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.
Volumetric (3D) compressive sensing spectral domain optical coherence tomography
Xu, Daguang; Huang, Yong; Kang, Jin U.
2014-01-01
In this work, we proposed a novel three-dimensional compressive sensing (CS) approach for spectral domain optical coherence tomography (SD OCT) volumetric image acquisition and reconstruction. Instead of taking a spectral volume whose size is the same as that of the volumetric image, our method uses a sub set of the original spectral volume that is under-sampled in all three dimensions, which reduces the amount of spectral measurements to less than 20% of that required by the Shan-non/Nyquist theory. The 3D image is recovered from the under-sampled spectral data dimension-by-dimension using the proposed three-step CS reconstruction strategy. Experimental results show that our method can significantly reduce the sampling rate required for a volumetric SD OCT image while preserving the image quality. PMID:25426320
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
3D multifocus astigmatism and compressed sensing (3D MACS) based superresolution reconstruction.
Huang, Jiaqing; Sun, Mingzhai; Gumpper, Kristyn; Chi, Yuejie; Ma, Jianjie
2015-03-01
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 ℓ 1-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
NASA Astrophysics Data System (ADS)
Chen, Ping-Feng; Krim, Hamid
2008-02-01
In this paper, we propose using two methods to determine the canonical views of 3D objects: minimum description length (MDL) criterion and compressive sensing method. MDL criterion searches for the description length that achieves the balance between model accuracy and parsimony. It takes the form of the sum of a likelihood and a penalizing term, where the likelihood is in favor of model accuracy such that more views assists the description of an object, while the second term penalizes lengthy description to prevent overfitting of the model. In order to devise the likelihood term, we propose a model to represent a 3D object as the weighted sum of multiple range images, which is used in the second method to determine the canonical views as well. In compressive sensing method, an intelligent way of parsimoniously sampling an object is presented. We make direct inference from Donoho1 and Candes'2 work, and adapt it to our model. Each range image is viewed as a projection, or a sample, of a 3D model, and by using compressive sensing theory, we are able to reconstruct the object with an overwhelming probability by scarcely sensing the object in a random manner. Compressive sensing is different from traditional compressing method in the sense that the former compress things in the sampling stage while the later collects a large number of samples and then compressing mechanism is carried out thereafter. Compressive sensing scheme is particularly useful when the number of sensors are limited or the sampling machinery cost much resource or time.
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
Fast imaging of laboratory core floods using 3D compressed sensing RARE MRI
NASA Astrophysics Data System (ADS)
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 16 min. 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 ± 0.03 ft day-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
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. PMID:26057610
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. PMID:22392604
NASA Astrophysics Data System (ADS)
van Gorp, Jetse S.; Bakker, Chris J. G.; Bouwman, Job G.; Smink, Jouke; Zijlstra, Frank; Seevinck, Peter R.
2015-01-01
In this study, we explore the potential of compressed sensing (CS) accelerated broadband 3D phase-encoded turbo spin-echo (3D-PE-TSE) for the purpose of geometrically undistorted imaging in the presence of field inhomogeneities. To achieve this goal 3D-PE-SE and 3D-PE-TSE sequences with broadband rf pulses and dedicated undersampling patterns were implemented on a clinical scanner. Additionally, a 3D multi-spectral spin-echo (ms3D-SE) sequence was implemented for reference purposes. First, we demonstrated the influence of susceptibility induced off-resonance effects on the spatial encoding of broadband 3D-SE, ms3D-SE, 3D-PE-SE and 3D-PE-TSE using a grid phantom containing a titanium implant (Δχ = 182 ppm) with x-ray CT as a gold standard. These experiments showed that the spatial encoding of 3D-PE-(T)SE was unaffected by susceptibility induced off-resonance effects, which caused geometrical distortions and/or signal hyper-intensities in broadband 3D-SE and, to a lesser extent, in ms3D-SE frequency encoded methods. Additionally, an SNR analysis was performed and the temporally resolved signal of 3D-PE-(T)SE sequences was exploited to retrospectively decrease the acquisition bandwidth and obtain field offset maps. The feasibility of CS acceleration was studied retrospectively and prospectively for the 3D-PE-SE sequence using an existing CS algorithm adapted for the reconstruction of 3D data with undersampling in all three phase encoded dimensions. CS was combined with turbo-acceleration by variable density undersampling and spherical stepwise T2 weighting by randomly sorting consecutive echoes in predefined spherical k-space layers. The CS-TSE combination resulted in an overall acceleration factor of 60, decreasing the original 3D-PE-SE scan time from 7 h to 7 min. Finally, CS accelerated 3D-PE-TSE in vivo images of a titanium screw were obtained within 10 min using a micro-coil demonstrating the feasibility of geometrically undistorted MRI near severe
van Gorp, Jetse S; Bakker, Chris J G; Bouwman, Job G; Smink, Jouke; Zijlstra, Frank; Seevinck, Peter R
2015-01-21
In this study, we explore the potential of compressed sensing (CS) accelerated broadband 3D phase-encoded turbo spin-echo (3D-PE-TSE) for the purpose of geometrically undistorted imaging in the presence of field inhomogeneities. To achieve this goal 3D-PE-SE and 3D-PE-TSE sequences with broadband rf pulses and dedicated undersampling patterns were implemented on a clinical scanner. Additionally, a 3D multi-spectral spin-echo (ms3D-SE) sequence was implemented for reference purposes. First, we demonstrated the influence of susceptibility induced off-resonance effects on the spatial encoding of broadband 3D-SE, ms3D-SE, 3D-PE-SE and 3D-PE-TSE using a grid phantom containing a titanium implant (Δχ = 182 ppm) with x-ray CT as a gold standard. These experiments showed that the spatial encoding of 3D-PE-(T)SE was unaffected by susceptibility induced off-resonance effects, which caused geometrical distortions and/or signal hyper-intensities in broadband 3D-SE and, to a lesser extent, in ms3D-SE frequency encoded methods. Additionally, an SNR analysis was performed and the temporally resolved signal of 3D-PE-(T)SE sequences was exploited to retrospectively decrease the acquisition bandwidth and obtain field offset maps. The feasibility of CS acceleration was studied retrospectively and prospectively for the 3D-PE-SE sequence using an existing CS algorithm adapted for the reconstruction of 3D data with undersampling in all three phase encoded dimensions. CS was combined with turbo-acceleration by variable density undersampling and spherical stepwise T2 weighting by randomly sorting consecutive echoes in predefined spherical k-space layers. The CS-TSE combination resulted in an overall acceleration factor of 60, decreasing the original 3D-PE-SE scan time from 7 h to 7 min. Finally, CS accelerated 3D-PE-TSE in vivo images of a titanium screw were obtained within 10 min using a micro-coil demonstrating the feasibility of geometrically undistorted MRI near severe
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
NASA Astrophysics Data System (ADS)
Park, Yeonok; Cho, Hyosung; Je, Uikyu; Hong, Daeki; Lee, Minsik; Park, Chulkyu; Cho, Heemoon; Choi, Sungil; Koo, Yangseo
2014-08-01
In practical applications of three-dimensional (3D) tomographic techniques, such as digital breast tomosynthesis (DBT), computed tomography (CT), etc., there are often challenges for accurate image reconstruction from incomplete data. In DBT, in particular, the limited-angle and few-view projection data are theoretically insufficient for exact reconstruction; thus, the use of common filtered-backprojection (FBP) algorithms leads to severe image artifacts, such as the loss of the average image value and edge sharpening. One possible approach to alleviate these artifacts may employ iterative statistical methods because they potentially yield reconstructed images that are in better accordance with the measured projection data. In this work, as another promising approach, we investigated potential applications to low-dose, accurate DBT imaging with a state-of-the-art reconstruction scheme based on compressed-sensing (CS) theory. We implemented an efficient CS-based DBT algorithm and performed systematic simulation works to investigate the imaging characteristics. We successfully obtained DBT images of substantially very high accuracy by using the algorithm and expect it to be applicable to developing the next-generation 3D breast X-ray imaging system.
Je, U K; Cho, H M; Hong, D K; Cho, H S; Park, Y O; Park, C K; Kim, K S; Lim, H W; Kim, G A; Park, S Y; Woo, T H; Cho, S I
2016-01-01
In this work, we propose a practical method that can combine the two functionalities of dental panoramic and cone-beam CT (CBCT) features in one by using a single panoramic detector. We implemented a CS-based reconstruction algorithm for the proposed method and performed a systematic simulation to demonstrate its viability for 3D dental X-ray imaging. We successfully reconstructed volumetric images of considerably high accuracy by using a panoramic detector having an active area of 198.4 mm × 6.4 mm and evaluated the reconstruction quality as a function of the pitch (p) and the angle step (Δθ). Our simulation results indicate that the CS-based reconstruction almost completely recovered the phantom structures, as in CBCT, for p≤2.0 and θ≤6°, indicating that it seems very promising for accurate image reconstruction even for large-pitch and few-view data. We expect the proposed method to be applicable to developing a cost-effective, volumetric dental X-ray imaging system. PMID:26494155
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.
3D MHD Simulations of Spheromak Compression
NASA Astrophysics Data System (ADS)
Stuber, James E.; Woodruff, Simon; O'Bryan, John; Romero-Talamas, Carlos A.; Darpa Spheromak Team
2015-11-01
The adiabatic compression of compact tori could lead to a compact and hence low cost fusion energy system. The critical scientific issues in spheromak compression relate both to confinement properties and to the stability of the configuration undergoing compression. We present results from the NIMROD code modified with the addition of magnetic field coils that allow us to examine the role of rotation on the stability and confinement of the spheromak (extending prior work for the FRC). We present results from a scan in initial rotation, from 0 to 100km/s. We show that strong rotational shear (10km/s over 1cm) occurs. We compare the simulation results with analytic scaling relations for adiabatic compression. Work performed under DARPA grant N66001-14-1-4044.
NASA Astrophysics Data System (ADS)
Lee, M. S.; Kim, H. J.; Cho, H. S.; Hong, D. K.; Je, U. K.; Oh, J. E.; Park, Y. O.; Lee, S. H.; Cho, H. M.; Choi, S. I.; Koo, Y. S.
2013-09-01
The most popular reconstruction algorithm for cone-beam computed tomography (CBCT) is based on the computationally-inexpensive filtered-backprojection (FBP) method. However, that method usually requires dense projections over the Nyquist samplings, which imposes severe restrictions on the imaging doses. Moreover, the algorithm tends to produce cone-beam artifacts as the cone angle is increased. Several variants of the FBP-based algorithm have been developed to overcome these difficulties, but problems with the cone-beam reconstruction still remain. In this study, we considered a compressed-sensing (CS)-based reconstruction algorithm for low-dose, high-quality dental CBCT images that exploited the sparsity of images with substantially high accuracy. We implemented the algorithm and performed systematic simulation works to investigate the imaging characteristics. CBCT images of high quality were successfully reconstructed by using the built-in CS-based algorithm, and the image qualities were evaluated quantitatively in terms of the universal-quality index (UQI) and the slice-profile quality index (SPQI).We expect the reconstruction algorithm developed in the work to be applicable to current dental CBCT systems, to reduce imaging doses, and to improve the image quality further.
Variance anisotropy in compressible 3-D MHD
NASA Astrophysics Data System (ADS)
Oughton, S.; Matthaeus, W. H.; Wan, Minping; Parashar, Tulasi
2016-06-01
We employ spectral method numerical simulations to examine the dynamical development of anisotropy of the variance, or polarization, of the magnetic and velocity field in compressible magnetohydrodynamic (MHD) turbulence. Both variance anisotropy and spectral anisotropy emerge under influence of a large-scale mean magnetic field B0; these are distinct effects, although sometimes related. Here we examine the appearance of variance parallel to B0, when starting from a highly anisotropic state. The discussion is based on a turbulence theoretic approach rather than a wave perspective. We find that parallel variance emerges over several characteristic nonlinear times, often attaining a quasi-steady level that depends on plasma beta. Consistency with solar wind observations seems to occur when the initial state is dominated by quasi-two-dimensional fluctuations.
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).
Tomographic compressive holographic reconstruction of 3D objects
NASA Astrophysics Data System (ADS)
Nehmetallah, G.; Williams, L.; Banerjee, P. P.
2012-10-01
Compressive holography with multiple projection tomography is applied to solve the inverse ill-posed problem of reconstruction of 3D objects with high axial accuracy. To visualize the 3D shape, we propose Digital Tomographic Compressive Holography (DiTCH), where projections from more than one direction as in tomographic imaging systems can be employed, so that a 3D shape with better axial resolution can be reconstructed. We compare DiTCH with single-beam holographic tomography (SHOT) which is based on Fresnel back-propagation. A brief theory of DiTCH is presented, and experimental results of 3D shape reconstruction of objects using DITCH and SHOT are compared.
Compression of 3D integral images using wavelet decomposition
NASA Astrophysics Data System (ADS)
Mazri, Meriem; Aggoun, Amar
2003-06-01
This paper presents a wavelet-based lossy compression technique for unidirectional 3D integral images (UII). The method requires the extraction of different viewpoint images from the integral image. A single viewpoint image is constructed by extracting one pixel from each microlens, then each viewpoint image is decomposed using a Two Dimensional Discrete Wavelet Transform (2D-DWT). The resulting array of coefficients contains several frequency bands. The lower frequency bands of the viewpoint images are assembled and compressed using a 3 Dimensional Discrete Cosine Transform (3D-DCT) followed by Huffman coding. This will achieve decorrelation within and between 2D low frequency bands from the different viewpoint images. The remaining higher frequency bands are Arithmetic coded. After decoding and decompression of the viewpoint images using an inverse 3D-DCT and an inverse 2D-DWT, each pixel from every reconstructed viewpoint image is put back into its original position within the microlens to reconstruct the whole 3D integral image. Simulations were performed on a set of four different grey level 3D UII using a uniform scalar quantizer with deadzone. The results for the average of the four UII intensity distributions are presented and compared with previous use of 3D-DCT scheme. It was found that the algorithm achieves better rate-distortion performance, with respect to compression ratio and image quality at very low bit rates.
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
2015-01-01
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
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
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.
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.
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.
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 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
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
Compression of point-texture 3D motion sequences
NASA Astrophysics Data System (ADS)
Song, In-Wook; Kim, Chang-Su; Lee, Sang-Uk
2005-10-01
In this work, we propose two compression algorithms for PointTexture 3D sequences: the octree-based scheme and the motion-compensated prediction scheme. The first scheme represents each PointTexture frame hierarchically using an octree. The geometry information in the octree nodes is encoded by the predictive partial matching (PPM) method. The encoder supports the progressive transmission of the 3D frame by transmitting the octree nodes in a top-down manner. The second scheme adopts the motion-compensated prediction to exploit the temporal correlation in 3D sequences. It first divides each frame into blocks, and then estimates the motion of each block using the block matching algorithm. In contrast to the motion-compensated 2D video coding, the prediction residual may take more bits than the original signal. Thus, in our approach, the motion compensation is used only for the blocks that can be replaced by the matching blocks. The other blocks are PPM-encoded. Extensive simulation results demonstrate that the proposed algorithms provide excellent compression performances.
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
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.
Compressive Sensing DNA Microarrays
2009-01-01
Compressive sensing microarrays (CSMs) are DNA-based sensors that operate using group testing and compressive sensing (CS) principles. In contrast to conventional DNA microarrays, in which each genetic sensor is designed to respond to a single target, in a CSM, each sensor responds to a set of targets. We study the problem of designing CSMs that simultaneously account for both the constraints from CS theory and the biochemistry of probe-target DNA hybridization. An appropriate cross-hybridization model is proposed for CSMs, and several methods are developed for probe design and CS signal recovery based on the new model. Lab experiments suggest that in order to achieve accurate hybridization profiling, consensus probe sequences are required to have sequence homology of at least 80% with all targets to be detected. Furthermore, out-of-equilibrium datasets are usually as accurate as those obtained from equilibrium conditions. Consequently, one can use CSMs in applications in which only short hybridization times are allowed. PMID:19158952
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
Compressed hyperspectral sensing
NASA Astrophysics Data System (ADS)
Tsagkatakis, Grigorios; Tsakalides, Panagiotis
2015-03-01
Acquisition of high dimensional Hyperspectral Imaging (HSI) data using limited dimensionality imaging sensors has led to restricted capabilities designs that hinder the proliferation of HSI. To overcome this limitation, novel HSI architectures strive to minimize the strict requirements of HSI by introducing computation into the acquisition process. A framework that allows the integration of acquisition with computation is the recently proposed framework of Compressed Sensing (CS). In this work, we propose a novel HSI architecture that exploits the sampling and recovery capabilities of CS to achieve a dramatic reduction in HSI acquisition requirements. In the proposed architecture, signals from multiple spectral bands are multiplexed before getting recorded by the imaging sensor. Reconstruction of the full hyperspectral cube is achieved by exploiting a dictionary of elementary spectral profiles in a unified minimization framework. Simulation results suggest that high quality recovery is possible from a single or a small number of multiplexed frames.
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.
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.
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.
Adaptive compressive sensing camera
NASA Astrophysics Data System (ADS)
Hsu, Charles; Hsu, Ming K.; Cha, Jae; Iwamura, Tomo; Landa, Joseph; Nguyen, Charles; Szu, Harold
2013-05-01
We have embedded Adaptive Compressive Sensing (ACS) algorithm on Charge-Coupled-Device (CCD) camera based on the simplest concept that each pixel is a charge bucket, and the charges comes from Einstein photoelectric conversion effect. Applying the manufactory design principle, we only allow altering each working component at a minimum one step. We then simulated what would be such a camera can do for real world persistent surveillance taking into account of diurnal, all weather, and seasonal variations. The data storage has saved immensely, and the order of magnitude of saving is inversely proportional to target angular speed. We did design two new components of CCD camera. Due to the matured CMOS (Complementary metal-oxide-semiconductor) technology, the on-chip Sample and Hold (SAH) circuitry can be designed for a dual Photon Detector (PD) analog circuitry for changedetection that predicts skipping or going forward at a sufficient sampling frame rate. For an admitted frame, there is a purely random sparse matrix [Φ] which is implemented at each bucket pixel level the charge transport bias voltage toward its neighborhood buckets or not, and if not, it goes to the ground drainage. Since the snapshot image is not a video, we could not apply the usual MPEG video compression and Hoffman entropy codec as well as powerful WaveNet Wrapper on sensor level. We shall compare (i) Pre-Processing FFT and a threshold of significant Fourier mode components and inverse FFT to check PSNR; (ii) Post-Processing image recovery will be selectively done by CDT&D adaptive version of linear programming at L1 minimization and L2 similarity. For (ii) we need to determine in new frames selection by SAH circuitry (i) the degree of information (d.o.i) K(t) dictates the purely random linear sparse combination of measurement data a la [Φ]M,N M(t) = K(t) Log N(t).
3-D Adaptive Sparsity Based Image Compression With Applications to Optical Coherence Tomography.
Fang, Leyuan; Li, Shutao; Kang, Xudong; Izatt, Joseph A; Farsiu, Sina
2015-06-01
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
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.
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.
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.
Advanced 3D imaging lidar concepts for long range sensing
NASA Astrophysics Data System (ADS)
Gordon, K. J.; Hiskett, P. A.; Lamb, R. A.
2014-06-01
Recent developments in 3D imaging lidar are presented. Long range 3D imaging using photon counting is now a possibility, offering a low-cost approach to integrated remote sensing with step changing advantages in size, weight and power compared to conventional analogue active imaging technology. We report results using a Geiger-mode array for time-of-flight, single photon counting lidar for depth profiling and determination of the shape and size of tree canopies and distributed surface reflections at a range of 9km, with 4μJ pulses with a frame rate of 100kHz using a low-cost fibre laser operating at a wavelength of λ=1.5 μm. The range resolution is less than 4cm providing very high depth resolution for target identification. This specification opens up several additional functionalities for advanced lidar, for example: absolute rangefinding and depth profiling for long range identification, optical communications, turbulence sensing and time-of-flight spectroscopy. Future concepts for 3D time-of-flight polarimetric and multispectral imaging lidar, with optical communications in a single integrated system are also proposed.
A Cartesian scheme for compressible multimaterial models in 3D
NASA Astrophysics Data System (ADS)
de Brauer, Alexia; Iollo, Angelo; Milcent, Thomas
2016-05-01
We model the three-dimensional interaction of compressible materials separated by sharp interfaces. We simulate fluid and hyperelastic solid flows in a fully Eulerian framework. The scheme is the same for all materials and can handle large deformations and frictionless contacts. Necessary conditions for hyperbolicity of the hyperelastic neohookean model in three dimensions are proved thanks to an explicit computation of the characteristic speeds. We present stiff multimaterial interactions including air-helium and water-air shock interactions, projectile-shield impacts in air and rebounds.
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
Compressed sensing based video multicast
NASA Astrophysics Data System (ADS)
Schenkel, Markus B.; Luo, Chong; Frossard, Pascal; Wu, Feng
2010-07-01
We propose a new scheme for wireless video multicast based on compressed sensing. It has the property of graceful degradation and, unlike systems adhering to traditional separate coding, it does not suffer from a cliff effect. Compressed sensing is applied to generate measurements of equal importance from a video such that a receiver with a better channel will naturally have more information at hands to reconstruct the content without penalizing others. We experimentally compare different random matrices at the encoder side in terms of their performance for video transmission. We further investigate how properties of natural images can be exploited to improve the reconstruction performance by transmitting a small amount of side information. And we propose a way of exploiting inter-frame correlation by extending only the decoder. Finally we compare our results with a different scheme targeting the same problem with simulations and find competitive results for some channel configurations.
Hyperspectral imaging using compressed sensing
NASA Astrophysics Data System (ADS)
Ramirez I., Gabriel Eduardo; Manian, Vidya B.
2012-06-01
Compressed sensing (CS) has attracted a lot of attention in recent years as a promising signal processing technique that exploits a signal's sparsity to reduce its size. It allows for simple compression that does not require a lot of additional computational power, and would allow physical implementation at the sensor using spatial light multiplexers using Texas Instruments (TI) digital micro-mirror device (DMD). The DMD can be used as a random measurement matrix, reflecting the image off the DMD is the equivalent of an inner product between the images individual pixels and the measurement matrix. CS however is asymmetrical, meaning that the signals recovery or reconstruction from the measurements does require a higher level of computation. This makes the prospect of working with the compressed version of the signal in implementations such as detection or classification much more efficient. If an initial analysis shows nothing of interest, the signal need not be reconstructed. Many hyper-spectral image applications are precisely focused on these areas, and would greatly benefit from a compression technique like CS that could help minimize the light sensor down to a single pixel, lowering costs associated with the cameras while reducing the large amounts of data generated by all the bands. The present paper will show an implementation of CS using a single pixel hyper-spectral sensor, and compare the reconstructed images to those obtained through the use of a regular sensor.
Fast spectrophotometry with compressive sensing
NASA Astrophysics Data System (ADS)
Starling, David; Storer, Ian
2015-03-01
Spectrophotometers and spectrometers have numerous applications in the physical sciences and engineering, resulting in a plethora of designs and requirements. A good spectrophotometer balances the need for high photometric precision, high spectral resolution, high durability and low cost. One way to address these design objectives is to take advantage of modern scanning and detection techniques. A common imaging method that has improved signal acquisition speed and sensitivity in limited signal scenarios is the single pixel camera. Such cameras utilize the sparsity of a signal to sample below the Nyquist rate via a process known as compressive sensing. Here, we show that a single pixel camera using compressive sensing algorithms and a digital micromirror device can replace the common scanning mechanisms found in virtually all spectrophotometers, providing a very low cost solution and improving data acquisition time. We evaluate this single pixel spectrophotometer by studying a variety of samples tested against commercial products. We conclude with an analysis of flame spectra and possible improvements for future designs.
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.
Athena3D: Flux-conservative Godunov-type algorithm for compressible magnetohydrodynamics
NASA Astrophysics Data System (ADS)
Hawley, John; Simon, Jake; Stone, James; Gardiner, Thomas; Teuben, Peter
2015-05-01
Written in FORTRAN, Athena3D, based on Athena (ascl:1010.014), is an implementation of a flux-conservative Godunov-type algorithm for compressible magnetohydrodynamics. Features of the Athena3D code include compressible hydrodynamics and ideal MHD in one, two or three spatial dimensions in Cartesian coordinates; adiabatic and isothermal equations of state; 1st, 2nd or 3rd order reconstruction using the characteristic variables; and numerical fluxes computed using the Roe scheme. In addition, it offers the ability to add source terms to the equations and is parallelized based on MPI.
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. PMID:25041926
Compressed Sensing Based Interior Tomography
Yu, Hengyong; Wang, Ge
2010-01-01
While the conventional wisdom is that the interior problem does not have a unique solution, by analytic continuation we recently showed that the interior problem can be uniquely and stably solved if we have a known sub-region inside a region-of-interest (ROI). However, such a known sub-region does not always readily available, and it is even impossible to find in some cases. Based on the compressed sensing theory, here we prove that if an object under reconstruction is essentially piecewise constant, a local ROI can be exactly and stably reconstructed via the total variation minimization. Because many objects in CT applications can be approximately modeled as piecewise constant, our approach is practically useful and suggests a new research direction of interior tomography. To illustrate the merits of our finding, we develop an iterative interior reconstruction algorithm that minimizes the total variation of a reconstructed image, and evaluate the performance in numerical simulation. PMID:19369711
Coherent radar imaging based on compressed sensing
NASA Astrophysics Data System (ADS)
Zhu, Qian; Volz, Ryan; Mathews, John D.
2015-12-01
High-resolution radar images in the horizontal spatial domain generally require a large number of different baselines that usually come with considerable cost. In this paper, aspects of compressed sensing (CS) are introduced to coherent radar imaging. We propose a single CS-based formalism that enables the full three-dimensional (3-D)—range, Doppler frequency, and horizontal spatial (represented by the direction cosines) domain—imaging. This new method can not only reduce the system costs and decrease the needed number of baselines by enabling spatial sparse sampling but also achieve high resolution in the range, Doppler frequency, and horizontal space dimensions. Using an assumption of point targets, a 3-D radar signal model for imaging has been derived. By comparing numerical simulations with the fast Fourier transform and maximum entropy methods at different signal-to-noise ratios, we demonstrate that the CS method can provide better performance in resolution and detectability given comparatively few available measurements relative to the number required by Nyquist-Shannon sampling criterion. These techniques are being applied to radar meteor observations.
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. PMID:26831987
Li, Xiangwei; Lan, Xuguang; Yang, Meng; Xue, Jianru; Zheng, Nanning
2014-01-01
Compressive Sensing Imaging (CSI) is a new framework for image acquisition, which enables the simultaneous acquisition and compression of a scene. Since the characteristics of Compressive Sensing (CS) acquisition are very different from traditional image acquisition, the general image compression solution may not work well. In this paper, we propose an efficient lossy compression solution for CS acquisition of images by considering the distinctive features of the CSI. First, we design an adaptive compressive sensing acquisition method for images according to the sampling rate, which could achieve better CS reconstruction quality for the acquired image. Second, we develop a universal quantization for the obtained CS measurements from CS acquisition without knowing any a priori information about the captured image. Finally, we apply these two methods in the CSI system for efficient lossy compression of CS acquisition. Simulation results demonstrate that the proposed solution improves the rate-distortion performance by 0.4∼2 dB comparing with current state-of-the-art, while maintaining a low computational complexity. PMID:25490597
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.
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.
Graph-Based Compression of Dynamic 3D Point Cloud Sequences.
Thanou, Dorina; Chou, Philip A; Frossard, Pascal
2016-04-01
This paper addresses the problem of compression of 3D point cloud sequences that are characterized by moving 3D positions and color attributes. As temporally successive point cloud frames share some similarities, motion estimation is key to effective compression of these sequences. It, however, remains a challenging problem as the point cloud frames have varying numbers of points without explicit correspondence information. We represent the time-varying geometry of these sequences with a set of graphs, and consider 3D positions and color attributes of the point clouds as signals on the vertices of the graphs. We then cast motion estimation as a feature-matching problem between successive graphs. The motion is estimated on a sparse set of representative vertices using new spectral graph wavelet descriptors. A dense motion field is eventually interpolated by solving a graph-based regularization problem. The estimated motion is finally used for removing the temporal redundancy in the predictive coding of the 3D positions and the color characteristics of the point cloud sequences. Experimental results demonstrate that our method is able to accurately estimate the motion between consecutive frames. Moreover, motion estimation is shown to bring a significant improvement in terms of the overall compression performance of the sequence. To the best of our knowledge, this is the first paper that exploits both the spatial correlation inside each frame (through the graph) and the temporal correlation between the frames (through the motion estimation) to compress the color and the geometry of 3D point cloud sequences in an efficient way. PMID:26891486
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.
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.
Turbo Compressed Sensing with Partial DFT Sensing Matrix
NASA Astrophysics Data System (ADS)
Ma, Junjie; Yuan, Xiaojun; Ping, Li
2015-02-01
In this letter, we propose a turbo compressed sensing algorithm with partial discrete Fourier transform (DFT) sensing matrices. Interestingly, the state evolution of the proposed algorithm is shown to be consistent with that derived using the replica method. Numerical results demonstrate that the proposed algorithm outperforms the well-known approximate message passing (AMP) algorithm when a partial DFT sensing matrix is involved.
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.
Engineering 3D Nanoplasmonic Assemblies for High Performance Spectroscopic Sensing.
Dinda, S; Suresh, V; Thoniyot, P; Balčytis, A; Juodkazis, S; Krishnamoorthy, S
2015-12-23
We demonstrate the fabrication of plasmonic sensors that comprise gold nanopillar arrays exhibiting high surface areas, and narrow gaps, through self-assembly of amphiphilic diblock copolymer micelles on silicon substrates. Silicon nanopillars with high integrity over arbitrary large areas are obtained using copolymer micelles as lithographic templates. The gaps between metal features are controlled by varying the thickness of the evaporated gold. The resulting gold metal nanopillar arrays exhibit an engineered surface topography, together with uniform and controlled separations down to sub-10 nm suitable for highly sensitive detection of molecular analytes by Surface Enhanced Raman Spectroscopy (SERS). The significance of the approach is demonstrated through the control exercised at each step, including template preparation and pattern-transfer steps. The approach is a promising means to address trade-offs between resolutions, throughput, and performance in the fabrication of nanoplasmonic assemblies for sensing applications. PMID:26523480
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.
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.
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
NASA Astrophysics Data System (ADS)
Pan, Zhongxiang; Gu, Bohong; Sun, Baozhong
2015-03-01
This paper reports the longitudinal compressive behaviour of 3D braided basalt fibre tows/epoxy composite materials under strain-rate range of 1,200-2,400 s-1 and temperature range of 23-210 °C both in experimental and finite element analyses (FEA). A split Hopkinson pressure bar system with a heating device was designed to test the longitudinal compressive behaviour of 3D braided composite materials. Testing results indicate that longitudinal compression modulus, specific energy absorption and peak stress decreased with elevated temperatures, whereas the failure strain increased with elevated temperatures. At some temperatures above the T g of epoxy resin, such as at 120 and 150 °C, strain distributions and deformations in fibre tows and epoxy resin tended to be the same. It results in relatively slighter damage status of the 3D braided composite material. The FEA results reveal that heating of the material due to the dissipative energy of the inelastic deformation and damage processes generated in resin is more than that in fibre tows. The braiding structure has a significant influence on thermomechanical failure via two aspects: distribution and accumulation of the heating leads to the development of the shear band paths along braiding angle; the buckling inflection segment rather than the straight segment generates the maximum of the heating in each fibre tows. The damage occurs at the early stage when the temperature is below T g, while at the temperature above T g, damage stage occurs at the rear of plastic deformation.
Remote sensing images fusion based on block compressed sensing
NASA Astrophysics Data System (ADS)
Yang, Sen-lin; Wan, Guo-bin; Zhang, Bian-lian; Chong, Xin
2013-08-01
A novel strategy for remote sensing images fusion is presented based on the block compressed sensing (BCS). Firstly, the multiwavelet transform (MWT) are employed for better sparse representation of remote sensing images. The sparse representations of block images are then compressive sampling by the BCS with an identical scrambled block hadamard operator. Further, the measurements are fused by a linear weighting rule in the compressive domain. And finally, the fused image is reconstructed by the gradient projection sparse reconstruction (GPSR) algorithm. Experiments result analyzes the selection of block dimension and sampling rating, as well as the convergence performance of the proposed method. The field test of remote sensing images fusion shows the validity of the proposed method.
Xu, Xiang; Li, Hui; Zhang, Qiangqiang; Hu, Han; Zhao, Zongbin; Li, Jihao; Li, Jingye; Qiao, Yu; Gogotsi, Yury
2015-04-28
Three-dimensional (3D) graphene aerogels (GA) show promise for applications in supercapacitors, electrode materials, gas sensors, and oil absorption due to their high porosity, mechanical strength, and electrical conductivity. However, the control, actuation, and response properties of graphene aerogels have not been well studied. In this paper, we synthesized 3D graphene aerogels decorated with Fe3O4 nanoparticles (Fe3O4/GA) by self-assembly of graphene with simultaneous decoration by Fe3O4 nanoparticles using a modified hydrothermal reduction process. The aerogels exhibit up to 52% reversible magnetic field-induced strain and strain-dependent electrical resistance that can be used to monitor the degree of compression/stretching of the material. The density of Fe3O4/GA is only about 5.8 mg cm(-3), making it an ultralight magnetic elastomer with potential applications in self-sensing soft actuators, microsensors, microswitches, and environmental remediation. PMID:25792130
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.
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.
Freestanding 3D mesoporous Co₃O₄@carbon foam nanostructures for ethanol gas sensing.
Li, Lei; Liu, Minmin; He, Shuijian; Chen, Wei
2014-08-01
Metal oxide materials have been widely used as gas-sensing platforms, and their sensing performances are largely dependent on the morphology and surface structure. Here, freestanding flower-like Co3O4 nanostructures supported on three-dimensional (3D) carbon foam (Co3O4@CF) were successfully synthesized by a facile and low-cost hydrothermal route and annealing procedure. The morphology and structure of the nanocomposites were studied by X-ray diffraction, X-ray photoelectron spectroscopy, energy-dispersive spectroscopy, and scanning electron microscopy (SEM). The SEM characterizations showed that the skeleton of the porous carbon foam was fully covered by flower-like Co3O4 nanostructures. Moreover, each Co3O4 nanoflower is composed of densely packed nanoneedles with a length of ~10 μm, which can largely enhance the surface area (about 286.117 m(2)/g) for ethanol sensing. Gas sensor based on the as-synthesized 3D Co3O4@CF nanostructures was fabricated to study the sensing performance for ethanol at a temperature range from 180 to 360 °C. Due to the 3D porous structure and the improvement in sensing surface/interface, the Co3O4@CF nanostructure exhibited enhanced sensing performance for ethanol detection with low resistance, fast response and recovery time, high sensitivity, and limit of detection as low as 15 ppm at 320 °C. The present study shows that such novel 3D metal oxide/carbon hybrid nanostructures are promising platforms for gas sensing. PMID:25011608
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.
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
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/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.
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
Compressive sensing in the EO/IR.
Gehm, M E; Brady, D J
2015-03-10
We investigate the utility of compressive sensing (CS) to electro-optic and infrared (EO/IR) applications. We introduce the field through a discussion of historical antecedents and the development of the modern CS framework. Basic economic arguments (in the broadest sense) are presented regarding the applicability of CS to the EO/IR and used to draw conclusions regarding application areas where CS would be most viable. A number of experimental success stories are presented to demonstrate the overall feasibility of the approaches, and we conclude with a discussion of open challenges to practical adoption of CS methods. PMID:25968399
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, 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 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
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.
Photon-counting passive 3D image sensing and processing for automatic target recognition
NASA Astrophysics Data System (ADS)
Yeom, Seokwon; Javidi, Bahram; Watson, Edward
2008-04-01
In this paper we overview the nonlinear matched filtering for photon counting recognition with 3D passive sensing. The first and second order statistical properties of the nonlinear matched filtering can improve the recognition performance compared to the linear matched filtering. Automatic target reconstruction and recognition are addressed for partially occluded objects. The recognition performance is shown to be improved significantly in the reconstruction space. The discrimination capability is analyzed in terms of Fisher ratio (FR) and receiver operating characteristic (ROC) curves.
Compressive rendering: a rendering application of compressed sensing.
Sen, Pradeep; Darabi, Soheil
2011-04-01
Recently, there has been growing interest in compressed sensing (CS), the new theory that shows how a small set of linear measurements can be used to reconstruct a signal if it is sparse in a transform domain. Although CS has been applied to many problems in other fields, in computer graphics, it has only been used so far to accelerate the acquisition of light transport. In this paper, we propose a novel application of compressed sensing by using it to accelerate ray-traced rendering in a manner that exploits the sparsity of the final image in the wavelet basis. To do this, we raytrace only a subset of the pixel samples in the spatial domain and use a simple, greedy CS-based algorithm to estimate the wavelet transform of the image during rendering. Since the energy of the image is concentrated more compactly in the wavelet domain, less samples are required for a result of given quality than with conventional spatial-domain rendering. By taking the inverse wavelet transform of the result, we compute an accurate reconstruction of the desired final image. Our results show that our framework can achieve high-quality images with approximately 75 percent of the pixel samples using a nonadaptive sampling scheme. In addition, we also perform better than other algorithms that might be used to fill in the missing pixel data, such as interpolation or inpainting. Furthermore, since the algorithm works in image space, it is completely independent of scene complexity. PMID:21311092
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 %.
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
Hyperspectral fluorescence microscopy based on compressed sensing
NASA Astrophysics Data System (ADS)
Studer, Vincent; Bobin, Jérome; Chahid, Makhlad; Mousavi, Hamed; Candes, Emmanuel; Dahan, Maxime
2012-03-01
In fluorescence microscopy, one can distinguish two kinds of imaging approaches, wide field and raster scan microscopy, differing by their excitation and detection scheme. In both imaging modalities the acquisition is independent of the information content of the image. Rather, the number of acquisitions N, is imposed by the Nyquist-Shannon theorem. However, in practice, many biological images are compressible (or, equivalently here, sparse), meaning that they depend on a number of degrees of freedom K that is smaller that their size N. Recently, the mathematical theory of compressed sensing (CS) has shown how the sensing modality could take advantage of the image sparsity to reconstruct images with no loss of information while largely reducing the number M of acquisition. Here we present a novel fluorescence microscope designed along the principles of CS. It uses a spatial light modulator (DMD) to create structured wide field excitation patterns and a sensitive point detector to measure the emitted fluorescence. On sparse fluorescent samples, we could achieve compression ratio N/M of up to 64, meaning that an image can be reconstructed with a number of measurements of only 1.5 % of its pixel number. Furthemore, we extend our CS acquisition scheme to an hyperspectral imaging system.
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.
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
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.
Binned progressive quantization for compressive sensing.
Wang, Liangjun; Wu, Xiaolin; Shi, Guangming
2012-06-01
Compressive sensing (CS) has been recently and enthusiastically promoted as a joint sampling and compression approach. The advantages of CS over conventional signal compression techniques are architectural: the CS encoder is made signal independent and computationally inexpensive by shifting the bulk of system complexity to the decoder. While these properties of CS allow signal acquisition and communication in some severely resource-deprived conditions that render conventional sampling and coding impossible, they are accompanied by rather disappointing rate-distortion performance. In this paper, we propose a novel coding technique that rectifies, to a certain extent, the problem of poor compression performance of CS and, at the same time, maintains the simplicity and universality of the current CS encoder design. The main innovation is a scheme of progressive fixed-rate scalar quantization with binning that enables the CS decoder to exploit hidden correlations between CS measurements, which was overlooked in the existing literature. Experimental results are presented to demonstrate the efficacy of the new CS coding technique. Encouragingly, on some test images, the new CS technique matches or even slightly outperforms JPEG. PMID:22374362
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.
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.
Compressive sensing with a spherical microphone array.
Fernandez-Grande, Efren; Xenaki, Angeliki
2016-02-01
A wave expansion method is proposed in this work, based on measurements with a spherical microphone array, and formulated in the framework provided by Compressive Sensing. The method promotes sparse solutions via ℓ1-norm minimization, so that the measured data are represented by few basis functions. This results in fine spatial resolution and accuracy. This publication covers the theoretical background of the method, including experimental results that illustrate some of the fundamental differences with the "conventional" least-squares approach. The proposed methodology is relevant for source localization, sound field reconstruction, and sound field analysis. PMID:26936583
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. PMID:27583683
Variable Quality Compression of Fluid Dynamical Data Sets Using a 3D DCT Technique
NASA Astrophysics Data System (ADS)
Loddoch, A.; Schmalzl, J.
2005-12-01
In this work we present a data compression scheme that is especially suited for the compression of data sets resulting from computational fluid dynamics (CFD). By adopting the concept of the JPEG compression standard and extending the approach of Schmalzl (Schmalzl, J. Using standard image compression algorithms to store data from computational fluid dynamics. Computers and Geosciences, 29, 10211031, 2003) we employ a three-dimensional discrete cosine transform of the data. The resulting frequency components are rearranged, quantized and finally stored using Huffman-encoding and standard variable length integer codes. The compression ratio and also the introduced loss of accuracy can be adjusted by means of two compression parameters to give the desired compression profile. Using the proposed technique compression ratios of more than 60:1 are possible with an mean error of the compressed data of less than 0.1%.
Spatial versus spectral compression ratio in compressive sensing of hyperspectral imaging
NASA Astrophysics Data System (ADS)
August, Yitzhak; Vachman, Chaim; Stern, Adrian
2013-05-01
Compressive hyperspectral imaging is based on the fact that hyperspectral data is highly redundant. However, there is no symmetry between the compressibility of the spatial and spectral domains, and that should be taken into account for optimal compressive hyperspectral imaging system design. Here we present a study of the influence of the ratio between the compression in the spatial and spectral domains on the performance of a 3D separable compressive hyperspectral imaging method we recently developed.
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.
Analysis of Compressive Sensing for Hyperspectral Remote Sensing Applications
NASA Astrophysics Data System (ADS)
Busuioceanu, Maria
Compressive Sensing (CS) systems capture data with fewer measurements than traditional sensors assuming that imagery is redundant and compressible in the spectral and spatial dimensions. This thesis utilizes a model of the Coded Aperture Snapshot Spectral Imager-Dual Disperser (CASSI-DD) to simulate CS measurements from traditionally sensed HyMap images. A novel reconstruction algorithm that combines spectral smoothing and spatial total variation (TV) is used to create high resolution hyperspectral imagery from the simulated CS measurements. This research examines the effect of the number of measurements, which corresponds to the percentage of physical data sampled, on the quality of simulated CS data as estimated through performance of spectral image processing algorithms. The effect of CS on the data cloud is explored through principal component analysis (PCA) and endmember extraction. The ultimate purpose of this thesis is to investigate the utility of the CS sensor model and reconstruction for various hyperspectral applications in order to identify the strengths and limitations of CS. While CS is shown to create useful imagery for visual analysis, the data cloud is altered and per-pixel spectral fidelity declines for CS reconstructions from only a small number of measurements. In some hyperspectral applications, many measurements are needed in order to obtain comparable results to traditionally sensed HSI, including atmospheric compensation and subpixel target detection. On the other hand, in hyperspectral applications where pixels must be dramatically altered in order to be misclassified, such as land classification or NDVI mapping, CS shows promise.
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.
Compressed sensing phase retrieval with phase diversity
NASA Astrophysics Data System (ADS)
Qin, Shun; Hu, Xinqi; Qin, Qiong
2014-01-01
The compressed sensing (CS) theory shows that sparse signal can be reconstructed accurately with some randomly observed measurements that are much fewer than what traditional method requires. Since it takes structure of signals into consideration, it has many advantages in the structured signals process. With CS, measuring can be speeded up and the cost of hardware can be decreased significantly. However, it faces great challenge in the amplitude-only measurement. In this article, we study the magnitude-only compressed sensing phase retrieval (CSPR) problem, and propose a practical recovery algorithm. In our algorithm, we introduce the powerful Hybrid-Input-Output algorithm with phase diversity to make our algorithm robust and efficient. A relaxed ℓ0 norm constrain is also introduced to help PR find a sparse solution with fewer measurements, which is demonstrated to be essential and effective to CSPR. We finally successfully apply it into complex-valued object recovery in THz imaging. The numerical results show that the proposed algorithm can recover the object pretty well with fewer measurements than what PR traditionally requires.
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.
Compressive sensing with a microwave photonic filter
NASA Astrophysics Data System (ADS)
Chen, Ying; Yu, Xianbin; Chi, Hao; Zheng, Shilie; Zhang, Xianmin; Jin, Xiaofeng; Galili, Michael
2015-03-01
In this letter, we present a novel approach to realizing photonics-assisted compressive sensing (CS) with the technique of microwave photonic filtering. In the proposed system, an input spectrally sparse signal to be captured and a random sequence are modulated on an optical carrier via two Mach-Zehnder modulators (MZMs). Therefore, the mixing process (the signal to be captured mixing with the random sequence) is realized in the optical domain. The mixed optical signal then propagates through a length of dispersive fiber. As the double-sideband modulation in a dispersive optical link leads to a frequency-dependent power fading, low-pass filtering required in the CS is then realized. A proof-of-concept experiment for compressive sampling and recovery of a signal containing three tones at 310 MHz, 1 GHz and 2 GHz with a compression factor up to 10 is successfully demonstrated. More simulation results are also presented to recover signals within wider bandwidth and with more frequency components.
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.
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.
NASA Astrophysics Data System (ADS)
Zeng, Li; Jansen, Christian; Unser, Michael A.; Hunziker, Patrick
2001-12-01
High resolution multidimensional image data yield huge datasets. For compression and analysis, 2D approaches are often used, neglecting the information coherence in higher dimensions, which can be exploited for improved compression. We designed a wavelet compression algorithm suited for data of arbitrary dimensions, and assessed its ability for compression of 4D medical images. Basically, separable wavelet transforms are done in each dimension, followed by quantization and standard coding. Results were compared with conventional 2D wavelet. We found that in 4D heart images, this algorithm allowed high compression ratios, preserving diagnostically important image features. For similar image quality, compression ratios using the 3D/4D approaches were typically much higher (2-4 times per added dimension) than with the 2D approach. For low-resolution images created with the requirement to keep predefined key diagnostic information (contractile function of the heart), compression ratios up to 2000 could be achieved. Thus, higher-dimensional wavelet compression is feasible, and by exploitation of data coherence in higher image dimensions allows much higher compression than comparable 2D approaches. The proven applicability of this approach to multidimensional medical imaging has important implications especially for the fields of image storage and transmission and, specifically, for the emerging field of telemedicine.
Hsu, Christina M. L.; Palmeri, Mark L.; Segars, W. Paul; Veress, Alexander I.; Dobbins, James T.
2011-01-01
Purpose: The authors previously introduced a methodology to generate a realistic three-dimensional (3D), high-resolution, computer-simulated breast phantom based on empirical data. One of the key components of such a phantom is that it provides a means to produce a realistic simulation of clinical breast compression. In the current study, they have evaluated a finite element (FE) model of compression and have demonstrated the effect of a variety of mechanical properties on the model using a dense mesh generated from empirical breast data. While several groups have demonstrated an effective compression simulation with lower density finite element meshes, the presented study offers a mesh density that is able to model the morphology of the inner breast structures more realistically than lower density meshes. This approach may prove beneficial for multimodality breast imaging research, since it provides a high level of anatomical detail throughout the simulation study. Methods: In this paper, the authors describe methods to improve the high-resolution performance of a FE compression model. In order to create the compressible breast phantom, dedicated breast CT data was segmented and a mesh was generated with 4-noded tetrahedral elements. Using an explicit FE solver to simulate breast compression, several properties were analyzed to evaluate their effect on the compression model including: mesh density, element type, density, and stiffness of various tissue types, friction between the skin and the compression plates, and breast density. Following compression, a simulated projection was generated to demonstrate the ability of the compressible breast phantom to produce realistic simulated mammographic images. Results: Small alterations in the properties of the breast model can change the final distribution of the tissue under compression by more than 1 cm; which ultimately results in different representations of the breast model in the simulated images. The model
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.
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.
Robust facial expression recognition via compressive sensing.
Zhang, Shiqing; Zhao, Xiaoming; Lei, Bicheng
2012-01-01
Recently, compressive sensing (CS) has attracted increasing attention in the areas of signal processing, computer vision and pattern recognition. In this paper, a new method based on the CS theory is presented for robust facial expression recognition. The CS theory is used to construct a sparse representation classifier (SRC). The effectiveness and robustness of the SRC method is investigated on clean and occluded facial expression images. Three typical facial features, i.e., the raw pixels, Gabor wavelets representation and local binary patterns (LBP), are extracted to evaluate the performance of the SRC method. Compared with the nearest neighbor (NN), linear support vector machines (SVM) and the nearest subspace (NS), experimental results on the popular Cohn-Kanade facial expression database demonstrate that the SRC method obtains better performance and stronger robustness to corruption and occlusion on robust facial expression recognition tasks. PMID:22737035
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
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
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
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.
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.
MSE optimal bit-rate allocation in JPEG2000 Part 2 compression applied to a 3D data set
NASA Astrophysics Data System (ADS)
Kosheleva, Olga M.; Cabrera, Sergio D.; Usevitch, Bryan E.; Aguirre, Alberto; Vidal, Edward, Jr.
2004-10-01
A bit rate allocation (BRA) strategy is needed to optimally compress three-dimensional (3-D) data on a per-slice basis, treating it as a collection of two-dimensional (2-D) slices/components. This approach is compatible with the framework of JPEG2000 Part 2 which includes the option of pre-processing the slices with a decorrelation transform in the cross-component direction so that slices of transform coefficients are compressed. In this paper, we illustrate the impact of a recently developed inter-slice rate-distortion optimal bit-rate allocation approach that is applicable to this compression system. The approach exploits the MSE optimality of all JPEG2000 bit streams for all slices when each is produced in the quality progressive mode. Each bit stream can be used to produce a rate-distortion curve (RDC) for each slice that is MSE optimal at each bit rate of interest. The inter-slice allocation approach uses all RDCs for all slices to optimally select an overall optimal set of bit rates for all the slices using a constrained optimization procedure. The optimization is conceptually similar to Post-Compression Rate-Distortion optimization that is used within JPEG2000 to optimize bit rates allocated to codeblocks. Results are presented for two types of data sets: a 3-D computed tomography (CT) medical image, and a 3-D metereological data set derived from a particular modeling program. For comparison purposes, compression results are also illustrated for the traditional log-variance approach and for a uniform allocation strategy. The approach is illustrated using two decorrelation tranforms (the Karhunen Loeve transform, and the discrete wavelet transform) for which the inter-slice allocation scheme has the most impact.
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
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
Accurate estimation of forest carbon stocks by 3-D remote sensing of individual trees.
Omasa, Kenji; Qiu, Guo Yu; Watanuki, Kenichi; Yoshimi, Kenji; Akiyama, Yukihide
2003-03-15
Forests are one of the most important carbon sinks on Earth. However, owing to the complex structure, variable geography, and large area of forests, accurate estimation of forest carbon stocks is still a challenge for both site surveying and remote sensing. For these reasons, the Kyoto Protocol requires the establishment of methodologies for estimating the carbon stocks of forests (Kyoto Protocol, Article 5). A possible solution to this challenge is to remotely measure the carbon stocks of every tree in an entire forest. Here, we present a methodology for estimating carbon stocks of a Japanese cedar forest by using a high-resolution, helicopter-borne 3-dimensional (3-D) scanning lidar system that measures the 3-D canopy structure of every tree in a forest. Results show that a digital image (10-cm mesh) of woody canopy can be acquired. The treetop can be detected automatically with a reasonable accuracy. The absolute error ranges for tree height measurements are within 42 cm. Allometric relationships of height to carbon stocks then permit estimation of total carbon storage by measurement of carbon stocks of every tree. Thus, we suggest that our methodology can be used to accurately estimate the carbon stocks of Japanese cedar forests at a stand scale. Periodic measurements will reveal changes in forest carbon stocks. PMID:12680675
NASA Astrophysics Data System (ADS)
Guienko, Guennadi; Levin, Eugene
2003-01-01
New ideas and solutions never come alone. Although automated feature extraction is not sufficiently mature to move from the realm of scientific investigation into the category of production technology, a new goal has arisen: 3D simulation of real-world objects, extracted from images. This task, which evolved from feature extraction and is not an easy task itself, becomes even more complex, multi-leveled, and often uncertain and fuzzy when one exploits time-sequenced multi-source remotely sensed visual data. The basic components of the process are familiar image processing tasks: fusion of various types of imagery, automatic recognition of objects, removng those objects from the source images, and replacing them in the images with their realistic simulated "twin" object rendering. This paper discusses how to aggregate the most appropriate approach to each task into one technological process in order to develop a Manipulator for Visual Simulation of 3D objects (ManVIS) that is independent or imagery/format/media. The technology could be made general by combining a number of competent special purpose algorithms under appropriate contextual, geometric, spatial, and temporal constraints derived from a-priori knowledge. This could be achieved by planning the simulation in an Open Structure Simulation Strategy Manager (O3SM) a distinct component of ManVIS building the simulation strategy before beginning actual image manipulation.
Research on compressive fusion for remote sensing images
NASA Astrophysics Data System (ADS)
Yang, Senlin; Wan, Guobin; Li, Yuanyuan; Zhao, Xiaoxia; Chong, Xin
2014-02-01
A compressive fusion of remote sensing images is presented based on the block compressed sensing (BCS) and non-subsampled contourlet transform (NSCT). Since the BCS requires small memory space and enables fast computation, firstly, the images with large amounts of data can be compressively sampled into block images with structured random matrix. Further, the compressive measurements are decomposed with NSCT and their coefficients are fused by a rule of linear weighting. And finally, the fused image is reconstructed by the gradient projection sparse reconstruction algorithm, together with consideration of blocking artifacts. The field test of remote sensing images fusion shows the validity of the proposed method.
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.
Remote sensing image compression assessment based on multilevel distortions
NASA Astrophysics Data System (ADS)
Jiang, Hongxu; Yang, Kai; Liu, Tingshan; Zhang, Yongfei
2014-01-01
The measurement of visual quality is of fundamental importance to remote sensing image compression, especially for image quality assessment and compression algorithm optimization. We exploit the distortion features of optical remote sensing image compression and propose a full-reference image quality metric based on multilevel distortions (MLD), which assesses image quality by calculating distortions of three levels (such as pixel-level, contexture-level, and content-level) between original images and compressed images. Based on this, a multiscale MLD (MMLD) algorithm is designed and it outperforms the other current methods in our testing. In order to validate the performance of our algorithm, a special remote sensing image compression distortion (RICD) database is constructed, involving 250 remote sensing images compressed with different algorithms and various distortions. Experimental results on RICD and Laboratory for Image and Video Engineering databases show that the proposed MMLD algorithm has better consistency with subjective perception values than current state-of-the-art methods in remote sensing image compression assessment, and the objective assessment results can show the distortion features and visual quality of compressed image well. It is suitable to be the evaluation criteria for optical remote sensing image compression.
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.
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.
Accelerated Compressed Sensing Based CT Image Reconstruction
Hashemi, SayedMasoud; Beheshti, Soosan; Gill, Patrick R.; Paul, Narinder S.; Cobbold, Richard S. C.
2015-01-01
In X-ray computed tomography (CT) an important objective is to reduce the radiation dose without significantly degrading the image quality. Compressed sensing (CS) enables the radiation dose to be reduced by producing diagnostic images from a limited number of projections. However, conventional CS-based algorithms are computationally intensive and time-consuming. We propose a new algorithm that accelerates the CS-based reconstruction by using a fast pseudopolar Fourier based Radon transform and rebinning the diverging fan beams to parallel beams. The reconstruction process is analyzed using a maximum-a-posterior approach, which is transformed into a weighted CS problem. The weights involved in the proposed model are calculated based on the statistical characteristics of the reconstruction process, which is formulated in terms of the measurement noise and rebinning interpolation error. Therefore, the proposed method not only accelerates the reconstruction, but also removes the rebinning and interpolation errors. Simulation results are shown for phantoms and a patient. For example, a 512 × 512 Shepp-Logan phantom when reconstructed from 128 rebinned projections using a conventional CS method had 10% error, whereas with the proposed method the reconstruction error was less than 1%. Moreover, computation times of less than 30 sec were obtained using a standard desktop computer without numerical optimization. PMID:26167200
Compressed sensing in imaging mass spectrometry
NASA Astrophysics Data System (ADS)
Bartels, Andreas; Dülk, Patrick; Trede, Dennis; Alexandrov, Theodore; Maaß, Peter
2013-12-01
Imaging mass spectrometry (IMS) is a technique of analytical chemistry for spatially resolved, label-free and multipurpose analysis of biological samples that is able to detect the spatial distribution of hundreds of molecules in one experiment. The hyperspectral IMS data is typically generated by a mass spectrometer analyzing the surface of the sample. In this paper, we propose a compressed sensing approach to IMS which potentially allows for faster data acquisition by collecting only a part of the pixels in the hyperspectral image and reconstructing the full image from this data. We present an integrative approach to perform both peak-picking spectra and denoising m/z-images simultaneously, whereas the state of the art data analysis methods solve these problems separately. We provide a proof of the robustness of the recovery of both the spectra and individual channels of the hyperspectral image and propose an algorithm to solve our optimization problem which is based on proximal mappings. The paper concludes with the numerical reconstruction results for an IMS dataset of a rat brain coronal section.
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. PMID:27244406
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
Plasmonic 3D-structures based on silver decorated nanotips for biological sensing
NASA Astrophysics Data System (ADS)
Coluccio, M. L.; Francardi, M.; Gentile, F.; Candeloro, P.; Ferrara, L.; Perozziello, G.; Di Fabrizio, E.
2016-01-01
Recent progresses in nanotechnology fabrication gives the opportunity to build highly functional nano-devices. 3D structures based on noble metals or covered by them can be realized down to the nano-scales, obtaining different devices with the functionalities of plasmonic nano-lenses or nano-probes. Here, nano-cones decorated with silver nano-grains were fabricated using advanced nano-fabrication techniques. In fabricating the cones, the angle of the apex was varied over a significant range and, in doing so, different geometries were realized. In depositing the silver nano-particles, the concentration of solution was varied, whereby different growth conditions were realized. The combined effect of tip geometry and growth conditions influences the size and distribution of the silver nano grains. The tips have the ability to guide or control the growth of the grains, in the sense that the nano-particles would preferentially distribute along the cone, and especially at the apex of the cone, with no o minor concentration effects on the substrate. The arrangement of metallic nano-particles into three-dimensional (3D) structures results in a Surface Enhanced Raman Spectroscopy (SERS) device with improved interface with analytes compared to bi-dimensional arrays of metallic nanoparticles. In the future, similar devices may find application in microfluidic devices, and in general in flow chambers, where the system can be inserted as to mimic a a nano-bait, for the recognition of specific biomarkers, or the manipulation and chemical investigation of single cells directly in native environments with good sensitivity, repeatability and selectivity.
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
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
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.
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.
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
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
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.
Chooi, Wai Hon; Chan, Barbara Pui
2016-01-01
Cells protect themselves from stresses through a cellular stress response. In the interverebral disc, such response was also demonstrated to be induced by various environmental stresses. However, whether compression loading will cause cellular stress response in the nucleus pulposus cells (NPCs) is not well studied. By using an in vitro collagen microencapsulation model, we investigated the effect of compression loading on the stress response of NPCs. Cell viability tests, and gene and protein expression experiments were conducted, with primers for the heat shock response (HSR: HSP70, HSF1, HSP27 and HSP90), and unfolded protein response (UPR: GRP78, GRP94, ATF4 and CHOP) genes and an antibody to HSP72. Different gene expression patterns occurred due to loading type throughout experiments. Increasing the loading strain for a short duration did not increase the stress response genes significantly, but over longer durations, HSP70 and HSP27 were upregulated. Longer loading durations also resulted in a continuous upregulation of HSR genes and downregulation of UPR genes, even after load removal. The rate of apoptosis did not increase significantly after loading, suggesting that stress response genes might play a role in cell survival following mechanical stress. These results demonstrate how mechanical stress might induce and control the expression of HSR and UPR genes in NPCs. PMID:27197886
Chooi, Wai Hon; Chan, Barbara Pui
2016-01-01
Cells protect themselves from stresses through a cellular stress response. In the interverebral disc, such response was also demonstrated to be induced by various environmental stresses. However, whether compression loading will cause cellular stress response in the nucleus pulposus cells (NPCs) is not well studied. By using an in vitro collagen microencapsulation model, we investigated the effect of compression loading on the stress response of NPCs. Cell viability tests, and gene and protein expression experiments were conducted, with primers for the heat shock response (HSR: HSP70, HSF1, HSP27 and HSP90), and unfolded protein response (UPR: GRP78, GRP94, ATF4 and CHOP) genes and an antibody to HSP72. Different gene expression patterns occurred due to loading type throughout experiments. Increasing the loading strain for a short duration did not increase the stress response genes significantly, but over longer durations, HSP70 and HSP27 were upregulated. Longer loading durations also resulted in a continuous upregulation of HSR genes and downregulation of UPR genes, even after load removal. The rate of apoptosis did not increase significantly after loading, suggesting that stress response genes might play a role in cell survival following mechanical stress. These results demonstrate how mechanical stress might induce and control the expression of HSR and UPR genes in NPCs. PMID:27197886
High-Frequency Subband Compressed Sensing MRI Using Quadruplet Sampling
Sung, Kyunghyun; Hargreaves, Brian A
2013-01-01
Purpose To presents and validates 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. Theory and Methods High- and low-spatial-frequency regions are defined in k-space based on the separation of wavelet subbands, and the conventional compressed sensing (CS) problem is transformed into one of localized k-space estimation. To better exploit wavelet-domain sparsity, CS can be used for high-spatial-frequency regions while parallel imaging can be used for low-spatial-frequency regions. Fourier undersampling is also customized to better accommodate each reconstruction method: random undersampling for CS and regular undersampling for parallel imaging. Results Examples using the proposed method demonstrate successful reconstruction of both low-spatial-frequency content and fine structures in high-resolution 3D breast imaging with a net acceleration of 11 to 12. Conclusion 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. PMID:23280540
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.
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.
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
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.
Design of real-time remote sensing image compression system
NASA Astrophysics Data System (ADS)
Wu, Wenbo; Lei, Ning; Wang, Kun; Wang, Qingyuan; Li, Tao
2013-08-01
This paper focuses on the issue of CCD remote sensing image compression. Compared with other images, CCD remote sensing image data is characterized with high speed and high quantized bits. A high speed CCD image compression system is proposed based on ADV212 chip. The system is mainly composed of three devices: FPGA, SRAM and ADV212. In this system, SRAM plays the role of data buffer, ADV212 focuses on data compression and the FPGA is used for image storage and interface bus control. Finally, a system platform is designed to test the performance of compression. Test results show that the proposed scheme can satisfy the real-time processing requirement and there is no obvious difference between the sourced image and the compressed image in respect of image quality.
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.
NASA Astrophysics Data System (ADS)
Bi, J.; Zhou, X. P.; Qian, Q. H.
2016-05-01
General particle dynamics (GPD), which is a novel meshless numerical method, is proposed to simulate the initiation, propagation and coalescence of 3D pre-existing penetrating and embedded flaws under biaxial compression. The failure process for rock-like materials subjected to biaxial compressive loads is investigated using the numerical code GPD3D. Moreover, internal crack evolution processes are successfully simulated using GPD3D. With increasing lateral stress, the secondary cracks keep growing in the samples, while the growth of the wing cracks is restrained. The samples are mainly split into fragments in a shear failure mode under biaxial compression, which is different from the splitting failure of the samples subjected to uniaxial compression. For specimens with macroscopic pre-existing flaws, the simulated types of cracks, the simulated coalescence types and the simulated failure modes are in good agreement with the experimental results.
Compressed sensing based on the improved wavelet transform for image processing
NASA Astrophysics Data System (ADS)
Pang, Peng; Gao, Wei; Song, Zongxi; XI, Jiang-bo
2014-09-01
Compressed sensing theory is a new sampling theory that can sample signal in a below sampling rate than the traditional Nyquist sampling theory. Compressed sensing theory that has given a revolutionary solution is a novel sampling and processing theory under the condition that the signal is sparse or compressible. This paper investigates how to improve the theory of CS and its application in imaging system. According to the properties of wavelet transform sub-bands, an improved compressed sensing algorithm based on the single layer wavelet transform was proposed. Based on the feature that the most information was preserved on the low-pass layer after the wavelet transform, the improved compressed sensing algorithm only measured the low-pass wavelet coefficients of the image but preserving the high-pass wavelet coefficients. The signal can be restricted exactly by using the appropriate reconstruction algorithms. The reconstruction algorithm is the key point that most researchers focus on and significant progress has been made. For the reconstruction, in order to improve the orthogonal matching pursuit (OMP) algorithm, increased the iteration layers make sure low-pass wavelet coefficients could be recovered by measurements exactly. Then the image could be reconstructed by using the inverse wavelet transform. Compared the original compressed sensing algorithm, simulation results demonstrated that the proposed algorithm decreased the processed data, signal processed time decreased obviously and the recovered image quality improved to some extent. The PSNR of the proposed algorithm was improved about 2 to 3 dB. Experimental results show that the proposed algorithm exhibits its superiority over other known CS reconstruction algorithms in the literature at the same measurement rates, while with a faster convergence speed.
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.
Compressive sensing by learning a Gaussian mixture model from measurements.
Yang, Jianbo; Liao, Xuejun; Yuan, Xin; Llull, Patrick; Brady, David J; Sapiro, Guillermo; Carin, Lawrence
2015-01-01
Compressive sensing of signals drawn from a Gaussian mixture model (GMM) admits closed-form minimum mean squared error reconstruction from incomplete linear measurements. An accurate GMM signal model is usually not available a priori, because it is difficult to obtain training signals that match the statistics of the signals being sensed. We propose to solve that problem by learning the signal model in situ, based directly on the compressive measurements of the signals, without resorting to other signals to train a model. A key feature of our method is that the signals being sensed are treated as random variables and are integrated out in the likelihood. We derive a maximum marginal likelihood estimator (MMLE) that maximizes the likelihood of the GMM of the underlying signals given only their linear compressive measurements. We extend the MMLE to a GMM with dominantly low-rank covariance matrices, to gain computational speedup. We report extensive experimental results on image inpainting, compressive sensing of high-speed video, and compressive hyperspectral imaging (the latter two based on real compressive cameras). The results demonstrate that the proposed methods outperform state-of-the-art methods by significant margins. PMID:25361508
Random filtering structure-based compressive sensing radar
NASA Astrophysics Data System (ADS)
Zhang, Jindong; Ban, YangYang; Zhu, Daiyin; Zhang, Gong
2014-12-01
Recently with an emerging theory of `compressive sensing' (CS), a radically new concept of compressive sensing radar (CSR) has been proposed in which the time-frequency plane is discretized into a grid. Random filtering is an interesting technique for efficiently acquiring signals in CS theory and can be seen as a linear time-invariant filter followed by decimation. In this paper, random filtering structure-based CSR system is investigated. Note that the sparse representation and sensing matrices are required to be as incoherent as possible; the methods for optimizing the transmit waveform and the FIR filter in the sensing matrix separately and simultaneously are presented to decrease the coherence between different target responses. Simulation results show that our optimized results lead to smaller coherence, with higher sparsity and better recovery accuracy observed in the CSR system than the nonoptimized transmit waveform and sensing matrix.
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
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.
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
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.
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.
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
Compressive sensing of frequency-hopping spread spectrum signals
NASA Astrophysics Data System (ADS)
Liu, Feng; Kim, Yookyung; Goodman, Nathan A.; Ashok, Amit; Bilgin, Ali
2012-06-01
In this paper, compressive sensing strategies for interception of Frequency-Hopping Spread Spectrum (FHSS) signals are introduced. Rapid switching of the carrier among many frequency channels using a pseudorandom sequence (unknown to the eavesdropper) makes FHSS signals dicult to intercept. The conventional approach to intercept FHSS signals necessitates capturing of all frequency channels and, thus, requires the Analog-to-Digital Converters (ADCs) to sample at very high rates. Using the fact that the FHSS signals have sparse instanta- neous spectra, we propose compressive sensing strategies for their interception. The proposed techniques are validated using Gaussian Frequency-Shift Keying (GFSK) modulated FHSS signals as dened by the Bluetooth specication.
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.
Dynamic measurement rate allocation for distributed compressive video sensing
NASA Astrophysics Data System (ADS)
Chen, Hung-Wei; Kang, Li-Wei; Lu, Chun-Shien
2010-07-01
We address an important issue of fully low-cost and low-complexity video encoding for use in resource limited sensors/devices. Conventional distributed video coding (DVC) does not actually meet this requirement because the acquisition of video sequences still relies on the high-cost mechanism (sampling + compression). Recently, we have proposed a distributed compressive video sensing (DCVS) framework to directly capture compressed video data called measurements, while exploiting correlations among successive frames for video reconstruction at the decoder. The core is to integrate the respective characteristics of DVC and compressive sensing (CS) to achieve CS-based single-pixel camera-compatible video encoder. At DCVS decoder, video reconstruction can be formulated as a convex unconstrained optimization problem via solving the sparse coefficients with respect to some basis functions. Nevertheless, the issue of measurement rate allocation has not been considered yet in the literature. Actually, different measurement rates should be adaptively assigned to different local regions by considering the sparsity of each region for improving reconstructed quality. This paper investigates dynamic measurement rate allocation in block-based DCVS, which can adaptively adjust measurement rates by estimating the sparsity of each block via feedback information. Simulation results have indicated the effectiveness of our scheme. It is worth noting that our goal is to develop a novel fully low-complexity video compression paradigm via the emerging compressive sensing and sparse representation technologies, and provide an alternative scheme adaptive to the environment, where raw video data is not available, instead of competing compression performances against the current compression standards (e.g., H.264/AVC) or DVC schemes which need raw data available for encoding.
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.
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.
Compressive sensing for spatial and spectral flame diagnostics
NASA Astrophysics Data System (ADS)
Starling, David; Ranalli, Joseph
2014-03-01
Compressive sensing has been a valuable resource for use in quantum imaging, low light level depth mapping of natural scenes, object tracking and even for the improvement of miniature spectrometers via post processing. Experimentally, many optical compressive sensing techniques utilize a single pixel camera composed of a digital micromirror device or spatial light modulator coupled to one shot-noise limited detector. This method has the advantages of fast acquisition time and high signal to noise ratio. One currently unexplored area of study is the use of these techniques in the context of flame diagnostics. Optical diagnostics are employed for a variety of purposes in flames, including imaging of the heat release region (via chemiluminescence) and spatially resolved species and temperature measurement (via spontaneous Raman scattering). Compressive sensing has a dual role in this field, where the signals of interest are generally sparse and the mean photon flux is very low at the appropriate wavelengths. We show here that compressive sensing is beneficial in particular for the study of laminar, steady flames using Raman spectroscopy and flame chemiluminescence imaging, without the use of intensified CCDs, commercial spectrometers or high intensity pulse lasers. We present results from a theoretical study with experimental data to follow.
Experimental evaluation of digital holographic reconstruction using compressive sensing
NASA Astrophysics Data System (ADS)
Banerjee, P.; Liu, H.; Williams, L.
2014-02-01
Compressive sensing is a new alternative to the conventional Fresnel approach for digital holographic reconstruction for sparse objects, and can show improved performance with respect to image quality and the depth of focus. In this work, we experimentally investigate the performance of the compressive sensing reconstruction approach and compare it with the Fresnel transform and the non-paraxial and paraxial transfer function back-propagation approach. The compressive sensing technique used is the so-called Two-Step Iterative Shrinkage/Thresholding algorithm. A He-Ne laser of 543.5 nm is used as the light source and a Gabor holographic recording system is used as the experimental setup. The test object comprises a dandelion seed parachute with few wings. We capture the holograms at several recording distances and then reconstruct the image using each method. Over the range of recording distances used, the non-paraxial and paraxial transfer function back-propagation approach yields identical results. We evaluate the depth resolution of the compressive sensing algorithm and compare it with that of the Fresnel approach and the non-paraxial back-propagation approach.
Canali, C; Mazzoni, C; Larsen, L B; Heiskanen, A; Martinsen, Ø G; Wolff, A; Dufva, M; Emnéus, J
2015-09-01
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. PMID:26198701
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
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.
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. PMID:26125379
NASA Astrophysics Data System (ADS)
Baumberger, Roland; Wehrens, Philip; Herwegh, Marco
2013-04-01
Geological 3D models are always just an approximation of a complex natural situation. This is especially true in regions, where hard underground data (e.g. bore holes, tunnel mappings and seismic data) is lacking. One of the key problems while developing valid geological 3D models is the three-dimensional spatial distribution of geological structures, particularly with increasing distance from the surface. In our study, we investigate the Alpine 3D Deformation of the crystalline rocks of the Aar massif (Haslital valley, Central Switzerland). Deformation in this area is dominated by different sets of large-scale shear zones, which acted under both ductile and brittle deformation conditions. The goal of our study is the prediction of the geometry and the evolution of the structures in 3D space and time. A key point in our project is the generation of a reliable 3D model of today's structures. In this sense, estimation of the reliability of the surface information for the extrapolation to depth is mandatory. Based on our data, a method will be presented that contributes to a possible solution of the questions addressed above. The basic idea consists of the fact that (i) mechanical anisotropies as shear zones and faults show prominent three-dimensional information in the landscape, (ii) these geometries can be used as input data for a geological 3D model and (iii) that the 3D information mentioned allows a projection to depth. As a great advantage of the study area, a large number of underground tunnels exist, which allow to evaluate the quality of the aforementioned extrapolations. The method is based on a combined remote-sensing and field work approach: morphological incisions recognized on digital elevation models as well as on aerial photos on the computer screen were evaluated, described and attributed in detail in the field. Our approach is based on a six step workflow: (1) Elaboration of a large-scale structural map of geological structures by means of remote-sensing
Real-time visual sensing system achieving high-speed 3D particle tracking with nanometer resolution.
Cheng, Peng; Jhiang, Sissy M; Menq, Chia-Hsiang
2013-11-01
This paper presents a real-time visual sensing system, which is created to achieve high-speed three-dimensional (3D) motion tracking of microscopic spherical particles in aqueous solutions with nanometer resolution. The system comprises a complementary metal-oxide-semiconductor (CMOS) camera, a field programmable gate array (FPGA), and real-time image processing programs. The CMOS camera has high photosensitivity and superior SNR. It acquires images of 128×120 pixels at a frame rate of up to 10,000 frames per second (fps) under the white light illumination from a standard 100 W halogen lamp. The real-time image stream is downloaded from the camera directly to the FPGA, wherein a 3D particle-tracking algorithm is implemented to calculate the 3D positions of the target particle in real time. Two important objectives, i.e., real-time estimation of the 3D position matches the maximum frame rate of the camera and the timing of the output data stream of the system is precisely controlled, are achieved. Two sets of experiments were conducted to demonstrate the performance of the system. First, the visual sensing system was used to track the motion of a 2 μm polystyrene bead, whose motion was controlled by a three-axis piezo motion stage. The ability to track long-range motion with nanometer resolution in all three axes is demonstrated. Second, it was used to measure the Brownian motion of the 2 μm polystyrene bead, which was stabilized in aqueous solution by a laser trapping system. PMID:24216655
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.
McIDAS-V: Advanced Visualization for 3D Remote Sensing Data
NASA Astrophysics Data System (ADS)
Rink, T.; Achtor, T. H.
2010-12-01
McIDAS-V is a Java-based, open-source, freely available software package for analysis and visualization of geophysical data. Its advanced capabilities provide very interactive 4-D displays, including 3D volumetric rendering and fast sub-manifold slicing, linked to an abstract mathematical data model with built-in metadata for units, coordinate system transforms and sampling topology. A Jython interface provides user defined analysis and computation in terms of the internal data model. These powerful capabilities to integrate data, analysis and visualization are being applied to hyper-spectral sounding retrievals, eg. AIRS and IASI, of moisture and cloud density to interrogate and analyze their 3D structure, as well as, validate with instruments such as CALIPSO, CloudSat and MODIS. The object oriented framework design allows for specialized extensions for novel displays and new sources of data. Community defined CF-conventions for gridded data are understood by the software, and can be immediately imported into the application. This presentation will show examples how McIDAS-V is used in 3-dimensional data analysis, display and evaluation.
NASA Astrophysics Data System (ADS)
Melchor, J. L., Jr.; Cabrera, S. D.; Aguirre, A.; Kosheleva, O. M.; Vidal, E., Jr.
2005-08-01
This paper describes an efficient algorithm and its Java implementation for a recently developed mean-squared error (MSE) rate-distortion optimal (RDO) inter-slice bit-rate allocation (BRA) scheme applicable to the JPEG2000 Part 2 (J2KP2) framework. Its performance is illustrated on hyperspectral imagery data using the J2KP2 with the Karhunen- Loeve transform (KLT) for decorrelation. The results are contrasted with those obtained using the traditional logvariance based BRA method and with the original RDO algorithm. The implementation has been developed as a Java plug-in to be incorporated into our evolving multi-dimensional data compression software tool denoted CompressMD. The RDO approach to BRA uses discrete rate distortion curves (RDCs) for each slice of transform coefficients. The generation of each point on a RDC requires a full decompression of that slice, therefore, the efficient version minimizes the number of RDC points needed from each slice by using a localized coarse-to-fine approach denoted RDOEfficient. The scheme is illustrated in detail using a subset of 10 bands of hyperspectral imagery data and is contrasted to the original RDO implementation and the traditional (log-variance) method of BRA showing that better results are obtained with the RDO methods. The three schemes are also tested on two hyperspectral imagery data sets with all bands present: the Cuprite radiance data from AVIRIS and a set derived from the Hyperion satellite. The results from the RDO and RDOEfficient are very close to each other in the MSE sense indicating that the adaptive approach can find almost the same BRA solution. Surprisingly, the traditional method also performs very close to the RDO methods, indicating that it is very close to being optimal for these types of data sets.
CMOS low data rate imaging method based on compressed sensing
NASA Astrophysics Data System (ADS)
Xiao, Long-long; Liu, Kun; Han, Da-peng
2012-07-01
Complementary metal-oxide semiconductor (CMOS) technology enables the integration of image sensing and image compression processing, making improvements on overall system performance possible. We present a CMOS low data rate imaging approach by implementing compressed sensing (CS). On the basis of the CS framework, the image sensor projects the image onto a separable two-dimensional (2D) basis set and measures the corresponding coefficients obtained. First, the electrical current output from the pixels in a column are combined, with weights specified by voltage, in accordance with Kirchhoff's law. The second computation is performed in an analog vector-matrix multiplier (VMM). Each element of the VMM considers the total value of each column as the input and multiplies it by a unique coefficient. Both weights and coefficients are reprogrammable through analog floating-gate (FG) transistors. The image can be recovered from a percentage of these measurements using an optimization algorithm. The percentage, which can be altered flexibly by programming on the hardware circuit, determines the image compression ratio. These novel designs facilitate image compression during the image-capture phase before storage, and have the potential to reduce power consumption. Experimental results demonstrate that the proposed method achieves a large image compression ratio and ensures imaging quality.
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.
Force sensing using 3D displacement measurements in linear elastic bodies
NASA Astrophysics Data System (ADS)
Feng, Xinzeng; Hui, Chung-Yuen
2016-04-01
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.
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.
Research of the wavelet based ECW remote sensing image compression technology
NASA Astrophysics Data System (ADS)
Zhang, Lan; Gu, Xingfa; Yu, Tao; Dong, Yang; Hu, Xinli; Xu, Hua
2007-11-01
This paper mainly study the wavelet based ECW remote sensing image compression technology. Comparing with the tradition compression technology JPEG and new compression technology JPEG2000 witch based on wavelet we can find that when compress quite large remote sensing image the ER Mapper Compressed Wavelet (ECW) can has significant advantages. The way how to use the ECW SDK was also discussed and prove that it's also the best and faster way to compress China-Brazil Earth Resource Satellite (CBERS) image.
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.
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. PMID:25415769
Synchronous radiation sensing and 3D urban mapping for improved source identification
NASA Astrophysics Data System (ADS)
Christie, Gordon; Stiltner, L. Justin; Kochersberger, Kevin; McLean, Morgan; Czaja, Wojtek
2014-05-01
The acquisition of synchronous EO imagery and gamma radiation data in aerial overflights of an unmanned aircraft can provide valuable spatial context for radioactive source mapping. Using image-based 3D reconstruction methods, a terrain map can be generated and used to reason about more likely radiation source locations. For instance, vehicles may be likely hiding places for nuclear materials, so a source model with assigned probability is used at the vehicle to reduce the overall uncertainty in position estimation. Environment reconstructions based on EO imagery with a mapped gamma radiation overlay provide intrinsic correlations between the datasets. Using radioactive material dispersion models or point source models, the derived correlations serve to enhance coarse gamma radiation data. The use of autonomous unmanned aircraft provide a valuable tool in acquiring these data as they are capable of accurate and repeatable position control while eliminating exposure danger to the operators. In this experiment, two sources (.084 Ci 137Ce and .00048 Ci 133Ba) were distributed in a field with varying terrain and a scan was conducted using the Virginia Tech Yamaha RMAX autonomous helicopter equipped with a two-camera imaging system and a NaI scintillation-type spectrometer. Terrain reconstruction was conducted using both structure from motion (SfM) and stereo vision techniques, and radiation data synchronized to the imagery was overlaid.
NASA Astrophysics Data System (ADS)
Weng, Jiawen; Clark, David C.; Kim, Myung K.
2016-05-01
A numerical reconstruction method based on compressive sensing (CS) for self-interference incoherent digital holography (SIDH) is proposed to achieve sectional imaging by single-shot in-line self-interference incoherent hologram. The sensing operator is built up based on the physical mechanism of SIDH according to CS theory, and a recovery algorithm is employed for image restoration. Numerical simulation and experimental studies employing LEDs as discrete point-sources and resolution targets as extended sources are performed to demonstrate the feasibility and validity of the method. The intensity distribution and the axial resolution along the propagation direction of SIDH by angular spectrum method (ASM) and by CS are discussed. The analysis result shows that compared to ASM the reconstruction by CS can improve the axial resolution of SIDH, and achieve sectional imaging. The proposed method may be useful to 3D analysis of dynamic systems.
Cognitive Radios Exploiting Gray Spaces via Compressed Sensing
NASA Astrophysics Data System (ADS)
Wieruch, Dennis; Jung, Peter; Wirth, Thomas; Dekorsy, Armin; Haustein, Thomas
2016-07-01
We suggest an interweave cognitive radio system with a gray space detector, which is properly identifying a small fraction of unused resources within an active band of a primary user system like 3GPP LTE. Therefore, the gray space detector can cope with frequency fading holes and distinguish them from inactive resources. Different approaches of the gray space detector are investigated, the conventional reduced-rank least squares method as well as the compressed sensing-based orthogonal matching pursuit and basis pursuit denoising algorithm. In addition, the gray space detector is compared with the classical energy detector. Simulation results present the receiver operating characteristic at several SNRs and the detection performance over further aspects like base station system load for practical false alarm rates. The results show, that especially for practical false alarm rates the compressed sensing algorithm are more suitable than the classical energy detector and reduced-rank least squares approach.
On ECG reconstruction using weighted-compressive sensing
Kassim, Ashraf A.
2014-01-01
The potential of the new weighted-compressive sensing approach for efficient reconstruction of electrocardiograph (ECG) signals is investigated. This is motivated by the observation that ECG signals are hugely sparse in the frequency domain and the sparsity changes slowly over time. The underlying idea of this approach is to extract an estimated probability model for the signal of interest, and then use this model to guide the reconstruction process. The authors show that the weighted-compressive sensing approach is able to achieve reconstruction performance comparable with the current state-of-the-art discrete wavelet transform-based method, but with substantially less computational cost to enable it to be considered for use in the next generation of miniaturised wearable ECG monitoring devices. PMID:26609381
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
On ECG reconstruction using weighted-compressive sensing.
Zonoobi, Dornoosh; Kassim, Ashraf A
2014-06-01
The potential of the new weighted-compressive sensing approach for efficient reconstruction of electrocardiograph (ECG) signals is investigated. This is motivated by the observation that ECG signals are hugely sparse in the frequency domain and the sparsity changes slowly over time. The underlying idea of this approach is to extract an estimated probability model for the signal of interest, and then use this model to guide the reconstruction process. The authors show that the weighted-compressive sensing approach is able to achieve reconstruction performance comparable with the current state-of-the-art discrete wavelet transform-based method, but with substantially less computational cost to enable it to be considered for use in the next generation of miniaturised wearable ECG monitoring devices. PMID:26609381
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.
3D Sensing Algorithms Towards Building an Intelligent Intensive Care Unit.
Lea, Colin; Facker, James; Hager, Gregory; Taylor, Russell; Saria, Suchi
2013-01-01
Intensive Care Units (ICUs) are chaotic places where hundreds of tasks are carried out by many different people. Timely and coordinated execution of these tasks are directly related to quality of patient outcomes. An improved understanding of the current care process can aid in improving quality. Our goal is to build towards a system that automatically catalogs various tasks being performed by the bedside. We propose a set of techniques using computer vision and machine learning to develop a system that passively senses the environment and identifies seven common actions such as documenting, checking up on a patient, and performing a procedure. Preliminary evaluation of our system on 5.5 hours of data from the Pediatric ICU obtains overall task recognition accuracy of 70%. Furthermore, we show how it can be used to summarize and visualize tasks. Our system provides a significant departure from current approaches used for quality improvement. With further improvement, we think that such a system could realistically be deployed in the ICU. PMID:24303253
Nichols, Alexander J.; Roussakis, Emmanuel; Klein, Oliver J.
2014-01-01
Hypoxia is an important factor that contributes to the development of drug-resistant cancer, yet few non-perturbative tools exist for studying oxygen in tissue. While progress has been made in the development of chemical probes for optical oxygen mapping, penetration into poorly perfused or avascular tumor regions remains problematic. Here we report a Click-Assembled Oxygen Sensing (CAOS) nanoconjugate and demonstrate its properties in an in vitro 3D spheroid cancer model. Our synthesis relies on sequential click-based ligation of poly(amidoamine)-like subunits for rapid assembly. Using near-infrared confocal phosphorescence microscopy, we demonstrate the ability of CAOS nanoconjugates to penetrate hundreds of microns into spheroids within hours and show their sensitivity to oxygen changes throughout the nodule. This proof-of-concept study demonstrates a modular approach that is readily extensible to a wide variety of oxygen and cellular sensors for depth-resolved imaging in tissue and tissue models. PMID:24590700
NASA Astrophysics Data System (ADS)
Huang, Jianfei; Zhu, Yihua; Yang, Xiaoling; Chen, Wei; Zhou, Ying; Li, Chunzhong
2014-12-01
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.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
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. PMID:24589833
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.
NASA Astrophysics Data System (ADS)
Geenen, T.; Heister, T.; Van Den Berg, A. P.; Jacobs, M.; Bangerth, W.
2011-12-01
We present high resolution 3D results of the complex mineral phase distribution in the transition zone obtained by numerical modelling of mantle convection. We extend the work by [Jacobs and van den Berg, 2011] to 3D and illustrate the efficiency of adaptive mesh refinement for capturing the complex spatial distribution and sharp phase transitions as predicted by their model. The underlying thermodynamical model is based on lattice dynamics which allows to predict thermophysical properties and seismic wave speeds for the applied magnesium-endmember olivine-pyroxene mineralogical model. The use of 3D geometry allows more realistic prediction of phase distribution and seismic wave speeds resulting from 3D flow processes involving the Earth's transition zone and more significant comparisons with interpretations from seismic tomography and seismic reflectivity studies aimed at the transition zone. Model results are generated with a recently developed geodynamics modeling application based on dealII (www.dealii.org). We extended this model to incorporate both a general thermodynamic model, represented by P,T space tabulated thermophysical properties, and a solution strategy that allows for compressible flow. When modeling compressible flow in the so called truncated anelastic approximation framework we have to adapt the solver strategy that has been proven by several authors to be highly efficient for incompressible flow to incorporate an extra term in the continuity equation. We present several possible solution strategies and discuss their implication in terms of robustness and computational efficiency.
Accurate reconstruction of hyperspectral images from compressive sensing measurements
NASA Astrophysics Data System (ADS)
Greer, John B.; Flake, J. C.
2013-05-01
The emerging field of Compressive Sensing (CS) provides a new way to capture data by shifting the heaviest burden of data collection from the sensor to the computer on the user-end. This new means of sensing requires fewer measurements for a given amount of information than traditional sensors. We investigate the efficacy of CS for capturing HyperSpectral Imagery (HSI) remotely. We also introduce a new family of algorithms for constructing HSI from CS measurements with Split Bregman Iteration [Goldstein and Osher,2009]. These algorithms combine spatial Total Variation (TV) with smoothing in the spectral dimension. We examine models for three different CS sensors: the Coded Aperture Snapshot Spectral Imager-Single Disperser (CASSI-SD) [Wagadarikar et al.,2008] and Dual Disperser (CASSI-DD) [Gehm et al.,2007] cameras, and a hypothetical random sensing model closer to CS theory, but not necessarily implementable with existing technology. We simulate the capture of remotely sensed images by applying the sensor forward models to well-known HSI scenes - an AVIRIS image of Cuprite, Nevada and the HYMAP Urban image. To measure accuracy of the CS models, we compare the scenes constructed with our new algorithm to the original AVIRIS and HYMAP cubes. The results demonstrate the possibility of accurately sensing HSI remotely with significantly fewer measurements than standard hyperspectral cameras.
A compressive sensing and unmixing scheme for hyperspectral data processing.
Li, Chengbo; Sun, Ting; Kelly, Kevin F; Zhang, Yin
2012-03-01
Hyperspectral data processing typically demands enormous computational resources in terms of storage, computation, and input/output throughputs, particularly when real-time processing is desired. In this paper, a proof-of-concept study is conducted on compressive sensing (CS) and unmixing for hyperspectral imaging. Specifically, we investigate a low-complexity scheme for hyperspectral data compression and reconstruction. In this scheme, compressed hyperspectral data are acquired directly by a device similar to the single-pixel camera based on the principle of CS. To decode the compressed data, we propose a numerical procedure to compute directly the unmixed abundance fractions of given endmembers, completely bypassing high-complexity tasks involving the hyperspectral data cube itself. The reconstruction model is to minimize the total variation of the abundance fractions subject to a preprocessed fidelity equation with a significantly reduced size and other side constraints. An augmented Lagrangian-type algorithm is developed to solve this model. We conduct extensive numerical experiments to demonstrate the feasibility and efficiency of the proposed approach, using both synthetic data and hardware-measured data. Experimental and computational evidences obtained from this paper indicate that the proposed scheme has a high potential in real-world applications. PMID:21914570
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
A new technique for hyperspectral compressive sensing using spectral unmixing
NASA Astrophysics Data System (ADS)
Martin, Gabriel; Bioucas Dias, José M.; Plaza, Antonio J.
2012-10-01
In Hyperspectral imaging the sensors measure the light refelcted by the earth surface in differents wavelenghts, usually the number of measures is between one and several hundreds per pixel. This generates huge data ammounts that must be transmitted to the earth and for subsequent processing. The real-time requirements of some applications make that the bandwidth required between the sensor and the earth station is very large. The Compressive Sensing (CS) framework tries to solve this problem. Althougth the hyperspectral images have thousands of bands usually most of the bands are highly correlated. The CS exploit this feature of the hyperspectral images and allow to represent most of the information in few bands instead of hundreds. This compressed version of the data can be sent to a earth station that will recover the original image using the corresponding algorithm. In this paper we describe an Compressive Sensing algorithm called Hyperspectral Coded Aperture (HYCA) that was developed in previous works. This algorithm has a parameter that need to be optimized empirically in order to get the better results. In this work we present a novel way to reconstruct the compressed images under the HYCA framework in which we do not need to optimize any parameter due to all parameters can be estimated automatically. The results show that this new way to reconstruct the images without the parameter provides similar results with respect to the best parameter setting for the old algorithm. The proposed approach have been tested using synthetic data and also we have used the dataset obtained by the AVIRIS sensor of NJPL over the Cuprite mining district in Nevada.
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
Saghi, Zineb; Divitini, Giorgio; Winter, Benjamin; Leary, Rowan; Spiecker, Erdmann; Ducati, Caterina; Midgley, Paul A
2016-01-01
Electron tomography is an invaluable method for 3D cellular imaging. The technique is, however, limited by the specimen geometry, with a loss of resolution due to a restricted tilt range, an increase in specimen thickness with tilt, and a resultant need for subjective and time-consuming manual segmentation. Here we show that 3D reconstructions of needle-shaped biological samples exhibit isotropic resolution, facilitating improved automated segmentation and feature detection. By using scanning transmission electron tomography, with small probe convergence angles, high spatial resolution is maintained over large depths of field and across the tilt range. Moreover, the application of compressed sensing methods to the needle data demonstrates how high fidelity reconstructions may be achieved with far fewer images (and thus greatly reduced dose) than needed by conventional methods. These findings open the door to high fidelity electron tomography over critically relevant length-scales, filling an important gap between existing 3D cellular imaging techniques. PMID:26555323
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.
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. PMID:26854730
On exploiting interbeat correlation in compressive sensing-based 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 data acquisition scheme with the potential to reduce the number of measurements required by the Nyquist sampling theorem to acquire sparse signals. We recently used the interbeat correlation to find the common support between jointly sparse adjacent heartbeats. In this paper, we fully exploit this correlation to find the magnitude, in addition to the support of the significant coefficients in the sparse domain. The approach used for this purpose is based on sparse Bayesian learning algorithms due to its superior performance compared to other reconstruction algorithms and the fact that being a probabilistic approach facilitates the incorporation of correlation information. The reconstruction includes, in the first place, the detection of the R peaks and the length normalization of ECG cycles to take advantage of the quasi-periodic structure. Since the common support reduces as the number of heartbeats increases, we propose the use of a sliding window where the support maintains approximately constant across cycles. The sparse Bayesian algorithm adaptively learns and exploits the high correlation between the heartbeats in the constructed window. Experimental results show that the proposed method reduces significantly the number of measurements required to achieve good reconstruction quality, validating the potential of using correlation information in compressed sensing-based ECG compression.
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.
Compressed Sensing Sodium MRI of Cartilage at 7T: Preliminary Study
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 to 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. PMID:22204825
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.
Infrared super-resolution imaging based on compressed sensing
NASA Astrophysics Data System (ADS)
Sui, Xiubao; Chen, Qian; Gu, Guohua; Shen, Xuewei
2014-03-01
The theoretical basis of traditional infrared super-resolution imaging method is Nyquist sampling theorem. The reconstruction premise is that the relative positions of the infrared objects in the low-resolution image sequences should keep fixed and the image restoration means is the inverse operation of ill-posed issues without fixed rules. The super-resolution reconstruction ability of the infrared image, algorithm's application area and stability of reconstruction algorithm are limited. To this end, we proposed super-resolution reconstruction method based on compressed sensing in this paper. In the method, we selected Toeplitz matrix as the measurement matrix and realized it by phase mask method. We researched complementary matching pursuit algorithm and selected it as the recovery algorithm. In order to adapt to the moving target and decrease imaging time, we take use of area infrared focal plane array to acquire multiple measurements at one time. Theoretically, the method breaks though Nyquist sampling theorem and can greatly improve the spatial resolution of the infrared image. The last image contrast and experiment data indicate that our method is effective in improving resolution of infrared images and is superior than some traditional super-resolution imaging method. The compressed sensing super-resolution method is expected to have a wide application prospect.
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. PMID:26698723
A compressed sensing approach for enhancing infrared imaging resolution
NASA Astrophysics Data System (ADS)
Xiao, Long-long; Liu, Kun; Han, Da-peng; Liu, Ji-ying
2012-11-01
This paper presents a novel approach for improving infrared imaging resolution by the use of Compressed Sensing (CS). Instead of sensing raw pixel data, the image sensor measures the compressed samples of the observed image through a coded aperture mask placed on the focal plane of the optical system, and then the image reconstruction can be conducted from these samples using an optimal algorithm. The resolution is determined by the size of the coded aperture mask other than that of the focal plane array (FPA). The attainable quality of the reconstructed image strongly depends on the choice of the coded aperture mode. Based on the framework of CS, we carefully design an optimum mask pattern and use a multiplexing scheme to achieve multiple samples. The gradient projection for sparse reconstruction (GPSR) algorithm is employed to recover the image. The mask radiation effect is discussed by theoretical analyses and numerical simulations. Experimental results are presented to show that the proposed method enhances infrared imaging resolution significantly and ensures imaging quality.
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.
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)
Zhou, Nanrun; Yang, Jianping; Tan, Changfa; Pan, Shumin; Zhou, Zhihong
2015-11-01
A new discrete fractional random transform based on two circular matrices is designed and a novel double-image encryption-compression scheme is proposed by combining compressive sensing with discrete fractional random transform. The two random circular matrices and the measurement matrix utilized in compressive sensing are constructed by using a two-dimensional sine Logistic modulation map. Two original images can be compressed, encrypted with compressive sensing and connected into one image. The resulting image is re-encrypted by Arnold transform and the discrete fractional random transform. Simulation results and security analysis demonstrate the validity and security of the scheme.
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.
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.
Jackman, Timothy M; Hussein, Amira I; Curtiss, Cameron; Fein, Paul M; Camp, Anderson; De Barros, Lidia; Morgan, Elise F
2016-04-01
The biomechanical mechanisms leading to vertebral fractures are not well understood. Clinical and laboratory evidence suggests that the vertebral endplate plays a key role in failure of the vertebra as a whole, but how this role differs for different types of vertebral loading is not known. Mechanical testing of human thoracic spine segments, in conjunction with time-lapsed micro-computed tomography, enabled quantitative assessment of deformations occurring throughout the entire vertebral body under axial compression combined with anterior flexion ("combined loading") and under axial compression only ("compression loading"). The resulting deformation maps indicated that endplate deflection was a principal feature of vertebral failure for both loading modes. Specifically, the onset of endplate deflection was temporally coincident with a pronounced drop in the vertebra's ability to support loads. The location of endplate deflection, and also vertebral strength, were associated with the porosity of the endplate and the microstructure of the underlying trabecular bone. However, the location of endplate deflection and the involvement of the cortex differed between the two types of loading. Under the combined loading, deflection initiated, and remained the largest, at the anterior central endplate or the anterior ring apophysis, depending in part on health of the adjacent intervertebral disc. This deflection was accompanied by outward bulging of the anterior cortex. In contrast, the location of endplate deflection was more varied in compression loading. For both loading types, the earliest progression to a mild fracture according to a quantitative morphometric criterion occurred only after much of the failure process had occurred. The outcomes of this work indicate that for two physiological loading modes, the vertebral endplate and underlying trabecular bone are critically involved in vertebral fracture. These outcomes provide a strong biomechanical rationale for
Recovering network topologies via Taylor expansion and compressive sensing.
Li, Guangjun; Wu, Xiaoqun; Liu, Juan; Lu, Jun-an; Guo, Chi
2015-04-01
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. PMID:25933650
Location constrained approximate message passing for compressed sensing MRI.
Sung, Kyunghyun; Daniel, Bruce L; Hargreaves, Brian A
2013-08-01
Iterative thresholding methods have been extensively studied as faster alternatives to convex optimization methods for solving large-sized problems in compressed sensing. A novel iterative thresholding method called LCAMP (Location Constrained Approximate Message Passing) is presented for reducing computational complexity and improving reconstruction accuracy when a nonzero location (or sparse support) constraint can be obtained from view shared images. LCAMP modifies the existing approximate message passing algorithm by replacing the thresholding stage with a location constraint, which avoids adjusting regularization parameters or thresholding levels. This work is first compared with other conventional reconstruction methods using random one-dimention signals and then applied to dynamic contrast-enhanced breast magnetic resonance imaging to demonstrate the excellent reconstruction accuracy (less than 2% absolute difference) and low computation time (5-10 s using Matlab) with highly undersampled three-dimentional data (244 × 128 × 48; overall reduction factor = 10). PMID:23042658
Compressive sensing spectroscopy with a single pixel camera.
Starling, David J; Storer, Ian; Howland, Gregory A
2016-07-01
Spectrometry requires high spectral resolution and high photometric precision while also balancing cost and complexity. We address these requirements by employing a compressive-sensing camera capable of improving signal acquisition speed and sensitivity in limited signal scenarios. In particular, we implement a fast single pixel spectrophotometer with no moving parts and measure absorption and emission spectra comparable with commercial products. Our method utilizes Hadamard matrices to sample the spectra and then minimizes the total variation of the signal. The experimental setup includes standard optics and a grating, a low-cost digital micromirror device, and an intensity detector. The resulting spectrometer produces a 512 pixel spectrum with low mean-squared error and up to a 90% reduction in data acquisition time when compared with a standard spectrophotometer. PMID:27409210
Radial Velocity Data Analysis with Compressed Sensing Techniques
NASA Astrophysics Data System (ADS)
Hara, Nathan C.; Boué, G.; Laskar, J.; Correia, A. C. M.
2016-09-01
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 processes 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
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.
Sampling theorems and compressive sensing on the sphere
NASA Astrophysics Data System (ADS)
McEwen, Jason D.; Puy, Gilles; Thiran, Jean-Philippe; Vandergheynst, Pierre; Van De Ville, Dimitri; Wiaux, Yves
2011-09-01
We discuss a novel sampling theorem on the sphere developed by McEwen & Wiaux recently through an association between the sphere and the torus. To represent a band-limited signal exactly, this new sampling theorem requires less than half the number of samples of other equiangular sampling theorems on the sphere, such as the canonical Driscoll & Healy sampling theorem. A reduction in the number of samples required to represent a band-limited signal on the sphere has important implications for compressive sensing, both in terms of the dimensionality and sparsity of signals. We illustrate the impact of this property with an inpainting problem on the sphere, where we show superior reconstruction performance when adopting the new sampling theorem.
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).
Compressed Sensing Inspired Image Reconstruction from Overlapped Projections
Yang, Lin; Lu, Yang; Wang, Ge
2010-01-01
The key idea discussed in this paper is to reconstruct an image from overlapped projections so that the data acquisition process can be shortened while the image quality remains essentially uncompromised. To perform image reconstruction from overlapped projections, the conventional reconstruction approach (e.g., filtered backprojection (FBP) algorithms) cannot be directly used because of two problems. First, overlapped projections represent an imaging system in terms of summed exponentials, which cannot be transformed into a linear form. Second, the overlapped measurement carries less information than the traditional line integrals. To meet these challenges, we propose a compressive sensing-(CS-) based iterative algorithm for reconstruction from overlapped data. This algorithm starts with a good initial guess, relies on adaptive linearization, and minimizes the total variation (TV). Then, we demonstrated the feasibility of this algorithm in numerical tests. PMID:20689701
Simultaneous measurement of complementary observables with compressive sensing.
Howland, Gregory A; Schneeloch, James; Lum, Daniel J; Howell, John C
2014-06-27
The more information a measurement provides about a quantum system's position statistics, the less information a subsequent measurement can provide about the system's momentum statistics. This information trade-off is embodied in the entropic formulation of the uncertainty principle. Traditionally, uncertainly relations correspond to resolution limits; increasing a detector's position sensitivity decreases its momentum sensitivity and vice versa. However, this is not required in general; for example, position information can instead be extracted at the cost of noise in momentum. Using random, partial projections in position followed by strong measurements in momentum, we efficiently determine the transverse-position and transverse-momentum distributions of an unknown optical field with a single set of measurements. The momentum distribution is directly imaged, while the position distribution is recovered using compressive sensing. At no point do we violate uncertainty relations; rather, we economize the use of information we obtain. PMID:25014815
Simultaneous Measurement of Complementary Observables with Compressive Sensing
NASA Astrophysics Data System (ADS)
Howland, Gregory A.; Schneeloch, James; Lum, Daniel J.; Howell, John C.
2014-06-01
The more information a measurement provides about a quantum system's position statistics, the less information a subsequent measurement can provide about the system's momentum statistics. This information trade-off is embodied in the entropic formulation of the uncertainty principle. Traditionally, uncertainly relations correspond to resolution limits; increasing a detector's position sensitivity decreases its momentum sensitivity and vice versa. However, this is not required in general; for example, position information can instead be extracted at the cost of noise in momentum. Using random, partial projections in position followed by strong measurements in momentum, we efficiently determine the transverse-position and transverse-momentum distributions of an unknown optical field with a single set of measurements. The momentum distribution is directly imaged, while the position distribution is recovered using compressive sensing. At no point do we violate uncertainty relations; rather, we economize the use of information we obtain.
Single image non-uniformity correction using compressive sensing
NASA Astrophysics Data System (ADS)
Jian, Xian-zhong; Lu, Rui-zhi; Guo, Qiang; Wang, Gui-pu
2016-05-01
A non-uniformity correction (NUC) method for an infrared focal plane array imaging system was proposed. The algorithm, based on compressive sensing (CS) of single image, overcame the disadvantages of "ghost artifacts" and bulk calculating costs in traditional NUC algorithms. A point-sampling matrix was designed to validate the measurements of CS on the time domain. The measurements were corrected using the midway infrared equalization algorithm, and the missing pixels were solved with the regularized orthogonal matching pursuit algorithm. Experimental results showed that the proposed method can reconstruct the entire image with only 25% pixels. A small difference was found between the correction results using 100% pixels and the reconstruction results using 40% pixels. Evaluation of the proposed method on the basis of the root-mean-square error, peak signal-to-noise ratio, and roughness index (ρ) proved the method to be robust and highly applicable.
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
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.
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.
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.
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
Compressed Sensing Photoacoustic Imaging Based on Fast Alternating Direction Algorithm
Liu, Xueyan; Peng, Dong; Guo, Wei; Ma, Xibo; Yang, Xin; Tian, Jie
2012-01-01
Photoacoustic imaging (PAI) has been employed to reconstruct endogenous optical contrast present in tissues. At the cost of longer calculations, a compressive sensing reconstruction scheme can achieve artifact-free imaging with fewer measurements. In this paper, an effective acceleration framework using the alternating direction method (ADM) was proposed for recovering images from limited-view and noisy observations. Results of the simulation demonstrated that the proposed algorithm could perform favorably in comparison to two recently introduced algorithms in computational efficiency and data fidelity. In particular, it ran considerably faster than these two methods. PAI with ADM can improve convergence speed with fewer ultrasonic transducers, enabling a high-performance and cost-effective PAI system for biomedical applications. PMID:23365553
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.
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.
Development of a Neutron Spectroscopic System Utilizing Compressed Sensing Measurements
NASA Astrophysics Data System (ADS)
Vargas, Danilo; Cable Kurwitz, R.; Carron, Igor; DePriest, K. Russell
2016-02-01
A new approach to neutron detection capable of gathering spectroscopic information has been demonstrated. The approach relies on an asymmetrical arrangement of materials, geometry, and an ability to change the orientation of the detector with respect to the neutron field. Measurements are used to unfold the energy characteristics of the neutron field using a new theoretical framework of compressed sensing. Recent theoretical results show that the number of multiplexed samples can be lower than the full number of traditional samples while providing the ability to have some super-resolution. Furthermore, the solution approach does not require a priori information or inclusion of physics models. Utilizing the MCNP code, a number of candidate detector geometries and materials were modeled. Simulations were carried out for a number of neutron energies and distributions with preselected orientations for the detector. The resulting matrix (A) consists of n rows associated with orientation and m columns associated with energy and distribution where n < m. The library of known responses is used for new measurements Y (n × 1) and the solver is able to determine the system, Y = Ax where x is a sparse vector. Therefore, energy spectrum measurements are a combination of the energy distribution information of the identified elements of A. This approach allows for determination of neutron spectroscopic information using a single detector system with analog multiplexing. The analog multiplexing allows the use of a compressed sensing solution similar to approaches used in other areas of imaging. A single detector assembly provides improved flexibility and is expected to reduce uncertainty associated with current neutron spectroscopy measurement.
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.
NASA Astrophysics Data System (ADS)
Yeom, Seokwon; Javidi, Bahram
In this research, a 3D object classification technique using a single hologram has been presented. The PCA-FLD classifier with feature vectors based on Gabor wavelets has been utilized for this purpose. Training and test data of the 3D objects were obtained by computational holographic imaging. We were able to classify 3D objects used in the experiments with a few reconstructed planes of the hologram. The Gabor approach appears to be a good feature extractor for hologram-based 3D classification. The FLD combined with the PCA proved to be a very efficient classifier even with a few training data. Substantial dimensionality reduction was achieved by using the proposed technique for 3D classification problem using holographic imaging. As a consequence, we were able to classify different classes of 3D objects using computer-reconstructed holographic images.
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.
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
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.
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
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.
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. PMID:26357126
Compressed sensing for chemical shift-based water-fat separation.
Doneva, Mariya; Börnert, Peter; Eggers, Holger; Mertins, Alfred; Pauly, John; Lustig, Michael
2010-12-01
Multi echo chemical shift-based water-fat separation methods allow for uniform fat suppression in the presence of main field inhomogeneities. However, these methods require additional scan time for chemical shift encoding. This work presents a method for water-fat separation from undersampled data (CS-WF), which combines compressed sensing and chemical shift-based water-fat separation. Undersampling was applied in the k-space and in the chemical shift encoding dimension to reduce the total scanning time. The method can reconstruct high quality water and fat images in 2D and 3D applications from undersampled data. As an extension, multipeak fat spectral models were incorporated into the CS-WF reconstruction to improve the water-fat separation quality. In 3D MRI, reduction factors of above three can be achieved, thus fully compensating the additional time needed in three-echo water-fat imaging. The method is demonstrated on knee and abdominal in vivo data. PMID:20859998
NASA Astrophysics Data System (ADS)
Leihong, Zhang; Dong, Liang; Bei, Li; Yi, Kang; Zilan, Pan; Dawei, Zhang; Xiuhua, Ma
2016-04-01
In order to improve the reconstruction accuracy and reduce the workload, the algorithm of compressive sensing based on the iterative threshold is combined with the method of adaptive selection of the training sample, and a new algorithm of adaptive compressive sensing is put forward. The three kinds of training sample are used to reconstruct the spectral reflectance of the testing sample based on the compressive sensing algorithm and adaptive compressive sensing algorithm, and the color difference and error are compared. The experiment results show that spectral reconstruction precision based on the adaptive compressive sensing algorithm is better than that based on the algorithm of compressive sensing.
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)
Strijker, Geertje; Beekman, Fred; Bertotti, Giovanni; Luthi, Stefan M.
2013-05-01
Stress distributions and deformation patterns in a medium with a pre-existing fracture set are analyzed as a function of the remote compressive stress orientation (σH) using finite element models with increasingly complex fracture configurations. Slip along the fractures causes deformation localization at the tips as wing cracks or shear zones. The deformation intensity is proportional to the amount of slip, attaining a peak value for α = 45° (α: angle between the fracture strike and σH) and slip is linearly proportional with fracture length. Wing cracks develop for high deformation intensities for 30° < α < 60°, whereas primary plastic shear zones develop for low deformation intensities. Additionally, two types of secondary shear zones develop for α < 30° and α > 60°, with constant angles of 135° and - 60° with σH, respectively. Mechanical interaction between fractures in a fracture zone, quantified as change in slip compared to an isolated fracture, decreases with increasing fracture separation. Fracture underlap elongates the fracture length and therefore increases the amount of slip, while fracture overlap exhibits the opposite effect. Fracture slip decreases with an increasing amount of directly adjacent fractures. Mechanical interaction becomes negligible for fracture configurations with spacing-to-length and spacing-to-overlap ratios exceeding 0.5 and that in this case fractures are decoupled. Independent of the pre-existing fracture configuration, the development of a secondary systematic fracture set driven by a remote stress rotation is dominated by σH; development of wing cracks or shear zones is restricted to the fracture tips. Blocks with tapered geometries are present in models with a variable fracture strike, where the maximum principal stress (σ1, applying the geological convention that compressive stresses are positive) trajectories consistently deviate from σH; the presence of two systematic σ1 trajectory orientations suggests
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.
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
Recent results of medium wave infrared compressive sensing.
Mahalanobis, A; Shilling, R; Murphy, R; Muise, R
2014-12-01
The application of compressive sensing (CS) for imaging has been extensively investigated and the underlying mathematical principles are well understood. The theory of CS is motivated by the sparse nature of real-world signals and images, and provides a framework in which high-resolution information can be recovered from low-resolution measurements. This, in turn, enables hardware concepts that require much fewer detectors than a conventional sensor. For infrared imagers there is a significant potential impact on the cost and footprint of the sensor. When smaller focal plane arrays (FPAs) to obtain large images are allowed, large formats FPAs are unnecessary. From a hardware standpoint, this benefit is independent of the actual level of compression and effective data rate reduction, which depend on the choice of codes and information recovery algorithm. Toward this end, we used a CS testbed for mid-wave infrared (MWIR) to experimentally show that information at high spatial resolution can be successfully recovered from measurements made with a small FPA. We describe the highly parallel and scalable CS architecture of the testbed, and its implementation using a reflective spatial light modulator and a focal plane array with variable pixel sizes. We also discuss the impact of real-world devices and the effect of sensor calibration that must be addressed in practice. Finally, we present preliminary results of image reconstruction, which demonstrate the testbed operation. These results experimentally confirm that high-resolution spatial information (for tasks such as imaging and target detection) can be successfully recovered from low-resolution measurements. We also discuss the potential system-level benefits of CS for infrared imaging, and some of the challenges that must be addressed in future infrared CS imagers designs. PMID:25607964
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
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.
Compressed sensing for wideband wavenumber tracking in dispersive shallow water.
Le Courtois, Florent; Bonnel, Julien
2015-08-01
In shallow water zones and at low frequency, seabed and water column properties can be estimated from the acoustic wavenumbers using inversion algorithms. When considering horizontal line arrays (HLA) and narrowband sources, the wavenumbers can be evaluated with classic spectral analysis methods. In this paper, a compressed sensing (CS) method for sparse recovery of the wavenumbers is proposed. This takes advantage of the few propagating modes and allows for spectral estimation when short HLA are used. The CS representation improves the wavenumber estimation, compared to the Fourier transform. However, for small arrays and several propagating modes, the CS generates interferences and does not allow proper wavenumber estimation. When considering broadband sources, it is possible to combine the wavenumbers estimated at several frequencies in order to build a frequency-wavenumber (f - k) representation. In this case, a post-processing tracking operation which improves the f - k resolution is presented. This relies on a general approach of waveguide physics and uses a particle filtering (PF) algorithm to track the wavenumbers. The consecutive use of CS and PF leads to a better wavenumber estimation. This methodology can be used for sources that are not at an end-fire position. It is illustrated by simulations and successfully applied on the Shallow Water 2006 data using the 32 sensor SHARK array. PMID:26328676
Interferometric Radio Transient Reconstruction in Compressed Sensing Framework
NASA Astrophysics Data System (ADS)
Jiang, M.; Girard, J. N.; Starck, J.-L.; Corbel, S.; Tasse, C.
2015-12-01
Imaging by aperture synthesis from interferometric data is a well-known, but strong ill-posed inverse problem. Strong and faint radio sources can be imaged unambiguously using time and frequency integration to gather more Fourier samples of the sky. , However, these imagers assumes a steady sky and the complexity of the problem increases when transients radio sources are also present in the data. Hopefully, in the context of transient imaging, the spatial and temporal information are separable which enable extension of an imager fit for a steady sky. We introduce independent spatial and temporal wavelet dictionaries to sparsely represent the transient in both spatial domain and temporal domain. These dictionaries intervenes in a new reconstruction method developed in the Compressed Sensing (CS) framework and using a primal-dual splitting algorithm. According to the preliminary tests in different noise regimes, this new ``Time-agile'' (or 2D-1D) method seems to be efficient in detecting and reconstructing the transients temporal dependence.
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. PMID:25967314
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
Compressive sensing of sparse radio frequency signals using optical mixing.
Valley, George C; Sefler, George A; Shaw, T Justin
2012-11-15
We demonstrate an optical mixing system for measuring properties of sparse radio frequency (RF) signals using compressive sensing (CS). Two types of sparse RF signals are investigated: (1) a signal that consists of a few 0.4 ns pulses in a 26.8 ns window and (2) a signal that consists of a few sinusoids at different frequencies. The RF is modulated onto the intensity of a repetitively pulsed, wavelength-chirped optical field, and time-wavelength-space mapping is used to map the optical field onto a 118-pixel, one-dimensional spatial light modulator (SLM). The SLM pixels are programmed with a pseudo-random bit sequence (PRBS) to form one row of the CS measurement matrix, and the optical throughput is integrated with a photodiode to obtain one value of the CS measurement vector. Then the PRBS is changed to form the second row of the mixing matrix and a second value of the measurement vector is obtained. This process is performed 118 times so that we can vary the dimensions of the CS measurement matrix from 1×118 to 118×118 (square). We use the penalized ℓ(1) norm method with stopping parameter λ (also called basis pursuit denoising) to recover pulsed or sinusoidal RF signals as a function of the small dimension of the measurement matrix and stopping parameter. For a square matrix, we also find that penalized ℓ(1) norm recovery performs better than conventional recovery using matrix inversion. PMID:23164876
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
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
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. PMID:25629590
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.
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.
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
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.
Compressed Sensing Electron tomography using adaptive dictionaries: a simulation study
NASA Astrophysics Data System (ADS)
AlAfeef, A.; Cockshott, P.; MacLaren, I.; McVitie, S.
2014-06-01
Electron tomography (ET) is an increasingly important technique for examining the three-dimensional morphologies of nanostructures. ET involves the acquisition of a set of 2D projection images to be reconstructed into a volumetric image by solving an inverse problem. However, due to limitations in the acquisition process this inverse problem is considered ill-posed (i.e., no unique solution exists). Furthermore reconstruction usually suffers from missing wedge artifacts (e.g., star, fan, blurring, and elongation artifacts). Compressed sensing (CS) has recently been applied to ET and showed promising results for reducing missing wedge artifacts caused by limited angle sampling. CS uses a nonlinear reconstruction algorithm that employs image sparsity as a priori knowledge to improve the accuracy of density reconstruction from a relatively small number of projections compared to other reconstruction techniques. However, The performance of CS recovery depends heavily on the degree of sparsity of the reconstructed image in the selected transform domain. Prespecified transformations such as spatial gradients provide sparse image representation, while synthesising the sparsifying transform based on the properties of the particular specimen may give even sparser results and can extend the application of CS to specimens that can not be sparsely represented with other transforms such as Total variation (TV). In this work, we show that CS reconstruction in ET can be significantly improved by tailoring the sparsity representation using a sparse dictionary learning principle.
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
Surface reconstruction in gradient-field domain using compressed sensing.
Rostami, Mohammad; Michailovich, Oleg V; Wang, Zhou
2015-05-01
Surface reconstruction from measurements of spatial gradient is an important computer vision problem with applications in photometric stereo and shape-from-shading. In the case of morphologically complex surfaces observed in the presence of shadowing and transparency artifacts, a relatively large dense gradient measurements may be required for accurate surface reconstruction. Consequently, due to hardware limitations of image acquisition devices, situations are possible in which the available sampling density might not be sufficiently high to allow for recovery of essential surface details. In this paper, the above problem is resolved by means of derivative compressed sensing (DCS). DCS can be viewed as a modification of the classical CS, which is particularly suited for reconstructions involving image/surface gradients. In DCS, a standard CS setting is augmented through incorporation of additional constraints arising from some intrinsic properties of potential vector fields. We demonstrate that using DCS results in reduction in the number of measurements as compared with the standard (dense) sampling, while producing estimates of higher accuracy and smaller variability as compared with CS-based estimates. The results of this study are further supported by a series of numerical experiments. PMID:25769157
Genetic optical design for a compressive sensing task
NASA Astrophysics Data System (ADS)
Horisaki, Ryoichi; Niihara, Takahiro; Tanida, Jun
2016-07-01
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.
Adaptive compressed sensing for spectral-domain optical coherence tomography
NASA Astrophysics Data System (ADS)
Wang, Yi; Chen, Xiaodong; Wang, Ting; Li, Hongxiao; Yu, Daoyin
2014-03-01
Spectral-domain optical coherence tomography (SD-OCT) is a non-contact and non-invasive method for measuring the change of biological tissues caused by pathological changes of body. CCD with huge number of pixels is usually used in SD-OCT to increase the detecting depth, thus enhancing the hardness of data transmission and storage. The usage of compressed sensing (CS) in SD-OCT is able to reduce the trouble of large data transfer and storage, thus eliminating the complexity of processing system. The traditional CS uses the same sampling model for SD-OCT images of different tissue, leading to reconstruction images with different quality. We proposed a CS with adaptive sampling model. The new model is based on uniform sampling model, and the interference spectral of SD-OCT is considered to adjust the local sampling ratio. Compared with traditional CS, adaptive CS can modify the sampling model for images of different tissue according to different interference spectral, getting reconstruction images with high quality without changing sampling model.
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
Compressed sensing MR image reconstruction exploiting TGV and wavelet sparsity.
Zhao, Di; 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
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.
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.
Hodgdon, M.L.; Oona, H.; Martinez, A.R.; Salon, S.; Wendling, P.; Krahenbuhl, L.; Nicolas, A.; Nicolas, L.
1989-01-01
We present herein the results of three electromagnetic field problems for compressed magnetic field generators and their associated power flow channels. The first problem is the computation of the transient magnetic field in a two-dimensional model of helical generator during loading. The second problem is the three-dimensional eddy current patterns in a section of an armature beneath a bifurcation point of a helical winding. Our third problem is the calculation of the three-dimensional electrostatic fields in a region known as the post-hole convolute in which a rod connects the inner and outer walls of a system of three concentric cylinders through a hole in the middle cylinder. While analytic solutions exist for many electromagnetic field problems in cases of special and ideal geometries, the solutions of these and similar problems for the proper analysis and design of compressed magnetic field generators and their related hardware require computer simulations. In earlier studies, computer models have been proposed, several based on research oriented hydrocodes to which uncoupled or partially coupled Maxwell's equations solvers are added. Although the hydrocode models address the problem of moving, deformable conductors, they are not useful for electromagnetic analysis, nor can they be considered design tools. For our studies, we take advantage of the commercial, electromagnetic computer-aided design software packages FLUX2D nd PHI3D that were developed for motor manufacturers and utilities industries. 4 refs., 6 figs.
Huang, Jian; Ma, Dayan; Chen, Feng; Bai, Min; Xu, Kewei; Zhao, Yongxi
2015-10-20
Surface-enhanced Raman scattering (SERS) has been considered as a promising sensing technique to detect low-level analytes. However, its practical application was hindered owing to the lack of uniform SERS substrates for ultrasensitive and reproducible assay. Herein, inspired by the natural cactus structure, we developed a cactus-like 3D nanostructure with uniform and high-density hotspots for highly efficient SERS sensing by both grafting the silicon nanoneedles onto Ag dendrites and subsequent decoration with Ag nanoparticles. The hierarchical scaffolds and high-density hotspots throughout the whole substrate result in great amplification of SERS signal. A high Raman enhancement factor of crystal violet up to 6.6 × 10(7) was achieved. Using malachite green (MG) as a model target, the fabricated SERS substrates exhibited good reproducibility (RSD ∼ 9.3%) and pushed the detection limit down to 10(-13) M with a wide linear range of 10(-12) M to 10(-7) M. Excellent selectivity was also demonstrated by facilely distinguishing MG from its derivative, some organics, and coexistent metal ions. Finally, the practicality and reliability of the 3D SERS substrates were confirmed by the quantitative analysis of spiked MG in environmental water with high recoveries (91.2% to 109.6%). By virtue of the excellent performance (good reproducibility, high sensitivity, and selectivity), the cactus-like 3D SERS substrate has great potential to become a versatile sensing platform in environmental monitoring, food safety, and medical diagnostics. PMID:26406111
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 R2 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. PMID:24489656
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. PMID:24489656
Parallel heterogeneous architectures for efficient OMP compressive sensing reconstruction
NASA Astrophysics Data System (ADS)
Kulkarni, Amey; Stanislaus, Jerome L.; Mohsenin, Tinoosh
2014-05-01
Compressive Sensing (CS) is a novel scheme, in which a signal that is sparse in a known transform domain can be reconstructed using fewer samples. The signal reconstruction techniques are computationally intensive and have sluggish performance, which make them impractical for real-time processing applications . The paper presents novel architectures for Orthogonal Matching Pursuit algorithm, one of the popular CS reconstruction algorithms. We show the implementation results of proposed architectures on FPGA, ASIC and on a custom many-core platform. For FPGA and ASIC implementation, a novel thresholding method is used to reduce the processing time for the optimization problem by at least 25%. Whereas, for the custom many-core platform, efficient parallelization techniques are applied, to reconstruct signals with variant signal lengths of N and sparsity of m. The algorithm is divided into three kernels. Each kernel is parallelized to reduce execution time, whereas efficient reuse of the matrix operators allows us to reduce area. Matrix operations are efficiently paralellized by taking advantage of blocked algorithms. For demonstration purpose, all architectures reconstruct a 256-length signal with maximum sparsity of 8 using 64 measurements. Implementation on Xilinx Virtex-5 FPGA, requires 27.14 μs to reconstruct the signal using basic OMP. Whereas, with thresholding method it requires 18 μs. ASIC implementation reconstructs the signal in 13 μs. However, our custom many-core, operating at 1.18 GHz, takes 18.28 μs to complete. Our results show that compared to the previous published work of the same algorithm and matrix size, proposed architectures for FPGA and ASIC implementations perform 1.3x and 1.8x respectively faster. Also, the proposed many-core implementation performs 3000x faster than the CPU and 2000x faster than the GPU.
Quantitative mapping of chemical compositions with MRI using compressed sensing
NASA Astrophysics Data System (ADS)
von Harbou, Erik; Fabich, Hilary T.; Benning, Martin; Tayler, Alexander B.; Sederman, Andrew J.; Gladden, Lynn F.; Holland, Daniel J.
2015-12-01
In this work, a magnetic resonance (MR) imaging method for accelerating the acquisition time of two dimensional concentration maps of different chemical species in mixtures by the use of compressed sensing (CS) is presented. Whilst 2D-concentration maps with a high spatial resolution are prohibitively time-consuming to acquire using full k -space sampling techniques, CS enables the reconstruction of quantitative concentration maps from sub-sampled k -space data. First, the method was tested by reconstructing simulated data. Then, the CS algorithm was used to reconstruct concentration maps of binary mixtures of 1,4-dioxane and cyclooctane in different samples with a field-of-view of 22 mm and a spatial resolution of 344 μm × 344 μm . Spiral based trajectories were used as sampling schemes. For the data acquisition, eight scans with slightly different trajectories were applied resulting in a total acquisition time of about 8 min. In contrast, a conventional chemical shift imaging experiment at the same resolution would require about 17 h. To get quantitative results, a careful weighting of the regularisation parameter (via the L-curve approach) or contrast-enhancing Bregman iterations are applied for the reconstruction of the concentration maps. Both approaches yield relative errors of the concentration map of less than 2 mol-% without any calibration prior to the measurement. The accuracy of the reconstructed concentration maps deteriorates when the reconstruction model is biased by systematic errors such as large inhomogeneities in the static magnetic field. The presented method is a powerful tool for the fast acquisition of concentration maps that can provide valuable information for the investigation of many phenomena in chemical engineering applications.
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
NASA Astrophysics Data System (ADS)
Oiknine, Yaniv; August, Isaac Y.; Revah, Liat; Stern, Adrian
2016-05-01
Recently we introduced a Compressive Sensing Miniature Ultra-Spectral Imaging (CS-MUSI) system. The system is based on a single Liquid Crystal (LC) cell and a parallel sensor array where the liquid crystal cell performs spectral encoding. Within the framework of compressive sensing, the CS-MUSI system is able to reconstruct ultra-spectral cubes captured with only an amount of ~10% samples compared to a conventional system. Despite the compression, the technique is extremely complex computationally, because reconstruction of ultra-spectral images requires processing huge data cubes of Gigavoxel size. Fortunately, the computational effort can be alleviated by using separable operation. An additional way to reduce the reconstruction effort is to perform the reconstructions on patches. In this work, we consider processing on various patch shapes. We present an experimental comparison between various patch shapes chosen to process the ultra-spectral data captured with CS-MUSI system. The patches may be one dimensional (1D) for which the reconstruction is carried out spatially pixel-wise, or two dimensional (2D) - working on spatial rows/columns of the ultra-spectral cube, as well as three dimensional (3D).
Compressive Sensing Based Bio-Inspired Shape Feature Detection CMOS Imager
NASA Technical Reports Server (NTRS)
Duong, Tuan A. (Inventor)
2015-01-01
A CMOS imager integrated circuit using compressive sensing and bio-inspired detection is presented which integrates novel functions and algorithms within a novel hardware architecture enabling efficient on-chip implementation.
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.
Elastic-Waveform Inversion with Compressive Sensing for Sparse Seismic Data
Lin, Youzuo; Huang, Lianjie
2015-01-26
Accurate velocity models of compressional- and shear-waves are essential for geothermal reservoir characterization and microseismic imaging. Elastic-waveform inversion of multi-component seismic data can provide high-resolution inversion results of subsurface geophysical properties. However, the method requires seismic data acquired using dense source and receiver arrays. In practice, seismic sources and/or geophones are often sparsely distributed on the surface and/or in a borehole, such as 3D vertical seismic profiling (VSP) surveys. We develop a novel elastic-waveform inversion method with compressive sensing for inversion of sparse seismic data. We employ an alternating-minimization algorithm to solve the optimization problem of our new waveform inversion method. We validate our new method using synthetic VSP data for a geophysical model built using geologic features found at the Raft River enhanced-geothermal-system (EGS) field. We apply our method to synthetic VSP data with a sparse source array and compare the results with those obtained with a dense source array. Our numerical results demonstrate that the velocity mode ls produced with our new method using a sparse source array are almost as accurate as those obtained using a dense source array.
Du, Pei-Yao; Gu, Wen; Liu, Xin
2016-06-01
Framework-isomeric three-dimensional (3D) Zn-Ln heterometallic metal-organic frameworks, {[Ln2Zn(abtc)2(H2O)4]·2H2O}∞ {Ln = Sm(1), Tb(2)}, were synthesized using a convenient solvothermal reaction. They can serve as excellent sensors for the specific identification of benzaldehyde and NO2(-) through a fluorescence quenching process. PMID:27117937
Choi, Jonghoon; Lee, Eun Kyu; Choo, Jaebum; Yuh, Junhan; Hong, Jong Wook
2015-09-01
Microfabricated systems equipped with 3D cell culture devices and in-situ cellular biosensing tools can be a powerful bionanotechnology platform to investigate a variety of biomedical applications. Various construction substrates such as plastics, glass, and paper are used for microstructures. When selecting a construction substrate, a key consideration is a porous microenvironment that allows for spheroid growth and mimics the extracellular matrix (ECM) of cell aggregates. Various bio-functionalized hydrogels are ideal candidates that mimic the natural ECM for 3D cell culture. When selecting an optimal and appropriate microfabrication method, both the intended use of the system and the characteristics and restrictions of the target cells should be carefully considered. For highly sensitive and near-cell surface detection of excreted cellular compounds, SERS-based microsystems capable of dual modal imaging have the potential to be powerful tools; however, the development of optical reporters and nanoprobes remains a key challenge. We expect that the microsystems capable of both 3D cell culture and cellular response monitoring would serve as excellent tools to provide fundamental cellular behavior information for various biomedical applications such as metastasis, wound healing, high throughput screening, tissue engineering, regenerative medicine, and drug discovery and development. PMID:26358782
High-resolution hyperspectral single-pixel imaging system based on compressive sensing
NASA Astrophysics Data System (ADS)
Magalha~es, Filipe; Abolbashari, Mehrdad; Araújo, Francisco M.; Correia, Miguel V.; Farahi, Faramarz
2012-07-01
For the first time, a high-resolution hyperspectral single-pixel imaging system based on compressive sensing is presented and demonstrated. The system integrates a digital micro-mirror device array to optically compress the image to be acquired and an optical spectrum analyzer to enable high spectral resolution. The system's ability to successfully reconstruct images with 10 pm spectral resolution is proven.
Chappard, Daniel; Terranova, Lisa; Mallet, Romain; Mercier, Philippe
2015-01-01
The 3D arrangement of porous granular biomaterials usable to fill bone defects has received little study. Granular biomaterials occupy 3D space when packed together in a manner that creates a porosity suitable for the invasion of vascular and bone cells. Granules of beta-tricalcium phosphate (β-TCP) were prepared with either 12.5 or 25 g of β-TCP powder in the same volume of slurry. When the granules were placed in a test tube, this produced 3D stacks with a high (HP) or low porosity (LP), respectively. Stacks of granules mimic the filling of a bone defect by a surgeon. The aim of this study was to compare the porosity of stacks of β-TCP granules with that of cores of trabecular bone. Biomechanical compression tests were done on the granules stacks. Bone cylinders were prepared from calf tibia plateau, constituted high-density (HD) blocks. Low-density (LD) blocks were harvested from aged cadaver tibias. Microcomputed tomography was used on the β-TCP granule stacks and the trabecular bone cores to determine porosity and specific surface. A vector-projection algorithm was used to image porosity employing a frontal plane image, which was constructed line by line from all images of a microCT stack. Stacks of HP granules had porosity (75.3 ± 0.4%) and fractal lacunarity (0.043 ± 0.007) intermediate between that of HD (respectively 69.1 ± 6.4%, p < 0.05 and 0.087 ± 0.045, p < 0.05) and LD bones (respectively 88.8 ± 1.57% and 0.037 ± 0.014), but exhibited a higher surface density (5.56 ± 0.11 mm2/mm3 vs. 2.06 ± 0.26 for LD, p < 0.05). LP granular arrangements created large pores coexisting with dense areas of material. Frontal plane analysis evidenced a more regular arrangement of β-TCP granules than bone trabecule. Stacks of HP granules represent a scaffold that resembles trabecular bone in its porous microarchitecture. PMID:26528240
Compressed Sensing Quantum Process Tomography for Superconducting Quantum Gates
NASA Astrophysics Data System (ADS)
Rodionov, Andrey
An important challenge in quantum information science and quantum computing is the experimental realization of high-fidelity quantum operations on multi-qubit systems. Quantum process tomography (QPT) is a procedure devised to fully characterize a quantum operation. We first present the results of the estimation of the process matrix for superconducting multi-qubit quantum gates using the full data set employing various methods: linear inversion, maximum likelihood, and least-squares. To alleviate the problem of exponential resource scaling needed to characterize a multi-qubit system, we next investigate a compressed sensing (CS) method for QPT of two-qubit and three-qubit quantum gates. Using experimental data for two-qubit controlled-Z gates, taken with both Xmon and superconducting phase qubits, we obtain estimates for the process matrices with reasonably high fidelities compared to full QPT, despite using significantly reduced sets of initial states and measurement configurations. We show that the CS method still works when the amount of data is so small that the standard QPT would have an underdetermined system of equations. We also apply the CS method to the analysis of the three-qubit Toffoli gate with simulated noise, and similarly show that the method works well for a substantially reduced set of data. For the CS calculations we use two different bases in which the process matrix is approximately sparse (the Pauli-error basis and the singular value decomposition basis), and show that the resulting estimates of the process matrices match with reasonably high fidelity. For both two-qubit and three-qubit gates, we characterize the quantum process by its process matrix and average state fidelity, as well as by the corresponding standard deviation defined via the variation of the state fidelity for different initial states. We calculate the standard deviation of the average state fidelity both analytically and numerically, using a Monte Carlo method. Overall
NASA Astrophysics Data System (ADS)
Schneiderwind, Sascha; Mason, Jack; Wiatr, Thomas; Papanikolaou, Ioannis; Reicherter, Klaus
2016-03-01
Two normal faults on the island of Crete and mainland Greece were studied to test an innovative workflow with the goal of obtaining a more objective palaeoseismic trench log, and a 3-D view of the sedimentary architecture within the trench walls. Sedimentary feature geometries in palaeoseismic trenches are related to palaeoearthquake magnitudes which are used in seismic hazard assessments. If the geometry of these sedimentary features can be more representatively measured, seismic hazard assessments can be improved. In this study more representative measurements of sedimentary features are achieved by combining classical palaeoseismic trenching techniques with multispectral approaches. A conventional trench log was firstly compared to results of ISO (iterative self-organising) cluster analysis of a true colour photomosaic representing the spectrum of visible light. Photomosaic acquisition disadvantages (e.g. illumination) were addressed by complementing the data set with active near-infrared backscatter signal image from t-LiDAR measurements. The multispectral analysis shows that distinct layers can be identified and it compares well with the conventional trench log. According to this, a distinction of adjacent stratigraphic units was enabled by their particular multispectral composition signature. Based on the trench log, a 3-D interpretation of attached 2-D ground-penetrating radar (GPR) profiles collected on the vertical trench wall was then possible. This is highly beneficial for measuring representative layer thicknesses, displacements, and geometries at depth within the trench wall. Thus, misinterpretation due to cutting effects is minimised. This manuscript combines multiparametric approaches and shows (i) how a 3-D visualisation of palaeoseismic trench stratigraphy and logging can be accomplished by combining t-LiDAR and GPR techniques, and (ii) how a multispectral digital analysis can offer additional advantages to interpret palaeoseismic and stratigraphic
NASA Astrophysics Data System (ADS)
DeJong, Andrew
Numerical models of fluid-structure interaction have grown in importance due to increasing interest in environmental energy harvesting, airfoil-gust interactions, and bio-inspired formation flying. Powered by increasingly powerful parallel computers, such models seek to explain the fundamental physics behind the complex, unsteady fluid-structure phenomena. To this end, a high-fidelity computational model based on the high-order spectral difference method on 3D unstructured, dynamic meshes has been developed. The spectral difference method constructs continuous solution fields within each element with a Riemann solver to compute the inviscid fluxes at the element interfaces and an averaging mechanism to compute the viscous fluxes. This method has shown promise in the past as a highly accurate, yet sufficiently fast method for solving unsteady viscous compressible flows. The solver is monolithically coupled to the equations of motion of an elastically mounted 3-degree of freedom rigid bluff body undergoing flow-induced lift, drag, and torque. The mesh is deformed using 4 methods: an analytic function, Laplace equation, biharmonic equation, and a bi-elliptic equation with variable diffusivity. This single system of equations -- fluid and structure -- is advanced through time using a 5-stage, 4th-order Runge-Kutta scheme. Message Passing Interface is used to run the coupled system in parallel on up to 240 processors. The solver is validated against previously published numerical and experimental data for an elastically mounted cylinder. The effect of adding an upstream body and inducing wake galloping is observed.
NASA Astrophysics Data System (ADS)
Fan, J.; Peng, L.; Li, K. H.; Tan, C. S.
2012-10-01
Low-temperature wafer-level Cu-to-Cu thermo-compression bonding and its reliability for hermetic sealing application have been investigated in this work. The volume of the encapsulated cavities is about 1.4×10-3 cm3 in accordance with the MIL-STD-883E standard prescribed for microelectronics packaging hermeticity measurement. The samples under test are bonded at 300 °C under a bonding force of 5500 N for 1 h in vacuum (˜2.5 × 10-4 mbar) with a 300 nm thick Cu diffusion layer and 50 nm thick Ti barrier layer which are deposited in an e-beam evaporator. The reliability test is accomplished through a temperature cycling test (TCT) from -40 to 125 °C up to 1000 cycles and a humidity test based on IPC/JEDEC J-STD-020 standard. In addition, an immersion in acid/base solution is applied to verify the corrosion resistance of the Cu frame for hermetic application. Excellent helium leak rate which is smaller than the reject limit defined by the MIL-STD-883E standard (method 1014.10) is detected for all the samples. These excellent helium leak rates show an outstanding bonding quality against harsh environment for hermetic encapsulation in 3D integration applications.
NASA Astrophysics Data System (ADS)
Snidero, M.; Amilibia, A.; Gratacos, O.; Muñoz, J. A.
2009-04-01
This work presents a methodological workflow for the 3D reconstruction of geological surfaces at regional scale, based on remote sensing data and geological maps. This workflow has been tested on the reconstruction of the Anaran anticline, located in the Zagros Fold and Thrust belt mountain front. The used remote sensing data-set is a combination of Aster and Spot images as well as a high resolution digital elevation model. A consistent spatial positioning of the complete data-set in a 3D environment is necessary to obtain satisfactory results during the reconstruction. The Aster images have been processed by the Optimum Index Factor (OIF) technique, in order to facilitate the geological mapping. By pansharpening of the resulting Aster image with the SPOT panchromatic one we obtain the final high-resolution image used during the 3D mapping. Structural data (dip data) has been acquired through the analysis of the 3D mapped geological traces. Structural analysis of the resulting data-set allows us to divide the structure in different cylindrical domains. Related plunge lines orientation has been used to project data along the structure, covering areas with little or no information. Once a satisfactory dataset has been acquired, we reconstruct a selected horizon following the dip-domain concept. By manual editing, the obtained surfaces have been adjusted to the mapped geological limits as well as to the modeled faults. With the implementation of the Discrete Smooth Interpolation (DSI) algorithm, the final surfaces have been reconstructed along the anticline. Up to date the results demonstrate that the proposed methodology is a powerful tool for 3D reconstruction of geological surfaces when working with remote sensing data, in very inaccessible areas (eg. Iran, China, Africa). It is especially useful in semiarid regions where the structure strongly controls the topography. The reconstructed surfaces clearly show the geometry in the different sectors of the structure
Compressive sensing beamforming based on covariance for acoustic imaging with noisy measurements.
Zhong, Siyang; Wei, Qingkai; Huang, Xun
2013-11-01
Compressive sensing, a newly emerging method from information technology, is applied to array beamforming and associated acoustic applications. A compressive sensing beamforming method (CSB-II) is developed based on sampling covariance matrix, assuming spatially sparse and incoherent signals, and then examined using both simulations and aeroacoustic measurements. The simulation results clearly show that the proposed CSB-II method is robust to sensing noise. In addition, aeroacoustic tests of a landing gear model demonstrate the good performance in terms of resolution and sidelobe rejection. PMID:24181989
NASA Astrophysics Data System (ADS)
Yu, Yangchun; Zeng, Wen; Xu, Mengxue; Peng, Xianghe
2016-05-01
In this paper, WO3·H2O with different nanostructures from 0D to 3D were successfully synthesized via a simple yet cost-effective hydrothermal method with the assistance of surfactants. The structures and morphologies of products were investigated by XRD and SEM. Besides, we systematically explained the evolution process and formation mechanisms of different WO3·H2O morphologies. It is noted that both the kinds and amounts of surfactants strongly affect the formation of WO3·H2O crystals, as reflected in the tailoring of WO3·H2O morphologies. Furthermore, the gas sensing performance of the as-prepared samples towards methanol was also investigated. 3D flower-like hierarchical architecture displayed outstanding response to target gas among the four samples. We hoped our results could be of great benefit to further investigations of synthesizing different dimensional WO3·H2O nanostructures and their gas sensing applications.
Research on Differential Coding Method for Satellite Remote Sensing Data Compression
NASA Astrophysics Data System (ADS)
Lin, Z. J.; Yao, N.; Deng, B.; Wang, C. Z.; Wang, J. H.
2012-07-01
Data compression, in the process of Satellite Earth data transmission, is of great concern to improve the efficiency of data transmission. Information amounts inherent to remote sensing images provide a foundation for data compression in terms of information theory. In particular, distinct degrees of uncertainty inherent to distinct land covers result in the different information amounts. This paper first proposes a lossless differential encoding method to improve compression rates. Then a district forecast differential encoding method is proposed to further improve the compression rates. Considering the stereo measurements in modern photogrammetry are basically accomplished by means of automatic stereo image matching, an edge protection operator is finally utilized to appropriately filter out high frequency noises which could help magnify the signals and further improve the compression rates. The three steps were applied to a Landsat TM multispectral image and a set of SPOT-5 panchromatic images of four typical land cover types (i.e., urban areas, farm lands, mountain areas and water bodies). Results revealed that the average code lengths obtained by the differential encoding method, compared with Huffman encoding, were more close to the information amounts inherent to remote sensing images. And the compression rates were improved to some extent. Furthermore, the compression rates of the four land cover images obtained by the district forecast differential encoding method were nearly doubled. As for the images with the edge features preserved, the compression rates are average four times as large as those of the original images.
NASA Astrophysics Data System (ADS)
Diner, D. J.; Kahn, R. A.; Hostetler, C. A.; Ferrare, R. A.; Hair, J. W.; Cairns, B.; Torres, O.
2006-05-01
extinction independently along with vertically resolved estimates of microphysical properties, thus representing a significant advance relative to simpler backscatter systems such as GLAS and CALIPSO. This fusion of satellite-based approaches is aimed at observing the 3-D distribution of aerosol abundances, sizes, shapes, and absorption, and would represent a major technological advance in our ability to monitor and characterize near-surface particulate matter from space.
Compressive sensing reconstruction of feed-forward connectivity in pulse-coupled nonlinear networks
NASA Astrophysics Data System (ADS)
Barranca, Victor J.; Zhou, Douglas; Cai, David
2016-06-01
Utilizing the sparsity ubiquitous in real-world network connectivity, we develop a theoretical framework for efficiently reconstructing sparse feed-forward connections in a pulse-coupled nonlinear network through its output activities. Using only a small ensemble of random inputs, we solve this inverse problem through the compressive sensing theory based on a hidden linear structure intrinsic to the nonlinear network dynamics. The accuracy of the reconstruction is further verified by the fact that complex inputs can be well recovered using the reconstructed connectivity. We expect this Rapid Communication provides a new perspective for understanding the structure-function relationship as well as compressive sensing principle in nonlinear network dynamics.
High capacity image steganography method based on framelet and compressive sensing
NASA Astrophysics Data System (ADS)
Xiao, Moyan; He, Zhibiao
2015-12-01
To improve the capacity and imperceptibility of image steganography, a novel high capacity and imperceptibility image steganography method based on a combination of framelet and compressive sensing (CS) is put forward. Firstly, SVD (Singular Value Decomposition) transform to measurement values obtained by compressive sensing technique to the secret data. Then the singular values in turn embed into the low frequency coarse subbands of framelet transform to the blocks of the cover image which is divided into non-overlapping blocks. Finally, use inverse framelet transforms and combine to obtain the stego image. The experimental results show that the proposed steganography method has a good performance in hiding capacity, security and imperceptibility.
Arroyo, Fangjun; Arroyo, Edward; Li, Xiezhang; Zhu, Jiehua
2014-01-01
The block cyclic projection method in the compressed sensing framework (BCPCS) was introduced for image reconstruction in computed tomography and its convergence had been proven in the case of unity relaxation (λ=1). In this paper, we prove its convergence with underrelaxation parameters λ∈(0,1). As a result, the convergence of compressed sensing based block component averaging algorithm (BCAVCS) and block diagonally-relaxed orthogonal projection algorithm (BDROPCS) with underrelaxation parameters under a certain condition are derived. Experiments are given to illustrate the convergence behavior of these algorithms with selected parameters. PMID:24699347
An Adaptive Data Collection Algorithm Based on a Bayesian Compressed Sensing Framework
Liu, Zhi; Zhang, Mengmeng; Cui, Jian
2014-01-01
For Wireless Sensor Networks, energy efficiency is always a key consideration in system design. Compressed sensing is a new theory which has promising prospects in WSNs. However, how to construct a sparse projection matrix is a problem. In this paper, based on a Bayesian compressed sensing framework, a new adaptive algorithm which can integrate routing and data collection is proposed. By introducing new target node selection metrics, embedding the routing structure and maximizing the differential entropy for each collection round, an adaptive projection vector is constructed. Simulations show that compared to reference algorithms, the proposed algorithm can decrease computation complexity and improve energy efficiency. PMID:24818659
Using computer vision and compressed sensing for wood plate surface detection
NASA Astrophysics Data System (ADS)
Zhang, Yizhuo; Liu, Sijia; Tu, Wenjun; Yu, Huiling; Li, Chao
2015-10-01
Aiming at detecting the random and complicated characteristic of wood surface, we proposed a comprehensive detection algorithm based on computer vision and compressed sensing. First, integral projection method was used to trace and locate the position of a wood plate; then surface images were obtained by blocks. Second, multiscaled features were extracted from image to express the surface characteristic. Third, particle swarm optimization algorithm was used for multiscaled features selection. Finally, the defects and textures were detected by compressed sensing classifier. Five types of wood samples, including radial texture, tangential texture, wormhole, live knot, and dead knot, were used for tests, and the average classification accuracy was 94.7%.
NASA Astrophysics Data System (ADS)
Cashmore, Matt. T.; Koutsourakis, George; Gottschalg, Ralph; Hall, Simon. R. G.
2016-04-01
Compressive sensing has been widely used in image compression and signal recovery techniques in recent years; however, it has received limited attention in the field of optical measurement. This paper describes the use of compressive sensing for measurements of photovoltaic (PV) solar cells, using fully random sensing matrices, rather than mapping an orthogonal basis set directly. Existing compressive sensing systems optically image the surface of the object under test, this contrasts with the method described, where illumination patterns defined by precalculated sensing matrices, probe PV devices. We discuss the use of spatially modulated light fields to probe a PV sample to produce a photocurrent map of the optical response. This allows for faster measurements than would be possible using traditional translational laser beam induced current techniques. Results produced to a 90% correlation to raster scanned measurements, which can be achieved with under 25% of the conventionally required number of data points. In addition, both crack and spot type defects are detected at resolutions comparable to electroluminescence techniques, with 50% of the number of measurements required for a conventional scan.
Foot and ankle compression improves joint position sense but not bipedal stance in older people.
Hijmans, Juha M; Zijlstra, Wiebren; Geertzen, Jan H B; Hof, At L; Postema, Klaas
2009-02-01
This study investigates the effects of foot and ankle compression on joint position sense (JPS) and balance in older people and young adults. 12 independently living healthy older persons (77-93 years) were recruited from a senior accommodation facility. 15 young adults (19-24 years) also participated. Compression was applied at the ankles and feet using medical compression hosiery. The mean velocity of the centre of pressure (CoP) displacements and the root mean square of the CoP velocity in both anteroposterior and mediolateral directions, were measured with a foot pressure plate. In older people, ankle compression was associated with an improvement of JPS towards normal values. However, a concurrent deterioration of their balance was found. In young adults compression had no effect on either JPS or balance. PMID:19019679
Application of Compressed Sensing to 2-D Ultrasonic Propagation Imaging System data
Mascarenas, David D.; Farrar, Charles R.; Chong, See Yenn; Lee, J.R.; Park, Gyu Hae; Flynn, Eric B.
2012-06-29
The Ultrasonic Propagation Imaging (UPI) System is a unique, non-contact, laser-based ultrasonic excitation and measurement system developed for structural health monitoring applications. The UPI system imparts laser-induced ultrasonic excitations at user-defined locations on a structure of interest. The response of these excitations is then measured by piezoelectric transducers. By using appropriate data reconstruction techniques, a time-evolving image of the response can be generated. A representative measurement of a plate might contain 800x800 spatial data measurement locations and each measurement location might be sampled at 500 instances in time. The result is a total of 640,000 measurement locations and 320,000,000 unique measurements. This is clearly a very large set of data to collect, store in memory and process. The value of these ultrasonic response images for structural health monitoring applications makes tackling these challenges worthwhile. Recently compressed sensing has presented itself as a candidate solution for directly collecting relevant information from sparse, high-dimensional measurements. The main idea behind compressed sensing is that by directly collecting a relatively small number of coefficients it is possible to reconstruct the original measurement. The coefficients are obtained from linear combinations of (what would have been the original direct) measurements. Often compressed sensing research is simulated by generating compressed coefficients from conventionally collected measurements. The simulation approach is necessary because the direct collection of compressed coefficients often requires compressed sensing analog front-ends that are currently not commercially available. The ability of the UPI system to make measurements at user-defined locations presents a unique capability on which compressed measurement techniques may be directly applied. The application of compressed sensing techniques on this data holds the potential to
NASA Astrophysics Data System (ADS)
Bruno, Oscar P.; Cubillos, Max
2016-02-01
This paper introduces alternating-direction implicit (ADI) solvers of higher order of time-accuracy (orders two to six) for the compressible Navier-Stokes equations in two- and three-dimensional curvilinear domains. The higher-order accuracy in time results from 1) An application of the backward differentiation formulae time-stepping algorithm (BDF) in conjunction with 2) A BDF-like extrapolation technique for certain components of the nonlinear terms (which makes use of nonlinear solves unnecessary), as well as 3) A novel application of the Douglas-Gunn splitting (which greatly facilitates handling of boundary conditions while preserving higher-order accuracy in time). As suggested by our theoretical analysis of the algorithms for a variety of special cases, an extensive set of numerical experiments clearly indicate that all of the BDF-based ADI algorithms proposed in this paper are "quasi-unconditionally stable" in the following sense: each algorithm is stable for all couples (h , Δt)of spatial and temporal mesh sizes in a problem-dependent rectangular neighborhood of the form (0 ,Mh) × (0 ,Mt). In other words, for each fixed value of Δt below a certain threshold, the Navier-Stokes solvers presented in this paper are stable for arbitrarily small spatial mesh-sizes. The second-order formulation has further been rigorously shown to be unconditionally stable for linear hyperbolic and parabolic equations in two-dimensional space. Although implicit ADI solvers for the Navier-Stokes equations with nominal second-order of temporal accuracy have been proposed in the past, the algorithms presented in this paper are the first ADI-based Navier-Stokes solvers for which second-order or better accuracy has been verified in practice under non-trivial (non-periodic) boundary conditions.
Split Bregman's optimization method for image construction in compressive sensing
NASA Astrophysics Data System (ADS)
Skinner, D.; Foo, S.; Meyer-Bäse, A.
2014-05-01
The theory of compressive sampling (CS) was reintroduced by Candes, Romberg and Tao, and D. Donoho in 2006. Using a priori knowledge that a signal is sparse, it has been mathematically proven that CS can defY Nyquist sampling theorem. Theoretically, reconstruction of a CS image relies on the minimization and optimization techniques to solve this complex almost NP-complete problem. There are many paths to consider when compressing and reconstructing an image but these methods have remained untested and unclear on natural images, such as underwater sonar images. The goal of this research is to perfectly reconstruct the original sonar image from a sparse signal while maintaining pertinent information, such as mine-like object, in Side-scan sonar (SSS) images. Goldstein and Osher have shown how to use an iterative method to reconstruct the original image through a method called Split Bregman's iteration. This method "decouples" the energies using portions of the energy from both the !1 and !2 norm. Once the energies are split, Bregman iteration is used to solve the unconstrained optimization problem by recursively solving the problems simultaneously. The faster these two steps or energies can be solved then the faster the overall method becomes. While the majority of CS research is still focused on the medical field, this paper will demonstrate the effectiveness of the Split Bregman's methods on sonar images.
Zhao, Anshun; Zhang, Zhaowei; Zhang, Penghui; Xiao, Shuang; Wang, Lu; Dong, Yue; Yuan, Hao; Li, Peiwu; Sun, Yimin; Jiang, Xueliang; Xiao, Fei
2016-09-28
Recent advances in on-body wearable medical apparatus and implantable devices drive the development of light-weight and bendable electrochemical sensors, which require the design of high-performance flexible electrode system. In this work, we reported a new type of freestanding and flexible electrode based on graphene paper (GP) supported 3D monolithic nanoporous gold (NPG) scaffold (NPG/GP), which was further modified by a layer of highly dense, well dispersed and ultrafine binary PtCo alloy nanoparticles via a facile and effective ultrasonic electrodeposition method. Our results demonstrated that benefited from the synergistic effect of the electrocatalytically active PtCo alloy nanoparticles, the large-active-area and highly conductive 3D NPG scaffold, and the mechanically strong and stable GP electrode substrate, the resultant PtCo alloy nanoparticles modified NPG/GP (PtCo/NPG/GP) exhibited high mechanical strength and good electrochemical sensing performances toward nonenzymatic detection of glucose, including a wide linear range from 35 μM- to 30 mM, a low detection limit of 5 μM (S/N = 3) and a high sensitivity of 7.84 μA cm(-2) mM(-1) as well as good selectivity, long-term stability and reproducibility. The practical application of the proposed PtCo/NPG/GP has also been demonstrated in in vitro detection of blood glucose in real clinic samples. PMID:27619087
Esfandyarpour, Rahim; Yang, Lu; Koochak, Zahra; Harris, James S; Davis, Ronald W
2016-02-01
The improvements in our ability to sequence and genotype DNA have opened up numerous avenues in the understanding of human biology and medicine with various applications, especially in medical diagnostics. But the realization of a label free, real time, high-throughput and low cost biosensing platforms to detect molecular interactions with a high level of sensitivity has been yet stunted due to two factors: one, slow binding kinetics caused by the lack of probe molecules on the sensors and two, limited mass transport due to the planar structure (two-dimensional) of the current biosensors. Here we present a novel three-dimensional (3D), highly sensitive, real-time, inexpensive and label-free nanotip array as a rapid and direct platform to sequence-specific DNA screening. Our nanotip sensors are designed to have a nano sized thin film as their sensing area (~ 20 nm), sandwiched between two sensing electrodes. The tip is then conjugated to a DNA oligonucleotide complementary to the sequence of interest, which is electrochemically detected in real-time via impedance changes upon the formation of a double-stranded helix at the sensor interface. This 3D configuration is specifically designed to improve the biomolecular hit rate and the detection speed. We demonstrate that our nanotip array effectively detects oligonucleotides in a sequence-specific and highly sensitive manner, yielding concentration-dependent impedance change measurements with a target concentration as low as 10 pM and discrimination against even a single mismatch. Notably, our nanotip sensors achieve this accurate, sensitive detection without relying on signal indicators or enhancing molecules like fluorophores. It can also easily be scaled for highly multiplxed detection with up to 5000 sensors/square centimeter, and integrated into microfluidic devices. The versatile, rapid, and sensitive performance of the nanotip array makes it an excellent candidate for point-of-care diagnostics, and high
Yang, Lu; koochak, Zahra; Harris, James S.; Davis, Ronald W.
2016-01-01
The improvements in our ability to sequence and genotype DNA have opened up numerous avenues in the understanding of human biology and medicine with various applications, especially in medical diagnostics. But the realization of a label free, real time, high-throughput and low cost biosensing platforms to detect molecular interactions with a high level of sensitivity has been yet stunted due to two factors: one, slow binding kinetics caused by the lack of probe molecules on the sensors and two, limited mass transport due to the planar structure (two-dimensional) of the current biosensors. Here we present a novel three-dimensional (3D), highly sensitive, real-time, inexpensive and label-free nanotip array as a rapid and direct platform to sequence-specific DNA screening. Our nanotip sensors are designed to have a nano sized thin film as their sensing area (~ 20 nm), sandwiched between two sensing electrodes. The tip is then conjugated to a DNA oligonucleotide complementary to the sequence of interest, which is electrochemically detected in real-time via impedance changes upon the formation of a double-stranded helix at the sensor interface. This 3D configuration is specifically designed to improve the biomolecular hit rate and the detection speed. We demonstrate that our nanotip array effectively detects oligonucleotides in a sequence-specific and highly sensitive manner, yielding concentration-dependent impedance change measurements with a target concentration as low as 10 pM and discrimination against even a single mismatch. Notably, our nanotip sensors achieve this accurate, sensitive detection without relying on signal indicators or enhancing molecules like fluorophores. It can also easily be scaled for highly multiplxed detection with up to 5000 sensors/square centimeter, and integrated into microfluidic devices. The versatile, rapid, and sensitive performance of the nanotip array makes it an excellent candidate for point-of-care diagnostics, and high
Remotely sensed image compression based on wavelet transform
NASA Technical Reports Server (NTRS)
Kim, Seong W.; Lee, Heung K.; Kim, Kyung S.; Choi, Soon D.
1995-01-01
In this paper, we present an image compression algorithm that is capable of significantly reducing the vast amount of information contained in multispectral images. The developed algorithm exploits the spectral and spatial correlations found in multispectral images. The scheme encodes the difference between images after contrast/brightness equalization to remove the spectral redundancy, and utilizes a two-dimensional wavelet transform to remove the spatial redundancy. the transformed images are then encoded by Hilbert-curve scanning and run-length-encoding, followed by Huffman coding. We also present the performance of the proposed algorithm with the LANDSAT MultiSpectral Scanner data. The loss of information is evaluated by PSNR (peak signal to noise ratio) and classification capability.
Three-Dimensional Inverse Transport Solver Based on Compressive Sensing Technique
NASA Astrophysics Data System (ADS)
Cheng, Yuxiong; Wu, Hongchun; Cao, Liangzhi; Zheng, Youqi
2013-09-01
According to the direct exposure measurements from flash radiographic image, a compressive sensing-based method for three-dimensional inverse transport problem is presented. The linear absorption coefficients and interface locations of objects are reconstructed directly at the same time. It is always very expensive to obtain enough measurements. With limited measurements, compressive sensing sparse reconstruction technique orthogonal matching pursuit is applied to obtain the sparse coefficients by solving an optimization problem. A three-dimensional inverse transport solver is developed based on a compressive sensing-based technique. There are three features in this solver: (1) AutoCAD is employed as a geometry preprocessor due to its powerful capacity in graphic. (2) The forward projection matrix rather than Gauss matrix is constructed by the visualization tool generator. (3) Fourier transform and Daubechies wavelet transform are adopted to convert an underdetermined system to a well-posed system in the algorithm. Simulations are performed and numerical results in pseudo-sine absorption problem, two-cube problem and two-cylinder problem when using compressive sensing-based solver agree well with the reference value.
Stand-off detection of explosives and precursors using compressive sensing Raman spectroscopy
NASA Astrophysics Data System (ADS)
Svanqvist, Mattias; Glimtoft, Martin; Ågren, Matilda; Nordberg, Markus; Östmark, Henric
2016-05-01
We present initial results on the performance of a compressive sensing setup for Raman imaging spectroscopy for standoff trace explosives detection. Hyperspectral image reconstruction is demonstrated under low signal conditions and successful spatial separation of substances with close lying Raman peaks is shown.
Curvelet-based compressive sensing for InSAR raw data
NASA Astrophysics Data System (ADS)
Costa, Marcello G.; da Silva Pinho, Marcelo; Fernandes, David
2015-10-01
The aim of this work is to evaluate the compression performance of SAR raw data for interferometry applications collected by airborne from BRADAR (Brazilian SAR System operating in X and P bands) using the new approach based on compressive sensing (CS) to achieve an effective recovery with a good phase preserving. For this framework is desirable a real-time capability, where the collected data can be compressed to reduce onboard storage and bandwidth required for transmission. In the CS theory, a sparse unknown signals can be recovered from a small number of random or pseudo-random measurements by sparsity-promoting nonlinear recovery algorithms. Therefore, the original signal can be significantly reduced. To achieve the sparse representation of SAR signal, was done a curvelet transform. The curvelets constitute a directional frame, which allows an optimal sparse representation of objects with discontinuities along smooth curves as observed in raw data and provides an advanced denoising optimization. For the tests were made available a scene of 8192 x 2048 samples in range and azimuth in X-band with 2 m of resolution. The sparse representation was compressed using low dimension measurements matrices in each curvelet subband. Thus, an iterative CS reconstruction method based on IST (iterative soft/shrinkage threshold) was adjusted to recover the curvelets coefficients and then the original signal. To evaluate the compression performance were computed the compression ratio (CR), signal to noise ratio (SNR), and because the interferometry applications require more reconstruction accuracy the phase parameters like the standard deviation of the phase (PSD) and the mean phase error (MPE) were also computed. Moreover, in the image domain, a single-look complex image was generated to evaluate the compression effects. All results were computed in terms of sparsity analysis to provides an efficient compression and quality recovering appropriated for inSAR applications
Super-resolution images fusion via compressed sensing and low-rank matrix decomposition
NASA Astrophysics Data System (ADS)
Ren, Kan; Xu, Fuyuan
2015-01-01
Most of available image fusion approaches cannot achieve higher spatial resolution than the multisource images. In this paper we propose a novel simultaneous images super-resolution and fusion approach via the recently developed compressed sensing and multiscale dictionaries learning technology. Under the sparse prior of image patches and the framework of compressed sensing, multisource images fusion is reduced to a task of signal recovery from the compressive measurements. Then a set of multiscale dictionaries are learned from some groups of example high-resolution (HR) image patches via a nonlinear optimization algorithm. Moreover, a linear weights fusion rule is advanced to obtain the fused high-resolution image at each scale. Finally the high-resolution image is derived by performing a low-rank decomposition on the recovered high-resolution images at multiple scales. Some experiments are taken to investigate the performance of our proposed method, and the results prove its superiority to the counterparts.
Cantisani, Marco; Guarnieri, Daniela; Biondi, Marco; Belli, Valentina; Profeta, Martina; Raiola, Luca; Netti, Paolo A
2015-11-01
The balance between dose-dependent tolerability, effectiveness and toxicity of systemically administered antitumor drugs is extremely delicate. This issue highlights the striking need for targeted release of chemotherapeutic drugs within tumors. In this work, a smart strategy of drug targeting to tumors relying upon biodegradable/biocompatible nanoparticles releasing cytotoxic drugs after sensing physiological variations intrinsic to the very nature of tumor tissues is exploited. Here, the well-known over-expression of matrix metallo-proteinase 2 (MMP2) enzyme in tumors has been chosen as a trigger for the release of a cytotoxic drug. Nanoparticles made up of a biodegradable poly(D,L-lactic-co-glycolic acid) (PLGA)--block--polyethylene glycol (PEG) copolymer (namely PELGA), blended with a tumor-activated prodrug (TAP) composed of a MMP2-sensitive peptide bound to doxorubicin (Dox) and to PLGA chain have been produced. The obtained devices are able to release Dox specifically upon MMP2 cleavage of the TAP. More interestingly, they can sense the differences in the expression levels of endogenous MMP2 protein, thus modulating drug penetration within a three-dimensional (3D) tumor spheroid matrix, accordingly. Therefore, the proposed nanoparticles hold promise as a useful tool for in vivo investigations aimed at an improved therapeutic efficacy of the conjugated drug payload. PMID:26340360
Ultra-Low Power Dynamic Knob in Adaptive Compressed Sensing Towards Biosignal Dynamics.
Wang, Aosen; Lin, Feng; Jin, Zhanpeng; Xu, Wenyao
2016-06-01
Compressed sensing (CS) is an emerging sampling paradigm in data acquisition. Its integrated analog-to-information structure can perform simultaneous data sensing and compression with low-complexity hardware. To date, most of the existing CS implementations have a fixed architectural setup, which lacks flexibility and adaptivity for efficient dynamic data sensing. In this paper, we propose a dynamic knob (DK) design to effectively reconfigure the CS architecture by recognizing the biosignals. Specifically, the dynamic knob design is a template-based structure that comprises a supervised learning module and a look-up table module. We model the DK performance in a closed analytic form and optimize the design via a dynamic programming formulation. We present the design on a 130 nm process, with a 0.058 mm (2) fingerprint and a 187.88 nJ/event energy-consumption. Furthermore, we benchmark the design performance using a publicly available dataset. Given the energy constraint in wireless sensing, the adaptive CS architecture can consistently improve the signal reconstruction quality by more than 70%, compared with the traditional CS. The experimental results indicate that the ultra-low power dynamic knob can provide an effective adaptivity and improve the signal quality in compressed sensing towards biosignal dynamics. PMID:26800548
NASA Astrophysics Data System (ADS)
Harvey, D. M.; Arshad, N. M.; Hobson, C. A.
2001-04-01
This paper examines the effects of data compression on fringe images. Using the JPEG still image compression method firstly comparisons of errors introduced in a standard test image and in fringe images are made. The work shows that at compression levels of 6 : 1 a 512×512×8 bit fringe image can be reduced in size to allow a CCD digital camera to be directly connected for image input to the parallel port of a PC. The errors introduced into angular and smooth fringe images by the compression and decompression process are small, 0.06% and 0.14%, respectively. This enabled successful fringe analysis by a phase stepping system, with compression levels up to 16 : 1 using JPEG, before any significant artefacts were introduced into the processed images.
YAMPA: Yet Another Matching Pursuit Algorithm for compressive sensing
NASA Astrophysics Data System (ADS)
Lodhi, Muhammad A.; Voronin, Sergey; Bajwa, Waheed U.
2016-05-01
State-of-the-art sparse recovery methods often rely on the restricted isometry property for their theoretical guarantees. However, they cannot explicitly incorporate metrics such as restricted isometry constants within their recovery procedures due to the computational intractability of calculating such metrics. This paper formulates an iterative algorithm, termed yet another matching pursuit algorithm (YAMPA), for recovery of sparse signals from compressive measurements. YAMPA differs from other pursuit algorithms in that: (i) it adapts to the measurement matrix using a threshold that is explicitly dependent on two computable coherence metrics of the matrix, and (ii) it does not require knowledge of the signal sparsity. Performance comparisons of YAMPA against other matching pursuit and approximate message passing algorithms are made for several types of measurement matrices. These results show that while state-of-the-art approximate message passing algorithms outperform other algorithms (including YAMPA) in the case of well-conditioned random matrices, they completely break down in the case of ill-conditioned measurement matrices. On the other hand, YAMPA and comparable pursuit algorithms not only result in reasonable performance for well-conditioned matrices, but their performance also degrades gracefully for ill-conditioned matrices. The paper also shows that YAMPA uniformly outperforms other pursuit algorithms for the case of thresholding parameters chosen in a clairvoyant fashion. Further, when combined with a simple and fast technique for selecting thresholding parameters in the case of ill-conditioned matrices, YAMPA outperforms other pursuit algorithms in the regime of low undersampling, although some of these algorithms can outperform YAMPA in the regime of high undersampling in this setting.
A new application of compressive sensing in MRI
NASA Astrophysics Data System (ADS)
Baselice, Fabio; Ferraioli, Giampaolo; Lenti, Flavia; Pascazio, Vito
2014-03-01
Image formation in Magnetic Resonance Imaging (MRI) is the procedure which allows the generation of the image starting from data acquired in the so called k-space. At the present, many image formation techniques have been presented, working with different k-space filling strategies. Recently, Compressive Sampling (CS) has been successfully used for image formation from non fully sampled k-space acquisitions, due to its interesting property of reconstructing signal from highly undetermined linear systems. The main advantage consists in greatly reducing the acquisition time. Within this manuscript, a novel application of CS to MRI field is presented, named Intra Voxel Analysis (IVA). The idea is to achieve the so-called super resolution, i.e. the possibility of distinguish anatomical structures smaller than the spatial resolution of the image. For this aim, multiple Spin Echo images acquired with different Echo Times are required. The output of the algorithm is the estimation of the number of contributions present in the same pixel, i.e. the number of tissues inside the same voxel, and their spin-spin relaxation times. This allows us not only to identify the number of involved tissues, but also to discriminate them. At the present, simulated case studies have been considered, obtaining interesting and promising results. In particular, a study on the required number of images, on the estimation noise and on the regularization parameter of different CS algorithms has been conducted. As future work, the method will be applied to real clinical datasets, in order to validate the estimations.
NASA Astrophysics Data System (ADS)
Akoguz, A.; Bozkurt, S.; Gozutok, A. A.; Alp, G.; Turan, E. G.; Bogaz, M.; Kent, S.
2016-06-01
High resolution level in satellite imagery came with its fundamental problem as big amount of telemetry data which is to be stored after the downlink operation. Moreover, later the post-processing and image enhancement steps after the image is acquired, the file sizes increase even more and then it gets a lot harder to store and consume much more time to transmit the data from one source to another; hence, it should be taken into account that to save even more space with file compression of the raw and various levels of processed data is a necessity for archiving stations to save more space. Lossless data compression algorithms that will be examined in this study aim to provide compression without any loss of data holding spectral information. Within this objective, well-known open source programs supporting related compression algorithms have been implemented on processed GeoTIFF images of Airbus Defence & Spaces SPOT 6 & 7 satellites having 1.5 m. of GSD, which were acquired and stored by ITU Center for Satellite Communications and Remote Sensing (ITU CSCRS), with the algorithms Lempel-Ziv-Welch (LZW), Lempel-Ziv-Markov chain Algorithm (LZMA & LZMA2), Lempel-Ziv-Oberhumer (LZO), Deflate & Deflate 64, Prediction by Partial Matching (PPMd or PPM2), Burrows-Wheeler Transform (BWT) in order to observe compression performances of these algorithms over sample datasets in terms of how much of the image data can be compressed by ensuring lossless compression.
The implementation of compressive sensing on an FPGA for chaotic radars
NASA Astrophysics Data System (ADS)
Ochoa, Hector A.; Hoe, David H.; Veeramachaneni, Dinesh
2015-05-01
Most of the advances in current radar systems are aimed at improving their resolution. As a result, their operating frequency has been increased from 10GHz up to 94GHz, and new millimeter-wave (100-300GHz) radar systems are currently being studied. One of the major concerns with these frequencies is the associated large bandwidth requirement. Compressive Sensing (CS), also known as Compressive Sampling, has been proposed as a solution to overcome the aforementioned problems by exploiting the sparsity of the radar signal. Using the CS method, a sparse signal can be reconstructed even if it is sampled below the Nyquist rate. This method provides a completely new way to reconstruct the signal using optimization techniques and a minimum number of observations. The objective of this research project is to investigate and develop a Chaotic Radar Imaging system that leverages Compressive Sensing (CS) technology to improve the image resolution without increasing the amount of processed data. In addition to demonstrating the validity of the proposed approach through simulations, this project seeks to develop and implement hardware prototypes for the proposed imaging radar system. Simulated chaotic radar data was generated and loaded to the FPGA board to test the algorithms and their performance. The results from implementing the Orthogonal Matching Pursuit (OMP), the Compressive Sensing Matching Pursuit (CSMP), and the Stagewise Orthogonal Matching Pursuit (StOMP) algorithms to a Xilinx ZedBoard will be presented.
Inverse filtering of radar signals using compressed sensing with application to meteors
NASA Astrophysics Data System (ADS)
Volz, Ryan; Close, Sigrid
2012-10-01
Compressed sensing, a method which relies on sparsity to reconstruct signals with relatively few measurements, provides a new approach to processing radar signals that is ideally suited to detailed imaging and identification of multiple targets. In this paper, we extend previously published theoretical work by investigating the practical problems associated with this approach. In deriving a discrete linear radar model that is suitable for compressed sensing, we discuss what the discrete model can tell us about continuously defined targets and show how sparsity in the latter translates to sparsity in the former. We provide details about how this problem can be solved when using large data sets. Through comparisons with matched filter processing, we validate our compressed sensing technique and demonstrate its application to meteors, where it has the potential to answer open questions about processes like fragmentation and flares. At the cost of computational complexity and an assumption of target sparsity, the benefits over pulse compression using a matched filter include no filtering sidelobes, noise removal, and higher possible range and Doppler frequency resolution.
Tang, Gang; Hou, Wei; Wang, Huaqing; Luo, Ganggang; Ma, Jianwei
2015-01-01
The Shannon sampling principle requires substantial amounts of data to ensure the accuracy of on-line monitoring of roller bearing fault signals. Challenges are often encountered as a result of the cumbersome data monitoring, thus a novel method focused on compressed vibration signals for detecting roller bearing faults is developed in this study. Considering that harmonics often represent the fault characteristic frequencies in vibration signals, a compressive sensing frame of characteristic harmonics is proposed to detect bearing faults. A compressed vibration signal is first acquired from a sensing matrix with information preserved through a well-designed sampling strategy. A reconstruction process of the under-sampled vibration signal is then pursued as attempts are conducted to detect the characteristic harmonics from sparse measurements through a compressive matching pursuit strategy. In the proposed method bearing fault features depend on the existence of characteristic harmonics, as typically detected directly from compressed data far before reconstruction completion. The process of sampling and detection may then be performed simultaneously without complete recovery of the under-sampled signals. The effectiveness of the proposed method is validated by simulations and experiments. PMID:26473858
Energy-efficient multi-mode compressed sensing system for implantable neural recordings.
Suo, Yuanming; Zhang, Jie; Xiong, Tao; Chin, Peter S; Etienne-Cummings, Ralph; Tran, Trac D
2014-10-01
Widely utilized in the field of Neuroscience, implantable neural recording devices could capture neuron activities with an acquisition rate on the order of megabytes per second. In order to efficiently transmit neural signals through wireless channels, these devices require compression methods that reduce power consumption. Although recent Compressed Sensing (CS) approaches have successfully demonstrated their power, their full potential is yet to be explored. Built upon our previous on-chip CS implementation, we propose an energy efficient multi-mode CS framework that focuses on improving the off-chip components, including (i) a two-stage sensing strategy, (ii) a sparsifying dictionary directly using data, (iii) enhanced compression performance from Full Signal CS mode and Spike Restoration mode to Spike CS + Restoration mode and; (iv) extension of our framework to the Tetrode CS recovery using joint sparsity. This new framework achieves energy efficiency, implementation simplicity and system flexibility simultaneously. Extensive experiments are performed on simulation and real datasets. For our Spike CS + Restoration mode, we achieve a compression ratio of 6% with a reconstruction SNDR > 10 dB and a classification accuracy > 95% for synthetic datasets. For real datasets, we get a 10% compression ratio with ∼ 10 dB for Spike CS + Restoration mode. PMID:25343768
Detection of Modified Matrix Encoding Using Machine Learning and Compressed Sensing
Allen, Josef D
2011-01-01
In recent years, driven by the development of steganalysis methods, steganographic algorithms have been evolved rapidly with the ultimate goal of an unbreakable embedding procedure, resulting in recent steganographic algorithms with minimum distortions, exemplified by the recent family of Modified Matrix Encoding (MME) algorithms, which has shown to be most difficult to be detected. In this paper we propose a compressed sensing based on approach for intrinsic steganalysis to detect MME stego messages. Compressed sensing is a recently proposed mathematical framework to represent an image (in general, a signal) using a sparse representation relative to an overcomplete dictionary by minimizing the l1-norm of resulting coefficients. Here we first learn a dictionary from a training set so that the performance will be optimized using the KSVD algorithm; since JPEG images are processed by 8x8 blocks, the training examples are 8x8 patches, rather than the entire images and this increases the generalization of compressed sensing. For each 8x8 block, we compute its sparse representation using OMP (orthogonal matching pursuit) algorithm. Using computed sparse representations, we train a support vector machine (SVM) to classify 8x8 blocks into stego and non-stego classes. Then given an input image, we first divide it into 8x8 blocks. For each 8x8 block, we compute its sparse representation and classify it using the trained SVM. After all the 8x8 blocks are classified, the entire image is classified based on the majority rule of 8x8 block classification results. This allows us to achieve a robust decision even when 8x8 blocks can be classified only with relatively low accuracy. We have tested the proposed algorithm on two datasets (Corel-1000 dataset and a remote sensing image dataset) and have achieved 100% accuracy on classifying images, even though the accuracy of classifying 8x8 blocks is only 80.89%. Key Words Compressed Sensing, Sparcity, Data Dictionary, Steganography
NASA Astrophysics Data System (ADS)
Sidorova, Elena
2013-04-01
the recognition of linear and ring features. The features of geological interest detected during the interpretation process were digitized using raster based GIS software. As results of collaboration between GIS and RS data analysis the new prospect areas were extracted from the study areas. Were revealed the geological structures in 3-D model, associated with mineralization, lineaments and ring structures. The complex analysis of model allowed proposing new potential ore areas for statement of prospecting work.
NASA Astrophysics Data System (ADS)
Chen, Yongyao; Liu, Haijun; Reilly, Michael; Bae, Hyungdae; Yu, Miao
2014-10-01
Acoustic sensors play an important role in many areas, such as homeland security, navigation, communication, health care and industry. However, the fundamental pressure detection limit hinders the performance of current acoustic sensing technologies. Here, through analytical, numerical and experimental studies, we show that anisotropic acoustic metamaterials can be designed to have strong wave compression effect that renders direct amplification of pressure fields in metamaterials. This enables a sensing mechanism that can help overcome the detection limit of conventional acoustic sensing systems. We further demonstrate a metamaterial-enhanced acoustic sensing system that achieves more than 20 dB signal-to-noise enhancement (over an order of magnitude enhancement in detection limit). With this system, weak acoustic pulse signals overwhelmed by the noise are successfully recovered. This work opens up new vistas for the development of metamaterial-based acoustic sensors with improved performance and functionalities that are highly desirable for many applications.
NASA Astrophysics Data System (ADS)
Otis, Collin; Ferrero, Pietro; Candler, Graham; Givi, Peyman
2013-11-01
The scalar filtered mass density function (SFMDF) methodology is implemented into the computer code US3D. This is an unstructured Eulerian finite volume hydrodynamic solver and has proven very effective for simulation of compressible turbulent flows. The resulting SFMDF-US3D code is employed for large eddy simulation (LES) on unstructured meshes. Simulations are conducted of subsonic and supersonic flows under non-reacting and reacting conditions. The consistency and the accuracy of the simulated results are assessed along with appraisal of the overall performance of the methodology. The SFMDF-US3D is now capable of simulating high speed flows in complex configurations.
Nichols, Alexander J; Roussakis, Emmanuel; Klein, Oliver J; Evans, Conor L
2014-04-01
Hypoxia is an important contributing factor to the development of drug-resistant cancer, yet few nonperturbative tools exist for studying oxygenation in tissues. While progress has been made in the development of chemical probes for optical oxygen mapping, penetration of such molecules into poorly perfused or avascular tumor regions remains problematic. A click-assembled oxygen-sensing (CAOS) nanoconjugate is reported and its properties demonstrated in an in vitro 3D spheroid cancer model. The synthesis relies on the sequential click-based ligation of poly(amidoamine)-like subunits for rapid assembly. Near-infrared confocal phosphorescence microscopy was used to demonstrate the ability of the CAOS nanoconjugates to penetrate hundreds of micrometers into spheroids within hours and to show their sensitivity to oxygen changes throughout the nodule. This proof-of-concept study demonstrates a modular approach that is readily extensible to a wide variety of oxygen and cellular sensors for depth-resolved imaging in tissue and tissue models. PMID:24590700
NASA Astrophysics Data System (ADS)
Waltermann, Christian; Baumann, Anna Lena; Bethmann, Konrad; Doering, Alexander; Koch, Jan; Angelmahr, Martin; Schade, Wolfgang
2015-05-01
Fiber Bragg grating based optical shape sensing is a new and promising approach to gather position and path information in environments where classical imaging systems fail. Especially a real-time in-vivo navigation of medical catheter or endoscope without any further requirements (such as the continuous exposure to x-rays) could provide a huge advantage in countless areas in medicine. Multicore fibers or bundles of glass fibers have been suggested for realizing such shape sensors, but to date all suffer from severe disadvantages. We present the realization of a third approach. With femtosecond laser pulses local waveguides are inscribed into the cladding of a standard single mode glass fiber. The evanescence field of the main fiber core couples to two S-shaped waveguides, which carry the light to high reflective fiber Bragg gratings located approx. 30 μm away from the centered fiber core in an orthogonal configuration. Part of the reflected light is coupled back to the fiber core and can be read out by a fiber Bragg grating interrogator. A typical spectrum is presented as well as the sensor signal for bending in all directions and with different radii. The entire sensor plane has an elongation of less than 4 mm and therefore enables even complicated and localized navigation applications such as medical catheters. Finally a complete 3D shape sensor in a single mode fiber is presented together with an exemplary application for motion capturing.
Structure-aware Bayesian compressive sensing for frequency-hopping spectrum estimation
NASA Astrophysics Data System (ADS)
Liu, Shengheng; Zhang, Yimin D.; Shan, Tao; Qin, Si; Amin, Moeness G.
2016-05-01
Frequency-hopping (FH) is one of the commonly used spread spectrum techniques that finds wide applications in communications and radar systems due to its capability of low probability of intercept, reduced interference, and desirable ambiguity property. In this paper, we consider the blind estimation of the instantaneous FH spectrum without the knowledge of hopping patterns. The FH signals are analyzed in the joint time-frequency domain, where FH signals manifest themselves as sparse entries, thus inviting compressive sensing and sparse reconstruction techniques for FH spectrum estimation. In particular, the signals' piecewise-constant frequency characteristics are exploited in the reconstruction of sparse quadratic time-frequency representations. The Bayesian compressive sensing methods are applied to provide high-resolution frequency estimation. The FH spectrum characteristics are used in the design of signal-dependent kernel within the framework of structure-aware sparse reconstruction.
Zhou, Fei; Nielson, Weston; Xia, Yi; Ozolins, Vidvuds
2014-10-27
First-principles prediction of lattice thermal conductivity K_{L} of strongly anharmonic crystals is a long-standing challenge in solid state physics. Using recent advances in information science, we propose a systematic and rigorous approach to this problem, compressive sensing lattice dynamics (CSLD). Compressive sensing is used to select the physically important terms in the lattice dynamics model and determine their values in one shot. Non-intuitively, high accuracy is achieved when the model is trained on first-principles forces in quasi-random atomic configurations. The method is demonstrated for Si, NaCl, and Cu_{12}Sb_{4}S_{13}, an earth-abundant thermoelectric with strong phononphonon interactions that limit the room-temperature K_{L} to values near the amorphous limit.
Zhou, Fei; Nielson, Weston; Xia, Yi; Ozoliņš, Vidvuds
2014-10-01
First-principles prediction of lattice thermal conductivity κ_{L} of strongly anharmonic crystals is a long-standing challenge in solid-state physics. Making use of recent advances in information science, we propose a systematic and rigorous approach to this problem, compressive sensing lattice dynamics. Compressive sensing is used to select the physically important terms in the lattice dynamics model and determine their values in one shot. Nonintuitively, high accuracy is achieved when the model is trained on first-principles forces in quasirandom atomic configurations. The method is demonstrated for Si, NaCl, and Cu_{12}Sb_{4}S_{13}, an earth-abundant thermoelectric with strong phonon-phonon interactions that limit the room-temperature κ_{L} to values near the amorphous limit.
NASA Astrophysics Data System (ADS)
Nakanishi-Ohno, Yoshinori; Haze, Masahiro; Yoshida, Yasuo; Hukushima, Koji; Hasegawa, Yukio; Okada, Masato
2016-09-01
We applied a method of compressed sensing to the observation of quasi-particle interference (QPI) by scanning tunneling microscopy/spectroscopy to improve efficiency and save measurement time. To solve an ill-posed problem owing to the scarcity of data, the compressed sensing utilizes the sparseness of QPI patterns in momentum space. We examined the performance of a sparsity-inducing algorithm called least absolute shrinkage and selection operator (LASSO), and demonstrated that LASSO enables us to recover a double-circle QPI pattern of the Ag(111) surface from a dataset whose size is less than that necessary for the conventional Fourier transformation method. In addition, the smallest number of data required for the recovery is discussed on the basis of cross validation.
Effect of downsampling and compressive sensing on audio-based continuous cough monitoring.
Casaseca-de-la-Higuera, Pablo; Lesso, Paul; McKinstry, Brian; Pinnock, Hilary; Rabinovich, Roberto; McCloughan, Lucy; Monge-Álvarez, Jesús
2015-01-01
This paper presents an efficient cough detection system based on simple decision-tree classification of spectral features from a smartphone audio signal. Preliminary evaluation on voluntary coughs shows that the system can achieve 98% sensitivity and 97.13% specificity when the audio signal is sampled at full rate. With this baseline system, we study possible efficiency optimisations by evaluating the effect of downsampling below the Nyquist rate and how the system performance at low sampling frequencies can be improved by incorporating compressive sensing reconstruction schemes. Our results show that undersampling down to 400 Hz can still keep sensitivity and specificity values above 90% despite of aliasing. Furthermore, the sparsity of cough signals in the time domain allows keeping performance figures close to 90% when sampling at 100 Hz using compressive sensing schemes. PMID:26737716
NASA Astrophysics Data System (ADS)
Béché, A.; Goris, B.; Freitag, B.; Verbeeck, J.
2016-02-01
The concept of compressed sensing was recently proposed to significantly reduce the electron dose in scanning transmission electron microscopy (STEM) while still maintaining the main features in the image. Here, an experimental setup based on an electromagnetic beam blanker placed in the condenser plane of a STEM is proposed. The beam blanker deflects the beam with a random pattern, while the scanning coils are moving the beam in the usual scan pattern. Experimental images at both the medium scale and high resolution are acquired and reconstructed based on a discrete cosine algorithm. The obtained results confirm that compressed sensing is highly attractive to limit beam damage in experimental STEM even though some remaining artifacts need to be resolved.
Super-resolution reconstruction of compressed sensing mammogram based on contourlet transform
NASA Astrophysics Data System (ADS)
Shen, Yan; Chen, Houjin; Yao, Chang; Qiao, Zhijun
2013-05-01
Calcification detection in mammogram is important in breast cancer diagnosis. A super-resolution reconstruction method is proposed to reconstruct mammogram image from one single low resolution mammogram based on the compressed sensing by the contourlet transform. The initial estimation of the super-resolution mammogram is obtained by the interpolation method of the low resolution mammogram reconstructed by compressed sensing, then contourlet transform is applied respectively to the initial estimation and the reconstructed low resolution mammogram. From the statistical characteristics of the mutiscale frequency bands between the initial estimation and the reconstructed low resolution mammogram, the thresholds are estimated to integrate the high frequency of the initial estimation and the low frequency of the reconstructed low resolution mammogram. The super-resolution mammogram is achieved through the reconstruction of contourlet inverse transform. The proposed method can retrieve some details of the low resolution images. The calcification in mammogram can be detected efficiently.
Zhou, Fei; Nielson, Weston; Xia, Yi; Ozolins, Vidvuds
2014-10-27
First-principles prediction of lattice thermal conductivity KL of strongly anharmonic crystals is a long-standing challenge in solid state physics. Using recent advances in information science, we propose a systematic and rigorous approach to this problem, compressive sensing lattice dynamics (CSLD). Compressive sensing is used to select the physically important terms in the lattice dynamics model and determine their values in one shot. Non-intuitively, high accuracy is achieved when the model is trained on first-principles forces in quasi-random atomic configurations. The method is demonstrated for Si, NaCl, and Cu12Sb4S13, an earth-abundant thermoelectric with strong phononphonon interactions that limit the room-temperature KLmore » to values near the amorphous limit.« less
Mcdaniel, J.C.; Fletcher, D.G.; Hartfield, R.J.; Hollo, S.D. NASA, Ames Research Center, Moffett Field, CA )
1991-12-01
A spatially-complete data set of the important primitive flow variables is presented for the complex, nonreacting, 3D unit combustor flow field employing transverse injection into a Mach 2 flow behind a rearward-facing step. A unique wind tunnel facility providing the capability for iodine seeding was built specifically for these measurements. Two optical techniques based on laser-induced-iodine fluorescence were developed and utilized for nonintrusive, in situ flow field measurements. LDA provided both mean and fluctuating velocity component measurements. A thermographic phosphor wall temperature measurement technique was developed and employed. Data from the 2D flow over a rearward-facing step and the complex 3D mixing flow with injection are reported. 25 refs.
NASA Astrophysics Data System (ADS)
Sejdić, Ervin; Movahedi, Faezeh; Zhang, Zhenwei; Kurosu, Atsuko; Coyle, James L.
2016-05-01
Acquiring swallowing accelerometry signals using a comprehensive sensing scheme may be a desirable approach for monitoring swallowing safety for longer periods of time. However, it needs to be insured that signal characteristics can be recovered accurately from compressed samples. In this paper, we considered this issue by examining the effects of the number of acquired compressed samples on the calculated swallowing accelerometry signal features. We used tri-axial swallowing accelerometry signals acquired from seventeen stroke patients (106 swallows in total). From acquired signals, we extracted typically considered signal features from time, frequency and time-frequency domains. Next, we compared these features from the original signals (sampled using traditional sampling schemes) and compressively sampled signals. Our results have shown we can obtain accurate estimates of signal features even by using only a third of original samples.
Monitoring and diagnosis of Alzheimer's disease using noninvasive compressive sensing EEG
NASA Astrophysics Data System (ADS)
Morabito, F. C.; Labate, D.; Morabito, G.; Palamara, I.; Szu, H.
2013-05-01
The majority of elderly with Alzheimer's Disease (AD) receive care at home from caregivers. In contrast to standard tethered clinical settings, a wireless, real-time, body-area smartphone-based remote monitoring of electroencephalogram (EEG) can be extremely advantageous for home care of those patients. Such wearable tools pave the way to personalized medicine, for example giving the opportunity to control the progression of the disease and the effect of drugs. By applying Compressive Sensing (CS) techniques it is in principle possible to overcome the difficulty raised by smartphones spatial-temporal throughput rate bottleneck. Unfortunately, EEG and other physiological signals are often non-sparse. In this paper, it is instead shown that the EEG of AD patients becomes actually more compressible with the progression of the disease. EEG of Mild Cognitive Impaired (MCI) subjects is also showing clear tendency to enhanced compressibility. This feature favor the use of CS techniques and ultimately the use of telemonitoring with wearable sensors.
NASA Astrophysics Data System (ADS)
Hollingsworth, Kieren Grant
2015-11-01
MRI is often the most sensitive or appropriate technique for important measurements in clinical diagnosis and research, but lengthy acquisition times limit its use due to cost and considerations of patient comfort and compliance. Once an image field of view and resolution is chosen, the minimum scan acquisition time is normally fixed by the amount of raw data that must be acquired to meet the Nyquist criteria. Recently, there has been research interest in using the theory of compressed sensing (CS) in MR imaging to reduce scan acquisition times. The theory argues that if our target MR image is sparse, having signal information in only a small proportion of pixels (like an angiogram), or if the image can be mathematically transformed to be sparse then it is possible to use that sparsity to recover a high definition image from substantially less acquired data. This review starts by considering methods of k-space undersampling which have already been incorporated into routine clinical imaging (partial Fourier imaging and parallel imaging), and then explains the basis of using compressed sensing in MRI. The practical considerations of applying CS to MRI acquisitions are discussed, such as designing k-space undersampling schemes, optimizing adjustable parameters in reconstructions and exploiting the power of combined compressed sensing and parallel imaging (CS-PI). A selection of clinical applications that have used CS and CS-PI prospectively are considered. The review concludes by signposting other imaging acceleration techniques under present development before concluding with a consideration of the potential impact and obstacles to bringing compressed sensing into routine use in clinical MRI.
OFDM and compressive sensing based GPR imaging using SAR focusing algorithm
NASA Astrophysics Data System (ADS)
Zhang, Yu; Xia, Tian
2015-04-01
This paper presents a new ground penetrating radar (GPR) design approach using orthogonal frequency division multiplexing (OFDM) and compressive sensing (CS) algorithms. OFDM technique is applied to leverage GPR operating speed with multiple frequency tones transmission and receiving concurrently, and CS technique allows utilizing reduced frequency tones without compromising data reconstruction accuracy. Combination of OFDM and CS boosts the radar operating efficiency. For GPR image reconstruction, a synthetic aperture radar (SAR) technique is implemented.
Hollingsworth, Kieren Grant
2015-11-01
MRI is often the most sensitive or appropriate technique for important measurements in clinical diagnosis and research, but lengthy acquisition times limit its use due to cost and considerations of patient comfort and compliance. Once an image field of view and resolution is chosen, the minimum scan acquisition time is normally fixed by the amount of raw data that must be acquired to meet the Nyquist criteria. Recently, there has been research interest in using the theory of compressed sensing (CS) in MR imaging to reduce scan acquisition times. The theory argues that if our target MR image is sparse, having signal information in only a small proportion of pixels (like an angiogram), or if the image can be mathematically transformed to be sparse then it is possible to use that sparsity to recover a high definition image from substantially less acquired data. This review starts by considering methods of k-space undersampling which have already been incorporated into routine clinical imaging (partial Fourier imaging and parallel imaging), and then explains the basis of using compressed sensing in MRI. The practical considerations of applying CS to MRI acquisitions are discussed, such as designing k-space undersampling schemes, optimizing adjustable parameters in reconstructions and exploiting the power of combined compressed sensing and parallel imaging (CS-PI). A selection of clinical applications that have used CS and CS-PI prospectively are considered. The review concludes by signposting other imaging acceleration techniques under present development before concluding with a consideration of the potential impact and obstacles to bringing compressed sensing into routine use in clinical MRI. PMID:26448064
NASA Astrophysics Data System (ADS)
Mejia, Yuri H.; Arguello, Henry
2016-05-01
Compressive sensing state-of-the-art proposes random Gaussian and Bernoulli as measurement matrices. Nev- ertheless, often the design of the measurement matrix is subject to physical constraints, and therefore it is frequently not possible that the matrix follows a Gaussian or Bernoulli distribution. Examples of these lim- itations are the structured and sparse matrices of the compressive X-Ray, and compressive spectral imaging systems. A standard algorithm for recovering sparse signals consists in minimizing an objective function that includes a quadratic error term combined with a sparsity-inducing regularization term. This problem can be solved using the iterative algorithms for solving linear inverse problems. This class of methods, which can be viewed as an extension of the classical gradient algorithm, is attractive due to its simplicity. However, current algorithms are slow for getting a high quality image reconstruction because they do not exploit the structured and sparsity characteristics of the compressive measurement matrices. This paper proposes the development of a gradient-based algorithm for compressive sensing reconstruction by including a filtering step that yields improved quality using less iterations. This algorithm modifies the iterative solution such that it forces to converge to a filtered version of the residual AT y, where y is the measurement vector and A is the compressive measurement matrix. We show that the algorithm including the filtering step converges faster than the unfiltered version. We design various filters that are motivated by the structure of AT y. Extensive simulation results using various sparse and structured matrices highlight the relative performance gain over the existing iterative process.
Channel estimation for OFDM system in atmospheric optical communication based on compressive sensing
NASA Astrophysics Data System (ADS)
Zhao, Qingsong; Hao, Shiqi; Geng, Hongjian; Sun, Han
2015-10-01
Orthogonal frequency division multiplexing (OFDM) technique applied to the atmospheric optical communication can improve data transmission rate, restrain pulse interference, and reduce effect of multipath caused by atmospheric scattering. Channel estimation, as one of the important modules in OFDM, has been investigated thoroughly and widely with great progress. In atmospheric optical communication system, channel estimation methods based on pilot are common approaches, such as traditional least-squares (LS) algorithm and minimum mean square error (MMSE) algorithm. However, sensitivity of the noise effects and high complexity of computation are shortcomings of LS algorithm and MMSE algorithm, respectively. Here, a new method based on compressive sensing is proposed to estimate the channel state information of atmospheric optical communication OFDM system, especially when the condition is closely associated with turbulence. Firstly, time-varying channel model is established under the condition of turbulence. Then, in consideration of multipath effect, sparse channel model is available for compressive sensing. And, the pilot signal is reconstructed with orthogonal matching tracking (OMP) algorithm, which is used for reconstruction. By contrast, the work of channel estimation is completed by LS algorithm as well. After that, simulations are conducted respectively in two different indexes -signal error rate (SER) and mean square error (MSE). Finally, result shows that compared with LS algorithm, the application of compressive sensing can improve the performance of SER and MSE. Theoretical analysis and simulation results show that the proposed method is reasonable and efficient.
NASA Astrophysics Data System (ADS)
Zhao, Rongqiang; Wang, Qiang; Shen, Yi
2015-11-01
We propose a new approach for Kronecker compressive sensing of hyperspectral (HS) images, including the imaging mechanism and the corresponding reconstruction method. The proposed mechanism is able to compress the data of all dimensions when sampling, which can be achieved by three fully independent sampling devices. As a result, the mechanism greatly reduces the control points and memory requirement. In addition, we can also select the suitable sparsifying bases and generate the corresponding optimized sensing matrices or change the distribution of sampling ratio for each dimension independently according to different HS images. As the cooperation of the mechanism, we combine the sparsity model and low multilinear-rank model to develop a reconstruction method. Analysis shows that our reconstruction method has a lower computational complexity than the traditional methods based on sparsity model. Simulations verify that the HS images can be reconstructed successfully with very few measurements. In summary, the proposed approach can reduce the complexity and improve the practicability for HS image compressive sensing.
Reconstructing high-dimensional two-photon entangled states via compressive sensing
Tonolini, Francesco; Chan, Susan; Agnew, Megan; Lindsay, Alan; Leach, Jonathan
2014-01-01
Accurately establishing the state of large-scale quantum systems is an important tool in quantum information science; however, the large number of unknown parameters hinders the rapid characterisation of such states, and reconstruction procedures can become prohibitively time-consuming. Compressive sensing, a procedure for solving inverse problems by incorporating prior knowledge about the form of the solution, provides an attractive alternative to the problem of high-dimensional quantum state characterisation. Using a modified version of compressive sensing that incorporates the principles of singular value thresholding, we reconstruct the density matrix of a high-dimensional two-photon entangled system. The dimension of each photon is equal to d = 17, corresponding to a system of 83521 unknown real parameters. Accurate reconstruction is achieved with approximately 2500 measurements, only 3% of the total number of unknown parameters in the state. The algorithm we develop is fast, computationally inexpensive, and applicable to a wide range of quantum states, thus demonstrating compressive sensing as an effective technique for measuring the state of large-scale quantum systems. PMID:25306850
Efficient Reconstruction of Heterogeneous Networks from Time Series via Compressed Sensing.
Ma, Long; Han, Xiao; Shen, Zhesi; Wang, Wen-Xu; Di, Zengru
2015-01-01
Recent years have witnessed a rapid development of network reconstruction approaches, especially for a series of methods based on compressed sensing. Although compressed-sensing based methods require much less data than conventional approaches, the compressed sensing for reconstructing heterogeneous networks has not been fully exploited because of hubs. Hub neighbors require much more data to be inferred than small-degree nodes, inducing a cask effect for the reconstruction of heterogeneous networks. Here, a conflict-based method is proposed to overcome the cast effect to considerably reduce data amounts for achieving accurate reconstruction. Moreover, an element elimination method is presented to use the partially available structural information to reduce data requirements. The integration of both methods can further improve the reconstruction performance than separately using each technique. These methods are validated by exploring two evolutionary games taking place in scale-free networks, where individual information is accessible and an attempt to decode the network structure from measurable data is made. The results demonstrate that for all of the cases, much data are saved compared to that in the absence of these two methods. Due to the prevalence of heterogeneous networks in nature and society and the high cost of data acquisition in large-scale networks, these approaches have wide applications in many fields and are valuable for understanding and controlling the collective dynamics of a variety of heterogeneous networked systems. PMID:26588832
NASA Astrophysics Data System (ADS)
Ravishankar, Saiprasad; Bresler, Yoram
2015-09-01
Compressed Sensing has been demonstrated to be a powerful tool for magnetic resonance imaging (MRI), where it enables accurate recovery of images from highly undersampled k-space measurements by exploiting the sparsity of the images or image patches in a transform domain or dictionary. In this work, we focus on blind compressed sensing, where the underlying sparse signal model is a priori unknown, and propose a framework to simultaneously reconstruct the underlying image as well as the unknown model from highly undersampled measurements. Specifically, our model is that the patches of the underlying MR image(s) are approximately sparse in a transform domain. We also extend this model to a union of transforms model that is better suited to capture the diversity of features in MR images. The proposed block coordinate descent type algorithms for blind compressed sensing are highly efficient. Our numerical experiments demonstrate the superior performance of the proposed framework for MRI compared to several recent image reconstruction methods. Importantly, the learning of a union of sparsifying transforms leads to better image reconstructions than a single transform.
NASA Astrophysics Data System (ADS)
Zhao, Hui; Li, Minghui; Wang, Ruyan; Liu, Yuanni; Song, Daiping
2014-09-01
Due to the spare multipath property of the channel, a channel estimation method, which is based on partial superimposed training sequence and compressed sensing theory, is proposed for line of sight optical orthogonal frequency division multiplexing communication systems. First, a continuous training sequence is added at variable power ratio to the cyclic prefix of orthogonal frequency division multiplexing symbols at the transmitter prior to transmission. Then the observation matrix of compressed sensing theory is structured by the use of the training symbols at receiver. Finally, channel state information is estimated using sparse signal reconstruction algorithm. Compared to traditional training sequences, the proposed partial superimposed training sequence not only improves the spectral efficiency, but also reduces the influence to information symbols. In addition, compared with classical least squares and linear minimum mean square error methods, the proposed compressed sensing theory based channel estimation method can improve both the estimation accuracy and the system performance. Simulation results are given to demonstrate the performance of the proposed method.
McClymont, Darryl; Teh, Irvin; Whittington, Hannah J.; Grau, Vicente
2015-01-01
Purpose Diffusion MRI requires acquisition of multiple diffusion‐weighted images, resulting in long scan times. Here, we investigate combining compressed sensing and a fast imaging sequence to dramatically reduce acquisition times in cardiac diffusion MRI. Methods Fully sampled and prospectively undersampled diffusion tensor imaging data were acquired in five rat hearts at acceleration factors of between two and six using a fast spin echo (FSE) sequence. Images were reconstructed using a compressed sensing framework, enforcing sparsity by means of decomposition by adaptive dictionaries. A tensor was fit to the reconstructed images and fiber tractography was performed. Results Acceleration factors of up to six were achieved, with a modest increase in root mean square error of mean apparent diffusion coefficient (ADC), fractional anisotropy (FA), and helix angle. At an acceleration factor of six, mean values of ADC and FA were within 2.5% and 5% of the ground truth, respectively. Marginal differences were observed in the fiber tracts. Conclusion We developed a new k‐space sampling strategy for acquiring prospectively undersampled diffusion‐weighted data, and validated a novel compressed sensing reconstruction algorithm based on adaptive dictionaries. The k‐space undersampling and FSE acquisition each reduced acquisition times by up to 6× and 8×, respectively, as compared to fully sampled spin echo imaging. Magn Reson Med 76:248–258, 2016. © 2015 Wiley Periodicals, Inc. PMID:26302363
NASA Astrophysics Data System (ADS)
Moré, G.; Pesquer, L.; Blanes, I.; Serra-Sagristà, J.; Pons, X.
2012-12-01
World coverage Digital Elevation Models (DEM) have progressively increased their spatial resolution (e.g., ETOPO, SRTM, or Aster GDEM) and, consequently, their storage requirements. On the other hand, lossy data compression facilitates accessing, sharing and transmitting large spatial datasets in environments with limited storage. However, since lossy compression modifies the original information, rigorous studies are needed to understand its effects and consequences. The present work analyzes the influence of DEM quality -modified by lossy compression-, on the radiometric correction of remote sensing imagery, and the eventual propagation of the uncertainty in the resulting land cover classification. Radiometric correction is usually composed of two parts: atmospheric correction and topographical correction. For topographical correction, DEM provides the altimetry information that allows modeling the incidence radiation on terrain surface (cast shadows, self shadows, etc). To quantify the effects of the DEM lossy compression on the radiometric correction, we use radiometrically corrected images for classification purposes, and compare the accuracy of two standard coding techniques for a wide range of compression ratios. The DEM has been obtained by resampling the DEM v.2 of Catalonia (ICC), originally having 15 m resolution, to the Landsat TM resolution. The Aster DEM has been used to fill the gaps beyond the administrative limits of Catalonia. The DEM has been lossy compressed with two coding standards at compression ratios 5:1, 10:1, 20:1, 100:1 and 200:1. The employed coding standards have been JPEG2000 and CCSDS-IDC; the former is an international ISO/ITU-T standard for almost any type of images, while the latter is a recommendation of the CCSDS consortium for mono-component remote sensing images. Both techniques are wavelet-based followed by an entropy-coding stage. Also, for large compression ratios, both techniques need a post processing for correctly
Leveraging EAP-Sparsity for Compressed Sensing of MS-HARDI in (k, q)-Space
Sun, Jiaqi; Sakhaee, Elham; Entezari, Alireza
2016-01-01
Compressed Sensing (CS) for the acceleration of MR scans has been widely investigated in the past decade. Lately, considerable progress has been made in achieving similar speed ups in acquiring multi-shell high angular resolution diffusion imaging (MS-HARDI) scans. Existing approaches in this context were primarily concerned with sparse reconstruction of the diffusion MR signal S(q) in the q-space. More recently, methods have been developed to apply the compressed sensing framework to the 6-dimensional joint (k, q)-space, thereby exploiting the redundancy in this 6D space. To guarantee accurate reconstruction from partial MS-HARDI data, the key ingredients of compressed sensing that need to be brought together are: (1) the function to be reconstructed needs to have a sparse representation, and (2) the data for reconstruction ought to be acquired in the dual domain (i.e., incoherent sensing) and (3) the reconstruction process involves a (convex) optimization. In this paper, we present a novel approach that uses partial Fourier sensing in the 6D space of (k, q) for the reconstruction of P(x, r). The distinct feature of our approach is a sparsity model that leverages surfacelets in conjunction with total variation for the joint sparse representation of P(x, r). Thus, our method stands to benefit from the practical guarantees for accurate reconstruction from partial (k, q)-space data. Further, we demonstrate significant savings in acquisition time over diffusion spectral imaging (DSI) which is commonly used as the benchmark for comparisons in reported literature. To demonstrate the benefits of this approach, we present several synthetic and real data examples. PMID:26221688
NASA Astrophysics Data System (ADS)
Wang, Haogang; Liao, Tien-Hao; Shi, Jiancheng; Yu, Zherui
2014-11-01
The forthcoming Water Cycle Observation Mission (WCOM) is to understand the water cycle system among land, atmosphere, and ocean. In both active and passive microwave remote sensing of soil moisture, the surface roughness plays an important role. Electromagnetic models of roughness provide tables of emissivities and backscattering coefficients that can be used to retrieve soil moisture. In this paper, a fast and accurate three dimensional solution of Maxwell's equations is developed and employed to solve rough soil surface scattering problem at L-band. The algorithm combines QR Pre-Ranked Multilevel UV(MLUV) factorization and Hierarchical Fast Far Field Approximation. It is implemented using OpenMP interface for fast parallel calculation. In this algorithm, 1) QR based rank predetermined algorithm is derived to further compress the UV matrix pairs obtained using coarse-coarse sampling; 2) at the finer levels, MLUV is used straightforwardly to factorize the interactions between groups, while at the coarsest level, interactions between groups in the interaction list are calculated using an elegantly derived Hierarchical Fast Far Field Approximation (HFAFFA) to accelerate the calculation of interactions between large groups while keeping the accuracy of this approximation; 3) OpenMP interface is used to parallelize this new algorithm. Numerical results including the incoherent bistatic scattering coefficients and the emissivity demonstrate the efficiency of this method.
NASA Technical Reports Server (NTRS)
Smith, Brandon; Jan, Darrell Leslie; Venkatapathy, Ethiraj
2015-01-01
Vehicles re-entering Earth's atmosphere require protection from the heat of atmospheric friction. The Orion Multi-Purpose Crew Vehicle (MPCV) has more demanding thermal protection system (TPS) requirements than the Low Earth Orbit (LEO) missions, especially in regions where the structural load passes through. The use of 2-dimensional laminate materials along with a metal insert, used in EFT1 flight test for the compression pad region, are deemed adequate but cannot be extended for Lunar return missions.
Efficient Sparse Signal Transmission over a Lossy Link Using Compressive Sensing
Wu, Liantao; Yu, Kai; Cao, Dongyu; Hu, Yuhen; Wang, Zhi
2015-01-01
Reliable data transmission over lossy communication link is expensive due to overheads for error protection. For signals that have inherent sparse structures, compressive sensing (CS) is applied to facilitate efficient sparse signal transmissions over lossy communication links without data compression or error protection. The natural packet loss in the lossy link is modeled as a random sampling process of the transmitted data, and the original signal will be reconstructed from the lossy transmission results using the CS-based reconstruction method at the receiving end. The impacts of packet lengths on transmission efficiency under different channel conditions have been discussed, and interleaving is incorporated to mitigate the impact of burst data loss. Extensive simulations and experiments have been conducted and compared to the traditional automatic repeat request (ARQ) interpolation technique, and very favorable results have been observed in terms of both accuracy of the reconstructed signals and the transmission energy consumption. Furthermore, the packet length effect provides useful insights for using compressed sensing for efficient sparse signal transmission via lossy links. PMID:26287195
NASA Astrophysics Data System (ADS)
Huang, Bingchao; Xu, Ke; Wan, Jianwei; Liu, Xu
2015-11-01
An efficient method and system for distributed compressive sensing of hyperspectral images is presented, which exploit the low rank and structure similarity property of hyperspectral imagery. In this paper, by integrating the respective characteristics of DSC and CS, a distributed compressive sensing framework is proposed to simultaneously capture and compress hyperspectral images. At the encoder, every band image is measured independently, where almost all computation burdens can be shifted to the decoder, resulting in a very low-complexity encoder. It is simple to operate and easy to hardware implementation. At the decoder, each band image is reconstructed by the method of total variation norm minimize. During each band reconstruction, the low rand structure of band images and spectrum structure similarity are used to give birth to the new regularizers. With combining the new regularizers and other regularizer, we can sufficiently exploit the spatial correlation, spectral correlation and spectral structural redundancy in hyperspectral imagery. A numerical optimization algorithm is also proposed to solve the reconstruction model by augmented Lagrangian multiplier method. Experimental results show that this method can effectively improve the reconstruction quality of hyperspectral images.
A closed-loop compressive-sensing-based neural recording system
NASA Astrophysics Data System (ADS)
Zhang, Jie; Mitra, Srinjoy; Suo, Yuanming; Cheng, Andrew; Xiong, Tao; Michon, Frederic; Welkenhuysen, Marleen; Kloosterman, Fabian; Chin, Peter S.; Hsiao, Steven; Tran, Trac D.; Yazicioglu, Firat; Etienne-Cummings, Ralph
2015-06-01
Objective. This paper describes a low power closed-loop compressive sensing (CS) based neural recording system. This system provides an efficient method to reduce data transmission bandwidth for implantable neural recording devices. By doing so, this technique reduces a majority of system power consumption which is dissipated at data readout interface. The design of the system is scalable and is a viable option for large scale integration of electrodes or recording sites onto a single device. Approach. The entire system consists of an application-specific integrated circuit (ASIC) with 4 recording readout channels with CS circuits, a real time off-chip CS recovery block and a recovery quality evaluation block that provides a closed feedback to adaptively adjust compression rate. Since CS performance is strongly signal dependent, the ASIC has been tested in vivo and with standard public neural databases. Main results. Implemented using efficient digital circuit, this system is able to achieve >10 times data compression on the entire neural spike band (500-6KHz) while consuming only 0.83uW (0.53 V voltage supply) additional digital power per electrode. When only the spikes are desired, the system is able to further compress the detected spikes by around 16 times. Unlike other similar systems, the characteristic spikes and inter-spike data can both be recovered which guarantes a >95% spike classification success rate. The compression circuit occupied 0.11mm2/electrode in a 180nm CMOS process. The complete signal processing circuit consumes <16uW/electrode. Significance. Power and area efficiency demonstrated by the system make it an ideal candidate for integration into large recording arrays containing thousands of electrode. Closed-loop recording and reconstruction performance evaluation further improves the robustness of the compression method, thus making the system more practical for long term recording.
NASA Astrophysics Data System (ADS)
Zhang, Yongfei; Cao, Haiheng; Jiang, Hongxu; Li, Bo
2016-04-01
As remote sensing image applications are often characterized with limited bandwidth and high-quality demands, higher coding performance of remote sensing images are desirable. The embedded block coding with optimal truncation (EBCOT) is the fundamental part of JPEG2000 image compression standard. However, EBCOT only considers correlation within a sub-band and utilizes a context template of eight spatially neighboring coefficients in prediction. The existing optimization methods in literature using the current context template prove little performance improvements. To address this problem, this paper presents a new mutual information (MI)-based context template selection and modeling method. By further considering the correlation across the sub-bands, the potential prediction coefficients, including neighbors, far neighbors, parent and parent neighbors, are comprehensively examined and selected in such a manner that achieves a nice trade-off between the MI-based correlation criterion and the prediction complexity. Based on the selected context template, a high-order prediction model, which jointly considers the weight and the significance state of each coefficient, is proposed. Experimental results show that the proposed algorithm consistently outperforms the benchmark JPEG2000 standard and state-of-the-art algorithms in term of coding efficiency at a competitive computational cost, which makes it desirable in real-time compression applications, especially for remote sensing images.
Estimation of sparse null space functions for compressed sensing in SPECT
NASA Astrophysics Data System (ADS)
Mukherjee, Joyeeta Mitra; Sidky, Emil; King, Michael A.
2014-03-01
Compressed sensing (CS) [1] is a novel sensing (acquisition) paradigm that applies to discrete-to-discrete system models and asserts exact recovery of a sparse signal from far fewer measurements than the number of unknowns [1- 2]. Successful applications of CS may be found in MRI [3, 4] and optical imaging [5]. Sparse reconstruction methods exploiting CS principles have been investigated for CT [6-8] to reduce radiation dose, and to gain imaging speed and image quality in optical imaging [9]. In this work the objective is to investigate the applicability of compressed sensing principles for a faster brain imaging protocol on a hybrid collimator SPECT system. As a proofof- principle we study the null space of the fan-beam collimator component of our system with regards to a particular imaging object. We illustrate the impact of object sparsity on the null space using pixel and Haar wavelet basis functions to represent a piecewise smooth phantom chosen as our object of interest.
Photonic compressive sensing with a micro-ring-resonator-based microwave photonic filter
NASA Astrophysics Data System (ADS)
Chen, Ying; Ding, Yunhong; Zhu, Zhijing; Chi, Hao; Zheng, Shilie; Zhang, Xianmin; Jin, Xiaofeng; Galili, Michael; Yu, Xianbin
2016-08-01
A novel approach to realize photonic compressive sensing (CS) with a multi-tap microwave photonic filter is proposed and demonstrated. The system takes both advantages of CS and photonics to capture wideband sparse signals with sub-Nyquist sampling rate. The low-pass filtering function required in the CS is realized in a photonic way by using a frequency comb and a dispersive element. The frequency comb is realized by shaping an amplified spontaneous emission (ASE) source with an on-chip micro-ring resonator, which is beneficial to the integration of photonic CS. A proof-of-concept experiment for a two-tone signal acquisition with frequencies of 350 MHz and 1.25 GHz is experimentally demonstrated with a compression factor up to 16.
Spencer, Austin P.; Spokoyny, Boris; Ray, Supratim; Sarvari, Fahad; Harel, Elad
2016-01-01
Compressive sensing allows signals to be efficiently captured by exploiting their inherent sparsity. Here we implement sparse sampling to capture the electronic structure and ultrafast dynamics of molecular systems using phase-resolved 2D coherent spectroscopy. Until now, 2D spectroscopy has been hampered by its reliance on array detectors that operate in limited spectral regions. Combining spatial encoding of the nonlinear optical response and rapid signal modulation allows retrieval of state-resolved correlation maps in a photosynthetic protein and carbocyanine dye. We report complete Hadamard reconstruction of the signals and compression factors as high as 10, in good agreement with array-detected spectra. Single-point array reconstruction by spatial encoding (SPARSE) Spectroscopy reduces acquisition times by about an order of magnitude, with further speed improvements enabled by fast scanning of a digital micromirror device. We envision unprecedented applications for coherent spectroscopy using frequency combs and super-continua in diverse spectral regions. PMID:26804546
Evaluation of the CASSI-DD hyperspectral compressive sensing imaging system
NASA Astrophysics Data System (ADS)
Busuioceanu, Maria; Messinger, David W.; Greer, John B.; Flake, J. Christopher
2013-05-01
Compressive Sensing (CS) systems capture data with fewer measurements than traditional sensors assuming that imagery is redundant and compressible in the spatial and spectral dimensions. We utilize a model of the Coded Aperture Snapshot Spectral Imager-Dual Disperser (CASSI-DD) CS model to simulate CS measurements from HyMap images. Flake et al's novel reconstruction algorithm, which combines a spectral smoothing parameter and spatial total variation (TV), is used to create high resolution hyperspectral imagery.1 We examine the e ect of the number of measurements, which corresponds to the percentage of physical data sampled, on the delity of simulated data. The impacts of the CS sensor model and reconstruction of the data cloud and the utility for various hyperspectral applications are described to identify the strengths and limitations of CS.
A modified JPEG-LS lossless compression method for remote sensing images
NASA Astrophysics Data System (ADS)
Deng, Lihua; Huang, Zhenghua
2015-12-01
As many variable length source coders, JPEG-LS is highly vulnerable to channel errors which occur in the transmission of remote sensing images. The error diffusion is one of the important factors which infect its robustness. The common method of improving the error resilience of JPEG-LS is dividing the image into many strips or blocks, and then coding each of them independently, but this method reduces the coding efficiency. In this paper, a block based JPEP-LS lossless compression method with an adaptive parameter is proposed. In the modified scheme, the threshold parameter RESET is adapted to an image and the compression efficiency is close to that of the conventional JPEG-LS.
NASA Astrophysics Data System (ADS)
Chan, Antony C. S.; Lam, Edmund Y.; Tsia, Kevin K.
2014-11-01
Fluorescence imaging using radio frequency-multiplexed excitation (FIRE) has emerged to enable an order-of-magnitude higher frame rate than the current technologies. Similar to all high-speed realtime imaging modalities, FIRE inherently generates massive image data continuously. While this technology entails high-throughput data sampling, processing, and storage in real-time, strategies in data compression on the fly is also beneficial. We here report that it is feasible to exploit the radio frequency-multiplexed excitation scheme in FIRE for implementing compressed sensing (CS) without any modification of the FIRE system. We numerically demonstrate that CS-FIRE can reduce the effective data rate by 95% without severe degradation of image quality.
Video compressed sensing using iterative self-similarity modeling and residual reconstruction
NASA Astrophysics Data System (ADS)
Kim, Yookyung; Oh, Han; Bilgin, Ali
2013-04-01
Compressed sensing (CS) has great potential for use in video data acquisition and storage because it makes it unnecessary to collect an enormous amount of data and to perform the computationally demanding compression process. We propose an effective CS algorithm for video that consists of two iterative stages. In the first stage, frames containing the dominant structure are estimated. These frames are obtained by thresholding the coefficients of similar blocks. In the second stage, refined residual frames are reconstructed from the original measurements and the measurements corresponding to the frames estimated in the first stage. These two stages are iterated until convergence. The proposed algorithm exhibits superior subjective image quality and significantly improves the peak-signal-to-noise ratio and the structural similarity index measure compared to other state-of-the-art CS algorithms.
NASA Astrophysics Data System (ADS)
Arias, Fernando X.; Sierra, Heidy; Rajadhyaksha, Milind; Arzuaga, Emmanuel
2016-03-01
Compressive Sensing (CS)-based technologies have shown potential to improve the efficiency of acquisition, manipulation, analysis and storage processes on signals and imagery with slight discernible loss in data performance. The CS framework relies on the reconstruction of signals that are presumed sparse in some domain, from a significantly small data collection of linear projections of the signal of interest. As a result, a solution to the underdetermined linear system resulting from this paradigm makes it possible to estimate the original signal with high accuracy. One common approach to solve the linear system is based on methods that minimize the L1-norm. Several fast algorithms have been developed for this purpose. This paper presents a study on the use of CS in high-resolution reflectance confocal microscopy (RCM) images of the skin. RCM offers a cell resolution level similar to that used in histology to identify cellular patterns for diagnosis of skin diseases. However, imaging of large areas (required for effective clinical evaluation) at such high-resolution can turn image capturing, processing and storage processes into a time consuming procedure, which may pose a limitation for use in clinical settings. We present an analysis on the compression ratio that may allow for a simpler capturing approach while reconstructing the required cellular resolution for clinical use. We provide a comparative study in compressive sensing and estimate its effectiveness in terms of compression ratio vs. image reconstruction accuracy. Preliminary results show that by using as little as 25% of the original number of samples, cellular resolution may be reconstructed with high accuracy.
Compressed Sensing/Sparse-Recovery Approach for Improved Range Resolution in Narrow-Band Radar
Costanzo, Sandra
2016-01-01
A compressed sensing/sparse-recovery procedure is adopted to obtain enhanced range resolution capability from the processing of data acquired with narrow-band SFCW radars. A mathematical formulation for the proposed approach is reported and validity limitations are fully discussed, by demonstrating the ability to identify a great number of targets, up to 20, in the range direction. Both numerical and experimental validations are presented, by assuming also noise conditions. The proposed method can be usefully applied for the accurate detection of parameters with very small variations, such as those involved in the monitoring of soil deformations or biological objects. PMID:27022617
Map-Based Compressive Sensing Model for Wireless Sensor Network Architecture, A Starting Point
NASA Astrophysics Data System (ADS)
Mahmudimanesh, Mohammadreza; Khelil, Abdelmajid; Yazdani, Nasser
Sub-Nyquist sampling techniques for Wireless Sensor Networks (WSN) are gaining increasing attention as an alternative method to capture natural events with desired quality while minimizing the number of active sensor nodes. Among those techniques, Compressive Sensing (CS) approaches are of special interest, because of their mathematically concrete foundations and efficient implementations. We describe how the geometrical representation of the sampling problem can influence the effectiveness and efficiency of CS algorithms. In this paper we introduce a Map-based model which exploits redundancy attributes of signals recorded from natural events to achieve an optimal representation of the signal.
DLA based compressed sensing for high resolution MR microscopy of neuronal tissue
NASA Astrophysics Data System (ADS)
Nguyen, Khieu-Van; Li, Jing-Rebecca; Radecki, Guillaume; Ciobanu, Luisa
2015-10-01
In this work we present the implementation of compressed sensing (CS) on a high field preclinical scanner (17.2 T) using an undersampling trajectory based on the diffusion limited aggregation (DLA) random growth model. When applied to a library of images this approach performs better than the traditional undersampling based on the polynomial probability density function. In addition, we show that the method is applicable to imaging live neuronal tissues, allowing significantly shorter acquisition times while maintaining the image quality necessary for identifying the majority of neurons via an automatic cell segmentation algorithm.
Exploring the origins of turbulence in multiphase flow using compressed sensing MRI.
Tayler, Alexander B; Holland, Daniel J; Sederman, Andrew J; Gladden, Lynn F
2012-06-29
Ultrafast magnetic resonance imaging, employing spiral reciprocal space sampling and compressed sensing image reconstruction, is used to acquire velocity maps of the liquid phase in gas-liquid multiphase flows. Velocity maps were acquired at a rate of 188 frames per second. The method enables quantitative characterization of the wake dynamics of single bubbles and bubble swarms. To illustrate this, we use the new technique to demonstrate the role of bubble wake vorticity in driving bubble secondary motions, and in governing the structure of turbulence in multiphase flows. PMID:23004990
Exploring the Origins of Turbulence in Multiphase Flow Using Compressed Sensing MRI
NASA Astrophysics Data System (ADS)
Tayler, Alexander B.; Holland, Daniel J.; Sederman, Andrew J.; Gladden, Lynn F.
2012-06-01
Ultrafast magnetic resonance imaging, employing spiral reciprocal space sampling and compressed sensing image reconstruction, is used to acquire velocity maps of the liquid phase in gas-liquid multiphase flows. Velocity maps were acquired at a rate of 188 frames per second. The method enables quantitative characterization of the wake dynamics of single bubbles and bubble swarms. To illustrate this, we use the new technique to demonstrate the role of bubble wake vorticity in driving bubble secondary motions, and in governing the structure of turbulence in multiphase flows.
DLA based compressed sensing for high resolution MR microscopy of neuronal tissue.
Nguyen, Khieu-Van; Li, Jing-Rebecca; Radecki, Guillaume; Ciobanu, Luisa
2015-10-01
In this work we present the implementation of compressed sensing (CS) on a high field preclinical scanner (17.2 T) using an undersampling trajectory based on the diffusion limited aggregation (DLA) random growth model. When applied to a library of images this approach performs better than the traditional undersampling based on the polynomial probability density function. In addition, we show that the method is applicable to imaging live neuronal tissues, allowing significantly shorter acquisition times while maintaining the image quality necessary for identifying the majority of neurons via an automatic cell segmentation algorithm. PMID:26367320
A novel reconstruction algorithm for bioluminescent tomography based on Bayesian compressive sensing
NASA Astrophysics Data System (ADS)
Wang, Yaqi; Feng, Jinchao; Jia, Kebin; Sun, Zhonghua; Wei, Huijun
2016-03-01
Bioluminescence tomography (BLT) is becoming a promising tool because it can resolve the biodistribution of bioluminescent reporters associated with cellular and subcellular function through several millimeters with to centimeters of tissues in vivo. However, BLT reconstruction is an ill-posed problem. By incorporating sparse a priori information about bioluminescent source, enhanced image quality is obtained for sparsity based reconstruction algorithm. Therefore, sparsity based BLT reconstruction algorithm has a great potential. Here, we proposed a novel reconstruction method based on Bayesian compressive sensing and investigated its feasibility and effectiveness with a heterogeneous phantom. The results demonstrate the potential and merits of the proposed algorithm.
Fast algorithms for nonconvex compression sensing: MRI reconstruction from very few data
Chartrand, Rick
2009-01-01
Compressive sensing is the reconstruction of sparse images or signals from very few samples, by means of solving a tractable optimization problem. In the context of MRI, this can allow reconstruction from many fewer k-space samples, thereby reducing scanning time. Previous work has shown that nonconvex optimization reduces still further the number of samples required for reconstruction, while still being tractable. In this work, we extend recent Fourier-based algorithms for convex optimization to the nonconvex setting, and obtain methods that combine the reconstruction abilities of previous nonconvex approaches with the computational speed of state-of-the-art convex methods.
Sparse OCT: optimizing compressed sensing in spectral domain optical coherence tomography
NASA Astrophysics Data System (ADS)
Liu, Xuan; Kang, Jin U.
2011-03-01
We applied compressed sensing (CS) to spectral domain optical coherence tomography (SD-OCT). Namely, CS was applied to the spectral data in reconstructing A-mode images. This would eliminate the need for a large amount of spectral data for image reconstruction and processing. We tested the CS method by randomly undersampling k-space SD-OCT signal. OCT images are reconstructed by solving an optimization problem that minimizes the l1 norm to enforce sparsity, subject to data consistency constraints. Variable density random sampling and uniform density random sampling were studied and compared, which shows the former undersampling scheme can achieve accurate signal recovery using less data.
Polarimetric SAR Tomopgraphy With TerraSAR-X By Means Of Distributed Compressed Sensing
NASA Astrophysics Data System (ADS)
Aguilera, E.; Nannini, M.; Antonello, A.; Marotti, L.; Prats, P.; Reigber, A.
2012-01-01
In SAR tomography, the vertical reflectivity function for every azimuth-range pixel is usually recovered by processing data collected using a defined repeat-pass acquisition geometry. A common and appealing approach is to generate a synthetic aperture in the elevation direction through imaging from parallel tracks. However, the quality of conventional reconstruction methods is generally dictated by the Nyquist rate, which can be considerably high. In an attempt to reduce this rate, we propose a new tomographic focusing approach that exploits correlations between neighboring azimuth-range pixels and polarimetric channels. As a matter of fact, this can be done under the framework of Distributed Com- pressed Sensing (DCS), which stems from Compressed Sensing (CS) theory, thus also exploiting sparsity in the tomographic signal. Results demonstrating the potential of the DCS methodology will be validated, for the first time, using dual-polarized data acquired at X-band by the TerraSAR-X spaceborne system.
Ultrashort echo time (UTE) imaging using gradient pre-equalization and compressed sensing.
Fabich, Hilary T; Benning, Martin; Sederman, Andrew J; Holland, Daniel J
2014-08-01
Ultrashort echo time (UTE) imaging is a well-known technique used in medical MRI, however, the implementation of the sequence remains non-trivial. This paper introduces UTE for non-medical applications and outlines a method for the implementation of UTE to enable accurate slice selection and short acquisition times. Slice selection in UTE requires fast, accurate switching of the gradient and r.f. pulses. Here a gradient "pre-equalization" technique is used to optimize the gradient switching and achieve an effective echo time of 10μs. In order to minimize the echo time, k-space is sampled radially. A compressed sensing approach is used to minimize the total acquisition time. Using the corrections for slice selection and acquisition along with novel image reconstruction techniques, UTE is shown to be a viable method to study samples of cork and rubber with a shorter signal lifetime than can typically be measured. Further, the compressed sensing image reconstruction algorithm is shown to provide accurate images of the samples with as little as 12.5% of the full k-space data set, potentially permitting real time imaging of short T2(*) materials. PMID:25036293
Gui, Guan; Xu, Li; Shan, Lin; Adachi, Fumiyuki
2014-01-01
In orthogonal frequency division modulation (OFDM) communication systems, channel state information (CSI) is required at receiver due to the fact that frequency-selective fading channel leads to disgusting intersymbol interference (ISI) over data transmission. Broadband channel model is often described by very few dominant channel taps and they can be probed by compressive sensing based sparse channel estimation (SCE) methods, for example, orthogonal matching pursuit algorithm, which can take the advantage of sparse structure effectively in the channel as for prior information. However, these developed methods are vulnerable to both noise interference and column coherence of training signal matrix. In other words, the primary objective of these conventional methods is to catch the dominant channel taps without a report of posterior channel uncertainty. To improve the estimation performance, we proposed a compressive sensing based Bayesian sparse channel estimation (BSCE) method which cannot only exploit the channel sparsity but also mitigate the unexpected channel uncertainty without scarifying any computational complexity. The proposed method can reveal potential ambiguity among multiple channel estimators that are ambiguous due to observation noise or correlation interference among columns in the training matrix. Computer simulations show that proposed method can improve the estimation performance when comparing with conventional SCE methods. PMID:24983012
Balanced Sparse Model for Tight Frames in Compressed Sensing Magnetic Resonance Imaging
Liu, Yunsong; Cai, Jian-Feng; Zhan, Zhifang; Guo, Di; Ye, Jing; Chen, Zhong; Qu, Xiaobo
2015-01-01
Compressed sensing has shown to be promising to accelerate magnetic resonance imaging. In this new technology, magnetic resonance images are usually reconstructed by enforcing its sparsity in sparse image reconstruction models, including both synthesis and analysis models. The synthesis model assumes that an image is a sparse combination of atom signals while the analysis model assumes that an image is sparse after the application of an analysis operator. Balanced model is a new sparse model that bridges analysis and synthesis models by introducing a penalty term on the distance of frame coefficients to the range of the analysis operator. In this paper, we study the performance of the balanced model in tight frame based compressed sensing magnetic resonance imaging and propose a new efficient numerical algorithm to solve the optimization problem. By tuning the balancing parameter, the new model achieves solutions of three models. It is found that the balanced model has a comparable performance with the analysis model. Besides, both of them achieve better results than the synthesis model no matter what value the balancing parameter is. Experiment shows that our proposed numerical algorithm constrained split augmented Lagrangian shrinkage algorithm for balanced model (C-SALSA-B) converges faster than previously proposed algorithms accelerated proximal algorithm (APG) and alternating directional method of multipliers for balanced model (ADMM-B). PMID:25849209
A COMPRESSED SENSING METHOD WITH ANALYTICAL RESULTS FOR LIDAR FEATURE CLASSIFICATION
Allen, Josef D
2011-01-01
We present an innovative way to autonomously classify LiDAR points into bare earth, building, vegetation, and other categories. One desirable product of LiDAR data is the automatic classification of the points in the scene. Our algorithm automatically classifies scene points using Compressed Sensing Methods via Orthogonal Matching Pursuit algorithms utilizing a generalized K-Means clustering algorithm to extract buildings and foliage from a Digital Surface Models (DSM). This technology reduces manual editing while being cost effective for large scale automated global scene modeling. Quantitative analyses are provided using Receiver Operating Characteristics (ROC) curves to show Probability of Detection and False Alarm of buildings vs. vegetation classification. Histograms are shown with sample size metrics. Our inpainting algorithms then fill the voids where buildings and vegetation were removed, utilizing Computational Fluid Dynamics (CFD) techniques and Partial Differential Equations (PDE) to create an accurate Digital Terrain Model (DTM) [6]. Inpainting preserves building height contour consistency and edge sharpness of identified inpainted regions. Qualitative results illustrate other benefits such as Terrain Inpainting s unique ability to minimize or eliminate undesirable terrain data artifacts. Keywords: Compressed Sensing, Sparsity, Data Dictionary, LiDAR, ROC, K-Means, Clustering, K-SVD, Orthogonal Matching Pursuit
A Reconstruction Method Based on AL0FGD for Compressed Sensing in Border Monitoring WSN System
Wang, Yan; Wu, Xi; Li, Wenzao; Zhang, Yi; Li, Zhi; Zhou, Jiliu
2014-01-01
In this paper, to monitor the border in real-time with high efficiency and accuracy, we applied the compressed sensing (CS) technology on the border monitoring wireless sensor network (WSN) system and proposed a reconstruction method based on approximately l0 norm and fast gradient descent (AL0FGD) for CS. In the frontend of the system, the measurement matrix was used to sense the border information in a compressed manner, and then the proposed reconstruction method was applied to recover the border information at the monitoring terminal. To evaluate the performance of the proposed method, the helicopter sound signal was used as an example in the experimental simulation, and three other typical reconstruction algorithms 1)split Bregman algorithm, 2)iterative shrinkage algorithm, and 3)smoothed approximate l0 norm (SL0), were employed for comparison. The experimental results showed that the proposed method has a better performance in recovering the helicopter sound signal in most cases, which could be used as a basis for further study of the border monitoring WSN system. PMID:25461759
A DMD-based hyperspectral imaging system using compressive sensing method
NASA Astrophysics Data System (ADS)
Sun, Zhongqiu; Chen, Bo; Cheng, Chengqi
2014-11-01
Hyperspectral Imaging Systems (HIS) are widely applied in many fields. However, in the traditional design of HIS, it is much time-consuming to acquire an integrated hyperspectral image. Compressive sensing is an efficient method to process sparse data, and a single-pixel camera which used the digital micromirror device (DMD) for accomplishing the CS algorithms had been developed. Nowadays, DMD achieved great development. The size of mirror array is increasing while switch speed of a single mirror becomes very fast. Consequently, researchers make efforts to design a HIS using CS method. CS method is a method to scale down the spatial information but the hyperspectral datacubes are still huge because of the thousands of bands. In this paper, we design a DMD-based spectrometer architecture using the method of compressed sensing principle, combined with DMD's spectral filter characteristics. In the new architecture, there are two DMDs. One is used for implementing the CS pattern, the other for filtering the bands. It has spectral simply adjustable advantages. With this new technology, we can reduce the amount of information which needs to be transmitted and processed in both spatial and spectral domain. We also present some simulation results of implementation procedures.
Secure biometric image sensor and authentication scheme based on compressed sensing.
Suzuki, Hiroyuki; Suzuki, Masamichi; Urabe, Takuya; Obi, Takashi; Yamaguchi, Masahiro; Ohyama, Nagaaki
2013-11-20
It is important to ensure the security of biometric authentication information, because its leakage causes serious risks, such as replay attacks using the stolen biometric data, and also because it is almost impossible to replace raw biometric information. In this paper, we propose a secure biometric authentication scheme that protects such information by employing an optical data ciphering technique based on compressed sensing. The proposed scheme is based on two-factor authentication, the biometric information being supplemented by secret information that is used as a random seed for a cipher key. In this scheme, a biometric image is optically encrypted at the time of image capture, and a pair of restored biometric images for enrollment and verification are verified in the authentication server. If any of the biometric information is exposed to risk, it can be reenrolled by changing the secret information. Through numerical experiments, we confirm that finger vein images can be restored from the compressed sensing measurement data. We also present results that verify the accuracy of the scheme. PMID:24513773
First-principles interatomic potentials for ten elemental metals via compressed sensing
NASA Astrophysics Data System (ADS)
Seko, Atsuto; Takahashi, Akira; Tanaka, Isao
2015-08-01
Interatomic potentials have been widely used in atomistic simulations such as molecular dynamics. Recently, frameworks to construct accurate interatomic potentials that combine a set of density functional theory (DFT) calculations with machine learning techniques have been proposed. One of these methods is to use compressed sensing to derive a sparse representation for the interatomic potential. This facilitates the control of the accuracy of interatomic potentials. In this study, we demonstrate the applicability of compressed sensing to deriving the interatomic potential of ten elemental metals, namely, Ag, Al, Au, Ca, Cu, Ga, In, K, Li, and Zn. For each elemental metal, the interatomic potential is obtained from DFT calculations using elastic net regression. The interatomic potentials are found to have prediction errors of less than 3.5 meV/atom, 0.03 eV/Å, and 0.15 GPa for the energy, force, and the stress tensor, respectively, which enable the accurate prediction of physical properties such as lattice constants and the phonon dispersion relationship.
Li, Qiyue; Qu, Xiaobo; Liu, Yunsong; Guo, Di; Lai, Zongying; Ye, Jing; Chen, Zhong
2015-06-01
Compressed sensing MRI (CS-MRI) is a promising technology to accelerate magnetic resonance imaging. Both improving the image quality and reducing the computation time are important for this technology. Recently, a patch-based directional wavelet (PBDW) has been applied in CS-MRI to improve edge reconstruction. However, this method is time consuming since it involves extensive computations, including geometric direction estimation and numerous iterations of wavelet transform. To accelerate computations of PBDW, we propose a general parallelization of patch-based processing by taking the advantage of multicore processors. Additionally, two pertinent optimizations, excluding smooth patches and pre-arranged insertion sort, that make use of sparsity in MR images are also proposed. Simulation results demonstrate that the acceleration factor with the parallel architecture of PBDW approaches the number of central processing unit cores, and that pertinent optimizations are also effective to make further accelerations. The proposed approaches allow compressed sensing MRI reconstruction to be accomplished within several seconds. PMID:25620521
Deformation corrected compressed sensing (DC-CS): a novel framework for accelerated dynamic MRI
Lingala, Sajan Goud; DiBella, Edward; Jacob, Mathews
2015-01-01
We propose a novel deformation corrected compressed sensing (DC-CS) framework to recover contrast enhanced dynamic magnetic resonance images from undersampled measurements. We introduce a formulation that is capable of handling a wide class of sparsity/compactness priors on the deformation corrected dynamic signal. In this work, we consider example compactness priors such as sparsity in temporal Fourier domain, sparsity in temporal finite difference domain, and nuclear norm penalty to exploit low rank structure. Using variable splitting, we decouple the complex optimization problem to simpler and well understood sub problems; the resulting algorithm alternates between simple steps of shrinkage based denoising, deformable registration, and a quadratic optimization step. Additionally, we employ efficient continuation strategies to reduce the risk of convergence to local minima. The decoupling enabled by the proposed scheme enables us to apply this scheme to contrast enhanced MRI applications. Through experiments on numerical phantom and in vivo myocardial perfusion MRI datasets, we observe superior image quality of the proposed DC-CS scheme in comparison to the classical k-t FOCUSS with motion estimation/correction scheme, and demonstrate reduced motion artifacts over classical compressed sensing schemes that utilize the compact priors on the original deformation uncorrected signal. PMID:25095251
Output-only modal identification by compressed sensing: Non-uniform low-rate random sampling
NASA Astrophysics Data System (ADS)
Yang, Yongchao; Nagarajaiah, Satish
2015-05-01
Modal identification or testing of structures consists of two phases, namely, data acquisition and data analysis. Some structures, such as aircrafts, high-speed machines, and plate-like civil structures, have active modes in the high-frequency range when subjected to high-speed or broadband excitation in their operational conditions. In the data acquisition stage, the Shannon-Nyquist sampling theorem indicates that capturing the high-frequency modes (signals) requires uniform high-rate sampling, resulting in sensing too many samples, which potentially impose burdens on the data transfer (especially in wireless platform) and data analysis stage. This paper explores a new-emerging, alternative, signal sampling and analysis technique, compressed sensing, and investigates the feasibility of a new method for output-only modal identification of structures in a non-uniform low-rate random sensing framework based on a combination of compressed sensing (CS) and blind source separation (BSS). Specifically, in the data acquisition stage, CS sensors sample few non-uniform low-rate random measurements of the structural responses signals, which turn out to be sufficient to capture the underlying mode information. Then in the data analysis stage, the proposed method uses the BSS technique, complexity pursuit (CP) recently explored by the authors, to directly decouple the non-uniform low-rate random samples of the structural responses, simultaneously yielding the mode shape matrix as well as the non-uniform low-rate random samples of the modal responses. Finally, CS with ℓ1-minimization recovers the uniform high-rate modal response from the CP-decoupled non-uniform low-rate random samples of the modal response, thereby enabling estimation of the frequency and damping ratio. Because CS sensors are currently in laboratory prototypes and not yet commercially available, their functionality-randomly sensing few non-uniform samples-is simulated in this study, which is performed on the
Continuous Compressed Sensing for Surface Dynamical Processes with Helium Atom Scattering
Jones, Alex; Tamtögl, Anton; Calvo-Almazán, Irene; Hansen, Anders
2016-01-01
Compressed Sensing (CS) techniques are used to measure and reconstruct surface dynamical processes with a helium spin-echo spectrometer for the first time. Helium atom scattering is a well established method for examining the surface structure and dynamics of materials at atomic sized resolution and the spin-echo technique opens up the possibility of compressing the data acquisition process. CS methods demonstrating the compressibility of spin-echo spectra are presented for several measurements. Recent developments on structured multilevel sampling that are empirically and theoretically shown to substantially improve upon the state of the art CS techniques are implemented. In addition, wavelet based CS approximations, founded on a new continuous CS approach, are used to construct continuous spectra. In order to measure both surface diffusion and surface phonons, which appear usually on different energy scales, standard CS techniques are not sufficient. However, the new continuous CS wavelet approach allows simultaneous analysis of surface phonons and molecular diffusion while reducing acquisition times substantially. The developed methodology is not exclusive to Helium atom scattering and can also be applied to other scattering frameworks such as neutron spin-echo and Raman spectroscopy. PMID:27301423
Compressed sensing of ECG signal for wireless system with new fast iterative method.
Tawfic, Israa; Kayhan, Sema
2015-12-01
Recent experiments in wireless body area network (WBAN) show that compressive sensing (CS) is a promising tool to compress the Electrocardiogram signal ECG signal. The performance of CS is based on algorithms use to reconstruct exactly or approximately the original signal. In this paper, we present two methods work with absence and presence of noise, these methods are Least Support Orthogonal Matching Pursuit (LS-OMP) and Least Support Denoising-Orthogonal Matching Pursuit (LSD-OMP). The algorithms achieve correct support recovery without requiring sparsity knowledge. We derive an improved restricted isometry property (RIP) based conditions over the best known results. The basic procedures are done by observational and analytical of a different Electrocardiogram signal downloaded them from PhysioBankATM. Experimental results show that significant performance in term of reconstruction quality and compression rate can be obtained by these two new proposed algorithms, and help the specialist gathering the necessary information from the patient in less time if we use Magnetic Resonance Imaging (MRI) application, or reconstructed the patient data after sending it through the network. PMID:26428598
Continuous Compressed Sensing for Surface Dynamical Processes with Helium Atom Scattering.
Jones, Alex; Tamtögl, Anton; Calvo-Almazán, Irene; Hansen, Anders
2016-01-01
Compressed Sensing (CS) techniques are used to measure and reconstruct surface dynamical processes with a helium spin-echo spectrometer for the first time. Helium atom scattering is a well established method for examining the surface structure and dynamics of materials at atomic sized resolution and the spin-echo technique opens up the possibility of compressing the data acquisition process. CS methods demonstrating the compressibility of spin-echo spectra are presented for several measurements. Recent developments on structured multilevel sampling that are empirically and theoretically shown to substantially improve upon the state of the art CS techniques are implemented. In addition, wavelet based CS approximations, founded on a new continuous CS approach, are used to construct continuous spectra. In order to measure both surface diffusion and surface phonons, which appear usually on different energy scales, standard CS techniques are not sufficient. However, the new continuous CS wavelet approach allows simultaneous analysis of surface phonons and molecular diffusion while reducing acquisition times substantially. The developed methodology is not exclusive to Helium atom scattering and can also be applied to other scattering frameworks such as neutron spin-echo and Raman spectroscopy. PMID:27301423
Continuous Compressed Sensing for Surface Dynamical Processes with Helium Atom Scattering
NASA Astrophysics Data System (ADS)
Jones, Alex; Tamtögl, Anton; Calvo-Almazán, Irene; Hansen, Anders
2016-06-01
Compressed Sensing (CS) techniques are used to measure and reconstruct surface dynamical processes with a helium spin-echo spectrometer for the first time. Helium atom scattering is a well established method for examining the surface structure and dynamics of materials at atomic sized resolution and the spin-echo technique opens up the possibility of compressing the data acquisition process. CS methods demonstrating the compressibility of spin-echo spectra are presented for several measurements. Recent developments on structured multilevel sampling that are empirically and theoretically shown to substantially improve upon the state of the art CS techniques are implemented. In addition, wavelet based CS approximations, founded on a new continuous CS approach, are used to construct continuous spectra. In order to measure both surface diffusion and surface phonons, which appear usually on different energy scales, standard CS techniques are not sufficient. However, the new continuous CS wavelet approach allows simultaneous analysis of surface phonons and molecular diffusion while reducing acquisition times substantially. The developed methodology is not exclusive to Helium atom scattering and can also be applied to other scattering frameworks such as neutron spin-echo and Raman spectroscopy.
A Compressed Sensing-Based Wearable Sensor Network for Quantitative Assessment of Stroke Patients.
Yu, Lei; Xiong, Daxi; Guo, Liquan; Wang, Jiping
2016-01-01
Clinical rehabilitation assessment is an important part of the therapy process because it is the premise for prescribing suitable rehabilitation interventions. However, the commonly used assessment scales have the following two drawbacks: (1) they are susceptible to subjective factors; (2) they only have several rating levels and are influenced by a ceiling effect, making it impossible to exactly detect any further improvement in the movement. Meanwhile, energy constraints are a primary design consideration in wearable sensor network systems since they are often battery-operated. Traditionally, for wearable sensor network systems that follow the Shannon/Nyquist sampling theorem, there are many data that need to be sampled and transmitted. This paper proposes a novel wearable sensor network system to monitor and quantitatively assess the upper limb motion function, based on compressed sensing technology. With the sparse representation model, less data is transmitted to the computer than with traditional systems. The experimental results show that the accelerometer signals of Bobath handshake and shoulder touch exercises can be compressed, and the length of the compressed signal is less than 1/3 of the raw signal length. More importantly, the reconstruction errors have no influence on the predictive accuracy of the Brunnstrom stage classification model. It also indicated that the proposed system can not only reduce the amount of data during the sampling and transmission processes, but also, the reconstructed accelerometer signals can be used for quantitative assessment without any loss of useful information. PMID:26861337
A Compressed Sensing-Based Wearable Sensor Network for Quantitative Assessment of Stroke Patients
Yu, Lei; Xiong, Daxi; Guo, Liquan; Wang, Jiping
2016-01-01
Clinical rehabilitation assessment is an important part of the therapy process because it is the premise for prescribing suitable rehabilitation interventions. However, the commonly used assessment scales have the following two drawbacks: (1) they are susceptible to subjective factors; (2) they only have several rating levels and are influenced by a ceiling effect, making it impossible to exactly detect any further improvement in the movement. Meanwhile, energy constraints are a primary design consideration in wearable sensor network systems since they are often battery-operated. Traditionally, for wearable sensor network systems that follow the Shannon/Nyquist sampling theorem, there are many data that need to be sampled and transmitted. This paper proposes a novel wearable sensor network system to monitor and quantitatively assess the upper limb motion function, based on compressed sensing technology. With the sparse representation model, less data is transmitted to the computer than with traditional systems. The experimental results show that the accelerometer signals of Bobath handshake and shoulder touch exercises can be compressed, and the length of the compressed signal is less than 1/3 of the raw signal length. More importantly, the reconstruction errors have no influence on the predictive accuracy of the Brunnstrom stage classification model. It also indicated that the proposed system can not only reduce the amount of data during the sampling and transmission processes, but also, the reconstructed accelerometer signals can be used for quantitative assessment without any loss of useful information. PMID:26861337
An infrared-visible image fusion scheme based on NSCT and compressed sensing
NASA Astrophysics Data System (ADS)
Zhang, Qiong; Maldague, Xavier
2015-05-01
Image fusion, as a research hot point nowadays in the field of infrared computer vision, has been developed utilizing different varieties of methods. Traditional image fusion algorithms are inclined to bring problems, such as data storage shortage and computational complexity increase, etc. Compressed sensing (CS) uses sparse sampling without knowing the priori knowledge and greatly reconstructs the image, which reduces the cost and complexity of image processing. In this paper, an advanced compressed sensing image fusion algorithm based on non-subsampled contourlet transform (NSCT) is proposed. NSCT provides better sparsity than the wavelet transform in image representation. Throughout the NSCT decomposition, the low-frequency and high-frequency coefficients can be obtained respectively. For the fusion processing of low-frequency coefficients of infrared and visible images , the adaptive regional energy weighting rule is utilized. Thus only the high-frequency coefficients are specially measured. Here we use sparse representation and random projection to obtain the required values of high-frequency coefficients, afterwards, the coefficients of each image block can be fused via the absolute maximum selection rule and/or the regional standard deviation rule. In the reconstruction of the compressive sampling results, a gradient-based iterative algorithm and the total variation (TV) method are employed to recover the high-frequency coefficients. Eventually, the fused image is recovered by inverse NSCT. Both the visual effects and the numerical computation results after experiments indicate that the presented approach achieves much higher quality of image fusion, accelerates the calculations, enhances various targets and extracts more useful information.
Estimation of the CSA-ODF using Bayesian Compressed Sensing of Multi-shell HARDI
Duarte-Carvajalino, Julio M.; Lenglet, Christophe; Xu, Junqian; Yacoub, Essa; Ugurbil, Kamil; Moeller, Steen; Carin, Lawrence; Sapiro, Guillermo
2013-01-01
Purpose Diffusion MRI provides important information about the brain white matter structures and has opened new avenues for neuroscience and translational research. However, acquisition time needed for advanced applications can still be a challenge in clinical settings. There is consequently a need to accelerate diffusion MRI acquisitions. Methods A multi-task Bayesian compressive sensing (MT-BCS) framework is proposed to directly estimate the constant solid angle orientation distribution function (CSA-ODF) from under-sampled (i.e., accelerated image acquisition) multi-shell high angular resolution diffusion imaging (HARDI) datasets, and accurately recover HARDI data at higher resolution in q-space. The proposed MT-BCS approach exploits the spatial redundancy of the data by modeling the statistical relationships within groups (clusters) of diffusion signal. This framework also provides uncertainty estimates of the computed CSA-ODF and diffusion signal, directly computed from the compressive measurements. Experiments validating the proposed framework are performed using realistic multi-shell synthetic images and in-vivo multi-shell high angular resolution HARDI datasets. Results Results indicate a practical reduction in the number of required diffusion volumes (q-space samples) by at least a factor of four to estimate the CSA-ODF from multi-shell data. Conclusion This work presents, for the first time, a multi-task Bayesian compressive sensing approach to simultaneously estimate the full posterior of the CSA-ODF and diffusion-weighted volumes from multi-shell HARDI acquisitions. It demonstrates improvement of the quality of acquired datasets via CS de-noising, and accurate estimation of the CSA-ODF, as well as enables a reduction in the acquisition time by a factor of two to four, especially when “staggered” q-space sampling schemes are used. The proposed MT-BCS framework can naturally be combined with parallel MR imaging to further accelerate HARDI acquisitions
NASA Astrophysics Data System (ADS)
Pletinckx, D.
2011-09-01
The current 3D hype creates a lot of interest in 3D. People go to 3D movies, but are we ready to use 3D in our homes, in our offices, in our communication? Are we ready to deliver real 3D to a general public and use interactive 3D in a meaningful way to enjoy, learn, communicate? The CARARE project is realising this for the moment in the domain of monuments and archaeology, so that real 3D of archaeological sites and European monuments will be available to the general public by 2012. There are several aspects to this endeavour. First of all is the technical aspect of flawlessly delivering 3D content over all platforms and operating systems, without installing software. We have currently a working solution in PDF, but HTML5 will probably be the future. Secondly, there is still little knowledge on how to create 3D learning objects, 3D tourist information or 3D scholarly communication. We are still in a prototype phase when it comes to integrate 3D objects in physical or virtual museums. Nevertheless, Europeana has a tremendous potential as a multi-facetted virtual museum. Finally, 3D has a large potential to act as a hub of information, linking to related 2D imagery, texts, video, sound. We describe how to create such rich, explorable 3D objects that can be used intuitively by the generic Europeana user and what metadata is needed to support the semantic linking.
3D digital breast tomosynthesis image reconstruction using anisotropic total variation minimization.
Seyyedi, Saeed; Yildirim, Isa
2014-01-01
This paper presents a compressed sensing based reconstruction method for 3D digital breast tomosynthesis (DBT) imaging. Algebraic reconstruction technique (ART) has been in use in DBT imaging by minimizing the isotropic total variation (TV) of the reconstructed image. The resolution in DBT differs in sagittal and axial directions which should be encountered during the TV minimization. In this study we develop a 3D anisotropic TV (ATV) minimization by considering the different resolutions in different directions. A customized 3D Shepp-logan phantom was generated to mimic a real DBT image by considering the overlapping tissue and directional resolution issues. Results of the ART, ART+3D TV and ART+3D ATV are compared using structural similarity (SSIM) diagram. PMID:25571377
Compact all-CMOS spatiotemporal compressive sensing video camera with pixel-wise coded exposure.
Zhang, Jie; Xiong, Tao; Tran, Trac; Chin, Sang; Etienne-Cummings, Ralph
2016-04-18
We present a low power all-CMOS implementation of temporal compressive sensing with pixel-wise coded exposure. This image sensor can increase video pixel resolution and frame rate simultaneously while reducing data readout speed. Compared to previous architectures, this system modulates pixel exposure at the individual photo-diode electronically without external optical components. Thus, the system provides reduction in size and power compare to previous optics based implementations. The prototype image sensor (127 × 90 pixels) can reconstruct 100 fps videos from coded images sampled at 5 fps. With 20× reduction in readout speed, our CMOS image sensor only consumes 14μW to provide 100 fps videos. PMID:27137331
Compressed Sensing for the Fast Computation of Matrices: Application to Molecular Vibrations
2015-01-01
This article presents a new method to compute matrices from numerical simulations based on the ideas of sparse sampling and compressed sensing. The method is useful for problems where the determination of the entries of a matrix constitutes the computational bottleneck. We apply this new method to an important problem in computational chemistry: the determination of molecular vibrations from electronic structure calculations, where our results show that the overall scaling of the procedure can be improved in some cases. Moreover, our method provides a general framework for bootstrapping cheap low-accuracy calculations in order to reduce the required number of expensive high-accuracy calculations, resulting in a significant 3× speed-up in actual calculations. PMID:27162943
Flame four-dimensional deflection tomography with compressed-sensing-revision reconstruction
NASA Astrophysics Data System (ADS)
Zhang, Bin; Zhao, Minmin; Liu, Zhigang; Wu, Zhaohang
2016-08-01
Deflection tomography with limited angle projections was investigated to visualize a premixed flame. A projection sampling system for deflection tomography was used to obtain chronological deflectogram arrays at six view angles with only a pair of gratings. A new iterative reconstruction algorithm with deflection angle compressed-sensing revision was developed to improve reconstruction-distribution quality from incomplete projection data. Numerical simulation and error analysis provided a good indication of algorithm precision and convergence. In the experiment, 150 fringes were processed, and temperature distributions in 20 cross-sections were reconstructed from projection data in four instants. Four-dimensional flame structures and temperature distributions in the flame interior were visualized using the visualization toolkit. The experimental reconstruction was then compared with the result obtained from computational fluid dynamic analysis.
NASA Astrophysics Data System (ADS)
Ouyang, Bing; Hou, Weilin; Gong, Cuiling; Caimi, Frank M.; Dalgleish, Fraser R.; Vuorenkoski, Anni K.
2016-05-01
The Compressive Line Sensing (CLS) active imaging system has been demonstrated to be effective in scattering mediums, such as turbid coastal water through simulations and test tank experiments. Since turbulence is encountered in many atmospheric and underwater surveillance applications, a new CLS imaging prototype was developed to investigate the effectiveness of the CLS concept in a turbulence environment. Compared with earlier optical bench top prototype, the new system is significantly more robust and compact. A series of experiments were conducted at the Naval Research Lab's optical turbulence test facility with the imaging path subjected to various turbulence intensities. In addition to validating the system design, we obtained some unexpected exciting results - in the strong turbulence environment, the time-averaged measurements using the new CLS imaging prototype improved both SNR and resolution of the reconstructed images. We will discuss the implications of the new findings, the challenges of acquiring data through strong turbulence environment, and future enhancements.
A computationally efficient OMP-based compressed sensing reconstruction for dynamic MRI
NASA Astrophysics Data System (ADS)
Usman, M.; Prieto, C.; Odille, F.; Atkinson, D.; Schaeffter, T.; Batchelor, P. G.
2011-04-01
Compressed sensing (CS) methods in MRI are computationally intensive. Thus, designing novel CS algorithms that can perform faster reconstructions is crucial for everyday applications. We propose a computationally efficient orthogonal matching pursuit (OMP)-based reconstruction, specifically suited to cardiac MR data. According to the energy distribution of a y-f space obtained from a sliding window reconstruction, we label the y-f space as static or dynamic. For static y-f space images, a computationally efficient masked OMP reconstruction is performed, whereas for dynamic y-f space images, standard OMP reconstruction is used. The proposed method was tested on a dynamic numerical phantom and two cardiac MR datasets. Depending on the field of view composition of the imaging data, compared to the standard OMP method, reconstruction speedup factors ranging from 1.5 to 2.5 are achieved.
Si, Weijian; Qu, Xinggen; Liu, Lutao
2014-01-01
A novel direction of arrival (DOA) estimation method in compressed sensing (CS) is presented, in which DOA estimation is considered as the joint sparse recovery from multiple measurement vectors (MMV). The proposed method is obtained by minimizing the modified-based covariance matching criterion, which is acquired by adding penalties according to the regularization method. This minimization problem is shown to be a semidefinite program (SDP) and transformed into a constrained quadratic programming problem for reducing computational complexity which can be solved by the augmented Lagrange method. The proposed method can significantly improve the performance especially in the scenarios with low signal to noise ratio (SNR), small number of snapshots, and closely spaced correlated sources. In addition, the Cramér-Rao bound (CRB) of the proposed method is developed and the performance guarantee is given according to a version of the restricted isometry property (RIP). The effectiveness and satisfactory performance of the proposed method are illustrated by simulation results. PMID:24678272
Off-Grid DOA Estimation Using Alternating Block Coordinate Descent in Compressed Sensing.
Si, Weijian; Qu, Xinggen; Qu, Zhiyu
2015-01-01
This paper presents a novel off-grid direction of arrival (DOA) estimation method to achieve the superior performance in compressed sensing (CS), in which DOA estimation problem is cast as a sparse reconstruction. By minimizing the mixed k-l norm, the proposed method can reconstruct the sparse source and estimate grid error caused by mismatch. An iterative process that minimizes the mixed k-l norm alternately over two sparse vectors is employed so that the nonconvex problem is solved by alternating convex optimization. In order to yield the better reconstruction properties, the block sparse source is exploited for off-grid DOA estimation. A block selection criterion is engaged to reduce the computational complexity. In addition, the proposed method is proved to have the global convergence. Simulation results show that the proposed method has the superior performance in comparisons to existing methods. PMID:26343658
An, Xinliang; Brittelle, Mack S; Lauzier, Pascal T; Gord, James R; Roy, Sukesh; Chen, Guang-Hong; Sanders, Scott T
2015-11-01
This paper introduces temperature imaging by total-variation-based compressed sensing (CS) tomography of H2O vapor absorption spectroscopy. A controlled laboratory setup is used to generate a constant two-dimensional temperature distribution in air (a roughly Gaussian temperature profile with a central temperature of 677 K). A wavelength-tunable laser beam is directed through the known distribution; the beam is translated and rotated using motorized stages to acquire complete absorption spectra in the 1330-1365 nm range at each of 64 beam locations and 60 view angles. Temperature reconstructions are compared to independent thermocouple measurements. Although the distribution studied is approximately axisymmetric, axisymmetry is not assumed and simulations show similar performance for arbitrary temperature distributions. We study the measurement error as a function of number of beams and view angles used in reconstruction to gauge the potential for application of CS in practical test articles where optical access is limited. PMID:26560573
NASA Astrophysics Data System (ADS)
Ozolins, Vidvuds; Xia, Yi; Nielson, Weston; Zhou, Fei
2015-03-01
Earth-abundant minerals such as tetrahedrite Cu12Sb4S13 have recently received attention as promising thermoelectrics due to a combination of a relatively high figure of merit (ZT > 1 at T = 700 K in tetrahedrite), good mechanical properties and inexpensive bulk processing methods. Like many large unit-cell thermoelectrics, these compounds often have complex chemical formulas with very large unit cells that pose challenges to our ability to study their lattice dynamical properties theoretically. Here we show that a recently introduced approach, compressive sensing lattice dynamics (CSLD) [F. Zhou et al., Phys. Rev. Lett. 113, 185501 (2014)] provides an accurate and computationally efficient platform for investigating anharmonic lattice dynamics in complex materials. We will discuss the basic ideas and illustrate the performance of CSLD for the lattice thermal conductivity κL of tetrahedrite, collusite, pyrite, and other earth-abundant mineral compounds.
Interference-based image encryption with silhouette removal by aid of compressive sensing
NASA Astrophysics Data System (ADS)
Gong, Qiong; Wang, Zhipeng; Lv, Xiaodong; Qin, Yi
2016-01-01
Compressive sensing (CS) offers the opportunity to reconstruct a signal from its sparse representation, either in the space domain or the transform domain. Exploiting this character, we propose a simple interference-based image encryption method. For encryption, a synthetic image, which contains sparse samples of the original image and the designated values, is analytically separated into two phase only masks (POMs). Consequently, only fragmentary data of the primary image can be directly collected in the traditional decryption scheme. However, the subsequent CS reconstruction will retrieve a high quality image from the fragmentary information. The proposed method has effectively suppressed the silhouette problem. Moreover, it has also some distinct advantages over the previous approaches.
Quantitative Inspection of Remanence of Broken Wire Rope Based on Compressed Sensing.
Zhang, Juwei; Tan, Xiaojiang
2016-01-01
Most traditional strong magnetic inspection equipment has disadvantages such as big excitation devices, high weight, low detection precision, and inconvenient operation. This paper presents the design of a giant magneto-resistance (GMR) sensor array collection system. The remanence signal is collected to acquire two-dimensional magnetic flux leakage (MFL) data on the surface of wire ropes. Through the use of compressed sensing wavelet filtering (CSWF), the image expression of wire ropes MFL on the surface was obtained. Then this was taken as the input of the designed back propagation (BP) neural network to extract three kinds of MFL image geometry features and seven invariant moments of defect images. Good results were obtained. The experimental results show that nondestructive inspection through the use of remanence has higher accuracy and reliability compared with traditional inspection devices, along with smaller volume, lighter weight and higher precision. PMID:27571077
A compressed-sensing approach for super-resolution reconstruction of diffusion MRI
Ning, Lipeng; Setsompop, Kawin; Michailovich, Oleg; Makris, Nikos; Westin, Carl-Fredrik; Rathi, Yogesh
2015-01-01
We present an innovative framework for reconstructing high-spatial-resolution diffusion magnetic resonance imaging (dMRI) from multiple low-resolution (LR) images. Our approach combines the twin concepts of compressed sensing (CS) and classical super-resolution to reduce acquisition time while increasing spatial resolution. We use sub-pixel-shifted LR images with down-sampled and non-overlapping diffusion directions to reduce acquisition time. The diffusion signal in the high resolution (HR) image is represented in a sparsifying basis of spherical ridgelets to model complex fiber orientations with reduced number of measurements. The HR image is obtained as the solution of a convex optimization problem which can be solved using the proposed algorithm based on the alternating direction method of multipliers (ADMM). We qualitatively and quantitatively evaluate the performance of our method on two sets of in-vivo human brain data and show its effectiveness in accurately recovering very high resolution diffusion images. PMID:26221667
An infrared image super-resolution reconstruction method based on compressive sensing
NASA Astrophysics Data System (ADS)
Mao, Yuxing; Wang, Yan; Zhou, Jintao; Jia, Haiwei
2016-05-01
Limited by the properties of infrared detector and camera lens, infrared images are often detail missing and indistinct in vision. The spatial resolution needs to be improved to satisfy the requirements of practical application. Based on compressive sensing (CS) theory, this thesis presents a single image super-resolution reconstruction (SRR) method. With synthetically adopting image degradation model, difference operation-based sparse transformation method and orthogonal matching pursuit (OMP) algorithm, the image SRR problem is transformed into a sparse signal reconstruction issue in CS theory. In our work, the sparse transformation matrix is obtained through difference operation to image, and, the measurement matrix is achieved analytically from the imaging principle of infrared camera. Therefore, the time consumption can be decreased compared with the redundant dictionary obtained by sample training such as K-SVD. The experimental results show that our method can achieve favorable performance and good stability with low algorithm complexity.
A CT reconstruction algorithm based on non-aliasing Contourlet transform and compressive sensing.
Deng, Lu-zhen; Feng, Peng; Chen, Mian-yi; He, Peng; Vo, Quang-sang; Wei, Biao
2014-01-01
Compressive sensing (CS) theory has great potential for reconstructing CT images from sparse-views projection data. Currently, total variation (TV-) based CT reconstruction method is a hot research point in medical CT field, which uses the gradient operator as the sparse representation approach during the iteration process. However, the images reconstructed by this method often suffer the smoothing problem; to improve the quality of reconstructed images, this paper proposed a hybrid reconstruction method combining TV and non-aliasing Contourlet transform (NACT) and using the Split-Bregman method to solve the optimization problem. Finally, the simulation results show that the proposed algorithm can reconstruct high-quality CT images from few-views projection using less iteration numbers, which is more effective in suppressing noise and artefacts than algebraic reconstruction technique (ART) and TV-based reconstruction method. PMID:25101142
Compressive sensing based spinning mode detections by in-duct microphone arrays
NASA Astrophysics Data System (ADS)
Yu, Wenjun; Huang, Xun
2016-05-01
This paper presents a compressive sensing based experimental method for detecting spinning modes of sound waves propagating inside a cylindrical duct system. This method requires fewer dynamic pressure sensors than the number required by the Shannon–Nyquist sampling theorem so long as the incident waves are sparse in spinning modes. In this work, the proposed new method is firstly validated by preparing some of the numerical simulations with representative set-ups. Then, a duct acoustic testing rig with a spinning mode synthesiser and an in-duct microphone array is built to experimentally demonstrate the new approach. Both the numerical simulations and the experiment results are satisfactory, even when the practical issue of the background noise pollution is taken into account. The approach is beneficial for sensory array tests of silent aeroengines in particular and some other engineering systems with duct acoustics in general.
Si, Weijian; Qu, Xinggen; Liu, Lutao
2014-01-01
A novel direction of arrival (DOA) estimation method in compressed sensing (CS) is presented, in which DOA estimation is considered as the joint sparse recovery from multiple measurement vectors (MMV). The proposed method is obtained by minimizing the modified-based covariance matching criterion, which is acquired by adding penalties according to the regularization method. This minimization problem is shown to be a semidefinite program (SDP) and transformed into a constrained quadratic programming problem for reducing computational complexity which can be solved by the augmented Lagrange method. The proposed method can significantly improve the performance especially in the scenarios with low signal to noise ratio (SNR), small number of snapshots, and closely spaced correlated sources. In addition, the Cramér-Rao bound (CRB) of the proposed method is developed and the performance guarantee is given according to a version of the restricted isometry property (RIP). The effectiveness and satisfactory performance of the proposed method are illustrated by simulation results. PMID:24678272
Off-Grid DOA Estimation Using Alternating Block Coordinate Descent in Compressed Sensing
Si, Weijian; Qu, Xinggen; Qu, Zhiyu
2015-01-01
This paper presents a novel off-grid direction of arrival (DOA) estimation method to achieve the superior performance in compressed sensing (CS), in which DOA estimation problem is cast as a sparse reconstruction. By minimizing the mixed k-l norm, the proposed method can reconstruct the sparse source and estimate grid error caused by mismatch. An iterative process that minimizes the mixed k-l norm alternately over two sparse vectors is employed so that the nonconvex problem is solved by alternating convex optimization. In order to yield the better reconstruction properties, the block sparse source is exploited for off-grid DOA estimation. A block selection criterion is engaged to reduce the computational complexity. In addition, the proposed method is proved to have the global convergence. Simulation results show that the proposed method has the superior performance in comparisons to existing methods. PMID:26343658
Accelerating PS model-based dynamic cardiac MRI using compressed sensing.
Zhang, Xiaoyong; Xie, Guoxi; Shi, Caiyun; Su, Shi; Zhang, Yongqin; Liu, Xin; Qiu, Bensheng
2016-02-01
High spatiotemporal resolution MRI is a challenging topic in dynamic MRI field. Partial separability (PS) model has been successfully applied to dynamic cardiac MRI by exploiting data redundancy. However, the model requires substantial preprocessing data to accurately estimate the model parameters before image reconstruction. Since compressed sensing (CS) is a potential technique to accelerate MRI by reducing the number of acquired data, the combination of PS and CS, named as Stepped-SparsePS, was introduced to accelerate the preprocessing data acquisition of PS in this work. The proposed Stepped-SparsePS method sequentially reconstructs a set of aliased dynamic images in each channel based on PS model and then the final dynamic images from the aliased images using CS. The results from numerical simulations and in vivo experiments demonstrate that Stepped-SparsePS could significantly reduce data acquisition time while preserving high spatiotemporal resolution. PMID:26552006
Wide-field fluorescence molecular tomography with compressive sensing based preconditioning
Yao, Ruoyang; Pian, Qi; Intes, Xavier
2015-01-01
Wide-field optical tomography based on structured light illumination and detection strategies enables efficient tomographic imaging of large tissues at very fast acquisition speeds. However, the optical inverse problem based on such instrumental approach is still ill-conditioned. Herein, we investigate the benefit of employing compressive sensing-based preconditioning to wide-field structured illumination and detection approaches. We assess the performances of Fluorescence Molecular Tomography (FMT) when using such preconditioning methods both in silico and with experimental data. Additionally, we demonstrate that such methodology could be used to select the subset of patterns that provides optimal reconstruction performances. Lastly, we compare preconditioning data collected using a normal base that offers good experimental SNR against that directly acquired with optimal designed base. An experimental phantom study is provided to validate the proposed technique. PMID:26713202
Chen, Minhua; Silva, Jorge; Paisley, John; Wang, Chunping; Dunson, David; Carin, Lawrence
2013-01-01
Nonparametric Bayesian methods are employed to constitute a mixture of low-rank Gaussians, for data x ∈ ℝN that are of high dimension N but are constrained to reside in a low-dimensional subregion of ℝN. The number of mixture components and their rank are inferred automatically from the data. The resulting algorithm can be used for learning manifolds and for reconstructing signals from manifolds, based on compressive sensing (CS) projection measurements. The statistical CS inversion is performed analytically. We derive the required number of CS random measurements needed for successful reconstruction, based on easily-computed quantities, drawing on block-sparsity properties. The proposed methodology is validated on several synthetic and real datasets. PMID:23894225
Compressed Sensing in a Fully Non-Mechanical 350 GHz Imaging Setting
NASA Astrophysics Data System (ADS)
Augustin, S.; Hieronymus, J.; Jung, P.; Hübers, H.-W.
2015-05-01
We investigate a single-pixel camera (SPC) that relies on non-mechanical scanning with a terahertz (THz) spatial light modulator (SLM) and Compressed Sensing (CS) for image generation. The camera is based on a 350 GHz multiplier source and a Golay cell detector. The SLM consists of a Germanium disc, which is illuminated by a halogen lamp. The light of the lamp is transmitted through a thin-film transistor (TFT) liquid crystal display (LCD). This enables the generation of light patterns on the Germanium disc, which in turn produce reflecting patterns for THz radiation. Using up to 1000 different patterns the pseudo-inverse reconstruction algorithm and the CS algorithm CoSaMP are evaluated with respect to image quality. It is shown that CS allows a reduction of the necessary measurements by a factor of three without compromising the image quality.
Sparse-view ultrasound diffraction tomography using compressed sensing with nonuniform FFT.
Hua, Shaoyan; Ding, Mingyue; Yuchi, Ming
2014-01-01
Accurate reconstruction of the object from sparse-view sampling data is an appealing issue for ultrasound diffraction tomography (UDT). In this paper, we present a reconstruction method based on compressed sensing framework for sparse-view UDT. Due to the piecewise uniform characteristics of anatomy structures, the total variation is introduced into the cost function to find a more faithful sparse representation of the object. The inverse problem of UDT is iteratively resolved by conjugate gradient with nonuniform fast Fourier transform. Simulation results show the effectiveness of the proposed method that the main characteristics of the object can be properly presented with only 16 views. Compared to interpolation and multiband method, the proposed method can provide higher resolution and lower artifacts with the same view number. The robustness to noise and the computation complexity are also discussed. PMID:24868241
Detection and Tracking of Moving Targets Behind Cluttered Environments Using Compressive Sensing
NASA Astrophysics Data System (ADS)
Dang, Vinh Quang
Detection and tracking of moving targets (target's motion, vibration, etc.) in cluttered environments have been receiving much attention in numerous applications, such as disaster search-and-rescue, law enforcement, urban warfare, etc. One of the popular techniques is the use of stepped frequency continuous wave radar due to its low cost and complexity. However, the stepped frequency radar suffers from long data acquisition time. This dissertation focuses on detection and tracking of moving targets and vibration rates of stationary targets behind cluttered medium such as wall using stepped frequency radar enhanced by compressive sensing. The application of compressive sensing enables the reconstruction of the target space using fewer random frequencies, which decreases the acquisition time. Hardware-accelerated parallelization on GPU is investigated for the Orthogonal Matching Pursuit reconstruction algorithm. For simulation purpose, two hybrid methods have been developed to calculate the scattered fields from the targets through the wall approaching the antenna system, and to convert the incoming fields into voltage signals at terminals of the receive antenna. The first method is developed based on the plane wave spectrum approach for calculating the scattered fields of targets behind the wall. The method uses Fast Multiple Method (FMM) to calculate scattered fields on a particular source plane, decomposes them into plane wave components, and propagates the plane wave spectrum through the wall by integrating wall transmission coefficients before constructing the fields on a desired observation plane. The second method allows one to calculate the complex output voltage at terminals of a receiving antenna which fully takes into account the antenna effects. This method adopts the concept of complex antenna factor in Electromagnetic Compatibility (EMC) community for its calculation.
An adaptive fusion approach for infrared and visible images based on NSCT and compressed sensing
NASA Astrophysics Data System (ADS)
Zhang, Qiong; Maldague, Xavier
2016-01-01
A novel nonsubsampled contourlet transform (NSCT) based image fusion approach, implementing an adaptive-Gaussian (AG) fuzzy membership method, compressed sensing (CS) technique, total variation (TV) based gradient descent reconstruction algorithm, is proposed for the fusion computation of infrared and visible images. Compared with wavelet, contourlet, or any other multi-resolution analysis method, NSCT has many evident advantages, such as multi-scale, multi-direction, and translation invariance. As is known, a fuzzy set is characterized by its membership function (MF), while the commonly known Gaussian fuzzy membership degree can be introduced to establish an adaptive control of the fusion processing. The compressed sensing technique can sparsely sample the image information in a certain sampling rate, and the sparse signal can be recovered by solving a convex problem employing gradient descent based iterative algorithm(s). In the proposed fusion process, the pre-enhanced infrared image and the visible image are decomposed into low-frequency subbands and high-frequency subbands, respectively, via the NSCT method as a first step. The low-frequency coefficients are fused using the adaptive regional average energy rule; the highest-frequency coefficients are fused using the maximum absolute selection rule; the other high-frequency coefficients are sparsely sampled, fused using the adaptive-Gaussian regional standard deviation rule, and then recovered by employing the total variation based gradient descent recovery algorithm. Experimental results and human visual perception illustrate the effectiveness and advantages of the proposed fusion approach. The efficiency and robustness are also analyzed and discussed through different evaluation methods, such as the standard deviation, Shannon entropy, root-mean-square error, mutual information and edge-based similarity index.
NASA Astrophysics Data System (ADS)
Chang, Chen-Ming; Grant, Alexander M.; Lee, Brian J.; Kim, Ealgoo; Hong, KeyJo; Levin, Craig S.
2015-08-01
In the field of information theory, compressed sensing (CS) had been developed to recover signals at a lower sampling rate than suggested by the Nyquist-Shannon theorem, provided the signals have a sparse representation with respect to some base. CS has recently emerged as a method to multiplex PET detector readouts thanks to the sparse nature of 511 keV photon interactions in a typical PET study. We have shown in our previous numerical studies that, at the same multiplexing ratio, CS achieves higher signal-to-noise ratio (SNR) compared to Anger and cross-strip multiplexing. In addition, unlike Anger logic, multiplexing by CS preserves the capability to resolve multi-hit events, in which multiple pixels are triggered within the resolving time of the detector. In this work, we characterized the time, energy and intrinsic spatial resolution of two CS detectors and a data acquisition system we have developed for a PET insert system for simultaneous PET/MRI. The CS detector comprises a 2× 4 mosaic of 4× 4 arrays of 3.2× 3.2× 20 mm3 lutetium-yttrium orthosilicate crystals coupled one-to-one to eight 4× 4 silicon photomultiplier arrays. The total number of 128 pixels is multiplexed down to 16 readout channels by CS. The energy, coincidence time and intrinsic spatial resolution achieved by two CS detectors were 15.4+/- 0.1 % FWHM at 511 keV, 4.5 ns FWHM and 2.3 mm FWHM, respectively. A series of experiments were conducted to measure the sources of time jitter that limit the time resolution of the current system, which provides guidance for potential system design improvements. These findings demonstrate the feasibility of compressed sensing as a promising multiplexing method for PET detectors.
Chang, Chen-Ming; Grant, Alexander M; Lee, Brian J; Kim, Ealgoo; Hong, KeyJo; Levin, Craig S
2015-08-21
In the field of information theory, compressed sensing (CS) had been developed to recover signals at a lower sampling rate than suggested by the Nyquist-Shannon theorem, provided the signals have a sparse representation with respect to some base. CS has recently emerged as a method to multiplex PET detector readouts thanks to the sparse nature of 511 keV photon interactions in a typical PET study. We have shown in our previous numerical studies that, at the same multiplexing ratio, CS achieves higher signal-to-noise ratio (SNR) compared to Anger and cross-strip multiplexing. In addition, unlike Anger logic, multiplexing by CS preserves the capability to resolve multi-hit events, in which multiple pixels are triggered within the resolving time of the detector. In this work, we characterized the time, energy and intrinsic spatial resolution of two CS detectors and a data acquisition system we have developed for a PET insert system for simultaneous PET/MRI. The CS detector comprises a 2 x 4 mosaic of 4 x 4 arrays of 3.2 x 3.2 x 20 mm(3) lutetium-yttrium orthosilicate crystals coupled one-to-one to eight 4 x 4 silicon photomultiplier arrays. The total number of 128 pixels is multiplexed down to 16 readout channels by CS. The energy, coincidence time and intrinsic spatial resolution achieved by two CS detectors were 15.4±0.1% FWHM at 511 keV, 4.5 ns FWHM and 2.3 mm FWHM, respectively. A series of experiments were conducted to measure the sources of time jitter that limit the time resolution of the current system, which provides guidance for potential system design improvements. These findings demonstrate the feasibility of compressed sensing as a promising multiplexing method for PET detectors. PMID:26237671
Optical image encryption via photon-counting imaging and compressive sensing based ptychography
NASA Astrophysics Data System (ADS)
Rawat, Nitin; Hwang, In-Chul; Shi, Yishi; Lee, Byung-Geun
2015-06-01
In this study, we investigate the integration of compressive sensing (CS) and photon-counting imaging (PCI) techniques with a ptychography-based optical image encryption system. Primarily, the plaintext real-valued image is optically encrypted and recorded via a classical ptychography technique. Further, the sparse-based representations of the original encrypted complex data can be produced by combining CS and PCI techniques with the primary encrypted image. Such a combination takes an advantage of reduced encrypted samples (i.e., linearly projected random compressive complex samples and photon-counted complex samples) that can be exploited to realize optical decryption, which inherently serves as a secret key (i.e., independent to encryption phase keys) and makes an intruder attack futile. In addition to this, recording fewer encrypted samples provides a substantial bandwidth reduction in online transmission. We demonstrate that the fewer sparse-based complex samples have adequate information to realize decryption. To the best of our knowledge, this is the first report on integrating CS and PCI with conventional ptychography-based optical image encryption.
Wang, Yishan; Doleschel, Sammy; Wunderlich, Ralf; Heinen, Stefan
2016-07-01
In this paper, a wearable and wireless ECG system is firstly designed with Bluetooth Low Energy (BLE). It can detect 3-lead ECG signals and is completely wireless. Secondly the digital Compressed Sensing (CS) is implemented to increase the energy efficiency of wireless ECG sensor. Different sparsifying basis, various compression ratio (CR) and several reconstruction algorithms are simulated and discussed. Finally the reconstruction is done by the android application (App) on smartphone to display the signal in real time. The power efficiency is measured and compared with the system without CS. The optimum satisfying basis built by 3-level decomposed db4 wavelet coefficients, 1-bit Bernoulli random matrix and the most suitable reconstruction algorithm are selected by the simulations and applied on the sensor node and App. The signal is successfully reconstructed and displayed on the App of smartphone. Battery life of sensor node is extended from 55 h to 67 h. The presented wireless ECG system with CS can significantly extend the battery life by 22 %. With the compact characteristic and long term working time, the system provides a feasible solution for the long term homecare utilization. PMID:27240841
Performance assessment of a single-pixel compressive sensing imaging system
NASA Astrophysics Data System (ADS)
Du Bosq, Todd W.; Preece, Bradley L.
2016-05-01
Conventional electro-optical and infrared (EO/IR) systems capture an image by measuring the light incident at each of the millions of pixels in a focal plane array. Compressive sensing (CS) involves capturing a smaller number of unconventional measurements from the scene, and then using a companion process known as sparse reconstruction to recover the image as if a fully populated array that satisfies the Nyquist criteria was used. Therefore, CS operates under the assumption that signal acquisition and data compression can be accomplished simultaneously. CS has the potential to acquire an image with equivalent information content to a large format array while using smaller, cheaper, and lower bandwidth components. However, the benefits of CS do not come without compromise. The CS architecture chosen must effectively balance between physical considerations (SWaP-C), reconstruction accuracy, and reconstruction speed to meet operational requirements. To properly assess the value of such systems, it is necessary to fully characterize the image quality, including artifacts and sensitivity to noise. Imagery of the two-handheld object target set at range was collected using a passive SWIR single-pixel CS camera for various ranges, mirror resolution, and number of processed measurements. Human perception experiments were performed to determine the identification performance within the trade space. The performance of the nonlinear CS camera was modeled with the Night Vision Integrated Performance Model (NV-IPM) by mapping the nonlinear degradations to an equivalent linear shift invariant model. Finally, the limitations of CS modeling techniques will be discussed.
3d-3d correspondence revisited
NASA Astrophysics Data System (ADS)
Chung, Hee-Joong; Dimofte, Tudor; Gukov, Sergei; Sułkowski, Piotr
2016-04-01
In fivebrane compactifications on 3-manifolds, we point out the importance of all flat connections in the proper definition of the effective 3d {N}=2 theory. The Lagrangians of some theories with the desired properties can be constructed with the help of homological knot invariants that categorify colored Jones polynomials. Higgsing the full 3d theories constructed this way recovers theories found previously by Dimofte-Gaiotto-Gukov. We also consider the cutting and gluing of 3-manifolds along smooth boundaries and the role played by all flat connections in this operation.
Mahrous, Hesham; Ward, Rabab
2016-01-01
This paper proposes a compressive sensing (CS) method for multi-channel electroencephalogram (EEG) signals in Wireless Body Area Network (WBAN) applications, where the battery life of sensors is limited. For the single EEG channel case, known as the single measurement vector (SMV) problem, the Block Sparse Bayesian Learning-BO (BSBL-BO) method has been shown to yield good results. This method exploits the block sparsity and the intra-correlation (i.e., the linear dependency) within the measurement vector of a single channel. For the multichannel case, known as the multi-measurement vector (MMV) problem, the Spatio-Temporal Sparse Bayesian Learning (STSBL-EM) method has been proposed. This method learns the joint correlation structure in the multichannel signals by whitening the model in the temporal and the spatial domains. Our proposed method represents the multi-channels signal data as a vector that is constructed in a specific way, so that it has a better block sparsity structure than the conventional representation obtained by stacking the measurement vectors of the different channels. To reconstruct the multichannel EEG signals, we modify the parameters of the BSBL-BO algorithm, so that it can exploit not only the linear but also the non-linear dependency structures in a vector. The modified BSBL-BO is then applied on the vector with the better sparsity structure. The proposed method is shown to significantly outperform existing SMV and also MMV methods. It also shows significant lower compression errors even at high compression ratios such as 10:1 on three different datasets. PMID:26861335
Mahrous, Hesham; Ward, Rabab
2016-01-01
This paper proposes a compressive sensing (CS) method for multi-channel electroencephalogram (EEG) signals in Wireless Body Area Network (WBAN) applications, where the battery life of sensors is limited. For the single EEG channel case, known as the single measurement vector (SMV) problem, the Block Sparse Bayesian Learning-BO (BSBL-BO) method has been shown to yield good results. This method exploits the block sparsity and the intra-correlation (i.e., the linear dependency) within the measurement vector of a single channel. For the multichannel case, known as the multi-measurement vector (MMV) problem, the Spatio-Temporal Sparse Bayesian Learning (STSBL-EM) method has been proposed. This method learns the joint correlation structure in the multichannel signals by whitening the model in the temporal and the spatial domains. Our proposed method represents the multi-channels signal data as a vector that is constructed in a specific way, so that it has a better block sparsity structure than the conventional representation obtained by stacking the measurement vectors of the different channels. To reconstruct the multichannel EEG signals, we modify the parameters of the BSBL-BO algorithm, so that it can exploit not only the linear but also the non-linear dependency structures in a vector. The modified BSBL-BO is then applied on the vector with the better sparsity structure. The proposed method is shown to significantly outperform existing SMV and also MMV methods. It also shows significant lower compression errors even at high compression ratios such as 10:1 on three different datasets. PMID:26861335
Deterministic matrices matching the compressed sensing phase transitions of Gaussian random matrices
Monajemi, Hatef; Jafarpour, Sina; Gavish, Matan; Donoho, David L.; Ambikasaran, Sivaram; Bacallado, Sergio; Bharadia, Dinesh; Chen, Yuxin; Choi, Young; Chowdhury, Mainak; Chowdhury, Soham; Damle, Anil; Fithian, Will; Goetz, Georges; Grosenick, Logan; Gross, Sam; Hills, Gage; Hornstein, Michael; Lakkam, Milinda; Lee, Jason; Li, Jian; Liu, Linxi; Sing-Long, Carlos; Marx, Mike; Mittal, Akshay; Monajemi, Hatef; No, Albert; Omrani, Reza; Pekelis, Leonid; Qin, Junjie; Raines, Kevin; Ryu, Ernest; Saxe, Andrew; Shi, Dai; Siilats, Keith; Strauss, David; Tang, Gary; Wang, Chaojun; Zhou, Zoey; Zhu, Zhen
2013-01-01
In compressed sensing, one takes samples of an N-dimensional vector using an matrix A, obtaining undersampled measurements . For random matrices with independent standard Gaussian entries, it is known that, when is k-sparse, there is a precisely determined phase transition: for a certain region in the (,)-phase diagram, convex optimization typically finds the sparsest solution, whereas outside that region, it typically fails. It has been shown empirically that the same property—with the same phase transition location—holds for a wide range of non-Gaussian random matrix ensembles. We report extensive experiments showing that the Gaussian phase transition also describes numerous deterministic matrices, including Spikes and Sines, Spikes and Noiselets, Paley Frames, Delsarte-Goethals Frames, Chirp Sensing Matrices, and Grassmannian Frames. Namely, for each of these deterministic matrices in turn, for a typical k-sparse object, we observe that convex optimization is successful over a region of the phase diagram that coincides with the region known for Gaussian random matrices. Our experiments considered coefficients constrained to for four different sets , and the results establish our finding for each of the four associated phase transitions. PMID:23277588
Huang, Wei; Xiao, Liang; Liu, Hongyi; Wei, Zhihui
2015-01-01
Due to the instrumental and imaging optics limitations, it is difficult to acquire high spatial resolution hyperspectral imagery (HSI). Super-resolution (SR) imagery aims at inferring high quality images of a given scene from degraded versions of the same scene. This paper proposes a novel hyperspectral imagery super-resolution (HSI-SR) method via dictionary learning and spatial-spectral regularization. The main contributions of this paper are twofold. First, inspired by the compressive sensing (CS) framework, for learning the high resolution dictionary, we encourage stronger sparsity on image patches and promote smaller coherence between the learned dictionary and sensing matrix. Thus, a sparsity and incoherence restricted dictionary learning method is proposed to achieve higher efficiency sparse representation. Second, a variational regularization model combing a spatial sparsity regularization term and a new local spectral similarity preserving term is proposed to integrate the spectral and spatial-contextual information of the HSI. Experimental results show that the proposed method can effectively recover spatial information and better preserve spectral information. The high spatial resolution HSI reconstructed by the proposed method outperforms reconstructed results by other well-known methods in terms of both objective measurements and visual evaluation. PMID:25608212
Huang, Wei; Xiao, Liang; Liu, Hongyi; Wei, Zhihui
2015-01-01
Due to the instrumental and imaging optics limitations, it is difficult to acquire high spatial resolution hyperspectral imagery (HSI). Super-resolution (SR) imagery aims at inferring high quality images of a given scene from degraded versions of the same scene. This paper proposes a novel hyperspectral imagery super-resolution (HSI-SR) method via dictionary learning and spatial-spectral regularization. The main contributions of this paper are twofold. First, inspired by the compressive sensing (CS) framework, for learning the high resolution dictionary, we encourage stronger sparsity on image patches and promote smaller coherence between the learned dictionary and sensing matrix. Thus, a sparsity and incoherence restricted dictionary learning method is proposed to achieve higher efficiency sparse representation. Second, a variational regularization model combing a spatial sparsity regularization term and a new local spectral similarity preserving term is proposed to integrate the spectral and spatial-contextual information of the HSI. Experimental results show that the proposed method can effectively recover spatial information and better preserve spectral information. The high spatial resolution HSI reconstructed by the proposed method outperforms reconstructed results by other well-known methods in terms of both objective measurements and visual evaluation. PMID:25608212
NASA Astrophysics Data System (ADS)
Li, Jia; Wang, Qiang; Yan, Wenjie; Shen, Yi
2015-12-01
Cooperative spectrum sensing exploits the spatial diversity to improve the detection of occupied channels in cognitive radio networks (CRNs). Cooperative compressive spectrum sensing (CCSS) utilizing the sparsity of channel occupancy further improves the efficiency by reducing the number of reports without degrading detection performance. In this paper, we firstly and mainly propose the referred multi-candidate orthogonal matrix matching pursuit (MOMMP) algorithms to efficiently and effectively detect occupied channels at fusion center (FC), where multi-candidate identification and orthogonal projection are utilized to respectively reduce the number of required iterations and improve the probability of exact identification. Secondly, two common but different approaches based on threshold and Gaussian distribution are introduced to realize the multi-candidate identification. Moreover, to improve the detection accuracy and energy efficiency, we propose the matrix construction based on shrinkage and gradient descent (MCSGD) algorithm to provide a deterministic filter coefficient matrix of low t-average coherence. Finally, several numerical simulations validate that our proposals provide satisfactory performance with higher probability of detection, lower probability of false alarm and less detection time.
Tam, Leo K.; Galiana, Gigi; Stockmann, Jason P.; Tagare, Hemant; Peters, Dana C.; Constable, R. Todd
2014-01-01
Purpose Nonlinear spatial encoding magnetic (SEM) field strategies such as O-space imaging have previously reported dispersed artifacts during accelerated scans. Compressed sensing (CS) has shown a sparsity-promoting convex program allows image reconstruction from a reduced data set when using the appropriate sampling. The development of a pseudo-random center placement (CP) O-space CS approach optimizes incoherence through SEM field modulation to reconstruct an image with reduced error. Theory and Methods The incoherence parameter determines the sparsity levels for which CS is valid and the related transform point spread function measures the maximum interference for a single point. The O-space acquisition is optimized for CS by perturbing the Z2 strength within 30% of the nominal value and demonstrated on a human 3T scanner. Results Pseudo-random CP O-space imaging is shown to improve incoherence between the sensing and sparse domains. Images indicate pseudo-random CP O-space has reduced mean squared error compared with a typical linear SEM field acquisition method. Conclusion Pseudo-random CP O-space imaging, with a nonlinear SEM field designed for CS, is shown to reduce mean squared error of images at high acceleration over linear encoding methods for a 2D slice when using an eight channel circumferential receiver array for parallel imaging. PMID:25042143
Wow! 3D Content Awakens the Classroom
ERIC Educational Resources Information Center
Gordon, Dan
2010-01-01
From her first encounter with stereoscopic 3D technology designed for classroom instruction, Megan Timme, principal at Hamilton Park Pacesetter Magnet School in Dallas, sensed it could be transformative. Last spring, when she began pilot-testing 3D content in her third-, fourth- and fifth-grade classrooms, Timme wasn't disappointed. Students…
NASA Astrophysics Data System (ADS)
Schneiderwind, S.; Mason, J.; Wiatr, T.; Papanikolaou, I.; Reicherter, K.
2015-09-01
Two normal faults on the Island of Crete and mainland Greece were studied to create and test an innovative workflow to make palaeoseismic trench logging more objective, and visualise the sedimentary architecture within the trench wall in 3-D. This is achieved by combining classical palaeoseismic trenching techniques with multispectral approaches. A conventional trench log was firstly compared to results of iso cluster analysis of a true colour photomosaic representing the spectrum of visible light. Passive data collection disadvantages (e.g. illumination) were addressed by complementing the dataset with active near-infrared backscatter signal image from t-LiDAR measurements. The multispectral analysis shows that distinct layers can be identified and it compares well with the conventional trench log. According to this, a distinction of adjacent stratigraphic units was enabled by their particular multispectral composition signature. Based on the trench log, a 3-D-interpretation of GPR data collected on the vertical trench wall was then possible. This is highly beneficial for measuring representative layer thicknesses, displacements and geometries at depth within the trench wall. Thus, misinterpretation due to cutting effects is minimised. Sedimentary feature geometries related to earthquake magnitude can be used to improve the accuracy of seismic hazard assessments. Therefore, this manuscript combines multiparametric approaches and shows: (i) how a 3-D visualisation of palaeoseismic trench stratigraphy and logging can be accomplished by combining t-LiDAR and GRP techniques, and (ii) how a multispectral digital analysis can offer additional advantages and a higher objectivity in the interpretation of palaeoseismic and stratigraphic information. The multispectral datasets are stored allowing unbiased input for future (re-)investigations.
NASA Astrophysics Data System (ADS)
Meulien Ohlmann, Odile
2013-02-01
Today the industry offers a chain of 3D products. Learning to "read" and to "create in 3D" becomes an issue of education of primary importance. 25 years professional experience in France, the United States and Germany, Odile Meulien set up a personal method of initiation to 3D creation that entails the spatial/temporal experience of the holographic visual. She will present some different tools and techniques used for this learning, their advantages and disadvantages, programs and issues of educational policies, constraints and expectations related to the development of new techniques for 3D imaging. Although the creation of display holograms is very much reduced compared to the creation of the 90ies, the holographic concept is spreading in all scientific, social, and artistic activities of our present time. She will also raise many questions: What means 3D? Is it communication? Is it perception? How the seeing and none seeing is interferes? What else has to be taken in consideration to communicate in 3D? How to handle the non visible relations of moving objects with subjects? Does this transform our model of exchange with others? What kind of interaction this has with our everyday life? Then come more practical questions: How to learn creating 3D visualization, to learn 3D grammar, 3D language, 3D thinking? What for? At what level? In which matter? for whom?
Wiscombe, Warren; Marshak, Alexander; Knyazikhin, Yuri; Chiu, Christine
2007-05-04
We have basically completed all the goals stated in the previous proposal and published or submitted journal papers thereon, the only exception being First-Principles Monte Carlo which has taken more time than expected. We finally finished the comprehensive book on 3D cloud radiative transfer (edited by Marshak and Davis and published by Springer), with many contributions by ARM scientists; this book was highlighted in the 2005 ARM Annual Report. We have also completed (for now) our pioneering work on new models of cloud drop clustering based on ARM aircraft FSSP data, with applications both to radiative transfer and to rainfall. This clustering work was highlighted in the FY07 “Our Changing Planet” (annual report of the US Climate Change Science Program). Our group published 22 papers, one book, and 5 chapters in that book, during this proposal period. All are listed at the end of this section. Below, we give brief highlights of some of those papers.
NASA Astrophysics Data System (ADS)
Tang, Jie; Nett, Brian E.; Chen, Guang-Hong
2009-10-01
Of all available reconstruction methods, statistical iterative reconstruction algorithms appear particularly promising since they enable accurate physical noise modeling. The newly developed compressive sampling/compressed sensing (CS) algorithm has shown the potential to accurately reconstruct images from highly undersampled data. The CS algorithm can be implemented in the statistical reconstruction framework as well. In this study, we compared the performance of two standard statistical reconstruction algorithms (penalized weighted least squares and q-GGMRF) to the CS algorithm. In assessing the image quality using these iterative reconstructions, it is critical to utilize realistic background anatomy as the reconstruction results are object dependent. A cadaver head was scanned on a Varian Trilogy system at different dose levels. Several figures of merit including the relative root mean square error and a quality factor which accounts for the noise performance and the spatial resolution were introduced to objectively evaluate reconstruction performance. A comparison is presented between the three algorithms for a constant undersampling factor comparing different algorithms at several dose levels. To facilitate this comparison, the original CS method was formulated in the framework of the statistical image reconstruction algorithms. Important conclusions of the measurements from our studies are that (1) for realistic neuro-anatomy, over 100 projections are required to avoid streak artifacts in the reconstructed images even with CS reconstruction, (2) regardless of the algorithm employed, it is beneficial to distribute the total dose to more views as long as each view remains quantum noise limited and (3) the total variation-based CS method is not appropriate for very low dose levels because while it can mitigate streaking artifacts, the images exhibit patchy behavior, which is potentially harmful for medical diagnosis.
Compressed histogram attribute profiles for the classification of VHR remote sensing images
NASA Astrophysics Data System (ADS)
Battiti, Romano; Demir, Begüm; Bruzzone, Lorenzo
2015-10-01
This paper presents a novel compressed histogram attribute profile (CHAP) for classification of very high resolution remote sensing images. The CHAP characterizes the marginal local distribution of attribute filter responses to model the texture information of each sample with a small number of image features. This is achieved based on a three steps algorithm. The first step is devoted to provide a complete characterization of spatial properties of objects in a scene. To this end, the attribute profile (AP) is initially built by the sequential application of attribute filters to the considered image. Then, to capture complete spatial characteristics of the structures in the scene a local histogram is calculated for each sample of each image in the AP. The local histograms of the same pixel location can contain redundant information since: i) adjacent histogram bins can provide similar information; and ii) the attributes obtained with similar attribute filter threshold values lead to redundant features. In the second step, to point out the redundancies the local histograms of the same pixel locations in the AP are organized into a 2D matrix representation, where columns are associated to the local histograms and rows represents a specific bin in all histograms of the considered sequence of filtered attributes in the profile. This representation results in the characterization of the texture information of each sample through a 2D texture descriptor. In the final step, a novel compression approach based on a uniform 2D quantization strategy is applied to remove the redundancy of the 2D texture descriptors. Finally the CHAP is classified by a Support Vector Machine classifier with histogram intersection kernel that is very effective for high dimensional histogram-based feature representations. Experimental results confirm the effectiveness of the proposed CHAP in terms of computational complexity, storage requirements and classification accuracy when compared to the
Effective low-power wearable wireless surface EMG sensor design based on analog-compressed sensing.
Balouchestani, Mohammadreza; Krishnan, Sridhar
2014-01-01
Surface Electromyography (sEMG) is a non-invasive measurement process that does not involve tools and instruments to break the skin or physically enter the body to investigate and evaluate the muscular activities produced by skeletal muscles. The main drawbacks of existing sEMG systems are: (1) they are not able to provide real-time monitoring; (2) they suffer from long processing time and low speed; (3) they are not effective for wireless healthcare systems because they consume huge power. In this work, we present an analog-based Compressed Sensing (CS) architecture, which consists of three novel algorithms for design and implementation of wearable wireless sEMG bio-sensor. At the transmitter side, two new algorithms are presented in order to apply the analog-CS theory before Analog to Digital Converter (ADC). At the receiver side, a robust reconstruction algorithm based on a combination of ℓ1-ℓ1-optimization and Block Sparse Bayesian Learning (BSBL) framework is presented to reconstruct the original bio-signals from the compressed bio-signals. The proposed architecture allows reducing the sampling rate to 25% of Nyquist Rate (NR). In addition, the proposed architecture reduces the power consumption to 40%, Percentage Residual Difference (PRD) to 24%, Root Mean Squared Error (RMSE) to 2%, and the computation time from 22 s to 9.01 s, which provide good background for establishing wearable wireless healthcare systems. The proposed architecture achieves robust performance in low Signal-to-Noise Ratio (SNR) for the reconstruction process. PMID:25526357
Effective Low-Power Wearable Wireless Surface EMG Sensor Design Based on Analog-Compressed Sensing
Balouchestani, Mohammadreza; Krishnan, Sridhar
2014-01-01
Surface Electromyography (sEMG) is a non-invasive measurement process that does not involve tools and instruments to break the skin or physically enter the body to investigate and evaluate the muscular activities produced by skeletal muscles. The main drawbacks of existing sEMG systems are: (1) they are not able to provide real-time monitoring; (2) they suffer from long processing time and low speed; (3) they are not effective for wireless healthcare systems because they consume huge power. In this work, we present an analog-based Compressed Sensing (CS) architecture, which consists of three novel algorithms for design and implementation of wearable wireless sEMG bio-sensor. At the transmitter side, two new algorithms are presented in order to apply the analog-CS theory before Analog to Digital Converter (ADC). At the receiver side, a robust reconstruction algorithm based on a combination of ℓ1-ℓ1-optimization and Block Sparse Bayesian Learning (BSBL) framework is presented to reconstruct the original bio-signals from the compressed bio-signals. The proposed architecture allows reducing the sampling rate to 25% of Nyquist Rate (NR). In addition, the proposed architecture reduces the power consumption to 40%, Percentage Residual Difference (PRD) to 24%, Root Mean Squared Error (RMSE) to 2%, and the computation time from 22 s to 9.01 s, which provide good background for establishing wearable wireless healthcare systems. The proposed architecture achieves robust performance in low Signal-to-Noise Ratio (SNR) for the reconstruction process. PMID:25526357
NASA Astrophysics Data System (ADS)
Qin, W.; Yin, J.; Yao, H.
2013-12-01
On May 24th 2013 a Mw 8.3 normal faulting earthquake occurred at a depth of approximately 600 km beneath the sea of Okhotsk, Russia. It is a rare mega earthquake that ever occurred at such a great depth. We use the time-domain iterative backprojection (IBP) method [1] and also the frequency-domain compressive sensing (CS) technique[2] to investigate the rupture process and energy radiation of this mega earthquake. We currently use the teleseismic P-wave data from about 350 stations of USArray. IBP is an improved method of the traditional backprojection method, which more accurately locates subevents (energy burst) during earthquake rupture and determines the rupture speeds. The total rupture duration of this earthquake is about 35 s with a nearly N-S rupture direction. We find that the rupture is bilateral in the beginning 15 seconds with slow rupture speeds: about 2.5km/s for the northward rupture and about 2 km/s for the southward rupture. After that, the northward rupture stopped while the rupture towards south continued. The average southward rupture speed between 20-35 s is approximately 5 km/s, lower than the shear wave speed (about 5.5 km/s) at the hypocenter depth. The total rupture length is about 140km, in a nearly N-S direction, with a southward rupture length about 100 km and a northward rupture length about 40 km. We also use the CS method, a sparse source inversion technique, to study the frequency-dependent seismic radiation of this mega earthquake. We observe clear along-strike frequency dependence of the spatial and temporal distribution of seismic radiation and rupture process. The results from both methods are generally similar. In the next step, we'll use data from dense arrays in southwest China and also global stations for further analysis in order to more comprehensively study the rupture process of this deep mega earthquake. Reference [1] Yao H, Shearer P M, Gerstoft P. Subevent location and rupture imaging using iterative backprojection for
ERIC Educational Resources Information Center
Hastings, S. K.
2002-01-01
Discusses 3 D imaging as it relates to digital representations in virtual library collections. Highlights include X-ray computed tomography (X-ray CT); the National Science Foundation (NSF) Digital Library Initiatives; output peripherals; image retrieval systems, including metadata; and applications of 3 D imaging for libraries and museums. (LRW)
Electron tomography image reconstruction using data-driven adaptive compressed sensing.
Al-Afeef, Ala'; Cockshott, W Paul; MacLaren, Ian; McVitie, Stephen
2016-05-01
Electron tomography (ET) is an increasingly important technique for the study of the three-dimensional morphologies of nanostructures. ET involves the acquisition of a set of two-dimensional projection images, followed by the reconstruction into a volumetric image by solving an inverse problem. However, due to limitations in the acquisition process, this inverse problem is ill-posed (i.e., a unique solution may not exist). Furthermore, reconstruction usually suffers from missing wedge artifacts (e.g., star, fan, blurring, and elongation artifacts). Recently, compressed sensing (CS) has been applied to ET and showed promising results for reducing missing wedge artifacts. This uses image sparsity as a priori knowledge to improve the accuracy of reconstruction, and can require fewer projections than other reconstruction techniques. The performance of CS relies heavily on the degree of sparsity in the selected transform domain and this depends essentially on the choice of sparsifying transform. We propose a new image reconstruction algorithm for ET that learns the sparsifying transform adaptively using a dictionary-based approach. We demonstrate quantitatively using simulations from complex phantoms that this new approach reconstructs the morphology with higher fidelity than either analytically based CS reconstruction algorithms or traditional weighted back projection from the same dataset. SCANNING 38:251-276, 2016. © 2015 Wiley Periodicals, Inc. PMID:26435074
Compressed sensing with gradient total variation for low-dose CBCT reconstruction
NASA Astrophysics Data System (ADS)
Seo, Chang-Woo; Cha, Bo Kyung; Jeon, Seongchae; Huh, Young; Park, Justin C.; Lee, Byeonghun; Baek, Junghee; Kim, Eunyoung
2015-06-01
This paper describes the improvement of convergence speed with gradient total variation (GTV) in compressed sensing (CS) for low-dose cone-beam computed tomography (CBCT) reconstruction. We derive a fast algorithm for the constrained total variation (TV)-based a minimum number of noisy projections. To achieve this task we combine the GTV with a TV-norm regularization term to promote an accelerated sparsity in the X-ray attenuation characteristics of the human body. The GTV is derived from a TV and enforces more efficient computationally and faster in convergence until a desired solution is achieved. The numerical algorithm is simple and derives relatively fast convergence. We apply a gradient projection algorithm that seeks a solution iteratively in the direction of the projected gradient while enforcing a non-negatively of the found solution. In comparison with the Feldkamp, Davis, and Kress (FDK) and conventional TV algorithms, the proposed GTV algorithm showed convergence in ≤18 iterations, whereas the original TV algorithm needs at least 34 iterations in reducing 50% of the projections compared with the FDK algorithm in order to reconstruct the chest phantom images. Future investigation includes improving imaging quality, particularly regarding X-ray cone-beam scatter, and motion artifacts of CBCT reconstruction.
A noise model for the design of a compressive sensing imaging system
NASA Astrophysics Data System (ADS)
Preece, Bradley; Du Bosq, Todd; Namazi, Nader; Nehmetallah, Georges; Kelly, Kevin F.
2015-05-01
The design and modeling of compressive sensing (CS) imagers is difficult due to the complexity and non-linearity of the system and reconstruction algorithm. The Night Vision Integrated Performance Model (NV-IPM) is a linear imaging system design tool that is very useful for complex system trade studies. The custom component generator, included in NV-IPM, will be used to include a recently published theory for CS that links measurement noise, easily calculated with NV-IPM, to the noise of the reconstructed CS image given the estimated sparsity of the scene and the number of measurements as input. As the sparsity will also depend on other factors such as the optical transfer function and the scene content, an empirical relationship will be developed between the linear model within NV-IPM and the non-linear reconstruction algorithm using measured test data. Using the theory, a CS imager varying the number of measurements will be compared to a notional traditional imager.
Fast and low-dose computed laminography using compressive sensing based technique
Abbas, Sajid Park, Miran Cho, Seungryong
2015-03-31
Computed laminography (CL) is well known for inspecting microstructures in the materials, weldments and soldering defects in high density packed components or multilayer printed circuit boards. The overload problem on x-ray tube and gross failure of the radio-sensitive electronics devices during a scan are among important issues in CL which needs to be addressed. The sparse-view CL can be one of the viable option to overcome such issues. In this work a numerical aluminum welding phantom was simulated to collect sparsely sampled projection data at only 40 views using a conventional CL scanning scheme i.e. oblique scan. A compressive-sensing inspired total-variation (TV) minimization algorithm was utilized to reconstruct the images. It is found that the images reconstructed using sparse view data are visually comparable with the images reconstructed using full scan data set i.e. at 360 views on regular interval. We have quantitatively confirmed that tiny structures such as copper and tungsten slags, and copper flakes in the reconstructed images from sparsely sampled data are comparable with the corresponding structure present in the fully sampled data case. A blurring effect can be seen near the edges of few pores at the bottom of the reconstructed images from sparsely sampled data, despite the overall image quality is reasonable for fast and low-dose NDT.
Stevens, Andrew J.; Yang, Hao; Carin, Lawrence; Arslan, Ilke; Browning, Nigel D.
2014-02-11
The use of high resolution imaging methods in the scanning transmission electron microscope (STEM) is limited in many cases by the sensitivity of the sample to the beam and the onset of electron beam damage (for example in the study of organic systems, in tomography and during in-situ experiments). To demonstrate that alternative strategies for image acquisition can help alleviate this beam damage issue, here we apply compressive sensing via Bayesian dictionary learning to high resolution STEM images. These experiments successively reduce the number of pixels in the image (thereby reducing the overall dose while maintaining the high resolution information) and show promising results for reconstructing images from this reduced set of randomly collected measurements. We show that this approach is valid for both atomic resolution images and nanometer resolution studies, such as those that might be used in tomography datasets, by applying the method to images of strontium titanate and zeolites. As STEM images are acquired pixel by pixel while the beam is scanned over the surface of the sample, these post acquisition manipulations of the images can, in principle, be directly implemented as a low-dose acquisition method with no change in the electron optics or alignment of the microscope itself.
An effective approach to attenuate random noise based on compressive sensing and curvelet transform
NASA Astrophysics Data System (ADS)
Liu, Wei; Cao, Siyuan; Chen, Yangkang; Zu, Shaohuan
2016-04-01
Random noise attenuation is an important step in seismic data processing. In this paper, we propose a novel denoising approach based on compressive sensing and the curvelet transform. We formulate the random noise attenuation problem as an L 1 norm regularized optimization problem. We propose to use the curvelet transform as the sparse transform in the optimization problem to regularize the sparse coefficients in order to separate signal and noise and to use the gradient projection for sparse reconstruction (GPSR) algorithm to solve the formulated optimization problem with an easy implementation and a fast convergence. We tested the performance of our proposed approach on both synthetic and field seismic data. Numerical results show that the proposed approach can effectively suppress the distortion near the edge of seismic events during the noise attenuation process and has high computational efficiency compared with the traditional curvelet thresholding and iterative soft thresholding based denoising methods. Besides, compared with f-x deconvolution, the proposed denoising method is capable of eliminating the random noise more effectively while preserving more useful signals.
On the Application of Compressed Sensing to Non-Time-Resolved PIV Measurements
NASA Astrophysics Data System (ADS)
Deem, Eric; Davis, Timothy; Cattafesta, Louis; Alvi, Farrukh
2014-11-01
Temporally resolved, full-field flow measurements are still impractical for most flows of interest. Fortunately, the spectral content of many flows can be described in a low dimensional space. This sparsity has inspired the recent adaptation of compressed sensing into the fluid mechanics community as a method for reconstructing spectral content of sub-Nyquist sampled data (arXiv:1401.7047). We apply this method to the analysis of several example fluid flow data sets, varying in spectral content. These data sets include the measured flow about a high-lift airfoil, an impinging jet, and a zero-net mass-flux (ZNMF) actuator. The Proper Orthogonal Decomposition (POD) is applied to the random PIV snapshots and we apply Orthogonal Matching Pursuit (OMP) to approximate the discrete Fourier transform of the POD coefficients. Additionally, reconstruction parameters are varied for to examine criteria regarding the probability of a successful reconstruction versus the degree of spectral sparsity. The advantages and restrictions of this method are discussed. This work is supported by the Air Force Office of Scientic Research (Grant FA9550-09-1-0257), monitored by Dr. Doug Smith, and the Florida Center for Advanced Aero-Propulsion.
Huang, Jianping; Wang, Lihui; Chu, Chunyu; Zhang, Yanli; Liu, Wanyu; Zhu, Yuemin
2016-04-29
Diffusion tensor magnetic resonance (DTMR) imaging and diffusion tensor imaging (DTI) have been widely used to probe noninvasively biological tissue structures. However, DTI suffers from long acquisition times, which limit its practical and clinical applications. This paper proposes a new Compressed Sensing (CS) reconstruction method that employs joint sparsity and rank deficiency to reconstruct cardiac DTMR images from undersampled k-space data. Diffusion-weighted images acquired in different diffusion directions were firstly stacked as columns to form the matrix. The matrix was row sparse in the transform domain and had a low rank. These two properties were then incorporated into the CS reconstruction framework. The underlying constrained optimization problem was finally solved by the first-order fast method. Experiments were carried out on both simulation and real human cardiac DTMR images. The results demonstrated that the proposed approach had lower reconstruction errors for DTI indices, including fractional anisotropy (FA) and mean diffusivities (MD), compared to the existing CS-DTMR image reconstruction techniques. PMID:27163322
Compressed sensing reconstruction of cardiac cine MRI using golden angle spiral trajectories
NASA Astrophysics Data System (ADS)
Tolouee, Azar; Alirezaie, Javad; Babyn, Paul
2015-11-01
In dynamic cardiac cine Magnetic Resonance Imaging (MRI), the spatiotemporal resolution is limited by the low imaging speed. Compressed sensing (CS) theory has been applied to improve the imaging speed and thus the spatiotemporal resolution. The purpose of this paper is to improve CS reconstruction of under sampled data by exploiting spatiotemporal sparsity and efficient spiral trajectories. We extend k-t sparse algorithm to spiral trajectories to achieve high spatio temporal resolutions in cardiac cine imaging. We have exploited spatiotemporal sparsity of cardiac cine MRI by applying a 2D + time wavelet-Fourier transform. For efficient coverage of k-space, we have used a modified version of multi shot (interleaved) spirals trajectories. In order to reduce incoherent aliasing artifact, we use different random undersampling pattern for each temporal frame. Finally, we have used nonuniform fast Fourier transform (NUFFT) algorithm to reconstruct the image from the non-uniformly acquired samples. The proposed approach was tested in simulated and cardiac cine MRI data. Results show that higher acceleration factors with improved image quality can be obtained with the proposed approach in comparison to the existing state-of-the-art method. The flexibility of the introduced method should allow it to be used not only for the challenging case of cardiac imaging, but also for other patient motion where the patient moves or breathes during acquisition.
Applying compressive sensing to TEM video: A substantial frame rate increase on any camera
Stevens, Andrew; Kovarik, Libor; Abellan, Patricia; Yuan, Xin; Carin, Lawrence; Browning, Nigel D.
2015-08-13
One of the main limitations of imaging at high spatial and temporal resolution during in-situ transmission electron microscopy (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 0.1 ms, the cameras are expensive and must replace existing detectors. In this paper, we examine the use of coded aperture compressive sensing (CS) methods to increase the frame rate of any camera with simple, low-cost hardware modifications. The coded aperture approach allows multiple sub-frames to be coded and integratedmore » into a single camera frame during the acquisition process, and then extracted upon readout using statistical CS inversion. Here we describe the background of CS and statistical methods in depth and simulate the frame rates and efficiencies for in-situ TEM experiments. Depending on the resolution and signal/noise of the image, it should be possible to increase the speed of any camera by more than an order of magnitude using this approach.« less
NASA Astrophysics Data System (ADS)
Gan, Shuwei; Wang, Shoudong; Chen, Yangkang; Chen, Xiaohong; Huang, Weiling; Chen, Hanming
2016-07-01
According to the compressive sensing (CS) theory in the signal-processing field, we proposed a new CS approach based on a fast projection onto convex sets (POCS) algorithm with sparsity constraint in the seislet transform domain. The seislet transform appears to be the sparest among the state-of-the-art sparse transforms. The FPOCS can obtain much faster convergence than conventional POCS (about two thirds of conventional iterations can be saved), while maintaining the same recovery performance. The FPOCS can obtain faster and better performance than FISTA for relatively cleaner data but will get slower and worse performance than FISTA, which becomes a reference to decide which algorithm to use in practice according the noise level in the seismic data. The seislet transform based CS approach can achieve obviously better data recovery results than f - k transform based scenarios, considering both signal-to-noise ratio (SNR), local similarity comparison, and visual observation, because of a much sparser structure in the seislet transform domain. We have used both synthetic and field data examples to demonstrate the superior performance of the proposed seislet-based FPOCS approach.
See-through obscurants via compressive sensing in degraded visual environment
NASA Astrophysics Data System (ADS)
Lau, Richard C.; Woodward, T. K.
2015-05-01
This paper proposes a new approach for seeing through obscurants in a severe degraded visual environment (DVE). The proposed method allows extraction of hidden information from the raw sensor data via computational imaging technologies. We show that although to the human eye a sensor captures a zero-visibility view of an object through dense obscurants, it is possible to recover the hidden visual information of the object and display its visual cue to the aircraft pilot to aid in landing and maneuvering. The proposed approach uses a compressive sensing algorithm incorporating an over-complete dictionary composed of pose transformation of the targeted object. Information on the recovered image is used in a feedback loop to further remove perception noise and maintain tracking of the object from a moving aircraft. The proposed algorithm is sensor agnostic and can be applied to images taken from Infrared, LIDAR, or RF imagery sensors. We quantify the upper bound of the DVE noise in which the proposed method is effective via simulation results.
Applying compressive sensing to TEM video: A substantial frame rate increase on any camera
Stevens, Andrew; Kovarik, Libor; Abellan, Patricia; Yuan, Xin; Carin, Lawrence; Browning, Nigel D.
2015-08-13
One of the main limitations of imaging at high spatial and temporal resolution during in-situ transmission electron microscopy (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 0.1 ms, the cameras are expensive and must replace existing detectors. In this paper, we examine the use of coded aperture compressive sensing (CS) methods to increase the frame rate 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 CS inversion. Here we describe the background of CS and statistical methods in depth and simulate the frame rates and efficiencies for in-situ TEM experiments. Depending on the resolution and signal/noise of the image, it should be possible to increase the speed of any camera by more than an order of magnitude using this approach.
NASA Astrophysics Data System (ADS)
Uzeler, Hande; Cakir, Serdar; Aytaç, Tayfun
2016-02-01
Compressive sensing (CS) is a signal processing technique that enables a signal that has a sparse representation in a known basis to be reconstructed using measurements obtained below the Nyquist rate. Single detector image reconstruction applications using CS have been shown to give promising results. In this study, we investigate the application of CS theory to single detector infrared (IR) rosette scanning systems which suffer from low performance compared to costly focal plane array (FPA) detectors. The single detector pseudoimaging rosette scanning system scans the scene with a specific pattern and performs processing to estimate the target location without forming an image. In this context, this generation of scanning systems may be improved by utilizing the samples obtained by the rosette scanning pattern in conjunction with the CS framework. For this purpose, we consider surface-to-air engagement scenarios using IR images containing aerial targets and flares. The IR images have been reconstructed from samples obtained with the rosette scanning pattern and other baseline sampling strategies. It has been shown that the proposed scheme exhibits good reconstruction performance and a large size FPA imaging performance can be achieved using a single IR detector with a rosette scanning pattern.
NASA Astrophysics Data System (ADS)
Zhao, Shengmei; Wang, Le; Liang, Wenqiang; Cheng, Weiwen; Gong, Longyan
2015-10-01
In this paper, we propose a high performance optical encryption (OE) scheme based on computational ghost imaging (GI) with QR code and compressive sensing (CS) technique, named QR-CGI-OE scheme. N random phase screens, generated by Alice, is a secret key and be shared with its authorized user, Bob. The information is first encoded by Alice with QR code, and the QR-coded image is then encrypted with the aid of computational ghost imaging optical system. Here, measurement results from the GI optical system's bucket detector are the encrypted information and be transmitted to Bob. With the key, Bob decrypts the encrypted information to obtain the QR-coded image with GI and CS techniques, and further recovers the information by QR decoding. The experimental and numerical simulated results show that the authorized users can recover completely the original image, whereas the eavesdroppers can not acquire any information about the image even the eavesdropping ratio (ER) is up to 60% at the given measurement times. For the proposed scheme, the number of bits sent from Alice to Bob are reduced considerably and the robustness is enhanced significantly. Meantime, the measurement times in GI system is reduced and the quality of the reconstructed QR-coded image is improved.
Long-term surface EMG monitoring using K-means clustering and compressive sensing
NASA Astrophysics Data System (ADS)
Balouchestani, Mohammadreza; Krishnan, Sridhar
2015-05-01
In this work, we present an advanced K-means clustering algorithm based on Compressed Sensing theory (CS) in combination with the K-Singular Value Decomposition (K-SVD) method for Clustering of long-term recording of surface Electromyography (sEMG) signals. The long-term monitoring of sEMG signals aims at recording of the electrical activity produced by muscles which are very useful procedure for treatment and diagnostic purposes as well as for detection of various pathologies. The proposed algorithm is examined for three scenarios of sEMG signals including healthy person (sEMG-Healthy), a patient with myopathy (sEMG-Myopathy), and a patient with neuropathy (sEMG-Neuropathr), respectively. The proposed algorithm can easily scan large sEMG datasets of long-term sEMG recording. We test the proposed algorithm with Principal Component Analysis (PCA) and Linear Correlation Coefficient (LCC) dimensionality reduction methods. Then, the output of the proposed algorithm is fed to K-Nearest Neighbours (K-NN) and Probabilistic Neural Network (PNN) classifiers in order to calclute the clustering performance. The proposed algorithm achieves a classification accuracy of 99.22%. This ability allows reducing 17% of Average Classification Error (ACE), 9% of Training Error (TE), and 18% of Root Mean Square Error (RMSE). The proposed algorithm also reduces 14% clustering energy consumption compared to the existing K-Means clustering algorithm.
Fast and low-dose computed laminography using compressive sensing based technique
NASA Astrophysics Data System (ADS)
Abbas, Sajid; Park, Miran; Cho, Seungryong
2015-03-01
Computed laminography (CL) is well known for inspecting microstructures in the materials, weldments and soldering defects in high density packed components or multilayer printed circuit boards. The overload problem on x-ray tube and gross failure of the radio-sensitive electronics devices during a scan are among important issues in CL which needs to be addressed. The sparse-view CL can be one of the viable option to overcome such issues. In this work a numerical aluminum welding phantom was simulated to collect sparsely sampled projection data at only 40 views using a conventional CL scanning scheme i.e. oblique scan. A compressive-sensing inspired total-variation (TV) minimization algorithm was utilized to reconstruct the images. It is found that the images reconstructed using sparse view data are visually comparable with the images reconstructed using full scan data set i.e. at 360 views on regular interval. We have quantitatively confirmed that tiny structures such as copper and tungsten slags, and copper flakes in the reconstructed images from sparsely sampled data are comparable with the corresponding structure present in the fully sampled data case. A blurring effect can be seen near the edges of few pores at the bottom of the reconstructed images from sparsely sampled data, despite the overall image quality is reasonable for fast and low-dose NDT.
A Compressive Sensing Approach for Glioma Margin Delineation Using Mass Spectrometry
Gholami, Behnood; Agar, Nathalie Y. R.; Jolesz, Ferenc A.; Haddad, Wassim M.; Tannenbaum, Allen R.
2013-01-01
Surgery, and specifically, tumor resection, is the primary treatment for most patients suffering from brain tumors. Medical imaging techniques, and in particular, magnetic resonance imaging are currently used in diagnosis as well as image-guided surgery procedures. However, studies show that computed tomography and magnetic resonance imaging fail to accurately identify the full extent of malignant brain tumors and their microscopic infiltration. Mass spectrometry is a well-known analytical technique used to identify molecules in a given sample based on their mass. In a recent study, it is proposed to use mass spectrometry as an intraoperative tool for discriminating tumor and non-tumor tissue. Integration of mass spectrometry with the resection module allows for tumor resection and immediate molecular analysis. In this paper, we propose a framework for tumor margin delineation using compressive sensing. Specifically, we show that the spatial distribution of tumor cell concentration can be efficiently reconstructed and updated using mass spectrometry information from the resected tissue. In addition, our proposed framework is model-free, and hence, requires no prior information of spatial distribution of the tumor cell concentration. PMID:22255629
Comparison and analysis of nonlinear algorithms for compressed sensing in MRI.
Yu, Yeyang; Hong, Mingjian; Liu, Feng; Wang, Hua; Crozier, Stuart
2010-01-01
Compressed sensing (CS) theory has been recently applied in Magnetic Resonance Imaging (MRI) to accelerate the overall imaging process. In the CS implementation, various algorithms have been used to solve the nonlinear equation system for better image quality and reconstruction speed. However, there are no explicit criteria for an optimal CS algorithm selection in the practical MRI application. A systematic and comparative study of those commonly used algorithms is therefore essential for the implementation of CS in MRI. In this work, three typical algorithms, namely, the Gradient Projection For Sparse Reconstruction (GPSR) algorithm, Interior-point algorithm (l(1)_ls), and the Stagewise Orthogonal Matching Pursuit (StOMP) algorithm are compared and investigated in three different imaging scenarios, brain, angiogram and phantom imaging. The algorithms' performances are characterized in terms of image quality and reconstruction speed. The theoretical results show that the performance of the CS algorithms is case sensitive; overall, the StOMP algorithm offers the best solution in imaging quality, while the GPSR algorithm is the most efficient one among the three methods. In the next step, the algorithm performances and characteristics will be experimentally explored. It is hoped that this research will further support the applications of CS in MRI. PMID:21097312
A compressive sensing-based approach for Preisach hysteresis model identification
NASA Astrophysics Data System (ADS)
Zhang, Jun; Torres, David; Sepúlveda, Nelson; Tan, Xiaobo
2016-07-01
The Preisach hysteresis model has been adopted extensively in magnetic and smart material-based systems. Fidelity of the model hinges on accurate identification of the Preisach density function. Existing work on the identification of the density function usually involves applying an input that provides sufficient excitation and measuring a large set of output data. In this paper, we propose a novel compressive sensing-based approach for Preisach model identification that requires fewer measurements. The proposed approach adopts the discrete cosine transform of the output data to obtain a sparse vector, where the order of all the output data is assumed to be known. The model parameters can be efficiently reconstructed using the proposed scheme. For comparison purposes, a constrained least-squares scheme using the same number of measurements is also considered. The root-mean-square error is adopted to examine the model identification performance. The proposed identification approach is shown to have better performance than the least-squares scheme through both simulation and experiments involving a vanadium dioxide ({{VO}}2)-integrated microactuator. This work was supported by the National Science Foundation (CMMI 1301243).
Compressed Sensing for Millimeter-wave Ground Based SAR/ISAR Imaging
NASA Astrophysics Data System (ADS)
Yiğit, Enes
2014-11-01
Millimeter-wave (MMW) ground based (GB) synthetic aperture radar (SAR) and inverse SAR (ISAR) imaging are the powerful tools for the detection of foreign object debris (FOD) and concealed objects that requires wide bandwidths and highly frequent samplings in both slow-time and fast-time domains according to Shannon/Nyquist sampling theorem. However, thanks to the compressive sensing (CS) theory GB-SAR/ISAR data can be reconstructed by much fewer random samples than the Nyquist rate. In this paper, the impact of both random frequency sampling and random spatial domain data collection of a SAR/ISAR sensor on reconstruction quality of a scene of interest was studied. To investigate the feasibility of using proposed CS framework, different experiments for various FOD-like and concealed object-like targets were carried out at the Ka and W band frequencies of the MMW. The robustness and effectiveness of the recommend CS-based reconstruction configurations were verified through a comparison among each other by using integrated side lobe ratios (ISLR) of the images.
Yao, Huajian; Shearer, Peter M.; Gerstoft, Peter
2013-01-01
Megathrust earthquakes rupture a broad zone of the subducting plate interface in both along-strike and along-dip directions. The along-dip rupture characteristics of megathrust events, e.g., their slip and energy radiation distribution, reflect depth-varying frictional properties of the slab interface. Here, we report high-resolution frequency-dependent seismic radiation of the four largest megathrust earthquakes in the past 10 y using a compressive-sensing (sparse source recovery) technique, resolving generally low-frequency radiation closer to the trench at shallower depths and high-frequency radiation farther from the trench at greater depths. Together with coseismic slip models and early aftershock locations, our results suggest depth-varying frictional properties at the subducting plate interfaces. The shallower portion of the slab interface (above ∼15 km) is frictionally stable or conditionally stable and is the source region for tsunami earthquakes with large coseismic slip, deficient high-frequency radiation, and few early aftershocks. The slab interface at intermediate depths (∼15–35 km) is the main unstable seismogenic zone for the nucleation of megathrust quakes, typically with large coseismic slip, abundant early aftershocks, and intermediate- to high-frequency radiation. The deeper portion of the slab interface (∼35–45 km) is seismically unstable, however with small coseismic slip, dominant high-frequency radiation, and relatively fewer aftershocks.
Harris, Peter; Philip, Rachel; Robinson, Stephen; Wang, Lian
2016-01-01
Monitoring ocean acoustic noise has been the subject of considerable recent study, motivated by the desire to assess the impact of anthropogenic noise on marine life. A combination of measuring ocean sound using an acoustic sensor network and modelling sources of sound and sound propagation has been proposed as an approach to estimating the acoustic noise map within a region of interest. However, strategies for developing a monitoring network are not well established. In this paper, considerations for designing a network are investigated using a simulated scenario based on the measurement of sound from ships in a shipping lane. Using models for the sources of the sound and for sound propagation, a noise map is calculated and measurements of the noise map by a sensor network within the region of interest are simulated. A compressive sensing algorithm, which exploits the sparsity of the representation of the noise map in terms of the sources, is used to estimate the locations and levels of the sources and thence the entire noise map within the region of interest. It is shown that although the spatial resolution to which the sound sources can be identified is generally limited, estimates of aggregated measures of the noise map can be obtained that are more reliable compared with those provided by other approaches. PMID:27011187
Compressed sensing MRI with singular value decomposition-based sparsity basis
NASA Astrophysics Data System (ADS)
Hong, Mingjian; Yu, Yeyang; Wang, Hua; Liu, Feng; Crozier, Stuart
2011-10-01
Compressed sensing MRI (CS-MRI) aims to significantly reduce the measurements required for image reconstruction in order to accelerate the overall imaging speed. The sparsity of the MR images in transformation bases is one of the fundamental criteria for CS-MRI performance. Sparser representations can require fewer samples necessary for a successful reconstruction or achieve better reconstruction quality with a given number of samples. Generally, there are two kinds of 'sparsifying' transforms: predefined transforms and data-adaptive transforms. The predefined transforms, such as the discrete cosine transform, discrete wavelet transform and identity transform have usually been used to provide sufficiently sparse representations for limited types of MR images, in view of their isolation to the object images. In this paper, we present singular value decomposition (SVD) as the data-adaptive 'sparsity' basis, which can sparsify a broader range of MR images and perform effective image reconstruction. The performance of this method was evaluated for MR images with varying content (for example, brain images, angiograms, etc), in terms of image quality, reconstruction time, sparsity and data fidelity. Comparison with other commonly used sparsifying transforms shows that the proposed method can significantly accelerate the reconstruction process and still achieve better image quality, providing a simple and effective alternative solution in the CS-MRI framework.
Worst configurations (instantons) for compressed sensing over reals: a channel coding approach
Chertkov, Michael; Chilappagari, Shashi K; Vasic, Bane
2010-01-01
We consider Linear Programming (LP) solution of a Compressed Sensing (CS) problem over reals, also known as the Basis Pursuit (BasP) algorithm. The BasP allows interpretation as a channel-coding problem, and it guarantees the error-free reconstruction over reals for properly chosen measurement matrix and sufficiently sparse error vectors. In this manuscript, we examine how the BasP performs on a given measurement matrix and develop a technique to discover sparse vectors for which the BasP fails. The resulting algorithm is a generalization of our previous results on finding the most probable error-patterns, so called instantons, degrading performance of a finite size Low-Density Parity-Check (LDPC) code in the error-floor regime. The BasP fails when its output is different from the actual error-pattern. We design CS-Instanton Search Algorithm (ISA) generating a sparse vector, called CS-instanton, such that the BasP fails on the instanton, while its action on any modification of the CS-instanton decreasing a properly defined norm is successful. We also prove that, given a sufficiently dense random input for the error-vector, the CS-ISA converges to an instanton in a small finite number of steps. Performance of the CS-ISA is tested on example of a randomly generated 512 * 120 matrix, that outputs the shortest instanton (error vector) pattern of length 11.
Marx, Sabrina; Hämmerle, Martin; Klonner, Carolin; Höfle, Bernhard
2016-01-01
The integration of local agricultural knowledge deepens the understanding of complex phenomena such as the association between climate variability, crop yields and undernutrition. Participatory Sensing (PS) is a concept which enables laymen to easily gather geodata with standard low-cost mobile devices, offering new and efficient opportunities for agricultural monitoring. This study presents a methodological approach for crop height assessment based on PS. In-field crop height variations of a maize field in Heidelberg, Germany, are gathered with smartphones and handheld GPS devices by 19 participants. The comparison of crop height values measured by the participants to reference data based on terrestrial laser scanning (TLS) results in R2 = 0.63 for the handheld GPS devices and R2 = 0.24 for the smartphone-based approach. RMSE for the comparison between crop height models (CHM) derived from PS and TLS data is 10.45 cm (GPS devices) and 14.69 cm (smartphones). Furthermore, the results indicate that incorporating participants’ cognitive abilities in the data collection process potentially improves the quality data captured with the PS approach. The proposed PS methods serve as a fundament to collect agricultural parameters on field-level by incorporating local people. Combined with other methods such as remote sensing, PS opens new perspectives to support agricultural development. PMID:27073917
Electrochemical and bio-sensing platform based on a novel 3D Cu nano-flowers/layered MoS₂ composite.
Lin, Xiaoyun; Ni, Yongnian; Kokot, Serge
2016-05-15
A novel 3D nano-flower-like Cu/multi-layer molybdenum disulfide composite (CuNFs/MoS2) modified glassy carbon electrode (GCE) has been successfully constructed. It was a highly sensitive and selective non-enzymatic hydrogen peroxide (H2O2) and glucose biosensor. The morphology of the obtained CuNFs-MoS2 nano-particles was investigated with the use of a scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD) and energy dispersive X-ray spectroscopy (EDS). The physicochemical properties of the modified electrode were characterized at each of the construction stages with the use of an electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV) techniques. The new sensor combined the advantages of MoS2 and CuNFs, and exhibited high electro-catalytic activity toward H2O2 and glucose. Quantitative analysis of H2O2 and glucose was carried out with the use of the amperometric i-t method. Linear ranges were obtained between 0.04-1.88 μM and 1.88-35.6 μM for H2O2 and 1-20 μM and 20-70 μM for glucose, and their corresponding limits of detection (LOD) were 0.021 μM and 0.32 μM. This novel sensor was successfully applied for the quantitative analysis of H2O2 in tap water and glucose in human serum samples. PMID:26773372
NASA Astrophysics Data System (ADS)
Lee, Chulhee; Youn, Sungwook; Baek, Jeoung Yeol; Serra Sagristà, Joan
2015-05-01
A number of compression methods have been used to compress hyperspectral images. However, these methods may fail to retain all the discriminant characteristics of hyperspectral images since some discriminant features may not be high in signal energy. Also, it has been reported that compression may improve classification performance in some cases. In this paper, we investigate these problems, and analyze potential discriminant information loss and classification performance improvement. We perform some experiments using various compression methods. We examine this phenomenon and its implication.
2015-04-23
A new type of graphene aerogel will make for better energy storage, sensors, nanoelectronics, catalysis and separations. Lawrence Livermore National Laboratory researchers have made graphene aerogel microlattices with an engineered architecture via a 3D printing technique known as direct ink writing. The research appears in the April 22 edition of the journal, Nature Communications. The 3D printed graphene aerogels have high surface area, excellent electrical conductivity, are lightweight, have mechanical stiffness and exhibit supercompressibility (up to 90 percent compressive strain). In addition, the 3D printed graphene aerogel microlattices show an order of magnitude improvement over bulk graphene materials and much better mass transport.
3-D Technology Approaches for Biological Ecologies
NASA Astrophysics Data System (ADS)
Liu, Liyu; Austin, Robert; U. S-China Physical-Oncology Sciences Alliance (PS-OA) Team
Constructing three dimensional (3-D) landscapes is an inevitable issue in deep study of biological ecologies, because in whatever scales in nature, all of the ecosystems are composed by complex 3-D environments and biological behaviors. Just imagine if a 3-D technology could help complex ecosystems be built easily and mimic in vivo microenvironment realistically with flexible environmental controls, it will be a fantastic and powerful thrust to assist researchers for explorations. For years, we have been utilizing and developing different technologies for constructing 3-D micro landscapes for biophysics studies in in vitro. Here, I will review our past efforts, including probing cancer cell invasiveness with 3-D silicon based Tepuis, constructing 3-D microenvironment for cell invasion and metastasis through polydimethylsiloxane (PDMS) soft lithography, as well as explorations of optimized stenting positions for coronary bifurcation disease with 3-D wax printing and the latest home designed 3-D bio-printer. Although 3-