers, each having eight platinum needle electrodes for parallel analysis. The electrode positions for current injection and voltage measurements were optimised by means of finite element simulations to maximise the sensitivity field distribution and spatial resolution. Eight different 4T combinations were experimentally tested in terms of the spatial sensitivity. The simulated sensitivity fields were validated using objects (phantoms) with different conductivity and size placed in different positions inside the chamber. This provided the detection limit (volume sensitivity) of 16.5%, i.e. the smallest detectable volume with respect to the size of the measurement chamber. Furthermore, the possibility for quick single frequency analysis was demonstrated by finding a common frequency of 250 kHz for all the presented electrode combinations. As final proof of concept, a high density of human hepatoblastoma (HepG2) cells were encapsulated in gelatin to form artificial 3D cell constructs and detected when placed in different positions inside large gelatin scaffolds. Taken together, these results open new perspectives for impedance-based sensing technologies for non-invasive monitoring in tissue engineering applications providing spatial information of constructs within biologically relevant 3D environments.
Compact 3D photonic crystals sensing platform with 45 degree angle polished fibers
NASA Astrophysics Data System (ADS)
Guo, Yuqing; Chen, Lu; Zhu, Jiali; Ni, Haibin; Xia, Wei; Wang, Ming
2017-07-01
Three dimensional photonic crystals are a kind of promising sensing materials in biology and chemistry. A compact structure, consists of planner colloidal crystals and 45 degree angle polished fiber, is proposed as a platform for accurate, fast, reliable three dimensional photonic crystals sensing in practice. This structure show advantages in compact size for integration and it is ease for large scale manufacture. Reflectivity of the 45 degree angle polished surface with and without a layer of Ag film are simulated by FDTD simulation. Refractive index sensing properties as well as mode distribution of this structure consists of both polystyrene opal and silica inverse opal film is investigated, and an experimental demonstration of silica inverse opal film is performed, which shows a sensitivity of 733 nm/RIU. Different kinds of three dimensional photonic crystals can also be applied in this structure for particular purpose.
Cladding waveguide gratings in standard single-mode fiber for 3D shape sensing.
Waltermann, Christian; Doering, Alexander; Köhring, Michael; Angelmahr, Martin; Schade, Wolfgang
2015-07-01
Femtosecond laser pulses were used for the direct point-by-point inscription of waveguides into the cladding of standard single-mode fibers. Homogeneous S-shaped waveguides have been processed as a bundle of overlapping lines without damaging the surrounding material. Within these structures, FBGs have been successfully inscribed and characterized. A sensor device to measure the bending direction of a fiber was created by two perpendicular inscribed cladding waveguides with FBG. Finally, a complete 3D shape sensor consisting of several bending sensor planes, capable of detecting bending radii even below 2.5 cm is demonstrated.
Kamesh Iyer, Srikant; Tasdizen, Tolga; Burgon, Nathan; Kholmovski, Eugene; Marrouche, Nassir; Adluru, Ganesh; DiBella, Edward
2016-09-01
Current late gadolinium enhancement (LGE) imaging of left atrial (LA) scar or fibrosis is relatively slow and requires 5-15min to acquire an undersampled (R=1.7) 3D navigated dataset. The GeneRalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) based parallel imaging method is the current clinical standard for accelerating 3D LGE imaging of the LA and permits an acceleration factor ~R=1.7. Two compressed sensing (CS) methods have been developed to achieve higher acceleration factors: a patch based collaborative filtering technique tested with acceleration factor R~3, and a technique that uses a 3D radial stack-of-stars acquisition pattern (R~1.8) with a 3D total variation constraint. The long reconstruction time of these CS methods makes them unwieldy to use, especially the patch based collaborative filtering technique. In addition, the effect of CS techniques on the quantification of percentage of scar/fibrosis is not known. We sought to develop a practical compressed sensing method for imaging the LA at high acceleration factors. In order to develop a clinically viable method with short reconstruction time, a Split Bregman (SB) reconstruction method with 3D total variation (TV) constraints was developed and implemented. The method was tested on 8 atrial fibrillation patients (4 pre-ablation and 4 post-ablation datasets). Blur metric, normalized mean squared error and peak signal to noise ratio were used as metrics to analyze the quality of the reconstructed images, Quantification of the extent of LGE was performed on the undersampled images and compared with the fully sampled images. Quantification of scar from post-ablation datasets and quantification of fibrosis from pre-ablation datasets showed that acceleration factors up to R~3.5 gave good 3D LGE images of the LA wall, using a 3D TV constraint and constrained SB methods. This corresponds to reducing the scan time by half, compared to currently used GRAPPA methods. Reconstruction of 3D LGE images
Investigation of inclined dual-fiber optical tweezers for 3D manipulation and force sensing.
Liu, Yuxiang; Yu, Miao
2009-08-03
Optical tweezers provide a versatile tool in biological and physical researches. Optical tweezers based on optical fibers are more flexible and ready to be integrated when compared with those based on microscope objectives. In this paper, the three-dimensional (3D) trapping ability of an inclined dual-fiber optical tweezers is demonstrated. The trapping efficiency with respect to displacement is experimentally calibrated along two dimensions. The system is studied numerically using a modified ray-optics model. The spring constants obtained in the experiment are predicted by simulations. It is found both experimentally and numerically that there is a critical value for the fiber inclination angle to retain the 3D trapping ability. The inclined dual-fiber optical tweezers are demonstrated to be more robust to z-axis misalignment than the counter-propagating fiber optical tweezers, which is a special case of th former when the fiber inclination angle is 90 masculine. This inclined dual-fiber optical tweezers can serve as both a manipulator and a force sensor in integrated systems, such as microfluidic systems and lab-on-a-chip systems.
Effects of 3D geometries on cellular gradient sensing and polarization
NASA Astrophysics Data System (ADS)
Spill, Fabian; Andasari, Vivi; Mak, Michael; Kamm, Roger D.; Zaman, Muhammad H.
2016-06-01
During cell migration, cells become polarized, change their shape, and move in response to various internal and external cues. Cell polarization is defined through the spatio-temporal organization of molecules such as PI3K or small GTPases, and is determined by intracellular signaling networks. It results in directional forces through actin polymerization and myosin contractions. Many existing mathematical models of cell polarization are formulated in terms of reaction-diffusion systems of interacting molecules, and are often defined in one or two spatial dimensions. In this paper, we introduce a 3D reaction-diffusion model of interacting molecules in a single cell, and find that cell geometry has an important role affecting the capability of a cell to polarize, or change polarization when an external signal changes direction. Our results suggest a geometrical argument why more roundish cells can repolarize more effectively than cells which are elongated along the direction of the original stimulus, and thus enable roundish cells to turn faster, as has been observed in experiments. On the other hand, elongated cells preferentially polarize along their main axis even when a gradient stimulus appears from another direction. Furthermore, our 3D model can accurately capture the effect of binding and unbinding of important regulators of cell polarization to and from the cell membrane. This spatial separation of membrane and cytosol, not possible to capture in 1D or 2D models, leads to marked differences of our model from comparable lower-dimensional models.
Microfluidic pressure sensing using trapped air compression.
Srivastava, Nimisha; Burns, Mark A
2007-05-01
We have developed a microfluidic method for measuring the fluid pressure head experienced at any location inside a microchannel. The principal component is a microfabricated sealed chamber with a single inlet and no exit; the entrance to the single inlet is positioned at the location where pressure is to be measured. The pressure measurement is then based on monitoring the movement of a liquid-air interface as it compresses air trapped inside the microfabricated sealed chamber and calculating the pressure using the ideal gas law. The method has been used to measure the pressure of the air stream and continuous liquid flow inside microfluidic channels (d approximately 50 microm). Further, a pressure drop has also been measured using multiple microfabricated sealed chambers. For air pressure, a resolution of 700 Pa within a full-scale range of 700-100 kPa was obtained. For liquids, pressure drops as low as 70 Pa were obtained in an operating range from 70 Pa to 10 kPa. Since the method primarily uses a microfluidic sealed chamber, it does not require additional fabrication steps and may easily be incorporated in several lab-on-a-chip fluidic applications for laminar as well as turbulent flow conditions.
Microfluidic pressure sensing using trapped air compression
Srivastava, Nimisha; Burns, Mark A.
2010-01-01
We have developed a microfluidic method for measuring the fluid pressure head experienced at any location inside a microchannel. The principal component is a microfabricated sealed chamber with a single inlet and no exit; the entrance to the single inlet is positioned at the location where pressure is to be measured. The pressure measurement is then based on monitoring the movement of a liquid–air interface as it compresses air trapped inside the microfabricated sealed chamber and calculating the pressure using the ideal gas law. The method has been used to measure the pressure of the air stream and continuous liquid flow inside microfluidic channels (d ~ 50 μm). Further, a pressure drop has also been measured using multiple microfabricated sealed chambers. For air pressure, a resolution of 700 Pa within a full-scale range of 700–100 kPa was obtained. For liquids, pressure drops as low as 70 Pa were obtained in an operating range from 70 Pa to 10 kPa. Since the method primarily uses a microfluidic sealed chamber, it does not require additional fabrication steps and may easily be incorporated in several lab-on-a-chip fluidic applications for laminar as well as turbulent flow conditions. PMID:17476384
Multimodal sensing: enhanced functionality with data compression
NASA Astrophysics Data System (ADS)
Krishna, Sanjay
2010-04-01
The past decade has seen a dramatic development in the infrared imaging systems. New material systems, novel fabrication schemes and creative read out circuit and system designs have all driven the third generation systems towards large format focal plane arrays with multicolor capability and high operating temperature. This paper explores the possibility of development of next generation infrared imagers. Could it be a bio-inspired infrared retina similar to the human eye? The conjecture is that the next generation systems will have two distinctive features that is present in the eye. They are (a) the ability to sense multimodal information including spectral, polarization, dynamic range, phase and (b) the intelligence to only send only small pieces of information to the central processing unit.
NASA Astrophysics Data System (ADS)
Chen, Tinghuan; Zhang, Meng; Wu, Jianhui; Yuen, Chau; Tong, You
2016-10-01
Because of simple encryption and compression procedure in single step, compressed sensing (CS) is utilized to encrypt and compress an image. Difference of sparsity levels among blocks of the sparsely transformed image degrades compression performance. In this paper, motivated by this difference of sparsity levels, we propose an encryption and compression approach combining Kronecker CS (KCS) with elementary cellular automata (ECA). In the first stage of encryption, ECA is adopted to scramble the sparsely transformed image in order to uniformize sparsity levels. A simple approximate evaluation method is introduced to test the sparsity uniformity. Due to low computational complexity and storage, in the second stage of encryption, KCS is adopted to encrypt and compress the scrambled and sparsely transformed image, where the measurement matrix with a small size is constructed from the piece-wise linear chaotic map. Theoretical analysis and experimental results show that our proposed scrambling method based on ECA has great performance in terms of scrambling and uniformity of sparsity levels. And the proposed encryption and compression method can achieve better secrecy, compression performance and flexibility.
Compressed sensing for real-time energy-efficient ECG compression on wireless body sensor nodes.
Mamaghanian, Hossein; Khaled, Nadia; Atienza, David; Vandergheynst, Pierre
2011-09-01
Wireless body sensor networks (WBSN) hold the promise to be a key enabling information and communications technology for next-generation patient-centric telecardiology or mobile cardiology solutions. Through enabling continuous remote cardiac monitoring, they have the potential to achieve improved personalization and quality of care, increased ability of prevention and early diagnosis, and enhanced patient autonomy, mobility, and safety. However, state-of-the-art WBSN-enabled ECG monitors still fall short of the required functionality, miniaturization, and energy efficiency. Among others, energy efficiency can be improved through embedded ECG compression, in order to reduce airtime over energy-hungry wireless links. In this paper, we quantify the potential of the emerging compressed sensing (CS) signal acquisition/compression paradigm for low-complexity energy-efficient ECG compression on the state-of-the-art Shimmer WBSN mote. Interestingly, our results show that CS represents a competitive alternative to state-of-the-art digital wavelet transform (DWT)-based ECG compression solutions in the context of WBSN-based ECG monitoring systems. More specifically, while expectedly exhibiting inferior compression performance than its DWT-based counterpart for a given reconstructed signal quality, its substantially lower complexity and CPU execution time enables it to ultimately outperform DWT-based ECG compression in terms of overall energy efficiency. CS-based ECG compression is accordingly shown to achieve a 37.1% extension in node lifetime relative to its DWT-based counterpart for "good" reconstruction quality.
Adaptive Grouping Distributed Compressive Sensing Reconstruction of Plant Hyperspectral Data.
Xu, Ping; Liu, Junfeng; Xue, Lingyun; Zhang, Jingcheng; Qiu, Bo
2017-06-07
With the development of hyperspectral technology, to establish an effective spectral data compressive reconstruction method that can improve data storage, transmission, and maintaining spectral information is critical for quantitative remote sensing research and application in vegetation. The spectral adaptive grouping distributed compressive sensing (AGDCS) algorithm is proposed, which enables a distributed compressed sensing reconstruction of plant hyperspectral data. The spectral characteristics of hyperspectral data are analyzed and the joint sparse model is constructed. The spectral bands are adaptively grouped and the hyperspectral data are compressed and reconstructed on the basis of grouping. The experimental results showed that, compared with orthogonal matching pursuit (OMP) and gradient projection for sparse reconstruction (GPSR), AGDCS can significantly improve the visual effect of image reconstruction in the spatial domain. The peak signal-to-noise ratio (PSNR) at a low sampling rate (the sampling rate is lower than 0.2) increases by 13.72 dB than OMP and 1.66 dB than GPSR. In the spectral domain, the average normalized root mean square error, the mean absolute percentage error, and the mean absolute error of AGDCS is 35.38%, 31.83%, and 33.33% lower than GPSR, respectively. Additionally, AGDCS can achieve relatively high reconstructed efficiency.
Adaptive Grouping Distributed Compressive Sensing Reconstruction of Plant Hyperspectral Data
Xu, Ping; Liu, Junfeng; Xue, Lingyun; Zhang, Jingcheng; Qiu, Bo
2017-01-01
With the development of hyperspectral technology, to establish an effective spectral data compressive reconstruction method that can improve data storage, transmission, and maintaining spectral information is critical for quantitative remote sensing research and application in vegetation. The spectral adaptive grouping distributed compressive sensing (AGDCS) algorithm is proposed, which enables a distributed compressed sensing reconstruction of plant hyperspectral data. The spectral characteristics of hyperspectral data are analyzed and the joint sparse model is constructed. The spectral bands are adaptively grouped and the hyperspectral data are compressed and reconstructed on the basis of grouping. The experimental results showed that, compared with orthogonal matching pursuit (OMP) and gradient projection for sparse reconstruction (GPSR), AGDCS can significantly improve the visual effect of image reconstruction in the spatial domain. The peak signal-to-noise ratio (PSNR) at a low sampling rate (the sampling rate is lower than 0.2) increases by 13.72 dB than OMP and 1.66 dB than GPSR. In the spectral domain, the average normalized root mean square error, the mean absolute percentage error, and the mean absolute error of AGDCS is 35.38%, 31.83%, and 33.33% lower than GPSR, respectively. Additionally, AGDCS can achieve relatively high reconstructed efficiency. PMID:28590433
Spatial Sense and Perspective: A 3-D Model of the Orion Constellation
NASA Astrophysics Data System (ADS)
Heyer, I.; Slater, T. F.; Slater, S. J.
2012-08-01
Building a scale model of the Orion constellation provides spatial perspective for students studying astronomy. For this activity, students read a passage from literature that refers to stars being strange when seen from a different point of view. From a data set of the seven major stars of Orion they construct a 3-D distance scale model. This involves the subject areas of astronomy, mathematics, literature and art, as well as the skill areas of perspective, relative distances, line-of-sight, and basic algebra. This model will appear from one side exactly the way we see it from Earth. But when looking at it from any other angle the familiar constellation will look very alien. Students are encouraged to come up with their own names and stories to go with these new constellations. This activity has been used for K-12 teacher professional development classes, and would be most suitable for grades 6-12.
Force sensing using 3D displacement measurements in linear elastic bodies
NASA Astrophysics Data System (ADS)
Feng, Xinzeng; Hui, Chung-Yuen
2016-07-01
In cell traction microscopy, the mechanical forces exerted by a cell on its environment is usually determined from experimentally measured displacement by solving an inverse problem in elasticity. In this paper, an innovative numerical method is proposed which finds the "optimal" traction to the inverse problem. When sufficient regularization is applied, we demonstrate that the proposed method significantly improves the widely used approach using Green's functions. Motivated by real cell experiments, the equilibrium condition of a slowly migrating cell is imposed as a set of equality constraints on the unknown traction. Our validation benchmarks demonstrate that the numeric solution to the constrained inverse problem well recovers the actual traction when the optimal regularization parameter is used. The proposed method can thus be applied to study general force sensing problems, which utilize displacement measurements to sense inaccessible forces in linear elastic bodies with a priori constraints.
NASA Astrophysics Data System (ADS)
Yu, H.-S.; Jackson, B. V.; Hick, P. P.; Buffington, A.; Odstrcil, D.; Wu, C.-C.; Davies, J. A.; Bisi, M. M.; Tokumaru, M.
2015-09-01
The University of California, San Diego, time-dependent analyses of the heliosphere provide three-dimensional (3D) reconstructions of solar wind velocities and densities from observations of interplanetary scintillation (IPS). Using data from the Solar-Terrestrial Environment Laboratory, Japan, these reconstructions provide a real-time prediction of the global solar-wind density and velocity throughout the whole heliosphere with a temporal cadence of about one day (ips.ucsd.edu). Updates to this modeling effort continue: in the present article, near-Sun results extracted from the time-dependent 3D reconstruction are used as inner boundary conditions to drive 3D-MHD models ( e.g. ENLIL and H3D-MHD). This allows us to explore the differences between the IPS kinematic-model data-fitting procedure and current 3D-MHD modeling techniques. The differences in these techniques provide interesting insights into the physical principles governing the expulsion of coronal mass ejections (CMEs). Here we detail for the first time several specific CMEs and an induced shock that occurred in September 2011 that demonstrate some of the issues resulting from these analyses.
3-D Numerical Simulations of Biofilm Dynamics with Quorum Sensing in a Flow Cell
2014-01-01
regulates exopolysaccharide production, motility, and virulence in pseudomonas syringae. Molecular Plant Microbe Interactions , 18(7):682–693, 2005. [25...transport of signaling molecules, the heterogenous structure in biofilm colonies and the solvent-biomass interaction can not be ignored, as they can...sensing in an aqueous environ- ment where hydrodynamic interaction between the various biofilm components and the ambient fluid flow is important. The
Zhu, Liang; Wu, Xi; Sun, Zhaoyong; Jin, Zhengyu; Weiland, Elisabeth; Raithel, Esther; Qian, Tianyi; Xue, Huadan
2017-09-26
The aims of this study were to prospectively evaluate image quality, duct visibility, and diagnostic performance in duct-related pathologies of compressed-sensing (CS) accelerated 3-dimensional (3D) magnetic resonance cholangiopancreatography (MRCP) prototype protocols and compare these with those of conventional 3D MRCP protocol in patients with suspected pancreatic diseases. The institutional review board approved this prospective study and all patients provided written informed consent. A total of 80 patients (47 men and 33 women; median age, 57 years; age range, 24-87 years) underwent 3D MRCP at 3.0 T. Three protocols were performed in each patient in random order: CS breath-hold (BH) protocol, CS navigator-triggered (NT) protocol, and conventional NT protocol. The acquisition time of each protocol was recorded. Image quality and duct visibility were independently rated in random order on a 5-point scale by 2 radiologists, who were blinded to the protocols. Receiver operating characteristic curves were generated, and area under the curve (Az value) was used to compare the diagnostic performance of each protocol in duct-related pathologies. Acquisition time was 17 seconds for the CS-BH and 134.1 ± 33.5 seconds for the CS-NT protocol, both being significantly shorter than the conventional NT protocol (364.7 ± 78.4 seconds; both P < 0.01). The CS-BH MRCP protocol showed significantly less artifacts compared with the CS-NT and conventional NT protocols (both P < 0.01). Visualization of bile ducts was comparable in all 3 protocols, whereas CS-NT and conventional NT MRCP depicted pancreatic duct better than CS-BH MRCP did (for proximal, middle, and distal segment; all P < 0.05). Compressed-sensing-NT MRCP had the highest diagnostic performance for detecting ductal anomalies, long-segment duct stenosis, abnormal branch ducts, and communication between cystic lesion and pancreatic duct (mean Az value, 0.943-0.983). Compressed-sensing MRCP is feasible in patients with
Image compression-encryption scheme based on hyper-chaotic system and 2D compressive sensing
NASA Astrophysics Data System (ADS)
Zhou, Nanrun; Pan, Shumin; Cheng, Shan; Zhou, Zhihong
2016-08-01
Most image encryption algorithms based on low-dimensional chaos systems bear security risks and suffer encryption data expansion when adopting nonlinear transformation directly. To overcome these weaknesses and reduce the possible transmission burden, an efficient image compression-encryption scheme based on hyper-chaotic system and 2D compressive sensing is proposed. The original image is measured by the measurement matrices in two directions to achieve compression and encryption simultaneously, and then the resulting image is re-encrypted by the cycle shift operation controlled by a hyper-chaotic system. Cycle shift operation can change the values of the pixels efficiently. The proposed cryptosystem decreases the volume of data to be transmitted and simplifies the keys distribution simultaneously as a nonlinear encryption system. Simulation results verify the validity and the reliability of the proposed algorithm with acceptable compression and security performance.
NASA Astrophysics Data System (ADS)
Liang, Lei; Li, Xinwu; Gao, Xizhang; Guo, Huadong
2015-01-01
The three-dimensional (3-D) structure of forests, especially the vertical structure, is an important parameter of forest ecosystem modeling for monitoring ecological change. Synthetic aperture radar tomography (TomoSAR) provides scene reflectivity estimation of vegetation along elevation coordinates. Due to the advantages of super-resolution imaging and a small number of measurements, distribution compressive sensing (DCS) inversion techniques for polarimetric SAR tomography were successfully developed and applied. This paper addresses the 3-D imaging of forested areas based on the framework of DCS using fully polarimetric (FP) multibaseline SAR interferometric (MB-InSAR) tomography at the P-band. A new DCS-based FP TomoSAR method is proposed: a new wavelet-based distributed compressive sensing FP TomoSAR method (FP-WDCS TomoSAR method). The method takes advantage of the joint sparsity between polarimetric channel signals in the wavelet domain to jointly inverse the reflectivity profiles in each channel. The method not only allows high accuracy and super-resolution imaging with a low number of acquisitions, but can also obtain the polarization information of the vertical structure of forested areas. The effectiveness of the techniques for polarimetric SAR tomography is demonstrated using FP P-band airborne datasets acquired by the ONERA SETHI airborne system over a test site in Paracou, French Guiana.
3D subsurface geological modeling using GIS, remote sensing, and boreholes data
NASA Astrophysics Data System (ADS)
Kavoura, Katerina; Konstantopoulou, Maria; Kyriou, Aggeliki; Nikolakopoulos, Konstantinos G.; Sabatakakis, Nikolaos; Depountis, Nikolaos
2016-08-01
The current paper presents the combined use of geological-geotechnical insitu data, remote sensing data and GIS techniques for the evaluation of a subsurface geological model. High accuracy Digital Surface Model (DSM), airphotos mosaic and satellite data, with a spatial resolution of 0.5m were used for an othophoto base map compilation of the study area. Geological - geotechnical data obtained from exploratory boreholes and the 1:5000 engineering geological maps were digitized and implemented in a GIS platform for a three - dimensional subsurface model evaluation. The study is located at the North part of Peloponnese along the new national road.
Optimal Compressed Sensing and Reconstruction of Unstructured Mesh Datasets
Salloum, Maher; Fabian, Nathan D.; Hensinger, David M.; ...
2017-08-09
Exascale computing promises quantities of data too large to efficiently store and transfer across networks in order to be able to analyze and visualize the results. We investigate compressed sensing (CS) as an in situ method to reduce the size of the data as it is being generated during a large-scale simulation. CS works by sampling the data on the computational cluster within an alternative function space such as wavelet bases and then reconstructing back to the original space on visualization platforms. While much work has gone into exploring CS on structured datasets, such as image data, we investigate itsmore » usefulness for point clouds such as unstructured mesh datasets often found in finite element simulations. We sample using a technique that exhibits low coherence with tree wavelets found to be suitable for point clouds. We reconstruct using the stagewise orthogonal matching pursuit algorithm that we improved to facilitate automated use in batch jobs. We analyze the achievable compression ratios and the quality and accuracy of reconstructed results at each compression ratio. In the considered case studies, we are able to achieve compression ratios up to two orders of magnitude with reasonable reconstruction accuracy and minimal visual deterioration in the data. Finally, our results suggest that, compared to other compression techniques, CS is attractive in cases where the compression overhead has to be minimized and where the reconstruction cost is not a significant concern.« less
Connell, Jodi L; Kim, Jiyeon; Shear, Jason B; Bard, Allen J; Whiteley, Marvin
2014-12-23
Microbes frequently live in nature as small, densely packed aggregates containing ∼10(1)-10(5) cells. These aggregates not only display distinct phenotypes, including resistance to antibiotics, but also, serve as building blocks for larger biofilm communities. Aggregates within these larger communities display nonrandom spatial organization, and recent evidence indicates that this spatial organization is critical for fitness. Studying single aggregates as well as spatially organized aggregates remains challenging because of the technical difficulties associated with manipulating small populations. Micro-3D printing is a lithographic technique capable of creating aggregates in situ by printing protein-based walls around individual cells or small populations. This 3D-printing strategy can organize bacteria in complex arrangements to investigate how spatial and environmental parameters influence social behaviors. Here, we combined micro-3D printing and scanning electrochemical microscopy (SECM) to probe quorum sensing (QS)-mediated communication in the bacterium Pseudomonas aeruginosa. Our results reveal that QS-dependent behaviors are observed within aggregates as small as 500 cells; however, aggregates larger than 2,000 bacteria are required to stimulate QS in neighboring aggregates positioned 8 μm away. These studies provide a powerful system to analyze the impact of spatial organization and aggregate size on microbial behaviors.
New non-Doppler remote sensing technique for 3D wind field mapping
NASA Astrophysics Data System (ADS)
Belen'kii, Mikhail S.; Gimmestad, Gary G.; Gurvich, Alexander V.
1994-06-01
A new approach to the statistical analysis of fluctuating, photon-limited signals that permits us to accumulate and process the lidar returns without averaging of the reflected energy fluctuations is developed. This approach requires recording the photocounts for each pulse in a series of pulses and then determining photocount statistics. Based on the semiclassical theory of photodetection and Mandel's formula, a relationship has been obtained between the time-space cross correlation function and the cross spectrum of the lidar returns and corresponding photocount statistics. It is shown that the relative uncertainties of measuring the cross correlation or the cross spectrum of the lidar returns is determined by the general number of photocounts, but not by their mean value. A fast-scanning lidar system, which is based on a new photocounting analysis approach, is described for 3D wind field mapping in the atmosphere at altitudes up to 5 km. A program for the experimental verification of the new approach is presented.
Alenezi, Mohammad R; Henley, Simon J; Emerson, Neil G; Silva, S Ravi P
2014-01-07
Facile and low cost hydrothermal routes are developed to fabricate three-dimensional (3D) hierarchical ZnO structures with high surface-to-volume ratios and an increased fraction of (0001) polar surfaces. Hierarchical ZnO nanowires (ZNWs) and nanodisks (ZNDs) assembled from initial ZnO nanostructures are prepared from sequential nucleation and growth following a hydrothermal process. These hierarchical ZnO structures display an enhancement of gas sensing performance and exhibit significantly improved sensitivity and fast response to acetone in comparison to other mono-morphological ZnO, such as nanoparticles, NWs, or NDs. In addition to the high surface-to-volume ratio due to its small size, the nanowire building blocks show the enhanced gas sensing properties mainly ascribed to the increased proportion of exposed active (0001) planes, and the formation of many nanojunctions at the interface between the initial ZnO nanostructure and secondary NWs. This work provides the route for structure induced enhancement of gas sensing performance by designing a desirable nanostructure, which could also be extended to synthesize other metal oxide nanostructures with superior gas sensing performance.
Huang, Jianfei; Zhu, Yihua; Yang, Xiaoling; Chen, Wei; Zhou, Ying; Li, Chunzhong
2015-01-14
Convenient determination of glucose in a sensitive, reliable and cost-effective way has aroused sustained research passion, bringing along assiduous investigation of high-performance electroactive nanomaterials to build enzymeless sensors. In addition to the intrinsic electrocatalytic capability of the sensing materials, electrode architecture at the microscale is also crucial for fully enhancing the performance. In this work, free-standing porous CuO nanowire (NW) was taken as a model sensing material to illustrate this point, where an in situ formed 3D CuO nanowire array (NWA) and CuO nanowires pile (NWP) immobilized with polymer binder by conventional drop-casting technique were both studied for enzymeless glucose sensing. The NWA electrode exhibited greatly promoted electrochemistry characterized by decreased overpotential for electro-oxidation of glucose and over 5-fold higher sensitivity compared to the NWP counterpart, benefiting from the binder-free nanoarray structure. Besides, its sensing performance was also satisfying in terms of rapidness, selectivity and durability. Further, the CuO NWA was utilized to fabricate a flexible sensor which showed excellent performance stability against mechanical bending. Thanks to its favorable electrode architecture, the CuO NWA is believed to offer opportunities for building high-efficiency flexible electrochemical devices.
Learning physical descriptors for materials science by compressed sensing
NASA Astrophysics Data System (ADS)
Ghiringhelli, Luca M.; Vybiral, Jan; Ahmetcik, Emre; Ouyang, Runhai; Levchenko, Sergey V.; Draxl, Claudia; Scheffler, Matthias
2017-02-01
The availability of big data in materials science offers new routes for analyzing materials properties and functions and achieving scientific understanding. Finding structure in these data that is not directly visible by standard tools and exploitation of the scientific information requires new and dedicated methodology based on approaches from statistical learning, compressed sensing, and other recent methods from applied mathematics, computer science, statistics, signal processing, and information science. In this paper, we explain and demonstrate a compressed-sensing based methodology for feature selection, specifically for discovering physical descriptors, i.e., physical parameters that describe the material and its properties of interest, and associated equations that explicitly and quantitatively describe those relevant properties. As showcase application and proof of concept, we describe how to build a physical model for the quantitative prediction of the crystal structure of binary compound semiconductors.
Compressed Sensing Based Fingerprint Identification for Wireless Transmitters
Zhao, Caidan; Wu, Xiongpeng; Huang, Lianfen; Yao, Yan; Chang, Yao-Chung
2014-01-01
Most of the existing fingerprint identification techniques are unable to distinguish different wireless transmitters, whose emitted signals are highly attenuated, long-distance propagating, and of strong similarity to their transient waveforms. Therefore, this paper proposes a new method to identify different wireless transmitters based on compressed sensing. A data acquisition system is designed to capture the wireless transmitter signals. Complex analytical wavelet transform is used to obtain the envelope of the transient signal, and the corresponding features are extracted by using the compressed sensing theory. Feature selection utilizing minimum redundancy maximum relevance (mRMR) is employed to obtain the optimal feature subsets for identification. The results show that the proposed method is more efficient for the identification of wireless transmitters with similar transient waveforms. PMID:24892053
Improved EEG source localization employing 3D sensing by "Flying Triangulation"
NASA Astrophysics Data System (ADS)
Ettl, Svenja; Rampp, Stefan; Fouladi-Movahed, Sarah; Dalal, Sarang S.; Willomitzer, Florian; Arold, Oliver; Stefan, Hermann; Häusler, Gerd
2013-04-01
With electroencephalography (EEG), a person's brain activity can be monitored over time and sources of activity localized. With this information, brain regions showing pathological activity, such as epileptic spikes, can be delineated. In cases of severe drug-resistant epilepsy, surgical resection of these brain regions may be the only treatment option. This requires a precise localization of the responsible seizure generators. They can be reconstructed from EEG data when the electrode positions are known. The standard method employs a "digitization pen" and has severe drawbacks: It is time consuming, the result is user-dependent, and the patient has to hold still. We present a novel method which overcomes these drawbacks. It is based on the optical "Flying Triangulation" (FlyTri) sensor which allows a motion-robust acquisition of precise 3D data. To compare the two methods, the electrode positions were determined with each method for a real-sized head model with EEG electrodes and their deviation to the ground-truth data calculated. The standard deviation for the current method was 3.39 mm while it was 0.98 mm for the new method. The influence of these results on the final EEG source localization was investigated by simulating EEG data. The digitization pen result deviates substantially from the true source location and time series. In contrast, the FlyTri result agrees with the original information. Our findings suggest that FlyTri might become a valuable tool in the field of medical brain research, because of its improved precision and contactless handling. Future applications might include co-registration of multimodal information.
Remote sensing solution using 3-D flash LADAR for automated control of aircraft
NASA Astrophysics Data System (ADS)
Neff, Brian J.; Fuka, Jennifer A.; Burwell, Alan C.; Gray, Stephen W.; Hubbard, Mason J.; Schenkel, Joseph W.
2015-09-01
The majority of image quality studies in the field of remote sensing have been performed on systems with conventional aperture functions. These systems have well-understood image quality tradeoffs, characterized by the General Image Quality Equation (GIQE). Advanced, next-generation imaging systems present challenges to both post-processing and image quality prediction. Examples include sparse apertures, synthetic apertures, coded apertures and phase elements. As a result of the non-conventional point spread functions of these systems, post-processing becomes a critical step in the imaging process and artifacts arise that are more complicated than simple edge overshoot. Previous research at the Rochester Institute of Technology's Digital Imaging and Remote Sensing Laboratory has resulted in a modeling methodology for sparse and segmented aperture systems, the validation of which will be the focus of this work. This methodology has predicted some unique post-processing artifacts that arise when a sparse aperture system with wavefront error is used over a large (panchromatic) spectral bandpass. Since these artifacts are unique to sparse aperture systems, they have not yet been observed in any real-world data. In this work, a laboratory setup and initial results for a model validation study will be described. Initial results will focus on the validation of spatial frequency response predictions and verification of post-processing artifacts. The goal of this study is to validate the artifact and spatial frequency response predictions of this model. This will allow model predictions to be used in image quality studies, such as aperture design optimization, and the signal-to-noise vs. post-processing artifact tradeoff resulting from choosing a panchromatic vs. multispectral system.
Multi-channel ECG data compression using compressed sensing in eigenspace.
Singh, A; Sharma, L N; Dandapat, S
2016-06-01
In recent years, compressed sensing (CS) has emerged as a potential alternative to traditional data compression techniques for resource-constrained telemonitoring applications. In the present work, a CS framework of data reduction is proposed for multi-channel electrocardiogram (MECG) signals in eigenspace. The sparsity of dimension-reduced eigenspace MECG signals is exploited to apply CS. First, principal component analysis (PCA) is applied over the MECG data to retain diagnostically important ECG features in a few principal eigenspace signals based on maximum variance. Then, the significant eigenspace signals are randomly projected over a sparse binary sensing matrix to obtain the reduced dimension compressive measurement vectors. The compressed measurements are quantized using a uniform quantizer and encoded by a lossless Huffman encoder. The signal recovery is carried out by an orthogonal matching pursuit (OMP) algorithm. The proposed method is evaluated on the MECG signals from PTB and CSE multilead measurement library databases. The average value of percentage root mean square difference (PRD) across the PTB database is found to be 5.24% at a compression ratio (CR)=17.76 in Lead V3 of PTB database. The visual signal quality of the reconstructed MECG signals is validated through mean opinion score (MOS), found to be 6.66%, which implies very good quality signal reconstruction. Copyright © 2016 Elsevier Ltd. All rights reserved.
Quantum tomography protocols with positivity are compressed sensing protocols
NASA Astrophysics Data System (ADS)
Kalev, Amir; Kosut, Robert L.; Deutsch, Ivan H.
2015-12-01
Characterising complex quantum systems is a vital task in quantum information science. Quantum tomography, the standard tool used for this purpose, uses a well-designed measurement record to reconstruct quantum states and processes. It is, however, notoriously inefficient. Recently, the classical signal reconstruction technique known as ‘compressed sensing’ has been ported to quantum information science to overcome this challenge: accurate tomography can be achieved with substantially fewer measurement settings, thereby greatly enhancing the efficiency of quantum tomography. Here we show that compressed sensing tomography of quantum systems is essentially guaranteed by a special property of quantum mechanics itself—that the mathematical objects that describe the system in quantum mechanics are matrices with non-negative eigenvalues. This result has an impact on the way quantum tomography is understood and implemented. In particular, it implies that the information obtained about a quantum system through compressed sensing methods exhibits a new sense of ‘informational completeness.’ This has important consequences on the efficiency of the data taking for quantum tomography, and enables us to construct informationally complete measurements that are robust to noise and modelling errors. Moreover, our result shows that one can expand the numerical tool-box used in quantum tomography and employ highly efficient algorithms developed to handle large dimensional matrices on a large dimensional Hilbert space. Although we mainly present our results in the context of quantum tomography, they apply to the general case of positive semidefinite matrix recovery.
Application of joint orthogonal bases in compressive sensing ghost image
NASA Astrophysics Data System (ADS)
Fan, Xiang; Chen, Yi; Cheng, Zheng-dong; Liang, Zheng-yu; Zhu, Bin
2016-11-01
Sparse decomposition is one of the core issue of compressive sensing ghost image. At this stage, traditional methods still have the problems of poor sparsity and low reconstruction accuracy, such as discrete fourier transform and discrete cosine transform. In order to solve these problems, joint orthogonal bases transform is proposed to optimize ghost imaging. First, introduce the principle of compressive sensing ghost imaging and point out that sparsity is related to the minimum sample data required for imaging. Then, analyze the development and principle of joint orthogonal bases in detail and find out it can use less nonzero coefficients to reach the same identification effect as other methods. So, joint orthogonal bases transform is able to provide the sparsest representation. Finally, the experimental setup is built in order to verify simulation results. Experimental results indicate that the PSNR of joint orthogonal bases is much higher than traditional methods by using same sample data in compressive sensing ghost image.Therefore, joint orthogonal bases transform can realize better imaging quality under less sample data, which can satisfy the system requirements of convenience and rapid speed in ghost image.
High-frequency subband compressed sensing MRI using quadruplet sampling.
Sung, Kyunghyun; Hargreaves, Brian A
2013-11-01
To present and validate a new method that formalizes a direct link between k-space and wavelet domains to apply separate undersampling and reconstruction for high- and low-spatial-frequency k-space data. High- and low-spatial-frequency regions are defined in k-space based on the separation of wavelet subbands, and the conventional compressed sensing problem is transformed into one of localized k-space estimation. To better exploit wavelet-domain sparsity, compressed sensing can be used for high-spatial-frequency regions, whereas parallel imaging can be used for low-spatial-frequency regions. Fourier undersampling is also customized to better accommodate each reconstruction method: random undersampling for compressed sensing and regular undersampling for parallel imaging. Examples using the proposed method demonstrate successful reconstruction of both low-spatial-frequency content and fine structures in high-resolution three-dimensional breast imaging with a net acceleration of 11-12. The proposed method improves the reconstruction accuracy of high-spatial-frequency signal content and avoids incoherent artifacts in low-spatial-frequency regions. This new formulation also reduces the reconstruction time due to the smaller problem size. Copyright © 2012 Wiley Periodicals, Inc.
3-D Printed Adjustable Microelectrode Arrays for Electrochemical Sensing and Biosensing.
Yang, Haipeng; Rahman, Taibur; Du, Dan; Panat, Rahul; Lin, Yuehe
2016-07-01
Printed Electronics has emerged as an important fabrication technique that overcomes several shortcomings of conventional lithography and provides custom rapid prototyping for various sensor applications. In this work, silver microelectrode arrays (MEA) with three different electrode spacing were fabricated using 3-D printing by the aerosol jet technology. The microelectrodes were printed at a length scale of about 15 μm, with the space between the electrodes accurately controlled to about 2 times (30 μm, MEA30), 6.6 times (100 μm, MEA100) and 12 times (180 μm, MEA180) the trace width, respectively. Hydrogen peroxide and glucose were chosen as model analytes to demonstrate the performance of the MEA for sensor applications. The electrodes are shown to reduce hydrogen peroxide with a reduction current proportional to the concentration of hydrogen peroxide for certain concentration ranges. Further, the sensitivity of the current for the three electrode configurations was shown to decrease with an increase in the microelectrode spacing (sensitivity of MEA30: MEA100: MEA180 was in the ratio of 3.7: 2.8: 1), demonstrating optimal MEA geometry for such applications. The noise of the different electrode configurations is also characterized and shows a dramatic reduction from MEA30 to MEA100 and MEA180 electrodes. Further, it is shown that the response current is proportional to MEA100 and MEA180 electrode areas, but not for the area of MEA30 electrode (the current density of MEA30 : MEA100 : MEA180 is 0.25 : 1 : 1), indicating that the MEA30 electrodes suffer from diffusion overlap from neighboring electrodes. The work thus establishes the lower limit of microelectrode spacing for our geometry. The lowest detection limit of the MEAs was calculated (with S/N = 3) to be 0.45 μM. Glucose oxidase was immobilized on MEA100 microelectrodes to demonstrate a glucose biosensor application. The sensitivity of glucose biosensor was 1.73 μAmM(-1) and the calculated value of
NASA Astrophysics Data System (ADS)
Wu, Guochun
2017-01-01
In this paper, we investigate the global existence and uniqueness of strong solutions to the initial boundary value problem for the 3D compressible Navier-Stokes equations without heat conductivity in a bounded domain with slip boundary. The global existence and uniqueness of strong solutions are obtained when the initial data is near its equilibrium in H2 (Ω). Furthermore, the exponential convergence rates of the pressure and velocity are also proved by delicate energy methods.
NASA Astrophysics Data System (ADS)
Zhou, Nanrun; Li, Haolin; Wang, Di; Pan, Shumin; Zhou, Zhihong
2015-05-01
Most of the existing image encryption techniques bear security risks for taking linear transform or suffer encryption data expansion for adopting nonlinear transformation directly. To overcome these difficulties, a novel image compression-encryption scheme is proposed by combining 2D compressive sensing with nonlinear fractional Mellin transform. In this scheme, the original image is measured by measurement matrices in two directions to achieve compression and encryption simultaneously, and then the resulting image is re-encrypted by the nonlinear fractional Mellin transform. The measurement matrices are controlled by chaos map. The Newton Smoothed l0 Norm (NSL0) algorithm is adopted to obtain the decryption image. Simulation results verify the validity and the reliability of this scheme.
Texture-based medical image retrieval in compressed domain using compressive sensing.
Yadav, Kuldeep; Srivastava, Avi; Mittal, Ankush; Ansari, M A
2014-01-01
Content-based image retrieval has gained considerable attention in today's scenario as a useful tool in many applications; texture is one of them. In this paper, we focus on texture-based image retrieval in compressed domain using compressive sensing with the help of DC coefficients. Medical imaging is one of the fields which have been affected most, as there had been huge size of image database and getting out the concerned image had been a daunting task. Considering this, in this paper we propose a new model of image retrieval process using compressive sampling, since it allows accurate recovery of image from far fewer samples of unknowns and it does not require a close relation of matching between sampling pattern and characteristic image structure with increase acquisition speed and enhanced image quality.
Perceptual rate-distortion optimized image compression based on block compressive sensing
NASA Astrophysics Data System (ADS)
Xu, Jin; Qiao, Yuansong; Wen, Quan; Fu, Zhizhong
2016-09-01
The emerging compressive sensing (CS) theory provides a paradigm for image compression. Most current efforts in CS-based image compression have been focused on enhancing the objective coding efficiency. In order to achieve a maximal perceptual quality under the measurements budget constraint, we propose a perceptual rate-distortion optimized (RDO) CS-based image codec in this paper. By incorporating both the human visual system characteristics and the signal sparsity into a RDO model designed for the block compressive sensing framework, the measurements allocation for each block is formulated as an optimization problem, which can be efficiently solved by the Lagrangian relaxation method. After the optimal measurement number is determined, each block is adaptively sampled using an image-dependent measurement matrix. To make our proposed codec applicable to different scenarios, we also propose two solutions to implement the perceptual RDO measurements allocation technique: one at the encoder side and the other at the decoder side. The experimental results show that our codec outperforms the other existing CS-based image codecs in terms of both objective and subjective performances. In particular, our codec can also achieve a low complexity encoder by adopting the decoder-based solution for the perceptual RDO measurements allocation.
Adam, Clayton; Izatt, Maree; Askin, Geoffrey
2010-01-01
Magnetic Resonance Imaging (MRI) offers a valuable research tool for the assessment of 3D spinal deformity in AIS, however the horizontal patient position imposed by conventional scanners removes the axial compressive loading on the spine. The objective of this study was to design, construct and test an MRI compatible compression device for research into the effect of axial loading on spinal deformity using supine MRI scans. The device was evaluated by performing unloaded and loaded supine MRI scans on a series of 10 AIS patients. The patient group had a mean initial (unloaded) major Cobb angle of 43+/-7 degrees, which increased to 50+/-9 degrees on application of the compressive load. The 7 degrees increase in mean Cobb angle is consistent with that reported by a previous study comparing standing versus supine posture in scoliosis patients (Torell et al, 1985. Spine 10:425-7).
3D printed disposable optics and lab-on-a-chip devices for chemical sensing with cell phones
NASA Astrophysics Data System (ADS)
Comina, G.; Suska, A.; Filippini, D.
2017-02-01
Digital manufacturing (DM) offers fast prototyping capabilities and great versatility to configure countless architectures at affordable development costs. Autonomous lab-on-a-chip (LOC) devices, conceived as only disposable accessory to interface chemical sensing to cell phones, require specific features that can be achieved using DM techniques. Here we describe stereo-lithography 3D printing (SLA) of optical components and unibody-LOC (ULOC) devices using consumer grade printers. ULOC devices integrate actuation in the form of check-valves and finger pumps, as well as the calibration range required for quantitative detection. Coupling to phone camera readout depends on the detection approach, and includes different types of optical components. Optical surfaces can be locally configured with a simple polishing-free post-processing step, and the representative costs are 0.5 US$/device, same as ULOC devices, both involving fabrication times of about 20 min.
Joint Sensing Matrix and Sparsifying Dictionary Optimization for Tensor Compressive Sensing
NASA Astrophysics Data System (ADS)
Ding, Xin; Chen, Wei; Wassell, Ian J.
2017-07-01
Tensor Compressive Sensing (TCS) is a multidimensional framework of Compressive Sensing (CS), and it is advantageous in terms of reducing the amount of storage, easing hardware implementations and preserving multidimensional structures of signals in comparison to a conventional CS system. In a TCS system, instead of using a random sensing matrix and a predefined dictionary, the average-case performance can be further improved by employing an optimized multidimensional sensing matrix and a learned multilinear sparsifying dictionary. In this paper, we propose a joint optimization approach of the sensing matrix and dictionary for a TCS system. For the sensing matrix design in TCS, an extended separable approach with a closed form solution and a novel iterative non-separable method are proposed when the multilinear dictionary is fixed. In addition, a multidimensional dictionary learning method that takes advantages of the multidimensional structure is derived, and the influence of sensing matrices is taken into account in the learning process. A joint optimization is achieved via alternately iterating the optimization of the sensing matrix and dictionary. Numerical experiments using both synthetic data and real images demonstrate the superiority of the proposed approaches.
Basha, Tamer A; Akçakaya, Mehmet; Goddu, Beth; Berg, Sophie; Nezafat, Reza
2015-01-01
The aim of this study was to implement and evaluate an accelerated three-dimensional (3D) cine phase contrast MRI sequence by combining a randomly sampled 3D k-space acquisition sequence with an echo planar imaging (EPI) readout. An accelerated 3D cine phase contrast MRI sequence was implemented by combining EPI readout with randomly undersampled 3D k-space data suitable for compressed sensing (CS) reconstruction. The undersampled data were then reconstructed using low-dimensional structural self-learning and thresholding (LOST). 3D phase contrast MRI was acquired in 11 healthy adults using an overall acceleration of 7 (EPI factor of 3 and CS rate of 3). For comparison, a single two-dimensional (2D) cine phase contrast scan was also performed with sensitivity encoding (SENSE) rate 2 and approximately at the level of the pulmonary artery bifurcation. The stroke volume and mean velocity in both the ascending and descending aorta were measured and compared between two sequences using Bland-Altman plots. An average scan time of 3 min and 30 s, corresponding to an acceleration rate of 7, was achieved for 3D cine phase contrast scan with one direction flow encoding, voxel size of 2 × 2 × 3 mm(3) , foot-head coverage of 6 cm and temporal resolution of 30 ms. The mean velocity and stroke volume in both the ascending and descending aorta were statistically equivalent between the proposed 3D sequence and the standard 2D cine phase contrast sequence. The combination of EPI with a randomly undersampled 3D k-space sampling sequence using LOST reconstruction allows a seven-fold reduction in scan time of 3D cine phase contrast MRI without compromising blood flow quantification.
An Energy Efficient Compressed Sensing Framework for the Compression of Electroencephalogram Signals
Fauvel, Simon; Ward, Rabab K.
2014-01-01
The use of wireless body sensor networks is gaining popularity in monitoring and communicating information about a person's health. In such applications, the amount of data transmitted by the sensor node should be minimized. This is because the energy available in these battery powered sensors is limited. In this paper, we study the wireless transmission of electroencephalogram (EEG) signals. We propose the use of a compressed sensing (CS) framework to efficiently compress these signals at the sensor node. Our framework exploits both the temporal correlation within EEG signals and the spatial correlations amongst the EEG channels. We show that our framework is up to eight times more energy efficient than the typical wavelet compression method in terms of compression and encoding computations and wireless transmission. We also show that for a fixed compression ratio, our method achieves a better reconstruction quality than the CS-based state-of-the art method. We finally demonstrate that our method is robust to measurement noise and to packet loss and that it is applicable to a wide range of EEG signal types. PMID:24434840
Exploiting prior knowledge in compressed sensing wireless ECG systems.
Polanía, Luisa F; Carrillo, Rafael E; Blanco-Velasco, Manuel; Barner, Kenneth E
2015-03-01
Recent results in telecardiology show that compressed sensing (CS) is a promising tool to lower energy consumption in wireless body area networks for electrocardiogram (ECG) monitoring. However, the performance of current CS-based algorithms, in terms of compression rate and reconstruction quality of the ECG, still falls short of the performance attained by state-of-the-art wavelet-based algorithms. In this paper, we propose to exploit the structure of the wavelet representation of the ECG signal to boost the performance of CS-based methods for compression and reconstruction of ECG signals. More precisely, we incorporate prior information about the wavelet dependencies across scales into the reconstruction algorithms and exploit the high fraction of common support of the wavelet coefficients of consecutive ECG segments. Experimental results utilizing the MIT-BIH Arrhythmia Database show that significant performance gains, in terms of compression rate and reconstruction quality, can be obtained by the proposed algorithms compared to current CS-based methods.
Lee, Seung Hyun; Lee, Young Han; Song, Ho-Taek; Suh, Jin-Suck
2017-10-01
To evaluate the feasibility of 3D fast spin-echo (FSE) imaging with compressed sensing (CS) for the assessment of shoulder. Twenty-nine patients who underwent shoulder MRI including image sets of axial 3D-FSE sequence without CS and with CS, using an acceleration factor of 1.5, were included. Quantitative assessment was performed by calculating the root mean square error (RMSE) and structural similarity index (SSIM). Two musculoskeletal radiologists compared image quality of 3D-FSE sequences without CS and with CS, and scored the qualitative agreement between sequences, using a five-point scale. Diagnostic agreement for pathologic shoulder lesions between the two sequences was evaluated. The acquisition time of 3D-FSE MRI was reduced using CS (3min 23s vs. 2min 22s). Quantitative evaluations showed a significant correlation between the two sequences (r=0.872-0.993, p<0.05) and SSIM was in an acceptable range (0.940-0.993; mean±standard deviation, 0.968±0.018). Qualitative image quality showed good to excellent agreement between 3D-FSE images without CS and with CS. Diagnostic agreement for pathologic shoulder lesions between the two sequences was very good (κ=0.915-1). The 3D-FSE sequence with CS is feasible in evaluating the shoulder joint with reduced scan time compared to 3D-FSE without CS. Copyright © 2017 Elsevier Inc. All rights reserved.
Applications of compressed sensing to coherent radar imaging
NASA Astrophysics Data System (ADS)
Zhu, Qian
Although meteoroids fragmentation has been observed and studied in the optical meteor community since the 1950s, no definitive fragmentation mechanisms for the relatively small meteoroids (mass .10.4 kg) have been proposed. This is in part due to the lack of observations to constrain physical mechanisms of the fragmentation process. While it is challenging to record fragmentation in faint optical meteors, observing meteors using HPLA (High-Power, Large- Aperture) radars can yield considerable information especially when employing coherent radar imaging (CRI). CRI can potentially resolve the fragmentation process in three spatial dimensions by monitoring the evolution of the plasma in the meteor head-echo, flare-echo, and trail-echo regions. On the other hand, the emerging field of compressed sensing (CS) provides a novel paradigm for signal acquisition and processing. Furthermore, it has been, and continues to be, applied with great success in radar systems, offering various benefits such as better resolution compared to traditional techniques, reduced resource requirements, and so forth. In this dissertation, we examine how CS can be incorporated to improve the performance of CRI using HPLA radars. We propose a single CS-based formalism that enables the threedimensions (3D).the range, Doppler frequency, and cross range (represented by the direction cosines) domain.coherent imaging. We show that the CS-based CRI can not only reduce the system costs and decrease the needed number of baselines by spatial sparse sampling, which can be much less than the number required by Nyquist-Shannon sampling criterion, but also achieve high resolution for target detection. We implement the CS-based CRI for meteor studies with observations conducted at the Jicamarca Radio Observatory (JRO) in Peru. We present the unprecedented resolved details of meteoroids fragmentation, including both along and transverse to the trajectory spreading of the developing plasma, apparently caused by
MAXAD distortion minimization for wavelet compression of remote sensing data
NASA Astrophysics Data System (ADS)
Alecu, Alin; Munteanu, Adrian; Schelkens, Peter; Cornelis, Jan P.; Dewitte, Steven
2001-12-01
In the context of compression of high resolution multi-spectral satellite image data consisting of radiances and top-of-the-atmosphere fluxes, it is vital that image calibration characteristics (luminance, radiance) must be preserved within certain limits in lossy image compression. Though existing compression schemes (SPIHT, JPEG2000, SQP) give good results as far as minimization of the global PSNR error is concerned, they fail to guarantee a maximum local error. With respect to this, we introduce a new image compression scheme, which guarantees a MAXAD distortion, defined as the maximum absolute difference between original pixel values and reconstructed pixel values. In terms of defining the Lagrangian optimization problem, this reflects in minimization of the rate given the MAXAD distortion. Our approach thus uses the l-infinite distortion measure, which is applied to the lifting scheme implementation of the 9-7 floating point Cohen-Daubechies-Feauveau (CDF) filter. Scalar quantizers, optimal in the D-R sense, are derived for every subband, by solving a global optimization problem that guarantees a user-defined MAXAD. The optimization problem has been defined and solved for the case of the 9-7 filter, and we show that our approach is valid and may be applied to any finite wavelet filters synthesized via lifting. The experimental assessment of our codec shows that our technique provides excellent results in applications such as those for remote sensing, in which reconstruction of image calibration characteristics within a tolerable local error (MAXAD) is perceived as being of crucial importance compared to obtaining an acceptable global error (PSNR), as is the case of existing quantizer design techniques.
Vascular masking for improved unfolding in 2D SENSE-accelerated 3D contrast-enhanced MR angiography.
Stinson, Eric G; Borisch, Eric A; Johnson, Casey P; Trzasko, Joshua D; Young, Phillip M; Riederer, Stephen J
2014-05-01
To describe and evaluate the method we refer to as "vascular masking" for improving signal-to-noise ratio (SNR) retention in sensitivity encoding (SENSE)-accelerated contrast-enhanced magnetic resonance angiography (CE-MRA). Vascular masking is a technique that restricts the SENSE unfolding of an accelerated subtraction angiogram to the voxels within the field of view known to have enhancing signal. This is a more restricted voxel set than that identified with conventional masking, which excludes only voxels in the air around the object. Thus, improved retention of SNR is expected. Evaluation was done in phantom and in vivo studies by comparing SNR and the g-factor in results reconstructed using vascular versus conventional masking. A radiological evaluation was also performed comparing conventional and vascular masking in R = 8 accelerated CE-MRA studies of the thighs (n = 21) and calves (n = 13). Images reconstructed with vascular masking showed a significant reduction in g-factor and improved retention of SNR versus those reconstructed with conventional masking. In the radiological evaluation, vascular masking consistently provided reduced background noise, improved luminal signal smoothness, and better small vessel conspicuity. Vascular masking provides improved SNR retention and improved depiction of the vasculature in accelerated, subtraction 3D CE-MRA of the thighs and calves. Copyright © 2013 Wiley Periodicals, Inc.
Vascular Masking for Improved Unfolding in 2D SENSE-Accelerated 3D Contrast-Enhanced MR Angiography
Stinson, Eric G.; Borisch, Eric A.; Johnson, Casey P.; Trzasko, Joshua D.; Young, Phillip M.; Riederer, Stephen J.
2013-01-01
Purpose To describe and evaluate the method we refer to as “vascular masking” for improving signal-to-noise ratio (SNR) retention in SENSE-accelerated contrast-enhanced MR angiography (CE-MRA). Materials and Methods Vascular masking is a technique which restricts the SENSE unfolding of an accelerated subtraction angiogram to the voxels within the field of view known to have enhancing signal. This is a more restricted voxel set than that identified with conventional masking which excludes only voxels in the air around the object. Thus, improved retention of SNR is expected. Evaluation was done in phantom and in vivo studies by comparing SNR and the g-factor in results reconstructed using vascular vs. conventional masking. A radiological evaluation was also performed comparing conventional and vascular masking in R = 8 accelerated CE-MRA studies of the thighs (n=21) and calves (n=13). Results Images reconstructed with vascular masking showed significant reduction in g-factor and improved retention of SNR vs. those reconstructed with conventional masking. In the radiological evaluation vascular masking consistently provided reduced background noise, improved luminal signal smoothness, and better small vessel conspicuity. Conclusion Vascular masking provides improved SNR retention and improved depiction of the vasculature in accelerated, subtraction 3D CE-MRA of the thighs and calves. PMID:23897776
Weavers, Paul T.; Borisch, Eric A.; Johnson, Casey P.; Riederer, Stephen J.
2013-01-01
Purpose In 2D SENSE-accelerated 3D Cartesian acquisition, the net acceleration factor R is the product of the two individual accelerations, R = RY × RZ. Acceleration Apportionment tailors acceleration parameters (RY, RZ) to improve parallel imaging performance on a patient- and coil-specific basis and is demonstrated in contrast-enhanced MR angiography. Methods A performance metric is defined based on coil sensitivity information which identifies the (RY, RZ) pair that optimally trades off image quality with scan time reduction on a patient-specific basis. Acceleration Apportionment is evaluated using retrospective analysis of contrast-enhanced MR angiography studies, and prospective studies in which optimally apportioned parameters are compared with standard acceleration parameters. Results The retrospective studies show strong variability in optimal acceleration parameters between anatomic regions and between patients. Prospective application of apportionment to foot contrast-enhanced MR angiography with an 8-channel receiver array provides a 20% increase in net acceleration with improved contrast-to-noise ratio. Application to 16-channel contrast-enhanced MR angiography of the feet and calves suggests 10% acceleration increase to R > 13 and no contrast-to-noise ratio loss. The specific implementation allows the optimum (RY, RZ) pair to be determined within one minute. Conclusion Optimum 2D SENSE acceleration parameters can be automatically chosen on a per-exam basis to allow improved performance without disrupting the clinical workflow. PMID:23450817
Weavers, Paul T; Borisch, Eric A; Johnson, Casey P; Riederer, Stephen J
2014-02-01
In 2D SENSE-accelerated 3D Cartesian acquisition, the net acceleration factor R is the product of the two individual accelerations, R = RY × RZ. Acceleration Apportionment tailors acceleration parameters (RY, RZ) to improve parallel imaging performance on a patient- and coil-specific basis and is demonstrated in contrast-enhanced MR angiography. A performance metric is defined based on coil sensitivity information which identifies the (RY, RZ) pair that optimally trades off image quality with scan time reduction on a patient-specific basis. Acceleration Apportionment is evaluated using retrospective analysis of contrast-enhanced MR angiography studies, and prospective studies in which optimally apportioned parameters are compared with standard acceleration parameters. The retrospective studies show strong variability in optimal acceleration parameters between anatomic regions and between patients. Prospective application of apportionment to foot contrast-enhanced MR angiography with an 8-channel receiver array provides a 20% increase in net acceleration with improved contrast-to-noise ratio. Application to 16-channel contrast-enhanced MR angiography of the feet and calves suggests 10% acceleration increase to R > 13 and no contrast-to-noise ratio loss. The specific implementation allows the optimum (RY, RZ) pair to be determined within one minute. Optimum 2D SENSE acceleration parameters can be automatically chosen on a per-exam basis to allow improved performance without disrupting the clinical workflow. Copyright © 2013 Wiley Periodicals, Inc.
Wu, Peihui; DeLassus, Elizabeth; Patra, Debabrata; Liao, Weiming; Sandell, Linda J
2013-05-01
The aim of this study was to investigate the effects of serum and compressive dynamic loading on the cartilaginous matrix spatiotemporal distribution around chondrocytes in vitro. Murine chondrocytes suspended in agarose were cultured in serum-free media or in varying concentrations of serum with or without compressive dynamic loading. Gene expression was assayed by quantitative polymerase chain reaction. Immunohistochemistry was performed for type II collagen and type VI collagen, aggrecan, or cartilage oligomeric matrix protein (COMP) to study the effect of serum and dynamic loading on the spatiotemporal distribution of cartilage matrix components. Chondrocytes in serum-free culture exhibited negligible differences in type II collagen, aggrecan, and COMP mRNA expression levels over 15 days of cultivation. However, higher serum concentrations decreased matrix gene expression. Expression of the matrix metalloproteinases (MMP)-3 and MMP-13 mRNA increased over time in serum-free or reduced serum levels, but was significantly suppressed in 10% fetal bovine serum (FBS). Compressive loading significantly stimulated MMP-3 expression on days 7 and 15. Immunohistochemical analysis demonstrated that maximum pericellular matrix deposition was achieved in 10% FBS culture in the absence of compressive loading. The pericellular distribution of type II and VI collagens, aggrecan, and COMP proteins tended to be more co-localized in the pericellular region from day 9 to day 21; compressive loading helped promote this co-localization of matrix proteins. The results of this study suggest that the quantity, quality, and spatial distribution of cartilaginous matrix can be altered by serum concentrations and compressive loading.
3D Imaging Millimeter Wave Circular Synthetic Aperture Radar
Zhang, Renyuan; Cao, Siyang
2017-01-01
In this paper, a new millimeter wave 3D imaging radar is proposed. The user just needs to move the radar along a circular track, and high resolution 3D imaging can be generated. The proposed radar uses the movement of itself to synthesize a large aperture in both the azimuth and elevation directions. It can utilize inverse Radon transform to resolve 3D imaging. To improve the sensing result, the compressed sensing approach is further investigated. The simulation and experimental result further illustrated the design. Because a single transceiver circuit is needed, a light, affordable and high resolution 3D mmWave imaging radar is illustrated in the paper. PMID:28629140
A high resolution spectrum reconstruction algorithm using compressive sensing theory
NASA Astrophysics Data System (ADS)
Zheng, Zhaoyu; Liang, Dakai; Liu, Shulin; Feng, Shuqing
2015-07-01
This paper proposes a quick spectrum scanning and reconstruction method using compressive sensing in composite structure. The strain field of corrugated structure is simulated by finite element analysis. Then the reflect spectrum is calculated using an improved transfer matrix algorithm. The K-means singular value decomposition sparse dictionary is trained . In the test the spectrum with limited sample points can be obtained and the high resolution spectrum is reconstructed by solving sparse representation equation. Compared with the other conventional basis, the effect of this method is better. The match rate of the recovered spectrum and the original spectrum is over 95%.
Multifrequency Bayesian compressive sensing methods for microwave imaging.
Poli, Lorenzo; Oliveri, Giacomo; Ding, Ping Ping; Moriyama, Toshifumi; Massa, Andrea
2014-11-01
The Bayesian retrieval of sparse scatterers under multifrequency transverse magnetic illuminations is addressed. Two innovative imaging strategies are formulated to process the spectral content of microwave scattering data according to either a frequency-hopping multistep scheme or a multifrequency one-shot scheme. To solve the associated inverse problems, customized implementations of single-task and multitask Bayesian compressive sensing are introduced. A set of representative numerical results is discussed to assess the effectiveness and the robustness against the noise of the proposed techniques also in comparison with some state-of-the-art deterministic strategies.
Object detection oriented video reconstruction using compressed sensing
NASA Astrophysics Data System (ADS)
Kang, Bin; Zhu, Wei-Ping; Yan, Jun
2015-12-01
Moving object detection plays a key role in video surveillance. A number of object detection methods have been proposed in the spatial domain. In this paper, we propose a compressed sensing (CS)-based algorithm for the detection of moving object in video sequences. First, we propose an object detection model to simultaneously reconstruct the foreground, background, and video sequence using the sampled measurement. Then, we use the reconstructed video sequence to estimate a confidence map to improve the foreground reconstruction result. Experimental results show that the proposed moving object detection algorithm outperforms the state-of-the-art approaches and is robust to the movement turbulence and sudden illumination changes.
Image reconstruction and compressive sensing in MIMO radar
NASA Astrophysics Data System (ADS)
Sun, Bing; Lopez, Juan; Qiao, Zhijun
2014-05-01
Multiple-input multiple-output (MIMO) radar utilizes the flexible configuration of transmitting and receiving antennas to construct images of target scenes. Because of the target scenes' sparsity, the compressive sensing (CS) technique can be used to realize a feasible reconstruction of the target scenes from undersampling data. This paper presents the signal model of MIMO radar and derive the corresponding CS measurement matrix, which shows success of the CS technique. Also the basis pursuit method and total-variation minimization method are adopted for different scenes' recovery. Numerical simulations are provided to illustrate the validity of reconstruction for one dimensional and two dimensional scenes.
Highly compressible fluorescent particles for pressure sensing in liquids
NASA Astrophysics Data System (ADS)
Cellini, F.; Peterson, S. D.; Porfiri, M.
2017-05-01
Pressure sensing in liquids is important for engineering applications ranging from industrial processing to naval architecture. Here, we propose a pressure sensor based on highly compressible polydimethylsiloxane foam particles embedding fluorescent Nile Red molecules. The particles display pressure sensitivities as low as 0.0018 kPa-1, which are on the same order of magnitude of sensitivities reported in commercial pressure-sensitive paints for air flows. We envision the application of the proposed sensor in particle image velocimetry toward an improved understanding of flow kinetics in liquids.
Predicting catastrophes in nonlinear dynamical systems by compressive sensing
Wang, Wen-Xu; Yang, Rui; Lai, Ying-Cheng; Kovanis, Vassilios; Grebogi, Celso
2013-01-01
An extremely challenging problem of significant interest is to predict catastrophes in advance of their occurrences. We present a general approach to predicting catastrophes in nonlinear dynamical systems under the assumption that the system equations are completely unknown and only time series reflecting the evolution of the dynamical variables of the system are available. Our idea is to expand the vector field or map of the underlying system into a suitable function series and then to use the compressive-sensing technique to accurately estimate the various terms in the expansion. Examples using paradigmatic chaotic systems are provided to demonstrate our idea and potential challenges are discussed. PMID:21568562
Compressed Sensing (CS) Imaging with Wide FOV and Dynamic Magnification
2011-03-14
Sponsor: Office of Naval Research March 14, 2011 ONR Program manager: Dr . Michael Duncan, Program Officer Tel: (703) 696-5787 Electro-Optics...significant wavelet coefficients at scale k is: /•OO \\k = 6(4 Kh) f(x;ak,0k) dr . (4) Jft Consider the (m,n)th DFT atom within the band Bk...34 Preprint. 2008. [6] M. Lustig , D. Donoho, and J. M. Pauly, "Sparse MRI: The application of compressed sensing for rapid MR imaging," Mag. Reson. in
Compressed sensing based virtual-detector photoacoustic microscopy in vivo.
Meng, Jing; Liu, Chengbo; Zheng, Jiaxiang; Lin, Riqiang; Song, Liang
2014-03-01
Photoacoustic microscopy (PAM) is becoming a vital tool for various biomedical studies, including functional and molecular imaging of cancer. However, due to the use of a focused ultrasonic transducer for photoacoustic detection, the image quality of conventional PAM degrades rapidly away from the ultrasonic focal zone. To improve the image quality of PAM for out-of-focus regions, we have developed compressed sensing based virtual-detector photoacoustic microscopy (CS-PAM). Through phantom and in vivo experiments, it has been demonstrated that CS-PAM can effectively extend the depth of focus of PAM, and thus may greatly expand its potential biomedical applications.
Image denoising by adaptive Compressed Sensing reconstructions and fusions
NASA Astrophysics Data System (ADS)
Meiniel, William; Angelini, Elsa; Olivo-Marin, Jean-Christophe
2015-09-01
In this work, Compressed Sensing (CS) is investigated as a denoising tool in bioimaging. The denoising algorithm exploits multiple CS reconstructions, taking advantage of the robustness of CS in the presence of noise via regularized reconstructions and the properties of the Fourier transform of bioimages. Multiple reconstructions at low sampling rates are combined to generate high quality denoised images using several sparsity constraints. We present different combination methods for the CS reconstructions and quantitatively compare the performance of our denoising methods to state-of-the-art ones.
Energy-efficient Compressed Sensing for ambulatory ECG monitoring.
Craven, Darren; McGinley, Brian; Kilmartin, Liam; Glavin, Martin; Jones, Edward
2016-04-01
Advances in Compressed Sensing (CS) are enabling promising low-energy implementation solutions for wireless Body Area Networks (BAN). While studies demonstrate the potential of CS in terms of overall energy efficiency compared to state-of-the-art lossy compression techniques, the performance of CS remains limited. The aim of this study is to improve the performance of CS-based compression for electrocardiogram (ECG) signals. This paper proposes a CS architecture that combines a novel redundancy removal scheme with quantization and Huffman entropy coding to effectively extend the Compression Ratio (CR). Reconstruction is performed using overcomplete sparse dictionaries created with Dictionary Learning (DL) techniques to exploit the highly structured nature of ECG signals. Performance of the proposed CS implementation is evaluated by analyzing energy-based distortion metrics and diagnostic metrics including QRS beat-detection accuracy across a range of CRs. The proposed CS approach offers superior performance to the most recent state-of-the-art CS implementations in terms of signal reconstruction quality across all CRs tested. Furthermore, QRS detection accuracy of the technique is compared with the well-known lossy Set Partitioning in Hierarchical Trees (SPIHT) compression technique. The proposed CS approach outperforms SPIHT in terms of achievable CR, using the area under the receiver operator characteristic (ROC) curve (AUC). For an application where a minimum AUC performance threshold of 0.9 is required, the proposed technique extends the CR from 64.6 to 90.45 compared with SPIHT, ensuring a 40% saving on wireless transmission costs. Therefore, the results highlight the potential of the proposed technique for ECG computer-aided diagnostic systems. Copyright © 2016 Elsevier Ltd. All rights reserved.
Rate and power efficient image compressed sensing and transmission
NASA Astrophysics Data System (ADS)
Olanigan, Saheed; Cao, Lei; Viswanathan, Ramanarayanan
2016-01-01
This paper presents a suboptimal quantization and transmission scheme for multiscale block-based compressed sensing images over wireless channels. The proposed method includes two stages: dealing with quantization distortion and transmission errors. First, given the total transmission bit rate, the optimal number of quantization bits is assigned to the sensed measurements in different wavelet sub-bands so that the total quantization distortion is minimized. Second, given the total transmission power, the energy is allocated to different quantization bit layers based on their different error sensitivities. The method of Lagrange multipliers with Karush-Kuhn-Tucker conditions is used to solve both optimization problems, for which the first problem can be solved with relaxation and the second problem can be solved completely. The effectiveness of the scheme is illustrated through simulation results, which have shown up to 10 dB improvement over the method without the rate and power optimization in medium and low signal-to-noise ratio cases.
Computational ghost imaging: advanced compressive sensing (CS) technique
NASA Astrophysics Data System (ADS)
Katkovnik, Vladimir; Astola, Jaakko
2012-10-01
A novel efficient variational technique for speckle imaging is discussed. It is developed with the main motivation to filter noise, to wipe out the typical diffraction artifacts and to achieve crisp imaging. A sparse modeling is used for the wave field at the object plane in order to overcome the loss of information due to the ill-posedness of forward propagation image formation operators. This flexible and data adaptive modeling relies on the recent progress in sparse imaging and compressive sensing (CS). Being in line with the general formalism of CS, we develop an original approach to wave field reconstruction.7 In this paper we demonstrate this technique in its application for computational amplitude ghost imaging (GI), where a spatial light modulator (SLM) is used in order to generate a speckle wave field sensing a transmitted mask object.
Miles, A R; Edwards, M J; Greenough, J A
2004-11-08
Perturbations on an interface driven by a strong blast wave grow in time due to a combination of Rayleigh-Taylor, Richtmyer-Meshkov, and decompression effects. In this paper, results from three-dimensional numerical simulations of such a system under drive conditions to be attainable on the National Ignition Facility [E. M. Campbell, Laser Part. Beams, 9(2), 209 (1991)] are presented. Using the multi-physics, adaptive mesh refinement, higher order Godunov Eulerian hydrocode, Raptor [L. H. Howell and J.A. Greenough, J. Comp. Phys. 184, 53 (2003)], the late nonlinear instability evolution, including transition to turbulence, is considered for various multimode perturbation spectra. The 3D post-transition state differs from the 2D result, but the process of transition proceeds similarly in both 2D and 3D. The turbulent mixing transition results in a reduction in the growth rate of the mixing layer relative to its pre-transition value and, in the case of the bubble front, relative to the 2D result. The post-transition spike front velocity is approximately the same in 2D and 3D. Implications for hydrodynamic mixing in core-collapse supernova are discussed.
NASA Astrophysics Data System (ADS)
Bito, Jo; Bahr, Ryan; Hester, Jimmy; Kimionis, John; Nauroze, Abdullah; Su, Wenjing; Tehrani, Bijan; Tentzeris, Manos M.
2017-05-01
In this paper, numerous inkjet-/3D-/4D-printed wearable flexible antennas, RF electronics, modules and sensors fabricated on paper and other polymer (e.g. LCP) substrates are introduced as a system-level solution for ultra-low-cost mass production of autonomous Biomonitoring, Positioning and Sensing applications. This paper briefly discusses the state-of-the-art area of fully-integrated wearable wireless sensor modules on paper or flexible LCP and show the first ever 4D sensor module integration on paper, as well as numerous 3D and 4D multilayer paper-based and LCP-based RF/microwave, flexible and wearable structures, that could potentially set the foundation for the truly convergent wireless sensor ad-hoc "on-body networks of the future with enhanced cognitive intelligence and "rugged" packaging. Also, some challenges concerning the power sources of "nearperpetual" wearable RF modules, including flexible miniaturized batteries as well as power-scavenging approaches involving electromagnetic and solar energy forms are discuessed. The final step of the paper will involve examples from mmW wearable (e.g. biomonitoring) antennas and RF modules, as well as the first examples of the integration of inkjet-printed nanotechnology-based (e.g.CNT) sensors on paper and organic substrates for Internet of Things (IoT) applications. It has to be noted that the paper will review and present challenges for inkjetprinted organic active and nonlinear devices as well as future directions in the area of environmentally-friendly "green") wearable RF electronics and "smart-skin conformal sensors.
Accelerated Whole-Brain Multi-Parameter Mapping using Blind Compressed Sensing
Bhave, Sampada; Lingala, Sajan Goud; Johnson, Casey P.; Magnotta, Vincent A.; Jacob, Mathews
2015-01-01
Purpose To introduce a blind compressed sensing (BCS) framework to accelerate multi-parameter MR mapping, and demonstrate its feasibility in high-resolution, whole-brain T1ρ and T2 mapping. Methods BCS models the evolution of magnetization at every pixel as a sparse linear combination of bases in a dictionary. Unlike compressed sensing (CS), the dictionary and the sparse coefficients are jointly estimated from under-sampled data. Large number of non-orthogonal bases in BCS accounts for more complex signals than low rank representations. The low degree of freedom of BCS, attributed to sparse coefficients, translates to fewer artifacts at high acceleration factors(R). Results From 2D retrospective under-sampling experiments, the mean square errors in T1ρ and T2 maps were observed to be within 0.1% up to R=10. BCS was observed to be more robust to patient-specific motion as compared to other CS schemes and resulted in minimal degradation of parameter maps in the presence of motion. Our results suggested that BCS can provide an acceleration factor of 8 in prospective 3D imaging with reasonable reconstructions. Conclusion BCS considerably reduces scan time for multi-parameter mapping of the whole brain with minimal artifacts, and is more robust to motion-induced signal changes compared to current CS and PCA based techniques. PMID:25850952
A wireless video multicasting scheme based on multi-scale compressed sensing
NASA Astrophysics Data System (ADS)
Wang, Anhong; Wu, Qingdian; Ma, Xiaoli; Zeng, Bing
2015-12-01
Video multicast is becoming more and more popular in wireless multimedia applications, in which one major challenge is to offer heterogeneous users with a graceful degradation against varying packet loss ratios and channel noise. In this paper, we propose a multi-scale compressed sensing-based wireless video multicast scheme, abbreviated as MCS-cast. The encoder of MCS-cast decomposes each video frame through a discrete wavelet transform (DWT) and explores an optimized compressed sensing (CS) rate to sample/measure each DWT level. The CS measurements are then packed in such a way that all packets are made as equally important as possible, while each packet includes different percentages of different DWT levels. Finally, the packets are transmitted via an analog-like modulator with mapping of the measurements into a very dense constellation. We demonstrate that because of larger percentages of more important DWT levels in each packet, packet loss leads to a much reduced influence on the reconstruction quality. Experimental results show that our MCS-cast preserves the property of graceful degradation for heterogeneous users and can outperform the state-of-the-art SoftCast by up to 3 dB in PSNR at high packet loss ratios (over the same noisy channel).
Optical scanning holography based on compressive sensing using a digital micro-mirror device
NASA Astrophysics Data System (ADS)
A-qian, Sun; Ding-fu, Zhou; Sheng, Yuan; You-jun, Hu; Peng, Zhang; Jian-ming, Yue; xin, Zhou
2017-02-01
Optical scanning holography (OSH) is a distinct digital holography technique, which uses a single two-dimensional (2D) scanning process to record the hologram of a three-dimensional (3D) object. Usually, these 2D scanning processes are in the form of mechanical scanning, and the quality of recorded hologram may be affected due to the limitation of mechanical scanning accuracy and unavoidable vibration of stepper motor's start-stop. In this paper, we propose a new framework, which replaces the 2D mechanical scanning mirrors with a Digital Micro-mirror Device (DMD) to modulate the scanning light field, and we call it OSH based on Compressive Sensing (CS) using a digital micro-mirror device (CS-OSH). CS-OSH can reconstruct the hologram of an object through the use of compressive sensing theory, and then restore the image of object itself. Numerical simulation results confirm this new type OSH can get a reconstructed image with favorable visual quality even under the condition of a low sample rate.
NASA Astrophysics Data System (ADS)
Li, P.; Turk, J.; Vu, Q.; Knosp, B.; Hristova-Veleva, S. M.; Lambrigtsen, B.; Poulsen, W. L.; Licata, S.
2009-12-01
NASA is planning a new field experiment, the Genesis and Rapid Intensification Processes (GRIP), in the summer of 2010 to better understand how tropical storms form and develop into major hurricanes. The DC-8 aircraft and the Global Hawk Unmanned Airborne System (UAS) will be deployed loaded with instruments for measurements including lightning, temperature, 3D wind, precipitation, liquid and ice water contents, aerosol and cloud profiles. During the field campaign, both the spaceborne and the airborne observations will be collected in real-time and integrated with the hurricane forecast models. This observation-model integration will help the campaign achieve its science goals by allowing team members to effectively plan the mission with current forecasts. To support the GRIP experiment, JPL developed a website for interactive visualization of all related remote-sensing observations in the GRIP’s geographical domain using the new Google Earth API. All the observations are collected in near real-time (NRT) with 2 to 5 hour latency. The observations include a 1KM blended Sea Surface Temperature (SST) map from GHRSST L2P products; 6-hour composite images of GOES IR; stability indices, temperature and vapor profiles from AIRS and AMSU-B; microwave brightness temperature and rain index maps from AMSR-E, SSMI and TRMM-TMI; ocean surface wind vectors, vorticity and divergence of the wind from QuikSCAT; the 3D precipitation structure from TRMM-PR and vertical profiles of cloud and precipitation from CloudSAT. All the NRT observations are collected from the data centers and science facilities at NASA and NOAA, subsetted, re-projected, and composited into hourly or daily data products depending on the frequency of the observation. The data products are then displayed on the 3D Google Earth plug-in at the JPL Tropical Cyclone Information System (TCIS) website. The data products offered by the TCIS in the Google Earth display include image overlays, wind vectors, clickable
NASA Astrophysics Data System (ADS)
Zinner, T.; Wind, G.; Platnick, S.; Ackerman, A. S.
2010-10-01
Remote sensing of cloud effective particle size with passive sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) is an important tool for cloud microphysical studies. As a measure of the radiatively relevant droplet size, effective radius can be retrieved with different combinations of visible through shortwave and midwave infrared channels. In practice, retrieved effective radii from these combinations can be quite different. This difference is perhaps indicative of different penetration depths and path lengths for the spectral reflectances used. In addition, operational liquid water cloud retrievals are based on the assumption of a relatively narrow distribution of droplet sizes; the role of larger precipitation particles in these distributions is neglected. Therefore, possible explanations for the discrepancy in some MODIS spectral size retrievals could include 3-D radiative transport effects, including sub-pixel cloud inhomogeneity, and/or the impact of drizzle formation. For three cloud cases the possible factors of influence are isolated and investigated in detail by the use of simulated cloud scenes and synthetic satellite data: marine boundary layer cloud scenes from large eddy simulations (LES) with detailed microphysics are combined with Monte Carlo radiative transfer calculations that explicitly account for the detailed droplet size distributions as well as 3-D radiative transfer to simulate MODIS observations. The operational MODIS optical thickness and effective radius retrieval algorithm is applied to these and the results are compared to the given LES microphysics. We investigate two types of marine cloud situations each with and without drizzle from LES simulations: (1) a typical daytime stratocumulus deck at two times in the diurnal cycle and (2) one scene with scattered cumulus. Only small impact of drizzle formation on the retrieved domain average and on the differences between the three effective radius retrievals is noticed
High dynamic range coherent imaging using compressed sensing.
He, Kuan; Sharma, Manoj Kumar; Cossairt, Oliver
2015-11-30
In both lensless Fourier transform holography (FTH) and coherent diffraction imaging (CDI), a beamstop is used to block strong intensities which exceed the limited dynamic range of the sensor, causing a loss in low-frequency information, making high quality reconstructions difficult or even impossible. In this paper, we show that an image can be recovered from high-frequencies alone, thereby overcoming the beamstop problem in both FTH and CDI. The only requirement is that the object is sparse in a known basis, a common property of most natural and manmade signals. The reconstruction method relies on compressed sensing (CS) techniques, which ensure signal recovery from incomplete measurements. Specifically, in FTH, we perform compressed sensing (CS) reconstruction of captured holograms and show that this method is applicable not only to standard FTH, but also multiple or extended reference FTH. For CDI, we propose a new phase retrieval procedure, which combines Fienup's hybrid input-output (HIO) method and CS. Both numerical simulations and proof-of-principle experiments are shown to demonstrate the effectiveness and robustness of the proposed CS-based reconstructions in dealing with missing data in both FTH and CDI.
A novel image fusion approach based on compressive sensing
NASA Astrophysics Data System (ADS)
Yin, Hongpeng; Liu, Zhaodong; Fang, Bin; Li, Yanxia
2015-11-01
Image fusion can integrate complementary and relevant information of source images captured by multiple sensors into a unitary synthetic image. The compressive sensing-based (CS) fusion approach can greatly reduce the processing speed and guarantee the quality of the fused image by integrating fewer non-zero coefficients. However, there are two main limitations in the conventional CS-based fusion approach. Firstly, directly fusing sensing measurements may bring greater uncertain results with high reconstruction error. Secondly, using single fusion rule may result in the problems of blocking artifacts and poor fidelity. In this paper, a novel image fusion approach based on CS is proposed to solve those problems. The non-subsampled contourlet transform (NSCT) method is utilized to decompose the source images. The dual-layer Pulse Coupled Neural Network (PCNN) model is used to integrate low-pass subbands; while an edge-retention based fusion rule is proposed to fuse high-pass subbands. The sparse coefficients are fused before being measured by Gaussian matrix. The fused image is accurately reconstructed by Compressive Sampling Matched Pursuit algorithm (CoSaMP). Experimental results demonstrate that the fused image contains abundant detailed contents and preserves the saliency structure. These also indicate that our proposed method achieves better visual quality than the current state-of-the-art methods.
Compressive Sensing Image Fusion Based on Particle Swarm Optimization Algorithm
NASA Astrophysics Data System (ADS)
Li, X.; Lv, J.; Jiang, S.; Zhou, H.
2017-09-01
In order to solve the problem that the spatial matching is difficult and the spectral distortion is large in traditional pixel-level image fusion algorithm. We propose a new method of image fusion that utilizes HIS transformation and the recently developed theory of compressive sensing that is called HIS-CS image fusion. In this algorithm, the particle swarm optimization algorithm is used to select the fusion coefficient ω. In the iterative process, the image fusion coefficient ω is taken as particle, and the optimal value is obtained by combining the optimal objective function. Then we use the compression-aware weighted fusion algorithm for remote sensing image fusion, taking the coefficient ω as the weight value. The algorithm ensures the optimal selection of fusion effect with a certain degree of self-adaptability. To evaluate the fused images, this paper uses five kinds of index parameters such as Entropy, Standard Deviation, Average Gradient, Degree of Distortion and Peak Signal-to-Noise Ratio. The experimental results show that the image fusion effect of the algorithm in this paper is better than that of traditional methods.
Statistical mechanics analysis of thresholding 1-bit compressed sensing
NASA Astrophysics Data System (ADS)
Xu, Yingying; Kabashima, Yoshiyuki
2016-08-01
The one-bit compressed sensing framework aims to reconstruct a sparse signal by only using the sign information of its linear measurements. To compensate for the loss of scale information, past studies in the area have proposed recovering the signal by imposing an additional constraint on the l 2-norm of the signal. Recently, an alternative strategy that captures scale information by introducing a threshold parameter to the quantization process was advanced. In this paper, we analyze the typical behavior of thresholding 1-bit compressed sensing utilizing the replica method of statistical mechanics, so as to gain an insight for properly setting the threshold value. Our result shows that fixing the threshold at a constant value yields better performance than varying it randomly when the constant is optimally tuned, statistically. Unfortunately, the optimal threshold value depends on the statistical properties of the target signal, which may not be known in advance. In order to handle this inconvenience, we develop a heuristic that adaptively tunes the threshold parameter based on the frequency of positive (or negative) values in the binary outputs. Numerical experiments show that the heuristic exhibits satisfactory performance while incurring low computational cost.
Structure assisted compressed sensing reconstruction of undersampled AFM images.
Oxvig, Christian Schou; Arildsen, Thomas; Larsen, Torben
2017-01-01
The use of compressed sensing in atomic force microscopy (AFM) can potentially speed-up image acquisition, lower probe-specimen interaction, or enable super resolution imaging. The idea in compressed sensing for AFM is to spatially undersample the specimen, i.e. only acquire a small fraction of the full image of it, and then use advanced computational techniques to reconstruct the remaining part of the image whenever this is possible. Our initial experiments have shown that it is possible to leverage inherent structure in acquired AFM images to improve image reconstruction. Thus, we have studied structure in the discrete cosine transform coefficients of typical AFM images. Based on this study, we propose a generic support structure model that may be used to improve the quality of the reconstructed AFM images. Furthermore, we propose a modification to the established iterative thresholding reconstruction algorithms that enables the use of our proposed structure model in the reconstruction process. Through a large set of reconstructions, the general reconstruction capability improvement achievable using our structured model is shown both quantitatively and qualitatively. Specifically, our experiments show that our proposed algorithm improves over established iterative thresholding algorithms by being able to reconstruct AFM images to a comparable quality using fewer measurements or equivalently obtaining a more detailed reconstruction for a fixed number of measurements. Copyright © 2016 Elsevier B.V. All rights reserved.
Multi-contrast Reconstruction with Bayesian Compressed Sensing
Bilgic, Berkin; Goyal, Vivek K; Adalsteinsson, Elfar
2011-01-01
Clinical imaging with structural MRI routinely relies on multiple acquisitions of the same region of interest under several different contrast preparations. This work presents a reconstruction algorithm based on Bayesian compressed sensing to jointly reconstruct a set of images from undersampled k-space data with higher fidelity than when the images are reconstructed either individually or jointly by a previously proposed algorithm, M-FOCUSS. The joint inference problem is formulated in a hierarchical Bayesian setting, wherein solving each of the inverse problems corresponds to finding the parameters (here, image gradient coefficients) associated with each of the images. The variance of image gradients across contrasts for a single volumetric spatial position is a single hyperparameter. All of the images from the same anatomical region, but with different contrast properties, contribute to the estimation of the hyperparameters, and once they are found, the k-space data belonging to each image are used independently to infer the image gradients. Thus, commonality of image spatial structure across contrasts is exploited without the problematic assumption of correlation across contrasts. Examples demonstrate improved reconstruction quality (up to a factor of 4 in root-mean-square error) compared to previous compressed sensing algorithms and show the benefit of joint inversion under a hierarchical Bayesian model. PMID:21671267
Coded strobing photography: compressive sensing of high speed periodic videos.
Veeraraghavan, Ashok; Reddy, Dikpal; Raskar, Ramesh
2011-04-01
We show that, via temporal modulation, one can observe and capture a high-speed periodic video well beyond the abilities of a low-frame-rate camera. By strobing the exposure with unique sequences within the integration time of each frame, we take coded projections of dynamic events. From a sequence of such frames, we reconstruct a high-speed video of the high-frequency periodic process. Strobing is used in entertainment, medical imaging, and industrial inspection to generate lower beat frequencies. But this is limited to scenes with a detectable single dominant frequency and requires high-intensity lighting. In this paper, we address the problem of sub-Nyquist sampling of periodic signals and show designs to capture and reconstruct such signals. The key result is that for such signals, the Nyquist rate constraint can be imposed on the strobe rate rather than the sensor rate. The technique is based on intentional aliasing of the frequency components of the periodic signal while the reconstruction algorithm exploits recent advances in sparse representations and compressive sensing. We exploit the sparsity of periodic signals in the Fourier domain to develop reconstruction algorithms that are inspired by compressive sensing.
Compressed sensing sodium MRI of cartilage at 7T: Preliminary study
NASA Astrophysics Data System (ADS)
Madelin, Guillaume; Chang, Gregory; Otazo, Ricardo; Jerschow, Alexej; Regatte, Ravinder R.
2012-01-01
Sodium MRI has been shown to be highly specific for glycosaminoglycan (GAG) content in articular cartilage, the loss of which is an early sign of osteoarthritis (OA). Quantitative sodium MRI techniques are therefore under development in order to detect and assess early biochemical degradation of cartilage, but due to low sodium NMR sensitivity and its low concentration, sodium images need long acquisition times (15-25 min) even at high magnetic fields and are typically of low resolution. In this preliminary study, we show that compressed sensing can be applied to reduce the acquisition time by a factor of 2 at 7T without losing sodium quantification accuracy. Alternatively, the nonlinear reconstruction technique can be used to denoise fully-sampled images. We expect to even further reduce this acquisition time by using parallel imaging techniques combined with SNR-improved 3D sequences at 3T and 7T.
High-Resolution Mesoscopic Fluorescence Molecular Tomography Based on Compressive Sensing
Yang, Fugang; Ozturk, Mehmet S.; Zhao, Lingling; Cong, Wenxiang; Wang, Ge
2017-01-01
Mesoscopic fluorescence molecular tomography (MFMT) is new imaging modality aiming at 3-D imaging of molecular probes in a few millimeter thick biological samples with high-spatial resolution. In this paper, we develop a compressive sensing-based reconstruction method with l1-norm regularization for MFMT with the goal of improving spatial resolution and stability of the optical inverse problem. Three-dimensional numerical simulations of anatomically accurate microvasculature and real data obtained from phantom experiments are employed to evaluate the merits of the proposed method. Experimental results show that the proposed method can achieve 80 μm spatial resolution for a biological sample of 3 mm thickness and more accurate quantifications of concentrations and locations for the fluorophore distribution than those of the conventional methods. PMID:25137718
NASA Astrophysics Data System (ADS)
Liu, Xingbin; Mei, Wenbo; Du, Huiqian
2016-05-01
In this paper, a novel approach based on compressive sensing and chaos is proposed for simultaneously compressing, fusing and encrypting multi-modal images. The sparsely represented source images are firstly measured with the key-controlled pseudo-random measurement matrix constructed using logistic map, which reduces the data to be processed and realizes the initial encryption. Then the obtained measurements are fused by the proposed adaptive weighted fusion rule. The fused measurement is further encrypted into the ciphertext through an iterative procedure including improved random pixel exchanging technique and fractional Fourier transform. The fused image can be reconstructed by decrypting the ciphertext and using a recovery algorithm. The proposed algorithm not only reduces data volume but also simplifies keys, which improves the efficiency of transmitting data and distributing keys. Numerical results demonstrate the feasibility and security of the proposed scheme.
Mouw, J K; Imler, S M; Levenston, M E
2007-01-01
Inhibition of various ion channels alters chondrocyte mechanotransduction in monolayer, but the mechanisms involved in chondrocyte mechanotransduction in three- dimensional culture remain unclear. The objective of this study was to investigate the effects of inhibiting putative ion-channel influenced mechanotransduction mechanisms on the chondrocyte responses to static and dynamic compression in three-dimensional culture. Bovine articular cartilage explants were used to investigate the dose-dependent inhibition and recovery of protein and sulfated glycosaminoglycan (sGAG) syntheses by four ion-channel inhibitors: 4-Aminopyridine (4AP), a K(+) channel blocker; Nifedipine (Nf), a Ca(2+) channel blocker; Gadolinium (Gd), a stretch-activated channel blocker; and Thapsigargin (Tg), which releases intracellular Ca(2+) stores by inhibiting ATP-dependent Ca(2+) pumps. Chondrocyte-seeded agarose gels were used to examine the influence of 20 h of static and dynamic loading in the presence of each of the inhibitors. Overall, treatment with the ion-channel inhibitors had a greater effect on sGAG synthesis, with the exception of Nf, which more substantially affected protein synthesis. Treatment with Tg significantly impaired both overall protein and sGAG synthesis, with a drastic reduction in sGAG synthesis. The inhibitors differentially influenced the responses to mechanical stimuli. Dynamic compression significantly upregulated protein synthesis but did not significantly affect sGAG synthesis with Nf or Tg treatment. Dynamic compression significantly upregulated both protein and sGAG synthesis rates with Gd treatment. There was no significant stimulation of either protein or sGAG synthesis by dynamic compression with 4AP treatment. Interruption of many ion-channel signaling mechanisms affected sGAG synthesis, suggesting a complicated, multi-pathway signaling process. Also, Ca(2+) signaling may be critical for the transduction of mechanical stimulus in regulating s
Nonconvex compressed sensing by nature-inspired optimization algorithms.
Liu, Fang; Lin, Leping; Jiao, Licheng; Li, Lingling; Yang, Shuyuan; Hou, Biao; Ma, Hongmei; Yang, Li; Xu, Jinghuan
2015-05-01
The l 0 regularized problem in compressed sensing reconstruction is nonconvex with NP-hard computational complexity. Methods available for such problems fall into one of two types: greedy pursuit methods and thresholding methods, which are characterized by suboptimal fast search strategies. Nature-inspired algorithms for combinatorial optimization are famous for their efficient global search strategies and superior performance for nonconvex and nonlinear problems. In this paper, we study and propose nonconvex compressed sensing for natural images by nature-inspired optimization algorithms. We get measurements by the block-based compressed sampling and introduce an overcomplete dictionary of Ridgelet for image blocks. An atom of this dictionary is identified by the parameters of direction, scale and shift. Of them, direction parameter is important for adapting to directional regularity. So we propose a two-stage reconstruction scheme (TS_RS) of nature-inspired optimization algorithms. In the first reconstruction stage, we design a genetic algorithm for a class of image blocks to acquire the estimation of atomic combinations in all directions; and in the second reconstruction stage, we adopt clonal selection algorithm to search better atomic combinations in the sub-dictionary resulted by the first stage for each image block further on scale and shift parameters. In TS_RS, to reduce the uncertainty and instability of the reconstruction problems, we adopt novel and flexible heuristic searching strategies, which include delicately designing the initialization, operators, evaluating methods, and so on. The experimental results show the efficiency and stability of the proposed TS_RS of nature-inspired algorithms, which outperforms classic greedy and thresholding methods.
Modelling compression sensing in ionic polymer metal composites
NASA Astrophysics Data System (ADS)
Volpini, Valentina; Bardella, Lorenzo; Rodella, Andrea; Cha, Youngsu; Porfiri, Maurizio
2017-03-01
Ionic polymer metal composites (IPMCs) consist of an ionomeric membrane, including mobile counterions, sandwiched between two thin noble metal electrodes. IPMCs find application as sensors and actuators, where an imposed mechanical loading generates a voltage across the electrodes, and, vice versa, an imposed electric field causes deformation. Here, we present a predictive modelling approach to elucidate the dynamic sensing response of IPMCs subject to a time-varying through-the-thickness compression (‘compression sensing’). The model relies on the continuum theory recently developed by Porfiri and co-workers, which couples finite deformations to the modified Poisson-Nernst-Planck (PNP) system governing the IPMC electrochemistry. For the ‘compression sensing’ problem we establish a perturbative closed-form solution along with a finite element (FE) solution. The systematic comparison between these two solutions is a central contribution of this study, offering insight on accuracy and mathematical complexity. The method of matched asymptotic expansions is employed to find the analytical solution. To this end, we uncouple the force balance from the modified PNP system and separately linearise the PNP equations in the ionomer bulk and in the boundary layers at the ionomer-electrode interfaces. Comparison with FE results for the fully coupled nonlinear system demonstrates the accuracy of the analytical solution to describe IPMC sensing for moderate deformation levels. We finally demonstrate the potential of the modelling scheme to accurately reproduce experimental results from the literature. The proposed model is expected to aid in the design of IPMC sensors, contribute to an improved understanding of IPMC electrochemomechanical response, and offer insight into the role of nonlinear phenomena across mechanics and electrochemistry.
Quantum Tomography via Compressed Sensing: Error Bounds, Sample Complexity and Efficient Estimators
2012-09-27
REPORT Quantum tomography via compressed sensing : error bounds, sample complexity and efficient estimators 14. ABSTRACT 16. SECURITY CLASSIFICATION OF...Box 12211 Research Triangle Park, NC 27709-2211 15. SUBJECT TERMS quantum tomography, compressed sensing Steven T Flammia, David Gross, Yi-Kai Liu... compressed sensing : error bounds, sample complexity and efficient estimators Report Title ABSTRACT Intuitively, if a density operator has small rank, then
AlAfeef, Ala; Bobynko, Joanna; Cockshott, W Paul; Craven, Alan J; Zuazo, Ian; Barges, Patrick; MacLaren, Ian
2016-11-01
We have investigated the use of DualEELS in elementally sensitive tilt series tomography in the scanning transmission electron microscope. A procedure is implemented using deconvolution to remove the effects of multiple scattering, followed by normalisation by the zero loss peak intensity. This is performed to produce a signal that is linearly dependent on the projected density of the element in each pixel. This method is compared with one that does not include deconvolution (although normalisation by the zero loss peak intensity is still performed). Additionally, we compare the 3D reconstruction using a new compressed sensing algorithm, DLET, with the well-established SIRT algorithm. VC precipitates, which are extracted from a steel on a carbon replica, are used in this study. It is found that the use of this linear signal results in a very even density throughout the precipitates. However, when deconvolution is omitted, a slight density reduction is observed in the cores of the precipitates (a so-called cupping artefact). Additionally, it is clearly demonstrated that the 3D morphology is much better reproduced using the DLET algorithm, with very little elongation in the missing wedge direction. It is therefore concluded that reliable elementally sensitive tilt tomography using EELS requires the appropriate use of DualEELS together with a suitable reconstruction algorithm, such as the compressed sensing based reconstruction algorithm used here, to make the best use of the limited data volume and signal to noise inherent in core-loss EELS. Copyright © 2016 Elsevier B.V. All rights reserved.
NASA Technical Reports Server (NTRS)
Harris, Julius E.; Iyer, Venkit; Radwan, Samir
1987-01-01
The application of stability theory in Laminar Flow Control (LFC) research requires that density and velocity profiles be specified throughout the viscous flow field of interest. These profile values must be as numerically accurate as possible and free of any numerically induced oscillations. Guidelines for the present research project are presented: develop an efficient and accurate procedure for solving the 3-D boundary layer equation for aerospace configurations; develop an interface program to couple selected 3-D inviscid programs that span the subsonic to hypersonic Mach number range; and document and release software to the LFC community. The interface program was found to be a dependable approach for developing a user friendly procedure for generating the boundary-layer grid and transforming an inviscid solution from a relatively coarse grid to a sufficiently fine boundary-layer grid. The boundary-layer program was shown to be fourth-order accurate in the direction normal to the wall boundary and second-order accurate in planes parallel to the boundary. The fourth-order accuracy allows accurate calculations with as few as one-fifth the number of grid points required for conventional second-order schemes.
An Efficient Distributed Compressed Sensing Algorithm for Decentralized Sensor Network.
Liu, Jing; Huang, Kaiyu; Zhang, Guoxian
2017-04-20
We consider the joint sparsity Model 1 (JSM-1) in a decentralized scenario, where a number of sensors are connected through a network and there is no fusion center. A novel algorithm, named distributed compact sensing matrix pursuit (DCSMP), is proposed to exploit the computational and communication capabilities of the sensor nodes. In contrast to the conventional distributed compressed sensing algorithms adopting a random sensing matrix, the proposed algorithm focuses on the deterministic sensing matrices built directly on the real acquisition systems. The proposed DCSMP algorithm can be divided into two independent parts, the common and innovation support set estimation processes. The goal of the common support set estimation process is to obtain an estimated common support set by fusing the candidate support set information from an individual node and its neighboring nodes. In the following innovation support set estimation process, the measurement vector is projected into a subspace that is perpendicular to the subspace spanned by the columns indexed by the estimated common support set, to remove the impact of the estimated common support set. We can then search the innovation support set using an orthogonal matching pursuit (OMP) algorithm based on the projected measurement vector and projected sensing matrix. In the proposed DCSMP algorithm, the process of estimating the common component/support set is decoupled with that of estimating the innovation component/support set. Thus, the inaccurately estimated common support set will have no impact on estimating the innovation support set. It is proven that under the condition the estimated common support set contains the true common support set, the proposed algorithm can find the true innovation set correctly. Moreover, since the innovation support set estimation process is independent of the common support set estimation process, there is no requirement for the cardinality of both sets; thus, the proposed DCSMP
An Efficient Distributed Compressed Sensing Algorithm for Decentralized Sensor Network
Liu, Jing; Huang, Kaiyu; Zhang, Guoxian
2017-01-01
We consider the joint sparsity Model 1 (JSM-1) in a decentralized scenario, where a number of sensors are connected through a network and there is no fusion center. A novel algorithm, named distributed compact sensing matrix pursuit (DCSMP), is proposed to exploit the computational and communication capabilities of the sensor nodes. In contrast to the conventional distributed compressed sensing algorithms adopting a random sensing matrix, the proposed algorithm focuses on the deterministic sensing matrices built directly on the real acquisition systems. The proposed DCSMP algorithm can be divided into two independent parts, the common and innovation support set estimation processes. The goal of the common support set estimation process is to obtain an estimated common support set by fusing the candidate support set information from an individual node and its neighboring nodes. In the following innovation support set estimation process, the measurement vector is projected into a subspace that is perpendicular to the subspace spanned by the columns indexed by the estimated common support set, to remove the impact of the estimated common support set. We can then search the innovation support set using an orthogonal matching pursuit (OMP) algorithm based on the projected measurement vector and projected sensing matrix. In the proposed DCSMP algorithm, the process of estimating the common component/support set is decoupled with that of estimating the innovation component/support set. Thus, the inaccurately estimated common support set will have no impact on estimating the innovation support set. It is proven that under the condition the estimated common support set contains the true common support set, the proposed algorithm can find the true innovation set correctly. Moreover, since the innovation support set estimation process is independent of the common support set estimation process, there is no requirement for the cardinality of both sets; thus, the proposed DCSMP
NASA Astrophysics Data System (ADS)
Zhou, Nanrun; Zhang, Aidi; Zheng, Fen; Gong, Lihua
2014-10-01
The existing ways to encrypt images based on compressive sensing usually treat the whole measurement matrix as the key, which renders the key too large to distribute and memorize or store. To solve this problem, a new image compression-encryption hybrid algorithm is proposed to realize compression and encryption simultaneously, where the key is easily distributed, stored or memorized. The input image is divided into 4 blocks to compress and encrypt, then the pixels of the two adjacent blocks are exchanged randomly by random matrices. The measurement matrices in compressive sensing are constructed by utilizing the circulant matrices and controlling the original row vectors of the circulant matrices with logistic map. And the random matrices used in random pixel exchanging are bound with the measurement matrices. Simulation results verify the effectiveness, security of the proposed algorithm and the acceptable compression performance.
NASA Astrophysics Data System (ADS)
Zhang, Lisha
We present fast and robust numerical algorithms for 3-D scattering from perfectly electrical conducting (PEC) and dielectric random rough surfaces in microwave remote sensing. The Coifman wavelets or Coiflets are employed to implement Galerkin's procedure in the method of moments (MoM). Due to the high-precision one-point quadrature, the Coiflets yield fast evaluations of the most off-diagonal entries, reducing the matrix fill effort from O(N2) to O( N). The orthogonality and Riesz basis of the Coiflets generate well conditioned impedance matrix, with rapid convergence for the conjugate gradient solver. The resulting impedance matrix is further sparsified by the matrix-formed standard fast wavelet transform (SFWT). By properly selecting multiresolution levels of the total transformation matrix, the solution precision can be enhanced while matrix sparsity and memory consumption have not been noticeably sacrificed. The unified fast scattering algorithm for dielectric random rough surfaces can asymptotically reduce to the PEC case when the loss tangent grows extremely large. Numerical results demonstrate that the reduced PEC model does not suffer from ill-posed problems. Compared with previous publications and laboratory measurements, good agreement is observed.
NASA Astrophysics Data System (ADS)
Wei, Ruiying; Guo, Boling; Li, Yin
2017-09-01
The Cauchy problem for the three-dimensional compressible magneto-micropolar fluid equations is considered. Existence of global-in-time smooth solutions is established under the condition that the initial data are small perturbations of some given constant state. Moreover, we obtain the time decay rates of the higher-order spatial derivatives of the solution by combining the Lp-Lq estimates for the linearized equations and the Fourier splitting method, if the initial perturbation is small in H3-norm and bounded in L1-norm.
NASA Astrophysics Data System (ADS)
Petrov, Mikhail A.; Kosatchyov, Nikolay V.; Petrov, Pavel A.
2016-10-01
The paper represents the results of the study concerning the investigation of the influence of the filling grade (material density) on the force characteristic during the uniaxial compression test of the cylindrical polymer probes produced by additive technology based on FDM. The authors have shown that increasing of the filling grate follows to the increase of the deformation forces. However, the dependency is not a linear function and characterized by soft-elastic model of material behaviour, which is typical for polymers partly crystallized structure.
Solwnd: A 3D Compressible MHD Code for Solar Wind Studies. Version 1.0: Cartesian Coordinates
NASA Technical Reports Server (NTRS)
Deane, Anil E.
1996-01-01
Solwnd 1.0 is a three-dimensional compressible MHD code written in Fortran for studying the solar wind. Time-dependent boundary conditions are available. The computational algorithm is based on Flux Corrected Transport and the code is based on the existing code of Zalesak and Spicer. The flow considered is that of shear flow with incoming flow that perturbs this base flow. Several test cases corresponding to pressure balanced magnetic structures with velocity shear flow and various inflows including Alfven waves are presented. Version 1.0 of solwnd considers a rectangular Cartesian geometry. Future versions of solwnd will consider a spherical geometry. Some discussions of this issue is presented.
Learning-based compressed sensing for infrared image super resolution
NASA Astrophysics Data System (ADS)
Zhao, Yao; Sui, Xiubao; Chen, Qian; Wu, Shaochi
2016-05-01
This paper presents an infrared image super-resolution method based on compressed sensing (CS). First, the reconstruction model under the CS framework is established and a Toeplitz matrix is selected as the sensing matrix. Compared with traditional learning-based methods, the proposed method uses a set of sub-dictionaries instead of two coupled dictionaries to recover high resolution (HR) images. And Toeplitz sensing matrix allows the proposed method time-efficient. Second, all training samples are divided into several feature spaces by using the proposed adaptive k-means classification method, which is more accurate than the standard k-means method. On the basis of this approach, a complex nonlinear mapping from the HR space to low resolution (LR) space can be converted into several compact linear mappings. Finally, the relationships between HR and LR image patches can be obtained by multi-sub-dictionaries and HR infrared images are reconstructed by the input LR images and multi-sub-dictionaries. The experimental results show that the proposed method is quantitatively and qualitatively more effective than other state-of-the-art methods.
NASA Astrophysics Data System (ADS)
Zeng, Jifang; Hu, Hong; Zhou, Lin
2017-06-01
More and more researches have been focused on auxetic composite materials and a number of composite structures have been fabricated, synthesized or theoretically predicted. Since their structures are complex, their mechanical behavior is very difficult to be characterized. The purpose of the present paper is to systematically investigate the negative Poisson’s ratio effect of a novel three-dimensional auxetic orthogonal textile composite under compression. Firstly, a set of equations are derived for the theoretical calculation of the Poisson’s ratio of the composite under uniaxial compression via an analytical analysis. Secondly, a finite element model (FEM) is created by ANSYS Parameter Design Language and is verified by experiment. The deviation between the simulation and experimental results are carefully discussed. Thirdly, the effects of geometry parameters and material properties on the negative Poisson’s ratio behavior of the composite are discussed based on the FEM simulated results. At last, a general basis is concluded. It is expected that the outcomes of this study could be useful to guide the design and fabrication of auxetic textile composites with required negative Poisson’s ratio behavior.
NASA Technical Reports Server (NTRS)
Donohue, James M.; Victor, Kenneth G.; Mcdaniel, James C., Jr.
1993-01-01
A computer-controlled technique, using planar laser-induced iodine fluorescence, for measuring complex compressible flowfields is presented. A new laser permits the use of a planar two-line temperature technique so that all parameters can be measured with the laser operated narrowband. Pressure and temperature measurements in a step flowfield show agreement within 10 percent of a CFD model except in regions close to walls. Deviation of near wall temperature measurements from the model was decreased from 21 percent to 12 percent compared to broadband planar temperature measurements. Computer-control of the experiment has been implemented, except for the frequency tuning of the laser. Image data storage and processing has been improved by integrating a workstation into the experimental setup reducing the data reduction time by a factor of 50.
NASA Technical Reports Server (NTRS)
Donohue, James M.; Victor, Kenneth G.; Mcdaniel, James C., Jr.
1993-01-01
A computer-controlled technique, using planar laser-induced iodine fluorescence, for measuring complex compressible flowfields is presented. A new laser permits the use of a planar two-line temperature technique so that all parameters can be measured with the laser operated narrowband. Pressure and temperature measurements in a step flowfield show agreement within 10 percent of a CFD model except in regions close to walls. Deviation of near wall temperature measurements from the model was decreased from 21 percent to 12 percent compared to broadband planar temperature measurements. Computer-control of the experiment has been implemented, except for the frequency tuning of the laser. Image data storage and processing has been improved by integrating a workstation into the experimental setup reducing the data reduction time by a factor of 50.
NASA Astrophysics Data System (ADS)
Matasci, Battista; Carrea, Dario; Jaboyedoff, Michel; Metzger, Richard; Stock, Greg; Putnam, Roger
2013-04-01
a TLS point cloud. We validated the mapping with field observations and high resolution digital photographs. TLS provides 3D data to precisely characterize the morphology of vertical and overhanging rock faces. With the recently developed methods it is possible to remotely map geologic limits and exfoliation joints, as well as to assess the density of potential failure mechanisms directly on the TLS point clouds. These advances in remote sensing methods provide new tools to locate the most probable future rockfall sources and provide key elements needed to evaluate the potential rockfall hazard of every area of the cliffs in Yosemite Valley.
Separate Magnitude and Phase Regularization via Compressed Sensing
Noll, Douglas C.; Nielsen, Jon-Fredrik; Fessler, Jeffrey A.
2012-01-01
Compressed sensing (CS) has been used for accelerating magnetic resonance imaging (MRI) acquisitions, but its use in applications with rapid spatial phase variations is challenging, e.g., proton resonance frequency shift (PRF-shift) thermometry and velocity mapping. Previously, an iterative MRI reconstruction with separate magnitude and phase regularization was proposed for applications where magnitude and phase maps are both of interest, but it requires fully sampled data and unwrapped phase maps. In this paper, CS is combined into this framework to reconstruct magnitude and phase images accurately from undersampled data. Moreover, new phase regularization terms are proposed to accommodate phase wrapping and to reconstruct images with encoded phase variations, e.g., PRF-shift thermometry and velocity mapping. The proposed method is demonstrated with simulated thermometry data and in-vivo velocity mapping data and compared to conventional phase corrected CS. PMID:22552571
Resolving intravoxel fiber architecture using nonconvex regularized blind compressed sensing.
Chu, C Y; Huang, J P; Sun, C Y; Liu, W Y; Zhu, Y M
2015-03-21
In diffusion magnetic resonance imaging, accurate and reliable estimation of intravoxel fiber architectures is a major prerequisite for tractography algorithms or any other derived statistical analysis. Several methods have been proposed that estimate intravoxel fiber architectures using low angular resolution acquisitions owing to their shorter acquisition time and relatively low b-values. But these methods are highly sensitive to noise. In this work, we propose a nonconvex regularized blind compressed sensing approach to estimate intravoxel fiber architectures in low angular resolution acquisitions. The method models diffusion-weighted (DW) signals as a sparse linear combination of unfixed reconstruction basis functions and introduces a nonconvex regularizer to enhance the noise immunity. We present a general solving framework to simultaneously estimate the sparse coefficients and the reconstruction basis. Experiments on synthetic, phantom, and real human brain DW images demonstrate the superiority of the proposed approach.
Fast Second Degree Total Variation Method for Image Compressive Sensing
Liu, Pengfei; Xiao, Liang; Zhang, Jun
2015-01-01
This paper presents a computationally efficient algorithm for image compressive sensing reconstruction using a second degree total variation (HDTV2) regularization. Firstly, a preferably equivalent formulation of the HDTV2 functional is derived, which can be formulated as a weighted L1-L2 mixed norm of second degree image derivatives under the spectral decomposition framework. Secondly, using the equivalent formulation of HDTV2, we introduce an efficient forward-backward splitting (FBS) scheme to solve the HDTV2-based image reconstruction model. Furthermore, from the averaged non-expansive operator point of view, we make a detailed analysis on the convergence of the proposed FBS algorithm. Experiments on medical images demonstrate that the proposed method outperforms several fast algorithms of the TV and HDTV2 reconstruction models in terms of peak signal to noise ratio (PSNR), structural similarity index (SSIM) and convergence speed. PMID:26361008
Uncovering transportation networks from traffic flux by compressed sensing
NASA Astrophysics Data System (ADS)
Tang, Si-Qi; Shen, Zhesi; Wang, Wen-Xu; Di, Zengru
2015-08-01
Transportation and communication networks are ubiquitous in nature and society. Uncovering the underlying topology as well as link weights, is fundamental to understanding traffic dynamics and designing effective control strategies to facilitate transmission efficiency. We develop a general method for reconstructing transportation networks from detectable traffic flux data using the aid of a compressed sensing algorithm. Our approach enables full reconstruction of network topology and link weights for both directed and undirected networks from relatively small amounts of data compared to the network size. The limited data requirement and certain resistance to noise allows our method to achieve real-time network reconstruction. We substantiate the effectiveness of our method through systematic numerical tests with respect to several different network structures and transmission strategies. We expect our approach to be widely applicable in a variety of transportation and communication systems.
Compressed-sensed-domain L1-PCA video surveillance
NASA Astrophysics Data System (ADS)
Liu, Ying; Pados, Dimitris A.
2015-05-01
We consider the problem of foreground and background extraction from compressed-sensed (CS) surveillance video. We propose, for the first time in the literature, a principal component analysis (PCA) approach that computes the low-rank subspace of the background scene directly in the CS domain. Rather than computing the conventional L2-norm-based principal components, which are simply the dominant left singular vectors of the CS measurement matrix, we compute the principal components under an L1-norm maximization criterion. The background scene is then obtained by projecting the CS measurement vector onto the L1 principal components followed by total-variation (TV) minimization image recovery. The proposed L1-norm procedure directly carries out low-rank background representation without reconstructing the video sequence and, at the same time, exhibits significant robustness against outliers in CS measurements compared to L2-norm PCA.
Compressed sensing MRI: a review of the clinical literature
Jaspan, Oren N; Fleysher, Roman
2015-01-01
MRI is one of the most dynamic and safe imaging techniques available in the clinic today. However, MRI acquisitions tend to be slow, limiting patient throughput and limiting potential indications for use while driving up costs. Compressed sensing (CS) is a method for accelerating MRI acquisition by acquiring less data through undersampling of k-space. This has the potential to mitigate the time-intensiveness of MRI. The limited body of research evaluating the effects of CS on MR images has been mostly positive with regards to its potential as a clinical tool. Studies have successfully accelerated MRI with this technology, with varying degrees of success. However, more must be performed before its diagnostic efficacy and benefits are clear. Studies involving a greater number radiologists and images must be completed, rating CS based on its diagnostic efficacy. Also, standardized methods for determining optimal imaging parameters must be developed. PMID:26402216
LED-based digital hologram reconstruction by compressive sensing
NASA Astrophysics Data System (ADS)
Weng, Jiawen; Yang, Chuping; Qin, Yi; Li, Hai
2015-10-01
LED-based digital hologram, considered as low-coherence digital hologram, is confined to in-line holography because the interference fringes could be observed only when the angle between the object and reference wave is small enough. So, phase-shifting technique is usually employed. But it is not fit for dynamic analysis for demanding more than one hologram. A numerical reconstruction method based on compressive sensing theory for single LED-based digital hologram is proposed to achieve dynamic analysis. By this method, the out-of-focus twin image and the coherent noise can be inhibited to some extent. The theory is presented in detail, and experimental result on LED-based digital holography with USAF pattern as test target, is performed to demonstrate the feasibility and validity of the method.
Recovering network topologies via Taylor expansion and compressive sensing
Li, Guangjun; Liu, Juan E-mail: liujuanjp@163.com; Wu, Xiaoqun E-mail: liujuanjp@163.com; Lu, Jun-an; Guo, Chi
2015-04-15
Gaining knowledge of the intrinsic topology of a complex dynamical network is the precondition to understand its evolutionary mechanisms and to control its dynamical and functional behaviors. In this article, a general framework is developed to recover topologies of complex networks with completely unknown node dynamics based on Taylor expansion and compressive sensing. Numerical simulations illustrate the feasibility and effectiveness of the proposed method. Moreover, this method is found to have good robustness to weak stochastic perturbations. Finally, the impact of two major factors on the topology identification performance is evaluated. This method provides a natural and direct point to reconstruct network topologies from measurable data, which is likely to have potential applicability in a wide range of fields.
Estimation of many-body quantum Hamiltonians via compressive sensing
Shabani, A.; Rabitz, H.; Mohseni, M.; Lloyd, S.; Kosut, R. L.
2011-07-15
We develop an efficient and robust approach for quantum measurement of nearly sparse many-body quantum Hamiltonians based on the method of compressive sensing. This work demonstrates that with only O(sln(d)) experimental configurations, consisting of random local preparations and measurements, one can estimate the Hamiltonian of a d-dimensional system, provided that the Hamiltonian is nearly s sparse in a known basis. The classical postprocessing is a convex optimization problem on the total Hilbert space which is generally not scalable. We numerically simulate the performance of this algorithm for three- and four-body interactions in spin-coupled quantum dots and atoms in optical lattices. Furthermore, we apply the algorithm to characterize Hamiltonian fine structure and unknown system-bath interactions.
A BLIND COMPRESSIVE SENSING FRAMEWORK FOR ACCELERATED DYNAMIC MRI.
Goud Lingala, Sajan; Jacob, Mathews
2012-01-01
We propose a novel blind compressive sensing (BCS) frame work to recover dynamic images from under-sampled measurements. This scheme models the the dynamic signal as a sparse linear combination of temporal basis functions, chosen from a large dictionary. The dictionary and the sparse coefficients are simultaneously estimated from the under-sampled measurements. Since the number of degrees of freedom of this model is much smaller than that of current low-rank methods, this scheme is expected to provide improved reconstructions for datasets with considerable inter-frame motion. We develop an efficient majorize-minimize algorithm to solve for the dynamic images. We use a continuation strategy to minimize the convergence of the algorithm to local minima. Numerical comparisons of the BCS scheme with low-rank methods demonstrate the significant improvement in performance in the presence of motion.
Radial velocity data analysis with compressed sensing techniques
NASA Astrophysics Data System (ADS)
Hara, Nathan C.; Boué, G.; Laskar, J.; Correia, A. C. M.
2017-01-01
We present a novel approach for analysing radial velocity data that combines two features: all the planets are searched at once and the algorithm is fast. This is achieved by utilizing compressed sensing techniques, which are modified to be compatible with the Gaussian process framework. The resulting tool can be used like a Lomb-Scargle periodogram and has the same aspect but with much fewer peaks due to aliasing. The method is applied to five systems with published radial velocity data sets: HD 69830, HD 10180, 55 Cnc, GJ 876 and a simulated very active star. The results are fully compatible with previous analysis, though obtained more straightforwardly. We further show that 55 Cnc e and f could have been respectively detected and suspected in early measurements from the Lick Observatory and Hobby-Eberly Telescope available in 2004, and that frequencies due to dynamical interactions in GJ 876 can be seen.
Underwater Acoustic Matched Field Imaging Based on Compressed Sensing
Yan, Huichen; Xu, Jia; Long, Teng; Zhang, Xudong
2015-01-01
Matched field processing (MFP) is an effective method for underwater target imaging and localizing, but its performance is not guaranteed due to the nonuniqueness and instability problems caused by the underdetermined essence of MFP. By exploiting the sparsity of the targets in an imaging area, this paper proposes a compressive sensing MFP (CS-MFP) model from wave propagation theory by using randomly deployed sensors. In addition, the model’s recovery performance is investigated by exploring the lower bounds of the coherence parameter of the CS dictionary. Furthermore, this paper analyzes the robustness of CS-MFP with respect to the displacement of the sensors. Subsequently, a coherence-excluding coherence optimized orthogonal matching pursuit (CCOOMP) algorithm is proposed to overcome the high coherent dictionary problem in special cases. Finally, some numerical experiments are provided to demonstrate the effectiveness of the proposed CS-MFP method. PMID:26457708
Compressed sensing sparse reconstruction for coherent field imaging
NASA Astrophysics Data System (ADS)
Bei, Cao; Xiu-Juan, Luo; Yu, Zhang; Hui, Liu; Ming-Lai, Chen
2016-04-01
Return signal processing and reconstruction plays a pivotal role in coherent field imaging, having a significant influence on the quality of the reconstructed image. To reduce the required samples and accelerate the sampling process, we propose a genuine sparse reconstruction scheme based on compressed sensing theory. By analyzing the sparsity of the received signal in the Fourier spectrum domain, we accomplish an effective random projection and then reconstruct the return signal from as little as 10% of traditional samples, finally acquiring the target image precisely. The results of the numerical simulations and practical experiments verify the correctness of the proposed method, providing an efficient processing approach for imaging fast-moving targets in the future. Project supported by the National Natural Science Foundation of China (Grant No. 61505248) and the Fund from Chinese Academy of Sciences, the Light of “Western” Talent Cultivation Plan “Dr. Western Fund Project” (Grant No. Y429621213).
Compressive sensing as a paradigm for building physics models
NASA Astrophysics Data System (ADS)
Nelson, Lance J.; Hart, Gus L. W.; Zhou, Fei; Ozoliņš, Vidvuds
2013-01-01
The widely accepted intuition that the important properties of solids are determined by a few key variables underpins many methods in physics. Though this reductionist paradigm is applicable in many physical problems, its utility can be limited because the intuition for identifying the key variables often does not exist or is difficult to develop. Machine learning algorithms (genetic programming, neural networks, Bayesian methods, etc.) attempt to eliminate the a priori need for such intuition but often do so with increased computational burden and human time. A recently developed technique in the field of signal processing, compressive sensing (CS), provides a simple, general, and efficient way of finding the key descriptive variables. CS is a powerful paradigm for model building; we show that its models are more physical and predict more accurately than current state-of-the-art approaches and can be constructed at a fraction of the computational cost and user effort.
Causal MRI reconstruction via Kalman prediction and compressed sensing correction.
Majumdar, Angshul
2017-02-04
This technical note addresses the problem of causal online reconstruction of dynamic MRI, i.e. given the reconstructed frames till the previous time instant, we reconstruct the frame at the current instant. Our work follows a prediction-correction framework. Given the previous frames, the current frame is predicted based on a Kalman estimate. The difference between the estimate and the current frame is then corrected based on the k-space samples of the current frame; this reconstruction assumes that the difference is sparse. The method is compared against prior Kalman filtering based techniques and Compressed Sensing based techniques. Experimental results show that the proposed method is more accurate than these and considerably faster.
Resolving intravoxel fiber architecture using nonconvex regularized blind compressed sensing
NASA Astrophysics Data System (ADS)
Chu, C. Y.; Huang, J. P.; Sun, C. Y.; Liu, W. Y.; Zhu, Y. M.
2015-03-01
In diffusion magnetic resonance imaging, accurate and reliable estimation of intravoxel fiber architectures is a major prerequisite for tractography algorithms or any other derived statistical analysis. Several methods have been proposed that estimate intravoxel fiber architectures using low angular resolution acquisitions owing to their shorter acquisition time and relatively low b-values. But these methods are highly sensitive to noise. In this work, we propose a nonconvex regularized blind compressed sensing approach to estimate intravoxel fiber architectures in low angular resolution acquisitions. The method models diffusion-weighted (DW) signals as a sparse linear combination of unfixed reconstruction basis functions and introduces a nonconvex regularizer to enhance the noise immunity. We present a general solving framework to simultaneously estimate the sparse coefficients and the reconstruction basis. Experiments on synthetic, phantom, and real human brain DW images demonstrate the superiority of the proposed approach.
Underwater Acoustic Matched Field Imaging Based on Compressed Sensing.
Yan, Huichen; Xu, Jia; Long, Teng; Zhang, Xudong
2015-10-07
Matched field processing (MFP) is an effective method for underwater target imaging and localizing, but its performance is not guaranteed due to the nonuniqueness and instability problems caused by the underdetermined essence of MFP. By exploiting the sparsity of the targets in an imaging area, this paper proposes a compressive sensing MFP (CS-MFP) model from wave propagation theory by using randomly deployed sensors. In addition, the model's recovery performance is investigated by exploring the lower bounds of the coherence parameter of the CS dictionary. Furthermore, this paper analyzes the robustness of CS-MFP with respect to the displacement of the sensors. Subsequently, a coherence-excluding coherence optimized orthogonal matching pursuit (CCOOMP) algorithm is proposed to overcome the high coherent dictionary problem in special cases. Finally, some numerical experiments are provided to demonstrate the effectiveness of the proposed CS-MFP method.
Compressive sensing based wireless sensor for structural health monitoring
NASA Astrophysics Data System (ADS)
Bao, Yuequan; Zou, Zilong; Li, Hui
2014-03-01
Data loss is a common problem for monitoring systems based on wireless sensors. Reliable communication protocols, which enhance communication reliability by repetitively transmitting unreceived packets, is one approach to tackle the problem of data loss. An alternative approach allows data loss to some extent and seeks to recover the lost data from an algorithmic point of view. Compressive sensing (CS) provides such a data loss recovery technique. This technique can be embedded into smart wireless sensors and effectively increases wireless communication reliability without retransmitting the data. The basic idea of CS-based approach is that, instead of transmitting the raw signal acquired by the sensor, a transformed signal that is generated by projecting the raw signal onto a random matrix, is transmitted. Some data loss may occur during the transmission of this transformed signal. However, according to the theory of CS, the raw signal can be effectively reconstructed from the received incomplete transformed signal given that the raw signal is compressible in some basis and the data loss ratio is low. This CS-based technique is implemented into the Imote2 smart sensor platform using the foundation of Illinois Structural Health Monitoring Project (ISHMP) Service Tool-suite. To overcome the constraints of limited onboard resources of wireless sensor nodes, a method called random demodulator (RD) is employed to provide memory and power efficient construction of the random sampling matrix. Adaptation of RD sampling matrix is made to accommodate data loss in wireless transmission and meet the objectives of the data recovery. The embedded program is tested in a series of sensing and communication experiments. Examples and parametric study are presented to demonstrate the applicability of the embedded program as well as to show the efficacy of CS-based data loss recovery for real wireless SHM systems.
Efficient 2D MRI relaxometry using compressed sensing
NASA Astrophysics Data System (ADS)
Bai, Ruiliang; Cloninger, Alexander; Czaja, Wojciech; Basser, Peter J.
2015-06-01
Potential applications of 2D relaxation spectrum NMR and MRI to characterize complex water dynamics (e.g., compartmental exchange) in biology and other disciplines have increased in recent years. However, the large amount of data and long MR acquisition times required for conventional 2D MR relaxometry limits its applicability for in vivo preclinical and clinical MRI. We present a new MR pipeline for 2D relaxometry that incorporates compressed sensing (CS) as a means to vastly reduce the amount of 2D relaxation data needed for material and tissue characterization without compromising data quality. Unlike the conventional CS reconstruction in the Fourier space (k-space), the proposed CS algorithm is directly applied onto the Laplace space (the joint 2D relaxation data) without compressing k-space to reduce the amount of data required for 2D relaxation spectra. This framework is validated using synthetic data, with NMR data acquired in a well-characterized urea/water phantom, and on fixed porcine spinal cord tissue. The quality of the CS-reconstructed spectra was comparable to that of the conventional 2D relaxation spectra, as assessed using global correlation, local contrast between peaks, peak amplitude and relaxation parameters, etc. This result brings this important type of contrast closer to being realized in preclinical, clinical, and other applications.
Experimental Investigations on Airborne Gravimetry Based on Compressed Sensing
Yang, Yapeng; Wu, Meiping; Wang, Jinling; Zhang, Kaidong; Cao, Juliang; Cai, Shaokun
2014-01-01
Gravity surveys are an important research topic in geophysics and geodynamics. This paper investigates a method for high accuracy large scale gravity anomaly data reconstruction. Based on the airborne gravimetry technology, a flight test was carried out in China with the strap-down airborne gravimeter (SGA-WZ) developed by the Laboratory of Inertial Technology of the National University of Defense Technology. Taking into account the sparsity of airborne gravimetry by the discrete Fourier transform (DFT), this paper proposes a method for gravity anomaly data reconstruction using the theory of compressed sensing (CS). The gravity anomaly data reconstruction is an ill-posed inverse problem, which can be transformed into a sparse optimization problem. This paper uses the zero-norm as the objective function and presents a greedy algorithm called Orthogonal Matching Pursuit (OMP) to solve the corresponding minimization problem. The test results have revealed that the compressed sampling rate is approximately 14%, the standard deviation of the reconstruction error by OMP is 0.03 mGal and the signal-to-noise ratio (SNR) is 56.48 dB. In contrast, the standard deviation of the reconstruction error by the existing nearest-interpolation method (NIPM) is 0.15 mGal and the SNR is 42.29 dB. These results have shown that the OMP algorithm can reconstruct the gravity anomaly data with higher accuracy and fewer measurements. PMID:24647125
Digital orthogonal receiver for wideband radar based on compressed sensing
NASA Astrophysics Data System (ADS)
Hou, Qingkai; Liu, Yang; Chen, Zengping; Su, Shaoying
2014-10-01
Digital orthogonal receiver is one of the key techniques in digital receiver of soft radar, and compressed sensing is attracting more and more attention in radar signal processing. In this paper, we propose a CS digital orthogonal receiver for wideband radar which utilizes compressed sampling in the acquisition of radar raw data. In order to reconstruct complex signal from sub-sampled raw data, a novel sparse dictionary is proposed to represent the real-valued radar raw signal sparsely. Using our dictionary and CS algorithm, we can reconstruct the complex-valued radar signal from sub-sampled echoes. Compared with conventional digital orthogonal radar receiver, the architecture of receiver in this paper is more simplified and the sampling frequency of ADC is reduced sharply. At the same time, the range profile can be obtained during the reconstruction, so the matched filtering can be eliminated in the receiver. Some experiments on ISAR imaging based on simulated data prove that the phase information of radar echoes is well reserved in our orthogonal receiver and the whole design is effective for wideband radar.
Compressive sensing optical coherence tomography using randomly accessible lasers
NASA Astrophysics Data System (ADS)
Harfouche, Mark; Satyan, Naresh; Vasilyev, Arseny; Yariv, Amnon
2014-05-01
We propose and demonstrate a novel a compressive sensing swept source optical coherence tomography (SSOCT) system that enables high speed images to be taken while maintaining the high resolution offered from a large bandwidth sweep. Conventional SSOCT systems sweep the optical frequency of a laser ω(t) to determine the depth of the reflectors at a given lateral location. A scatterer located at delay τ appears as a sinusoid cos (ω(t)τ ) at the photodetector. The finite optical chirp rate and the speed of analog to digital and digital to analog converters limit the acquisition rate of an axial scan. The proposed acquisition modality enables much faster image acquisition rates by interrogating the beat signal at randomly selected optical frequencies while preserving resolution and depth of field. The system utilizes a randomly accessible laser, a modulated grating Y-branch laser, to sample the interference pattern from a scene at randomly selected optical frequencies over an optical bandwidth of 5 THz , corresponding to a resolution of 30 μm in air. The depth profile is then reconstructed using an l1 minimization algorithm with a LASSO constraint. Signal-dependent noise sources, shot noise and phase noise, are analyzed and taken into consideration during the recovery. Redundant dictionaries are used to improve the reconstruction of the depth profile. A compression by a factor of 10 for sparse targets up to a depth of 15 mm in noisy environments is shown.
Motion-compensated compressed sensing for dynamic imaging
NASA Astrophysics Data System (ADS)
Sundaresan, Rajagopalan; Kim, Yookyung; Nadar, Mariappan S.; Bilgin, Ali
2010-08-01
The recently introduced Compressed Sensing (CS) theory explains how sparse or compressible signals can be reconstructed from far fewer samples than what was previously believed possible. The CS theory has attracted significant attention for applications such as Magnetic Resonance Imaging (MRI) where long acquisition times have been problematic. This is especially true for dynamic MRI applications where high spatio-temporal resolution is needed. For example, in cardiac cine MRI, it is desirable to acquire the whole cardiac volume within a single breath-hold in order to avoid artifacts due to respiratory motion. Conventional MRI techniques do not allow reconstruction of high resolution image sequences from such limited amount of data. Vaswani et al. recently proposed an extension of the CS framework to problems with partially known support (i.e. sparsity pattern). In their work, the problem of recursive reconstruction of time sequences of sparse signals was considered. Under the assumption that the support of the signal changes slowly over time, they proposed using the support of the previous frame as the "known" part of the support for the current frame. While this approach works well for image sequences with little or no motion, motion causes significant change in support between adjacent frames. In this paper, we illustrate how motion estimation and compensation techniques can be used to reconstruct more accurate estimates of support for image sequences with substantial motion (such as cardiac MRI). Experimental results using phantoms as well as real MRI data sets illustrate the improved performance of the proposed technique.
RZA-NLMF algorithm-based adaptive sparse sensing for realizing compressive sensing
NASA Astrophysics Data System (ADS)
Gui, Guan; Xu, Li; Adachi, Fumiyuki
2014-12-01
Nonlinear sparse sensing (NSS) techniques have been adopted for realizing compressive sensing in many applications such as radar imaging. Unlike the NSS, in this paper, we propose an adaptive sparse sensing (ASS) approach using the reweighted zero-attracting normalized least mean fourth (RZA-NLMF) algorithm which depends on several given parameters, i.e., reweighted factor, regularization parameter, and initial step size. First, based on the independent assumption, Cramer-Rao lower bound (CRLB) is derived as for the performance comparisons. In addition, reweighted factor selection method is proposed for achieving robust estimation performance. Finally, to verify the algorithm, Monte Carlo-based computer simulations are given to show that the ASS achieves much better mean square error (MSE) performance than the NSS.
NASA Astrophysics Data System (ADS)
He, Jinping; Ruan, Ningjuan; Zhao, Haibo; Liu, Yuchen
2016-10-01
Remote sensing features are varied and complicated. There is no comprehensive coverage dictionary for reconstruction. The reconstruction precision is not guaranteed. Aiming at the above problems, a novel reconstruction method with multiple compressed sensing data based on energy compensation is proposed in this paper. The multiple measured data and multiple coding matrices compose the reconstruction equation. It is locally solved through the Orthogonal Matching Pursuit (OMP) algorithm. Then the initial reconstruction image is obtained. Further assuming the local image patches have the same compensation gray value, the mathematical model of compensation value is constructed by minimizing the error of multiple estimated measured values and actual measured values. After solving the minimization, the compensation values are added to the initial reconstruction image. Then the final energy compensation image is obtained. The experiments prove that the energy compensation method is superior to those without compensation. Our method is more suitable for remote sensing features.
Zheng, Pengfei; Yao, Qingqiang; Xu, Peng; Wang, Liming
2017-05-01
To investigate the feasibility and accuracy of a drill template based on computer-aided design (CAD) and 3D printing technology for the placement of screws in Locking Compression Pediatric Hip Plate (LCP-PHP). LCP-PHP was used in 11 children [5 with femoral neck fracture and 6 with development dysplasia of hip (DDH)]. Using the CT data, the proximal femur model was created by a 3D printer. Fracture reduction and the placement of the screw in the femoral neck and the LCP-PHP were simulated by the computer. The navigation template was designed by the software to match the proximal femur. After the feasibility of the 3D model operation was demonstrated before the operation, the guide pins and the screws were inserted with the help of the navigate template in the operation. During surgery, the navigation template for each case was matched to the bony markers of the proximal femur. With the help of the template, in femoral neck fracture cases, three screws could be accurately inserted into the femoral neck to implant the LCP-PHP and stabilize the fracture. The template for DDH includes all operation parameters and steps for proximal femoral varus rotation and shortening osteotomy, which made the surgery much easier to perform. Radiographs taken after surgery showed that the postoperative results closely corresponded to the preoperative computer simulation. The average time taken for LCP-PHP placement was 26.5 min; radiography was used during surgery only for an average of 6.0 times. Postoperative radiographs showed good results. With the use of CAD and 3D printing technology, accurate placement of individualized navigation template of LCP-PHP can be achieved. This technology can reduce intraoperative damage to the femoral neck epiphysis, decrease operation time, reduce intraoperative hemorrhage, and decrease radiation exposure to patients and personnel during the surgery.
Visualization of Astronomical Nebulae via Distributed Multi-GPU Compressed Sensing Tomography.
Wenger, S; Ament, M; Guthe, S; Lorenz, D; Tillmann, A; Weiskopf, D; Magnor, M
2012-12-01
The 3D visualization of astronomical nebulae is a challenging problem since only a single 2D projection is observable from our fixed vantage point on Earth. We attempt to generate plausible and realistic looking volumetric visualizations via a tomographic approach that exploits the spherical or axial symmetry prevalent in some relevant types of nebulae. Different types of symmetry can be implemented by using different randomized distributions of virtual cameras. Our approach is based on an iterative compressed sensing reconstruction algorithm that we extend with support for position-dependent volumetric regularization and linear equality constraints. We present a distributed multi-GPU implementation that is capable of reconstructing high-resolution datasets from arbitrary projections. Its robustness and scalability are demonstrated for astronomical imagery from the Hubble Space Telescope. The resulting volumetric data is visualized using direct volume rendering. Compared to previous approaches, our method preserves a much higher amount of detail and visual variety in the 3D visualization, especially for objects with only approximate symmetry.
2015-03-26
medical imaging , e.g., magnetic resonance imaging (MRI). Since the early 1980s, MRI has granted doctors the ability to distinguish between healthy tissue ...Recent Advances in Compressed Sensing: Discrete Uncertainty Principles and Fast Hyperspectral Imaging THESIS MARCH 2015 Megan E. Lewis, Second...IN COMPRESSED SENSING: DISCRETE UNCERTAINTY PRINCIPLES AND FAST HYPERSPECTRAL IMAGING THESIS Presented to the Faculty Department of Mathematics and
Enhanced compressive wideband frequency spectrum sensing for dynamic spectrum access
NASA Astrophysics Data System (ADS)
Liu, Yipeng; Wan, Qun
2012-12-01
Wideband spectrum sensing detects the unused spectrum holes for dynamic spectrum access (DSA). Too high sampling rate is the main challenge. Compressive sensing (CS) can reconstruct sparse signal with much fewer randomized samples than Nyquist sampling with high probability. Since survey shows that the monitored signal is sparse in frequency domain, CS can deal with the sampling burden. Random samples can be obtained by the analog-to-information converter. Signal recovery can be formulated as the combination of an L0 norm minimization and a linear measurement fitting constraint. In DSA, the static spectrum allocation of primary radios means the bounds between different types of primary radios are known in advance. To incorporate this a priori information, we divide the whole spectrum into sections according to the spectrum allocation policy. In the new optimization model, the minimization of the L2 norm of each section is used to encourage the cluster distribution locally, while the L0 norm of the L2 norms is minimized to give sparse distribution globally. Because the L2/L0 optimization is not convex, an iteratively re-weighted L2/L1 optimization is proposed to approximate it. Simulations demonstrate the proposed method outperforms others in accuracy, denoising ability, etc.
Resolution enhancement for ISAR imaging via improved statistical compressive sensing
NASA Astrophysics Data System (ADS)
Zhang, Lei; Wang, Hongxian; Qiao, Zhi-jun
2016-12-01
Developing compressed sensing (CS) theory reveals that optimal reconstruction of an unknown signal can be achieved from very limited observations by utilizing signal sparsity. For inverse synthetic aperture radar (ISAR), the image of an interesting target is generally constructed by limited strong scattering centers, representing strong spatial sparsity. Such prior sparsity intrinsically paves a way to improved ISAR imaging performance. In this paper, we develop a super-resolution algorithm for forming ISAR images from limited observations. When the amplitude of the target scattered field follows an identical Laplace probability distribution, the approach converts super-resolution imaging into sparsity-driven optimization in the Bayesian statistics sense. We show that improved performance is achievable by taking advantage of the meaningful spatial structure of the scattered field. Further, we use the nonidentical Laplace distribution with small scale on strong signal components and large scale on noise to discriminate strong scattering centers from noise. A maximum likelihood estimator combined with a bandwidth extrapolation technique is also developed to estimate the scale parameters. Real measured data processing indicates the proposal can reconstruct the high-resolution image though only limited pulses even with low SNR, which shows advantages over current super-resolution imaging methods.
Compressed Sensing Techniques Applied to Ultrasonic Imaging of Cargo Containers.
López, Yuri Álvarez; Lorenzo, José Ángel Martínez
2017-01-15
One of the key issues in the fight against the smuggling of goods has been the development of scanners for cargo inspection. X-ray-based radiographic system scanners are the most developed sensing modality. However, they are costly and use bulky sources that emit hazardous, ionizing radiation. Aiming to improve the probability of threat detection, an ultrasonic-based technique, capable of detecting the footprint of metallic containers or compartments concealed within the metallic structure of the inspected cargo, has been proposed. The system consists of an array of acoustic transceivers that is attached to the metallic structure-under-inspection, creating a guided acoustic Lamb wave. Reflections due to discontinuities are detected in the images, provided by an imaging algorithm. Taking into consideration that the majority of those images are sparse, this contribution analyzes the application of Compressed Sensing (CS) techniques in order to reduce the amount of measurements needed, thus achieving faster scanning, without compromising the detection capabilities of the system. A parametric study of the image quality, as a function of the samples needed in spatial and frequency domains, is presented, as well as the dependence on the sampling pattern. For this purpose, realistic cargo inspection scenarios have been simulated.
Vibration-based monitoring and diagnostics using compressive sensing
NASA Astrophysics Data System (ADS)
Ganesan, Vaahini; Das, Tuhin; Rahnavard, Nazanin; Kauffman, Jeffrey L.
2017-04-01
Vibration data from mechanical systems carry important information that is useful for characterization and diagnosis. Standard approaches rely on continually streaming data at a fixed sampling frequency. For applications involving continuous monitoring, such as Structural Health Monitoring (SHM), such approaches result in high volume data and rely on sensors being powered for prolonged durations. Furthermore, for spatial resolution, structures are instrumented with a large array of sensors. This paper shows that both volume of data and number of sensors can be reduced significantly by applying Compressive Sensing (CS) in vibration monitoring applications. The reduction is achieved by using random sampling and capitalizing on the sparsity of vibration signals in the frequency domain. Preliminary experimental results validating CS-based frequency recovery are also provided. By exploiting the sparsity of mode shapes, CS can also enable efficient spatial reconstruction using fewer spatially distributed sensors. CS can thereby reduce the cost and power requirement of sensing as well as streamline data storage and processing in monitoring applications. In well-instrumented structures, CS can enable continued monitoring in case of sensor or computational failures.
Compressed Sensing Techniques Applied to Ultrasonic Imaging of Cargo Containers
Álvarez López, Yuri; Martínez Lorenzo, José Ángel
2017-01-01
One of the key issues in the fight against the smuggling of goods has been the development of scanners for cargo inspection. X-ray-based radiographic system scanners are the most developed sensing modality. However, they are costly and use bulky sources that emit hazardous, ionizing radiation. Aiming to improve the probability of threat detection, an ultrasonic-based technique, capable of detecting the footprint of metallic containers or compartments concealed within the metallic structure of the inspected cargo, has been proposed. The system consists of an array of acoustic transceivers that is attached to the metallic structure-under-inspection, creating a guided acoustic Lamb wave. Reflections due to discontinuities are detected in the images, provided by an imaging algorithm. Taking into consideration that the majority of those images are sparse, this contribution analyzes the application of Compressed Sensing (CS) techniques in order to reduce the amount of measurements needed, thus achieving faster scanning, without compromising the detection capabilities of the system. A parametric study of the image quality, as a function of the samples needed in spatial and frequency domains, is presented, as well as the dependence on the sampling pattern. For this purpose, realistic cargo inspection scenarios have been simulated. PMID:28098841
Determination of nonlinear genetic architecture using compressed sensing.
Ho, Chiu Man; Hsu, Stephen D H
2015-01-01
One of the fundamental problems of modern genomics is to extract the genetic architecture of a complex trait from a data set of individual genotypes and trait values. Establishing this important connection between genotype and phenotype is complicated by the large number of candidate genes, the potentially large number of causal loci, and the likely presence of some nonlinear interactions between different genes. Compressed Sensing methods obtain solutions to under-constrained systems of linear equations. These methods can be applied to the problem of determining the best model relating genotype to phenotype, and generally deliver better performance than simply regressing the phenotype against each genetic variant, one at a time. We introduce a Compressed Sensing method that can reconstruct nonlinear genetic models (i.e., including epistasis, or gene-gene interactions) from phenotype-genotype (GWAS) data. Our method uses L1-penalized regression applied to nonlinear functions of the sensing matrix. The computational and data resource requirements for our method are similar to those necessary for reconstruction of linear genetic models (or identification of gene-trait associations), assuming a condition of generalized sparsity, which limits the total number of gene-gene interactions. An example of a sparse nonlinear model is one in which a typical locus interacts with several or even many others, but only a small subset of all possible interactions exist. It seems plausible that most genetic architectures fall in this category. We give theoretical arguments suggesting that the method is nearly optimal in performance, and demonstrate its effectiveness on broad classes of nonlinear genetic models using simulated human genomes and the small amount of currently available real data. A phase transition (i.e., dramatic and qualitative change) in the behavior of the algorithm indicates when sufficient data is available for its successful application. Our results indicate
A mosaic approach for unmanned airship remote sensing images based on compressive sensing
NASA Astrophysics Data System (ADS)
Yang, Jilian; Zhang, Aiwu; Sun, Weidong
2011-12-01
The recently-emerged compressive sensing (CS) theory goes against the Nyquist-Shannon (NS) sampling theory and shows that signals can be recovered from far fewer samples than what the NS sampling theorem states. In this paper, to solve the problems in image fusion step of the full-scene image mosaic for the multiple images acquired by a low-altitude unmanned airship, a novel information mutual complement (IMC) model based on CS theory is proposed. IMC model rests on a similar concept that was termed as the joint sparsity models (JSMs) in distributed compressive sensing (DCS) theory, but the measurement matrix in our IMC model is rearranged in order for the multiple images to be reconstructed as one combination. The experimental results of the BP and TSW-CS algorithm with our IMC model certified the effectiveness and adaptability of this proposed approach, and demonstrated that it is possible to substantially reduce the measurement rates of the signal ensemble with good performance in the compressive domain.
The development of an underwater pulsed compressive line sensing imaging system
NASA Astrophysics Data System (ADS)
Ouyang, Bing; Gong, Sue; Hou, Weilin; Dalgleish, Fraser R.; Caimi, Frank M.; Vuorenkoski, Anni K.
2017-05-01
Compressive Line Sensing (CLS) imaging system is a compressive sensing (CS) based imaging system with the goal of developing a compact and resource efficient imaging system for the degraded visual environment. In the CLS system, each line segment is sensed independently; however, the correlation among the adjacent lines (sources) is exploited via the joint sparsity in the distributed compressing sensing model during signal reconstruction. Several different CLS prototypes have been developed. This paper discusses the development of a pulsed CLS system. Initial experimental results using this system in a turbid water environment are presented.
Compressed sensing based simultaneous black- and gray-blood carotid vessel wall MR imaging.
Li, Bo; Li, Hao; Kong, Hanjing; Dong, Li; Zhang, Jue; Fang, Jing
2017-05-01
In this study, we sought to demonstrate the blood suppression performance, image quality and morphological measurements for compressed sensing (CS) based simultaneous 3D black- and gray-blood imaging sequence (CS-siBLAG) in carotid vessel wall MR imaging. Seven healthy volunteers and five patients were recruited. Healthy subjects underwent five CS-siBLAG scans with 1, 2, 3, 4 and 5-fold accelerations. Signal-to-tissue ratio (STR) and contrast-to-tissue ratio (CTR) were computed as the measures of flowing signal suppression performance and the image quality for black-blood imaging of the technique. Vessel lumen area (LA) and wall area (WA) were compared between fully sampled acquisition and each accelerated acquisition. Patients underwent three CS-siBLAG scans with 1, 3 and 5-fold accelerations as well as a 3D time of flight (3D TOF) scan. Two radiologists reviewed the under-sampled black- and gray-blood image quality. STR and CTR values obtained with 2 to 5-fold accelerations were not significantly different from those with full acquisition. LA and WA measured at 2×, 3×, 4× and 5× were all highly correlated to the corresponding values at 1×. For patients imaging, two radiologists both found that the dual-contrast images at 3× acceleration exhibited comparable image quality to that of the fully sampled acquisition, and that the images at 5× exhibited slightly blurred vessel wall and outer vessel wall boundaries. By combining the CS under-sampling pattern and reconstruction, pseudo-centric phase encoding order and dual blood contrast sequences, this technique provides spatially registered black- and gray-blood images and excellent visualization for vessel wall imaging and gray-blood imaging in a short scan time. Copyright © 2017 Elsevier Inc. All rights reserved.
NASA Astrophysics Data System (ADS)
Castaldo, Raffaele; De Novellis, Vincenzo; Lollino, Piernicola; Manunta, Michele; Tizzani, Pietro
2015-04-01
The new challenge that the research in slopes instabilities phenomena is going to tackle is the effective integration and joint exploitation of remote sensing measurements with in situ data and observations to study and understand the sub-surface interactions, the triggering causes, and, in general, the long term behaviour of the investigated landslide phenomenon. In this context, a very promising approach is represented by Finite Element (FE) techniques, which allow us to consider the intrinsic complexity of the mass movement phenomena and to effectively benefit from multi source observations and data. In this context, we perform a three dimensional (3D) numerical model of the Ivancich (Assisi, Central Italy) instability phenomenon. In particular, we apply an inverse FE method based on a Genetic Algorithm optimization procedure, benefitting from advanced DInSAR measurements, retrieved through the full resolution Small Baseline Subset (SBAS) technique, and an inclinometric array distribution. To this purpose we consider the SAR images acquired from descending orbit by the COSMO-SkyMed (CSK) X-band radar constellation, from December 2009 to February 2012. Moreover the optimization input dataset is completed by an array of eleven inclinometer measurements, from 1999 to 2006, distributed along the unstable mass. The landslide body is formed of debris material sliding on a arenaceous marl substratum, with a thin shear band detected using borehole and inclinometric data, at depth ranging from 20 to 60 m. Specifically, we consider the active role of this shear band in the control of the landslide evolution process. A large field monitoring dataset of the landslide process, including at-depth piezometric and geological borehole observations, were available. The integration of these datasets allows us to develop a 3D structural geological model of the considered slope. To investigate the dynamic evolution of a landslide, various physical approaches can be considered
Jones, Stacy; Sinha, Sudarson Sekhar; Pramanik, Avijit; Ray, Paresh Chandra
2016-11-03
Drug resistant superbug infection is one of the foremost threats to human health. Plasmonic nanoparticles can be used for ultrasensitive bio-imaging and photothermal killing by amplification of electromagnetic fields at nanoscale "hot spots". One of the main challenges to plasmonic imaging and photothermal killing is design of a plasmonic substrate with a large number of "hot spots". Driven by this need, this article reports design of a three-dimensional (3D) plasmonic "hot spot"-based substrate using gold nanoparticle attached hybrid graphene oxide (GO), free from the traditional 2D limitations. Experimental results show that the 3D substrate has capability for highly sensitive label-free sensing and generates high photothermal heat. Reported data using p-aminothiophenol conjugated 3D substrate show that the surface enhanced Raman spectroscopy (SERS) enhancement factor for the 3D "hot spot"-based substrate is more than two orders of magnitude greater than that for the two-dimensional (2D) substrate and five orders of magnitude greater than that for the zero-dimensional (0D) p-aminothiophenol conjugated gold nanoparticle. 3D-Finite-Difference Time-Domain (3D-FDTD) simulation calculations indicate that the SERS enhancement factor can be greater than 10(4) because of the bent assembly structure in the 3D substrate. Results demonstrate that the 3D-substrate-based SERS can be used for fingerprint identification of several multi-drug resistant superbugs with detection limits of 5 colony forming units per mL. Experimental data show that 785 nm near infrared (NIR) light generates around two times more photothermal heat for the 3D substrate with respect to the 2D substrate, and allows rapid and effective killing of 100% of the multi-drug resistant superbugs within 5 minutes.
COxSwAIN: Compressive Sensing for Advanced Imaging and Navigation
NASA Technical Reports Server (NTRS)
Kurwitz, Richard; Pulley, Marina; LaFerney, Nathan; Munoz, Carlos
2015-01-01
The COxSwAIN project focuses on building an image and video compression scheme that can be implemented in a small or low-power satellite. To do this, we used Compressive Sensing, where the compression is performed by matrix multiplications on the satellite and reconstructed on the ground. Our paper explains our methodology and demonstrates the results of the scheme, being able to achieve high quality image compression that is robust to noise and corruption.
Online sparse representation for remote sensing compressed-sensed video sampling
NASA Astrophysics Data System (ADS)
Wang, Jie; Liu, Kun; Li, Sheng-liang; Zhang, Li
2014-11-01
Most recently, an emerging Compressed Sensing (CS) theory has brought a major breakthrough for data acquisition and recovery. It asserts that a signal, which is highly compressible in a known basis, can be reconstructed with high probability through sampling frequency which is well below Nyquist Sampling Frequency. When applying CS to Remote Sensing (RS) Video imaging, it can directly and efficiently acquire compressed image data by randomly projecting original data to obtain linear and non-adaptive measurements. In this paper, with the help of distributed video coding scheme which is a low-complexity technique for resource limited sensors, the frames of a RS video sequence are divided into Key frames (K frames) and Non-Key frames (CS frames). In other words, the input video sequence consists of many groups of pictures (GOPs) and each GOP consists of one K frame followed by several CS frames. Both of them are measured based on block, but at different sampling rates. In this way, the major encoding computation burden will be shifted to the decoder. At the decoder, the Side Information (SI) is generated for the CS frames using traditional Motion-Compensated Interpolation (MCI) technique according to the reconstructed key frames. The over-complete dictionary is trained by dictionary learning methods based on SI. These learning methods include ICA-like, PCA, K-SVD, MOD, etc. Using these dictionaries, the CS frames could be reconstructed according to sparse-land model. In the numerical experiments, the reconstruction performance of ICA algorithm, which is often evaluated by Peak Signal-to-Noise Ratio (PSNR), has been made compared with other online sparse representation algorithms. The simulation results show its advantages in reducing reconstruction time and robustness in reconstruction performance when applying ICA algorithm to remote sensing video reconstruction.
Spatially Regularized Compressed Sensing for High Angular Resolution Diffusion Imaging
Rathi, Yogesh; Dolui, Sudipto
2013-01-01
Despite the relative recency of its inception, the theory of compressive sampling (aka compressed sensing) (CS) has already revolutionized multiple areas of applied sciences, a particularly important instance of which is medical imaging. Specifically, the theory has provided a different perspective on the important problem of optimal sampling in magnetic resonance imaging (MRI), with an ever-increasing body of works reporting stable and accurate reconstruction of MRI scans from the number of spectral measurements which would have been deemed unacceptably small as recently as five years ago. In this paper, the theory of CS is employed to palliate the problem of long acquisition times, which is known to be a major impediment to the clinical application of high angular resolution diffusion imaging (HARDI). Specifically, we demonstrate that a substantial reduction in data acquisition times is possible through minimization of the number of diffusion encoding gradients required for reliable reconstruction of HARDI scans. The success of such a minimization is primarily due to the availability of spherical ridgelet transformation, which excels in sparsifying HARDI signals. What makes the resulting reconstruction procedure even more accurate is a combination of the sparsity constraints in the diffusion domain with additional constraints imposed on the estimated diffusion field in the spatial domain. Accordingly, the present paper describes an original way to combine the diffusion-and spatial-domain constraints to achieve a maximal reduction in the number of diffusion measurements, while sacrificing little in terms of reconstruction accuracy. Finally, details are provided on an efficient numerical scheme which can be used to solve the aforementioned reconstruction problem by means of standard and readily available estimation tools. The paper is concluded with experimental results which support the practical value of the proposed reconstruction methodology. PMID:21536524
A Computational model for compressed sensing RNAi cellular screening
2012-01-01
Background RNA interference (RNAi) becomes an increasingly important and effective genetic tool to study the function of target genes by suppressing specific genes of interest. This system approach helps identify signaling pathways and cellular phase types by tracking intensity and/or morphological changes of cells. The traditional RNAi screening scheme, in which one siRNA is designed to knockdown one specific mRNA target, needs a large library of siRNAs and turns out to be time-consuming and expensive. Results In this paper, we propose a conceptual model, called compressed sensing RNAi (csRNAi), which employs a unique combination of group of small interfering RNAs (siRNAs) to knockdown a much larger size of genes. This strategy is based on the fact that one gene can be partially bound with several small interfering RNAs (siRNAs) and conversely, one siRNA can bind to a few genes with distinct binding affinity. This model constructs a multi-to-multi correspondence between siRNAs and their targets, with siRNAs much fewer than mRNA targets, compared with the conventional scheme. Mathematically this problem involves an underdetermined system of equations (linear or nonlinear), which is ill-posed in general. However, the recently developed compressed sensing (CS) theory can solve this problem. We present a mathematical model to describe the csRNAi system based on both CS theory and biological concerns. To build this model, we first search nucleotide motifs in a target gene set. Then we propose a machine learning based method to find the effective siRNAs with novel features, such as image features and speech features to describe an siRNA sequence. Numerical simulations show that we can reduce the siRNA library to one third of that in the conventional scheme. In addition, the features to describe siRNAs outperform the existing ones substantially. Conclusions This csRNAi system is very promising in saving both time and cost for large-scale RNAi screening experiments which
A computational model for compressed sensing RNAi cellular screening.
Tan, Hua; Fan, Jing; Bao, Jiguang; Dy, Jennifer G; Zhou, Xiaobo
2012-12-27
RNA interference (RNAi) becomes an increasingly important and effective genetic tool to study the function of target genes by suppressing specific genes of interest. This system approach helps identify signaling pathways and cellular phase types by tracking intensity and/or morphological changes of cells. The traditional RNAi screening scheme, in which one siRNA is designed to knockdown one specific mRNA target, needs a large library of siRNAs and turns out to be time-consuming and expensive. In this paper, we propose a conceptual model, called compressed sensing RNAi (csRNAi), which employs a unique combination of group of small interfering RNAs (siRNAs) to knockdown a much larger size of genes. This strategy is based on the fact that one gene can be partially bound with several small interfering RNAs (siRNAs) and conversely, one siRNA can bind to a few genes with distinct binding affinity. This model constructs a multi-to-multi correspondence between siRNAs and their targets, with siRNAs much fewer than mRNA targets, compared with the conventional scheme. Mathematically this problem involves an underdetermined system of equations (linear or nonlinear), which is ill-posed in general. However, the recently developed compressed sensing (CS) theory can solve this problem. We present a mathematical model to describe the csRNAi system based on both CS theory and biological concerns. To build this model, we first search nucleotide motifs in a target gene set. Then we propose a machine learning based method to find the effective siRNAs with novel features, such as image features and speech features to describe an siRNA sequence. Numerical simulations show that we can reduce the siRNA library to one third of that in the conventional scheme. In addition, the features to describe siRNAs outperform the existing ones substantially. This csRNAi system is very promising in saving both time and cost for large-scale RNAi screening experiments which may benefit the biological
Choi, Kihwan; Fahimian, Benjamin P; Li, Tianfang; Suh, Tae-Suk; Lei, Xing
2013-01-01
In four-dimensional (4D) cone-beam computed tomography (CBCT), there is a spatio-temporal tradeoff that currently limits the accuracy. The aim of this study is to develop a Bregman iteration based formalism for high quality 4D CBCT image reconstruction from a limited number of low-dose projections. The 4D CBCT problem is first divided into multiple 3D CBCT subproblems by grouping the projection images corresponding to the phases. To maximally utilize the information from the under-sampled projection data, a compressed sensing (CS) method with Bregman iterations is employed for solving each subproblem. We formulate an unconstrained optimization problem based on least-square criterion regularized by total-variation. The least-square criterion reflects the inconsistency between the measured and the estimated line integrals. Furthermore, the unconstrained problem is updated and solved repeatedly by Bregman iterations. The performance of the proposed algorithm is demonstrated through a series of simulation studies and phantom experiments, and the results are compared to those of previously implemented compressed sensing technique using other gradient-based methods as well as conventional filtered back-projection (FBP) results. The simulation and experimental studies have shown that artifact suppressed images can be obtained with as small as 41 projections per phase, which is adequate for clinical 4D CBCT reconstruction. With such small number of projections, the conventional FDK failed to yield meaningful 4D CBCT images, and CS technique using conjugate gradient was not able to recover sharp edges. The proposed method significantly reduces the radiation dose and scanning time to achieve the high quality images compared to the 4D CBCT imaging based on the conventional FDK technique and the existing CS techniques.
Li, Bo; Li, Hao; Dong, Li; Huang, Guofu
2017-07-20
In this study, we sought to investigate the feasibility of fast carotid artery MR angiography (MRA) by combining three-dimensional time-of-flight (3D TOF) with compressed sensing method (CS-3D TOF). A pseudo-sequential phase encoding order was developed for CS-3D TOF to generate hyper-intense vessel and suppress background tissues in under-sampled 3D k-space. Seven healthy volunteers and one patient with carotid artery stenosis were recruited for this study. Five sequential CS-3D TOF scans were implemented at 1, 2, 3, 4 and 5-fold acceleration factors for carotid artery MRA. Blood signal-to-tissue ratio (BTR) values for fully-sampled and under-sampled acquisitions were calculated and compared in seven subjects. Blood area (BA) was measured and compared between fully sampled acquisition and each under-sampled one. There were no significant differences between the fully-sampled dataset and each under-sampled in BTR comparisons (P>0.05 for all comparisons). The carotid vessel BAs measured from the images of CS-3D TOF sequences with 2, 3, 4 and 5-fold acceleration scans were all highly correlated with that of the fully-sampled acquisition. The contrast between blood vessels and background tissues of the images at 2 to 5-fold acceleration is comparable to that of fully sampled images. The images at 2× to 5× exhibit the comparable lumen definition to the corresponding images at 1×. By combining the pseudo-sequential phase encoding order, CS reconstruction, and 3D TOF sequence, this technique provides excellent visualizations for carotid vessel and calcifications in a short scan time. It has the potential to be integrated into current multiple blood contrast imaging protocol. Copyright © 2017. Published by Elsevier Inc.
Liu, Xiaodong; Xia, Yidong; Luo, Hong; ...
2016-10-05
A comparative study of two classes of third-order implicit time integration schemes is presented for a third-order hierarchical WENO reconstructed discontinuous Galerkin (rDG) method to solve the 3D unsteady compressible Navier-Stokes equations: — 1) the explicit first stage, single diagonally implicit Runge-Kutta (ESDIRK3) scheme, and 2) the Rosenbrock-Wanner (ROW) schemes based on the differential algebraic equations (DAEs) of Index-2. Compared with the ESDIRK3 scheme, a remarkable feature of the ROW schemes is that, they only require one approximate Jacobian matrix calculation every time step, thus considerably reducing the overall computational cost. A variety of test cases, ranging from inviscid flowsmore » to DNS of turbulent flows, are presented to assess the performance of these schemes. Here, numerical experiments demonstrate that the third-order ROW scheme for the DAEs of index-2 can not only achieve the designed formal order of temporal convergence accuracy in a benchmark test, but also require significantly less computing time than its ESDIRK3 counterpart to converge to the same level of discretization errors in all of the flow simulations in this study, indicating that the ROW methods provide an attractive alternative for the higher-order time-accurate integration of the unsteady compressible Navier-Stokes equations.« less
Liu, Xiaodong; Xia, Yidong; Luo, Hong; Xuan, Lijun
2016-10-05
A comparative study of two classes of third-order implicit time integration schemes is presented for a third-order hierarchical WENO reconstructed discontinuous Galerkin (rDG) method to solve the 3D unsteady compressible Navier-Stokes equations: — 1) the explicit first stage, single diagonally implicit Runge-Kutta (ESDIRK3) scheme, and 2) the Rosenbrock-Wanner (ROW) schemes based on the differential algebraic equations (DAEs) of Index-2. Compared with the ESDIRK3 scheme, a remarkable feature of the ROW schemes is that, they only require one approximate Jacobian matrix calculation every time step, thus considerably reducing the overall computational cost. A variety of test cases, ranging from inviscid flows to DNS of turbulent flows, are presented to assess the performance of these schemes. Here, numerical experiments demonstrate that the third-order ROW scheme for the DAEs of index-2 can not only achieve the designed formal order of temporal convergence accuracy in a benchmark test, but also require significantly less computing time than its ESDIRK3 counterpart to converge to the same level of discretization errors in all of the flow simulations in this study, indicating that the ROW methods provide an attractive alternative for the higher-order time-accurate integration of the unsteady compressible Navier-Stokes equations.
Improved Compressive Sensing of Natural Scenes Using Localized Random Sampling
Barranca, Victor J.; Kovačič, Gregor; Zhou, Douglas; Cai, David
2016-01-01
Compressive sensing (CS) theory demonstrates that by using uniformly-random sampling, rather than uniformly-spaced sampling, higher quality image reconstructions are often achievable. Considering that the structure of sampling protocols has such a profound impact on the quality of image reconstructions, we formulate a new sampling scheme motivated by physiological receptive field structure, localized random sampling, which yields significantly improved CS image reconstructions. For each set of localized image measurements, our sampling method first randomly selects an image pixel and then measures its nearby pixels with probability depending on their distance from the initially selected pixel. We compare the uniformly-random and localized random sampling methods over a large space of sampling parameters, and show that, for the optimal parameter choices, higher quality image reconstructions can be consistently obtained by using localized random sampling. In addition, we argue that the localized random CS optimal parameter choice is stable with respect to diverse natural images, and scales with the number of samples used for reconstruction. We expect that the localized random sampling protocol helps to explain the evolutionarily advantageous nature of receptive field structure in visual systems and suggests several future research areas in CS theory and its application to brain imaging. PMID:27555464
Pitfalls in compressed sensing reconstruction and how to avoid them.
Shchukina, Alexandra; Kasprzak, Paweł; Dass, Rupashree; Nowakowski, Michał; Kazimierczuk, Krzysztof
2016-11-11
Multidimensional NMR can provide unmatched spectral resolution, which is crucial when dealing with samples of biological macromolecules. The resolution, however, comes at the high price of long experimental time. Non-uniform sampling (NUS) of the evolution time domain allows to suppress this limitation by sampling only a small fraction of the data, but requires sophisticated algorithms to reconstruct omitted data points. A significant group of such algorithms known as compressed sensing (CS) is based on the assumption of sparsity of a reconstructed spectrum. Several papers on the application of CS in multidimensional NMR have been published in the last years, and the developed methods have been implemented in most spectral processing software. However, the publications rarely show the cases when NUS reconstruction does not work perfectly or explain how to solve the problem. On the other hand, every-day users of NUS develop their rules-of-thumb, which help to set up the processing in an optimal way, but often without a deeper insight. In this paper, we discuss several sources of problems faced in CS reconstructions: low sampling level, missassumption of spectral sparsity, wrong stopping criterion and attempts to extrapolate the signal too much. As an appendix, we provide MATLAB codes of several CS algorithms used in NMR. We hope that this work will explain the mechanism of NUS reconstructions and help readers to set up acquisition and processing parameters. Also, we believe that it might be helpful for algorithm developers.
Compressive sensing for direct millimeter-wave holographic imaging.
Qiao, Lingbo; Wang, Yingxin; Shen, Zongjun; Zhao, Ziran; Chen, Zhiqiang
2015-04-10
Direct millimeter-wave (MMW) holographic imaging, which provides both the amplitude and phase information by using the heterodyne mixing technique, is considered a powerful tool for personnel security surveillance. However, MWW imaging systems usually suffer from the problem of high cost or relatively long data acquisition periods for array or single-pixel systems. In this paper, compressive sensing (CS), which aims at sparse sampling, is extended to direct MMW holographic imaging for reducing the number of antenna units or the data acquisition time. First, following the scalar diffraction theory, an exact derivation of the direct MMW holographic reconstruction is presented. Then, CS reconstruction strategies for complex-valued MMW images are introduced based on the derived reconstruction formula. To pursue the applicability for near-field MMW imaging and more complicated imaging targets, three sparsity bases, including total variance, wavelet, and curvelet, are evaluated for the CS reconstruction of MMW images. We also discuss different sampling patterns for single-pixel, linear array and two-dimensional array MMW imaging systems. Both simulations and experiments demonstrate the feasibility of recovering MMW images from measurements at 1/2 or even 1/4 of the Nyquist rate.
Compressive-Sensing-Based Structure Identification for Multilayer Networks.
Mei, Guofeng; Wu, Xiaoqun; Wang, Yingfei; Hu, Mi; Lu, Jun-An; Chen, Guanrong
2017-02-13
The coexistence of multiple types of interactions within social, technological, and biological networks has motivated the study of the multilayer nature of real-world networks. Meanwhile, identifying network structures from dynamical observations is an essential issue pervading over the current research on complex networks. This paper addresses the problem of structure identification for multilayer networks, which is an important topic but involves a challenging inverse problem. To clearly reveal the formalism, the simplest two-layer network model is considered and a new approach to identifying the structure of one layer is proposed. Specifically, if the interested layer is sparsely connected and the node behaviors of the other layer are observable at a few time points, then a theoretical framework is established based on compressive sensing and regularization. Some numerical examples illustrate the effectiveness of the identification scheme, its requirement of a relatively small number of observations, as well as its robustness against small noise. It is noteworthy that the framework can be straightforwardly extended to multilayer networks, thus applicable to a variety of real-world complex systems.
Improved Compressive Sensing of Natural Scenes Using Localized Random Sampling
NASA Astrophysics Data System (ADS)
Barranca, Victor J.; Kovačič, Gregor; Zhou, Douglas; Cai, David
2016-08-01
Compressive sensing (CS) theory demonstrates that by using uniformly-random sampling, rather than uniformly-spaced sampling, higher quality image reconstructions are often achievable. Considering that the structure of sampling protocols has such a profound impact on the quality of image reconstructions, we formulate a new sampling scheme motivated by physiological receptive field structure, localized random sampling, which yields significantly improved CS image reconstructions. For each set of localized image measurements, our sampling method first randomly selects an image pixel and then measures its nearby pixels with probability depending on their distance from the initially selected pixel. We compare the uniformly-random and localized random sampling methods over a large space of sampling parameters, and show that, for the optimal parameter choices, higher quality image reconstructions can be consistently obtained by using localized random sampling. In addition, we argue that the localized random CS optimal parameter choice is stable with respect to diverse natural images, and scales with the number of samples used for reconstruction. We expect that the localized random sampling protocol helps to explain the evolutionarily advantageous nature of receptive field structure in visual systems and suggests several future research areas in CS theory and its application to brain imaging.
A Secure LFSR Based Random Measurement Matrix for Compressive Sensing
NASA Astrophysics Data System (ADS)
George, Sudhish N.; Pattathil, Deepthi P.
2014-11-01
In this paper, a novel approach for generating the secure measurement matrix for compressive sensing (CS) based on linear feedback shift register (LFSR) is presented. The basic idea is to select the different states of LFSR as the random entries of the measurement matrix and normalize these values to get independent and identically distributed (i.i.d.) random variables with zero mean and variance , where N is the number of input samples. The initial seed for the LFSR system act as the key to the user to provide security. Since the measurement matrix is generated from the LFSR system, and memory overload to store the measurement matrix is avoided in the proposed system. Moreover, the proposed system can provide security maintaining the robustness to noise of the CS system. The proposed system is validated through different block-based CS techniques of images. To enhance security, the different blocks of images are measured with different measurement matrices so that the proposed encryption system can withstand known plaintext attack. A modulo division circuit is used to reseed the LFSR system to generate multiple random measurement matrices, whereby after each fundamental period of LFSR, the feedback polynomial of the modulo circuit is modified in terms of a chaotic value. The proposed secure robust CS paradigm for images is subjected to several forms of attacks and is proven to be resistant against the same. From experimental analysis, it is proven that the proposed system provides better performance than its counterparts.
Compressed Sensing Doppler Ultrasound Reconstruction Using Block Sparse Bayesian Learning.
Lorintiu, Oana; Liebgott, Herve; Friboulet, Denis
2016-04-01
In this paper we propose a framework for using duplex Doppler ultrasound systems. These type of systems need to interleave the acquisition and display of a B-mode image and of the pulsed Doppler spectrogram. In a recent study (Richy , 2013), we have shown that compressed sensing-based reconstruction of Doppler signal allowed reducing the number of Doppler emissions and yielded better results than traditional interpolation and at least equivalent or even better depending on the configuration than the study estimating the signal from sparse data sets given in Jensen, 2006. We propose here to improve over this study by using a novel framework for randomly interleaving Doppler and US emissions. The proposed method reconstructs the Doppler signal segment by segment using a block sparse Bayesian learning (BSBL) algorithm based CS reconstruction. The interest of using such framework in the context of duplex Doppler is linked to the unique ability of BSBL to exploit block-correlated signals and to recover non-sparse signals. The performance of the technique is evaluated from simulated data as well as experimental in vivo data and compared to the recent results in Richy , 2013.
Sparse radar imaging using 2D compressed sensing
NASA Astrophysics Data System (ADS)
Hou, Qingkai; Liu, Yang; Chen, Zengping; Su, Shaoying
2014-10-01
Radar imaging is an ill-posed linear inverse problem and compressed sensing (CS) has been proved to have tremendous potential in this field. This paper surveys the theory of radar imaging and a conclusion is drawn that the processing of ISAR imaging can be denoted mathematically as a problem of 2D sparse decomposition. Based on CS, we propose a novel measuring strategy for ISAR imaging radar and utilize random sub-sampling in both range and azimuth dimensions, which will reduce the amount of sampling data tremendously. In order to handle 2D reconstructing problem, the ordinary solution is converting the 2D problem into 1D by Kronecker product, which will increase the size of dictionary and computational cost sharply. In this paper, we introduce the 2D-SL0 algorithm into the reconstruction of imaging. It is proved that 2D-SL0 can achieve equivalent result as other 1D reconstructing methods, but the computational complexity and memory usage is reduced significantly. Moreover, we will state the results of simulating experiments and prove the effectiveness and feasibility of our method.
Single-snapshot DOA estimation by using Compressed Sensing
NASA Astrophysics Data System (ADS)
Fortunati, Stefano; Grasso, Raffaele; Gini, Fulvio; Greco, Maria S.; LePage, Kevin
2014-12-01
This paper deals with the problem of estimating the directions of arrival (DOA) of multiple source signals from a single observation vector of an array data. In particular, four estimation algorithms based on the theory of compressed sensing (CS), i.e., the classical ℓ 1 minimization (or Least Absolute Shrinkage and Selection Operator, LASSO), the fast smooth ℓ 0 minimization, and the Sparse Iterative Covariance-Based Estimator, SPICE and the Iterative Adaptive Approach for Amplitude and Phase Estimation, IAA-APES algorithms, are analyzed, and their statistical properties are investigated and compared with the classical Fourier beamformer (FB) in different simulated scenarios. We show that unlike the classical FB, a CS-based beamformer (CSB) has some desirable properties typical of the adaptive algorithms (e.g., Capon and MUSIC) even in the single snapshot case. Particular attention is devoted to the super-resolution property. Theoretical arguments and simulation analysis provide evidence that a CS-based beamformer can achieve resolution beyond the classical Rayleigh limit. Finally, the theoretical findings are validated by processing a real sonar dataset.
Genetic optical design for a compressive sensing task
NASA Astrophysics Data System (ADS)
Horisaki, Ryoichi; Niihara, Takahiro; Tanida, Jun
2016-10-01
We present a sophisticated optical design method for reducing the number of photodetectors for a specific sensing task. The chosen design parameter is the point spread function, and the selected task is object recognition. The point spread function is optimized iteratively with a genetic algorithm for object recognition based on a neural network. In the experimental demonstration, binary classification of face and non-face datasets was performed with a single measurement using two photodetectors. A spatial light modulator operating in the amplitude modulation mode was provided in the imaging optics and was used to modulate the point spread function. In each generation of the genetic algorithm, the classification accuracy with a pattern displayed on the spatial light modulator was fed-back to the next generation to find better patterns. The proposed method increased the accuracy by about 30 % compared with a conventional imaging system in which the point spread function was the delta function. This approach is practically useful for compressing the cost, size, and observation time of optical sensors in specific applications, and robust for imperfections in optical elements.
Filter for speckle noise reduction based on compressive sensing
NASA Astrophysics Data System (ADS)
Leportier, Thibault; Park, Min-Chul
2016-12-01
In holographic reconstruction, speckle noise is a serious factor that may degrade the image quality greatly. Several methods have been proposed, so far, to filter speckle from hologram reconstruction. The first approach is based on averaging several speckle patterns. The second solution is to apply a filter on the reconstructed image. In the first case, several holograms should be acquired, while compromise between speckle reduction and edge preservation is usually a challenge in the case of digital filtering. We propose a method to filter speckle noise based on compressive sensing (CS). CS is a method that has been demonstrated recently to reconstruct images with a sampling inferior to the Nyquist rate. By applying several times the CS algorithm on the hologram reconstruction with different initial downsampling, several versions of the same images can be reconstructed with slightly different speckle patterns. Then, speckle noise can be greatly decreased while preserving sharpness of the image. We demonstrate the effectiveness of our proposed method with simulations as well as with holograms acquired by phase-shifting method.
Improved Compressive Sensing of Natural Scenes Using Localized Random Sampling.
Barranca, Victor J; Kovačič, Gregor; Zhou, Douglas; Cai, David
2016-08-24
Compressive sensing (CS) theory demonstrates that by using uniformly-random sampling, rather than uniformly-spaced sampling, higher quality image reconstructions are often achievable. Considering that the structure of sampling protocols has such a profound impact on the quality of image reconstructions, we formulate a new sampling scheme motivated by physiological receptive field structure, localized random sampling, which yields significantly improved CS image reconstructions. For each set of localized image measurements, our sampling method first randomly selects an image pixel and then measures its nearby pixels with probability depending on their distance from the initially selected pixel. We compare the uniformly-random and localized random sampling methods over a large space of sampling parameters, and show that, for the optimal parameter choices, higher quality image reconstructions can be consistently obtained by using localized random sampling. In addition, we argue that the localized random CS optimal parameter choice is stable with respect to diverse natural images, and scales with the number of samples used for reconstruction. We expect that the localized random sampling protocol helps to explain the evolutionarily advantageous nature of receptive field structure in visual systems and suggests several future research areas in CS theory and its application to brain imaging.
Adaptive compressed sensing recovery utilizing the property of signal's autocorrelations.
Fu, Changjun; Ji, Xiangyang; Dai, Qionghai
2012-05-01
Perfect compressed sensing (CS) recovery can be achieved when a certain basis space is found to sparsely represent the original signal. However, due to the diversity of the signals, there does not exist a universal predetermined basis space that can sparsely represent all kinds of signals, which results in an unsatisfying performance. To improve the accuracy of recovered signal, this paper proposes an adaptive basis CS reconstruction algorithm by minimizing the rank of an accumulated matrix (MRAM), whose eigenvectors approximate the optimal basis sparsely representing the original signal. The accumulated matrix is constructed to efficiently exploit the second-order statistical property of the signal's autocorrelations. Based on the theory of matrix completion, MRAM reconstructs the original signal from its random projections under the observation that the constructed accumulated matrix is of low rank for most natural signals such as periodic signals and those coming from an autoregressive stationary process. Experimental results show that the proposed MRAM efficiently improves the reconstruction quality compared with the existing algorithms.
A novel hybrid total variation minimization algorithm for compressed sensing
NASA Astrophysics Data System (ADS)
Li, Hongyu; Wang, Yong; Liang, Dong; Ying, Leslie
2017-05-01
Compressed sensing (CS) is a technology to acquire and reconstruct sparse signals below the Nyquist rate. For images, total variation of the signal is usually minimized to promote sparseness of the image in gradient. However, similar to all L1-minimization algorithms, total variation has the issue of penalizing large gradient, thus causing large errors on image edges. Many non-convex penalties have been proposed to address the issue of L1 minimization. For example, homotopic L0 minimization algorithms have shown success in reconstructing images from magnetic resonance imaging (MRI). Homotopic L0 minimization may suffer from local minimum which may not be sufficiently robust when the signal is not strictly sparse or the measurements are contaminated by noise. In this paper, we propose a hybrid total variation minimization algorithm to integrate the benefits of both L1 and homotopic L0 minimization algorithms for image recovery from reduced measurements. The algorithm minimizes the conventional total variation when the gradient is small, and minimizes the L0 of gradient when the gradient is large. The transition between L1 and L0 of the gradients is determined by an auto-adaptive threshold. The proposed algorithm has the benefits of L1 minimization being robust to noise/approximation errors, and also the benefits of L0 minimization requiring fewer measurements for recovery. Experimental results using MRI data are presented to demonstrate the proposed hybrid total variation minimization algorithm yields improved image quality over other existing methods in terms of the reconstruction accuracy.
Compressive Sensing Based Design of Sparse Tripole Arrays
Hawes, Matthew; Liu, Wei; Mihaylova, Lyudmila
2015-01-01
This paper considers the problem of designing sparse linear tripole arrays. In such arrays at each antenna location there are three orthogonal dipoles, allowing full measurement of both the horizontal and vertical components of the received waveform. We formulate this problem from the viewpoint of Compressive Sensing (CS). However, unlike for isotropic array elements (single antenna), we now have three complex valued weight coefficients associated with each potential location (due to the three dipoles), which have to be simultaneously minimised. If this is not done, we may only set the weight coefficients of individual dipoles to be zero valued, rather than complete tripoles, meaning some dipoles may remain at each location. Therefore, the contributions of this paper are to formulate the design of sparse tripole arrays as an optimisation problem, and then we obtain a solution based on the minimisation of a modified l1 norm or a series of iteratively solved reweighted minimisations, which ensure a truly sparse solution. Design examples are provided to verify the effectiveness of the proposed methods and show that a good approximation of a reference pattern can be achieved using fewer tripoles than a Uniform Linear Array (ULA) of equivalent length. PMID:26690436
The fast algorithm of spark in compressive sensing
NASA Astrophysics Data System (ADS)
Xie, Meihua; Yan, Fengxia
2017-01-01
Compressed Sensing (CS) is an advanced theory on signal sampling and reconstruction. In CS theory, the reconstruction condition of signal is an important theory problem, and spark is a good index to study this problem. But the computation of spark is NP hard. In this paper, we study the problem of computing spark. For some special matrixes, for example, the Gaussian random matrix and 0-1 random matrix, we obtain some conclusions. Furthermore, for Gaussian random matrix with fewer rows than columns, we prove that its spark equals to the number of its rows plus one with probability 1. For general matrix, two methods are given to compute its spark. One is the method of directly searching and the other is the method of dual-tree searching. By simulating 24 Gaussian random matrixes and 18 0-1 random matrixes, we tested the computation time of these two methods. Numerical results showed that the dual-tree searching method had higher efficiency than directly searching, especially for those matrixes which has as much as rows and columns.
Compressed Sensing MR Image Reconstruction Exploiting TGV and Wavelet Sparsity
Du, Huiqian; Han, Yu; Mei, Wenbo
2014-01-01
Compressed sensing (CS) based methods make it possible to reconstruct magnetic resonance (MR) images from undersampled measurements, which is known as CS-MRI. The reference-driven CS-MRI reconstruction schemes can further decrease the sampling ratio by exploiting the sparsity of the difference image between the target and the reference MR images in pixel domain. Unfortunately existing methods do not work well given that contrast changes are incorrectly estimated or motion compensation is inaccurate. In this paper, we propose to reconstruct MR images by utilizing the sparsity of the difference image between the target and the motion-compensated reference images in wavelet transform and gradient domains. The idea is attractive because it requires neither the estimation of the contrast changes nor multiple times motion compensations. In addition, we apply total generalized variation (TGV) regularization to eliminate the staircasing artifacts caused by conventional total variation (TV). Fast composite splitting algorithm (FCSA) is used to solve the proposed reconstruction problem in order to improve computational efficiency. Experimental results demonstrate that the proposed method can not only reduce the computational cost but also decrease sampling ratio or improve the reconstruction quality alternatively. PMID:25371704
Potential of compressed sensing in quantitative MR imaging of cancer
Smith, David S.; Li, Xia; Abramson, Richard G.; Chad Quarles, C.; Yankeelov, Thomas E.
2013-01-01
Abstract Classic signal processing theory dictates that, in order to faithfully reconstruct a band-limited signal (e.g., an image), the sampling rate must be at least twice the maximum frequency contained within the signal, i.e., the Nyquist frequency. Recent developments in applied mathematics, however, have shown that it is often possible to reconstruct signals sampled below the Nyquist rate. This new method of compressed sensing (CS) requires that the signal have a concise and extremely dense representation in some mathematical basis. Magnetic resonance imaging (MRI) is particularly well suited for CS approaches, owing to the flexibility of data collection in the spatial frequency (Fourier) domain available in most MRI protocols. With custom CS acquisition and reconstruction strategies, one can quickly obtain a small subset of the full data and then iteratively reconstruct images that are consistent with the acquired data and sparse by some measure. Successful use of CS results in a substantial decrease in the time required to collect an individual image. This extra time can then be harnessed to increase spatial resolution, temporal resolution, signal-to-noise, or any combination of the three. In this article, we first review the salient features of CS theory and then discuss the specific barriers confronting CS before it can be readily incorporated into clinical quantitative MRI studies of cancer. We finally illustrate applications of the technique by describing examples of CS in dynamic contrast-enhanced MRI and dynamic susceptibility contrast MRI. PMID:24434808
Compressed sensing methods for DNA microarrays, RNA interference, and metagenomics.
Rao, Aditya; P, Deepthi; Renumadhavi, C H; Chandra, M Girish; Srinivasan, Rajgopal
2015-02-01
Compressed sensing (CS) is a sparse signal sampling methodology for efficiently acquiring and reconstructing a signal from relatively few measurements. Recent work shows that CS is well-suited to be applied to problems in genomics, including probe design in microarrays, RNA interference (RNAi), and taxonomic assignment in metagenomics. The principle of using different CS recovery methods in these applications has thus been established, but a comprehensive study of using a wide range of CS methods has not been done. For each of these applications, we apply three hitherto unused CS methods, namely, l1-magic, CoSaMP, and l1-homotopy, in conjunction with CS measurement matrices such as randomly generated CS m matrix, Hamming matrix, and projective geometry-based matrix. We find that, in RNAi, the l1-magic (the standard package for l1 minimization) and l1-homotopy methods show significant reduction in reconstruction error compared to the baseline. In metagenomics, we find that l1-homotopy as well as CoSaMP estimate concentration with significantly reduced time when compared to the GPSR and WGSQuikr methods.
Implementation of compressive sensing for preclinical cine-MRI
NASA Astrophysics Data System (ADS)
Tan, Elliot; Yang, Ming; Ma, Lixin; Zheng, Yahong Rosa
2014-03-01
This paper presents a practical implementation of Compressive Sensing (CS) for a preclinical MRI machine to acquire randomly undersampled k-space data in cardiac function imaging applications. First, random undersampling masks were generated based on Gaussian, Cauchy, wrapped Cauchy and von Mises probability distribution functions by the inverse transform method. The best masks for undersampling ratios of 0.3, 0.4 and 0.5 were chosen for animal experimentation, and were programmed into a Bruker Avance III BioSpec 7.0T MRI system through method programming in ParaVision. Three undersampled mouse heart datasets were obtained using a fast low angle shot (FLASH) sequence, along with a control undersampled phantom dataset. ECG and respiratory gating was used to obtain high quality images. After CS reconstructions were applied to all acquired data, resulting images were quantitatively analyzed using the performance metrics of reconstruction error and Structural Similarity Index (SSIM). The comparative analysis indicated that CS reconstructed images from MRI machine undersampled data were indeed comparable to CS reconstructed images from retrospective undersampled data, and that CS techniques are practical in a preclinical setting. The implementation achieved 2 to 4 times acceleration for image acquisition and satisfactory quality of image reconstruction.
Calibration of a speckle-based compressive sensing receiver
NASA Astrophysics Data System (ADS)
Sefler, George A.; Shaw, T. Justin; Stapleton, Andrew D.; Valley, George C.
2017-02-01
Optical speckle in a multimode waveguide has been proposed to perform the function of a compressive sensing (CS) measurement matrix (MM) in a receiver for GHz-band radio frequency (RF) signals. Unlike other devices used for the CS MM, e.g. the digital micromirror device (DMD) used in the single pixel camera, the elements of the speckle MM are not known before use and must be measured and calibrated. In our system, the RF signal is modulated on a repetitively pulsed chirped wavelength laser source, generated from mode-locked laser pulses that have been dispersed in time or from an electrically addressed distributed Bragg reflector laser. Next, the optical beam with RF propagates through a multimode fiber or waveguide, which applies different weights in wavelength (or equivalently time) and space and performs the function of the CS MM. The output of the guide is directed to or imaged on a bank of photodiodes with integration time set to the pulse length of the chirp waveform. The output of each photodiode is digitized by an analog-to-digital converter (ADC), and the data from these ADCs are used to form the CS measurement vector. Accurate recovery of the RF signal from CS measurements depends critically on knowledge of the weights in the MM. Here we present results using a stable wavelength laser source to probe the guide.
UWB radar echo signal detection based on compressive sensing
NASA Astrophysics Data System (ADS)
Xia, Shugao; Sichina, Jeffrey; Liu, Fengshan
2013-05-01
Ultra-wideband (UWB) technology has been widely utilized in radar system because of the advantage of the ability of high spatial resolution and object-distinction capability. A major challenge in UWB signal processing is the requirement for very high sampling rate under Shannon-Nyquist sampling theorem which exceeds the current ADC capacity. Recently, new approaches based on the Finite Rate of Innovation (FRI) allow significant reduction in the sampling rate. A system for sampling UWB radar echo signal at an ultra-low sampling rate and the estimation of time-delays is presented in the paper. An ultra-low rate sampling scheme based on FRI is applied, which often results in sparse parameter extraction for UWB radar signal detection. The parameters such as time-delays are estimated using the framework of compressed sensing based on total-variation norm minimization. With this system, the UWB radar signal can be accurately reconstructed and detected with overwhelming probability at the rate much lower than Nyquist rate. The simulation results show that the proposed method is effective for sampling and detecting UWB radar signal at an ultra-low sampling rate.
Active remote sensing of snow using NMM3D/DMRT and comparison with CLPX II airborne data
Xu, X.; Liang, D.; Tsang, L.; Andreadis, K.M.; Josberger, E.G.; Lettenmaier, D.P.; Cline, D.W.; Yueh, S.H.
2010-01-01
We applied the Numerical Maxwell Model of three-dimensional simulations (NMM3D) in the Dense Media Radiative Theory (DMRT) to calculate backscattering coefficients. The particles' positions are computer-generated and the subsequent Foldy-Lax equations solved numerically. The phase matrix in NMM3D has significant cross-polarization, particularly when the particles are densely packed. The NMM3D model is combined with DMRT in calculating the microwave scattering by dry snow. The NMM3D/DMRT equations are solved by an iterative solution up to the second order in the case of small to moderate optical thickness. The numerical results of NMM3D/DMRT are illustrated and compared with QCA/DMRT. The QCA/DMRT and NMM3D/DMRT results are also applied to compare with data from two specific datasets from the second Cold Land Processes Experiment (CLPX II) in Alaska and Colorado. The data are obtained at the Ku-band (13.95 GHz) observations using airborne imaging polarimetric scatterometer (POLSCAT). It is shown that the model predictions agree with the field measurements for both co-polarization and cross-polarization. For the Alaska region, the average snow depth and snow density are used as the inputs for DMRT. The grain size, selected from within the range of the ground measurements, is used as a best-fit parameter within the range. For the Colorado region, we use the Variable Infiltration Capacity Model (VIC) to obtain the input snow profiles for NMM3D/DMRT. ?? 2010 IEEE.
Feng, Li; Axel, Leon; Chandarana, Hersh; Block, Kai Tobias; Sodickson, Daniel K; Otazo, Ricardo
2016-02-01
To develop a novel framework for free-breathing MRI called XD-GRASP, which sorts dynamic data into extra motion-state dimensions using the self-navigation properties of radial imaging and reconstructs the multidimensional dataset using compressed sensing. Radial k-space data are continuously acquired using the golden-angle sampling scheme and sorted into multiple motion-states based on respiratory and/or cardiac motion signals derived directly from the data. The resulting undersampled multidimensional dataset is reconstructed using a compressed sensing approach that exploits sparsity along the new dynamic dimensions. The performance of XD-GRASP is demonstrated for free-breathing three-dimensional (3D) abdominal imaging, two-dimensional (2D) cardiac cine imaging and 3D dynamic contrast-enhanced (DCE) MRI of the liver, comparing against reconstructions without motion sorting in both healthy volunteers and patients. XD-GRASP separates respiratory motion from cardiac motion in cardiac imaging, and respiratory motion from contrast enhancement in liver DCE-MRI, which improves image quality and reduces motion-blurring artifacts. XD-GRASP represents a new use of sparsity for motion compensation and a novel way to handle motions in the context of a continuous acquisition paradigm. Instead of removing or correcting motion, extra motion-state dimensions are reconstructed, which improves image quality and also offers new physiological information of potential clinical value. © 2015 Wiley Periodicals, Inc.
NASA Astrophysics Data System (ADS)
Lim, Se Hoon
Compressive holography estimates images from incomplete data by using sparsity priors. Compressive holography combines digital holography and compressive sensing. Digital holography consists of computational image estimation from data captured by an electronic focal plane array. Compressive sensing enables accurate data reconstruction by prior knowledge on desired signal. Computational and optical co-design optimally supports compressive holography in the joint computational and optical domain. This dissertation explores two examples of compressive holography: estimation of 3D tomographic images from 2D data and estimation of images from under sampled apertures. Compressive holography achieves single shot holographic tomography using decompressive inference. In general, 3D image reconstruction suffers from underdetermined measurements with a 2D detector. Specifically, single shot holographic tomography shows the uniqueness problem in the axial direction because the inversion is ill-posed. Compressive sensing alleviates the ill-posed problem by enforcing some sparsity constraints. Holographic tomography is applied for video-rate microscopic imaging and diffuse object imaging. In diffuse object imaging, sparsity priors are not valid in coherent image basis due to speckle. So incoherent image estimation is designed to hold the sparsity in incoherent image basis by support of multiple speckle realizations. High pixel count holography achieves high resolution and wide field-of-view imaging. Coherent aperture synthesis can be one method to increase the aperture size of a detector. Scanning-based synthetic aperture confronts a multivariable global optimization problem due to time-space measurement errors. A hierarchical estimation strategy divides the global problem into multiple local problems with support of computational and optical co-design. Compressive sparse aperture holography can be another method. Compressive sparse sampling collects most of significant field
Lee, M; Suh, T; Han, B; Xing, L; Jenkins, C
2015-06-15
Purpose: To develop and validate an innovative method of using depth sensing cameras and 3D printing techniques for Total Body Irradiation (TBI) treatment planning and compensator fabrication. Methods: A tablet with motion tracking cameras and integrated depth sensing was used to scan a RANDOTM phantom arranged in a TBI treatment booth to detect and store the 3D surface in a point cloud (PC) format. The accuracy of the detected surface was evaluated by comparison to extracted measurements from CT scan images. The thickness, source to surface distance and off-axis distance of the phantom at different body section was measured for TBI treatment planning. A 2D map containing a detailed compensator design was calculated to achieve uniform dose distribution throughout the phantom. The compensator was fabricated using a 3D printer, silicone molding and tungsten powder. In vivo dosimetry measurements were performed using optically stimulated luminescent detectors (OSLDs). Results: The whole scan of the anthropomorphic phantom took approximately 30 seconds. The mean error for thickness measurements at each section of phantom compare to CT was 0.44 ± 0.268 cm. These errors resulted in approximately 2% dose error calculation and 0.4 mm tungsten thickness deviation for the compensator design. The accuracy of 3D compensator printing was within 0.2 mm. In vivo measurements for an end-to-end test showed the overall dose difference was within 3%. Conclusion: Motion cameras and depth sensing techniques proved to be an accurate and efficient tool for TBI patient measurement and treatment planning. 3D printing technique improved the efficiency and accuracy of the compensator production and ensured a more accurate treatment delivery.
Accelerated 3D catheter visualization from triplanar MR projection images.
Schirra, Carsten Oliver; Weiss, Steffen; Krueger, Sascha; Caulfield, Denis; Pedersen, Steen F; Razavi, Reza; Kozerke, Sebastian; Schaeffter, Tobias
2010-07-01
One major obstacle for MR-guided catheterizations is long acquisition times associated with visualizing interventional devices. Therefore, most techniques presented hitherto rely on single-plane imaging to visualize the catheter. Recently, accelerated three-dimensional (3D) imaging based on compressed sensing has been proposed to reduce acquisition times. However, frame rates with this technique remain low, and the 3D reconstruction problem yields a considerable computational load. In X-ray angiography, it is well understood that the shape of interventional devices can be derived in 3D space from a limited number of projection images. In this work, this fact is exploited to develop a method for 3D visualization of active catheters from multiplanar two-dimensional (2D) projection MR images. This is favorable to 3D MRI as the overall number of acquired profiles, and consequently the acquisition time, is reduced. To further reduce measurement times, compressed sensing is employed. Furthermore, a novel single-channel catheter design is presented that combines a solenoidal tip coil in series with a single-loop antenna, enabling simultaneous tip tracking and shape visualization. The tracked tip and catheter properties provide constraints for compressed sensing reconstruction and subsequent 2D/3D curve fitting. The feasibility of the method is demonstrated in phantoms and in an in vivo pig experiment.
NASA Astrophysics Data System (ADS)
Siddeq, M. M.; Rodrigues, M. A.
2015-09-01
Image compression techniques are widely used on 2D image 2D video 3D images and 3D video. There are many types of compression techniques and among the most popular are JPEG and JPEG2000. In this research, we introduce a new compression method based on applying a two level discrete cosine transform (DCT) and a two level discrete wavelet transform (DWT) in connection with novel compression steps for high-resolution images. The proposed image compression algorithm consists of four steps. (1) Transform an image by a two level DWT followed by a DCT to produce two matrices: DC- and AC-Matrix, or low and high frequency matrix, respectively, (2) apply a second level DCT on the DC-Matrix to generate two arrays, namely nonzero-array and zero-array, (3) apply the Minimize-Matrix-Size algorithm to the AC-Matrix and to the other high-frequencies generated by the second level DWT, (4) apply arithmetic coding to the output of previous steps. A novel decompression algorithm, Fast-Match-Search algorithm (FMS), is used to reconstruct all high-frequency matrices. The FMS-algorithm computes all compressed data probabilities by using a table of data, and then using a binary search algorithm for finding decompressed data inside the table. Thereafter, all decoded DC-values with the decoded AC-coefficients are combined in one matrix followed by inverse two levels DCT with two levels DWT. The technique is tested by compression and reconstruction of 3D surface patches. Additionally, this technique is compared with JPEG and JPEG2000 algorithm through 2D and 3D root-mean-square-error following reconstruction. The results demonstrate that the proposed compression method has better visual properties than JPEG and JPEG2000 and is able to more accurately reconstruct surface patches in 3D.
K.M. Bergen; S.J. Goetz; R.O. Dubayah; G.M. Henebry; C.T. Hunsaker; M.L. Imhoff; R.F. Nelson; G.G. Parker; V.C. Radeloff
2009-01-01
Biodiversity and habitat face increasing pressures due to human and natural influences that alter vegetation structure. Because of the inherent difficulty of measuring forested vegetation three-dimensional (3-D) structure on the ground, this important component of biodiversity and habitat has been, until recently, largely restricted to local measurements, or at larger...
Prior image constrained compressed sensing: Implementation and performance evaluation
Lauzier, Pascal Thériault; Tang, Jie; Chen, Guang-Hong
2012-01-01
Purpose: Prior image constrained compressed sensing (PICCS) is an image reconstruction framework which incorporates an often available prior image into the compressed sensing objective function. The images are reconstructed using an optimization procedure. In this paper, several alternative unconstrained minimization methods are used to implement PICCS. The purpose is to study and compare the performance of each implementation, as well as to evaluate the performance of the PICCS objective function with respect to image quality. Methods: Six different minimization methods are investigated with respect to convergence speed and reconstruction accuracy. These minimization methods include the steepest descent (SD) method and the conjugate gradient (CG) method. These algorithms require a line search to be performed. Thus, for each minimization algorithm, two line searching algorithms are evaluated: a backtracking (BT) line search and a fast Newton-Raphson (NR) line search. The relative root mean square error is used to evaluate the reconstruction accuracy. The algorithm that offers the best convergence speed is used to study the performance of PICCS with respect to the prior image parameter α and the data consistency parameter λ. PICCS is studied in terms of reconstruction accuracy, low-contrast spatial resolution, and noise characteristics. A numerical phantom was simulated and an animal model was scanned using a multirow detector computed tomography (CT) scanner to yield the projection datasets used in this study. Results: For λ within a broad range, the CG method with Fletcher-Reeves formula and NR line search offers the fastest convergence for an equal level of reconstruction accuracy. Using this minimization method, the reconstruction accuracy of PICCS was studied with respect to variations in α and λ. When the number of view angles is varied between 107, 80, 64, 40, 20, and 16, the relative root mean square error reaches a minimum value for α ≈ 0.5. For
NASA Astrophysics Data System (ADS)
Liang, Jinyang; Gao, Liang; Hai, Pengfei; Li, Chiye; Wang, Lihong V.
2016-02-01
We applied compressed ultrafast photography (CUP), a computational imaging technique, to acquire three-dimensional (3D) images. The approach unites image encryption, compression, and acquisition in a single measurement, thereby allowing efficient and secure data transmission. By leveraging the time-of-flight (ToF) information of pulsed light reflected by the object, we can reconstruct a volumetric image (150 mm×150 mm×1050 mm, x × y × z) from a single camera snapshot. Furthermore, we demonstrated high-speed 3D videography of a moving object at 75 frames per second using the ToF-CUP camera.
Accelerated diffusion spectrum imaging with compressed sensing using adaptive dictionaries.
Bilgic, Berkin; Setsompop, Kawin; Cohen-Adad, Julien; Yendiki, Anastasia; Wald, Lawrence L; Adalsteinsson, Elfar
2012-12-01
Diffusion spectrum imaging offers detailed information on complex distributions of intravoxel fiber orientations at the expense of extremely long imaging times (∼1 h). Recent work by Menzel et al. demonstrated successful recovery of diffusion probability density functions from sub-Nyquist sampled q-space by imposing sparsity constraints on the probability density functions under wavelet and total variation transforms. As the performance of compressed sensing reconstruction depends strongly on the level of sparsity in the selected transform space, a dictionary specifically tailored for diffusion probability density functions can yield higher fidelity results. To our knowledge, this work is the first application of adaptive dictionaries in diffusion spectrum imaging, whereby we reduce the scan time of whole brain diffusion spectrum imaging acquisition from 50 to 17 min while retaining high image quality. In vivo experiments were conducted with the 3T Connectome MRI. The root-mean-square error of the reconstructed "missing" diffusion images were calculated by comparing them to a gold standard dataset (obtained from acquiring 10 averages of diffusion images in these missing directions). The root-mean-square error from the proposed reconstruction method is up to two times lower than that of Menzel et al.'s method and is actually comparable to that of the fully-sampled 50 minute scan. Comparison of tractography solutions in 18 major white-matter pathways also indicated good agreement between the fully-sampled and 3-fold accelerated reconstructions. Further, we demonstrate that a dictionary trained using probability density functions from a single slice of a particular subject generalizes well to other slices from the same subject, as well as to slices from other subjects. Copyright © 2012 Wiley Periodicals, Inc.
Diagnostic quality assessment of compressed sensing accelerated magnetic resonance neuroimaging.
Kayvanrad, Mohammad; Lin, Amy; Joshi, Rohit; Chiu, Jack; Peters, Terry
2016-08-01
To determine the efficacy of compressed sensing (CS) reconstructions for specific clinical magnetic resonance neuroimaging applications beyond more conventional acceleration techniques such as parallel imaging (PI) and low-resolution acquisitions. Raw k-space data were acquired from five healthy volunteers on a 3T scanner using a 32-channel head coil using T2 -FLAIR, FIESTA-C, time of flight (TOF), and spoiled gradient echo (SPGR) sequences. In a series of blinded studies, three radiologists independently evaluated CS, PI (GRAPPA), and low-resolution images at up to 5× accelerations. Synthetic T2 -FLAIR images with artificial lesions were used to assess diagnostic accuracy for CS reconstructions. CS reconstructions were of diagnostically acceptable quality at up to 4× acceleration for T2 -FLAIR and FIESTA-C (average qualitative scores 3.7 and 4.3, respectively, on a 5-point scale at 4× acceleration), and at up to 3× acceleration for TOF and SPGR (average scores 4.0 and 3.7, respectively, at 3× acceleration). The qualitative scores for CS reconstructions were significantly better than low-resolution images for T2 -FLAIR, FIESTA-C, and TOF and significantly better than GRAPPA for TOF and SPGR (Wilcoxon signed rank test, P < 0.05) with no significant difference found otherwise. Diagnostic accuracy was acceptable for both CS and low-resolution images at up to 3× acceleration (area under the ROC curve 0.97 and 0.96, respectively.) Mild to moderate accelerations are possible for those sequences by a combined CS and PI reconstruction. Nevertheless, for certain sequences/applications one might mildly reduce the acquisition time by appropriately reducing the imaging resolution rather than the more complicated CS reconstruction. J. Magn. Reson. Imaging 2016;44:433-444. © 2016 Wiley Periodicals, Inc.
Undersampling strategies for compressed sensing accelerated MR spectroscopic imaging
NASA Astrophysics Data System (ADS)
Vidya Shankar, Rohini; Hu, Houchun Harry; Bikkamane Jayadev, Nutandev; Chang, John C.; Kodibagkar, Vikram D.
2017-03-01
Compressed sensing (CS) can accelerate magnetic resonance spectroscopic imaging (MRSI), facilitating its widespread clinical integration. The objective of this study was to assess the effect of different undersampling strategy on CS-MRSI reconstruction quality. Phantom data were acquired on a Philips 3 T Ingenia scanner. Four types of undersampling masks, corresponding to each strategy, namely, low resolution, variable density, iterative design, and a priori were simulated in Matlab and retrospectively applied to the test 1X MRSI data to generate undersampled datasets corresponding to the 2X - 5X, and 7X accelerations for each type of mask. Reconstruction parameters were kept the same in each case(all masks and accelerations) to ensure that any resulting differences can be attributed to the type of mask being employed. The reconstructed datasets from each mask were statistically compared with the reference 1X, and assessed using metrics like the root mean square error and metabolite ratios. Simulation results indicate that both the a priori and variable density undersampling masks maintain high fidelity with the 1X up to five-fold acceleration. The low resolution mask based reconstructions showed statistically significant differences from the 1X with the reconstruction failing at 3X, while the iterative design reconstructions maintained fidelity with the 1X till 4X acceleration. In summary, a pilot study was conducted to identify an optimal sampling mask in CS-MRSI. Simulation results demonstrate that the a priori and variable density masks can provide statistically similar results to the fully sampled reference. Future work would involve implementing these two masks prospectively on a clinical scanner.
Rapid MR spectroscopic imaging of lactate using compressed sensing
NASA Astrophysics Data System (ADS)
Vidya Shankar, Rohini; Agarwal, Shubhangi; Geethanath, Sairam; Kodibagkar, Vikram D.
2015-03-01
Imaging lactate metabolism in vivo may improve cancer targeting and therapeutics due to its key role in the development, maintenance, and metastasis of cancer. The long acquisition times associated with magnetic resonance spectroscopic imaging (MRSI), which is a useful technique for assessing metabolic concentrations, are a deterrent to its routine clinical use. The objective of this study was to combine spectral editing and prospective compressed sensing (CS) acquisitions to enable precise and high-speed imaging of the lactate resonance. A MRSI pulse sequence with two key modifications was developed: (1) spectral editing components for selective detection of lactate, and (2) a variable density sampling mask for pseudo-random under-sampling of the k-space `on the fly'. The developed sequence was tested on phantoms and in vivo in rodent models of cancer. Datasets corresponding to the 1X (fully-sampled), 2X, 3X, 4X, 5X, and 10X accelerations were acquired. The under-sampled datasets were reconstructed using a custom-built algorithm in MatlabTM, and the fidelity of the CS reconstructions was assessed in terms of the peak amplitudes, SNR, and total acquisition time. The accelerated reconstructions demonstrate a reduction in the scan time by up to 90% in vitro and up to 80% in vivo, with negligible loss of information when compared with the fully-sampled dataset. The proposed unique combination of spectral editing and CS facilitated rapid mapping of the spatial distribution of lactate at high temporal resolution. This technique could potentially be translated to the clinic for the routine assessment of lactate changes in solid tumors.
Deconvolution of serum cortisol levels by using compressed sensing.
Faghih, Rose T; Dahleh, Munther A; Adler, Gail K; Klerman, Elizabeth B; Brown, Emery N
2014-01-01
The pulsatile release of cortisol from the adrenal glands is controlled by a hierarchical system that involves corticotropin releasing hormone (CRH) from the hypothalamus, adrenocorticotropin hormone (ACTH) from the pituitary, and cortisol from the adrenal glands. Determining the number, timing, and amplitude of the cortisol secretory events and recovering the infusion and clearance rates from serial measurements of serum cortisol levels is a challenging problem. Despite many years of work on this problem, a complete satisfactory solution has been elusive. We formulate this question as a non-convex optimization problem, and solve it using a coordinate descent algorithm that has a principled combination of (i) compressed sensing for recovering the amplitude and timing of the secretory events, and (ii) generalized cross validation for choosing the regularization parameter. Using only the observed serum cortisol levels, we model cortisol secretion from the adrenal glands using a second-order linear differential equation with pulsatile inputs that represent cortisol pulses released in response to pulses of ACTH. Using our algorithm and the assumption that the number of pulses is between 15 to 22 pulses over 24 hours, we successfully deconvolve both simulated datasets and actual 24-hr serum cortisol datasets sampled every 10 minutes from 10 healthy women. Assuming a one-minute resolution for the secretory events, we obtain physiologically plausible timings and amplitudes of each cortisol secretory event with R (2) above 0.92. Identification of the amplitude and timing of pulsatile hormone release allows (i) quantifying of normal and abnormal secretion patterns towards the goal of understanding pathological neuroendocrine states, and (ii) potentially designing optimal approaches for treating hormonal disorders.
Compressive Sensing Based Bio-Inspired Shape Feature Detection CMOS Imager