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
2015-03-01
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
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
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
2015-01-01
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
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
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 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
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.
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
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.
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.
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.
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.
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
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
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.
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.
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.
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.
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
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.
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
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.
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.
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
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.
Real-time sensing of mouth 3-D position and orientation
NASA Astrophysics Data System (ADS)
Burdea, Grigore C.; Dunn, Stanley M.; Mallik, Matsumita; Jun, Heesung
1990-07-01
A key problem in using digital subtraction radiography in dentistry is the ability to reposition the X-ray source and patient so as to reproduce an identical imaging geometry. In this paper we describe an approach to solving this problem based on real time sensing of the 3-D position and orientation of the patient's mouth. The research described here is part of a program which has a long term goal to develop an automated digital subtraction radiography system. This will allow the patient and X-ray source to be accurately repositioned without the mechanical fixtures that are presently used to preserve the imaging geometry. If we can measure the position and orientation of the mouth, then the desired position of the source can be computed as the product of the transformation matrices describing the desired imaging geometry and the position vector of the targeted tooth. Position and orientation of the mouth is measured by a real time sensing device using low-frequency magnetic field technology. We first present the problem of repositioning the patient and source and then outline our analytic solution. Then we describe an experimental setup to measure the accuracy, reproducibility and resolution of the sensor and present results of preliminary experiments.
Pope, Paul A; Ranken, Doug M
2010-01-01
A method for abstracting a 3D model by shrinking a triangular mesh, defined upon a best fitting ellipsoid surrounding the model, onto the model's surface has been previously described. This ''shrinkwrap'' process enables a semi-regular mesh to be defined upon an object's surface. This creates a useful data structure for conducting remote sensing simulations and image processing. However, using a best fitting ellipsoid having a graticule-based tessellation to seed the shrinkwrap process suffers from a mesh which is too dense at the poles. To achieve a more regular mesh, the use of a best fitting, subdivided icosahedron was tested. By subdividing each of the twenty facets of the icosahedron into regular triangles of a predetermined size, arbitrarily dense, highly-regular starting meshes can be created. Comparisons of the meshes resulting from these two seed surfaces are described. Use of a best fitting icosahedron-based mesh as the seed surface in the shrinkwrap process is preferable to using a best fitting ellipsoid. The impacts to remote sensing simulations, specifically generation of synthetic imagery, is illustrated.
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
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.
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
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
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
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.
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
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.
Finite element methods of analysis for 3D inviscid compressible flows
NASA Technical Reports Server (NTRS)
Peraire, Jaime
1990-01-01
The applicants have developed a finite element based approach for the solution of three-dimensional compressible flows. The procedure enables flow solutions to be obtained on tetrahedral discretizations of computational domains of complex form. A further development was the incorporation of a solution adaptive mesh strategy in which the adaptivity is achieved by complete remeshing of the solution domain. During the previous year, the applicants were working with the Advanced Aerodynamics Concepts Branch at NASA Ames Research Center with an implementation of the basic meshing and solution procedure. The objective of the work to be performed over this twelve month period was the transfer of the adaptive mesh technology and also the undertaking of basic research into alternative flow algorithms for the Euler equations on unstructured meshes.
Johnson, Christopher; Sheshadri, Priyanka; Ketchum, Jessica M; Narayanan, Lokesh K; Weinberger, Paul M; Shirwaiker, Rohan A
2016-06-01
Infection or damage to the trachea, a thin walled and cartilage reinforced conduit that connects the pharynx and larynx to the lungs, leads to serious respiratory medical conditions which can often prove fatal. Current clinical strategies for complex tracheal reconstruction are of limited availability and efficacy, but tissue engineering and regenerative medicine approaches may provide viable alternatives. In this study, we have developed a new "hybrid graft" approach that utilizes decellularized tracheal tissue along with a resorbable polymer scaffold, and holds promise for potential clinical applications. First, we evaluated the effect of our decellularization process on the compression properties of porcine tracheal segments, and noted approximately 63% decrease in resistance to compression following decellularization. Next we developed four C-shape scaffold designs by varying the base geometry and thickness, and fabricated polycaprolactone scaffolds using a combination of 3D-Bioplotting and thermally-assisted forming. All scaffolds designs were evaluated in vitro under three different environmental testing conditions to determine the design that offered the best resistance to compression. These were further studied to determine the effect of gamma radiation sterilization and cyclic compression loading. Finally, hybrid grafts were developed by securing these optimal design scaffolds to decellularized tracheal segments and evaluated in vitro under physiological testing conditions. Results show that the resistance to compression offered by the hybrid grafts created using gamma radiation sterilized scaffolds was comparable to that of fresh tracheal segments. Given that current clinical attempts at tracheal transplantation using decellularized tissue have been fraught with luminal collapse and complications, our data support the possibility that future embodiments using a hybrid graft approach may reduce the need for intraluminal stenting in tracheal transplant
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.
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
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
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
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.
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.
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
2016-04-01
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.
Force sensing using 3D displacement measurements in linear elastic bodies
NASA Astrophysics Data System (ADS)
Feng, Xinzeng; Hui, Chung-Yuen
2016-07-01
In cell traction microscopy, the mechanical forces exerted by a cell on its environment is usually determined from experimentally measured displacement by solving an inverse problem in elasticity. In this paper, an innovative numerical method is proposed which finds the "optimal" traction to the inverse problem. When sufficient regularization is applied, we demonstrate that the proposed method significantly improves the widely used approach using Green's functions. Motivated by real cell experiments, the equilibrium condition of a slowly migrating cell is imposed as a set of equality constraints on the unknown traction. Our validation benchmarks demonstrate that the numeric solution to the constrained inverse problem well recovers the actual traction when the optimal regularization parameter is used. The proposed method can thus be applied to study general force sensing problems, which utilize displacement measurements to sense inaccessible forces in linear elastic bodies with a priori constraints.
NASA Astrophysics Data System (ADS)
Yu, H.-S.; Jackson, B. V.; Hick, P. P.; Buffington, A.; Odstrcil, D.; Wu, C.-C.; Davies, J. A.; Bisi, M. M.; Tokumaru, M.
2015-09-01
The University of California, San Diego, time-dependent analyses of the heliosphere provide three-dimensional (3D) reconstructions of solar wind velocities and densities from observations of interplanetary scintillation (IPS). Using data from the Solar-Terrestrial Environment Laboratory, Japan, these reconstructions provide a real-time prediction of the global solar-wind density and velocity throughout the whole heliosphere with a temporal cadence of about one day (ips.ucsd.edu). Updates to this modeling effort continue: in the present article, near-Sun results extracted from the time-dependent 3D reconstruction are used as inner boundary conditions to drive 3D-MHD models ( e.g. ENLIL and H3D-MHD). This allows us to explore the differences between the IPS kinematic-model data-fitting procedure and current 3D-MHD modeling techniques. The differences in these techniques provide interesting insights into the physical principles governing the expulsion of coronal mass ejections (CMEs). Here we detail for the first time several specific CMEs and an induced shock that occurred in September 2011 that demonstrate some of the issues resulting from these analyses.
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.
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
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.
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.
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
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.
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
NASA Astrophysics Data System (ADS)
Zhou, Nanrun; Li, Haolin; Wang, Di; Pan, Shumin; Zhou, Zhihong
2015-05-01
Most of the existing image encryption techniques bear security risks for taking linear transform or suffer encryption data expansion for adopting nonlinear transformation directly. To overcome these difficulties, a novel image compression-encryption scheme is proposed by combining 2D compressive sensing with nonlinear fractional Mellin transform. In this scheme, the original image is measured by measurement matrices in two directions to achieve compression and encryption simultaneously, and then the resulting image is re-encrypted by the nonlinear fractional Mellin transform. The measurement matrices are controlled by chaos map. The Newton Smoothed l0 Norm (NSL0) algorithm is adopted to obtain the decryption image. Simulation results verify the validity and the reliability of this scheme.
Texture-based medical image retrieval in compressed domain using compressive sensing.
Yadav, Kuldeep; Srivastava, Avi; Mittal, Ankush; Ansari, M A
2014-01-01
Content-based image retrieval has gained considerable attention in today's scenario as a useful tool in many applications; texture is one of them. In this paper, we focus on texture-based image retrieval in compressed domain using compressive sensing with the help of DC coefficients. Medical imaging is one of the fields which have been affected most, as there had been huge size of image database and getting out the concerned image had been a daunting task. Considering this, in this paper we propose a new model of image retrieval process using compressive sampling, since it allows accurate recovery of image from far fewer samples of unknowns and it does not require a close relation of matching between sampling pattern and characteristic image structure with increase acquisition speed and enhanced image quality. PMID:24589833
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
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
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.
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 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.
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
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
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.
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
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.
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.
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.
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.
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.