Sensing and compressing 3-D models
Krumm, J. [Sandia National Labs., Albuquerque, NM (United States). Intelligent System Sensors and Controls Dept.
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
Sun, Qi; Tang, Yanlong; Hu, Ping
2013-07-01
3D data is easier to acquire for family entertainment purpose today because of the mass-production, cheapness and portability of domestic RGBD sensors, e.g., Microsoft Kinect. However, the accuracy of facial modeling is affected by the roughness and instability of the raw input data from such sensors. To overcome this problem, we introduce compressive sensing (CS) method to build a novel 3D super-resolution scheme to reconstruct high-resolution facial models from rough samples captured by Kinect. Unlike the simple frame fusion super-resolution method, this approach aims to acquire compressed samples for storage before a high-resolution image is produced. In this scheme, depth frames are firstly captured and then each of them is measured into compressed samples using sparse coding. Next, the samples are fused to produce an optimal one and finally a high-resolution image is recovered from the fused sample. This framework is able to recover 3D facial model of a given user from compressed simples and this can reducing storage space as well as measurement cost in future devices e.g., single-pixel depth cameras. Hence, this work can potentially be applied into future applications, such as access control system using face recognition, and smart phones with depth cameras, which need high resolution and little measure time.
3D multifocus astigmatism and compressed sensing (3D MACS) based superresolution reconstruction.
Huang, Jiaqing; Sun, Mingzhai; Gumpper, Kristyn; Chi, Yuejie; Ma, Jianjie
2015-03-01
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) localiza...
A new combined prior based reconstruction method for compressed sensing in 3D ultrasound imaging
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.
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 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
Compressive Sensing in High-resolution 3D SAR Tomography of Urban Scenarios
Liao Ming-sheng
2015-04-01
Full Text Available In modern high resolution SAR data, due to the intrinsic side-looking geometry of SAR sensors, layover and foreshortening issues inevitably arise, especially in dense urban areas. SAR tomography provides a new way of overcoming these problems by exploiting the back-scattering property for each pixel. However, traditional non-parametric spectral estimators, e.g. Truncated Singular Value Decomposition (TSVD, are limited by their poor elevation resolution, which is not comparable to the azimuth and slant-range resolution. In this paper, the Compressive Sensing (CS approach using Basis Pursuit (BP and TWo-step Iterative Shrinkage/Thresholding (TWIST are introduced. Experimental studies with real spotlight-mode TerraSAR-X dataset are carried out using both BP and TWIST, to demonstrate the merits of compressive sensing approaches in terms of robustness, computational efficiency, and super-resolution capability.
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
Wech, T.; Koestler, H. [Wuerzburg Univ. (Germany). Inst. of Radiology; Wuerzburg Univ. (Germany). Comprehensive Heart Failure Center; Pickl, W.; Tran-Gia, J.; Ritter, C.; Hahn, D. [Wuerzburg Univ. (Germany). Inst. of Radiology; Beer, M. [Wuerzburg Univ. (Germany). Inst. of Radiology; Graz Univ. (Austria). University Hospital Radiology
2014-01-15
Purpose: The aim of this study was to perform functional MR imaging of the whole heart in a single breath-hold using an undersampled 3 D trajectory for data acquisition in combination with compressed sensing for image reconstruction. Materials and Methods: Measurements were performed using an SSFP sequence on a 3 T whole-body system equipped with a 32-channel body array coil. A 3 D radial stack-of-stars sampling scheme was utilized enabling efficient undersampling of the k-space and thereby accelerating data acquisition. Compressed sensing was applied for the reconstruction of the missing data. A validation study was performed based on a fully sampled dataset acquired by standard Cartesian cine imaging of 2 D slices on a healthy volunteer. The results were investigated with regard to systematic errors and resolution losses possibly introduced by the developed reconstruction. Subsequently, the proposed technique was applied for in-vivo functional cardiac imaging of the whole heart in a single breath-hold of 27 s. The developed technique was tested on three healthy volunteers to examine its reproducibility. Results: By means of the results of the simulation (temporal resolution: 47 ms, spatial resolution: 1.4 x 1.4 x 8 mm, 3 D image matrix: 208 x 208 x 10), an overall acceleration factor of 10 has been found where the compressed sensing reconstructed image series shows only very low systematic errors and a slight in-plane resolution loss of 15 %. The results of the in-vivo study (temporal resolution: 40.5 ms, spatial resolution: 2.1 x 2.1 x 8 mm, 3 D image matrix: 224 x 224 x 12) performed with an acceleration factor of 10.7 confirm the overall good image quality of the presented technique for undersampled acquisitions. Conclusion: The combination of 3 D radial data acquisition and model-based compressed sensing reconstruction allows high acceleration factors enabling cardiac functional imaging of the whole heart within only one breath-hold. The image quality in the
Purpose: The aim of this study was to perform functional MR imaging of the whole heart in a single breath-hold using an undersampled 3 D trajectory for data acquisition in combination with compressed sensing for image reconstruction. Materials and Methods: Measurements were performed using an SSFP sequence on a 3 T whole-body system equipped with a 32-channel body array coil. A 3 D radial stack-of-stars sampling scheme was utilized enabling efficient undersampling of the k-space and thereby accelerating data acquisition. Compressed sensing was applied for the reconstruction of the missing data. A validation study was performed based on a fully sampled dataset acquired by standard Cartesian cine imaging of 2 D slices on a healthy volunteer. The results were investigated with regard to systematic errors and resolution losses possibly introduced by the developed reconstruction. Subsequently, the proposed technique was applied for in-vivo functional cardiac imaging of the whole heart in a single breath-hold of 27 s. The developed technique was tested on three healthy volunteers to examine its reproducibility. Results: By means of the results of the simulation (temporal resolution: 47 ms, spatial resolution: 1.4 x 1.4 x 8 mm, 3 D image matrix: 208 x 208 x 10), an overall acceleration factor of 10 has been found where the compressed sensing reconstructed image series shows only very low systematic errors and a slight in-plane resolution loss of 15 %. The results of the in-vivo study (temporal resolution: 40.5 ms, spatial resolution: 2.1 x 2.1 x 8 mm, 3 D image matrix: 224 x 224 x 12) performed with an acceleration factor of 10.7 confirm the overall good image quality of the presented technique for undersampled acquisitions. Conclusion: The combination of 3 D radial data acquisition and model-based compressed sensing reconstruction allows high acceleration factors enabling cardiac functional imaging of the whole heart within only one breath-hold. The image quality in the
ICER-3D Hyperspectral Image Compression Software
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
3D Video Compression and Transmission
Zamarin, Marco; Forchhammer, Søren
In this short paper we provide a brief introduction to 3D and multi-view video technologies - like three-dimensional television and free-viewpoint video - focusing on the aspects related to data compression and transmission. Geometric information represented by depth maps is introduced as well...
Compressed sensing electron tomography
Leary, Rowan, E-mail: rkl26@cam.ac.uk [Department of Materials Science and Metallurgy, University of Cambridge, Pembroke Street, Cambridge CB2 3QZ (United Kingdom); Saghi, Zineb; Midgley, Paul A. [Department of Materials Science and Metallurgy, University of Cambridge, Pembroke Street, Cambridge CB2 3QZ (United Kingdom); Holland, Daniel J. [Department of Chemical Engineering and Biotechnology, University of Cambridge, New Museums Site, Pembroke Street, Cambridge CB2 3RA (United Kingdom)
2013-08-15
The recent mathematical concept of compressed sensing (CS) asserts that a small number of well-chosen measurements can suffice to reconstruct signals that are amenable to sparse or compressible representation. In addition to powerful theoretical results, the principles of CS are being exploited increasingly across a range of experiments to yield substantial performance gains relative to conventional approaches. In this work we describe the application of CS to electron tomography (ET) reconstruction and demonstrate the efficacy of CS–ET with several example studies. Artefacts present in conventional ET reconstructions such as streaking, blurring of object boundaries and elongation are markedly reduced, and robust reconstruction is shown to be possible from far fewer projections than are normally used. The CS–ET approach enables more reliable quantitative analysis of the reconstructions as well as novel 3D studies from extremely limited data. - Highlights: • Compressed sensing (CS) theory and its application to electron tomography (ET) is described. • The practical implementation of CS–ET is outlined and its efficacy demonstrated with examples. • High fidelity tomographic reconstruction is possible from a small number of images. • The CS–ET reconstructions can be more reliably segmented and analysed quantitatively. • CS–ET is applicable to different image content by choice of an appropriate sparsifying transform.
Compression of 3D models with NURBS
Santa Cruz Ducci, Diego; Ebrahimi, Touradj
2005-01-01
With recent progress in computing, algorithmics and telecommunications, 3D models are increasingly used in various multimedia applications. Examples include visualization, gaming, entertainment and virtual reality. In the multimedia domain 3D models have been traditionally represented as polygonal meshes. This piecewise planar representation can be thought of as the analogy of bitmap images for 3D surfaces. As bitmap images, they enjoy great flexibility and are particularly well suited to des...
3D MHD Simulations of Spheromak Compression
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.
Compressed sensing & sparse filtering
Carmi, Avishy Y; Godsill, Simon J
2013-01-01
This book is aimed at presenting concepts, methods and algorithms ableto cope with undersampled and limited data. One such trend that recently gained popularity and to some extent revolutionised signal processing is compressed sensing. Compressed sensing builds upon the observation that many signals in nature are nearly sparse (or compressible, as they are normally referred to) in some domain, and consequently they can be reconstructed to within high accuracy from far fewer observations than traditionally held to be necessary.Â Apart from compressed sensing this book contains other related app
Compressed Sensing in Astronomy
Bobin, J; Ottensamer, R
2008-01-01
Recent advances in signal processing have focused on the use of sparse representations in various applications. A new field of interest based on sparsity has recently emerged: compressed sensing. This theory is a new sampling framework that provides an alternative to the well-known Shannon sampling theory. In this paper we investigate how compressed sensing (CS) can provide new insights into astronomical data compression and more generally how it paves the way for new conceptions in astronomical remote sensing. We first give a brief overview of the compressed sensing theory which provides very simple coding process with low computational cost, thus favoring its use for real-time applications often found on board space mission. We introduce a practical and effective recovery algorithm for decoding compressed data. In astronomy, physical prior information is often crucial for devising effective signal processing methods. We particularly point out that a CS-based compression scheme is flexible enough to account ...
Efficient traveltime compression for 3D prestack Kirchhoff migration
Alkhalifah, Tariq
2010-12-13
Kirchhoff 3D prestack migration, as part of its execution, usually requires repeated access to a large traveltime table data base. Access to this data base implies either a memory intensive or I/O bounded solution to the storage problem. Proper compression of the traveltime table allows efficient 3D prestack migration without relying on the usually slow access to the computer hard drive. Such compression also allows for faster access to desirable parts of the traveltime table. Compression is applied to the traveltime field for each source location on the surface on a regular grid using 3D Chebyshev polynomial or cosine transforms of the traveltime field represented in the spherical coordinates or the Celerity domain. We obtain practical compression levels up to and exceeding 20 to 1. In fact, because of the smaller size traveltime table, we obtain exceptional traveltime extraction speed during migration that exceeds conventional methods. Additional features of the compression include better interpolation of traveltime tables and more stable estimates of amplitudes from traveltime curvatures. Further compression is achieved using bit encoding, by representing compression parameters values with fewer bits. © 2010 European Association of Geoscientists & Engineers.
Novel 3D Compression Methods for Geometry, Connectivity and Texture
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
Controllability of the 3D compressible Euler system
Nersisyan, Hayk
2009-01-01
The paper is devoted to the controllability problem for 3D compressible Euler system. The control is a finite-dimensional external force acting only on the velocity equation. We show that the velocity and density of the fluid are simultaneously controllable. In particular, the system is approximately controllable and exactly controllable in projections
3D Mesh Compression and Transmission for Mobile Robotic Applications
Bailin Yang
2016-01-01
Full Text Available Mobile robots are useful for environment exploration and rescue operations. In such applications, it is crucial to accurately analyse and represent an environment, providing appropriate inputs for motion planning in order to support robot navigation and operations. 2D mapping methods are simple but cannot handle multilevel or multistory environments. To address this problem, 3D mapping methods generate structural 3D representations of the robot operating environment and its objects by 3D mesh reconstruction. However, they face the challenge of efficiently transmitting those 3D representations to system modules for 3D mapping, motion planning, and robot operation visualization. This paper proposes a quality-driven mesh compression and transmission method to address this. Our method is efficient, as it compresses a mesh by quantizing its transformed vertices without the need to spend time constructing an a-priori structure over the mesh. A visual distortion function is developed to govern the level of quantization, allowing mesh transmission to be controlled under different network conditions or time constraints. Our experiments demonstrate how the visual quality of a mesh can be manipulated by the visual distortion function.
3D Face Compression and Recognition using Spherical Wavelet Parametrization
Rabab M. Ramadan
2012-09-01
Full Text Available In this research an innovative fully automated 3D face compression and recognition system is presented. Several novelties are introduced to make the system performance robust and efficient. These novelties include: First, an automatic pose correction and normalization process by using curvature analysis for nose tip detection and iterative closest point (ICP image registration. Second, the use of spherical based wavelet coefficients for efficient representation of the 3D face. The spherical wavelet transformation is used to decompose the face image into multi-resolution sub images characterizing the underlying functions in a local fashion in both spacial and frequency domains. Two representation features based on spherical wavelet parameterization of the face image were proposed for the 3D face compression and recognition. Principle component analysis (PCA is used to project to a low resolution sub-band. To evaluate the performance of the proposed approach, experiments were performed on the GAVAB face database. Experimental results show that the spherical wavelet coefficients yield excellent compression capabilities with minimal set of features. Haar wavelet coefficients extracted from the face geometry image was found to generate good recognition results that outperform other methods working on the GAVAB database.
Je, U.K.; Lee, M.S.; Cho, H.S., E-mail: hscho1@yonsei.ac.kr; 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.
In practical applications of three-dimensional (3D) tomographic imaging, there are often challenges for image reconstruction from insufficient sampling data. In computed tomography (CT), for example, image reconstruction from sparse views and/or limited-angle (<360°) views would enable fast scanning with reduced imaging doses to the patient. In this study, we investigated and implemented a reconstruction algorithm based on the compressed-sensing (CS) theory, which exploits the sparseness of the gradient image with substantially high accuracy, for potential applications to low-dose, high-accurate dental cone-beam CT (CBCT). We performed systematic simulation works to investigate the image characteristics and also performed experimental works by applying the algorithm to a commercially-available dental CBCT system to demonstrate its effectiveness for image reconstruction in insufficient sampling problems. We successfully reconstructed CBCT images of superior accuracy from insufficient sampling data and evaluated the reconstruction quality quantitatively. Both simulation and experimental demonstrations of the CS-based reconstruction from insufficient data indicate that the CS-based algorithm can be applied directly to current dental CBCT systems for reducing the imaging doses and further improving the image quality
Improving 3D Wavelet-Based Compression of Hyperspectral Images
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
Mroueh, Youssef; Rosasco, Lorenzo
2013-01-01
We introduce q-ary compressive sensing, an extension of 1-bit compressive sensing. We propose a novel sensing mechanism and a corresponding recovery procedure. The recovery properties of the proposed approach are analyzed both theoretically and empirically. Results in 1-bit compressive sensing are recovered as a special case. Our theoretical results suggest a tradeoff between the quantization parameter q, and the number of measurements m in the control of the error of the resulting recovery a...
Compressive Sensing DNA Microarrays
Sheikh Mona A
2009-01-01
Full Text Available 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.
Compressed hyperspectral sensing
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.
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.
Compressive sensing for urban radar
Amin, Moeness
2014-01-01
With the emergence of compressive sensing and sparse signal reconstruction, approaches to urban radar have shifted toward relaxed constraints on signal sampling schemes in time and space, and to effectively address logistic difficulties in data acquisition. Traditionally, these challenges have hindered high resolution imaging by restricting both bandwidth and aperture, and by imposing uniformity and bounds on sampling rates.Compressive Sensing for Urban Radar is the first book to focus on a hybrid of two key areas: compressive sensing and urban sensing. It explains how reliable imaging, tracki
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
Compressive Sensing for Quantum Imaging
Howland, Gregory A.
This thesis describes the application of compressive sensing to several challenging problems in quantum imaging with practical and fundamental implications. Compressive sensing is a measurement technique that compresses a signal during measurement such that it can be dramatically undersampled. Compressive sensing has been shown to be an extremely efficient measurement technique for imaging, particularly when detector arrays are not available. The thesis first reviews compressive sensing through the lens of quantum imaging and quantum measurement. Four important applications and their corresponding experiments are then described in detail. The first application is a compressive sensing, photon-counting lidar system. A novel depth mapping technique that uses standard, linear compressive sensing is described. Depth maps up to 256 x 256 pixel transverse resolution are recovered with depth resolution less than 2.54 cm. The first three-dimensional, photon counting video is recorded at 32 x 32 pixel resolution and 14 frames-per-second. The second application is the use of compressive sensing for complementary imaging---simultaneously imaging the transverse-position and transverse-momentum distributions of optical photons. This is accomplished by taking random, partial projections of position followed by imaging the momentum distribution on a cooled CCD camera. The projections are shown to not significantly perturb the photons' momenta while allowing high resolution position images to be reconstructed using compressive sensing. A variety of objects and their diffraction patterns are imaged including the double slit, triple slit, alphanumeric characters, and the University of Rochester logo. The third application is the use of compressive sensing to characterize spatial entanglement of photon pairs produced by spontaneous parametric downconversion. The technique gives a theoretical speedup N2/log N for N-dimensional entanglement over the standard raster scanning technique
Compressed sensing for distributed systems
Coluccia, Giulio; Magli, Enrico
2015-01-01
This book presents a survey of the state-of-the art in the exciting and timely topic of compressed sensing for distributed systems. It has to be noted that, while compressed sensing has been studied for some time now, its distributed applications are relatively new. Remarkably, such applications are ideally suited to exploit all the benefits that compressed sensing can provide. The objective of this book is to provide the reader with a comprehensive survey of this topic, from the basic concepts to different classes of centralized and distributed reconstruction algorithms, as well as a comparison of these techniques. This book collects different contributions on these aspects. It presents the underlying theory in a complete and unified way for the first time, presenting various signal models and their use cases. It contains a theoretical part collecting latest results in rate-distortion analysis of distributed compressed sensing, as well as practical implementations of algorithms obtaining performance close to...
Forest mapping and monitoring using active 3D remote sensing
VASTARANTA Mikko
2012-01-01
The main aim in forest mapping and monitoring is to produce accurate information for forest managers with the use of efficient methodologies. For example, it is important to locate harvesting sites and stands where forest operations should be carried out as well as to provide updates regarding forest growth, among other changes in forest structure. In recent years, remote sensing (RS) has taken a significant technological leap forward. It has become possible to acquire three-dimensional (3D),...
3D temperature field reconstruction using ultrasound sensing system
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.
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.
Single-shot 3D sensing with improved data density
Willomitzer, Florian; Faber, Christian; Häusler, Gerd
2014-01-01
We introduce a novel concept for motion robust optical 3D-sensing. The concept is based on multi-line triangulation. The aim is to evaluate a large number of projected lines (high data density) in a large measurement volume with high precision. Implementing all those three attributes at the same time allows for the "perfect" real-time 3D movie camera (our long term goal). The key problem towards this goal is ambiguous line indexing: we will demonstrate that the necessary information for unique line indexing can be acquired by two synchronized cameras and a back projection scheme. The introduced concept preserves high lateral resolution, since the lines are as narrow as the sampling theorem allows, no spatial bandwidth is consumed by encoding of the lines. In principle, the distance uncertainty is only limited by shot noise and coherent noise. The concept can be also advantageously implemented with a hand-guided sensor and real-time registration, for a complete and dense 3D-acquisition of complicated scenes.
Compressive Sensing in Communication Systems
Fyhn, Karsten
2013-01-01
Wireless communication is omnipresent today, but this development has led to frequency spectrum becoming a limited resource. Furthermore, wireless devices become more and more energy-limited, due to the demand for continual wireless communication of higher and higher amounts of information. The...... need for cheaper, smarter and more energy efficient wireless devices is greater now than ever. This thesis addresses this problem and concerns the application of the recently developed sampling theory of compressive sensing in communication systems. Compressive sensing is the merging of signal...... acquisition and compression. It allows for sampling a signal with a rate below the bound dictated by the celebrated Shannon-Nyquist sampling theorem. In some communication systems this necessary minimum sample rate, dictated by the Shannon-Nyquist sampling theorem, is so high it is at the limit of what the...
Efficient reconfigurable architectures for 3D medical image compression
Afandi, Ahmad
2010-01-01
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University. Recently, the more widespread use of three-dimensional (3-D) imaging modalities, such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and ultrasound (US) have generated a massive amount of volumetric data. These have provided an impetus to the development of other applications, in particular telemedicine and teleradiology. In thes...
3D Compressible Melt Transport with Adaptive Mesh Refinement
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
On Finite Alphabet Compressive Sensing
Das, Abhik Kumar; Vishwanath, Sriram
2013-01-01
This paper considers the problem of compressive sensing over a finite alphabet, where the finite alphabet may be inherent to the nature of the data or a result of quantization. There are multiple examples of finite alphabet based static as well as time-series data with inherent sparse structure; and quantizing real values is an essential step while handling real data in practice. We show that there are significant benefits to analyzing the problem while incorporating its finite alphabet natur...
Neurovascular compressive sites at the root entry zone of the trigeminal nerve were investigated in 25 patients with trigeminal neuralgia by using the fine three-dimensional (3D) magnetic resonance (MR) angiogram, obtained by a 3D time-of-flight, spoiled gradient-recalled sequence. The characteristic 3D MR angiographic findings of the offending vessels were obtained at the neurovascular compressive sites 19/23 (83%), including intermittent MR signal intensity within the vessels in 14/23 (61%), and unclear margin of the vessels in 5/23 (22%). Those abnormal 3D MR angiographic findings were commonly observed at the site of neurovascular compression in conjunction with moderate degree (grade II) and severe degree (grade III) in 19/20 (95%) of the actual nerve compression by the offending vessels. Abnormal findings with 3D MR angiograms may provide flow-related information to suggest a certain neurovascular compression upon the trigeminal nerve by the offending vessels. Those novel 3D MR angiographic findings may be useful for the diagnosis and decision-making process to execute the microvascular decompression surgery in patients with trigeminal neuralgia. (author)
Compression of 3D meshes based on a decomposition into singular vectors
El Mostafa RAJAALLAH
2016-06-01
Full Text Available Compression of 3D meshes is an important operation for many applications involving transfer and storage of 3D objects data in their process, such as research by content and navigation in 3D objects databases. Compression is to reduce the space needed to store and/or display the 3D mesh. To do that, we usually try to project it in a frequency space where information is less correlated. The approach suggested in this work is based on a decomposition into singular vectors of the laplacian transform of the adjacency matrix of the vertices of the 3D mesh. The obtained results show the invariance of this approach to a normalization and very encouraging performance semantically.
Hyperspectral imaging using compressed sensing
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.
Immersive 3D Visualization of Remote Sensing Data
Surbhi Rautji
2013-10-01
Full Text Available Immersive 3D Visualization is a java based Engine f or viewing the Data of aerial images in the Three Dimensional with provision for simulation and fly t hrough. This application is based on java Technolog y and works as standalone application, applet or in t he browser. Immersive 3D Visualization is a good application to use where the area of interest is to view the interested regions rather than the big im ages. The Immersive 3D Visualization is a application tha t is designed on Java 3D Technology and Open Graphics Libraries. The Java3D Technology of envisa ges the three Dimensional view of the picture and Open Graphics Libraries allows one to view the rend ered data on to the screen. This visualization application is java based hence no portability issu es. The Data for this work is collected from the va rious sites like Google earth, USGS web site for the data viewing . This work takes an advantage of modelling the 3D DEM data on the visualized portion of the screen , and thus this approach is optimized. We are assumed that the DEM is coming from the other sourc e or it is simulated Data in order to view the thre e- dimensional pictures. The Process of Collecting the Data and projecting on to the screen is done by collectively Open Graphics Libraries and Java 3D Te chnology. In this work any image can be viewed as 3D with the use of DEM data, which can be created w ith use of certain selected values, where as in the case of Google Earth it is not possible to see your own image in 3D. The work done here can be used fo r selected region of interest, unlike Google Earth, w hich is used for continuous streaming of data. Our work on 3D immersive visualisation can be useful to GIS analyst to view their own images in 3D
Fast spectrophotometry with compressive sensing
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.
Immersive 3D Visualization of Remote Sensing Data
Surbhi Rautji; Deepak Gaur; Karan Khare
2013-01-01
Immersive 3D Visualization is a java based Engine f or viewing the Data of aerial images in the Three Dimensional with provision for simulation and fly t hrough. This application is based on java Technolog y and works as standalone application, applet or in t he browser. Immersive 3D Visualization is a good application to use where the area of interest is to view the interested regions rather than the b...
A New Method for EEG Compressive Sensing
FIRA, M.; GORAS, L.
2012-01-01
The paper investigates the possibility of using compressive sensing techniques for the acquisition and reconstruction of EEG signals containing the evoked potential P300. A method of EEG compressive sensing based on the physiological correlation of EEG channels is proposed. The reconstruction of 55 EEG channels signals acquired by compressive sensing uses a dictionary consisting of EEG signals from other nine channels with normal acquisition.
Athena3D: Flux-conservative Godunov-type algorithm for compressible magnetohydrodynamics
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.
Contributions in compression of 3D medical images and 2D images
The huge amounts of volumetric data generated by current medical imaging techniques in the context of an increasing demand for long term archiving solutions, as well as the rapid development of distant radiology make the use of compression inevitable. Indeed, if the medical community has sided until now with compression without losses, most of applications suffer from compression ratios which are too low with this kind of compression. In this context, compression with acceptable losses could be the most appropriate answer. So, we propose a new loss coding scheme based on 3D (3 dimensional) Wavelet Transform and Dead Zone Lattice Vector Quantization 3D (DZLVQ) for medical images. Our algorithm has been evaluated on several computerized tomography (CT) and magnetic resonance image volumes. The main contribution of this work is the design of a multidimensional dead zone which enables to take into account correlations between neighbouring elementary volumes. At high compression ratios, we show that it can out-perform visually and numerically the best existing methods. These promising results are confirmed on head CT by two medical patricians. The second contribution of this document assesses the effect with-loss image compression on CAD (Computer-Aided Decision) detection performance of solid lung nodules. This work on 120 significant lungs images shows that detection did not suffer until 48:1 compression and still was robust at 96:1. The last contribution consists in the complexity reduction of our compression scheme. The first allocation dedicated to 2D DZLVQ uses an exponential of the rate-distortion (R-D) functions. The second allocation for 2D and 3D medical images is based on block statistical model to estimate the R-D curves. These R-D models are based on the joint distribution of wavelet vectors using a multidimensional mixture of generalized Gaussian (MMGG) densities. (author)
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
Restricted isometry properties and nonconvex compressive sensing
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 CT scans with a small number of x-rays and reducing MRI scanning time. The potential benefits extend to any application of compressive sensing.
Lossy compression of hyperspectral images using shearlet transform and 3D SPECK
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.
Accelerated fluorine-19 MRI cell tracking using compressed sensing
Zhong, Jia; Mills, Parker H.; Hitchens, T. Kevin; Ahrens, Eric T.
2012-01-01
Cell tracking using perfluorocarbon (PFC) labels and fluorine-19 (19F) MRI is a noninvasive approach to visualize and quantify cell populations in vivo. In this study, we investigated three-dimensional (3D) compressed sensing (CS) methods to accelerate 19F MRI data acquisition for cell tracking and evaluate the impact of acceleration on 19F signal quantification. We show that a greater than eight-fold reduction in imaging time was feasible without pronounced image degradation and with minimal...
Compressed sensing and sparsity in photoacoustic tomography
Haltmeier, Markus; Moon, Sunghwan; Burgholzer, Peter
2016-01-01
Increasing the imaging speed is a central aim in photoacoustic tomography. In this work we address this issue using techniques of compressed sensing. We demonstrate that the number of measurements can significantly be reduced by allowing general linear measurements instead of point wise pressure values. A main requirement in compressed sensing is the sparsity of the unknowns to be recovered. For that purpose we develop the concept of sparsifying temporal transforms for three dimensional photoacoustic tomography. Reconstruction results for simulated and for experimental data verify that the proposed compressed sensing scheme allows to significantly reducing the number of spatial measurements without reducing the spatial resolution.
First application of the 3D-MHB on dynamic compressive behavior of UHPC
Cadoni Ezio
2015-01-01
Full Text Available 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.
Huimin Yu
2012-01-01
The asymptotic behavior (as well as the global existence) of classical solutions to the 3D compressible Euler equations are considered. For polytropic perfect gas $(P(\\rho )={P}_{0}{\\rho }^{\\gamma })$ , time asymptotically, it has been proved by Pan and Zhao (2009) that linear damping and slip boundary effect make the density satisfying the porous medium equation and the momentum obeying the classical Darcy's law. In this paper, we use a more general method and extend this resu...
3D-Web-GIS RFID Location Sensing System for Construction Objects
Chien-Ho Ko
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 alg...
Compressive Sensing Image Sensors-Hardware Implementation
Shahram Shirani; M. Jamal Deen; Mohammadreza Dadkhah
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 implementa...
Spectral imaging with dual compressed sensing
Liu, Xue-feng; Yu, Wen-Kai; Yao, Xu-Ri; Dai, Bin; Li, Long-Zhen; Wang, Chao; Zhai, Guang-Jie
2015-01-01
We experimentally demonstrated a spectral imaging scheme with dual compressed sensing. With the dimensions of spectral and spatial information both compressed, the spectral image of a colored object can be obtained with only a single point detector. The effect of spatial and spectral modulation numbers on the imaging quality is also analyzed. Our scheme provides a stable, highly consistent approach of spectral imaging.
Spectral Compressive Sensing with Polar Interpolation
Fyhn, Karsten; Dadkhahi, Hamid; F. Duarte, Marco
2013-01-01
this paper, we introduce a greedy recovery algorithm that leverages a band-exclusion function and a polar interpolation function to address these two issues in spectral compressive sensing. Our algorithm is geared towards line spectral estimation from compressive measurements and outperforms most...
Compressive sensing exploiting wavelet-domain dependencies for ECG compression
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.
Compressive Sensing for MIMO Radar
Yu, Yao; Poor, H Vincent
2009-01-01
Multiple-input multiple-output (MIMO) radar systems have been shown to achieve superior resolution as compared to traditional radar systems with the same number of transmit and receive antennas. This paper considers a distributed MIMO radar scenario, in which each transmit element is a node in a wireless network, and investigates the use of compressive sampling for direction-of-arrival (DOA) estimation. According to the theory of compressive sampling, a signal that is sparse in some domain can be recovered based on far fewer samples than required by the Nyquist sampling theorem. The DOA of targets form a sparse vector in the angle space, and therefore, compressive sampling can be applied for DOA estimation. The proposed approach achieves the superior resolution of MIMO radar with far fewer samples than other approaches. This is particularly useful in a distributed scenario, in which the results at each receive node need to be transmitted to a fusion center for further processing.
Expanding Window Compressed Sensing for Non-Uniform Compressible Signals
Sung Ho Cho
2012-09-01
Full Text Available Many practical compressible signals like image signals or the networked data in wireless sensor networks have non-uniform support distribution in their sparse representation domain. Utilizing this prior information, a novel compressed sensing (CS scheme with unequal protection capability is proposed in this paper by introducing a windowing strategy called expanding window compressed sensing (EW-CS. According to the importance of different parts of the signal, the signal is divided into several nested subsets, i.e., the expanding windows. Each window generates its own measurements using a random sensing matrix. The more significant elements are contained by more windows, so they are captured by more measurements. This design makes the EW-CS scheme have more convenient implementation and better overall recovery quality for non-uniform compressible signals than ordinary CS schemes. These advantages are theoretically analyzed and experimentally confirmed. Moreover, the EW-CS scheme is applied to the compressed acquisition of image signals and networked data where it also has superior performance than ordinary CS and the existing unequal protection CS schemes.
MRI Sequence Images Compression Method Based on Improved 3D SPIHT%基于改进3D SPIHT的MRI序列图像压缩方法
蒋行国; 李丹; 陈真诚
2013-01-01
目的 研究一种有效的MRI序列图像压缩方法.方法 以2组不同数量、不同层厚的MRI序列图像为例,针对3D SPIHT算法运算复杂度,在对D型、L型表项重复判断的不足上,提出了一种改进的3DSPIHT方法;同时,根据MRI序列图像的相关性特点,提出了分组编/解码的方法,结合3D小波变换和应用改进的3D SPIHT方法,实现了MRI序列图像压缩.结果 分组结合改进3D SPIHT方法与2DSPIHT,3D SPIHT相比,能够得到较好重构图像,同时,峰值信噪比(PSNR)提高了1～8 dB左右.结论 在相同码率下,分组结合改进3D SPIHT的方法提高了PSNR和图像恢复质量,可以更好地解决大量MRI序列图像存储与传输问题.%Objective To propose an effective MRI sequence image compression method for solving the storage and transmission problem of large amounts of MRI sequence images. Methods Aimed at alleviating the complexity of computation of 3D Set Partitioning in Hierarchical Trees( SPIHT) algorithm and the deficiency that D or L type table were judged repeatedly, an improved 3 D SPIHT method was presented and two groups of MRI sequence images with different numbers and slice thickness were taken as examples. At the same time, according to the correlation characteristics of MRI sequence images, a method that images were divided into groups and then coded/decoded was put forward in this paper. It combined with 3D wavelet transform and the improved 3D SPIHT method, the MRI sequence image compression was achieved. Results Comparing with the 2D SPIHT and 3D SPIHT methods, the grouping combined with the improved 3D SPIHT method could obtain better reconstructed images and Peak Signal Noise Ratio (PSNR) could be improved by 1 ～ 8 dB as well. Conclusion At the same bit rate, PSNR and image quality of recovery can be improved by the grouping combined with the improved 3D SPIHT method and the storage and transmission problem of large amounts of MRI sequence images can be solved.
Remote sensing images fusion based on block compressed sensing
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.
DART : a 3D model for remote sensing images and radiative budget of earth surfaces
Gastellu-Etchegorry, J.P.; Grau, E.; Lauret, N.
2012-01-01
Modeling the radiative behavior and the energy budget of land surfaces is relevant for many scientific domains such as the study of vegetation functioning with remotely acquired information. DART model (Discrete Anisotropic Radiative Transfer) is developed since 1992. It is one of the most complete 3D models in this domain. It simulates radiative transfer (R.T.) in the optical domain: 3D radiative budget and remote sensing images (i.e., radiance, reflectance, brightness temperature) of vegeta...
The possibilities of compressed sensing based migration
Aldawood, Ali
2013-09-22
Linearized waveform inversion or Least-square migration helps reduce migration artifacts caused by limited acquisition aperture, coarse sampling of sources and receivers, and low subsurface illumination. However, leastsquare migration, based on L2-norm minimization of the misfit function, tends to produce a smeared (smoothed) depiction of the true subsurface reflectivity. Assuming that the subsurface reflectivity distribution is a sparse signal, we use a compressed-sensing (Basis Pursuit) algorithm to retrieve this sparse distribution from a small number of linear measurements. We applied a compressed-sensing algorithm to image a synthetic fault model using dense and sparse acquisition geometries. Tests on synthetic data demonstrate the ability of compressed-sensing to produce highly resolved migrated images. We, also, studied the robustness of the Basis Pursuit algorithm in the presence of Gaussian random noise.
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
Xampling: Compressed Sensing of Analog Signals
Mishali, Moshe; Eldar, Yonina C.
2011-01-01
Xampling generalizes compressed sensing (CS) to reduced-rate sampling of analog signals. A unified framework is introduced for low rate sampling and processing of signals lying in a union of subspaces. Xampling consists of two main blocks: Analog compression that narrows down the input bandwidth prior to sampling with commercial devices followed by a nonlinear algorithm that detects the input subspace prior to conventional signal processing. A variety of analog CS applications are reviewed wi...
Statistical Compressed Sensing of Gaussian Mixture Models
Yu, Guoshen
2011-01-01
A novel framework of compressed sensing, namely statistical compressed sensing (SCS), that aims at efficiently sampling a collection of signals that follow a statistical distribution, and achieving accurate reconstruction on average, is introduced. SCS based on Gaussian models is investigated in depth. For signals that follow a single Gaussian model, with Gaussian or Bernoulli sensing matrices of O(k) measurements, considerably smaller than the O(k log(N/k)) required by conventional CS based on sparse models, where N is the signal dimension, and with an optimal decoder implemented via linear filtering, significantly faster than the pursuit decoders applied in conventional CS, the error of SCS is shown tightly upper bounded by a constant times the best k-term approximation error, with overwhelming probability. The failure probability is also significantly smaller than that of conventional sparsity-oriented CS. Stronger yet simpler results further show that for any sensing matrix, the error of Gaussian SCS is u...
Compressive sensing for neutrospheric water vapor tomography using GNSS and InSAR observations
Heublein, Marion; Zhu, Xiao Xiang; Alshawaf, Fadwa; Mayer, Michael; Bamler, Richard; Hinz, Stefan
2015-01-01
This paper presents the innovative Compressive Sensing (CS) concept for tomographic reconstruction of 3D neutrospheric water vapor fields using data from Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR). The Precipitable Water Vapor (PWV) input data are derived from simulations of the Weather Research and Forecasting modeling system. We apply a Compressive Sensing based approach for tomographic inversion. Using the Cosine transform, a sparse repr...
Compressed Sensing Applied to Weather Radar
Mishra, Kumar Vijay; Kruger, Anton; Krajewski, Witold F.
2014-01-01
We propose an innovative meteorological radar, which uses reduced number of spatiotemporal samples without compromising the accuracy of target information. Our approach extends recent research on compressed sensing (CS) for radar remote sensing of hard point scatterers to volumetric targets. The previously published CS-based radar techniques are not applicable for sampling weather since the precipitation echoes lack sparsity in both range-time and Doppler domains. We propose an alternative ap...
Mechano-sensing and cell migration: a 3D model approach
Cell migration is essential for tissue development in different physiological and pathological conditions. It is a complex process orchestrated by chemistry, biological factors, microstructure and surrounding mechanical properties. Focusing on the mechanical interactions, cells do not only exert forces on the matrix that surrounds them, but they also sense and react to mechanical cues in a process called mechano-sensing. Here, we hypothesize the involvement of mechano-sensing in the regulation of directional cell migration through a three-dimensional (3D) matrix. For this purpose, we develop a 3D numerical model of individual cell migration, which incorporates the mechano-sensing process of the cell as the main mechanism regulating its movement. Consistent with this hypothesis, we found that factors, such as substrate stiffness, boundary conditions and external forces, regulate specific and distinct cell movements
Compressive sensing with a microwave photonic filter
Chen, Ying; Yu, Xianbin; Chi, Hao;
2015-01-01
In this letter, we present a novel approach to realizing photonics-assisted compressive sensing (CS) with the technique of microwave photonic fi ltering. 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...
Compressive sensing with a spherical microphone array
Fernandez Grande, Efren; Xenaki, Angeliki
2016-01-01
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 ‘1-norm minimization, so that the measured data are represented by few basis functions...... localization, sound field reconstruction, and sound field analysis....
Compressive sensing for high resolution radar imaging
Anitori, L.; Otten, M.P.G.; Hoogeboom, P.
2010-01-01
In this paper we present some preliminary results on the application of Compressive Sensing (CS) to high resolution radar imaging. CS is a recently developed theory which allows reconstruction of sparse signals with a number of measurements much lower than what is required by the Shannon sampling th
Qiao Liu
2013-01-01
In this article, we extend the well-known Serrin's blow-up criterion for solutions of the 3-D incompressible Navier-Stokes equations to the 3-D compressible nematic liquid crystal flows where the initial vacuum is allowed. It is proved that for the initial-boundary value problem of the 3-D compressible nematic liquid crystal flows in a bounded domain, the strong solution exists globally if the velocity satisfies the Serrin's condition and $L^1(0,T;L^{infty})$-norm of the gradient of th...
Qiao Liu
2013-04-01
Full Text Available In this article, we extend the well-known Serrin's blow-up criterion for solutions of the 3-D incompressible Navier-Stokes equations to the 3-D compressible nematic liquid crystal flows where the initial vacuum is allowed. It is proved that for the initial-boundary value problem of the 3-D compressible nematic liquid crystal flows in a bounded domain, the strong solution exists globally if the velocity satisfies the Serrin's condition and $L^1(0,T;L^{infty}$-norm of the gradient of the velocity is bounded.
Xu Xinying
2012-01-01
In this paper; we prove a blow-up criterion of strong solutions to the 3-D viscous and non-resistive magnetohydrodynamic equations for compressible heat-conducting flows with initial vacuum.This blow-up criterion depends only on the gradient of velocity and the temperature,which is similar to the one for compressible Navier-Stokes equations.
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
Zi-Yi Guo; Jing Chen; Guang Yang; Qian-Yu Tang; Cai-Xiang Chen; Shui-Xi Fu; Dan Yu
2012-01-01
Objective: To evaluate the anatomical characteristics and patterns of neurovascular compression in patients suffering trigeminal neuralgia, using 3D high-resolution magnetic resonance imaging methods and fusion technologies. Methods: The analysis of the anatomy of the facial nerve, brain stem and the vascular structures related to this nerve was made in 100 consecutive patients for TN. 3D high resolution MRI studies (3D SPGR, T1 enhanced 3D MP-RAGE and T2/T1 3D FIESTA) simultaneous visualization were used to assessed using the software 3D DOCTOR. Results: In 93 patients (93%), there were one or several locals of neurovascular compression (NVC). The superior cerebellar artery was involved in 71 cases (76%), the other vessels including the antero-inferior cerebellar artery, the basilar artery, the vertebral artery, and some venous structures. The mean distance between NVC and nerve origin site in the brainstem was (3.76 ± 2.90) mm). In 39 patients (42%), the vascular compression was located proximally and in 42 (45%) the compression was located distally. Nerve dislocation or distortion by the vessel was observed in 30 cases (32%). Conclusions: This 3D high resolution MRI and image fusion technology could be useful for diagnostic and therapeutic decisions in TN.
Object specific reconstruction using compressively sensed data
Compressed sensing holds the promise for radically novel sensors that can perfectly reconstruct images using considerably less samples of data than required by the otherwise general Shannon sampling theorem. In surveillance systems however, it is also desirable to cue regions of the image where objects of interest may exist. Thus in this paper, we are interested in imaging interesting objects in a scene, without necessarily seeking perfect reconstruction of the whole image. We show that our goals are achieved by minimizing a modified L2-norm criterion with good results when the reconstruction of only specific objects is of interest. The method yields a simple closed form analytical solution that does not require iterative processing. Objects can be meaningfully sensed in considerable detail while heavily compressing the scene elsewhere. Essentially, this embeds the object detection and clutter discrimination function in the sensing and imaging process.
Inductively Driven, 3D Liner Compression of a Magnetized Plasma to Megabar Energy Densities
Slough, John [MSNW LLC, Redmond, WA (United States)
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
Compressive Sensing for Spectroscopy and Polarimetry
Ramos, A Asensio
2009-01-01
We demonstrate through numerical simulations with real data the feasibility of using compressive sensing techniques for the acquisition of spectro-polarimetric data. This allows us to combine the measurement and the compression process into one consistent framework. Signals are recovered thanks to a sparse reconstruction scheme from projections of the signal of interest onto appropriately chosen vectors, typically noise-like vectors. The compressibility properties of spectral lines are analyzed in detail. The results shown in this paper demonstrate that, thanks to the compressibility properties of spectral lines, it is feasible to reconstruct the signals using only a small fraction of the information that is measured nowadays. We investigate in depth the quality of the reconstruction as a function of the amount of data measured and the influence of noise. This change of paradigm also allows us to define new instrumental strategies and to propose modifications to existing instruments in order to take advantage...
Coding Strategies and Implementations of Compressive Sensing
Tsai, Tsung-Han
This dissertation studies the coding strategies of computational imaging to overcome the limitation of conventional sensing techniques. The information capacity of conventional sensing is limited by the physical properties of optics, such as aperture size, detector pixels, quantum efficiency, and sampling rate. These parameters determine the spatial, depth, spectral, temporal, and polarization sensitivity of each imager. To increase sensitivity in any dimension can significantly compromise the others. This research implements various coding strategies subject to optical multidimensional imaging and acoustic sensing in order to extend their sensing abilities. The proposed coding strategies combine hardware modification and signal processing to exploiting bandwidth and sensitivity from conventional sensors. We discuss the hardware architecture, compression strategies, sensing process modeling, and reconstruction algorithm of each sensing system. Optical multidimensional imaging measures three or more dimensional information of the optical signal. Traditional multidimensional imagers acquire extra dimensional information at the cost of degrading temporal or spatial resolution. Compressive multidimensional imaging multiplexes the transverse spatial, spectral, temporal, and polarization information on a two-dimensional (2D) detector. The corresponding spectral, temporal and polarization coding strategies adapt optics, electronic devices, and designed modulation techniques for multiplex measurement. This computational imaging technique provides multispectral, temporal super-resolution, and polarization imaging abilities with minimal loss in spatial resolution and noise level while maintaining or gaining higher temporal resolution. The experimental results prove that the appropriate coding strategies may improve hundreds times more sensing capacity. Human auditory system has the astonishing ability in localizing, tracking, and filtering the selected sound sources or
3D-Web-GIS RFID Location Sensing System for Construction Objects
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
3D-Web-GIS RFID location sensing system for construction objects.
Ko, Chien-Ho
2013-01-01
Construction site managers could benefit from being able to visualize on-site construction objects. Radio frequency identification (RFID) technology has been shown to improve the efficiency of construction object management. The objective of this study is to develop a 3D-Web-GIS RFID location sensing system for construction objects. An RFID 3D location sensing algorithm combining Simulated Annealing (SA) and a gradient descent method is proposed to determine target object location. In the algorithm, SA is used to stabilize the search process and the gradient descent method is used to reduce errors. The locations of the analyzed objects are visualized using the 3D-Web-GIS system. A real construction site is used to validate the applicability of the proposed method, with results indicating that the proposed approach can provide faster, more accurate, and more stable 3D positioning results than other location sensing algorithms. The proposed system allows construction managers to better understand worksite status, thus enhancing managerial efficiency. PMID:23864821
L. Zhang
2015-01-01
Full Text Available In solar wind, dissipation of slow-mode magnetosonic waves may play a significant role in heating the solar wind, and these modes contribute essentially to the solar wind compressible turbulence. Most previous identifications of slow waves utilized the characteristic negative correlation between δ|B| and δρ. However, that criterion does not well identify quasi-parallel slow waves, for which δ|B| is negligible compared to δρ. Here we present a new method of identification, which will be used in 3-D compressible simulation. It is based on two criteria: (1 that VpB0 (phase speed projected along B0 is around ± cs, and that (2 there exists a clear correlation of δv|| and δρ. Our research demonstrates that if vA > cs, slow waves possess correlation between δv|| and δρ, with δρ / δv|| ≈ ± ρ0 / cs. This method helps us to distinguish slow-mode waves from fast and Alfvén waves, both of which do not have this polarity relation. The criteria are insensitive to the propagation angle θk B, defined as the angle between wave vector k and B0; they can be applied with a wide range of β if only vA > cs. In our numerical simulation, we have identified four cases of slow wave trains with this method. The slow wave trains seem to deform, probably caused by interaction with other waves; as a result, fast or Alfvén waves may be produced during the interaction and seem to propagate bidirectionally away. Our identification and analysis of the wave trains provide useful methods for investigations of compressible turbulence in the solar wind or in similar environments, and will thus deepen understandings of slow waves in the turbulence.
Compressive Sensing for Spread Spectrum Receivers
Fyhn, Karsten; Jensen, Tobias Lindstrøm; Larsen, Torben; Jensen, Søren Holdt
2013-01-01
With the advent of ubiquitous computing there are two design parameters of wireless communication devices that become very important: power efficiency and production cost. Compressive sensing enables the receiver in such devices to sample below the Shannon-Nyquist sampling rate, which may lead to a...... Compressive Spread Spectrum (CSS), allows for a simple, effective receiver design. Furthermore, we numerically evaluate the proposed receiver in terms of bit error rate under different signal to noise ratio conditions and compare it with other receiver structures. These numerical experiments show that though...
Dynamic Spectrum Detection Via Compressive Sensing
Michael Odeyomi
2012-04-01
Full Text Available Spectrum congestion is a major concern in both military and commercial wireless networks. To support growing demand for ubiquitous spectrum usage, Cognitive Radio is a new paradigm in wireless communication that can be used to exploit unused part of the spectrum by dynamically adjusting its operating parameters. While cognitive radio technology is a promising solution to the spectral congestion problem, efficient methods for detecting white spaces in wideband radio spectrum remain a challenge. Conventional methods of detection are forced to use the high sampling rate requirement of Nyquist criterion. In this paper, the feasibility and efficacy of using compressive sensing (CS algorithms inconjunction with Haar wavelet for identifying spectrum holes in the wideband spectrum is explored. Compressive sensing is an emerging theory that shows that it’s possible to achieve good reconstruction, at sampling rates lower than that specified by Nyquist. CS approach is robust in AWGN and fading channel.
Compressive Sensing Image Sensors-Hardware Implementation
Shahram Shirani
2013-04-01
Full Text Available 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.
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
A Compressed Sensing Wire-Tap Channel
Reeves, Galen; Milosavljevic, Nebojsa; Gastpar, Michael
2011-01-01
A multiplicative Gaussian wire-tap channel inspired by compressed sensing is studied. Lower and upper bounds on the secrecy capacity are derived, and shown to be relatively tight in the large system limit for a large class of compressed sensing matrices. Surprisingly, it is shown that the secrecy capacity of this channel is nearly equal to the capacity without any secrecy constraint provided that the channel of the eavesdropper is strictly worse than the channel of the intended receiver. In other words, the eavesdropper can see almost everything and yet learn almost nothing. This behavior, which contrasts sharply with that of many commonly studied wiretap channels, is made possible by the fact that a small number of linear projections can make a crucial difference in the ability to estimate sparse vectors.
Multichannel Compressive Sensing MRI Using Noiselet Encoding
Pawar, Kamlesh; Zhang, Jingxin
2014-01-01
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, and presents a method to design the pulse sequence for the noiselet encoding. This novel encoding scheme is combined with the multichannel compressive sensing (MCS) framework to take the advantage of multichannel data acquisition used in MRI scanners. An empirical RIP a...
Restricted Conformal Property of Compressive Sensing
Cheng, Tao
2014-01-01
Energy and direction are tow basic properties of a vector. A discrete signal is a vector in nature. RIP of compressive sensing can not show the direction information of a signal but show the energy information of a signal. Hence, RIP is not complete. Orthogonal matrices can preserve angles and lengths. Preservation of length can show energies of signals like RIP do; and preservation of angle can show directions of signals. Therefore, Restricted Conformal Property (RCP) is proposed according t...
Compressed Sensing-Based Direct Conversion Receiver
Pierzchlewski, Jacek; Arildsen, Thomas; Larsen, Torben
2012-01-01
Due to the continuously increasing computational power of modern data receivers it is possible to move more and more processing from the analog to the digital domain. This paper presents a compressed sensing approach to relaxing the analog filtering requirements prior to the ADCs in a direct conversion receiver. In the presented solution, the filtered down-convertedradio signals are randomly sampled with an average sampling frequency lower than its Nyquist rate, and then reconstructed in a DS...
Robust Facial Expression Recognition via Compressive Sensing
Shiqing Zhang; Xiaoming Zhao; Bicheng Lei
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, ...
3D printed sensing patches with embedded polymer optical fibre Bragg gratings
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.
Gaudeau, Y
2006-12-15
The huge amounts of volumetric data generated by current medical imaging techniques in the context of an increasing demand for long term archiving solutions, as well as the rapid development of distant radiology make the use of compression inevitable. Indeed, if the medical community has sided until now with compression without losses, most of applications suffer from compression ratios which are too low with this kind of compression. In this context, compression with acceptable losses could be the most appropriate answer. So, we propose a new loss coding scheme based on 3D (3 dimensional) Wavelet Transform and Dead Zone Lattice Vector Quantization 3D (DZLVQ) for medical images. Our algorithm has been evaluated on several computerized tomography (CT) and magnetic resonance image volumes. The main contribution of this work is the design of a multidimensional dead zone which enables to take into account correlations between neighbouring elementary volumes. At high compression ratios, we show that it can out-perform visually and numerically the best existing methods. These promising results are confirmed on head CT by two medical patricians. The second contribution of this document assesses the effect with-loss image compression on CAD (Computer-Aided Decision) detection performance of solid lung nodules. This work on 120 significant lungs images shows that detection did not suffer until 48:1 compression and still was robust at 96:1. The last contribution consists in the complexity reduction of our compression scheme. The first allocation dedicated to 2D DZLVQ uses an exponential of the rate-distortion (R-D) functions. The second allocation for 2D and 3D medical images is based on block statistical model to estimate the R-D curves. These R-D models are based on the joint distribution of wavelet vectors using a multidimensional mixture of generalized Gaussian (MMGG) densities. (author)
Biomedical sensor design using analog compressed sensing
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
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
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
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.
Self-calibration and biconvex compressive sensing
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.
Enablement of scientific remote sensing missions with in-space 3D printing
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
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.
张映辉; 吴国春
2014-01-01
We investigate the global existence and asymptotic behavior of classical solutions for the 3D compressible non-isentropic damped Euler equations on a periodic domain. The global existence and uniqueness of classical solutions are obtained when the initial data is near an equilibrium. Furthermore, the exponential convergence rates of the pressure and velocity are also proved by delicate energy methods.
Convex Feasibility Programming for Compressed Sensing
Carmi, Avishy
2010-01-01
We present a computationally-efficient method for recovering sparse signals from a series of noisy observations, known as the problem of compressed sensing (CS). CS theory requires solving a convex constrained minimization problem. We propose to transform this optimization problem into a convex feasibility problem (CFP), and solve it using subgradient projection methods, which are iterative, fast, robust and convergent schemes for solving CFPs. As opposed to some of the recently-introduced CS algorithms, such as Bayesian CS and gradient projections for sparse reconstruction, which become prohibitively inefficient as the problem dimension and sparseness degree increase, the newly-proposed methods exhibit a marked robustness with respect to these factors. This renders the subgradient projection methods highly viable for large-scale compressible scenarios.
Compressed Sensing-Based Direct Conversion Receiver
Pierzchlewski, Jacek; Arildsen, Thomas; Larsen, Torben
2012-01-01
conversion receiver. In the presented solution, the filtered down-converted radio signals are randomly sampled with an average sampling frequency lower than its Nyquist rate, and then reconstructed in a DSP system. To enable compressed sensing, this approach exploits the frequency domain sparsity of the down......-converted radio signals. As shown in an experiment presented in the article, when the proposed method is used, it is possible to relax the requirements for the quadrature down-converter filters. A random sampling device and an additional digital signal processing module is the price to pay for these relaxed...
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.
Spatial versus spectral compression ratio in compressive sensing of hyperspectral imaging
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.
Analysis of Compressive Sensing for Hyperspectral Remote Sensing Applications
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.
Towards 4D intervention guidance using compressed sensing
Interventional radiology is nowadays usually guided with projection radiography using mono- or biplane systems. Due to the projective nature of this guidance imaging certain intraprocedural situations remain unclear. Although helpful, the use of 3D CT is limited due to radiation dose. Using advanced reconstruction techniques incorporating prior knowledge, one could overcome these limitations without exceeding dose limitations. Intervention guidance is especially appealing to those algorithms, because certain constrains apply to useful images in intervention guidance that vary relevantly from other CT applications. These are: key relevance of high contrast structures, sparse temporal updates and little relevance of absolute CT values. In this paper the principal usability of reconstruction algorithms for intervention guidance is tested. Compressed sensing algorithms PICCS and ASD-POCS are compared to the McKinnon-Bates and Feldkamp-Davis-Kress algorithm. Animal experiments as well as simulations are performed. An outlook towards 4D intervention guidance is provided. (orig.)
Compressed sensing phase retrieval with phase diversity
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
Palangi, Hamid; Ward, Rabab; Deng, Li
2016-09-01
Various studies that address the compressed sensing problem with Multiple Measurement Vectors (MMVs) have been recently carried. These studies assume the vectors of the different channels to be jointly sparse. In this paper, we relax this condition. Instead we assume that these sparse vectors depend on each other but that this dependency is unknown. We capture this dependency by computing the conditional probability of each entry in each vector being non-zero, given the "residuals" of all previous vectors. To estimate these probabilities, we propose the use of the Long Short-Term Memory (LSTM)[1], a data driven model for sequence modelling that is deep in time. To calculate the model parameters, we minimize a cross entropy cost function. To reconstruct the sparse vectors at the decoder, we propose a greedy solver that uses the above model to estimate the conditional probabilities. By performing extensive experiments on two real world datasets, we show that the proposed method significantly outperforms the general MMV solver (the Simultaneous Orthogonal Matching Pursuit (SOMP)) and a number of the model-based Bayesian methods. The proposed method does not add any complexity to the general compressive sensing encoder. The trained model is used just at the decoder. As the proposed method is a data driven method, it is only applicable when training data is available. In many applications however, training data is indeed available, e.g. in recorded images and videos.
Compressive sensing with a microwave photonic filter
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
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.
Breaking the coherence barrier: A new theory for compressed sensing
Adcock, Ben; Hansen, Anders C.; Poon, Clarice; Roman, Bogdan
2013-01-01
This paper provides an extension of compressed sensing which bridges a substantial gap between existing theory and its current use in real-world applications. It introduces a mathematical framework that generalizes the three standard pillars of compressed sensing - namely, sparsity, incoherence and uniform random subsampling - to three new concepts: asymptotic sparsity, asymptotic incoherence and multilevel random sampling. The new theorems show that compressed sensing is also possible, and r...
Lithographic VCSEL array multimode and single mode sources for sensing and 3D imaging
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.
In this paper we combine a stochastic 3D microstructure model of a fiber based gas diffusion layer of polymer electrolyte fuel cells with a Lattice Boltzmann model for fluid transport. We focus on a simple approach of compressing the planar oriented virtual geometry of paper-type gas diffusion layer from Toray. Material parameters – permeability and tortuosity – are calculated from simulation of one phase, one component gas flow in stochastic geometries. We analyze the statistical spread of simulation results on ensembles of the virtual geometry, both uncompressed and compressed. The influence of the compression is discussed with regard to the Kozeny–Carman equation. The effective transport properties calculated from transport simulations in compressed gas diffusion layers agree well with a trend based on the Kozeny–Carman equation
Yong, Wang
2015-01-01
In the present paper, we study the uniform regularity and vanishing dissipation limit for the full compressible Navier-Stokes system whose viscosity and heat conductivity are allowed to vanish at different order. The problem is studied in a 3-D bounded domain with Navier-slip type boundary conditions \\eqref{1.9}. It is shown that there exists a unique strong solution to the full compressible Navier-Stokes system with the boundary conditions \\eqref{1.9} in a finite time interval which is indep...
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.
Robust Facial Expression Recognition via Compressive Sensing
Shiqing Zhang
2012-03-01
Full Text Available 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.
Multichannel Compressive Sensing MRI Using Noiselet Encoding
Pawar, Kamlesh; Egan, Gary; Zhang, Jingxin
2015-01-01
The incoherence between measurement and sparsifying transform matrices and the restricted isometry property (RIP) of measurement matrix are two of the key factors in determining the performance of compressive sensing (CS). In CS-MRI, the randomly under-sampled Fourier matrix is used as the measurement matrix and the wavelet transform is usually used as sparsifying transform matrix. However, the incoherence between the randomly under-sampled Fourier matrix and the wavelet matrix is not optimal, which can deteriorate the performance of CS-MRI. Using the mathematical result that noiselets are maximally incoherent with wavelets, this paper introduces the noiselet unitary bases as the measurement matrix to improve the incoherence and RIP in CS-MRI. Based on an empirical RIP analysis that compares the multichannel noiselet and multichannel Fourier measurement matrices in CS-MRI, we propose a multichannel compressive sensing (MCS) framework to take the advantage of multichannel data acquisition used in MRI scanners. Simulations are presented in the MCS framework to compare the performance of noiselet encoding reconstructions and Fourier encoding reconstructions at different acceleration factors. The comparisons indicate that multichannel noiselet measurement matrix has better RIP than that of its Fourier counterpart, and that noiselet encoded MCS-MRI outperforms Fourier encoded MCS-MRI in preserving image resolution and can achieve higher acceleration factors. To demonstrate the feasibility of the proposed noiselet encoding scheme, a pulse sequences with tailored spatially selective RF excitation pulses was designed and implemented on a 3T scanner to acquire the data in the noiselet domain from a phantom and a human brain. The results indicate that noislet encoding preserves image resolution better than Fouirer encoding. PMID:25965548
Multichannel compressive sensing MRI using noiselet encoding.
Kamlesh Pawar
Full Text Available 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.
Compressed Sensing via Iterative Support Detection
Wang, Yilun
2009-01-01
We present a new compressive sensing reconstruction method "ISD". ISD addresses failed cases of L1-based construction due to insufficient measurements. ISD will learn from wrong solutions and come up with new minimization problems that return signals that are either correct or better. Specifically, from an incorrect signal ISD detects an index set I that includes components most likely to be true nonzeros, obtains a new signal x by solving min{sum_{i not in I} |x_i| : Ax = b}, and repeats such support detection and minimization using latest x and I from one another until convergence. We introduce an efficient implementation of ISD, called threshold-ISD, for recovering signals with fast decaying distributions of nonzeros from compressive measurements. Numerical experiments show that threshold-ISD has significant overall advantages over the classical L1 minimization approach, as well as two other state-of-the-art algorithms such as the iterative reweighted L1 minimization algorithm (IRL1) and the iterative rewe...
Compressed Sensing Electron Tomography for Determining Biological Structure.
Guay, Matthew D; Czaja, Wojciech; Aronova, Maria A; Leapman, Richard D
2016-01-01
There has been growing interest in applying compressed sensing (CS) theory and practice to reconstruct 3D volumes at the nanoscale from electron tomography datasets of inorganic materials, based on known sparsity in the structure of interest. Here we explore the application of CS for visualizing the 3D structure of biological specimens from tomographic tilt series acquired in the scanning transmission electron microscope (STEM). CS-ET reconstructions match or outperform commonly used alternative methods in full and undersampled tomogram recovery, but with less significant performance gains than observed for the imaging of inorganic materials. We propose that this disparity stems from the increased structural complexity of biological systems, as supported by theoretical CS sampling considerations and numerical results in simulated phantom datasets. A detailed analysis of the efficacy of CS-ET for undersampled recovery is therefore complicated by the structure of the object being imaged. The numerical nonlinear decoding process of CS shares strong connections with popular regularized least-squares methods, and the use of such numerical recovery techniques for mitigating artifacts and denoising in reconstructions of fully sampled datasets remains advantageous. This article provides a link to the software that has been developed for CS-ET reconstruction of electron tomographic data sets. PMID:27291259
Compressed Sensing Electron Tomography for Determining Biological Structure
Guay, Matthew D.; Czaja, Wojciech; Aronova, Maria A.; Leapman, Richard D.
2016-06-01
There has been growing interest in applying compressed sensing (CS) theory and practice to reconstruct 3D volumes at the nanoscale from electron tomography datasets of inorganic materials, based on known sparsity in the structure of interest. Here we explore the application of CS for visualizing the 3D structure of biological specimens from tomographic tilt series acquired in the scanning transmission electron microscope (STEM). CS-ET reconstructions match or outperform commonly used alternative methods in full and undersampled tomogram recovery, but with less significant performance gains than observed for the imaging of inorganic materials. We propose that this disparity stems from the increased structural complexity of biological systems, as supported by theoretical CS sampling considerations and numerical results in simulated phantom datasets. A detailed analysis of the efficacy of CS-ET for undersampled recovery is therefore complicated by the structure of the object being imaged. The numerical nonlinear decoding process of CS shares strong connections with popular regularized least-squares methods, and the use of such numerical recovery techniques for mitigating artifacts and denoising in reconstructions of fully sampled datasets remains advantageous. This article provides a link to the software that has been developed for CS-ET reconstruction of electron tomographic data sets.
Compressed Sensing Electron Tomography for Determining Biological Structure
Guay, Matthew D.; Czaja, Wojciech; Aronova, Maria A.; Leapman, Richard D.
2016-01-01
There has been growing interest in applying compressed sensing (CS) theory and practice to reconstruct 3D volumes at the nanoscale from electron tomography datasets of inorganic materials, based on known sparsity in the structure of interest. Here we explore the application of CS for visualizing the 3D structure of biological specimens from tomographic tilt series acquired in the scanning transmission electron microscope (STEM). CS-ET reconstructions match or outperform commonly used alternative methods in full and undersampled tomogram recovery, but with less significant performance gains than observed for the imaging of inorganic materials. We propose that this disparity stems from the increased structural complexity of biological systems, as supported by theoretical CS sampling considerations and numerical results in simulated phantom datasets. A detailed analysis of the efficacy of CS-ET for undersampled recovery is therefore complicated by the structure of the object being imaged. The numerical nonlinear decoding process of CS shares strong connections with popular regularized least-squares methods, and the use of such numerical recovery techniques for mitigating artifacts and denoising in reconstructions of fully sampled datasets remains advantageous. This article provides a link to the software that has been developed for CS-ET reconstruction of electron tomographic data sets.
Laser speckle reduction based on compressive sensing and edge detection
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.
Detection Performance of Compressive Sensing Applied to Radar
Anitori, L.; Otten, M.P.G.; Hoogeboom, P.
2011-01-01
In this paper some results are presented on detection performance of radar using Compressive Sensing. Compressive sensing is a recently developed theory which allows reconstruction of sparse signals with a number of measurements much lower than implied by the Nyquist rate. In this work the behavior
False Alarm Probability Estimation for Compressive Sensing Radar
Anitori, L.; Otten, M.P.G.; Hoogeboom, P.
2011-01-01
In this paper false alarm probability (FAP) estimation of a radar using Compressive Sensing (CS) in the frequency domain is investigated. Compressive Sensing is a recently proposed technique which allows reconstruction of sparse signal from sub-Nyquist rate measurements. The estimation of the FAP is
Compressive Sensing with Directly Recoverable Optimal Basis and Applications in Spectrum Sensing
Gwon, Youngjune Lee; Kung, H. T.; Vlah, Dario
2011-01-01
We describe a method of integrating Karhunen-Loeve Transform (KLT) into compressive sensing, which can as a result leverage KLT’s optimality in revealing the sparsity of a signal. We present two complementary results: (1) by using the KLT to find the optimal basis for decoding we can drastically reduce the number of measurements for compressive sensing used in applications such as spectrum sensing; (2) by using compressive sensing we can compute the KLT basis directly from measurements of the...
Research on compressive fusion for remote sensing images
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.
Sayed, Ahmed; Layne, Ginger; Abraham, Jame; Mukdadi, Osama
2013-01-01
The main objective of this article is to introduce a new nonlinear elastography based classification method for human breast masses. Multi-compression elastography imaging is elucidated in this study to differentiate malignant from benign lesions, based on their nonlinear mechanical behavior under compression. Three classification parameters were used and compared in this work: a new nonlinear parameter based on a power-law behavior of the strain difference between breast masses and healthy t...
Hyperspectral images lossless compression using the 3D binary EZW algorithm
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.
Compressed Sensing: How Sharp Is the Restricted Isometry Property?
Blanchard, Jeffrey D.; Cartis, Coralia; Tanner, Jared
2011-01-01
Compressed Sensing (CS) seeks to recover an unknown vector with $N$ entries by making far fewer than $N$ measurements; it posits that the number of compressed sensing measurements should be comparable to the information content of the vector, not simply $N$. CS combines the important task of compression directly with the measurement task. Since its introduction in 2004 there have been hundreds of manuscripts on CS, a large fraction of which develop algorithms to recover a signal from its comp...
Remote sensing image compression assessment based on multilevel distortions
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.
Is "Compressed Sensing" compressive? Can it beat the Nyquist Sampling Approach?
Yaroslavsky, L
2015-01-01
Measurement redundancy required for sampling and restoration of signals/images using "Compressed sensing (sampling)" techniques is compared with that of their more traditional alternatives. It is shown that "Compressed sensing" is not more compressive than the conventional sampling and that it is inferior in this respect to other available methods of sampling with reduced redundancy such as DPCM coding or random sparse sampling and restoration of image band-limited approximations. It is also shown that assertions that "Compressed sensing" can beat the Nyquist sampling approach are rooted in misinterpretation of the sampling theory.
Global Solutions of the Equations of 3D Compressible Magnetohydrodynamics with Zero Resistivity
Suen, Anthony
2012-01-01
We prove the global-in-time existence of H^2 solutions of the equations of compressible magnetohydrodynamics with zero magnetic resistivity in three space dimensions. Initial data are taken to be small in H^2 modulo a constant state and initial densities are positive and essentially bounded. The present work generalizes the results obtained by Kawashima.
Compressed Sensing, Pseudodictionary-Based, Superresolution Reconstruction
Chun-mei Li
2016-01-01
Full Text Available The spatial resolution of digital images is the critical factor that affects photogrammetry precision. Single-frame, superresolution, image reconstruction is a typical underdetermined, inverse problem. To solve this type of problem, a compressive, sensing, pseudodictionary-based, superresolution reconstruction method is proposed in this study. The proposed method achieves pseudodictionary learning with an available low-resolution image and uses the K-SVD algorithm, which is based on the sparse characteristics of the digital image. Then, the sparse representation coefficient of the low-resolution image is obtained by solving the norm of l0 minimization problem, and the sparse coefficient and high-resolution pseudodictionary are used to reconstruct image tiles with high resolution. Finally, single-frame-image superresolution reconstruction is achieved. The proposed method is applied to photogrammetric images, and the experimental results indicate that the proposed method effectively increase image resolution, increase image information content, and achieve superresolution reconstruction. The reconstructed results are better than those obtained from traditional interpolation methods in aspect of visual effects and quantitative indicators.
Compressed sensing in imaging mass spectrometry
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.
Compressed sensing in imaging mass spectrometry
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. (paper)
Energy Preserved Sampling for Compressed Sensing MRI
Yudong Zhang
2014-01-01
Full Text Available 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.
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
Plasmonic 3D-structures based on silver decorated nanotips for biological sensing
Coluccio, M. L.
2015-05-01
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. © 2015 Elsevier Ltd.
Plasmonic 3D-structures based on silver decorated nanotips for biological sensing
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.
Hong-De Li
2015-01-01
Full Text Available Background: Percutaneous vertebroplasty (PVP has been gradually used for osteoporotic vertebral compression fracture (OVCF treatment, but severe osteoporotic vertebral body compression fractures (sOVCFs due to the difficulty in performing a puncture and the characteristics of the fractured vertebrae, it has been considered as a contraindication to PVP. The aim of the following study was to evaluate the feasibility of a unilateral, three-dimensional (3D, accurate puncture in percutaneous vertebroplasty (PVP for a single, severely osteoporotic vertebral body compression fracture (ssOVCFs. Materials and Methods: 57 patients received PVP in the current study. Feasibility of a unilateral approach was judged before surgery using the 64-slice helical computed tomography (CT multiplanar reconstruction technique, a 3D accurate puncture plan was then determined. The skin bone distance, puncture angle and needle insertion depth were recorded during surgery. 2D CT rechecking was performed for any complication at day 1 after operation. Preoperative and postoperative numerical data were compared. Results: The procedure was completed smoothly in all patients. 2D CT scanning at day 1 after operation did not show any puncture related complications. Visual analog scoring (VAS showed that the score at day 3 after surgery was reduced to 1.7 ± 0.4 (0-2.9 scale from the preoperative 7.9 ± 2.1 (6.1-9.5 scale. No significant differences in measure numerical data were found before and after the surgery. At 12 months followup three patients presented with nonadjacent level fractures, VAS for other patients were 1.2 ± 0.3 (0-2.1 scale. Conclusions: Application of CT scanning for a unilateral 3D puncture design helps realize an accurate puncture in PVP. It is a safe and effective method for ssOVCFs treatment.
) object based methods for the extraction of information are a key element to link individual workflow elements - improvements, especially concerning the level of automation in this area are developed and demonstrated; (2) in addition to established geovisualisation techniques, the application of recent developments in 3D geovisualisation using freely available virtual globes can be meaningful to communicate results - analytical 3D views, a new method to effectively provide relevant information in virtual globes is presented; (3) transferability of the workflow to different fields of application is shown to be successful - up to a certain degree even independently from underlying data sources. (author)
Numerical simulation of complex 3D compressible viscous flows through rotating blade passage
Despotović M.; Babić Milun; Milovanović D.; Šušteršič Vanja
2003-01-01
This paper describes a three-dimensional compressible Navier-Stokes code, which has been developed for analysis of turbocompressor blade rows and other internal flows. Despite numerous numerical techniques and statement that Computational Fluid Dynamics has reached state of the art, issues related to successful simulations represent valuable database of how particular technique behave for a specifie problem. This paper deals with rapid numerical method accurate enough to be used as a design ...
Can compressed sensing beat the Nyquist sampling rate?
Yaroslavsky, L
2015-01-01
Data saving capability of "Compressed sensing (sampling)" in signal discretization is disputed and found to be far below the theoretical upper bound defined by the signal sparsity. On a simple and intuitive example, it is demonstrated that, in a realistic scenario for signals that are believed to be sparse, one can achieve a substantially larger saving than compressing sensing can. It is also shown that frequent assertions in the literature that "Compressed sensing" can beat the Nyquist sampling approach are misleading substitution of terms and are rooted in misinterpretation of the sampling theory.
Castruccio, Stefano
2015-04-02
One of the main challenges when working with modern climate model ensembles is the increasingly larger size of the data produced, and the consequent difficulty in storing large amounts of spatio-temporally resolved information. Many compression algorithms can be used to mitigate this problem, but since they are designed to compress generic scientific data sets, they do not account for the nature of climate model output and they compress only individual simulations. In this work, we propose a different, statistics-based approach that explicitly accounts for the space-time dependence of the data for annual global three-dimensional temperature fields in an initial condition ensemble. The set of estimated parameters is small (compared to the data size) and can be regarded as a summary of the essential structure of the ensemble output; therefore, it can be used to instantaneously reproduce the temperature fields in an ensemble with a substantial saving in storage and time. The statistical model exploits the gridded geometry of the data and parallelization across processors. It is therefore computationally convenient and allows to fit a non-trivial model to a data set of one billion data points with a covariance matrix comprising of 10^18 entries.
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.
Adaptive Compressive Spectrum Sensing for Wideband Cognitive Radios
Sun, Hongjian; Chiu, Wei-Yu; Nallanathan, A.
2013-01-01
This letter presents an adaptive spectrum sensing algorithm that detects wideband spectrum using sub-Nyquist sampling rates. By taking advantage of compressed sensing (CS), the proposed algorithm reconstructs the wideband spectrum from compressed samples. Furthermore, an l2 norm validation approach is proposed that enables cognitive radios (CRs) to automatically terminate the signal acquisition once the current spectral recovery is satisfactory, leading to enhanced CR throughput. Numerical re...
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.
Encryption of Messages and Images Using Compressed Sensing
Daňková, M.
2015-01-01
The article deals with compressed sensing used to encrypt data. It allows performing signal capturing, its compression and encryption at the same time. The measurement matrix is generated using a secret key and is exploited for encryption. The article shows an example of its utilization at text and image message, moreover the Arnold transform is used in colour images for increasing security.
Reducing disk storage of full-3D seismic waveform tomography (F3DT) through lossy online compression
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.
Numerical simulation of complex 3D compressible viscous flows through rotating blade passage
Despotović M.
2003-01-01
Full Text Available This paper describes a three-dimensional compressible Navier-Stokes code, which has been developed for analysis of turbocompressor blade rows and other internal flows. Despite numerous numerical techniques and statement that Computational Fluid Dynamics has reached state of the art, issues related to successful simulations represent valuable database of how particular technique behave for a specifie problem. This paper deals with rapid numerical method accurate enough to be used as a design tool. The mathematical model is based on System of Favre averaged Navier-Stokes equations that are written in relative frame of reference, which rotates with constant angular velocity around axis of rotation. The governing equations are solved using finite volume method applied on structured grids. The numerical procedure is based on the explicit multistage Runge-Kutta scheme that is coupled with modem numerical procedures for convergence acceleration. To demonstrate the accuracy of the described numerical method developed software is applied to numerical analysis of flow through impeller of axial turbocompressor, and obtained results are compared with available experimental data.
Optimized Compressed Sensing Matrix Design for Noisy Communication Channels
Shirazinia, Amirpasha; Dey, Subhrakanti
2014-01-01
We investigate a power-constrained sensing matrix design problem for a compressed sensing framework. We adopt a mean square error (MSE) performance criterion for sparse source reconstruction in a system where the source-to-sensor channel and the sensor-to-decoder communication channel are noisy. Our proposed sensing matrix design procedure relies upon minimizing a lower-bound on the MSE. Under certain conditions, we derive closed-form solutions to the optimization problem. Through numerical e...
Spread spectrum compressed sensing MRI using chirp radio frequency pulses
Qu, Xiaobo; Zhuang, Xiaoxing; Yan, Zhiyu; Guo, Di; Chen, Zhong
2013-01-01
Compressed sensing has shown great potential in reducing data acquisition time in magnetic resonance imaging (MRI). Recently, a spread spectrum compressed sensing MRI method modulates an image with a quadratic phase. It performs better than the conventional compressed sensing MRI with variable density sampling, since the coherence between the sensing and sparsity bases are reduced. However, spread spectrum in that method is implemented via a shim coil which limits its modulation intensity and is not convenient to operate. In this letter, we propose to apply chirp (linear frequency-swept) radio frequency pulses to easily control the spread spectrum. To accelerate the image reconstruction, an alternating direction algorithm is modified by exploiting the complex orthogonality of the quadratic phase encoding. Reconstruction on the acquired data demonstrates that more image features are preserved using the proposed approach than those of conventional CS-MRI.
Li-bo Zhang; Zhi-liang Zhu; Ben-qiang Yang; Wen-yuan Liu; Hong-feng Zhu; Ming-yu Zou
2015-01-01
This paper presents a solution to satisfy the increasing requirements for secure medical image transmission and storage over public networks. The proposed scheme can simultaneously encrypt and compress the medical image using compressive sensing (CS) and pixel swapping based permutation approach. In the CS phase, the plain image is compressed and encrypted by chaos-based Bernoulli measurement matrix, which is generated under the control of the introduced Chebyshev map. The quantized measureme...
National Aeronautics and Space Administration — Eye safe 3D Imaging LIDARS when combined with advanced very high sensitivity, large format receivers can provide a robust wide area search capability in a very...
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.
Compressed Sensing for Moving Imagery in Medical Imaging
Bilen, Cagdas; Selesnick, Ivan
2012-01-01
Numerous applications in signal processing have benefited from the theory of compressed sensing which shows that it is possible to reconstruct signals sampled below the Nyquist rate when certain conditions are satisfied. One of these conditions is that there exists a known transform that represents the signal with a sufficiently small number of non-zero coefficients. However when the signal to be reconstructed is composed of moving images or volumes, it is challenging to form such regularization constraints with traditional transforms such as wavelets. In this paper, we present a motion compensating prior for such signals that is derived directly from the optical flow constraint and can utilize the motion information during compressed sensing reconstruction. Proposed regularization method can be used in a wide variety of applications involving compressed sensing and images or volumes of moving and deforming objects. It is also shown that it is possible to estimate the signal and the motion jointly or separate...
Conroy, Parker; Bareiss, Daman; Beall, Matt; Berg, Jur van den
2014-01-01
In this paper, we present an implementation of 3-D reciprocal collision avoidance on real quadrotor helicopters where each quadrotor senses the relative position and velocity of other quadrotors using an on-board camera. We show that using our approach, quadrotors are able to successfully avoid pairwise collisions in GPS and motion-capture denied environments, without communication between the quadrotors, and even when human operators deliberately attempt to induce collisions. To our knowledg...
Multi-resolution Compressive Sensing Reconstruction
Gonzalez, Adriana; Jiang, Hong; Huang, Gang; Jacques, Laurent
2016-01-01
We consider the problem of reconstructing an image from compressive measurements using a multi-resolution grid. In this context, the reconstructed image is divided into multiple regions, each one with a different resolution. This problem arises in situations where the image to reconstruct contains a certain region of interest (RoI) that is more important than the rest. Through a theoretical analysis and simulation experiments we show that the multi-resolution reconstruction provides a higher ...
Compressive sensing of frequency-hopping spread spectrum signals
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.
Muhammad, Haseena Bashir; Canali, Chiara; Heiskanen, Arto;
2014-01-01
We present an 8-channel bioreactor array with integrated bioimpedance sensors, which enables perfusion culture of cells seeded onto porous 3D scaffolds. Results show the capability of the system for monitoring cell proliferation within the scaffolds through a culture period of 19 days....
Discovery of a quorum sensing modulator pharmacophore by 3D small-molecule microarray screening
Marsden, David M; Nicholson, Rebecca L; Skindersoe, Mette E;
2010-01-01
The screening of large arrays of drug-like small-molecules was traditionally a time consuming and resource intensive task. New methodology developed within our laboratories provides an attractive low cost, 3D microarray-assisted screening platform that could be used to rapidly assay thousands of...
Muhammad, Haseena Bashir; Canali, Chiara; Heiskanen, Arto; Hemmingsen, Mette; Wolff, Anders; Dufva, Martin; Emnéus, Jenny
2014-01-01
We present an 8-channel bioreactor array with integrated bioimpedance sensors, which enables perfusion culture of cells seeded onto porous 3D scaffolds. Results show the capability of the system for monitoring cell proliferation within the scaffolds through a culture period of 19 days.
Lipschitz bounds for noise robustness in compressive sensing: two algorithms
Nicodème, Marc; Dossal, Charles; Turcu, Flavius; Berthoumieu, Yannick
2014-01-01
The paper deals with estimating the local noise robustness in a compressive sensing framework. We provide two algorithms which estimate, for each vector x that can be recovered by $\\ell^1$ minimization, the Lipschitz bounds relating the $\\ell^1$-reconstruction error to the measurement error (or noise) for a given sensing matrix. Classical theoretical estimations, such as those based on the restricted isometry property, theoretically give error bounds estimates depending on RIP constants. Unfo...
Polymer optical fibers integrated directly into 3D orthogonal woven composites for sensing
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. (paper)
Compressive sensing in a photonic link with optical integration
Chen, Ying; Yu, Xianbin; Chi, Hao;
2014-01-01
In this Letter, we present a novel structure to realize photonics-assisted compressive sensing (CS) with optical integration. In the system, a spectrally sparse signal modulates a multiwavelength continuous-wave light and then is mixed with a random sequence in optical domain. The optical signal...
Compressive sensing for spatial and spectral flame diagnostics
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.
Joint Sparsity and Frequency Estimation for Spectral Compressive Sensing
Nielsen, Jesper Kjær; Christensen, Mads Græsbøll; Jensen, Søren Holdt
Parameter estimation from compressively sensed signals has recently received some attention. We here also consider this problem in the context of frequency sparse signals which are encountered in many application. Existing methods perform the estimation using finite dictionaries or incorporate va...
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
Compressed Sensing and Matrix Completion with Constant Proportion of Corruptions
Li, Xiaodong
2011-01-01
We improve existing results in the field of compressed sensing and matrix completion when sampled data may be grossly corrupted. We introduce three new theorems. 1) In compressed sensing, we show that if the m \\times n sensing matrix has independent Gaussian entries, then one can recover a sparse signal x exactly by tractable \\ell1 minimimization even if a positive fraction of the measurements are arbitrarily corrupted, provided the number of nonzero entries in x is O(m/(log(n/m) + 1)). 2) In the very general sensing model introduced in "A probabilistic and RIPless theory of compressed sensing" by Candes and Plan, and assuming a positive fraction of corrupted measurements, exact recovery still holds if the signal now has O(m/(log^2 n)) nonzero entries. 3) Finally, we prove that one can recover an n \\times n low-rank matrix from m corrupted sampled entries by tractable optimization provided the rank is on the order of O(m/(n log^2 n)); again, this holds when there is a positive fraction of corrupted samples.
Multimodal sensing: enhanced functionality with data compression
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.
Baumberger, Roland; Wehrens, Philip; Herwegh, Marco
2013-04-01
Geological 3D models are always just an approximation of a complex natural situation. This is especially true in regions, where hard underground data (e.g. bore holes, tunnel mappings and seismic data) is lacking. One of the key problems while developing valid geological 3D models is the three-dimensional spatial distribution of geological structures, particularly with increasing distance from the surface. In our study, we investigate the Alpine 3D Deformation of the crystalline rocks of the Aar massif (Haslital valley, Central Switzerland). Deformation in this area is dominated by different sets of large-scale shear zones, which acted under both ductile and brittle deformation conditions. The goal of our study is the prediction of the geometry and the evolution of the structures in 3D space and time. A key point in our project is the generation of a reliable 3D model of today's structures. In this sense, estimation of the reliability of the surface information for the extrapolation to depth is mandatory. Based on our data, a method will be presented that contributes to a possible solution of the questions addressed above. The basic idea consists of the fact that (i) mechanical anisotropies as shear zones and faults show prominent three-dimensional information in the landscape, (ii) these geometries can be used as input data for a geological 3D model and (iii) that the 3D information mentioned allows a projection to depth. As a great advantage of the study area, a large number of underground tunnels exist, which allow to evaluate the quality of the aforementioned extrapolations. The method is based on a combined remote-sensing and field work approach: morphological incisions recognized on digital elevation models as well as on aerial photos on the computer screen were evaluated, described and attributed in detail in the field. Our approach is based on a six step workflow: (1) Elaboration of a large-scale structural map of geological structures by means of remote-sensing
Real-time visual sensing system achieving high-speed 3D particle tracking with nanometer resolution.
Cheng, Peng; Jhiang, Sissy M; Menq, Chia-Hsiang
2013-11-01
This paper presents a real-time visual sensing system, which is created to achieve high-speed three-dimensional (3D) motion tracking of microscopic spherical particles in aqueous solutions with nanometer resolution. The system comprises a complementary metal-oxide-semiconductor (CMOS) camera, a field programmable gate array (FPGA), and real-time image processing programs. The CMOS camera has high photosensitivity and superior SNR. It acquires images of 128×120 pixels at a frame rate of up to 10,000 frames per second (fps) under the white light illumination from a standard 100 W halogen lamp. The real-time image stream is downloaded from the camera directly to the FPGA, wherein a 3D particle-tracking algorithm is implemented to calculate the 3D positions of the target particle in real time. Two important objectives, i.e., real-time estimation of the 3D position matches the maximum frame rate of the camera and the timing of the output data stream of the system is precisely controlled, are achieved. Two sets of experiments were conducted to demonstrate the performance of the system. First, the visual sensing system was used to track the motion of a 2 μm polystyrene bead, whose motion was controlled by a three-axis piezo motion stage. The ability to track long-range motion with nanometer resolution in all three axes is demonstrated. Second, it was used to measure the Brownian motion of the 2 μm polystyrene bead, which was stabilized in aqueous solution by a laser trapping system. PMID:24216655
Effects of 3D geometries on cellular gradient sensing and polarization
Spill, Fabian; Andasari, Vivi; Mak, Michael; Kamm, Roger D.; Zaman, Muhammad H.
2016-06-01
During cell migration, cells become polarized, change their shape, and move in response to various internal and external cues. Cell polarization is defined through the spatio-temporal organization of molecules such as PI3K or small GTPases, and is determined by intracellular signaling networks. It results in directional forces through actin polymerization and myosin contractions. Many existing mathematical models of cell polarization are formulated in terms of reaction–diffusion systems of interacting molecules, and are often defined in one or two spatial dimensions. In this paper, we introduce a 3D reaction–diffusion model of interacting molecules in a single cell, and find that cell geometry has an important role affecting the capability of a cell to polarize, or change polarization when an external signal changes direction. Our results suggest a geometrical argument why more roundish cells can repolarize more effectively than cells which are elongated along the direction of the original stimulus, and thus enable roundish cells to turn faster, as has been observed in experiments. On the other hand, elongated cells preferentially polarize along their main axis even when a gradient stimulus appears from another direction. Furthermore, our 3D model can accurately capture the effect of binding and unbinding of important regulators of cell polarization to and from the cell membrane. This spatial separation of membrane and cytosol, not possible to capture in 1D or 2D models, leads to marked differences of our model from comparable lower-dimensional models.
McIDAS-V: Advanced Visualization for 3D Remote Sensing Data
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.
CMOS low data rate imaging method based on compressed sensing
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
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
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.
Impact of drizzle and 3D cloud structure on remote sensing of cloud effective radius
Zinner, Tobias; Wind, Gala; Platnick, Steven; Ackerman, Andy
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. The resulting effective radii are often quite different, indicative of different penetration depths for the spectral radiances used. Operational liquid water cloud retrievals are based on the assumption of a ...
On ECG Compressed Sensing using Specific Overcomplete Dictionaries
CLEJU, N.
2010-11-01
Full Text Available The paper presents a number of results regarding the construction of specific overcomplete dictionaries for ECG compressed sensing (CS. The dictionaries were built using normal and patological cardiac patterns extracted from 24 recordings of the MIT-BIH Arrhythmia Database. It has been shown that the compression results obtained using the CS concept based on specific dictionaries are better that those using the wavelet overcomplete dictionaries. Starting from the concept of sparse signal with respect to a given overcomplete dictionary the paper present several results regarding the possibility of simple pattern classification as well.
Compressive MUSIC: A Missing Link Between Compressive Sensing and Array Signal Processing
Kim, Jong Min; Ye, Jong Chul
2010-01-01
Multiple measurement vector (MMV) problem addresses identification of unknown input vectors that share common sparse support sets, and has many practical applications. Even though MMV problems had been traditionally addressed within the context of sensory array signal processing, recent research trend is to apply compressive sensing (CS) theory due to its capability to estimate sparse support even with insufficient number of snapshots, in which cases classical array signal processing approaches fail. However, CS approaches guarantees the accurate recovery of support in a probabilistic manner, which often shows inferior performance in the regime where the traditional array signal processing approaches succeed. The main contribution of the present article is, therefore, a unified approach that unveils a {missing link} between compressive sensing and array signal processing approaches for the multiple measurement vector problem. The new algorithm, which we call {\\em compressive MUSIC}, identifies the parts of su...
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.
A mobile sensing system for real-time 3D weld pool surface measurement in manual GTAW
An innovative mobile sensing system has been developed to non-intrusively monitor the manual pipe gas tungsten arc welding (GTAW) process. The system consists of a projective torch held by a welder, and a sensory helmet on the welder’s head. The three-dimensional (3D) weld pool surface is effectively measured in real-time as the welder performs the weld despite the movements of the torch and the helmet. In this study, the sensing system is first analyzed by numerical simulations in which the adjustment boundaries of the torch and helmet, i.e. their translation and orientation ranges, for effective sensing have been determined. Then, the sensing system is further evaluated using a simulation platform in which the movements of the helmet/welder’s head is mimicked by a tripod head with 6 degree-of-freedom (DOF), and a convex spherical mirror with a comparable size of a typical weld pool in a GTAW process is applied as a weld pool substitute. The effectiveness of the proposed system is validated by successfully capturing laser reflections from the mirror without great constraints on the welder’s movements of the torch and the helmet. Based on the analysis of the spatial relations among the torch, the helmet and the weld pool, an innovative real-time algorithm is proposed to reconstruct the 3D weld pool surface. The effectiveness and robustness of the algorithm have been verified by accurately reconstructing the convex spherical mirror surface despite the movements of the torch and the helmet in the simulation platform. (paper)
Li-bo Zhang
2015-01-01
Full Text Available This paper presents a solution to satisfy the increasing requirements for secure medical image transmission and storage over public networks. The proposed scheme can simultaneously encrypt and compress the medical image using compressive sensing (CS and pixel swapping based permutation approach. In the CS phase, the plain image is compressed and encrypted by chaos-based Bernoulli measurement matrix, which is generated under the control of the introduced Chebyshev map. The quantized measurements are then encrypted by permutation-diffusion type chaotic cipher for the second level protection. Simulations and extensive security analyses have been performed. The results demonstrate that at a large scale of compression ratio the proposed cryptosystem can provide satisfactory security level and reconstruction quality.
Image compression-encryption scheme based on hyper-chaotic system and 2D compressive sensing
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.
3D Visual Sensing of the Human Hand for the Remote Operation of a Robotic Hand
Pablo Gil
2014-02-01
Full Text Available New low cost sensors and open free libraries for 3D image processing are making important advances in robot vision applications possible, such as three- dimensional object recognition, semantic mapping, navigation and localization of robots, human detection and/or gesture recognition for human-machine interaction. In this paper, a novel method for recognizing and tracking the fingers of a human hand is presented. This method is based on point clouds from range images captured by a RGBD sensor. It works in real time and it does not require visual marks, camera calibration or previous knowledge of the environment. Moreover, it works successfully even when multiple objects appear in the scene or when the ambient light is changed. Furthermore, this method was designed to develop a human interface to control domestic or industrial devices, remotely. In this paper, the method was tested by operating a robotic hand. Firstly, the human hand was recognized and the fingers were detected. Secondly, the movement of the fingers was analysed and mapped to be imitated by a robotic hand.
Weng, Jiawen; Clark, David C.; Kim, Myung K.
2016-05-01
A numerical reconstruction method based on compressive sensing (CS) for self-interference incoherent digital holography (SIDH) is proposed to achieve sectional imaging by single-shot in-line self-interference incoherent hologram. The sensing operator is built up based on the physical mechanism of SIDH according to CS theory, and a recovery algorithm is employed for image restoration. Numerical simulation and experimental studies employing LEDs as discrete point-sources and resolution targets as extended sources are performed to demonstrate the feasibility and validity of the method. The intensity distribution and the axial resolution along the propagation direction of SIDH by angular spectrum method (ASM) and by CS are discussed. The analysis result shows that compared to ASM the reconstruction by CS can improve the axial resolution of SIDH, and achieve sectional imaging. The proposed method may be useful to 3D analysis of dynamic systems.
Cognitive Radios Exploiting Gray Spaces via Compressed Sensing
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.
Compressed Sensing with Linear Correlation Between Signal and Measurement Noise
Arildsen, Thomas; Larsen, Torben
2014-01-01
Existing convex relaxation-based approaches to reconstruction in compressed sensing assume that noise in the measurements is independent of the signal of interest. We consider the case of noise being linearly correlated with the signal and introduce a simple technique for improving compressed...... sensing reconstruction from such measurements. The technique is based on a linear model of the correlation of additive noise with the signal. The modification of the reconstruction algorithm based on this model is very simple and has negligible additional computational cost compared to standard...... reconstruction algorithms, but is not known in existing literature. The proposed technique reduces reconstruction error considerably in the case of linearly correlated measurements and noise. Numerical experiments confirm the efficacy of the technique. The technique is demonstrated with application to low...
Statistical mechanics analysis of thresholding 1-bit compressed sensing
Xu, Yingying
2016-01-01
The one-bit compressed sensing framework aims to reconstruct a sparse signal by only using the sign information of its linear measurements. To compensate for the loss of scale information, past studies in the area have proposed recovering the signal by imposing an additional constraint on the L2-norm of the signal. Recently, an alternative strategy that captures scale information by introducing a threshold parameter to the quantization process was advanced. In this paper, we analyze the typical behavior of the thresholding 1-bit compressed sensing utilizing the replica method of statistical mechanics, so as to gain an insight for properly setting the threshold value. Our result shows that, fixing the threshold at a constant value yields better performance than varying it randomly when the constant is optimally tuned, statistically. Unfortunately, the optimal threshold value depends on the statistical properties of the target signal, which may not be known in advance. In order to handle this inconvenience, we ...
Compressed sensing for wide-field radio interferometric imaging
McEwen, J D
2010-01-01
For the next generation of radio interferometric telescopes it is of paramount importance to incorporate wide field-of-view (WFOV) considerations in interferometric imaging, otherwise the fidelity of reconstructed images will suffer greatly. We extend compressed sensing techniques for interferometric imaging to a WFOV and recover images in the spherical coordinate space in which they naturally live, eliminating any distorting projection. The effectiveness of the spread spectrum phenomenon, highlighted recently by one of the authors, is enhanced when going to a WFOV, while sparsity is promoted by recovering images directly on the sphere. Both of these properties act to improve the quality of reconstructed interferometric images. We quantify the performance of compressed sensing reconstruction techniques through simulations, highlighting the superior reconstruction quality achieved by recovering interferometric images directly on the sphere rather than the plane.
Sampling theory, a renaissance compressive sensing and other developments
2015-01-01
Reconstructing or approximating objects from seemingly incomplete information is a frequent challenge in mathematics, science, and engineering. A multitude of tools designed to recover hidden information are based on Shannon’s classical sampling theorem, a central pillar of Sampling Theory. The growing need to efficiently obtain precise and tailored digital representations of complex objects and phenomena requires the maturation of available tools in Sampling Theory as well as the development of complementary, novel mathematical theories. Today, research themes such as Compressed Sensing and Frame Theory re-energize the broad area of Sampling Theory. This volume illustrates the renaissance that the area of Sampling Theory is currently experiencing. It touches upon trendsetting areas such as Compressed Sensing, Finite Frames, Parametric Partial Differential Equations, Quantization, Finite Rate of Innovation, System Theory, as well as sampling in Geometry and Algebraic Topology.
Weighted algorithms for compressed sensing and matrix completion
Gaïffas, Stéphane
2011-01-01
This paper is about iteratively reweighted basis-pursuit algorithms for compressed sensing and matrix completion problems. In a first part, we give a theoretical explanation of the fact that reweighted basis pursuit can improve a lot upon basis pursuit for exact recovery in compressed sensing. We exhibit a condition that links the accuracy of the weights to the RIP and incoherency constants, which ensures exact recovery. In a second part, we introduce a new algorithm for matrix completion, based on the idea of iterative reweighting. Since a weighted nuclear "norm" is typically non-convex, it cannot be used easily as an objective function. So, we define a new estimator based on a fixed-point equation. We give empirical evidences of the fact that this new algorithm leads to strong improvements over nuclear norm minimization on simulated and real matrix completion problems.
Signal Recovery in Compressed Sensing via Universal Priors
Baron, Dror
2012-01-01
We study the compressed sensing (CS) signal estimation problem where an input is measured via a linear matrix multiplication under additive noise. While this setup usually assumes sparsity or compressibility in the observed signal during recovery, the signal structure that can be leveraged is often not known a priori. In this paper, we consider universal CS recovery, where the statistics of a stationary ergodic signal source are estimated simultaneously with the signal itself. We focus on a maximum a posteriori (MAP) estimation framework that leverages universal priors such as Kolmogorov complexity and minimum description length. We provide theoretical results that support the algorithmic feasibility of universal MAP estimation through a Markov Chain Monte Carlo implementation. We also include simulation results that showcase the promise of universality in CS, particularly for low-complexity sources that do not exhibit standard sparsity or compressibility.
Compressed Sensing for Time-Frequency Gravitational Wave Data Analysis
Addesso, Paolo; Marano, Stefano; Matta, Vincenzo; Principe, Maria; Pinto, Innocenzo M
2016-01-01
The potential of compressed sensing for obtaining sparse time-frequency representations for gravitational wave data analysis is illustrated by comparison with existing methods, as regards i) shedding light on the fine structure of noise transients (glitches) in preparation of their classification, and ii) boosting the performance of waveform consistency tests in the detection of unmodeled transient gravitational wave signals using a network of detectors affected by unmodeled noise transient
Application of Compressive Sensing in Cognitive Radio Communications: A Survey
Sharma, Shree Krishna; Lagunas Targarona, Eva; Chatzinotas, Symeon; Ottersten, Björn
2016-01-01
Compressive Sensing (CS) has received much attention in several fields such as digital image processing, wireless channel estimation, radar imaging, and Cognitive Radio (CR) communications. Out of these areas, this survey paper focuses on the application of CS in CR communications. Due to the underutilization of the allocated radio spectrum, spectrum occupancy is usually sparse in different domains such as time, frequency and space. Such a sparse nature of the spectrum occupancy has inspired ...
Compressed Sensing MR Image Reconstruction Exploiting TGV and Wavelet Sparsity
Di Zhao; Huiqian Du; Yu Han; Wenbo Mei
2014-01-01
Compressed sensing (CS) based methods make it possible to reconstruct magnetic resonance (MR) images from undersampled measurements, which is known as CS-MRI. The reference-driven CS-MRI reconstruction schemes can further decrease the sampling ratio by exploiting the sparsity of the difference image between the target and the reference MR images in pixel domain. Unfortunately existing methods do not work well given that contrast changes are incorrectly estimated or motion compensation is inac...
Frame-Based Compressed Sensing Of Speech Signal
B Chaitanya
2014-01-01
In this paper, compressed sensing (CS) of speech signal using Frame-based adaptive technique has been proposed. we propose 3 sampling strategies in a frame-based adaptive CS framework. The first stage is frame based adaptive CS framework. In this stage, each speech sequence is divided into non-overlapping frames and all frames in a speech sequence are processed independently. Second stage is Partial Sampling and Frame Analysis. This stage can estimate the amount of intensity c...
Compressive Sensing for Feedback Reduction in MIMO Broadcast Channels
Qaseem, Syed T.; Al-Naffouri, Tareq Y.
2009-01-01
We propose a generalized feedback model and compressive sensing based opportunistic feedback schemes for feedback resource reduction in MIMO Broadcast Channels under the assumption that both uplink and downlink channels undergo block Rayleigh fading. Feedback resources are shared and are opportunistically accessed by users who are strong, i.e. users whose channel quality information is above a certain fixed threshold. Strong users send same feedback information on all shared channels. They ar...
Development of a Neutron Spectroscopic System Utilizing Compressed Sensing Measurements
Vargas Danilo; Kurwitz R. Cable; Carron Igor; DePriest K. Russell
2016-01-01
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 tha...
A Compressive Sensing Based Approach to Sparse Wideband Array Design
Hawes, M.B.; Liu, W
2014-01-01
Sparse wideband sensor array design for sensor location optimisation is highly nonlinear and it is traditionally solved by genetic algorithms, simulated annealing or other similar optimization methods. However, this is an extremely time-consuming process and more efficient solutions are needed. In this work, this problem is studied from the viewpoint of compressive sensing and a formulation based on a modified $l_1$ norm is derived. As there are multiple coefficients associated with each sens...
Early Detection of Rogue Waves Using Compressive Sensing
Bayindir, Cihan
2016-01-01
We discuss the possible usage of the compressive sampling for the early detection of rogue waves in a chaotic sea state. One of the promising techniques for the early detection of the oceanic rogue waves is to measure the triangular Fourier spectra which begin to appear at the early stages of their development. For the early detection of the rogue waves it is possible to treat such a spectrum as a sparse signal since we would mainly be interested in the high amplitude triangular region located at the central wavenumber. Therefore compressive sampling can be a very efficient tool for the rogue wave early warning systems. Compressed measurements can be acquired by remote sensing techniques such as coherent SAR which measure the ocean surface fluctuation or by insitu techniques such as spectra measuring tools mounted on a ship hull or bottom mounted pressure gauges. By employing a numerical approach we show that triangular Fourier spectra can be sensed by compressed measurements at the early stages of the develo...
Residual Distributed Compressive Video Sensing Based on Double Side Information
CHEN Jian; SU Kai-Xiong; WANG Wei-Xing; LAN Cheng-Dong
2014-01-01
Compressed sensing (CS) is a novel technology to acquire and reconstruct sparse signals below the Nyquist rate. It has great potential in image and video acquisition and processing. To effectively improve the sparsity of signal being measured and reconstructing efficiency, an encoding and decoding model of residual distributed compressive video sensing based on double side information (RDCVS-DSI) is proposed in this paper. Exploiting the characteristics of image itself in the frequency domain and the correlation between successive frames, the model regards the video frame in low quality as the first side information in the process of coding, and generates the second side information for the non-key frames using motion estimation and compensation technology at its decoding end. Performance analysis and simulation experiments show that the RDCVS-DSI model can rebuild the video sequence with high fidelity in the consumption of quite low complexity. About 1∼5 dB gain in the average peak signal-to-noise ratio of the reconstructed frames is observed, and the speed is close to the least complex DCVS, when compared with prior works on compressive video sensing.
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
A CLASS OF DETERMINISTIC CONSTRUCTION OF BINARY COMPRESSED SENSING MATRICES
Li Dandan; Liu Xinji; Xia Shutao; Jiang Yong
2012-01-01
Compressed Sensing (CS) is an emerging technology in the field of signal processing,which can recover a sparse signal by taking very few samples and solving a linear programming problem.In this paper,we study the application of Low-Density Parity-Check (LDPC) Codes in CS.Firstly,we find a sufficient condition for a binary matrix to satisfy the Restricted Isometric Property (RIP).Then,by employing the LDPC codes based on Berlekamp-Justesen (B-J) codes,we construct two classes of binary structured matrices and show that these matrices satisfy RIP.Thus,the proposed matrices could be used as sensing matrices for CS.Finally,simulation results show that the performance of the Droposed matrices can be comparable with the widely used random sensing matrices.
A. Labibzadeh
2008-01-01
Full Text Available In recent years, the material behavior dependence of laboratory concrete specimens built with the same concrete mixture under the same load conditions to their geometrical sizes is well established. This phenomenon which is observed not only in concrete but also in most quasi-brittle materials such as rock, ceramic or composite materials is now called as size effect. Many of the existing structural analyzing codes are not able to consider this important feature of concrete structures especially under compressive loadings. However we know that the main purpose of concrete application in structural members is to resist compression. The aim of this study is to show the ability of author's recently developed 3D finite elements code equipped with the proposed author's newly micro-planes damage based model for considering of compressive size effect of plane concrete. To do so, two different sizes of cubic concrete specimens are modeled with mentioned code under the uniaxial compressive test and their fracture mechanisms, pre-peak and post-peak strain-stress paths are investigated. Obtained results reveal the good coincidence with experimental evidences. In fact, the combination of proposed micro-planes damage based model and developed presented 3D finite elements technique creates a powerful numerical tool to capture and predict precisely strain localization and fracture mechanism in the specimens and consequently to assess properly the compressive size effect of plane concrete in analysis and design.
Canali, Chiara; Mazzoni, Chiara; Larsen, Layla Bashir;
2015-01-01
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...
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
Bull, D. J.
2014-01-01
In this thesis, particle-toughened and untoughened, carbon fibre composite material systems with quasi-isotropic layups were investigated. This was to understand better the toughening behaviour leading to increased impact damage resistance and post-impact compression damage tolerance performance. To achieve this, mechanical testing and conventional ultrasonic C-scan methods were combined with damage assessments using several 3D X-ray computed tomography techniques. These consisted of lab base...
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
Compressive MUSIC: A Missing Link Between Compressive Sensing and Array Signal Processing
Kim, Jong Min; Lee, Ok Kyun; Ye, Jong Chul
2010-01-01
The multiple measurement vector (MMV) problem addresses the identification of unknown input vectors that share common sparse support. Even though MMV problems had been traditionally addressed within the context of sensor array signal processing, the recent trend is to apply compressive sensing (CS) due to its capability to estimate sparse support even with an insufficient number of snapshots, in which case classical array signal processing fails. However, CS guarantees the accurate recovery i...
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
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.
Compressed Sensing for Denoising in Adaptive System Identification
Hosseini, Seyed Hossein
2012-01-01
We propose a new technique for adaptive identification of sparse systems based on the compressed sensing (CS) theory. We manipulate the transmitted pilot (input signal) and the received signal such that the weights of adaptive filter approach the compressed version of the sparse system instead of the original system. To this end, we use random filter structure at the transmitter to form the measurement matrix according to the CS framework. The original sparse system can be reconstructed by the conventional recovery algorithms. As a result, the denoising property of CS can be deployed in the proposed method at the recovery stage. The experiments indicate significant performance improvement of proposed method compared to the conventional LMS method which directly identifies the sparse system. Furthermore, at low levels of sparsity, our method outperforms a specialized identification algorithm that promotes sparsity.
An Energy Efficient Compressed Sensing Framework for the Compression of Electroencephalogram Signals
Simon Fauvel
2014-01-01
Full Text Available 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.
A new technique for hyperspectral compressive sensing using spectral unmixing
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.
Hyperspectral data compression
Motta, Giovanni; Storer, James A
2006-01-01
Provides a survey of results in the field of compression of remote sensed 3D data, with a particular interest in hyperspectral imagery. This work covers topics such as compression architecture, lossless compression, lossy techniques, and more. It also describes a lossless algorithm based on vector quantization.
The Effect of Spatial Coupling on Compressive Sensing
Kudekar, Shrinivas
2010-01-01
Recently, it was observed that spatially-coupled LDPC code ensembles approach the Shannon capacity for a class of binary-input memoryless symmetric (BMS) channels. The fundamental reason for this was attributed to a "threshold saturation" phenomena derived by Kudekar, Richardson and Urbanke. In particular, it was shown that the belief propagation (BP) threshold of the spatially coupled codes is equal to the maximum a posteriori (MAP) decoding threshold of the underlying constituent codes. In this sense, the BP threshold is saturated to its maximum value. Moreover, it has been empirically observed that the same phenomena also occurs when transmitting over more general classes of BMS channels. In this paper, we show that the effect of spatial coupling is not restricted to the realm of channel coding. The effect of coupling also manifests itself in compressed sensing. Specifically, we show that spatially-coupled measurement matrices have an improved sparsity to sampling threshold for reconstruction algorithms ba...
Image Deblurring Using Derivative Compressed Sensing for Optical Imaging Application
Rostami, Mohammad; Wang, Zhou
2011-01-01
Reconstruction of multidimensional signals from the samples of their partial derivatives is known to be a standard problem in inverse theory. Such and similar problems routinely arise in numerous areas of applied sciences, including optical imaging, laser interferometry, computer vision, remote sensing and control. Though being ill-posed in nature, the above problem can be solved in a unique and stable manner, provided proper regularization and relevant boundary conditions. In this paper, however, a more challenging setup is addressed, in which one has to recover an image of interest from its noisy and blurry version, while the only information available about the imaging system at hand is the amplitude of the generalized pupil function (GPF) along with partial observations of the gradient of GPF's phase. In this case, the phase-related information is collected using a simplified version of the Shack-Hartmann interferometer, followed by recovering the entire phase by means of derivative compressed sensing. Su...
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
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 (15-25 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.
T. Zinner
2010-01-01
Full Text Available Remote sensing of cloud effective particle size with passive sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS is an important tool for cloud microphysical studies. As a measure of the radiatively relevant droplet size, effective radius can be retrieved with different combinations of visible through shortwave and midwave infrared channels. In practice, retrieved effective radii from these combinations can be quite different. This difference is perhaps indicative of different penetration depths and path lengths for the spectral reflectances used. In addition, operational liquid water cloud retrievals are based on the assumption of a relatively narrow distribution of droplet sizes; the role of larger precipitation particles in these distributions is neglected. Therefore, possible explanations for the discrepancy in some MODIS spectral size retrievals could include 3-D radiative transport effects, including sub-pixel cloud inhomogeneity, and/or the impact of drizzle formation.
The possible factors of influence are isolated and investigated in detail by the use of simulated cloud scenes and synthetic satellite data: marine boundary layer cloud scenes from large eddy simulations (LES with detailed microphysics are combined with Monte Carlo radiative transfer calculations that explicitly account for the detailed droplet size distributions as well as 3-D radiative transfer to simulate MODIS observations. The operational MODIS optical thickness and effective radius retrieval algorithm is applied to these and the results are compared to the given LES microphysics.
We investigate two types of marine cloud situations each with and without drizzle from LES simulations: (1 a typical daytime stratocumulus deck at two times in the diurnal cycle and (2 one scene with scattered cumulus. Only small impact of drizzle formation on the retrieved domain average and on the differences between the three effective radius retrievals
T. Zinner
2010-10-01
Full Text Available Remote sensing of cloud effective particle size with passive sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS is an important tool for cloud microphysical studies. As a measure of the radiatively relevant droplet size, effective radius can be retrieved with different combinations of visible through shortwave and midwave infrared channels. In practice, retrieved effective radii from these combinations can be quite different. This difference is perhaps indicative of different penetration depths and path lengths for the spectral reflectances used. In addition, operational liquid water cloud retrievals are based on the assumption of a relatively narrow distribution of droplet sizes; the role of larger precipitation particles in these distributions is neglected. Therefore, possible explanations for the discrepancy in some MODIS spectral size retrievals could include 3-D radiative transport effects, including sub-pixel cloud inhomogeneity, and/or the impact of drizzle formation.
For three cloud cases the possible factors of influence are isolated and investigated in detail by the use of simulated cloud scenes and synthetic satellite data: marine boundary layer cloud scenes from large eddy simulations (LES with detailed microphysics are combined with Monte Carlo radiative transfer calculations that explicitly account for the detailed droplet size distributions as well as 3-D radiative transfer to simulate MODIS observations. The operational MODIS optical thickness and effective radius retrieval algorithm is applied to these and the results are compared to the given LES microphysics.
We investigate two types of marine cloud situations each with and without drizzle from LES simulations: (1 a typical daytime stratocumulus deck at two times in the diurnal cycle and (2 one scene with scattered cumulus. Only small impact of drizzle formation on the retrieved domain average and on the differences between the three
Compressed-sensing application - Pre-stack kirchhoff migration
Aldawood, Ali
2013-01-01
Least-squares migration is a linearized form of waveform inversion that aims to enhance the spatial resolution of the subsurface reflectivity distribution and reduce the migration artifacts due to limited recording aperture, coarse sampling of sources and receivers, and low subsurface illumination. Least-squares migration, however, due to the nature of its minimization process, tends to produce smoothed and dispersed versions of the reflectivity of the subsurface. Assuming that the subsurface reflectivity distribution is sparse, we propose the addition of a non-quadratic L1-norm penalty term on the model space in the objective function. This aims to preserve the sparse nature of the subsurface reflectivity series and enhance resolution. We further use a compressed-sensing algorithm to solve the linear system, which utilizes the sparsity assumption to produce highly resolved migrated images. Thus, the Kirchhoff migration implementation is formulated as a Basis Pursuit denoise (BPDN) problem to obtain the sparse reflectivity model. Applications on synthetic data show that reflectivity models obtained using this compressed-sensing algorithm are highly accurate with optimal resolution.
High dynamic range coherent imaging using compressed sensing.
He, Kuan; Sharma, Manoj Kumar; Cossairt, Oliver
2015-11-30
In both lensless Fourier transform holography (FTH) and coherent diffraction imaging (CDI), a beamstop is used to block strong intensities which exceed the limited dynamic range of the sensor, causing a loss in low-frequency information, making high quality reconstructions difficult or even impossible. In this paper, we show that an image can be recovered from high-frequencies alone, thereby overcoming the beamstop problem in both FTH and CDI. The only requirement is that the object is sparse in a known basis, a common property of most natural and manmade signals. The reconstruction method relies on compressed sensing (CS) techniques, which ensure signal recovery from incomplete measurements. Specifically, in FTH, we perform compressed sensing (CS) reconstruction of captured holograms and show that this method is applicable not only to standard FTH, but also multiple or extended reference FTH. For CDI, we propose a new phase retrieval procedure, which combines Fienup's hybrid input-output (HIO) method and CS. Both numerical simulations and proof-of-principle experiments are shown to demonstrate the effectiveness and robustness of the proposed CS-based reconstructions in dealing with missing data in both FTH and CDI. PMID:26698723
A compressed sensing approach for enhancing infrared imaging resolution
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.
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.
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.
Robust Compressive Wideband Spectrum Sensing with Sampling Distortion
Liu, Yipeng
2010-01-01
Too high sampling rate is the bottleneck to wideband spectrum sensing for cognitive radio (CR). As the survey shows that the sensed signal has a sparse representation in frequency domain, compressed sensing (CS) can be used to transfer the sampling burden to the digital signal processor. But the standard sparse signal recovery ways do not consider the distortion in the analogue to information converter (AIC) which randomly samples the received signal with sub-Nyquist rate. In practice various non-ideal physical effects would lead to distortion in AIC. Here we model the sampling distortion as a bounded additive noise. An anti-sampling-distortion constraint (ASDC) in the form of a mixed \\ell 2 and \\ell 1 norm is deduced from the sparse signal model with bounded sampling error. And the sparse constraint is in the form of minimization of the \\ell 1 norm of the estimated signal. Then we combine the sparse constraint with the ASDC to get a novel robust sparse signal recovery operator with sampling distortion. Numer...
Compressive Wideband Spectrum Sensing for Fixed Frequency Spectrum Allocation
Liu, Yipeng
2010-01-01
Too high sampling rate is the bottleneck to wideband spectrum sensing for cognitive radio (CR). As the survey shows that the sensed signal has a sparse representation in frequency domain in the mass, compressed sensing (CS) can be used to transfer the sampling burden to the digital signal processor. An analog to information converter (AIC) can randomly sample the received signal with sub-Nyquist rate to obtained the random measurements. Considering that the static frequency spectrum allocation of primary radios means the bounds between different primary radios is known in advance, here we incorporate information of the spectrum boundaries between different primary user as a priori information to obtain a mixed l2/l1 norm denoising operator (MNDO). In the MNDO, the estimated power spectrum density (PSD) vector is divided into block sections with bounds corresponding different allocated primary radios. Different from previous standard l1-norm constraint on the whole PSD vector, a sum of the l2 norm of each sect...
Learning-based compressed sensing for infrared image super resolution
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.
Vibha Tiwari
2015-12-01
Full Text Available Compressive sensing theory enables faithful reconstruction of signals, sparse in domain $ \\Psi $, at sampling rate lesser than Nyquist criterion, while using sampling or sensing matrix $ \\Phi $ which satisfies restricted isometric property. The role played by sensing matrix $ \\Phi $ and sparsity matrix $ \\Psi $ is vital in faithful reconstruction. If the sensing matrix is dense then it takes large storage space and leads to high computational cost. In this paper, effort is made to design sparse sensing matrix with least incurred computational cost while maintaining quality of reconstructed image. The design approach followed is based on sparse block circulant matrix (SBCM with few modifications. The other used sparse sensing matrix consists of 15 ones in each column. The medical images used are acquired from US, MRI and CT modalities. The image quality measurement parameters are used to compare the performance of reconstructed medical images using various sensing matrices. It is observed that, since Gram matrix of dictionary matrix ($ \\Phi \\Psi \\mathrm{} $ is closed to identity matrix in case of proposed modified SBCM, therefore, it helps to reconstruct the medical images of very good quality.
Bull, D. J.; Helfen, L.; Sinclair, I.; Spearing, S.M.
2012-01-01
3D X-ray computed tomography (CT) was used to study the effects of particle toughening within unidirectional carbon fibre reinforced polymer (CFRP) materials subjected to impact damage, followed by ex situ CT of compression after impact (CAI) tests at incremental loads. A multi-scale approach utilizing synchrotron radiation CT and laminography was used to study the damage micro-mechanisms of impact-loaded specimens, and micro-focus CT (?CT) assessed damage at meso- and macro-scopic levels. Fo...
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
Radial Velocity Data Analysis with Compressed Sensing Techniques
Hara, Nathan C; Laskar, Jacques; Correia, Alexandre C M
2016-01-01
We present a novel approach for analysing radial velocity data that combines two features: all the planets are searched at once and the algorithm is fast. This is achieved by utilizing compressed sensing techniques, which are modified to be compatible with the Gaussian 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.
Single image non-uniformity correction using compressive sensing
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.
Compressed sensing in MRI – mathematical preliminaries and basic examples
Błaszczyk Łukasz
2016-03-01
Full Text Available In magnetic resonance imaging (MRI, k-space sampling, due to physical restrictions, is very time-consuming. It cannot be much improved using classical Nyquist-based sampling theory. Recent developments utilize the fact that MR images are sparse in some representations (i.e. wavelet coefficients. This new theory, created by Candès and Romberg, called compressed sensing (CS, shows that images with sparse representations can be recovered from randomly undersampled k-space data, by using nonlinear reconstruction algorithms (i.e. l1-norm minimization. Throughout this paper, mathematical preliminaries of CS are outlined, in the form introduced by Candès. We describe the main conditions for measurement matrices and recovery algorithms and present a basic example, showing that while the method really works (reducing the time of MR examination, there are some major problems that need to be taken into consideration.
Simultaneous Measurement of Complementary Observables with Compressive Sensing
Howland, Gregory A; Lum, Daniel J; Howell, John C
2016-01-01
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, uncertainty 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 o...