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
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
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
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.
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
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.
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 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...
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.
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
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.
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 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 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
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...
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.
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
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...
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, ...
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...
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...
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
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.
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.
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.
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.
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.
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.
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.
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...
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.)
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.
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.
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
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.
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.
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.
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
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.
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...
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.
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.
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.
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, 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.
Multimode waveguide speckle patterns for compressive sensing.
Valley, George C; Sefler, George A; Justin Shaw, T
2016-06-01
Compressive sensing (CS) of sparse gigahertz-band RF signals using microwave photonics may achieve better performances with smaller size, weight, and power than electronic CS or conventional Nyquist rate sampling. The critical element in a CS system is the device that produces the CS measurement matrix (MM). We show that passive speckle patterns in multimode waveguides potentially provide excellent MMs for CS. We measure and calculate the MM for a multimode fiber and perform simulations using this MM in a CS system. We show that the speckle MM exhibits the sharp phase transition and coherence properties needed for CS and that these properties are similar to those of a sub-Gaussian MM with the same mean and standard deviation. We calculate the MM for a multimode planar waveguide and find dimensions of the planar guide that give a speckle MM with a performance similar to that of the multimode fiber. The CS simulations show that all measured and calculated speckle MMs exhibit a robust performance with equal amplitude signals that are sparse in time, in frequency, and in wavelets (Haar wavelet transform). The planar waveguide results indicate a path to a microwave photonic integrated circuit for measuring sparse gigahertz-band RF signals using CS. PMID:27244406
Energy Preserved Sampling for Compressed Sensing MRI
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.
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)
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 ...
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.
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.
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.
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.
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.
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.
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.
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....
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 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.
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...
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
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.
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.
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.
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.
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 ...
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.
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.
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 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...
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 ...
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...
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...
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...
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...
Huang, Jianfei; Zhu, Yihua; Yang, Xiaoling; Chen, Wei; Zhou, Ying; Li, Chunzhong
2014-12-01
Convenient determination of glucose in a sensitive, reliable and cost-effective way has aroused sustained research passion, bringing along assiduous investigation of high-performance electroactive nanomaterials to build enzymeless sensors. In addition to the intrinsic electrocatalytic capability of the sensing materials, electrode architecture at the microscale is also crucial for fully enhancing the performance. In this work, free-standing porous CuO nanowire (NW) was taken as a model sensing material to illustrate this point, where an in situ formed 3D CuO nanowire array (NWA) and CuO nanowires pile (NWP) immobilized with polymer binder by conventional drop-casting technique were both studied for enzymeless glucose sensing. The NWA electrode exhibited greatly promoted electrochemistry characterized by decreased overpotential for electro-oxidation of glucose and over 5-fold higher sensitivity compared to the NWP counterpart, benefiting from the binder-free nanoarray structure. Besides, its sensing performance was also satisfying in terms of rapidness, selectivity and durability. Further, the CuO NWA was utilized to fabricate a flexible sensor which showed excellent performance stability against mechanical bending. Thanks to its favorable electrode architecture, the CuO NWA is believed to offer opportunities for building high-efficiency flexible electrochemical devices.Convenient determination of glucose in a sensitive, reliable and cost-effective way has aroused sustained research passion, bringing along assiduous investigation of high-performance electroactive nanomaterials to build enzymeless sensors. In addition to the intrinsic electrocatalytic capability of the sensing materials, electrode architecture at the microscale is also crucial for fully enhancing the performance. In this work, free-standing porous CuO nanowire (NW) was taken as a model sensing material to illustrate this point, where an in situ formed 3D CuO nanowire array (NWA) and CuO nanowires
Texture-based medical image retrieval in compressed domain using compressive sensing.
Yadav, Kuldeep; Srivastava, Avi; Mittal, Ankush; Ansari, M A
2014-01-01
Content-based image retrieval has gained considerable attention in today's scenario as a useful tool in many applications; texture is one of them. In this paper, we focus on texture-based image retrieval in compressed domain using compressive sensing with the help of DC coefficients. Medical imaging is one of the fields which have been affected most, as there had been huge size of image database and getting out the concerned image had been a daunting task. Considering this, in this paper we propose a new model of image retrieval process using compressive sampling, since it allows accurate recovery of image from far fewer samples of unknowns and it does not require a close relation of matching between sampling pattern and characteristic image structure with increase acquisition speed and enhanced image quality. PMID:24589833
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.
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...
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...
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.
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.
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
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
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...
Single-Iteration Algorithm for Compressive Sensing Reconstruction
S. Stanković
2014-06-01
Full Text Available A single-iteration algorithm is proposed for the reconstruction of sparse signal from its incomplete set of observations. Recently, the reconstruction algorithms have been intensively developed within the Compressive Sensing framework. Most of the existing solutions are based either on l1-norm optimization methods or greedy iterative procedures with a priori known number of components or predefined number of iterations. We propose a simple non-iterative algorithm based on the analysis of noise-effect that appears in the frequency domain as a consequence of missing samples. The noise variance can be related and controlled by the number of missing samples. Accordingly, it is possible to keep the level of spectral noise below the signal components, such as to be able to accurately detect signal support and to reconstruct the entire signal. The theory is proven on various examples with multicomponent signals.
Estimation of many-body quantum Hamiltonians via compressive sensing
Shabani, A.; Rabitz, H. [Department of Chemistry, Princeton University, Princeton, New Jersey 08544 (United States); Mohseni, M. [Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 (United States); Lloyd, S. [Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 (United States); Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 (United States); Kosut, R. L. [SC Solutions, Sunnyvale, California 94085 (United States)
2011-07-15
We develop an efficient and robust approach for quantum measurement of nearly sparse many-body quantum Hamiltonians based on the method of compressive sensing. This work demonstrates that with only O(sln(d)) experimental configurations, consisting of random local preparations and measurements, one can estimate the Hamiltonian of a d-dimensional system, provided that the Hamiltonian is nearly s sparse in a known basis. The classical postprocessing is a convex optimization problem on the total Hilbert space which is generally not scalable. We numerically simulate the performance of this algorithm for three- and four-body interactions in spin-coupled quantum dots and atoms in optical lattices. Furthermore, we apply the algorithm to characterize Hamiltonian fine structure and unknown system-bath interactions.
Estimation of many-body quantum Hamiltonians via compressive sensing
We develop an efficient and robust approach for quantum measurement of nearly sparse many-body quantum Hamiltonians based on the method of compressive sensing. This work demonstrates that with only O(sln(d)) experimental configurations, consisting of random local preparations and measurements, one can estimate the Hamiltonian of a d-dimensional system, provided that the Hamiltonian is nearly s sparse in a known basis. The classical postprocessing is a convex optimization problem on the total Hilbert space which is generally not scalable. We numerically simulate the performance of this algorithm for three- and four-body interactions in spin-coupled quantum dots and atoms in optical lattices. Furthermore, we apply the algorithm to characterize Hamiltonian fine structure and unknown system-bath interactions.
Sampling theorems and compressive sensing on the sphere
McEwen, J D; Thiran, J -Ph; Vandergheynst, P; Van De Ville, D; Wiaux, Y
2011-01-01
We discuss a novel sampling theorem on the sphere developed by McEwen & Wiaux recently through an association between the sphere and the torus. To represent a band-limited signal exactly, this new sampling theorem requires less than half the number of samples of other equiangular sampling theorems on the sphere, such as the canonical Driscoll & Healy sampling theorem. A reduction in the number of samples required to represent a band-limited signal on the sphere has important implications for compressive sensing, both in terms of the dimensionality and sparsity of signals. We illustrate the impact of this property with an inpainting problem on the sphere, where we show superior reconstruction performance when adopting the new sampling theorem.
Compressed sensing for radio interferometric imaging: review and future direction
McEwen, J D
2011-01-01
Radio interferometry is a powerful technique for astronomical imaging. The theory of Compressed Sensing (CS) has been applied recently to the ill-posed inverse problem of recovering images from the measurements taken by radio interferometric telescopes. We review novel CS radio interferometric imaging techniques, both at the level of acquisition and reconstruction, and discuss their superior performance relative to traditional approaches. In order to remain as close to the theory of CS as possible, these techniques necessarily consider idealised interferometric configurations. To realise the enhancement in quality provided by these novel techniques on real radio interferometric observations, their extension to realistic interferometric configurations is now of considerable importance. We also chart the future direction of research required to achieve this goal.
Multi-Sparse Signal Recovery for Compressive Sensing
Liu, Yipeng; Matic, Vladimir; De Vos, Maarten; Van Huffel, Sabine
2012-01-01
Signal recovery is one of the key techniques of Compressive sensing (CS). It reconstructs the original signal from the linear sub-Nyquist measurements. Classical methods exploit the sparsity in one domain to formulate the L0 norm optimization. Recent investigation shows that some signals are sparse in multiple domains. To further improve the signal reconstruction performance, we can exploit this multi-sparsity to generate a new convex programming model. The latter is formulated with multiple sparsity constraints in multiple domains and the linear measurement fitting constraint. It improves signal recovery performance by additional a priori information. Since some EMG signals exhibit sparsity both in time and frequency domains, we take them as example in numerical experiments. Results show that the newly proposed method achieves better performance for multi-sparse signals.
Location constrained approximate message passing for compressed sensing MRI.
Sung, Kyunghyun; Daniel, Bruce L; Hargreaves, Brian A
2013-08-01
Iterative thresholding methods have been extensively studied as faster alternatives to convex optimization methods for solving large-sized problems in compressed sensing. A novel iterative thresholding method called LCAMP (Location Constrained Approximate Message Passing) is presented for reducing computational complexity and improving reconstruction accuracy when a nonzero location (or sparse support) constraint can be obtained from view shared images. LCAMP modifies the existing approximate message passing algorithm by replacing the thresholding stage with a location constraint, which avoids adjusting regularization parameters or thresholding levels. This work is first compared with other conventional reconstruction methods using random one-dimention signals and then applied to dynamic contrast-enhanced breast magnetic resonance imaging to demonstrate the excellent reconstruction accuracy (less than 2% absolute difference) and low computation time (5-10 s using Matlab) with highly undersampled three-dimentional data (244 × 128 × 48; overall reduction factor = 10). PMID:23042658
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.
Compressive Sensing for Feedback Reduction in Wireless Multiuser Networks
Elkhalil, Khalil
2015-05-01
User/relay selection is a simple technique that achieves spatial diversity in multiuser networks. However, for user/relay selection algorithms to make a selection decision, channel state information (CSI) from all cooperating users/relays is usually required at a central node. This requirement poses two important challenges. Firstly, CSI acquisition generates a great deal of feedback overhead (air-time) that could result in significant transmission delays. Secondly, the fed-back channel information is usually corrupted by additive noise. This could lead to transmission outages if the central node selects the set of cooperating relays based on inaccurate feedback information. Motivated by the aforementioned challenges, we propose a limited feedback user/relay selection scheme that is based on the theory of compressed sensing. Firstly, we introduce a limited feedback relay selection algorithm for a multicast relay network. The proposed algorithm exploits the theory of compressive sensing to first obtain the identity of the “strong” relays with limited feedback air-time. Following that, the CSI of the selected relays is estimated using minimum mean square error estimation without any additional feedback. To minimize the effect of noise on the fed-back CSI, we introduce a back-off strategy that optimally backs-off on the noisy received CSI. In the second part of the thesis, we propose a feedback reduction scheme for full-duplex relay-aided multiuser networks. The proposed scheme permits the base station (BS) to obtain channel state information (CSI) from a subset of strong users under substantially reduced feedback overhead. More specifically, we cast the problem of user identification and CSI estimation as a block sparse signal recovery problem in compressive sensing (CS). Using existing CS block recovery algorithms, we first obtain the identity of the strong users and then estimate their CSI using the best linear unbiased estimator (BLUE). Moreover, we derive the
Compressive sensing for feedback reduction in MIMO broadcast channels
Eltayeb, Mohammed E.
2014-09-01
In multi-antenna broadcast networks, the base stations (BSs) rely on the channel state information (CSI) of the users to perform user scheduling and downlink transmission. However, in networks with large number of users, obtaining CSI from all users is arduous, if not impossible, in practice. This paper proposes channel feedback reduction techniques based on the theory of compressive sensing (CS), which permits the BS to obtain CSI with acceptable recovery guarantees under substantially reduced feedback overhead. Additionally, assuming noisy CS measurements at the BS, inexpensive ways for improving post-CS detection are explored. The proposed techniques are shown to reduce the feedback overhead, improve CS detection at the BS, and achieve a sum-rate close to that obtained by noiseless dedicated feedback channels.
Underwater Acoustic Matched Field Imaging Based on Compressed Sensing
Huichen Yan
2015-10-01
Full Text Available Matched field processing (MFP is an effective method for underwater target imaging and localizing, but its performance is not guaranteed due to the nonuniqueness and instability problems caused by the underdetermined essence of MFP. By exploiting the sparsity of the targets in an imaging area, this paper proposes a compressive sensing MFP (CS-MFP model from wave propagation theory by using randomly deployed sensors. In addition, the model’s recovery performance is investigated by exploring the lower bounds of the coherence parameter of the CS dictionary. Furthermore, this paper analyzes the robustness of CS-MFP with respect to the displacement of the sensors. Subsequently, a coherence-excluding coherence optimized orthogonal matching pursuit (CCOOMP algorithm is proposed to overcome the high coherent dictionary problem in special cases. Finally, some numerical experiments are provided to demonstrate the effectiveness of the proposed CS-MFP method.
Development of a Neutron Spectroscopic System Utilizing Compressed Sensing Measurements
Vargas Danilo
2016-01-01
Full Text Available A new approach to neutron detection capable of gathering spectroscopic information has been demonstrated. The approach relies on an asymmetrical arrangement of materials, geometry, and an ability to change the orientation of the detector with respect to the neutron field. Measurements are used to unfold the energy characteristics of the neutron field using a new theoretical framework of compressed sensing. Recent theoretical results show that the number of multiplexed samples can be lower than the full number of traditional samples while providing the ability to have some super-resolution. Furthermore, the solution approach does not require a priori information or inclusion of physics models. Utilizing the MCNP code, a number of candidate detector geometries and materials were modeled. Simulations were carried out for a number of neutron energies and distributions with preselected orientations for the detector. The resulting matrix (A consists of n rows associated with orientation and m columns associated with energy and distribution where n < m. The library of known responses is used for new measurements Y (n × 1 and the solver is able to determine the system, Y = Ax where x is a sparse vector. Therefore, energy spectrum measurements are a combination of the energy distribution information of the identified elements of A. This approach allows for determination of neutron spectroscopic information using a single detector system with analog multiplexing. The analog multiplexing allows the use of a compressed sensing solution similar to approaches used in other areas of imaging. A single detector assembly provides improved flexibility and is expected to reduce uncertainty associated with current neutron spectroscopy measurement.
Compressive sensing based wireless sensor for structural health monitoring
Bao, Yuequan; Zou, Zilong; Li, Hui
2014-03-01
Data loss is a common problem for monitoring systems based on wireless sensors. Reliable communication protocols, which enhance communication reliability by repetitively transmitting unreceived packets, is one approach to tackle the problem of data loss. An alternative approach allows data loss to some extent and seeks to recover the lost data from an algorithmic point of view. Compressive sensing (CS) provides such a data loss recovery technique. This technique can be embedded into smart wireless sensors and effectively increases wireless communication reliability without retransmitting the data. The basic idea of CS-based approach is that, instead of transmitting the raw signal acquired by the sensor, a transformed signal that is generated by projecting the raw signal onto a random matrix, is transmitted. Some data loss may occur during the transmission of this transformed signal. However, according to the theory of CS, the raw signal can be effectively reconstructed from the received incomplete transformed signal given that the raw signal is compressible in some basis and the data loss ratio is low. This CS-based technique is implemented into the Imote2 smart sensor platform using the foundation of Illinois Structural Health Monitoring Project (ISHMP) Service Tool-suite. To overcome the constraints of limited onboard resources of wireless sensor nodes, a method called random demodulator (RD) is employed to provide memory and power efficient construction of the random sampling matrix. Adaptation of RD sampling matrix is made to accommodate data loss in wireless transmission and meet the objectives of the data recovery. The embedded program is tested in a series of sensing and communication experiments. Examples and parametric study are presented to demonstrate the applicability of the embedded program as well as to show the efficacy of CS-based data loss recovery for real wireless SHM systems.
Blow-up criterions of strong solutions to 3D compressible Navier-Stokes equations with vacuum
Wen, Huanyao; Zhu, Changjiang
2011-01-01
In the paper, we establish a blow-up criterion in terms of the integrability of the density for strong solutions to the Cauchy problem of compressible isentropic Navier-Stokes equations in \\mathbb{R}^3 with vacuum, under the assumptions on the coefficients of viscosity: \\frac{29\\mu}{3}>\\lambda. This extends the corresponding results in [20, 36] where a blow-up criterion in terms of the upper bound of the density was obtained under the condition 7\\mu>\\lambda. As a byproduct, the restriction 7\\...
Bø Fløystad, Jostein; Skjønsfjell, Eirik Torbjørn Bakken; Guizar-Sicairos, Manuel; Høydalsvik, Kristin; He, Jianying; Andreasen, Jens Wenzel; Zhang, Zhiliang; Breiby, Dag Werner
2015-01-01
Phase-contrast three-dimensional tomograms showing in unprecedented detail the mechanical response of a micro-composite subjected to a mechanical compression test are reported. The X-ray ptychography images reveal the deformation and fracture processes of a 10 μm diameter composite, consisting of a spherical polymer bead coated with a nominally 210 nm metal shell. The beginning delamination of the shell from the core can be directly observed at an engineering strain of a few percent. Pre-exis...
Zhensheng GAO; Zhong TAN; Guochun WU
2014-01-01
In this paper, we are concerned with the global existence and convergence rates of the smooth solutions for the compressible magnetohydrodynamic equations without heat conductivity, which is a hyperbolic-parabolic system. The global solutions are obtained by combining the local existence and a priori estimates if H3-norm of the initial perturbation around a constant states is small enough and its L1-norm is bounded. A priori decay-in-time estimates on the pressure, velocity and magnetic field are used to get the uniform bound of entropy. Moreover, the optimal convergence rates are also obtained.
Bø Fløystad, Jostein; Skjønsfjell, Eirik Torbjørn Bakken; Guizar-Sicairos, Manuel;
2015-01-01
spherical polymer bead coated with a nominally 210 nm metal shell. The beginning delamination of the shell from the core can be directly observed at an engineering strain of a few percent. Pre-existing defects are shown to dictate the deformation behavior of both core and shell. The strain state of the...... increasingly compressed polymer core is assessed quantitatively through the local densification at sub-micron resolution, supported by finite element analysis. Nanoscale mechanics is of rapidly growing importance in materials science, biotechnology and medicine, and this study demonstrates the use of coherent...
Motion-compensated compressed sensing for dynamic imaging
Sundaresan, Rajagopalan; Kim, Yookyung; Nadar, Mariappan S.; Bilgin, Ali
2010-08-01
The recently introduced Compressed Sensing (CS) theory explains how sparse or compressible signals can be reconstructed from far fewer samples than what was previously believed possible. The CS theory has attracted significant attention for applications such as Magnetic Resonance Imaging (MRI) where long acquisition times have been problematic. This is especially true for dynamic MRI applications where high spatio-temporal resolution is needed. For example, in cardiac cine MRI, it is desirable to acquire the whole cardiac volume within a single breath-hold in order to avoid artifacts due to respiratory motion. Conventional MRI techniques do not allow reconstruction of high resolution image sequences from such limited amount of data. Vaswani et al. recently proposed an extension of the CS framework to problems with partially known support (i.e. sparsity pattern). In their work, the problem of recursive reconstruction of time sequences of sparse signals was considered. Under the assumption that the support of the signal changes slowly over time, they proposed using the support of the previous frame as the "known" part of the support for the current frame. While this approach works well for image sequences with little or no motion, motion causes significant change in support between adjacent frames. In this paper, we illustrate how motion estimation and compensation techniques can be used to reconstruct more accurate estimates of support for image sequences with substantial motion (such as cardiac MRI). Experimental results using phantoms as well as real MRI data sets illustrate the improved performance of the proposed technique.
Sequestration of carbon dioxide in unmineable coal seams is an option to combat climate change and an opportunity to enhance coalbed methane production. Prediction of sequestration potential in coal requires characterization of porosity, permeability, sorption capacity and the magnitude of swelling due to carbon dioxide uptake or shrinkage due to methane and water loss. Unfortunately, the majority of data characterizing coal-gas systems have been obtained from powdered, unconfined coal samples. Little is known about confined coal behavior during carbon dioxide uptake and methane desorption. The present work focuses on the characterization of lithotype specific deformation, and strain behavior during CO2 uptake at simulated in-situ stress conditions. It includes the evaluation of three-dimensional strain induced by the confining stress, the sorption, and the desorption of carbon dioxide. X-ray computed tomography allowed three-dimensional characterization of the bituminous coal deformation samples under hydrostatic stress. The application of 6.9 MPa of confining stress contributes an average of - 0.34% volumetric strain. Normal strains due to confining stress were - 0.08%, - 0.15% and - 0.11% along the x, y and z axes respectively. Gas injection pressure was 3.1 MPa and the excess sorption was 0.85 mmol/g. Confined coal exposed to CO2 for 26 days displays an average volumetric expansion of 0.4%. Normal strains due to CO2 sorption were 0.11%, 0.22% and 0.11% along x, y and z axes. Drainage of the CO2 induced an average of - 0.33% volumetric shrinkage. Normal strains due to CO2 desorption were - 0.23%, - 0.08% and - 0.02% along x, y and z axes. Alternating positive and negative strain values observed along the sample length during compression, sorption and desorption respectively emphasized that both localized compression/compaction and expansion of coal will occur during CO2 sequestration. (author)
Hongtao Li; Chaoyu Wang; Xiaohua Zhu
2015-01-01
A novel compressive sensing- (CS-) based direction-of-arrival (DOA) estimation algorithm is proposed to solve the performance degradation of the CS-based DOA estimation in the presence of sensing matrix mismatching. Firstly, a DOA sparse sensing model is set up in the presence of sensing matrix mismatching. Secondly, combining the Dantzig selector (DS) algorithm and least-absolute shrinkage and selection operator (LASSO) algorithm, a CS-based DOA estimation algorithm which performs iterative ...
Two-Dimensional DOA Estimation in Compressed Sensing with Compressive-Reduced Dimension-lp-MUSIC
Weijian Si
2015-01-01
Full Text Available This paper presents a novel two-dimensional (2D direction of arrival (DOA estimation method in compressed sensing (CS to remove the estimation failure problem and achieve superior performance. The proposed method separates the steering vector into two parts to construct two corresponding noise subspaces by introducing electric angles. Then, electric angles are estimated based on the constructed noise subspaces. In order to estimate the azimuth and elevation angles in terms of estimates of electric angles, arc-tangent operations are exploited. The arc-tangent is a one-to-one function and allows the value of the argument to be larger than unity so that the proposed method never fails. The proposed method can avoid pair matching to reduce the computational complexity and extend the number of snapshots to improve performance. Simulation results show that the proposed method can avoid estimation failure occurrence and has superior performance as compared to existing methods.
Castaldo, Raffaele; De Novellis, Vincenzo; Lollino, Piernicola; Manunta, Michele; Tizzani, Pietro
2015-04-01
The new challenge that the research in slopes instabilities phenomena is going to tackle is the effective integration and joint exploitation of remote sensing measurements with in situ data and observations to study and understand the sub-surface interactions, the triggering causes, and, in general, the long term behaviour of the investigated landslide phenomenon. In this context, a very promising approach is represented by Finite Element (FE) techniques, which allow us to consider the intrinsic complexity of the mass movement phenomena and to effectively benefit from multi source observations and data. In this context, we perform a three dimensional (3D) numerical model of the Ivancich (Assisi, Central Italy) instability phenomenon. In particular, we apply an inverse FE method based on a Genetic Algorithm optimization procedure, benefitting from advanced DInSAR measurements, retrieved through the full resolution Small Baseline Subset (SBAS) technique, and an inclinometric array distribution. To this purpose we consider the SAR images acquired from descending orbit by the COSMO-SkyMed (CSK) X-band radar constellation, from December 2009 to February 2012. Moreover the optimization input dataset is completed by an array of eleven inclinometer measurements, from 1999 to 2006, distributed along the unstable mass. The landslide body is formed of debris material sliding on a arenaceous marl substratum, with a thin shear band detected using borehole and inclinometric data, at depth ranging from 20 to 60 m. Specifically, we consider the active role of this shear band in the control of the landslide evolution process. A large field monitoring dataset of the landslide process, including at-depth piezometric and geological borehole observations, were available. The integration of these datasets allows us to develop a 3D structural geological model of the considered slope. To investigate the dynamic evolution of a landslide, various physical approaches can be considered
Recent results of medium wave infrared compressive sensing.
Mahalanobis, A; Shilling, R; Murphy, R; Muise, R
2014-12-01
The application of compressive sensing (CS) for imaging has been extensively investigated and the underlying mathematical principles are well understood. The theory of CS is motivated by the sparse nature of real-world signals and images, and provides a framework in which high-resolution information can be recovered from low-resolution measurements. This, in turn, enables hardware concepts that require much fewer detectors than a conventional sensor. For infrared imagers there is a significant potential impact on the cost and footprint of the sensor. When smaller focal plane arrays (FPAs) to obtain large images are allowed, large formats FPAs are unnecessary. From a hardware standpoint, this benefit is independent of the actual level of compression and effective data rate reduction, which depend on the choice of codes and information recovery algorithm. Toward this end, we used a CS testbed for mid-wave infrared (MWIR) to experimentally show that information at high spatial resolution can be successfully recovered from measurements made with a small FPA. We describe the highly parallel and scalable CS architecture of the testbed, and its implementation using a reflective spatial light modulator and a focal plane array with variable pixel sizes. We also discuss the impact of real-world devices and the effect of sensor calibration that must be addressed in practice. Finally, we present preliminary results of image reconstruction, which demonstrate the testbed operation. These results experimentally confirm that high-resolution spatial information (for tasks such as imaging and target detection) can be successfully recovered from low-resolution measurements. We also discuss the potential system-level benefits of CS for infrared imaging, and some of the challenges that must be addressed in future infrared CS imagers designs. PMID:25607964
Accelerated radial Fourier-velocity encoding using compressed sensing
Hilbert, Fabian; Han, Dietbert [Wuerzburg Univ. (Germany). Inst. of Radiology; Wech, Tobias; Koestler, Herbert [Wuerzburg Univ. (Germany). Inst. of Radiology; Wuerzburg Univ. (Germany). Comprehensive Heart Failure Center (CHFC)
2014-10-01
Purpose:Phase Contrast Magnetic Resonance Imaging (MRI) is a tool for non-invasive determination of flow velocities inside blood vessels. Because Phase Contrast MRI only measures a single mean velocity per voxel, it is only applicable to vessels significantly larger than the voxel size. In contrast, Fourier Velocity Encoding measures the entire velocity distribution inside a voxel, but requires a much longer acquisition time. For accurate diagnosis of stenosis in vessels on the scale of spatial resolution, it is important to know the velocity distribution of a voxel. Our aim was to determine velocity distributions with accelerated Fourier Velocity Encoding in an acquisition time required for a conventional Phase Contrast image. Materials and Methods:We imaged the femoral artery of healthy volunteers with ECG - triggered, radial CINE acquisition. Data acquisition was accelerated by undersampling, while missing data were reconstructed by Compressed Sensing. Velocity spectra of the vessel were evaluated by high resolution Phase Contrast images and compared to spectra from fully sampled and undersampled Fourier Velocity Encoding. By means of undersampling, it was possible to reduce the scan time for Fourier Velocity Encoding to the duration required for a conventional Phase Contrast image. Results:Acquisition time for a fully sampled data set with 12 different Velocity Encodings was 40 min. By applying a 12.6 - fold retrospective undersampling, a data set was generated equal to 3:10 min acquisition time, which is similar to a conventional Phase Contrast measurement. Velocity spectra from fully sampled and undersampled Fourier Velocity Encoded images are in good agreement and show the same maximum velocities as compared to velocity maps from Phase Contrast measurements. Conclusion: Compressed Sensing proved to reliably reconstruct Fourier Velocity Encoded data. Our results indicate that Fourier Velocity Encoding allows an accurate determination of the velocity
Shihua Cao; Qihui Wang; Yaping Yuan; Junyang Yu
2014-01-01
Anomaly event detection is one of the research hotspots in wireless sensor networks. Aiming at the disadvantages of current detection solutions, a novel anomaly event detection algorithm based on compressed sensing and iteration is proposed. Firstly, a measured value can be sensed in each node, based on the compressed sensing. Then the problem of anomaly event detection is modeled as the minimization problem of weighted l1 norm, and OMP algorithm is adopted for solving the problem iteratively...
2014-01-01
MATLAB simulation software used for the PhD thesis "Acquisition of Multi-Band Signals via Compressed Sensing......MATLAB simulation software used for the PhD thesis "Acquisition of Multi-Band Signals via Compressed Sensing...
The formulation and validation of an efficient spatially second-order accurate finite volume total variation diminishing (TVD) scheme in general curvilinear coordinates for the 3-D compressible Navier-Stokes equations in strong conservation law form is presented. Approximate factorization and flux splitting are not utilized; therefore the associated extra, unwanted numerical dissipation is avoided, especially in complex viscous problems. A general line relaxation procedure is used to accelerate the convergence rate for geometrically complex steady-state problems since it is completely vectorizable. The implicit operator is cast in a delta-form using a TVD preconditioning matrix. The explicit part is evaluated using the same finite-volume TVD procedures developed by the authors in previous publications. This procedure preserves the high-resolution TVD characteristics in the steady state even at high CFL numbers. Numerical results are presented which demonstrate the efficiency, accuracy, and convergence characteristics for single and multi-body inviscid and turbulent flows. 21 refs
Genetic optical design for a compressive sensing task
Horisaki, Ryoichi; Niihara, Takahiro; Tanida, Jun
2016-07-01
We present a sophisticated optical design method for reducing the number of photodetectors for a specific sensing task. The chosen design parameter is the point spread function, and the selected task is object recognition. The point spread function is optimized iteratively with a genetic algorithm for object recognition based on a neural network. In the experimental demonstration, binary classification of face and non-face datasets was performed with a single measurement using two photodetectors. A spatial light modulator operating in the amplitude modulation mode was provided in the imaging optics and was used to modulate the point spread function. In each generation of the genetic algorithm, the classification accuracy with a pattern displayed on the spatial light modulator was fed-back to the next generation to find better patterns. The proposed method increased the accuracy by about 30 % compared with a conventional imaging system in which the point spread function was the delta function. This approach is practically useful for compressing the cost, size, and observation time of optical sensors in specific applications, and robust for imperfections in optical elements.
Improved Compressive Sensing of Natural Scenes Using Localized Random Sampling.
Barranca, Victor J; Kovačič, Gregor; Zhou, Douglas; Cai, David
2016-01-01
Compressive sensing (CS) theory demonstrates that by using uniformly-random sampling, rather than uniformly-spaced sampling, higher quality image reconstructions are often achievable. Considering that the structure of sampling protocols has such a profound impact on the quality of image reconstructions, we formulate a new sampling scheme motivated by physiological receptive field structure, localized random sampling, which yields significantly improved CS image reconstructions. For each set of localized image measurements, our sampling method first randomly selects an image pixel and then measures its nearby pixels with probability depending on their distance from the initially selected pixel. We compare the uniformly-random and localized random sampling methods over a large space of sampling parameters, and show that, for the optimal parameter choices, higher quality image reconstructions can be consistently obtained by using localized random sampling. In addition, we argue that the localized random CS optimal parameter choice is stable with respect to diverse natural images, and scales with the number of samples used for reconstruction. We expect that the localized random sampling protocol helps to explain the evolutionarily advantageous nature of receptive field structure in visual systems and suggests several future research areas in CS theory and its application to brain imaging. PMID:27555464
Implementation of compressive sensing for preclinical cine-MRI
Tan, Elliot; Yang, Ming; Ma, Lixin; Zheng, Yahong Rosa
2014-03-01
This paper presents a practical implementation of Compressive Sensing (CS) for a preclinical MRI machine to acquire randomly undersampled k-space data in cardiac function imaging applications. First, random undersampling masks were generated based on Gaussian, Cauchy, wrapped Cauchy and von Mises probability distribution functions by the inverse transform method. The best masks for undersampling ratios of 0.3, 0.4 and 0.5 were chosen for animal experimentation, and were programmed into a Bruker Avance III BioSpec 7.0T MRI system through method programming in ParaVision. Three undersampled mouse heart datasets were obtained using a fast low angle shot (FLASH) sequence, along with a control undersampled phantom dataset. ECG and respiratory gating was used to obtain high quality images. After CS reconstructions were applied to all acquired data, resulting images were quantitatively analyzed using the performance metrics of reconstruction error and Structural Similarity Index (SSIM). The comparative analysis indicated that CS reconstructed images from MRI machine undersampled data were indeed comparable to CS reconstructed images from retrospective undersampled data, and that CS techniques are practical in a preclinical setting. The implementation achieved 2 to 4 times acceleration for image acquisition and satisfactory quality of image reconstruction.
Interferometric radio transient reconstruction in compressed sensing framework
Jiang, M; Starck, J -L; Corbel, S; Tasse, C
2015-01-01
Imaging by aperture synthesis from interferometric data is a well-known, but is a strong ill-posed inverse problem. Strong and faint radio sources can be imaged unambiguously using time and frequency integration to gather more Fourier samples of the sky. However, these imagers assumes a steady sky and the complexity of the problem increases when transients radio sources are also present in the data. Hopefully, in the context of transient imaging, the spatial and temporal information are separable which enable extension of an imager fit for a steady sky. We introduce independent spatial and temporal wavelet dictionaries to sparsely represent the transient in both spatial domain and temporal domain. These dictionaries intervenes in a new reconstruction method developed in the Compressed Sensing (CS) framework and using a primal-dual splitting algorithm. According to the preliminary tests in different noise regimes, this new "Time-agile" (or 2D-1D) method seems to be efficient in detecting and reconstructing the...
Compressed sensing MR image reconstruction exploiting TGV and wavelet sparsity.
Zhao, Di; Du, Huiqian; Han, Yu; Mei, Wenbo
2014-01-01
Compressed sensing (CS) based methods make it possible to reconstruct magnetic resonance (MR) images from undersampled measurements, which is known as CS-MRI. The reference-driven CS-MRI reconstruction schemes can further decrease the sampling ratio by exploiting the sparsity of the difference image between the target and the reference MR images in pixel domain. Unfortunately existing methods do not work well given that contrast changes are incorrectly estimated or motion compensation is inaccurate. In this paper, we propose to reconstruct MR images by utilizing the sparsity of the difference image between the target and the motion-compensated reference images in wavelet transform and gradient domains. The idea is attractive because it requires neither the estimation of the contrast changes nor multiple times motion compensations. In addition, we apply total generalized variation (TGV) regularization to eliminate the staircasing artifacts caused by conventional total variation (TV). Fast composite splitting algorithm (FCSA) is used to solve the proposed reconstruction problem in order to improve computational efficiency. Experimental results demonstrate that the proposed method can not only reduce the computational cost but also decrease sampling ratio or improve the reconstruction quality alternatively. PMID:25371704
Compressed Sensing MR Image Reconstruction Exploiting TGV and Wavelet Sparsity
Di Zhao
2014-01-01
Full Text Available Compressed sensing (CS based methods make it possible to reconstruct magnetic resonance (MR images from undersampled measurements, which is known as CS-MRI. The reference-driven CS-MRI reconstruction schemes can further decrease the sampling ratio by exploiting the sparsity of the difference image between the target and the reference MR images in pixel domain. Unfortunately existing methods do not work well given that contrast changes are incorrectly estimated or motion compensation is inaccurate. In this paper, we propose to reconstruct MR images by utilizing the sparsity of the difference image between the target and the motion-compensated reference images in wavelet transform and gradient domains. The idea is attractive because it requires neither the estimation of the contrast changes nor multiple times motion compensations. In addition, we apply total generalized variation (TGV regularization to eliminate the staircasing artifacts caused by conventional total variation (TV. Fast composite splitting algorithm (FCSA is used to solve the proposed reconstruction problem in order to improve computational efficiency. Experimental results demonstrate that the proposed method can not only reduce the computational cost but also decrease sampling ratio or improve the reconstruction quality alternatively.
The possibilities of compressed-sensing-based Kirchhoff prestack migration
Aldawood, Ali
2014-05-08
An approximate subsurface reflectivity distribution of the earth is usually obtained through the migration process. However, conventional migration algorithms, including those based on the least-squares approach, yield structure descriptions that are slightly smeared and of low resolution caused by the common migration artifacts due to limited aperture, coarse sampling, band-limited source, and low subsurface illumination. To alleviate this problem, we use the fact that minimizing the L1-norm of a signal promotes its sparsity. Thus, we formulated the Kirchhoff migration problem as a compressed-sensing (CS) basis pursuit denoise problem to solve for highly focused migrated images compared with those obtained by standard and least-squares migration algorithms. The results of various subsurface reflectivity models revealed that solutions computed using the CS based migration provide a more accurate subsurface reflectivity location and amplitude. We applied the CS algorithm to image synthetic data from a fault model using dense and sparse acquisition geometries. Our results suggest that the proposed approach may still provide highly resolved images with a relatively small number of measurements. We also evaluated the robustness of the basis pursuit denoise algorithm in the presence of Gaussian random observational noise and in the case of imaging the recorded data with inaccurate migration velocities.
Compressed Sensing Electron tomography using adaptive dictionaries: a simulation study
AlAfeef, A.; Cockshott, P.; MacLaren, I.; McVitie, S.
2014-06-01
Electron tomography (ET) is an increasingly important technique for examining the three-dimensional morphologies of nanostructures. ET involves the acquisition of a set of 2D projection images to be reconstructed into a volumetric image by solving an inverse problem. However, due to limitations in the acquisition process this inverse problem is considered ill-posed (i.e., no unique solution exists). Furthermore reconstruction usually suffers from missing wedge artifacts (e.g., star, fan, blurring, and elongation artifacts). Compressed sensing (CS) has recently been applied to ET and showed promising results for reducing missing wedge artifacts caused by limited angle sampling. CS uses a nonlinear reconstruction algorithm that employs image sparsity as a priori knowledge to improve the accuracy of density reconstruction from a relatively small number of projections compared to other reconstruction techniques. However, The performance of CS recovery depends heavily on the degree of sparsity of the reconstructed image in the selected transform domain. Prespecified transformations such as spatial gradients provide sparse image representation, while synthesising the sparsifying transform based on the properties of the particular specimen may give even sparser results and can extend the application of CS to specimens that can not be sparsely represented with other transforms such as Total variation (TV). In this work, we show that CS reconstruction in ET can be significantly improved by tailoring the sparsity representation using a sparse dictionary learning principle.
Compressed Sensing Electron tomography using adaptive dictionaries: a simulation study
Electron tomography (ET) is an increasingly important technique for examining the three-dimensional morphologies of nanostructures. ET involves the acquisition of a set of 2D projection images to be reconstructed into a volumetric image by solving an inverse problem. However, due to limitations in the acquisition process this inverse problem is considered ill-posed (i.e., no unique solution exists). Furthermore reconstruction usually suffers from missing wedge artifacts (e.g., star, fan, blurring, and elongation artifacts). Compressed sensing (CS) has recently been applied to ET and showed promising results for reducing missing wedge artifacts caused by limited angle sampling. CS uses a nonlinear reconstruction algorithm that employs image sparsity as a priori knowledge to improve the accuracy of density reconstruction from a relatively small number of projections compared to other reconstruction techniques. However, The performance of CS recovery depends heavily on the degree of sparsity of the reconstructed image in the selected transform domain. Prespecified transformations such as spatial gradients provide sparse image representation, while synthesising the sparsifying transform based on the properties of the particular specimen may give even sparser results and can extend the application of CS to specimens that can not be sparsely represented with other transforms such as Total variation (TV). In this work, we show that CS reconstruction in ET can be significantly improved by tailoring the sparsity representation using a sparse dictionary learning principle
Passive Source Localization Using Compressively Sensed Towed Array
N. Suresh Kumar
2013-12-01
Full Text Available The objective of this work is to estimate the sparse angular power spectrum using a towed acoustic pressure sensor (APS array. In a passive towed array sonar, any reduction in the analog sensor signal conditioning receiver hardware housed inside the array tube, significantly improves the signal integrity and hence the localization performance. In this paper, a novel sparse acoustic pressure sensor (SAPS array architecture is proposed to estimate the direction of arrival (DOA of multiple acoustic sources. Bearing localization is effectively achieved by customizing the Capons spatial filter algorithm to suit the SAPS array architecture. Apart from the Monte Carlo simulations, the acoustic performance of the SAPS array with compressively sensed minimum variance distortionless response (CS-MVDR filter is demonstrated using a real passive towed array data. The proposed sparse towed array architecture promises a significant reduction in the analog signal acquisition receiver hardware, transmission data rate, number of snapshots and software complexity.Defence Science Journal, 2013, 63(6, pp.630-635, DOI:http://dx.doi.org/10.14429/dsj.63.5765
Single-snapshot DOA estimation by using Compressed Sensing
Fortunati, Stefano; Grasso, Raffaele; Gini, Fulvio; Greco, Maria S.; LePage, Kevin
2014-12-01
This paper deals with the problem of estimating the directions of arrival (DOA) of multiple source signals from a single observation vector of an array data. In particular, four estimation algorithms based on the theory of compressed sensing (CS), i.e., the classical ℓ 1 minimization (or Least Absolute Shrinkage and Selection Operator, LASSO), the fast smooth ℓ 0 minimization, and the Sparse Iterative Covariance-Based Estimator, SPICE and the Iterative Adaptive Approach for Amplitude and Phase Estimation, IAA-APES algorithms, are analyzed, and their statistical properties are investigated and compared with the classical Fourier beamformer (FB) in different simulated scenarios. We show that unlike the classical FB, a CS-based beamformer (CSB) has some desirable properties typical of the adaptive algorithms (e.g., Capon and MUSIC) even in the single snapshot case. Particular attention is devoted to the super-resolution property. Theoretical arguments and simulation analysis provide evidence that a CS-based beamformer can achieve resolution beyond the classical Rayleigh limit. Finally, the theoretical findings are validated by processing a real sonar dataset.
The application of Compressed Sensing for Longitudinal MRI
Weizman, Lior; Bashat, Dafna Ben
2014-01-01
Purpose: The mutual similarity of the follow-up scans in longitudinal studies is exploited on top of the well known sparse transform domains for rapid MRI by reducing the number of k-space measurements. Theory and Methods: A framework for adaptive Compressed Sensing (CS) MRI that exploits the redundancy of the acquired data in longitudinal studies is proposed. The baseline MR scan is utilized both in the sampling stage, with adaptive CS, and in the reconstruction stage, with weighted CS. In adaptive CS, k-space sampling locations are optimized such that the acquired data is focused on the change between the follow-up MRI and the former one. Weighted CS uses the locations of the nonzero coefficients in the sparse domains as a prior in the recovery process. Results: Experiments demonstrate that our longitudinal adaptive CS MRI (LACS-MRI) scheme provides reconstruction quality which outperforms traditional CS MRI for rapid MRI. Examples are shown on patients with brain tumors and demonstrate improved spatial res...
Compressive sensing of sparse radio frequency signals using optical mixing.
Valley, George C; Sefler, George A; Shaw, T Justin
2012-11-15
We demonstrate an optical mixing system for measuring properties of sparse radio frequency (RF) signals using compressive sensing (CS). Two types of sparse RF signals are investigated: (1) a signal that consists of a few 0.4 ns pulses in a 26.8 ns window and (2) a signal that consists of a few sinusoids at different frequencies. The RF is modulated onto the intensity of a repetitively pulsed, wavelength-chirped optical field, and time-wavelength-space mapping is used to map the optical field onto a 118-pixel, one-dimensional spatial light modulator (SLM). The SLM pixels are programmed with a pseudo-random bit sequence (PRBS) to form one row of the CS measurement matrix, and the optical throughput is integrated with a photodiode to obtain one value of the CS measurement vector. Then the PRBS is changed to form the second row of the mixing matrix and a second value of the measurement vector is obtained. This process is performed 118 times so that we can vary the dimensions of the CS measurement matrix from 1×118 to 118×118 (square). We use the penalized ℓ(1) norm method with stopping parameter λ (also called basis pursuit denoising) to recover pulsed or sinusoidal RF signals as a function of the small dimension of the measurement matrix and stopping parameter. For a square matrix, we also find that penalized ℓ(1) norm recovery performs better than conventional recovery using matrix inversion. PMID:23164876
LS-CS: Compressive Sensing on Least Squares Residual
Vaswani, Namrata
2009-01-01
We consider the problem of recursively reconstructing time sequences of sparse signals (with unknown and time-varying sparsity patterns) from a limited number of linear incoherent measurements with additive noise. The signals are sparse in some transform domain referred to as the sparsity basis and the sparsity pattern is assumed to change slowly with time. The idea of our proposed solution, LS-CS-residual (LS-CS), is to replace compressed sensing (CS) on the observation by CS on the least squares (LS) observation residual computed using the previous estimate of the support. We bound the CS-residual error and show that when the number of available measurements is small, the bound is much smaller than that on CS error if the sparsity pattern changes slowly enough. We also obtain conditions for "stability" of LS-CS over time for a simple deterministic signal model of coefficient addition/removal and coefficient magnitude increase/decrease which has bounded signal power. By "stability", we mean that the number o...
Opportunistic Relay Selection in Multicast Relay Networks using Compressive Sensing
Elkhalil, Khalil
2014-12-01
Relay selection is a simple technique that achieves spatial diversity in cooperative relay networks. However, for relay selection algorithms to make a selection decision, channel state information (CSI) from all cooperating relays is usually required at a central node. This requirement poses two important challenges. Firstly, CSI acquisition generates a great deal of feedback overhead (air-time) that could result in significant transmission delays. Secondly, the fed back channel information is usually corrupted by additive noise. This could lead to transmission outages if the central node selects the set of cooperating relays based on inaccurate feedback information. In this paper, we introduce a limited feedback relay selection algorithm for a multicast relay network. The proposed algorithm exploits the theory of compressive sensing to first obtain the identity of the “strong” relays with limited feedback. Following that, the CSI of the selected relays is estimated using linear minimum mean square error estimation. To minimize the effect of noise on the fed back CSI, we introduce a back-off strategy that optimally backs-off on the noisy estimated CSI. For a fixed group size, we provide closed form expressions for the scaling law of the maximum equivalent SNR for both Decode and Forward (DF) and Amplify and Forward (AF) cases. Numerical results show that the proposed algorithm drastically reduces the feedback air-time and achieves a rate close to that obtained by selection algorithms with dedicated error-free feedback channels.
Compressive Sensing Based Design of Sparse Tripole Arrays
Matthew Hawes
2015-12-01
Full Text Available This paper considers the problem of designing sparse linear tripole arrays. In such arrays at each antenna location there are three orthogonal dipoles, allowing full measurement of both the horizontal and vertical components of the received waveform. We formulate this problem from the viewpoint of Compressive Sensing (CS. However, unlike for isotropic array elements (single antenna, we now have three complex valued weight coefficients associated with each potential location (due to the three dipoles, which have to be simultaneously minimised. If this is not done, we may only set the weight coefficients of individual dipoles to be zero valued, rather than complete tripoles, meaning some dipoles may remain at each location. Therefore, the contributions of this paper are to formulate the design of sparse tripole arrays as an optimisation problem, and then we obtain a solution based on the minimisation of a modified l 1 norm or a series of iteratively solved reweighted minimisations, which ensure a truly sparse solution. Design examples are provided to verify the effectiveness of the proposed methods and show that a good approximation of a reference pattern can be achieved using fewer tripoles than a Uniform Linear Array (ULA of equivalent length.
Compressive Sensing Based Design of Sparse Tripole Arrays.
Hawes, Matthew; Liu, Wei; Mihaylova, Lyudmila
2015-01-01
This paper considers the problem of designing sparse linear tripole arrays. In such arrays at each antenna location there are three orthogonal dipoles, allowing full measurement of both the horizontal and vertical components of the received waveform. We formulate this problem from the viewpoint of Compressive Sensing (CS). However, unlike for isotropic array elements (single antenna), we now have three complex valued weight coefficients associated with each potential location (due to the three dipoles), which have to be simultaneously minimised. If this is not done, we may only set the weight coefficients of individual dipoles to be zero valued, rather than complete tripoles, meaning some dipoles may remain at each location. Therefore, the contributions of this paper are to formulate the design of sparse tripole arrays as an optimisation problem, and then we obtain a solution based on the minimisation of a modified l1 norm or a series of iteratively solved reweighted minimisations, which ensure a truly sparse solution. Design examples are provided to verify the effectiveness of the proposed methods and show that a good approximation of a reference pattern can be achieved using fewer tripoles than a Uniform Linear Array (ULA) of equivalent length. PMID:26690436
Active remote sensing of snow using NMM3D/DMRT and comparison with CLPX II airborne data
Xu, X.; Liang, D.; Tsang, L.; Andreadis, K.M.; Josberger, E.G.; Lettenmaier, D.P.; Cline, D.W.; Yueh, S.H.
2010-01-01
We applied the Numerical Maxwell Model of three-dimensional simulations (NMM3D) in the Dense Media Radiative Theory (DMRT) to calculate backscattering coefficients. The particles' positions are computer-generated and the subsequent Foldy-Lax equations solved numerically. The phase matrix in NMM3D has significant cross-polarization, particularly when the particles are densely packed. The NMM3D model is combined with DMRT in calculating the microwave scattering by dry snow. The NMM3D/DMRT equations are solved by an iterative solution up to the second order in the case of small to moderate optical thickness. The numerical results of NMM3D/DMRT are illustrated and compared with QCA/DMRT. The QCA/DMRT and NMM3D/DMRT results are also applied to compare with data from two specific datasets from the second Cold Land Processes Experiment (CLPX II) in Alaska and Colorado. The data are obtained at the Ku-band (13.95 GHz) observations using airborne imaging polarimetric scatterometer (POLSCAT). It is shown that the model predictions agree with the field measurements for both co-polarization and cross-polarization. For the Alaska region, the average snow depth and snow density are used as the inputs for DMRT. The grain size, selected from within the range of the ground measurements, is used as a best-fit parameter within the range. For the Colorado region, we use the Variable Infiltration Capacity Model (VIC) to obtain the input snow profiles for NMM3D/DMRT. ?? 2010 IEEE.
Purpose: To develop and validate an innovative method of using depth sensing cameras and 3D printing techniques for Total Body Irradiation (TBI) treatment planning and compensator fabrication. Methods: A tablet with motion tracking cameras and integrated depth sensing was used to scan a RANDOTM phantom arranged in a TBI treatment booth to detect and store the 3D surface in a point cloud (PC) format. The accuracy of the detected surface was evaluated by comparison to extracted measurements from CT scan images. The thickness, source to surface distance and off-axis distance of the phantom at different body section was measured for TBI treatment planning. A 2D map containing a detailed compensator design was calculated to achieve uniform dose distribution throughout the phantom. The compensator was fabricated using a 3D printer, silicone molding and tungsten powder. In vivo dosimetry measurements were performed using optically stimulated luminescent detectors (OSLDs). Results: The whole scan of the anthropomorphic phantom took approximately 30 seconds. The mean error for thickness measurements at each section of phantom compare to CT was 0.44 ± 0.268 cm. These errors resulted in approximately 2% dose error calculation and 0.4 mm tungsten thickness deviation for the compensator design. The accuracy of 3D compensator printing was within 0.2 mm. In vivo measurements for an end-to-end test showed the overall dose difference was within 3%. Conclusion: Motion cameras and depth sensing techniques proved to be an accurate and efficient tool for TBI patient measurement and treatment planning. 3D printing technique improved the efficiency and accuracy of the compensator production and ensured a more accurate treatment delivery
Lee, M; Suh, T [Department of Biomedical Engineering, College of Medicine, The Catholic University of Korea, Seoul (Korea, Republic of); Research Institute of Biomedical Engineering, College of Medicine, The Catholic University of Korea, Seoul (Korea, Republic of); Han, B; Xing, L [Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, CA (United States); Jenkins, C [Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, CA (United States); Department of Mechanical Engineering, Stanford University, Palo Alto, CA (United States)
2015-06-15
Purpose: To develop and validate an innovative method of using depth sensing cameras and 3D printing techniques for Total Body Irradiation (TBI) treatment planning and compensator fabrication. Methods: A tablet with motion tracking cameras and integrated depth sensing was used to scan a RANDOTM phantom arranged in a TBI treatment booth to detect and store the 3D surface in a point cloud (PC) format. The accuracy of the detected surface was evaluated by comparison to extracted measurements from CT scan images. The thickness, source to surface distance and off-axis distance of the phantom at different body section was measured for TBI treatment planning. A 2D map containing a detailed compensator design was calculated to achieve uniform dose distribution throughout the phantom. The compensator was fabricated using a 3D printer, silicone molding and tungsten powder. In vivo dosimetry measurements were performed using optically stimulated luminescent detectors (OSLDs). Results: The whole scan of the anthropomorphic phantom took approximately 30 seconds. The mean error for thickness measurements at each section of phantom compare to CT was 0.44 ± 0.268 cm. These errors resulted in approximately 2% dose error calculation and 0.4 mm tungsten thickness deviation for the compensator design. The accuracy of 3D compensator printing was within 0.2 mm. In vivo measurements for an end-to-end test showed the overall dose difference was within 3%. Conclusion: Motion cameras and depth sensing techniques proved to be an accurate and efficient tool for TBI patient measurement and treatment planning. 3D printing technique improved the efficiency and accuracy of the compensator production and ensured a more accurate treatment delivery.
Huang, Jian; Ma, Dayan; Chen, Feng; Bai, Min; Xu, Kewei; Zhao, Yongxi
2015-10-20
Surface-enhanced Raman scattering (SERS) has been considered as a promising sensing technique to detect low-level analytes. However, its practical application was hindered owing to the lack of uniform SERS substrates for ultrasensitive and reproducible assay. Herein, inspired by the natural cactus structure, we developed a cactus-like 3D nanostructure with uniform and high-density hotspots for highly efficient SERS sensing by both grafting the silicon nanoneedles onto Ag dendrites and subsequent decoration with Ag nanoparticles. The hierarchical scaffolds and high-density hotspots throughout the whole substrate result in great amplification of SERS signal. A high Raman enhancement factor of crystal violet up to 6.6 × 10(7) was achieved. Using malachite green (MG) as a model target, the fabricated SERS substrates exhibited good reproducibility (RSD ∼ 9.3%) and pushed the detection limit down to 10(-13) M with a wide linear range of 10(-12) M to 10(-7) M. Excellent selectivity was also demonstrated by facilely distinguishing MG from its derivative, some organics, and coexistent metal ions. Finally, the practicality and reliability of the 3D SERS substrates were confirmed by the quantitative analysis of spiked MG in environmental water with high recoveries (91.2% to 109.6%). By virtue of the excellent performance (good reproducibility, high sensitivity, and selectivity), the cactus-like 3D SERS substrate has great potential to become a versatile sensing platform in environmental monitoring, food safety, and medical diagnostics. PMID:26406111
Assessment of Crime & its Mapping Using Remote Sensing & 3D Geo-Spatial Model for Chennai City
Lenin Barath Kumar.D
2014-03-01
Full Text Available This study integrates a combined set of methods and techniques for 3D Geo-spatial virtual environment and mapping property crimes in Chennai city for a period of three years (2010-2012. The creation and presentation of 3D city model are achieved through Google Sketch up using Google satellite image and hotspot areas are generated based on reliable crime record from (CCRB Chennai police department. The relationship between crime clusters and their spatial neighborhood are analyzed through kernel density estimation function this lead to hotspot crime incidents. The intensity of property crimes in higher rate and in lower rate is differentiated through 3D modelling and hotspot mapping. Although, the creation and display of 3D city models for large, wide areas is difficult, it is vital for planning and designing safer cities and as well as public places. The particular region of a city display the intensity of crime whereas, this lead to nefarious activity over the regions. The study has provided valuable information concerning property crimes in Chennai city, including the social and infrastructural characteristics of these areas that contribute to the localized criminal activity.
Deconvolution of serum cortisol levels by using compressed sensing.
Faghih, Rose T; Dahleh, Munther A; Adler, Gail K; Klerman, Elizabeth B; Brown, Emery N
2014-01-01
The pulsatile release of cortisol from the adrenal glands is controlled by a hierarchical system that involves corticotropin releasing hormone (CRH) from the hypothalamus, adrenocorticotropin hormone (ACTH) from the pituitary, and cortisol from the adrenal glands. Determining the number, timing, and amplitude of the cortisol secretory events and recovering the infusion and clearance rates from serial measurements of serum cortisol levels is a challenging problem. Despite many years of work on this problem, a complete satisfactory solution has been elusive. We formulate this question as a non-convex optimization problem, and solve it using a coordinate descent algorithm that has a principled combination of (i) compressed sensing for recovering the amplitude and timing of the secretory events, and (ii) generalized cross validation for choosing the regularization parameter. Using only the observed serum cortisol levels, we model cortisol secretion from the adrenal glands using a second-order linear differential equation with pulsatile inputs that represent cortisol pulses released in response to pulses of ACTH. Using our algorithm and the assumption that the number of pulses is between 15 to 22 pulses over 24 hours, we successfully deconvolve both simulated datasets and actual 24-hr serum cortisol datasets sampled every 10 minutes from 10 healthy women. Assuming a one-minute resolution for the secretory events, we obtain physiologically plausible timings and amplitudes of each cortisol secretory event with R (2) above 0.92. Identification of the amplitude and timing of pulsatile hormone release allows (i) quantifying of normal and abnormal secretion patterns towards the goal of understanding pathological neuroendocrine states, and (ii) potentially designing optimal approaches for treating hormonal disorders. PMID:24489656
On the Projection Matrices Influence in the Classification of Compressed Sensed ECG Signals
Monica Fira
2012-08-01
Full Text Available In this paper the classification results of compressed sensed ECG signals based on various types of projection matrices is investigated. The compressed signals are classified using the KNN (K-Nearest Neighbour algorithm. A comparative analysis is made with respect to the projection matrices used, as well as of the results obtained in the case of the original (uncompressed signals for various compression ratios. For Bernoulli projection matrices it has been observed that the classification results for compressed cardiac cycles are comparable to those obtained for uncompressed cardiac cycles. Thus, for normal uncompressed cardiac cycles a classification ratio of 91.33% was obtained, while for the signals compressed with a Bernoulli matrix, up to a compression ratio of 15:1 classification rates of approximately 93% were obtained. Significant improvements of classification in the compressed space take place up to a compression ratio of 30:1.
Bilen, Cagdas; Selesnick, Ivan
2012-01-01
Magnetic Resonance Imaging (MRI) is one of the fields that the compressed sensing theory is well utilized to reduce the scan time significantly leading to faster imaging or higher resolution images. It has been shown that a small fraction of the overall measurements are sufficient to reconstruct images with the combination of compressed sensing and parallel imaging. Various reconstruction algorithms has been proposed for compressed sensing, among which Augmented Lagrangian based methods have been shown to often perform better than others for many different applications. In this paper, we propose new Augmented Lagrangian based solutions to the compressed sensing reconstruction problem with analysis and synthesis prior formulations. We also propose a computational method which makes use of properties of the sampling pattern to significantly improve the speed of the reconstruction for the proposed algorithms in Cartesian sampled MRI. The proposed algorithms are shown to outperform earlier methods especially for ...
Elastic-Waveform Inversion with Compressive Sensing for Sparse Seismic Data
Lin, Youzuo [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Huang, Lianjie [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2015-01-26
Accurate velocity models of compressional- and shear-waves are essential for geothermal reservoir characterization and microseismic imaging. Elastic-waveform inversion of multi-component seismic data can provide high-resolution inversion results of subsurface geophysical properties. However, the method requires seismic data acquired using dense source and receiver arrays. In practice, seismic sources and/or geophones are often sparsely distributed on the surface and/or in a borehole, such as 3D vertical seismic profiling (VSP) surveys. We develop a novel elastic-waveform inversion method with compressive sensing for inversion of sparse seismic data. We employ an alternating-minimization algorithm to solve the optimization problem of our new waveform inversion method. We validate our new method using synthetic VSP data for a geophysical model built using geologic features found at the Raft River enhanced-geothermal-system (EGS) field. We apply our method to synthetic VSP data with a sparse source array and compare the results with those obtained with a dense source array. Our numerical results demonstrate that the velocity mode ls produced with our new method using a sparse source array are almost as accurate as those obtained using a dense source array.
Nakanishi-Ohno, Yoshinori; Haze, Masahiro; Yoshida, Yasuo; Hukushima, Koji; Hasegawa, Yukio; Okada, Masato
2016-01-01
We applied a method of compressed sensing to the observation of quasi-particle interference (QPI) by scanning tunneling microscopy/spectroscopy to improve efficiency and save measurement time. To solve an ill-posed problem owing to the scarcity of data, the compressed sensing utilizes the sparseness of QPI patterns in momentum space. We examined the performance of a sparsity-inducing algorithm called least absolute shrinkage and selection operator (LASSO), and demonstrated that LASSO enables ...
Compressed correlation-matching for spectrum sensing in sparse wideband regimes
Font Segura, Josep; Vázquez Grau, Gregorio; Riba Sagarra, Jaume
2011-01-01
In this paper, we consider a novel compressed correlation-matching (CCM) approach for spectrum sensing of wideband sparse signals. We derive a general closed-form estimate of the wideband sparse signal level from compressed observations, while providing physical interpretation of the problem. The formulation allows straightforward application to signal processing problems of interest, such as generalized likelihood ratio test (GLRT) spectrum sensing for wideband cognitive radio. Simu...
An advanced scheme of compressed sensing of acceleration data for telemonintoring of human gait
Wu, Jianning; Xu, Haidong
2016-01-01
Background The compressed sensing (CS) of acceleration data has been drawing increasing attention in gait telemonitoring application. In such application, there still exist some challenging issues including high energy consumption of body-worn device for acceleration data acquisition and the poor reconstruction performance due to nonsparsity of acceleration data. Thus, the novel scheme of compressive sensing of acceleration data is needed urgently for solutions that are found to these issues....
Localization with a Mobile Beacon Based on Compressive Sensing in Wireless Sensor Networks
Bing Cui; Hui Huang; Yunlong Xu; Chunhui Zhao
2013-01-01
Node localization technology is especially important to most applications of wireless sensor network. However, each mobile beacon needs to broadcast its current position many times, and locating all the nodes must take a long time in most of the localization algorithms. In order to solve these problems, we propose a novel range-free localization algorithm—localization with a mobile beacon based on compressive sensing (LMCS). LMCS makes use of compressive sensing (CS) to get the related degree...
Huaxin Yu; Xiaofeng Qiu; Xiaofei Zhang; Chenghua Wang; Gang Yang
2015-01-01
We investigate the topic of two-dimensional direction of arrival (2D-DOA) estimation for rectangular array. This paper links angle estimation problem to compressive sensing trilinear model and derives a compressive sensing trilinear model-based angle estimation algorithm which can obtain the paired 2D-DOA estimation. The proposed algorithm not only requires no spectral peak searching but also has better angle estimation performance than estimation of signal parameters via rotational invarianc...
Du, Pei-Yao; Gu, Wen; Liu, Xin
2016-06-01
Framework-isomeric three-dimensional (3D) Zn-Ln heterometallic metal-organic frameworks, {[Ln2Zn(abtc)2(H2O)4]·2H2O}∞ {Ln = Sm(1), Tb(2)}, were synthesized using a convenient solvothermal reaction. They can serve as excellent sensors for the specific identification of benzaldehyde and NO2(-) through a fluorescence quenching process. PMID:27117937
Choi, Jonghoon; Lee, Eun Kyu; Choo, Jaebum; Yuh, Junhan; Hong, Jong Wook
2015-09-01
Microfabricated systems equipped with 3D cell culture devices and in-situ cellular biosensing tools can be a powerful bionanotechnology platform to investigate a variety of biomedical applications. Various construction substrates such as plastics, glass, and paper are used for microstructures. When selecting a construction substrate, a key consideration is a porous microenvironment that allows for spheroid growth and mimics the extracellular matrix (ECM) of cell aggregates. Various bio-functionalized hydrogels are ideal candidates that mimic the natural ECM for 3D cell culture. When selecting an optimal and appropriate microfabrication method, both the intended use of the system and the characteristics and restrictions of the target cells should be carefully considered. For highly sensitive and near-cell surface detection of excreted cellular compounds, SERS-based microsystems capable of dual modal imaging have the potential to be powerful tools; however, the development of optical reporters and nanoprobes remains a key challenge. We expect that the microsystems capable of both 3D cell culture and cellular response monitoring would serve as excellent tools to provide fundamental cellular behavior information for various biomedical applications such as metastasis, wound healing, high throughput screening, tissue engineering, regenerative medicine, and drug discovery and development. PMID:26358782
High-resolution hyperspectral single-pixel imaging system based on compressive sensing
Magalha~es, Filipe; Abolbashari, Mehrdad; Araújo, Francisco M.; Correia, Miguel V.; Farahi, Faramarz
2012-07-01
For the first time, a high-resolution hyperspectral single-pixel imaging system based on compressive sensing is presented and demonstrated. The system integrates a digital micro-mirror device array to optically compress the image to be acquired and an optical spectrum analyzer to enable high spectral resolution. The system's ability to successfully reconstruct images with 10 pm spectral resolution is proven.
Compressed Sensing Quantum Process Tomography for Superconducting Quantum Gates
Rodionov, Andrey
An important challenge in quantum information science and quantum computing is the experimental realization of high-fidelity quantum operations on multi-qubit systems. Quantum process tomography (QPT) is a procedure devised to fully characterize a quantum operation. We first present the results of the estimation of the process matrix for superconducting multi-qubit quantum gates using the full data set employing various methods: linear inversion, maximum likelihood, and least-squares. To alleviate the problem of exponential resource scaling needed to characterize a multi-qubit system, we next investigate a compressed sensing (CS) method for QPT of two-qubit and three-qubit quantum gates. Using experimental data for two-qubit controlled-Z gates, taken with both Xmon and superconducting phase qubits, we obtain estimates for the process matrices with reasonably high fidelities compared to full QPT, despite using significantly reduced sets of initial states and measurement configurations. We show that the CS method still works when the amount of data is so small that the standard QPT would have an underdetermined system of equations. We also apply the CS method to the analysis of the three-qubit Toffoli gate with simulated noise, and similarly show that the method works well for a substantially reduced set of data. For the CS calculations we use two different bases in which the process matrix is approximately sparse (the Pauli-error basis and the singular value decomposition basis), and show that the resulting estimates of the process matrices match with reasonably high fidelity. For both two-qubit and three-qubit gates, we characterize the quantum process by its process matrix and average state fidelity, as well as by the corresponding standard deviation defined via the variation of the state fidelity for different initial states. We calculate the standard deviation of the average state fidelity both analytically and numerically, using a Monte Carlo method. Overall
The MUSIC algorithm for sparse objects: a compressed sensing analysis
The multiple signal classification (MUSIC) algorithm, and its extension for imaging sparse extended objects, with noisy data is analyzed by compressed sensing (CS) techniques. A thresholding rule is developed to augment the standard MUSIC algorithm. The notion of restricted isometry property (RIP) and an upper bound on the restricted isometry constant (RIC) are employed to establish sufficient conditions for the exact localization by MUSIC with or without noise. In the noiseless case, the sufficient condition gives an upper bound on the numbers of random sampling and incident directions necessary for exact localization. In the noisy case, the sufficient condition assumes additionally an upper bound for the noise-to-object ratio in terms of the RIC and the dynamic range of objects. This bound points to the super-resolution capability of the MUSIC algorithm. Rigorous comparison of performance between MUSIC and the CS minimization principle, basis pursuit denoising (BPDN), is given. In general, the MUSIC algorithm guarantees to recover, with high probability, s scatterers with n=O(s2) random sampling and incident directions and sufficiently high frequency. For the favorable imaging geometry where the scatterers are distributed on a transverse plane MUSIC guarantees to recover, with high probability, s scatterers with a median frequency and n=O(s) random sampling/incident directions. Moreover, for the problems of spectral estimation and source localizations both BPDN and MUSIC guarantee, with high probability, to identify exactly the frequencies of random signals with the number n=O(s) of sampling times. However, in the absence of abundant realizations of signals, BPDN is the preferred method for spectral estimation. Indeed, BPDN can identify the frequencies approximately with just one realization of signals with the recovery error at worst linearly proportional to the noise level. Numerical results confirm that BPDN outperforms MUSIC in the well-resolved case while
Low-temperature wafer-level Cu-to-Cu thermo-compression bonding and its reliability for hermetic sealing application have been investigated in this work. The volume of the encapsulated cavities is about 1.4×10−3 cm3 in accordance with the MIL-STD-883E standard prescribed for microelectronics packaging hermeticity measurement. The samples under test are bonded at 300 °C under a bonding force of 5500 N for 1 h in vacuum (∼2.5 × 10−4 mbar) with a 300 nm thick Cu diffusion layer and 50 nm thick Ti barrier layer which are deposited in an e-beam evaporator. The reliability test is accomplished through a temperature cycling test (TCT) from −40 to 125 °C up to 1000 cycles and a humidity test based on IPC/JEDEC J-STD-020 standard. In addition, an immersion in acid/base solution is applied to verify the corrosion resistance of the Cu frame for hermetic application. Excellent helium leak rate which is smaller than the reject limit defined by the MIL-STD-883E standard (method 1014.10) is detected for all the samples. These excellent helium leak rates show an outstanding bonding quality against harsh environment for hermetic encapsulation in 3D integration applications. (paper)
DeJong, Andrew
Numerical models of fluid-structure interaction have grown in importance due to increasing interest in environmental energy harvesting, airfoil-gust interactions, and bio-inspired formation flying. Powered by increasingly powerful parallel computers, such models seek to explain the fundamental physics behind the complex, unsteady fluid-structure phenomena. To this end, a high-fidelity computational model based on the high-order spectral difference method on 3D unstructured, dynamic meshes has been developed. The spectral difference method constructs continuous solution fields within each element with a Riemann solver to compute the inviscid fluxes at the element interfaces and an averaging mechanism to compute the viscous fluxes. This method has shown promise in the past as a highly accurate, yet sufficiently fast method for solving unsteady viscous compressible flows. The solver is monolithically coupled to the equations of motion of an elastically mounted 3-degree of freedom rigid bluff body undergoing flow-induced lift, drag, and torque. The mesh is deformed using 4 methods: an analytic function, Laplace equation, biharmonic equation, and a bi-elliptic equation with variable diffusivity. This single system of equations -- fluid and structure -- is advanced through time using a 5-stage, 4th-order Runge-Kutta scheme. Message Passing Interface is used to run the coupled system in parallel on up to 240 processors. The solver is validated against previously published numerical and experimental data for an elastically mounted cylinder. The effect of adding an upstream body and inducing wake galloping is observed.
Various surface treatments are applied for surface oxide removal prior to wafer-level Cu-to-Cu thermo-compression bonding and the bonding quality is systematically analyzed in this work. Three methods are investigated: self-assembled monolayer (SAM) passivation, forming gas annealing and acetic acid wet cleaning. The surface conditions are carefully examined including roughness, contact angle and x-ray photoelectron spectroscopy (XPS) scan. The wafer pairs are bonded at 250 °C under a bonding force of 5500 N for a duration of 1 h in a vacuum environment. The bonding medium consists of a Cu (300 nm) bonding layer and a Ti (50 nm) barrier layer. The bonding quality investigation consists of two parts: hermeticity based on helium leak test and mechanical strength using four-point bending method. Although all samples under test with different surface treatment methods present an excellent hermetic seal and a robust mechanical support, the measurement results show that samples bonded after SAM passivation exhibit the best hermeticity and bonding strength for 3D integration application. (paper)
Snidero, M.; Amilibia, A.; Gratacos, O.; Muñoz, J. A.
2009-04-01
This work presents a methodological workflow for the 3D reconstruction of geological surfaces at regional scale, based on remote sensing data and geological maps. This workflow has been tested on the reconstruction of the Anaran anticline, located in the Zagros Fold and Thrust belt mountain front. The used remote sensing data-set is a combination of Aster and Spot images as well as a high resolution digital elevation model. A consistent spatial positioning of the complete data-set in a 3D environment is necessary to obtain satisfactory results during the reconstruction. The Aster images have been processed by the Optimum Index Factor (OIF) technique, in order to facilitate the geological mapping. By pansharpening of the resulting Aster image with the SPOT panchromatic one we obtain the final high-resolution image used during the 3D mapping. Structural data (dip data) has been acquired through the analysis of the 3D mapped geological traces. Structural analysis of the resulting data-set allows us to divide the structure in different cylindrical domains. Related plunge lines orientation has been used to project data along the structure, covering areas with little or no information. Once a satisfactory dataset has been acquired, we reconstruct a selected horizon following the dip-domain concept. By manual editing, the obtained surfaces have been adjusted to the mapped geological limits as well as to the modeled faults. With the implementation of the Discrete Smooth Interpolation (DSI) algorithm, the final surfaces have been reconstructed along the anticline. Up to date the results demonstrate that the proposed methodology is a powerful tool for 3D reconstruction of geological surfaces when working with remote sensing data, in very inaccessible areas (eg. Iran, China, Africa). It is especially useful in semiarid regions where the structure strongly controls the topography. The reconstructed surfaces clearly show the geometry in the different sectors of the structure
Task-Driven Adaptive Statistical Compressive Sensing of Gaussian Mixture Models
Duarte-Carvajalino, Julio M; Carin, Lawrence; Sapiro, Guillermo
2012-01-01
A framework for adaptive and non-adaptive statistical compressive sensing is developed, where a statistical model replaces the standard sparsity model of classical compressive sensing. We propose within this framework optimal task-specific sensing protocols specifically and jointly designed for classification and reconstruction. A two-step adaptive sensing paradigm is developed, where online sensing is applied to detect the signal class in the first step, followed by a reconstruction step adapted to the detected class and the observed samples. The approach is based on information theory, here tailored for Gaussian mixture models (GMMs), where an information-theoretic objective relationship between the sensed signals and a representation of the specific task of interest is maximized. Experimental results using synthetic signals, Landsat satellite attributes, and natural images of different sizes and with different noise levels show the improvements achieved using the proposed framework when compared to more st...
Zinner, T.; Wind, G.; Platnick, S.; Ackerman, A. S.
2010-01-01
Remote sensing of cloud effective particle size with passive sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) is an important tool for cloud microphysical studies. As a measure of the radiatively relevant droplet size, effective radius can be retrieved with different combinations of visible through shortwave and midwave infrared channels. In practice, retrieved effective radii from these combinations can be quite different. This difference is perhaps indicative of differ...
In this manuscript we have successfully synthesized a large scale 3D dendritic α-Fe2O3 hierarchical structure via a hydrothermal reaction. The crystallinity, composition, purity, morphology of the synthesized α-Fe2O3 are characterized by powder X-ray diffraction (PXRD), field emission scanning electron microscopic (FESEM), energy dispersive X-ray spectroscopic (EDS). FESEM image reveals that the individual α-Fe2O3 dendrite consists of a long central trunk with secondary and tertiary branches. For electrochemical H2O2 sensing we have carried out cyclic voltammetry (CV), amperometric i-t measurement. It has been found that the current density vs. H2O2 concentration calibration curve is linear in nature. The present study reveals that the dendritic α-Fe2O3 hierarchical structure exhibits very sensitive electrochemical sensing capability towards H2O2 reduction
Preparation of Au-sensitized 3D hollow SnO2 microspheres with an enhanced sensing performance
Highlights: • A hollow microsphere was synthesized. • The microsphere has a size of 600–900 nm. • The microsphere exhibits excellent sensing performances. -- Abstract: A controlled synthesis of three-dimensional Au-sensitized SnO2 hollow microspheres that composed of numerous nanorod subunits has been realized via a facile one-pot hydrothermal method without employing any templates or capping agents. The structural and morphological characteristics of the pure and Au-loaded SnO2 microspheres are revealed by X-ray diffraction, field-emission scanning electron microscope, and high resolution transmission electron microscope, respectively. The characterized results reveal that SnO2 hollow microspheres have the diameter of 600–900 nm with a rutile structure, which are composed of high densities of secondary SnO2 nanorods with the size of 20–50 nm. Furthermore, the gas sensors based on the pure and Au-loaded SnO2 have been fabricated, and the Au-loaded hierarchical SnO2 hollow microspheres exhibit more excellent sensing performances than that of pure SnO2 to acetone. The response and recovery times of the Au-loaded SnO2 microspheres are 0.9 s and 21 s when the sensor is exposed to 5 ppm acetone at the optimal operating temperature of 220 °C. These improved acetone-sensing performances are attributed to the catalytic effect of Au and the enhanced electron depletion at the surface of SnO2 hollow microspheres
Yu, Yangchun; Zeng, Wen; Xu, Mengxue; Peng, Xianghe
2016-05-01
In this paper, WO3·H2O with different nanostructures from 0D to 3D were successfully synthesized via a simple yet cost-effective hydrothermal method with the assistance of surfactants. The structures and morphologies of products were investigated by XRD and SEM. Besides, we systematically explained the evolution process and formation mechanisms of different WO3·H2O morphologies. It is noted that both the kinds and amounts of surfactants strongly affect the formation of WO3·H2O crystals, as reflected in the tailoring of WO3·H2O morphologies. Furthermore, the gas sensing performance of the as-prepared samples towards methanol was also investigated. 3D flower-like hierarchical architecture displayed outstanding response to target gas among the four samples. We hoped our results could be of great benefit to further investigations of synthesizing different dimensional WO3·H2O nanostructures and their gas sensing applications.
Total Variation Minimization Based Compressive Wideband Spectrum Sensing for Cognitive Radios
Liu, Yipeng
2011-01-01
Wideband spectrum sensing is a critical component of a functioning cognitive radio system. Its major challenge is the too high sampling rate requirement. Compressive sensing (CS) promises to be able to deal with it. Nearly all the current CS based compressive wideband spectrum sensing methods exploit only the frequency sparsity to perform. Motivated by the achievement of a fast and robust detection of the wideband spectrum change, total variation mnimization is incorporated to exploit the temporal and frequency structure information to enhance the sparse level. As a sparser vector is obtained, the spectrum sensing period would be shorten and sensing accuracy would be enhanced. Both theoretical evaluation and numerical experiments can demonstrate the performance improvement.
Bruno, Oscar
2015-01-01
This paper introduces alternating-direction implicit (ADI) solvers of higher order of time-accuracy (orders two to six) for the compressible Navier-Stokes equations in two- and three-dimensional curvilinear domains. The higher-order accuracy in time results from 1) An application of the backward differentiation formulae time-stepping algorithm (BDF) in conjunction with 2) A BDF-like extrapolation technique for certain components of the nonlinear terms (which makes use of nonlinear solves unnecessary), as well as 3) A novel application of the Douglas-Gunn splitting (which greatly facilitates handling of boundary conditions while preserving higher-order accuracy in time). As suggested by our theoretical analysis of the algorithms for a variety of special cases, an extensive set of numerical experiments clearly indicate that all of the BDF-based ADI algorithms proposed in this paper are "quasi-unconditionally stable" in the following sense: each algorithm is stable for all couples $(h,\\Delta t)$ of spatial and t...
Antonio Miguel Martínez-Graña
2016-01-01
Full Text Available The key focus of this paper is to establish a procedure that combines the use of Geographical Information Systems (GIS and remote sensing in order to achieve simulation and modeling of the landscape impact caused by construction. The procedure should be easily and inexpensively developed. With the aid of 3D virtual reconstruction and visualization, this paper proposes that the technologies of remote sensing and GIS can be applied to the landscape for post-urbanization environmental restoration. The goal is to create a rural zone in an urban development sector that integrates the residential areas and local infrastructure into the surrounding natural environment in order to measure the changes to the preliminary urban design. The units of the landscape are determined by means of two cartographic methods: (1 indirect, using the components of the landscape; and (2 direct methods, using the landscape’s elements. The visual basins are calculated for the most transited by the population points, while establishing the zones that present major impacts for the urbanization of their landscape. Based on this, the different construction types are distributed (one-family houses, blocks of houses, etc., selecting the types of plant masses either with ornamentals or integration depending on the zone; integrating water channels, creating a water channel in recirculation and green spaces and leisure time facilities. The techniques of remote sensing and GIS allow for the visualization and modeling of the urbanization in 3D, simulating the virtual reality of the infrastructure as well as the actions that need to be taken for restoration, thereby providing at a low cost an understanding of landscape integration before it takes place.
Wavelet Transform-Based Distributed Compressed Sensing in Wireless Sensor Networks
Hu Haifeng; Yang Zhen; Bao Jianmin
2012-01-01
Wireless Sensor Networks （WSN） are mainly characterized by a potentially large number of distributed sensor nodes which collectively transmit information about sensed events to the sink. In this paper, we present a Distributed Wavelet Basis Gener- ation （DWBG） algorithm performing at the sink to obtain the distributed wavelet basis in WSN. And on this basis, a Wavelet Transform-based Distributed Compressed Sensing （WTDCS） algorithm is proposed to compress and reconstruct the sensed data with spatial correlation. Finally, we make a detailed analysis of relationship between reconstruction performance and WTDCS algorithm parameters such as the compression ratio, the channel Signal-to-Noise Ratio （SNR）, the observation noise power and the correlation decay parameter by simulation. The simulation results show that WTDCS can achieve high performance in terms of energy and reconstruction accuracy, as compared to the conventional distributed wavelet transform algorithm.
Compressive Sensing in Speech from LPC using Gradient Projection for Sparse Reconstruction
Viral Modha
2015-02-01
Full Text Available This paper presents compressive sensing technique used for speech reconstruction using linear predictive coding because the speech is more sparse in LPC. DCT of a speech is taken and the DCT points of sparse speech are thrown away arbitrarily. This is achieved by making some point in DCT domain to be zero by multiplying with mask functions. From the incomplete points in DCT domain, the original speech is reconstructed using compressive sensing and the tool used is Gradient Projection for Sparse Reconstruction. The performance of the result is compared with direct IDCT subjectively. The experiment is done and it is observed that the performance is better for compressive sensing than the DCT.
Li, Edward; Shafiee, Mohammad Javad; Haider, Masoom A; Wong, Alexander
2015-01-01
Magnetic Resonance Imaging (MRI) is a crucial medical imaging technology for the screening and diagnosis of frequently occurring cancers. However image quality may suffer by long acquisition times for MRIs due to patient motion, as well as result in great patient discomfort. Reducing MRI acquisition time can reduce patient discomfort and as a result reduces motion artifacts from the acquisition process. Compressive sensing strategies, when applied to MRI, have been demonstrated to be effective at decreasing acquisition times significantly by sparsely sampling the \\emph{k}-space during the acquisition process. However, such a strategy requires advanced reconstruction algorithms to produce high quality and reliable images from compressive sensing MRI. This paper proposes a new reconstruction approach based on cross-domain stochastically fully connected conditional random fields (CD-SFCRF) for compressive sensing MRI. The CD-SFCRF introduces constraints in both \\emph{k}-space and spatial domains within a stochas...
Compressed Sensing-Based MRI Reconstruction Using Complex Double-Density Dual-Tree DWT
Zangen Zhu
2013-01-01
Full Text Available Undersampling k-space data is an efficient way to speed up the magnetic resonance imaging (MRI process. As a newly developed mathematical framework of signal sampling and recovery, compressed sensing (CS allows signal acquisition using fewer samples than what is specified by Nyquist-Shannon sampling theorem whenever the signal is sparse. As a result, CS has great potential in reducing data acquisition time in MRI. In traditional compressed sensing MRI methods, an image is reconstructed by enforcing its sparse representation with respect to a basis, usually wavelet transform or total variation. In this paper, we propose an improved compressed sensing-based reconstruction method using the complex double-density dual-tree discrete wavelet transform. Our experiments demonstrate that this method can reduce aliasing artifacts and achieve higher peak signal-to-noise ratio (PSNR and structural similarity (SSIM index.
Design and Exploration of Low-Power Analog to Information Conversion Based on Compressed Sensing
Mamaghanian, Hossein; Khaled, Nadia; Atienza Alonso, David; Vandergheynst, Pierre
2012-01-01
The long-standing analog-to-digital conversion paradigm based on Shannon/Nyquist sampling has been challenged lately, mostly in situations such as radar and communication signal processing where signal bandwidth is so large that sampling architectures constraints are simply not manageable. Compressed Sensing (CS) [1], [2], [3] is a new emerging signal acquisition/compression paradigm that offers a striking alternative to traditional signal acquisition. CS states that a signal having a sparse ...
Constructing Maximum-Lifetime Data-Gathering Tree in WSNs Based on Compressed Sensing
Chen, Zhengyu; Yang, Geng; Chen, Lei; Xu, Jian
2016-01-01
Data gathering is one of the most important operations in many wireless sensor networks (WSNs) applications. In order to implement data gathering, a tree structure rooted at the sink is usually defined. In most wireless sensor networks, nodes are powered by batteries with limited energy. Prolonging network lifetime is a critical issue for WSNs. As a technique for signal processing, compressed sensing (CS) is being increasingly applied to wireless sensor networks for saving energy. Compressive...
Jie TANG; Nett, Brian E; Chen, Guang-Hong
2009-01-01
Of all available reconstruction methods, statistical iterative reconstruction algorithms appear particularly promising since they enable accurate physical noise modeling. The newly developed compressive sampling/compressed sensing (CS) algorithm has shown the potential to accurately reconstruct images from highly undersampled data. The CS algorithm can be implemented in the statistical reconstruction framework as well. In this study, we compared the performance of two standard statistical rec...
Efficient Sparse Signal Transmission over a Lossy Link Using Compressive Sensing
Liantao Wu; Kai Yu; Dongyu Cao; Yuhen Hu; Zhi Wang
2015-01-01
Reliable data transmission over lossy communication link is expensive due to overheads for error protection. For signals that have inherent sparse structures, compressive sensing (CS) is applied to facilitate efficient sparse signal transmissions over lossy communication links without data compression or error protection. The natural packet loss in the lossy link is modeled as a random sampling process of the transmitted data, and the original signal will be reconstructed from the lossy trans...
Compressive sensing reconstruction of feed-forward connectivity in pulse-coupled nonlinear networks
Barranca, Victor J.; Zhou, Douglas; Cai, David
2016-06-01
Utilizing the sparsity ubiquitous in real-world network connectivity, we develop a theoretical framework for efficiently reconstructing sparse feed-forward connections in a pulse-coupled nonlinear network through its output activities. Using only a small ensemble of random inputs, we solve this inverse problem through the compressive sensing theory based on a hidden linear structure intrinsic to the nonlinear network dynamics. The accuracy of the reconstruction is further verified by the fact that complex inputs can be well recovered using the reconstructed connectivity. We expect this Rapid Communication provides a new perspective for understanding the structure-function relationship as well as compressive sensing principle in nonlinear network dynamics.
Using computer vision and compressed sensing for wood plate surface detection
Zhang, Yizhuo; Liu, Sijia; Tu, Wenjun; Yu, Huiling; Li, Chao
2015-10-01
Aiming at detecting the random and complicated characteristic of wood surface, we proposed a comprehensive detection algorithm based on computer vision and compressed sensing. First, integral projection method was used to trace and locate the position of a wood plate; then surface images were obtained by blocks. Second, multiscaled features were extracted from image to express the surface characteristic. Third, particle swarm optimization algorithm was used for multiscaled features selection. Finally, the defects and textures were detected by compressed sensing classifier. Five types of wood samples, including radial texture, tangential texture, wormhole, live knot, and dead knot, were used for tests, and the average classification accuracy was 94.7%.
Pilotless recovery of clipped OFDM signals by compressive sensing over reliable data carriers
Al-Safadi, Ebrahim B.
2012-06-01
In this paper we propose a novel method of clipping mitigation in OFDM using compressive sensing that completely avoids using reserved tones or channel-estimation pilots. The method builds on selecting the most reliable perturbations from the constellation lattice upon decoding at the receiver (in the frequency domain), and performs compressive sensing over these observations in order to completely recover the sparse nonlinear distortion in the time domain. As such, the method provides a practical solution to the problem of initial erroneous decoding decisions in iterative ML methods, and the ability to recover the distorted signal in one shot. © 2012 IEEE.
High capacity image steganography method based on framelet and compressive sensing
Xiao, Moyan; He, Zhibiao
2015-12-01
To improve the capacity and imperceptibility of image steganography, a novel high capacity and imperceptibility image steganography method based on a combination of framelet and compressive sensing (CS) is put forward. Firstly, SVD (Singular Value Decomposition) transform to measurement values obtained by compressive sensing technique to the secret data. Then the singular values in turn embed into the low frequency coarse subbands of framelet transform to the blocks of the cover image which is divided into non-overlapping blocks. Finally, use inverse framelet transforms and combine to obtain the stego image. The experimental results show that the proposed steganography method has a good performance in hiding capacity, security and imperceptibility.
A compressive sensing approach to the calculation of the inverse data space
Khan, B. H.
2012-01-01
Seismic processing in the Inverse Data Space (IDS) has its advantages like the task of removing the multiples simply becomes muting the zero offset and zero time data in the inverse domain. Calculation of the Inverse Data Space by sparse inversion techniques has seen mitigation of some artifacts. We reformulate the problem by taking advantage of some of the developments from the field of Compressive Sensing. The seismic data is compressed at the sensor level by recording projections of the traces. We then process this compressed data directly to estimate the inverse data space. Due to the smaller number of data set we also gain in terms of computational complexity.
An ECG Compressed Sensing Method of Low Power Body Area Network
Jizhong Liu
2013-07-01
Full Text Available Aimed at low power problem in body area network, an ECG compressed sensing method of low power body area network based on the compressed sensing theory was proposed. Random binary matrices were used as the sensing matrix to measure ECG signals on the sensor nodes. After measured value is transmitted to remote monitoring center, ECG signal sparse representation under the discrete cosine transform and block sparse Bayesian learning reconstruction algorithm is used to reconstruct the ECG signals. The simulation results show that the 30% of overall signal can get reconstruction signal which’s SNR is more than 60dB, each numbers in each rank of sensing matrix can be controlled below 5, which reduces the power of sensor node sampling, calculation and transmission. The method has the advantages of low power, high accuracy of signal reconstruction and easy to hardware implementation.
A Data-Driven Compressive Sensing Framework for Long-Term Health Monitoring
Xu, Kai; Wang, Yuhao; Li, Yixing; Ren, Fengbo
2016-01-01
Compressive sensing (CS) is a promising technology for realizing energy-efficient wireless sensors for long-term health monitoring. In this paper, we propose a data-driven CS framework that learns signal characteristics and individual variability from patients' data to significantly enhance CS performance and noise resilience. This is accomplished by a co-training approach that optimizes both the sensing matrix and dictionary towards improved restricted isometry property (RIP) and signal spar...
Compressed Sensing over $\\ell_p$-balls: Minimax Mean Square Error
Donoho, David; Maleki, Arian; Montanari, Andrea
2011-01-01
We consider the compressed sensing problem, where the object $x_0 \\in \\bR^N$ is to be recovered from incomplete measurements $y = Ax_0 + z$; here the sensing matrix $A$ is an $n \\times N$ random matrix with iid Gaussian entries and $n 0$. We study an asymptotic regime in which $n$ and $N$ both tend to infinity with limiting ratio $n/N = \\delta \\in (0,1)$, both in the noisy ($z \
Accelerated High-Resolution Photoacoustic Tomography via Compressed Sensing
Arridge, Simon; Betcke, Marta; Cox, Ben; Huynh, Nam; Lucka, Felix; Ogunlade, Olumide; Zhang, Edward
2016-01-01
Current 3D photoacoustic tomography (PAT) systems offer either high image quality or high frame rates but are not able to deliver high spatial and temporal resolution simultaneously, which limits their ability to image dynamic processes in living tissue. A particular example is the planar Fabry-Perot (FP) scanner, which yields high-resolution images but takes several minutes to sequentially map the photoacoustic field on the sensor plane, point-by-point. However, as the spatio-temporal complexity of many absorbing tissue structures is rather low, the data recorded in such a conventional, regularly sampled fashion is often highly redundant. We demonstrate that combining variational image reconstruction methods using spatial sparsity constraints with the development of novel PAT acquisition systems capable of sub-sampling the acoustic wave field can dramatically increase the acquisition speed while maintaining a good spatial resolution: First, we describe and model two general spatial sub-sampling schemes. Then...
Application of Compressed Sensing to 2-D Ultrasonic Propagation Imaging System data
Mascarenas, David D. [Los Alamos National Laboratory; Farrar, Charles R. [Los Alamos National Laboratory; Chong, See Yenn [Engineering Institute-Korea; Lee, J.R. [Engineering Institute-Korea; Park, Gyu Hae [Los Alamos National Laboratory; Flynn, Eric B. [Los Alamos National Laboratory
2012-06-29
The Ultrasonic Propagation Imaging (UPI) System is a unique, non-contact, laser-based ultrasonic excitation and measurement system developed for structural health monitoring applications. The UPI system imparts laser-induced ultrasonic excitations at user-defined locations on a structure of interest. The response of these excitations is then measured by piezoelectric transducers. By using appropriate data reconstruction techniques, a time-evolving image of the response can be generated. A representative measurement of a plate might contain 800x800 spatial data measurement locations and each measurement location might be sampled at 500 instances in time. The result is a total of 640,000 measurement locations and 320,000,000 unique measurements. This is clearly a very large set of data to collect, store in memory and process. The value of these ultrasonic response images for structural health monitoring applications makes tackling these challenges worthwhile. Recently compressed sensing has presented itself as a candidate solution for directly collecting relevant information from sparse, high-dimensional measurements. The main idea behind compressed sensing is that by directly collecting a relatively small number of coefficients it is possible to reconstruct the original measurement. The coefficients are obtained from linear combinations of (what would have been the original direct) measurements. Often compressed sensing research is simulated by generating compressed coefficients from conventionally collected measurements. The simulation approach is necessary because the direct collection of compressed coefficients often requires compressed sensing analog front-ends that are currently not commercially available. The ability of the UPI system to make measurements at user-defined locations presents a unique capability on which compressed measurement techniques may be directly applied. The application of compressed sensing techniques on this data holds the potential to
Bruno, Oscar P.; Cubillos, Max
2016-02-01
This paper introduces alternating-direction implicit (ADI) solvers of higher order of time-accuracy (orders two to six) for the compressible Navier-Stokes equations in two- and three-dimensional curvilinear domains. The higher-order accuracy in time results from 1) An application of the backward differentiation formulae time-stepping algorithm (BDF) in conjunction with 2) A BDF-like extrapolation technique for certain components of the nonlinear terms (which makes use of nonlinear solves unnecessary), as well as 3) A novel application of the Douglas-Gunn splitting (which greatly facilitates handling of boundary conditions while preserving higher-order accuracy in time). As suggested by our theoretical analysis of the algorithms for a variety of special cases, an extensive set of numerical experiments clearly indicate that all of the BDF-based ADI algorithms proposed in this paper are "quasi-unconditionally stable" in the following sense: each algorithm is stable for all couples (h , Δt)of spatial and temporal mesh sizes in a problem-dependent rectangular neighborhood of the form (0 ,Mh) × (0 ,Mt). In other words, for each fixed value of Δt below a certain threshold, the Navier-Stokes solvers presented in this paper are stable for arbitrarily small spatial mesh-sizes. The second-order formulation has further been rigorously shown to be unconditionally stable for linear hyperbolic and parabolic equations in two-dimensional space. Although implicit ADI solvers for the Navier-Stokes equations with nominal second-order of temporal accuracy have been proposed in the past, the algorithms presented in this paper are the first ADI-based Navier-Stokes solvers for which second-order or better accuracy has been verified in practice under non-trivial (non-periodic) boundary conditions.
Zhao, Anshun; Zhang, Zhaowei; Zhang, Penghui; Xiao, Shuang; Wang, Lu; Dong, Yue; Yuan, Hao; Li, Peiwu; Sun, Yimin; Jiang, Xueliang; Xiao, Fei
2016-09-28
Recent advances in on-body wearable medical apparatus and implantable devices drive the development of light-weight and bendable electrochemical sensors, which require the design of high-performance flexible electrode system. In this work, we reported a new type of freestanding and flexible electrode based on graphene paper (GP) supported 3D monolithic nanoporous gold (NPG) scaffold (NPG/GP), which was further modified by a layer of highly dense, well dispersed and ultrafine binary PtCo alloy nanoparticles via a facile and effective ultrasonic electrodeposition method. Our results demonstrated that benefited from the synergistic effect of the electrocatalytically active PtCo alloy nanoparticles, the large-active-area and highly conductive 3D NPG scaffold, and the mechanically strong and stable GP electrode substrate, the resultant PtCo alloy nanoparticles modified NPG/GP (PtCo/NPG/GP) exhibited high mechanical strength and good electrochemical sensing performances toward nonenzymatic detection of glucose, including a wide linear range from 35 μM- to 30 mM, a low detection limit of 5 μM (S/N = 3) and a high sensitivity of 7.84 μA cm(-2) mM(-1) as well as good selectivity, long-term stability and reproducibility. The practical application of the proposed PtCo/NPG/GP has also been demonstrated in in vitro detection of blood glucose in real clinic samples. PMID:27619087
Esfandyarpour, Rahim; Yang, Lu; Koochak, Zahra; Harris, James S; Davis, Ronald W
2016-02-01
The improvements in our ability to sequence and genotype DNA have opened up numerous avenues in the understanding of human biology and medicine with various applications, especially in medical diagnostics. But the realization of a label free, real time, high-throughput and low cost biosensing platforms to detect molecular interactions with a high level of sensitivity has been yet stunted due to two factors: one, slow binding kinetics caused by the lack of probe molecules on the sensors and two, limited mass transport due to the planar structure (two-dimensional) of the current biosensors. Here we present a novel three-dimensional (3D), highly sensitive, real-time, inexpensive and label-free nanotip array as a rapid and direct platform to sequence-specific DNA screening. Our nanotip sensors are designed to have a nano sized thin film as their sensing area (~ 20 nm), sandwiched between two sensing electrodes. The tip is then conjugated to a DNA oligonucleotide complementary to the sequence of interest, which is electrochemically detected in real-time via impedance changes upon the formation of a double-stranded helix at the sensor interface. This 3D configuration is specifically designed to improve the biomolecular hit rate and the detection speed. We demonstrate that our nanotip array effectively detects oligonucleotides in a sequence-specific and highly sensitive manner, yielding concentration-dependent impedance change measurements with a target concentration as low as 10 pM and discrimination against even a single mismatch. Notably, our nanotip sensors achieve this accurate, sensitive detection without relying on signal indicators or enhancing molecules like fluorophores. It can also easily be scaled for highly multiplxed detection with up to 5000 sensors/square centimeter, and integrated into microfluidic devices. The versatile, rapid, and sensitive performance of the nanotip array makes it an excellent candidate for point-of-care diagnostics, and high
Spotlight SAR sparse sampling and imaging method based on compressive sensing
XU HuaPing; YOU YaNan; LI ChunSheng; ZHANG LvQian
2012-01-01
Spotlight synthetic aperture radar (SAR) emits a chirp signal and the echo bandwidth can be reduced through dechirp processing,where the A/D sampling rate decreases accordingly at the receiver.Compressive sensing allows the compressible signal to be reconstructed with a high probability using only a few samples by solving a linear program problem.This paper presents a novel signal sampling and imaging method for application to spotlight SAR based on compressive sensing.The signal is randomly sampled after dechirp processing to form a low-dimensional sample set,and the dechirp basis is imported to reconstruct the dechirp signal.Matching pursuit (MP) is used as a reconstruction algorithm.The reconstructed signal uses polar format algorithm (PFA) for imaging.Although our novel mechanism increases the system complexity to an extent,the data storage requirements can be compressed considerably. Several simulations verify the feasibility and accuracy of spotlight SAR signal processing via compressive sensing,and the method still obtains acceptable imaging results with 10％ of the original echo data.
Three-Dimensional Inverse Transport Solver Based on Compressive Sensing Technique
Cheng, Yuxiong; Wu, Hongchun; Cao, Liangzhi; Zheng, Youqi
2013-09-01
According to the direct exposure measurements from flash radiographic image, a compressive sensing-based method for three-dimensional inverse transport problem is presented. The linear absorption coefficients and interface locations of objects are reconstructed directly at the same time. It is always very expensive to obtain enough measurements. With limited measurements, compressive sensing sparse reconstruction technique orthogonal matching pursuit is applied to obtain the sparse coefficients by solving an optimization problem. A three-dimensional inverse transport solver is developed based on a compressive sensing-based technique. There are three features in this solver: (1) AutoCAD is employed as a geometry preprocessor due to its powerful capacity in graphic. (2) The forward projection matrix rather than Gauss matrix is constructed by the visualization tool generator. (3) Fourier transform and Daubechies wavelet transform are adopted to convert an underdetermined system to a well-posed system in the algorithm. Simulations are performed and numerical results in pseudo-sine absorption problem, two-cube problem and two-cylinder problem when using compressive sensing-based solver agree well with the reference value.
Photonic compressive sensing with a micro-ring-resonator-based microwave photonic filter
Chen, Ying; Ding, Yunhong; Zhu, Zhijing;
2015-01-01
A novel approach to realize photonic compressive sensing (CS) with a multi-tap microwave photonic filter is proposed and demonstrated. The system takes both advantages of CS and photonics to capture wideband sparse signals with sub-Nyquist sampling rate. The low-pass filtering function required in...
Back to the sampling theory: a practical and less redundant alternative to Compressed sensing
Yaroslavsky, Leonid
2016-01-01
A method is suggested for restoration of images of N samples from their M
Owodunni, Damilola S.
2014-04-01
In this paper, compressed sensing techniques are proposed to linearize commercial power amplifiers driven by orthogonal frequency division multiplexing signals. The nonlinear distortion is considered as a sparse phenomenon in the time-domain, and three compressed sensing based algorithms are presented to estimate and compensate for these distortions at the receiver using a few and, at times, even no frequency-domain free carriers (i.e. pilot carriers). The first technique is a conventional compressed sensing approach, while the second incorporates a priori information about the distortions to enhance the estimation. Finally, the third technique involves an iterative data-aided algorithm that does not require any pilot carriers and hence allows the system to work at maximum bandwidth efficiency. The performances of all the proposed techniques are evaluated on a commercial power amplifier and compared. The error vector magnitude and symbol error rate results show the ability of compressed sensing to compensate for the amplifier\\'s nonlinear distortions. © 2013 Elsevier B.V.
Ultra-Low Power Dynamic Knob in Adaptive Compressed Sensing Towards Biosignal Dynamics.
Wang, Aosen; Lin, Feng; Jin, Zhanpeng; Xu, Wenyao
2016-06-01
Compressed sensing (CS) is an emerging sampling paradigm in data acquisition. Its integrated analog-to-information structure can perform simultaneous data sensing and compression with low-complexity hardware. To date, most of the existing CS implementations have a fixed architectural setup, which lacks flexibility and adaptivity for efficient dynamic data sensing. In this paper, we propose a dynamic knob (DK) design to effectively reconfigure the CS architecture by recognizing the biosignals. Specifically, the dynamic knob design is a template-based structure that comprises a supervised learning module and a look-up table module. We model the DK performance in a closed analytic form and optimize the design via a dynamic programming formulation. We present the design on a 130 nm process, with a 0.058 mm (2) fingerprint and a 187.88 nJ/event energy-consumption. Furthermore, we benchmark the design performance using a publicly available dataset. Given the energy constraint in wireless sensing, the adaptive CS architecture can consistently improve the signal reconstruction quality by more than 70%, compared with the traditional CS. The experimental results indicate that the ultra-low power dynamic knob can provide an effective adaptivity and improve the signal quality in compressed sensing towards biosignal dynamics. PMID:26800548
Block-Based Compressed Sensing for Neutron Radiation Image Using WDFB
Wei Jin
2015-01-01
Full Text Available An ideal compression method for neutron radiation image should have high compression ratio while keeping more details of the original image. Compressed sensing (CS, which can break through the restrictions of sampling theorem, is likely to offer an efficient compression scheme for the neutron radiation image. Combining wavelet transform with directional filter banks, a novel nonredundant multiscale geometry analysis transform named Wavelet Directional Filter Banks (WDFB is constructed and applied to represent neutron radiation image sparsely. Then, the block-based CS technique is introduced and a high performance CS scheme for neutron radiation image is proposed. By performing two-step iterative shrinkage algorithm the problem of L1 norm minimization is solved to reconstruct neutron radiation image from random measurements. The experiment results demonstrate that the scheme not only improves the quality of reconstructed image obviously but also retains more details of original image.
A new application of compressive sensing in MRI
Baselice, Fabio; Ferraioli, Giampaolo; Lenti, Flavia; Pascazio, Vito
2014-03-01
Image formation in Magnetic Resonance Imaging (MRI) is the procedure which allows the generation of the image starting from data acquired in the so called k-space. At the present, many image formation techniques have been presented, working with different k-space filling strategies. Recently, Compressive Sampling (CS) has been successfully used for image formation from non fully sampled k-space acquisitions, due to its interesting property of reconstructing signal from highly undetermined linear systems. The main advantage consists in greatly reducing the acquisition time. Within this manuscript, a novel application of CS to MRI field is presented, named Intra Voxel Analysis (IVA). The idea is to achieve the so-called super resolution, i.e. the possibility of distinguish anatomical structures smaller than the spatial resolution of the image. For this aim, multiple Spin Echo images acquired with different Echo Times are required. The output of the algorithm is the estimation of the number of contributions present in the same pixel, i.e. the number of tissues inside the same voxel, and their spin-spin relaxation times. This allows us not only to identify the number of involved tissues, but also to discriminate them. At the present, simulated case studies have been considered, obtaining interesting and promising results. In particular, a study on the required number of images, on the estimation noise and on the regularization parameter of different CS algorithms has been conducted. As future work, the method will be applied to real clinical datasets, in order to validate the estimations.
Akoguz, A.; Bozkurt, S.; Gozutok, A. A.; Alp, G.; Turan, E. G.; Bogaz, M.; Kent, S.
2016-06-01
High resolution level in satellite imagery came with its fundamental problem as big amount of telemetry data which is to be stored after the downlink operation. Moreover, later the post-processing and image enhancement steps after the image is acquired, the file sizes increase even more and then it gets a lot harder to store and consume much more time to transmit the data from one source to another; hence, it should be taken into account that to save even more space with file compression of the raw and various levels of processed data is a necessity for archiving stations to save more space. Lossless data compression algorithms that will be examined in this study aim to provide compression without any loss of data holding spectral information. Within this objective, well-known open source programs supporting related compression algorithms have been implemented on processed GeoTIFF images of Airbus Defence & Spaces SPOT 6 & 7 satellites having 1.5 m. of GSD, which were acquired and stored by ITU Center for Satellite Communications and Remote Sensing (ITU CSCRS), with the algorithms Lempel-Ziv-Welch (LZW), Lempel-Ziv-Markov chain Algorithm (LZMA & LZMA2), Lempel-Ziv-Oberhumer (LZO), Deflate & Deflate 64, Prediction by Partial Matching (PPMd or PPM2), Burrows-Wheeler Transform (BWT) in order to observe compression performances of these algorithms over sample datasets in terms of how much of the image data can be compressed by ensuring lossless compression.
A Novel Coherence Reduction Method in Compressed Sensing for DOA Estimation
Jing Liu
2013-01-01
Full Text Available A novel method named as coherent column replacement method is proposed to reduce the coherence of a partially deterministic sensing matrix, which is comprised of highly coherent columns and random Gaussian columns. The proposed method is to replace the highly coherent columns with random Gaussian columns to obtain a new sensing matrix. The measurement vector is changed accordingly. It is proved that the original sparse signal could be reconstructed well from the newly changed measurement vector based on the new sensing matrix with large probability. This method is then extended to a more practical condition when highly coherent columns and incoherent columns are considered, for example, the direction of arrival (DOA estimation problem in phased array radar system using compressed sensing. Numerical simulations show that the proposed method succeeds in identifying multiple targets in a sparse radar scene, where the compressed sensing method based on the original sensing matrix fails. The proposed method also obtains more precise estimation of DOA using one snapshot compared with the traditional estimation methods such as Capon, APES, and GLRT, based on hundreds of snapshots.
Inverse filtering of radar signals using compressed sensing with application to meteors
Volz, Ryan; Close, Sigrid
2012-10-01
Compressed sensing, a method which relies on sparsity to reconstruct signals with relatively few measurements, provides a new approach to processing radar signals that is ideally suited to detailed imaging and identification of multiple targets. In this paper, we extend previously published theoretical work by investigating the practical problems associated with this approach. In deriving a discrete linear radar model that is suitable for compressed sensing, we discuss what the discrete model can tell us about continuously defined targets and show how sparsity in the latter translates to sparsity in the former. We provide details about how this problem can be solved when using large data sets. Through comparisons with matched filter processing, we validate our compressed sensing technique and demonstrate its application to meteors, where it has the potential to answer open questions about processes like fragmentation and flares. At the cost of computational complexity and an assumption of target sparsity, the benefits over pulse compression using a matched filter include no filtering sidelobes, noise removal, and higher possible range and Doppler frequency resolution.
Quantum tomography via compressed sensing: error bounds, sample complexity and efficient estimators
Intuitively, if a density operator has small rank, then it should be easier to estimate from experimental data, since in this case only a few eigenvectors need to be learned. We prove two complementary results that confirm this intuition. Firstly, we show that a low-rank density matrix can be estimated using fewer copies of the state, i.e. the sample complexity of tomography decreases with the rank. Secondly, we show that unknown low-rank states can be reconstructed from an incomplete set of measurements, using techniques from compressed sensing and matrix completion. These techniques use simple Pauli measurements, and their output can be certified without making any assumptions about the unknown state. In this paper, we present a new theoretical analysis of compressed tomography, based on the restricted isometry property for low-rank matrices. Using these tools, we obtain near-optimal error bounds for the realistic situation where the data contain noise due to finite statistics, and the density matrix is full-rank with decaying eigenvalues. We also obtain upper bounds on the sample complexity of compressed tomography, and almost-matching lower bounds on the sample complexity of any procedure using adaptive sequences of Pauli measurements. Using numerical simulations, we compare the performance of two compressed sensing estimators—the matrix Dantzig selector and the matrix Lasso—with standard maximum-likelihood estimation (MLE). We find that, given comparable experimental resources, the compressed sensing estimators consistently produce higher fidelity state reconstructions than MLE. In addition, the use of an incomplete set of measurements leads to faster classical processing with no loss of accuracy. Finally, we show how to certify the accuracy of a low-rank estimate using direct fidelity estimation, and describe a method for compressed quantum process tomography that works for processes with small Kraus rank and requires only Pauli eigenstate preparations
Detection of Modified Matrix Encoding Using Machine Learning and Compressed Sensing
Allen, Josef D [ORNL
2011-01-01
In recent years, driven by the development of steganalysis methods, steganographic algorithms have been evolved rapidly with the ultimate goal of an unbreakable embedding procedure, resulting in recent steganographic algorithms with minimum distortions, exemplified by the recent family of Modified Matrix Encoding (MME) algorithms, which has shown to be most difficult to be detected. In this paper we propose a compressed sensing based on approach for intrinsic steganalysis to detect MME stego messages. Compressed sensing is a recently proposed mathematical framework to represent an image (in general, a signal) using a sparse representation relative to an overcomplete dictionary by minimizing the l1-norm of resulting coefficients. Here we first learn a dictionary from a training set so that the performance will be optimized using the KSVD algorithm; since JPEG images are processed by 8x8 blocks, the training examples are 8x8 patches, rather than the entire images and this increases the generalization of compressed sensing. For each 8x8 block, we compute its sparse representation using OMP (orthogonal matching pursuit) algorithm. Using computed sparse representations, we train a support vector machine (SVM) to classify 8x8 blocks into stego and non-stego classes. Then given an input image, we first divide it into 8x8 blocks. For each 8x8 block, we compute its sparse representation and classify it using the trained SVM. After all the 8x8 blocks are classified, the entire image is classified based on the majority rule of 8x8 block classification results. This allows us to achieve a robust decision even when 8x8 blocks can be classified only with relatively low accuracy. We have tested the proposed algorithm on two datasets (Corel-1000 dataset and a remote sensing image dataset) and have achieved 100% accuracy on classifying images, even though the accuracy of classifying 8x8 blocks is only 80.89%. Key Words Compressed Sensing, Sparcity, Data Dictionary, Steganography
Chen, Yongyao; Liu, Haijun; Reilly, Michael; Bae, Hyungdae; Yu, Miao
2014-01-01
Acoustic sensors play an important role in many areas, such as homeland security, navigation, communication, health care and industry. However, the fundamental pressure detection limit hinders the performance of current acoustic sensing technologies. Here, through analytical, numerical and experimental studies, we show that anisotropic acoustic metamaterials can be designed to have strong wave compression effect that renders direct amplification of pressure fields in metamaterials. This enables a sensing mechanism that can help overcome the detection limit of conventional acoustic sensing systems. We further demonstrate a metamaterial-enhanced acoustic sensing system that achieves more than 20 dB signal-to-noise enhancement (over an order of magnitude enhancement in detection limit). With this system, weak acoustic pulse signals overwhelmed by the noise are successfully recovered. This work opens up new vistas for the development of metamaterial-based acoustic sensors with improved performance and functionalities that are highly desirable for many applications. PMID:25316410
Dynamic compression of carbon/epoxy quasi-isotropic in-plane 3D composites%碳/环氧面内准各向三维复合材料的动态压缩性能
孙颖; 张鹤江; 郝露; 陈利
2015-01-01
利用置换法三维织物非织造技术结合树脂传递模塑（RTM）工艺，制备了碳/环氧面内准各向三维复合材料和三维正交复合材料。采用岛津万能材料试验机和分离式霍普金森压杆（SHPB）测试系统，对复合材料进行了面内和厚度方向的准静态及动态压缩性能试验，研究了碳/环氧面内准各向三维复合材料不同应变率下的压缩应力-应变关系以及破坏模式。对比分析结果表明：无论面内还是厚度方向，碳/环氧面内准各向三维复合材料的压缩性能都是应变率敏感的，并且其破坏应力和应变的应变率敏感程度大于三维正交复合材料；在面内和厚度方向准静态压缩载荷作用下，面内±45°纱线有效地抑制了面内准各向三维复合材料剪切带的形成和扩展，使得脆性破坏形貌更加均匀；随着应变率增加，面内准各向三维复合材料面内方向以剪切破坏为主，断口比三维正交复合材料粗糙，厚度方向二者破坏模式相同。%Carbon/epoxy quasi-isotropic in-plane 3D composites and 3D orthogonal composites were made by non-woven manufacture technique for 3D fabric and resin transfer molding (RTM)process. The in-plane and through-thickness compression properties at different strain rates of carbon/epoxy quasi-isotropic in-plane 3D composites and 3D orthogonal composites were tested using SHIMADZU universal material testing machine and SHPB measurement system. The compression stress-strain relationships, failure modes under quasi-static and high strain rates were investigated. The results show that the compression performance of carbon/epoxy quasi-isotropic in-plane 3D composites is strain rate dependent in the directions of in-plane and through-thickness. The compression properties of the composites are more strain-rate sensitive than those of 3D orthogonal composites. Under quasi-static loading, the production and spread of shear bonds are
Compressed Sensing Methods in Radio Receivers Exposed to Noise and Interference
Pierzchlewski, Jacek
Currently we observe a dramatical increase of wireless data trac and a rising number of mobile radio transceivers like Bluetooth, WiFi or LTE. Wireless networks become more robust and deliver data rates many times higher than rates available a couple of years ago. LTE, which is the currently...... is a mathematical tool which allows for sub-Nyquist signal sampling. In this thesis the author opens a new possibility of relaxing requirements for analog signal ltering in a direct conversion receiver by applying compressed sensing. In the proposed solution,high-order low-pass lters which separate......, is proposed. The author shows that by applying appropriate signal acquisition it is possible to lter out the wanted signal with a reduced signal sampling frequency and reduced lter order. A compressed-sensing based direct conversion receiver is proposed. An LTE signal generator, which is a side accomplishment...
Zhou, Fei [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Nielson, Weston [Univ. of California, Los Angeles, CA (United States); Xia, Yi [Univ. of California, Los Angeles, CA (United States); Ozoliņš, Vidvuds [Univ. of California, Los Angeles, CA (United States)
2014-10-01
First-principles prediction of lattice thermal conductivity κ_{L} of strongly anharmonic crystals is a long-standing challenge in solid-state physics. Making use of recent advances in information science, we propose a systematic and rigorous approach to this problem, compressive sensing lattice dynamics. Compressive sensing is used to select the physically important terms in the lattice dynamics model and determine their values in one shot. Nonintuitively, high accuracy is achieved when the model is trained on first-principles forces in quasirandom atomic configurations. The method is demonstrated for Si, NaCl, and Cu_{12}Sb_{4}S_{13}, an earth-abundant thermoelectric with strong phonon-phonon interactions that limit the room-temperature κ_{L} to values near the amorphous limit.
A compressed sensing based method with support refinement for impulse noise cancelation in DSL
Quadeer, Ahmed Abdul
2013-06-01
This paper presents a compressed sensing based method to suppress impulse noise in digital subscriber line (DSL). The proposed algorithm exploits the sparse nature of the impulse noise and utilizes the carriers, already available in all practical DSL systems, for its estimation and cancelation. Specifically, compressed sensing is used for a coarse estimate of the impulse position, an a priori information based maximum aposteriori probability (MAP) metric for its refinement, followed by least squares (LS) or minimum mean square error (MMSE) estimation for estimating the impulse amplitudes. Simulation results show that the proposed scheme achieves higher rate as compared to other known sparse estimation algorithms in literature. The paper also demonstrates the superior performance of the proposed scheme compared to the ITU-T G992.3 standard that utilizes RS-coding for impulse noise refinement in DSL signals. © 2013 IEEE.
Ali, Anum Z.
2013-12-01
Linearization of user equipment power amplifiers driven by orthogonal frequency division multiplexing signals is addressed in this paper. Particular attention is paid to the power efficient operation of an orthogonal frequency division multiple access cognitive radio system and realization of such a system using compressed sensing. Specifically, precompensated overdriven amplifiers are employed at the mobile terminal. Over-driven amplifiers result in in-band distortions and out of band interference. Out of band interference mostly occupies the spectrum of inactive users, whereas the in-band distortions are mitigated using compressed sensing at the receiver. It is also shown that the performance of the proposed scheme can be further enhanced using multiple measurements of the distortion signal in single-input multi-output systems. Numerical results verify the ability of the proposed setup to improve error vector magnitude, bit error rate, outage capacity and mean squared error. © 2011 IEEE.
Cabrelli, Carlos; Jaffard, Stephane; Molter, Ursula
2016-01-01
This volume is a selection of written notes corresponding to courses taught at the CIMPA School: "New Trends in Applied Harmonic Analysis: Sparse Representations, Compressed Sensing and Multifractal Analysis". New interactions between harmonic analysis and signal and image processing have seen striking development in the last 10 years, and several technological deadlocks have been solved through the resolution of deep theoretical problems in harmonic analysis. New Trends in Applied Harmonic Analysis focuses on two particularly active areas that are representative of such advances: multifractal analysis, and sparse representation and compressed sensing. The contributions are written by leaders in these areas, and covers both theoretical aspects and applications. This work should prove useful not only to PhD students and postdocs in mathematics and signal and image processing, but also to researchers working in related topics.
Primary User Localization Algorithm Based on Compressive Sensing in Cognitive Radio Networks
Fang Ye
2016-04-01
Full Text Available In order to locate source signal more accurately in authorized frequency bands, a novel primary user localization algorithm based on compressive sensing (PU-CSL in cognitive radio networks (CRNs is proposed in this paper. In comparison to existing centroid locating algorithms, PU-CSL shows higher locating accuracy for integrally exploring correlation between source signal and secondary users (SUs. Energy detection is first adopted for collecting the energy fingerprint of source signal at each SU, then degree of correlation between source signal and SUs is reconstructed based on compressive sensing (CS, which determines weights of centroid coordinates. A weighted centroid scheme is finally utilized to estimate source position. Simulation results show that PU-CSL has smaller maximum error of positioning and root-mean-square error. Moreover, the proposed PU-CSL algorithm possess excellent location accuracy and strong anti-noise performance.
Sejdić, Ervin; Movahedi, Faezeh; Zhang, Zhenwei; Kurosu, Atsuko; Coyle, James L.
2016-05-01
Acquiring swallowing accelerometry signals using a comprehensive sensing scheme may be a desirable approach for monitoring swallowing safety for longer periods of time. However, it needs to be insured that signal characteristics can be recovered accurately from compressed samples. In this paper, we considered this issue by examining the effects of the number of acquired compressed samples on the calculated swallowing accelerometry signal features. We used tri-axial swallowing accelerometry signals acquired from seventeen stroke patients (106 swallows in total). From acquired signals, we extracted typically considered signal features from time, frequency and time-frequency domains. Next, we compared these features from the original signals (sampled using traditional sampling schemes) and compressively sampled signals. Our results have shown we can obtain accurate estimates of signal features even by using only a third of original samples.
MRI is often the most sensitive or appropriate technique for important measurements in clinical diagnosis and research, but lengthy acquisition times limit its use due to cost and considerations of patient comfort and compliance. Once an image field of view and resolution is chosen, the minimum scan acquisition time is normally fixed by the amount of raw data that must be acquired to meet the Nyquist criteria. Recently, there has been research interest in using the theory of compressed sensing (CS) in MR imaging to reduce scan acquisition times. The theory argues that if our target MR image is sparse, having signal information in only a small proportion of pixels (like an angiogram), or if the image can be mathematically transformed to be sparse then it is possible to use that sparsity to recover a high definition image from substantially less acquired data. This review starts by considering methods of k-space undersampling which have already been incorporated into routine clinical imaging (partial Fourier imaging and parallel imaging), and then explains the basis of using compressed sensing in MRI. The practical considerations of applying CS to MRI acquisitions are discussed, such as designing k-space undersampling schemes, optimizing adjustable parameters in reconstructions and exploiting the power of combined compressed sensing and parallel imaging (CS-PI). A selection of clinical applications that have used CS and CS-PI prospectively are considered. The review concludes by signposting other imaging acceleration techniques under present development before concluding with a consideration of the potential impact and obstacles to bringing compressed sensing into routine use in clinical MRI. (topical review)
Interpolated Compressed Sensing for 2D Multiple Slice Fast MR Imaging
Yong Pang; Xiaoliang Zhang
2013-01-01
Sparse MRI has been introduced to reduce the acquisition time and raw data size by undersampling the k-space data. However, the image quality, particularly the contrast to noise ratio (CNR), decreases with the undersampling rate. In this work, we proposed an interpolated Compressed Sensing (iCS) method to further enhance the imaging speed or reduce data size without significant sacrifice of image quality and CNR for multi-slice two-dimensional sparse MR imaging in humans. This method utilizes...
Deformation corrected compressed sensing (DC-CS): a novel framework for accelerated dynamic MRI
Lingala, Sajan Goud; DiBella, Edward; Jacob, Mathews
2014-01-01
We propose a novel deformation corrected compressed sensing (DC-CS) framework to recover dynamic magnetic resonance images from undersampled measurements. We introduce a generalized formulation that is capable of handling a wide class of sparsity/compactness priors on the deformation corrected dynamic signal. In this work, we consider example compactness priors such as sparsity in temporal Fourier domain, sparsity in temporal finite difference domain, and nuclear norm penalty to exploit low r...
Ultrawideband Impulse Radar Through-the-Wall Imaging with Compressive Sensing
Graeme E. Smith; Ahmad Hoorfar; Moeness G. Amin; Wenji Zhang; Fauzia Ahmad
2012-01-01
Compressive Sensing (CS) provides a new perspective for addressing radar applications requiring large amount of measurements and long data acquisition time; both issues are inherent in through-the-wall radar imaging (TWRI). Most CS techniques applied to TWRI consider stepped-frequency radar platforms. In this paper, the impulse radar two-dimensional (2D) TWRI problem is cast within the framework of CS and solved by the sparse constraint optimization performed on time-domain samples. Instead o...
On Compressed Sensing and the Estimation of Continuous Parameters From Noisy Observations
Nielsen, Jesper Kjær; Christensen, Mads Græsbøll; Jensen, Søren Holdt
2012-01-01
Compressed sensing (CS) has in recent years become a very popular way of sampling sparse signals. This sparsity is measured with respect to some known dictionary consisting of a finite number of atoms. Most models for real world signals, however, are parametrised by continuous parameters corresponding to a dictionary with an infinite number of atoms. Examples of such parameters are the temporal and spatial frequency. In this paper, we analyse how CS affects the estimation performance of any u...
Fast compressed sensing analysis for super-resolution imaging using L1-homotopy
Babcock, Hazen P; Moffitt, Jeffrey R.; Cao, Yunlong; Zhuang, Xiaowei
2013-01-01
In super-resolution imaging techniques based on single-molecule switching and localization, the time to acquire a super-resolution image is limited by the maximum density of fluorescent emitters that can be accurately localized per imaging frame. In order to increase the imaging rate, several methods have been recently developed to analyze images with higher emitter densities. One powerful approach uses methods based on compressed sensing to increase the analyzable emitter density per imaging...
Guojin Liu; Qian Zhang; Yuyuan Yang; Zhenzhi Yin; Bin Zhu
2015-01-01
Recent years have witnessed pilot deployments of inexpensive wireless sensor networks (WSNs) for volcanic eruption detection, where the volcano-seismic signals were collected and processed by sensor nodes. However, it is faced with the limitation of energy resources and the transmission bottleneck of sensors in WSN. In this paper, a Model-Based Adaptive Iterative Hard Thresholding (MAIHT) compressive sensing scheme is developed, where a large number of inexpensive sensors are used to collect ...
Hollingsworth, Kieren Grant
2015-11-01
MRI is often the most sensitive or appropriate technique for important measurements in clinical diagnosis and research, but lengthy acquisition times limit its use due to cost and considerations of patient comfort and compliance. Once an image field of view and resolution is chosen, the minimum scan acquisition time is normally fixed by the amount of raw data that must be acquired to meet the Nyquist criteria. Recently, there has been research interest in using the theory of compressed sensing (CS) in MR imaging to reduce scan acquisition times. The theory argues that if our target MR image is sparse, having signal information in only a small proportion of pixels (like an angiogram), or if the image can be mathematically transformed to be sparse then it is possible to use that sparsity to recover a high definition image from substantially less acquired data. This review starts by considering methods of k-space undersampling which have already been incorporated into routine clinical imaging (partial Fourier imaging and parallel imaging), and then explains the basis of using compressed sensing in MRI. The practical considerations of applying CS to MRI acquisitions are discussed, such as designing k-space undersampling schemes, optimizing adjustable parameters in reconstructions and exploiting the power of combined compressed sensing and parallel imaging (CS-PI). A selection of clinical applications that have used CS and CS-PI prospectively are considered. The review concludes by signposting other imaging acceleration techniques under present development before concluding with a consideration of the potential impact and obstacles to bringing compressed sensing into routine use in clinical MRI.
Mejia, Yuri H.; Arguello, Henry
2016-05-01
Compressive sensing state-of-the-art proposes random Gaussian and Bernoulli as measurement matrices. Nev- ertheless, often the design of the measurement matrix is subject to physical constraints, and therefore it is frequently not possible that the matrix follows a Gaussian or Bernoulli distribution. Examples of these lim- itations are the structured and sparse matrices of the compressive X-Ray, and compressive spectral imaging systems. A standard algorithm for recovering sparse signals consists in minimizing an objective function that includes a quadratic error term combined with a sparsity-inducing regularization term. This problem can be solved using the iterative algorithms for solving linear inverse problems. This class of methods, which can be viewed as an extension of the classical gradient algorithm, is attractive due to its simplicity. However, current algorithms are slow for getting a high quality image reconstruction because they do not exploit the structured and sparsity characteristics of the compressive measurement matrices. This paper proposes the development of a gradient-based algorithm for compressive sensing reconstruction by including a filtering step that yields improved quality using less iterations. This algorithm modifies the iterative solution such that it forces to converge to a filtered version of the residual AT y, where y is the measurement vector and A is the compressive measurement matrix. We show that the algorithm including the filtering step converges faster than the unfiltered version. We design various filters that are motivated by the structure of AT y. Extensive simulation results using various sparse and structured matrices highlight the relative performance gain over the existing iterative process.
To improve the image reconstruction quality of electrical capacitance tomography (ECT), a new ECT image reconstruction method based on compressed sensing principle is proposed. The capacitances’ collection of the ECT system is regarded as linear measurement of compressed sensing; the measurement matrix is designed by randomly realigning the row vectors of a new sensitivity matrix, which is reconstructed by zero vectors’ expansion of the original sensitivity matrix, and the capacitance vectors expanded in the same way are regarded as measurement signals of compressed sensing. The original signal, i.e. permittivity distribution of some object inside a pipeline, is recovered by orthogonal matching pursuit algorithm. Finally, the simulation experiments are performed. The experimental results have shown that the average image reconstruction error and correlation coefficient obtained by this method are better than the corresponding indicators obtained by linear back-projection algorithm, Landweber iterative algorithm and total variation regularization algorithm. Therefore, it is a kind of ECT image reconstruction method with high accuracy, which also provides a new method and means for ECT research. (paper)
Zhao, Rongqiang; Wang, Qiang; Shen, Yi
2015-11-01
We propose a new approach for Kronecker compressive sensing of hyperspectral (HS) images, including the imaging mechanism and the corresponding reconstruction method. The proposed mechanism is able to compress the data of all dimensions when sampling, which can be achieved by three fully independent sampling devices. As a result, the mechanism greatly reduces the control points and memory requirement. In addition, we can also select the suitable sparsifying bases and generate the corresponding optimized sensing matrices or change the distribution of sampling ratio for each dimension independently according to different HS images. As the cooperation of the mechanism, we combine the sparsity model and low multilinear-rank model to develop a reconstruction method. Analysis shows that our reconstruction method has a lower computational complexity than the traditional methods based on sparsity model. Simulations verify that the HS images can be reconstructed successfully with very few measurements. In summary, the proposed approach can reduce the complexity and improve the practicability for HS image compressive sensing.
Toofanny Rudesh D
2011-08-01
Full Text Available Abstract Background Molecular dynamics (MD simulations offer the ability to observe the dynamics and interactions of both whole macromolecules and individual atoms as a function of time. Taken in context with experimental data, atomic interactions from simulation provide insight into the mechanics of protein folding, dynamics, and function. The calculation of atomic interactions or contacts from an MD trajectory is computationally demanding and the work required grows exponentially with the size of the simulation system. We describe the implementation of a spatial indexing algorithm in our multi-terabyte MD simulation database that significantly reduces the run-time required for discovery of contacts. The approach is applied to the Dynameomics project data. Spatial indexing, also known as spatial hashing, is a method that divides the simulation space into regular sized bins and attributes an index to each bin. Since, the calculation of contacts is widely employed in the simulation field, we also use this as the basis for testing compression of data tables. We investigate the effects of compression of the trajectory coordinate tables with different options of data and index compression within MS SQL SERVER 2008. Results Our implementation of spatial indexing speeds up the calculation of contacts over a 1 nanosecond (ns simulation window by between 14% and 90% (i.e., 1.2 and 10.3 times faster. For a 'full' simulation trajectory (51 ns spatial indexing reduces the calculation run-time between 31 and 81% (between 1.4 and 5.3 times faster. Compression resulted in reduced table sizes but resulted in no significant difference in the total execution time for neighbour discovery. The greatest compression (~36% was achieved using page level compression on both the data and indexes. Conclusions The spatial indexing scheme significantly decreases the time taken to calculate atomic contacts and could be applied to other multidimensional neighbor discovery
Wang, Haogang; Liao, Tien-Hao; Shi, Jiancheng; Yu, Zherui
2014-11-01
The forthcoming Water Cycle Observation Mission (WCOM) is to understand the water cycle system among land, atmosphere, and ocean. In both active and passive microwave remote sensing of soil moisture, the surface roughness plays an important role. Electromagnetic models of roughness provide tables of emissivities and backscattering coefficients that can be used to retrieve soil moisture. In this paper, a fast and accurate three dimensional solution of Maxwell's equations is developed and employed to solve rough soil surface scattering problem at L-band. The algorithm combines QR Pre-Ranked Multilevel UV(MLUV) factorization and Hierarchical Fast Far Field Approximation. It is implemented using OpenMP interface for fast parallel calculation. In this algorithm, 1) QR based rank predetermined algorithm is derived to further compress the UV matrix pairs obtained using coarse-coarse sampling; 2) at the finer levels, MLUV is used straightforwardly to factorize the interactions between groups, while at the coarsest level, interactions between groups in the interaction list are calculated using an elegantly derived Hierarchical Fast Far Field Approximation (HFAFFA) to accelerate the calculation of interactions between large groups while keeping the accuracy of this approximation; 3) OpenMP interface is used to parallelize this new algorithm. Numerical results including the incoherent bistatic scattering coefficients and the emissivity demonstrate the efficiency of this method.
Song Zha
2014-04-01
Full Text Available This paper considers the problem of compressed wideband spectrum sensing in wideband cognitive radio when partial occupancy status is known. While performing wideband spectrum sensing, incomplete prior information on the occupancy status may be obtained from coexisting narrowband detectors, collaborative sensing nodes or remote database. In this paper, we present a new optimization model in order to incorporate this prior information and then propose a particular weighting strategy in the reconstruction algorithm based on weighted l1 minimization to solve it. Numerical simulation results demonstrate that the use of partially known occupancy status leads to an improvement in detection performance and the proposed approach exploits such prior information effectively. As incorrect prior information is unavoidable in practical situation, cases in which the prior information is non-ideal are also investigated via simulations
The authors present the results of three electromagnetic field problems for compressed magnetic field generators and their associated power flow channels. The first problem is the computation of the transient magnetic field in a two-dimensional model of a helical generator during loading. The second problem is the three-dimensional eddy current patterns in a section of an armature beneath a bifurcation point of a helical winding. The authors' third problem is the calculation of the three-dimensional electrostatic fields in a region known as the post-hole convolute in which a rod connects the inner and outer walls of a system of three concentric cylinders through a hole in the middle cylinder. While analytic solutions exist for many electromagnetic filed problems in cases of special and ideal geometries, the solution of these and similar problems for the proper analysis and design of compressed magnetic field generators and their related hardware require computer simulations
Smith, Brandon; Jan, Darrell Leslie; Venkatapathy, Ethiraj
2015-01-01
Vehicles re-entering Earth's atmosphere require protection from the heat of atmospheric friction. The Orion Multi-Purpose Crew Vehicle (MPCV) has more demanding thermal protection system (TPS) requirements than the Low Earth Orbit (LEO) missions, especially in regions where the structural load passes through. The use of 2-dimensional laminate materials along with a metal insert, used in EFT1 flight test for the compression pad region, are deemed adequate but cannot be extended for Lunar return missions.
Topologic compression for 3D mesh model based on Face Fixer method%基于边扩张算法和熵编码的3D网格模型的拓扑信息压缩
许敏; 李钢; 吴石虎; 刘宁
2011-01-01
Firstly, three categorizes methods of compressing polygon mesh topologic information without triangulations were summarized in the paper. Then, the Face Fixer algorithm based on edge conquering was studied. Finally, several 3D mesh models were compressed after topologic encoding when using the same order adaptive arithmetic coder and range coder. The experiments results showed that range coder is superior to arithmetic coder in compression ratio and velocity with the increasing model size. Thus, for larger model, the adaptive range coder is preferred to compress.%本文总结了三类不经三角剖分直接编码多边形网格模型拓扑信息的单分辨率压缩法,对其中基于边区域扩张的Face Fixer算法进行了研究,并分别应用同阶自适应区间编码法和算术编码法对三角形网格模型和多边形网格模型进行了压缩.实验结果表明:随着模型数据量的增大,区间编码的压缩率和压缩速度反而高于算术编码,因而对于大数据量的网格模型,更适直采用区间编码来压缩.
Huang, Bingchao; Xu, Ke; Wan, Jianwei; Liu, Xu
2015-11-01
An efficient method and system for distributed compressive sensing of hyperspectral images is presented, which exploit the low rank and structure similarity property of hyperspectral imagery. In this paper, by integrating the respective characteristics of DSC and CS, a distributed compressive sensing framework is proposed to simultaneously capture and compress hyperspectral images. At the encoder, every band image is measured independently, where almost all computation burdens can be shifted to the decoder, resulting in a very low-complexity encoder. It is simple to operate and easy to hardware implementation. At the decoder, each band image is reconstructed by the method of total variation norm minimize. During each band reconstruction, the low rand structure of band images and spectrum structure similarity are used to give birth to the new regularizers. With combining the new regularizers and other regularizer, we can sufficiently exploit the spatial correlation, spectral correlation and spectral structural redundancy in hyperspectral imagery. A numerical optimization algorithm is also proposed to solve the reconstruction model by augmented Lagrangian multiplier method. Experimental results show that this method can effectively improve the reconstruction quality of hyperspectral images.
Efficient Sparse Signal Transmission over a Lossy Link Using Compressive Sensing
Liantao Wu
2015-08-01
Full Text Available Reliable data transmission over lossy communication link is expensive due to overheads for error protection. For signals that have inherent sparse structures, compressive sensing (CS is applied to facilitate efficient sparse signal transmissions over lossy communication links without data compression or error protection. The natural packet loss in the lossy link is modeled as a random sampling process of the transmitted data, and the original signal will be reconstructed from the lossy transmission results using the CS-based reconstruction method at the receiving end. The impacts of packet lengths on transmission efficiency under different channel conditions have been discussed, and interleaving is incorporated to mitigate the impact of burst data loss. Extensive simulations and experiments have been conducted and compared to the traditional automatic repeat request (ARQ interpolation technique, and very favorable results have been observed in terms of both accuracy of the reconstructed signals and the transmission energy consumption. Furthermore, the packet length effect provides useful insights for using compressed sensing for efficient sparse signal transmission via lossy links.
A closed-loop compressive-sensing-based neural recording system
Zhang, Jie; Mitra, Srinjoy; Suo, Yuanming; Cheng, Andrew; Xiong, Tao; Michon, Frederic; Welkenhuysen, Marleen; Kloosterman, Fabian; Chin, Peter S.; Hsiao, Steven; Tran, Trac D.; Yazicioglu, Firat; Etienne-Cummings, Ralph
2015-06-01
Objective. This paper describes a low power closed-loop compressive sensing (CS) based neural recording system. This system provides an efficient method to reduce data transmission bandwidth for implantable neural recording devices. By doing so, this technique reduces a majority of system power consumption which is dissipated at data readout interface. The design of the system is scalable and is a viable option for large scale integration of electrodes or recording sites onto a single device. Approach. The entire system consists of an application-specific integrated circuit (ASIC) with 4 recording readout channels with CS circuits, a real time off-chip CS recovery block and a recovery quality evaluation block that provides a closed feedback to adaptively adjust compression rate. Since CS performance is strongly signal dependent, the ASIC has been tested in vivo and with standard public neural databases. Main results. Implemented using efficient digital circuit, this system is able to achieve >10 times data compression on the entire neural spike band (500-6KHz) while consuming only 0.83uW (0.53 V voltage supply) additional digital power per electrode. When only the spikes are desired, the system is able to further compress the detected spikes by around 16 times. Unlike other similar systems, the characteristic spikes and inter-spike data can both be recovered which guarantes a >95% spike classification success rate. The compression circuit occupied 0.11mm2/electrode in a 180nm CMOS process. The complete signal processing circuit consumes <16uW/electrode. Significance. Power and area efficiency demonstrated by the system make it an ideal candidate for integration into large recording arrays containing thousands of electrode. Closed-loop recording and reconstruction performance evaluation further improves the robustness of the compression method, thus making the system more practical for long term recording.
IDMA-Based Compressed Sensing for Ocean Monitoring Information Acquisition with Sensor Networks
Gongliang Liu
2014-01-01
Full Text Available The ocean monitoring sensor network is a typically energy-limited and bandwidth-limited system, and the technical bottleneck of which is the asymmetry between the demand for large-scale and high-resolution information acquisition and the limited network resources. The newly arising compressed sensing theory provides a chance for breaking through the bottleneck. In view of this and considering the potential advantages of the emerging interleave-division multiple access (IDMA technology in underwater channels, this paper proposes an IDMA-based compressed sensing scheme in underwater sensor networks with applications to environmental monitoring information acquisition. Exploiting the sparse property of the monitored objects, only a subset of sensors is required to measure and transmit the measurements to the monitoring center for accurate information reconstruction, reducing the requirements for channel bandwidth and energy consumption significantly. Furthermore, with the aid of the semianalytical technique of IDMA, the optimal sensing probability of each sensor is determined to minimize the reconstruction error of the information map. Simulation results with real oceanic monitoring data validate the efficiency of the proposed scheme.
Shihua Cao
2014-03-01
Full Text Available Anomaly event detection is one of the research hotspots in wireless sensor networks. Aiming at the disadvantages of current detection solutions, a novel anomaly event detection algorithm based on compressed sensing and iteration is proposed. Firstly, a measured value can be sensed in each node, based on the compressed sensing. Then the problem of anomaly event detection is modeled as the minimization problem of weighted l1 norm, and OMP algorithm is adopted for solving the problem iteratively. And then the result of problem solving is judged according to detection functions. Finally, in the light of the judgment results, the weight value is updated for beginning a new round iteration. The loop won't stop until all the anomaly events are detected in wireless sensor networks. Simulation experimental results show the proposed algorithm has a better omission detection rate and false alarm rate in different noisy environments. In addition, the detection quality of this algorithm is higher than those of the traditional ones.
Efficient adaptation of complex-valued noiselet sensing matrices for compressed single-pixel imaging
Pastuszczak, Anna; Mikołajczyk, Michał; Kotyński, Rafał
2016-01-01
Minimal mutual coherence of discrete noiselets and Haar wavelets makes this pair of bases an essential choice for the measurement and compression matrices in compressed-sensing-based single-pixel detectors. In this paper we propose an efficient way of using complex-valued and non-binary noiselet functions for object sampling in single-pixel cameras with binary spatial light modulators and incoherent illumination. The proposed method allows to determine m complex noiselet coefficients from m+1 binary sampling measurements. Further, we introduce a modification to the complex fast noiselet transform, which enables computationally-efficient real-time generation of the binary noiselet-based patterns using efficient integer calculations on bundled patterns. The proposed method is verified experimentally with a single-pixel camera system using a binary spatial light modulator.
Spatio-temporal Compressed Sensing with Coded Apertures and Keyed Exposures
Harmany, Zachary T; Willett, Rebecca M
2011-01-01
Optical systems which measure independent random projections of a scene according to compressed sensing (CS) theory face a myriad of practical challenges related to the size of the physical platform, photon efficiency, the need for high temporal resolution, and fast reconstruction in video settings. This paper describes a coded aperture and keyed exposure approach to compressive measurement in optical systems. The proposed projections satisfy the Restricted Isometry Property for sufficiently sparse scenes, and hence are compatible with theoretical guarantees on the video reconstruction quality. These concepts can be implemented in both space and time via either amplitude modulation or phase shifting, and this paper describes the relative merits of the two approaches in terms of theoretical performance, noise and hardware considerations, and experimental results. Fast numerical algorithms which account for the nonnegativity of the projections and temporal correlations in a video sequence are developed and appl...
Evaluation of the CASSI-DD hyperspectral compressive sensing imaging system
Busuioceanu, Maria; Messinger, David W.; Greer, John B.; Flake, J. Christopher
2013-05-01
Compressive Sensing (CS) systems capture data with fewer measurements than traditional sensors assuming that imagery is redundant and compressible in the spatial and spectral dimensions. We utilize a model of the Coded Aperture Snapshot Spectral Imager-Dual Disperser (CASSI-DD) CS model to simulate CS measurements from HyMap images. Flake et al's novel reconstruction algorithm, which combines a spectral smoothing parameter and spatial total variation (TV), is used to create high resolution hyperspectral imagery.1 We examine the e ect of the number of measurements, which corresponds to the percentage of physical data sampled, on the delity of simulated data. The impacts of the CS sensor model and reconstruction of the data cloud and the utility for various hyperspectral applications are described to identify the strengths and limitations of CS.
Photonic compressive sensing with a micro-ring-resonator-based microwave photonic filter
Chen, Ying; Ding, Yunhong; Zhu, Zhijing; Chi, Hao; Zheng, Shilie; Zhang, Xianmin; Jin, Xiaofeng; Galili, Michael; Yu, Xianbin
2016-08-01
A novel approach to realize photonic compressive sensing (CS) with a multi-tap microwave photonic filter is proposed and demonstrated. The system takes both advantages of CS and photonics to capture wideband sparse signals with sub-Nyquist sampling rate. The low-pass filtering function required in the CS is realized in a photonic way by using a frequency comb and a dispersive element. The frequency comb is realized by shaping an amplified spontaneous emission (ASE) source with an on-chip micro-ring resonator, which is beneficial to the integration of photonic CS. A proof-of-concept experiment for a two-tone signal acquisition with frequencies of 350 MHz and 1.25 GHz is experimentally demonstrated with a compression factor up to 16.
Yu, Yao; Sun, Shunqiao; Petropulu, Athina P.
2013-05-01
Compressive sensing (CS) based multi-input multi-output (MIMO) radar systems that explore the sparsity of targets in the target space enable either the same localization performance as traditional methods but with significantly fewer measurements, or significantly improved performance with the same number of measurements. However, the enabling assumption, i.e., the target sparsity, diminishes in the presence of clutter, since clutters is highly correlated with the desire target echoes. This paper proposes an approach to suppress clutter in the context of CS MIMO radars. Assuming that the clutter covariance is known, Capon beamforming is applied at the fusion center on compressively obtained data, which are forwarded by the receive antennas. Subsequently, the target is estimated using CS theory, by exploiting the sparsity of the beamformed signals.
Dagiuklas, Tasos
2015-01-01
This book describes recent innovations in 3D media and technologies, with coverage of 3D media capturing, processing, encoding, and adaptation, networking aspects for 3D Media, and quality of user experience (QoE). The contributions are based on the results of the FP7 European Project ROMEO, which focuses on new methods for the compression and delivery of 3D multi-view video and spatial audio, as well as the optimization of networking and compression jointly across the future Internet. The delivery of 3D media to individual users remains a highly challenging problem due to the large amount of data involved, diverse network characteristics and user terminal requirements, as well as the user’s context such as their preferences and location. As the number of visual views increases, current systems will struggle to meet the demanding requirements in terms of delivery of consistent video quality to fixed and mobile users. ROMEO will present hybrid networking solutions that combine the DVB-T2 and DVB-NGH broadcas...
Dagiuklas, Tasos
2014-01-01
This book describes recent innovations in 3D media and technologies, with coverage of 3D media capturing, processing, encoding, and adaptation, networking aspects for 3D Media, and quality of user experience (QoE). The main contributions are based on the results of the FP7 European Projects ROMEO, which focus on new methods for the compression and delivery of 3D multi-view video and spatial audio, as well as the optimization of networking and compression jointly across the Future Internet (www.ict-romeo.eu). The delivery of 3D media to individual users remains a highly challenging problem due to the large amount of data involved, diverse network characteristics and user terminal requirements, as well as the user’s context such as their preferences and location. As the number of visual views increases, current systems will struggle to meet the demanding requirements in terms of delivery of constant video quality to both fixed and mobile users. ROMEO will design and develop hybrid-networking solutions that co...
Lucas, Laurent; Loscos, Céline
2013-01-01
While 3D vision has existed for many years, the use of 3D cameras and video-based modeling by the film industry has induced an explosion of interest for 3D acquisition technology, 3D content and 3D displays. As such, 3D video has become one of the new technology trends of this century.The chapters in this book cover a large spectrum of areas connected to 3D video, which are presented both theoretically and technologically, while taking into account both physiological and perceptual aspects. Stepping away from traditional 3D vision, the authors, all currently involved in these areas, provide th
Beane, Andy
2012-01-01
The essential fundamentals of 3D animation for aspiring 3D artists 3D is everywhere--video games, movie and television special effects, mobile devices, etc. Many aspiring artists and animators have grown up with 3D and computers, and naturally gravitate to this field as their area of interest. Bringing a blend of studio and classroom experience to offer you thorough coverage of the 3D animation industry, this must-have book shows you what it takes to create compelling and realistic 3D imagery. Serves as the first step to understanding the language of 3D and computer graphics (CG)Covers 3D anim
LP Decoding meets LP Decoding: A Connection between Channel Coding and Compressed Sensing
Dimakis, Alexandros G
2009-01-01
This is a tale of two linear programming decoders, namely channel coding linear programming decoding (CC-LPD) and compressed sensing linear programming decoding (CS-LPD). So far, they have evolved quite independently. The aim of the present paper is to show that there is a tight connection between, on the one hand, CS-LPD based on a zero-one measurement matrix over the reals and, on the other hand, CC-LPD of the binary linear code that is obtained by viewing this measurement matrix as a binary parity-check matrix. This connection allows one to translate performance guarantees from one setup to the other.
Exploring the Origins of Turbulence in Multiphase Flow Using Compressed Sensing MRI
Tayler, Alexander B.; Holland, Daniel J.; Sederman, Andrew J.; Gladden, Lynn F.
2012-06-01
Ultrafast magnetic resonance imaging, employing spiral reciprocal space sampling and compressed sensing image reconstruction, is used to acquire velocity maps of the liquid phase in gas-liquid multiphase flows. Velocity maps were acquired at a rate of 188 frames per second. The method enables quantitative characterization of the wake dynamics of single bubbles and bubble swarms. To illustrate this, we use the new technique to demonstrate the role of bubble wake vorticity in driving bubble secondary motions, and in governing the structure of turbulence in multiphase flows.
Implications for compressed sensing of a new sampling theorem on the sphere
McEwen, J D; Thiran, J -Ph; Vandergheynst, P; Van De Ville, D; Wiaux, Y
2011-01-01
A sampling theorem on the sphere has been developed recently, requiring half as many samples as alternative equiangular sampling theorems on the sphere. A reduction by a factor of two in the number of samples required to represent a band-limited signal on the sphere exactly has important implications for compressed sensing, both in terms of the dimensionality and sparsity of signals. We illustrate the impact of this property with an inpainting problem on the sphere, where we show the superior reconstruction performance when adopting the new sampling theorem compared to the alternative.
Bandit Theory meets Compressed Sensing for high dimensional Stochastic Linear Bandit
Carpentier, Alexandra
2012-01-01
We consider a linear stochastic bandit problem where the dimension $K$ of the unknown parameter $\\theta$ is larger than the sampling budget $n$. In such cases, it is in general impossible to derive sub-linear regret bounds since usual linear bandit algorithms have a regret in $O(K\\sqrt{n})$. In this paper we assume that $\\theta$ is $S-$sparse, i.e. has at most $S-$non-zero components, and that the space of arms is the unit ball for the $||.||_2$ norm. We combine ideas from Compressed Sensing and Bandit Theory and derive algorithms with regret bounds in $O(S\\sqrt{n})$.
A novel reconstruction algorithm for bioluminescent tomography based on Bayesian compressive sensing
Wang, Yaqi; Feng, Jinchao; Jia, Kebin; Sun, Zhonghua; Wei, Huijun
2016-03-01
Bioluminescence tomography (BLT) is becoming a promising tool because it can resolve the biodistribution of bioluminescent reporters associated with cellular and subcellular function through several millimeters with to centimeters of tissues in vivo. However, BLT reconstruction is an ill-posed problem. By incorporating sparse a priori information about bioluminescent source, enhanced image quality is obtained for sparsity based reconstruction algorithm. Therefore, sparsity based BLT reconstruction algorithm has a great potential. Here, we proposed a novel reconstruction method based on Bayesian compressive sensing and investigated its feasibility and effectiveness with a heterogeneous phantom. The results demonstrate the potential and merits of the proposed algorithm.
Clipping Noise Cancellation for OFDM and OFDMA Systems Using Compressed Sensing
Kim, Kee-Hoon; No, Jong-Seon; Chung, Habong
2011-01-01
In this paper, we propose clipping noise cancellation scheme using compressed sensing (CS) for orthogonal frequency division multiplexing (OFDM) systems. In the proposed scheme, only the data tones with high reliability are exploited in reconstructing the clipping noise instead of the whole data tones. For reconstructing the clipping noise using a fraction of the data tones at the receiver, the CS technique is applied. The proposed scheme is also applicable to interleaved orthogonal frequency division multiple access (OFDMA) systems due to the decomposition of fast Fourier transform (FFT) structure. Numerical analysis shows that the proposed scheme performs well for clipping noise cancellation of both OFDM and OFDMA systems.
DLA based compressed sensing for high resolution MR microscopy of neuronal tissue
Nguyen, Khieu-Van; Li, Jing-Rebecca; Radecki, Guillaume; Ciobanu, Luisa
2015-10-01
In this work we present the implementation of compressed sensing (CS) on a high field preclinical scanner (17.2 T) using an undersampling trajectory based on the diffusion limited aggregation (DLA) random growth model. When applied to a library of images this approach performs better than the traditional undersampling based on the polynomial probability density function. In addition, we show that the method is applicable to imaging live neuronal tissues, allowing significantly shorter acquisition times while maintaining the image quality necessary for identifying the majority of neurons via an automatic cell segmentation algorithm.
Compressed Sensing/Sparse-Recovery Approach for Improved Range Resolution in Narrow-Band Radar
Costanzo, Sandra
2016-01-01
A compressed sensing/sparse-recovery procedure is adopted to obtain enhanced range resolution capability from the processing of data acquired with narrow-band SFCW radars. A mathematical formulation for the proposed approach is reported and validity limitations are fully discussed, by demonstrating the ability to identify a great number of targets, up to 20, in the range direction. Both numerical and experimental validations are presented, by assuming also noise conditions. The proposed method can be usefully applied for the accurate detection of parameters with very small variations, such as those involved in the monitoring of soil deformations or biological objects. PMID:27022617
Novel additive manufacturing processes are increasingly recognized as ideal techniques to produce 3D biodegradable structures with optimal pore size and spatial distribution, providing an adequate mechanical support for tissue regeneration while shaping in-growing tissues. With regard to the mechanical and biological performances of 3D scaffolds, pore size and geometry play a crucial role. In this study, a novel integrated automated system for the production and in vitro culture of 3D constructs, known as BioCell Printing, was used only to manufacture poly(ε-caprolactone) scaffolds for tissue engineering; the influence of pore size and shape on their mechanical and biological performances was investigated. Imposing a single lay-down pattern of 0°/90° and varying the filament distance, it was possible to produce scaffolds with square interconnected pores with channel sizes falling in the range of 245–433 µm, porosity 49–57% and a constant road width. Three different lay-down patterns were also adopted (0°/90°, 0°/60/120° and 0°/45°/90°/135°), thus resulting in scaffolds with quadrangular, triangular and complex internal geometries, respectively. Mechanical compression tests revealed a decrease of scaffold stiffness with the increasing porosity and number of deposition angles (from 0°/90° to 0°/45°/90°/135°). Results from biological analysis, carried out using human mesenchymal stem cells, suggest a strong influence of pore size and geometry on cell viability. On the other hand, after 21 days of in vitro static culture, it was not possible to detect any significant variation in terms of cell morphology promoted by scaffold topology. As a first systematic analysis, the obtained results clearly demonstrate the potential of the BioCell Printing process to produce 3D scaffolds with reproducible well organized architectures and tailored mechanical properties. (paper)
Polarimetric SAR Tomopgraphy With TerraSAR-X By Means Of Distributed Compressed Sensing
Aguilera, E.; Nannini, M.; Antonello, A.; Marotti, L.; Prats, P.; Reigber, A.
2012-01-01
In SAR tomography, the vertical reflectivity function for every azimuth-range pixel is usually recovered by processing data collected using a defined repeat-pass acquisition geometry. A common and appealing approach is to generate a synthetic aperture in the elevation direction through imaging from parallel tracks. However, the quality of conventional reconstruction methods is generally dictated by the Nyquist rate, which can be considerably high. In an attempt to reduce this rate, we propose a new tomographic focusing approach that exploits correlations between neighboring azimuth-range pixels and polarimetric channels. As a matter of fact, this can be done under the framework of Distributed Com- pressed Sensing (DCS), which stems from Compressed Sensing (CS) theory, thus also exploiting sparsity in the tomographic signal. Results demonstrating the potential of the DCS methodology will be validated, for the first time, using dual-polarized data acquired at X-band by the TerraSAR-X spaceborne system.
A DMD-based hyperspectral imaging system using compressive sensing method
Sun, Zhongqiu; Chen, Bo; Cheng, Chengqi
2014-11-01
Hyperspectral Imaging Systems (HIS) are widely applied in many fields. However, in the traditional design of HIS, it is much time-consuming to acquire an integrated hyperspectral image. Compressive sensing is an efficient method to process sparse data, and a single-pixel camera which used the digital micromirror device (DMD) for accomplishing the CS algorithms had been developed. Nowadays, DMD achieved great development. The size of mirror array is increasing while switch speed of a single mirror becomes very fast. Consequently, researchers make efforts to design a HIS using CS method. CS method is a method to scale down the spatial information but the hyperspectral datacubes are still huge because of the thousands of bands. In this paper, we design a DMD-based spectrometer architecture using the method of compressed sensing principle, combined with DMD's spectral filter characteristics. In the new architecture, there are two DMDs. One is used for implementing the CS pattern, the other for filtering the bands. It has spectral simply adjustable advantages. With this new technology, we can reduce the amount of information which needs to be transmitted and processed in both spatial and spectral domain. We also present some simulation results of implementation procedures.
Rohit Thanki
2013-07-01
Full Text Available Biometric systems are those which utilize or are capability of utilizing characteristics of human for enrolment, verification or authentication. Biometrics based authentication system that use physiological and/or behavioral traits likes fingerprint, face, signature, palm print and voice are accept and use by many organization now days. But these biometric authentication systems are vulnerable against different attacks when biometric data transfer from one check point to other check point. In this paper, by combined compressive sensing theory and the correlation approach of White Gaussian Noise based robust digital watermarking technique is proposed for biometric embedding. The technique is divided into five steps: generation of linear measurement from watermark biometric data, embedding linear measurement into another biometric data, extraction of secure biometric from linear measurement, compressive sensing recovery process of biometric data and recognition of reconstructed biometric data with enrol biometric data for user authenticity. The proposed technique is providing more security and more payload capacity compare to traditional watermarking technique in spatial domain. The proposed technique is evaluated based on parameter like Peak Signal to Noise Ratio and Similarity Factor between reconstruct biometric data and enrolled biometric data. This proposed technique is increased overall data storage capacity in term secure enrolled biometric data of biometric authentication system
Amina Noor
2013-01-01
Full Text Available This paper proposes a novel algorithm for inferring gene regulatory networks which makes use of cubature Kalman filter (CKF and Kalman filter (KF techniques in conjunction with compressed sensing methods. The gene network is described using a state-space model. A nonlinear model for the evolution of gene expression is considered, while the gene expression data is assumed to follow a linear Gaussian model. The hidden states are estimated using CKF. The system parameters are modeled as a Gauss-Markov process and are estimated using compressed sensing-based KF. These parameters provide insight into the regulatory relations among the genes. The Cramér-Rao lower bound of the parameter estimates is calculated for the system model and used as a benchmark to assess the estimation accuracy. The proposed algorithm is evaluated rigorously using synthetic data in different scenarios which include different number of genes and varying number of sample points. In addition, the algorithm is tested on the DREAM4 in silico data sets as well as the in vivo data sets from IRMA network. The proposed algorithm shows superior performance in terms of accuracy, robustness, and scalability.
Compressive Sensing Based Opportunistic Protocol for Throughput Improvement in Wireless Networks
Qaseem, Syed T
2009-01-01
A key feature in the design of any MAC protocol is the throughput it can provide. In wireless networks, the channel of a user is not fixed but varies randomly. Thus, in order to maximize the throughput of the MAC protocol at any given time, only users with large channel gains should be allowed to transmit. In this paper, compressive sensing based opportunistic protocol for throughput improvement in wireless networks is proposed. The protocol is based on the traditional protocol of R-ALOHA which allows users to compete for channel access before reserving the channel to the best user. We use compressive sensing to find the best user, and show that the proposed protocol requires less time for reservation and so it outperforms other schemes proposed in literature. This makes the protocol particularly suitable for enhancing R-ALOHA in fast fading environments. We consider both analog and digital versions of the protocol where the channel gains sent by the user are analog and digital, respectively.
Gui, Guan; Xu, Li; Shan, Lin; Adachi, Fumiyuki
2014-01-01
In orthogonal frequency division modulation (OFDM) communication systems, channel state information (CSI) is required at receiver due to the fact that frequency-selective fading channel leads to disgusting intersymbol interference (ISI) over data transmission. Broadband channel model is often described by very few dominant channel taps and they can be probed by compressive sensing based sparse channel estimation (SCE) methods, for example, orthogonal matching pursuit algorithm, which can take the advantage of sparse structure effectively in the channel as for prior information. However, these developed methods are vulnerable to both noise interference and column coherence of training signal matrix. In other words, the primary objective of these conventional methods is to catch the dominant channel taps without a report of posterior channel uncertainty. To improve the estimation performance, we proposed a compressive sensing based Bayesian sparse channel estimation (BSCE) method which cannot only exploit the channel sparsity but also mitigate the unexpected channel uncertainty without scarifying any computational complexity. The proposed method can reveal potential ambiguity among multiple channel estimators that are ambiguous due to observation noise or correlation interference among columns in the training matrix. Computer simulations show that proposed method can improve the estimation performance when comparing with conventional SCE methods. PMID:24983012
Guan Gui
2014-01-01
Full Text Available In orthogonal frequency division modulation (OFDM communication systems, channel state information (CSI is required at receiver due to the fact that frequency-selective fading channel leads to disgusting intersymbol interference (ISI over data transmission. Broadband channel model is often described by very few dominant channel taps and they can be probed by compressive sensing based sparse channel estimation (SCE methods, for example, orthogonal matching pursuit algorithm, which can take the advantage of sparse structure effectively in the channel as for prior information. However, these developed methods are vulnerable to both noise interference and column coherence of training signal matrix. In other words, the primary objective of these conventional methods is to catch the dominant channel taps without a report of posterior channel uncertainty. To improve the estimation performance, we proposed a compressive sensing based Bayesian sparse channel estimation (BSCE method which cannot only exploit the channel sparsity but also mitigate the unexpected channel uncertainty without scarifying any computational complexity. The proposed method can reveal potential ambiguity among multiple channel estimators that are ambiguous due to observation noise or correlation interference among columns in the training matrix. Computer simulations show that proposed method can improve the estimation performance when comparing with conventional SCE methods.
Balanced sparse model for tight frames in compressed sensing magnetic resonance imaging.
Yunsong Liu
Full Text Available Compressed sensing has shown to be promising to accelerate magnetic resonance imaging. In this new technology, magnetic resonance images are usually reconstructed by enforcing its sparsity in sparse image reconstruction models, including both synthesis and analysis models. The synthesis model assumes that an image is a sparse combination of atom signals while the analysis model assumes that an image is sparse after the application of an analysis operator. Balanced model is a new sparse model that bridges analysis and synthesis models by introducing a penalty term on the distance of frame coefficients to the range of the analysis operator. In this paper, we study the performance of the balanced model in tight frame based compressed sensing magnetic resonance imaging and propose a new efficient numerical algorithm to solve the optimization problem. By tuning the balancing parameter, the new model achieves solutions of three models. It is found that the balanced model has a comparable performance with the analysis model. Besides, both of them achieve better results than the synthesis model no matter what value the balancing parameter is. Experiment shows that our proposed numerical algorithm constrained split augmented Lagrangian shrinkage algorithm for balanced model (C-SALSA-B converges faster than previously proposed algorithms accelerated proximal algorithm (APG and alternating directional method of multipliers for balanced model (ADMM-B.
A Compressed Sensing-Based Wearable Sensor Network for Quantitative Assessment of Stroke Patients.
Yu, Lei; Xiong, Daxi; Guo, Liquan; Wang, Jiping
2016-01-01
Clinical rehabilitation assessment is an important part of the therapy process because it is the premise for prescribing suitable rehabilitation interventions. However, the commonly used assessment scales have the following two drawbacks: (1) they are susceptible to subjective factors; (2) they only have several rating levels and are influenced by a ceiling effect, making it impossible to exactly detect any further improvement in the movement. Meanwhile, energy constraints are a primary design consideration in wearable sensor network systems since they are often battery-operated. Traditionally, for wearable sensor network systems that follow the Shannon/Nyquist sampling theorem, there are many data that need to be sampled and transmitted. This paper proposes a novel wearable sensor network system to monitor and quantitatively assess the upper limb motion function, based on compressed sensing technology. With the sparse representation model, less data is transmitted to the computer than with traditional systems. The experimental results show that the accelerometer signals of Bobath handshake and shoulder touch exercises can be compressed, and the length of the compressed signal is less than 1/3 of the raw signal length. More importantly, the reconstruction errors have no influence on the predictive accuracy of the Brunnstrom stage classification model. It also indicated that the proposed system can not only reduce the amount of data during the sampling and transmission processes, but also, the reconstructed accelerometer signals can be used for quantitative assessment without any loss of useful information. PMID:26861337
A Compressed Sensing-Based Wearable Sensor Network for Quantitative Assessment of Stroke Patients
Lei Yu
2016-02-01
Full Text Available Clinical rehabilitation assessment is an important part of the therapy process because it is the premise for prescribing suitable rehabilitation interventions. However, the commonly used assessment scales have the following two drawbacks: (1 they are susceptible to subjective factors; (2 they only have several rating levels and are influenced by a ceiling effect, making it impossible to exactly detect any further improvement in the movement. Meanwhile, energy constraints are a primary design consideration in wearable sensor network systems since they are often battery-operated. Traditionally, for wearable sensor network systems that follow the Shannon/Nyquist sampling theorem, there are many data that need to be sampled and transmitted. This paper proposes a novel wearable sensor network system to monitor and quantitatively assess the upper limb motion function, based on compressed sensing technology. With the sparse representation model, less data is transmitted to the computer than with traditional systems. The experimental results show that the accelerometer signals of Bobath handshake and shoulder touch exercises can be compressed, and the length of the compressed signal is less than 1/3 of the raw signal length. More importantly, the reconstruction errors have no influence on the predictive accuracy of the Brunnstrom stage classification model. It also indicated that the proposed system can not only reduce the amount of data during the sampling and transmission processes, but also, the reconstructed accelerometer signals can be used for quantitative assessment without any loss of useful information.
Compressed sensing of ECG signal for wireless system with new fast iterative method.
Tawfic, Israa; Kayhan, Sema
2015-12-01
Recent experiments in wireless body area network (WBAN) show that compressive sensing (CS) is a promising tool to compress the Electrocardiogram signal ECG signal. The performance of CS is based on algorithms use to reconstruct exactly or approximately the original signal. In this paper, we present two methods work with absence and presence of noise, these methods are Least Support Orthogonal Matching Pursuit (LS-OMP) and Least Support Denoising-Orthogonal Matching Pursuit (LSD-OMP). The algorithms achieve correct support recovery without requiring sparsity knowledge. We derive an improved restricted isometry property (RIP) based conditions over the best known results. The basic procedures are done by observational and analytical of a different Electrocardiogram signal downloaded them from PhysioBankATM. Experimental results show that significant performance in term of reconstruction quality and compression rate can be obtained by these two new proposed algorithms, and help the specialist gathering the necessary information from the patient in less time if we use Magnetic Resonance Imaging (MRI) application, or reconstructed the patient data after sending it through the network. PMID:26428598
Continuous Compressed Sensing for Surface Dynamical Processes with Helium Atom Scattering
Jones, Alex; Tamtögl, Anton; Calvo-Almazán, Irene; Hansen, Anders
2016-06-01
Compressed Sensing (CS) techniques are used to measure and reconstruct surface dynamical processes with a helium spin-echo spectrometer for the first time. Helium atom scattering is a well established method for examining the surface structure and dynamics of materials at atomic sized resolution and the spin-echo technique opens up the possibility of compressing the data acquisition process. CS methods demonstrating the compressibility of spin-echo spectra are presented for several measurements. Recent developments on structured multilevel sampling that are empirically and theoretically shown to substantially improve upon the state of the art CS techniques are implemented. In addition, wavelet based CS approximations, founded on a new continuous CS approach, are used to construct continuous spectra. In order to measure both surface diffusion and surface phonons, which appear usually on different energy scales, standard CS techniques are not sufficient. However, the new continuous CS wavelet approach allows simultaneous analysis of surface phonons and molecular diffusion while reducing acquisition times substantially. The developed methodology is not exclusive to Helium atom scattering and can also be applied to other scattering frameworks such as neutron spin-echo and Raman spectroscopy.
Continuous Compressed Sensing for Surface Dynamical Processes with Helium Atom Scattering.
Jones, Alex; Tamtögl, Anton; Calvo-Almazán, Irene; Hansen, Anders
2016-01-01
Compressed Sensing (CS) techniques are used to measure and reconstruct surface dynamical processes with a helium spin-echo spectrometer for the first time. Helium atom scattering is a well established method for examining the surface structure and dynamics of materials at atomic sized resolution and the spin-echo technique opens up the possibility of compressing the data acquisition process. CS methods demonstrating the compressibility of spin-echo spectra are presented for several measurements. Recent developments on structured multilevel sampling that are empirically and theoretically shown to substantially improve upon the state of the art CS techniques are implemented. In addition, wavelet based CS approximations, founded on a new continuous CS approach, are used to construct continuous spectra. In order to measure both surface diffusion and surface phonons, which appear usually on different energy scales, standard CS techniques are not sufficient. However, the new continuous CS wavelet approach allows simultaneous analysis of surface phonons and molecular diffusion while reducing acquisition times substantially. The developed methodology is not exclusive to Helium atom scattering and can also be applied to other scattering frameworks such as neutron spin-echo and Raman spectroscopy. PMID:27301423
Continuous Compressed Sensing for Surface Dynamical Processes with Helium Atom Scattering
Jones, Alex; Tamtögl, Anton; Calvo-Almazán, Irene; Hansen, Anders
2016-01-01
Compressed Sensing (CS) techniques are used to measure and reconstruct surface dynamical processes with a helium spin-echo spectrometer for the first time. Helium atom scattering is a well established method for examining the surface structure and dynamics of materials at atomic sized resolution and the spin-echo technique opens up the possibility of compressing the data acquisition process. CS methods demonstrating the compressibility of spin-echo spectra are presented for several measurements. Recent developments on structured multilevel sampling that are empirically and theoretically shown to substantially improve upon the state of the art CS techniques are implemented. In addition, wavelet based CS approximations, founded on a new continuous CS approach, are used to construct continuous spectra. In order to measure both surface diffusion and surface phonons, which appear usually on different energy scales, standard CS techniques are not sufficient. However, the new continuous CS wavelet approach allows simultaneous analysis of surface phonons and molecular diffusion while reducing acquisition times substantially. The developed methodology is not exclusive to Helium atom scattering and can also be applied to other scattering frameworks such as neutron spin-echo and Raman spectroscopy. PMID:27301423
Quantum Tomography via Compressed Sensing: Error Bounds, Sample Complexity, and Efficient Estimators
Flammia, Steven T; Liu, Yi-Kai; Eisert, Jens
2012-01-01
Intuitively, if a density operator has only a few non-zero eigenvalues, then it should be easier to estimate from experimental data, since in this case only a few eigenvectors need to be learned. We exhibit two complementary ways of making this intuition precise. On the one hand, we show that the sample complexity decreases with the rank of the density operator. In other words, fewer copies of the state need to be prepared in order to estimate a low-rank density matrix. On the other hand---and maybe more surprisingly---we prove that unknown low-rank states may be reconstructed using an incomplete set of measurement settings. The method does not require any a priori assumptions about the unknown state, uses only simple Pauli measurements, and can be efficiently and unconditionally certified. Our results extend earlier work on compressed tomography, building on ideas from compressed sensing and matrix completion. Instrumental to the improved analysis are new error bounds for compressed tomography, based on the ...
An infrared-visible image fusion scheme based on NSCT and compressed sensing
Zhang, Qiong; Maldague, Xavier
2015-05-01
Image fusion, as a research hot point nowadays in the field of infrared computer vision, has been developed utilizing different varieties of methods. Traditional image fusion algorithms are inclined to bring problems, such as data storage shortage and computational complexity increase, etc. Compressed sensing (CS) uses sparse sampling without knowing the priori knowledge and greatly reconstructs the image, which reduces the cost and complexity of image processing. In this paper, an advanced compressed sensing image fusion algorithm based on non-subsampled contourlet transform (NSCT) is proposed. NSCT provides better sparsity than the wavelet transform in image representation. Throughout the NSCT decomposition, the low-frequency and high-frequency coefficients can be obtained respectively. For the fusion processing of low-frequency coefficients of infrared and visible images , the adaptive regional energy weighting rule is utilized. Thus only the high-frequency coefficients are specially measured. Here we use sparse representation and random projection to obtain the required values of high-frequency coefficients, afterwards, the coefficients of each image block can be fused via the absolute maximum selection rule and/or the regional standard deviation rule. In the reconstruction of the compressive sampling results, a gradient-based iterative algorithm and the total variation (TV) method are employed to recover the high-frequency coefficients. Eventually, the fused image is recovered by inverse NSCT. Both the visual effects and the numerical computation results after experiments indicate that the presented approach achieves much higher quality of image fusion, accelerates the calculations, enhances various targets and extracts more useful information.
Hologram Compression Based on Compressive Sensing%基于压缩传感的全息图压缩研究
李科; 李军
2012-01-01
提出将压缩传感理论用于数字全息图的压缩研究.研究了2种不同交换域对全息图稀疏化的影响,针对全息图的特点选取合适的稀疏域,然后根据压缩传感理论直接获取图像的压缩表示,最后利用得到的压缩数据采用2种不同算法重构全息图并对比其重构效果.实验证实这种压缩方法是可行的,并且在傅里叶稀疏域下使用正交匹配追踪(OMP)重构算法能获得1.5％的压缩率.%Hologram is the image that contains the amplitude and phase information of an object, and the compression of hologram is different from the nature image. This paper presents a new theory for digital hologram compression using compressive sensing. A study of the impact of two transform domains to the hologram sparse is first presented, which selects the appropriate transform domain according to the character of hologram, then it directly acquires the compression data of hologram using compressive sensing theory, recovery the hologram using these compression data by two reconstruction algorithms and make a comparison of their construction effects. The main purpose of this paper is to explore the feasibility of the CS framework for hologram compression. The experiments demonstrate that the method proposed is available and it can achieve the compression ratio of 5% when the OMP reconstruction algorithm is used in Fourier transform domain.
D. Pletinckx
2012-09-01
Full Text Available The current 3D hype creates a lot of interest in 3D. People go to 3D movies, but are we ready to use 3D in our homes, in our offices, in our communication? Are we ready to deliver real 3D to a general public and use interactive 3D in a meaningful way to enjoy, learn, communicate? The CARARE project is realising this for the moment in the domain of monuments and archaeology, so that real 3D of archaeological sites and European monuments will be available to the general public by 2012. There are several aspects to this endeavour. First of all is the technical aspect of flawlessly delivering 3D content over all platforms and operating systems, without installing software. We have currently a working solution in PDF, but HTML5 will probably be the future. Secondly, there is still little knowledge on how to create 3D learning objects, 3D tourist information or 3D scholarly communication. We are still in a prototype phase when it comes to integrate 3D objects in physical or virtual museums. Nevertheless, Europeana has a tremendous potential as a multi-facetted virtual museum. Finally, 3D has a large potential to act as a hub of information, linking to related 2D imagery, texts, video, sound. We describe how to create such rich, explorable 3D objects that can be used intuitively by the generic Europeana user and what metadata is needed to support the semantic linking.
This book explains modeling of solid works 3D and application of 3D CAD/CAM. The contents of this book are outline of modeling such as CAD and 2D and 3D, solid works composition, method of sketch, writing measurement fixing, selecting projection, choosing condition of restriction, practice of sketch, making parts, reforming parts, modeling 3D, revising 3D modeling, using pattern function, modeling necessaries, assembling, floor plan, 3D modeling method, practice floor plans for industrial engineer data aided manufacturing, processing of CAD/CAM interface.
Xu, Kai; Li, Yixing; Ren, Fengbo
2016-01-01
Wireless body area network (WBAN) is emerging in the mobile healthcare area to replace the traditional wire-connected monitoring devices. As wireless data transmission dominates power cost of sensor nodes, it is beneficial to reduce the data size without much information loss. Compressive sensing (CS) is a perfect candidate to achieve this goal compared to existing compression techniques. In this paper, we proposed a general framework that utilize CS and online dictionary learning (ODL) toget...
Preparation and Compressive Properties of 3D Woven Sandwich Composites%三维机织夹芯复合材料的制备与压缩性能研究
王梦远; 曹海建; 钱坤; 袁守忍
2013-01-01
Using glass fiber as raw material,two systems of warp (one for the upper and lower surface,the other for the folder core layer) and one system of weft were employed to make the new 3D sandwich fabric whose meridional cross-section was a rectangle on the SU111-automatic rapier.Using the epoxy resin E-44 and the type of 9055 curing agent as the matrix system,the woven fabric was made into 3D woven sandwich composites through the hand lay-up process.The compressive properties of 3D woven sandwich composites were studied,and the relationship between structure and compressive properties was analyzed and compared with the "8"-shaped hollow composite material.The results show that the compressive strength of the "8"-shaped hollow composite material whose spacing of core material is 5 mm is better than 3D woven sandwich composites whose spacing of core material is 25 mm.But the elastic modulus of the lattar is higher than the former.The results are extremely valuable in guiding the optimization and mechanical property study on this kind of materials.%以玻璃纤维为原料,采用2个系统经纱(一个为上下表层经纱,另一个为夹芯层经纱)、1个系统纬纱,在SU111型全自动剑杆织机上制织经向截面为“口”字形的新型三维夹芯织物.以环氧树脂E-44与9055型固化剂为基体体系,采用手糊成型工艺将上述机织物复合制成三维机织夹芯复合材料.研究三维机织夹芯复合材料的压缩性能,分析材料结构与压缩性能之间的关系,并与“8”字形中空复合材料进行比较.结果表明,芯材间距为5mm的“8”字形中空复合材料的压缩强度高于芯材间距为25 mm的三维机织夹芯复合材料,但是后者的弹性模量高于前者.实验结果对该类结构材料的优化设计与力学性能研究具有极其重要的指导价值.
Felician ALECU
2010-01-01
Full Text Available Many professionals and 3D artists consider Blender as being the best open source solution for 3D computer graphics. The main features are related to modeling, rendering, shading, imaging, compositing, animation, physics and particles and realtime 3D/game creation.
Compact all-CMOS spatiotemporal compressive sensing video camera with pixel-wise coded exposure.
Zhang, Jie; Xiong, Tao; Tran, Trac; Chin, Sang; Etienne-Cummings, Ralph
2016-04-18
We present a low power all-CMOS implementation of temporal compressive sensing with pixel-wise coded exposure. This image sensor can increase video pixel resolution and frame rate simultaneously while reducing data readout speed. Compared to previous architectures, this system modulates pixel exposure at the individual photo-diode electronically without external optical components. Thus, the system provides reduction in size and power compare to previous optics based implementations. The prototype image sensor (127 × 90 pixels) can reconstruct 100 fps videos from coded images sampled at 5 fps. With 20× reduction in readout speed, our CMOS image sensor only consumes 14μW to provide 100 fps videos. PMID:27137331
Kang, Jaewook; Kim, Kiseon
2012-01-01
In this paper, we investigate a Bayesian sparse reconstruction algorithm called compressive sensing via Bayesian support detection (CS-BSD). This algorithm is quite robust against measurement noise and achieves the performance of a minimum mean square error (MMSE) estimator that has support knowledge beyond a certain SNR threshold. The key idea behind CS-BSD is that reconstruction takes a detection-directed estimation structure consisting of two parts: support detection and signal value estimation. Belief propagation (BP) and a Bayesian hypothesis test perform support detection, and an MMSE estimator finds the signal values belonging to the support set. CS-BSD converges faster than other BP-based algorithms, and it can be converted to a parallel architecture to become much faster. Numerical results are provided to verify the superiority of CS-BSD compared to recent algorithms.
Interference-based image encryption with silhouette removal by aid of compressive sensing
Gong, Qiong; Wang, Zhipeng; Lv, Xiaodong; Qin, Yi
2016-01-01
Compressive sensing (CS) offers the opportunity to reconstruct a signal from its sparse representation, either in the space domain or the transform domain. Exploiting this character, we propose a simple interference-based image encryption method. For encryption, a synthetic image, which contains sparse samples of the original image and the designated values, is analytically separated into two phase only masks (POMs). Consequently, only fragmentary data of the primary image can be directly collected in the traditional decryption scheme. However, the subsequent CS reconstruction will retrieve a high quality image from the fragmentary information. The proposed method has effectively suppressed the silhouette problem. Moreover, it has also some distinct advantages over the previous approaches.
Flame four-dimensional deflection tomography with compressed-sensing-revision reconstruction
Zhang, Bin; Zhao, Minmin; Liu, Zhigang; Wu, Zhaohang
2016-08-01
Deflection tomography with limited angle projections was investigated to visualize a premixed flame. A projection sampling system for deflection tomography was used to obtain chronological deflectogram arrays at six view angles with only a pair of gratings. A new iterative reconstruction algorithm with deflection angle compressed-sensing revision was developed to improve reconstruction-distribution quality from incomplete projection data. Numerical simulation and error analysis provided a good indication of algorithm precision and convergence. In the experiment, 150 fringes were processed, and temperature distributions in 20 cross-sections were reconstructed from projection data in four instants. Four-dimensional flame structures and temperature distributions in the flame interior were visualized using the visualization toolkit. The experimental reconstruction was then compared with the result obtained from computational fluid dynamic analysis.
A computationally efficient OMP-based compressed sensing reconstruction for dynamic MRI
Usman, M.; Prieto, C.; Odille, F.; Atkinson, D.; Schaeffter, T.; Batchelor, P. G.
2011-04-01
Compressed sensing (CS) methods in MRI are computationally intensive. Thus, designing novel CS algorithms that can perform faster reconstructions is crucial for everyday applications. We propose a computationally efficient orthogonal matching pursuit (OMP)-based reconstruction, specifically suited to cardiac MR data. According to the energy distribution of a y-f space obtained from a sliding window reconstruction, we label the y-f space as static or dynamic. For static y-f space images, a computationally efficient masked OMP reconstruction is performed, whereas for dynamic y-f space images, standard OMP reconstruction is used. The proposed method was tested on a dynamic numerical phantom and two cardiac MR datasets. Depending on the field of view composition of the imaging data, compared to the standard OMP method, reconstruction speedup factors ranging from 1.5 to 2.5 are achieved.
Two-stage through-the-wall radar image formation using compressive sensing
Tang, Van Ha; Bouzerdoum, Abdesselam; Phung, Son Lam
2013-04-01
We introduce a robust image-formation approach for through-the-wall radar imaging (TWRI). The proposed approach consists of two stages involving compressive sensing (CS) followed by delay-and-sum (DS) beamforming. In the first stage, CS is used to reconstruct a complete set of measurements from a small subset collected with a reduced number of transceivers and frequencies. DS beamforming is then applied to form the image using the reconstructed measurements. To promote sparsity of the CS solution, an overcomplete Gabor dictionary is employed to sparsely represent the imaged scene. The new approach requires far fewer measurement samples than the conventional DS beamforming and CS-based TWRI methods to reconstruct a high-quality image of the scene. Experimental results based on simulated and real data demonstrate the effectiveness and robustness of the proposed two-stage image formation technique, especially when the measurement set is drastically reduced.
Optimal Arrays for Compressed Sensing in Snapshot-Mode Radio Interferometry
Fannjiang, Clara
2015-01-01
Radio interferometry has always faced the problem of incomplete sampling of the Fourier plane. A possible remedy can be found in the promising new theory of compressed sensing (CS), which allows for the accurate recovery of sparse signals from sub-Nyquist sampling given certain measurement conditions. We provide an introductory assessment of optimal arrays for CS in snapshot-mode radio interferometry, using orthogonal matching pursuit (OMP), a widely used CS recovery algorithm similar in some respects to CLEAN. We focus on centrally condensed (specifically, Gaussian) arrays versus uniform arrays, and the principle of randomization versus deterministic arrays such as the VLA. The theory of CS is grounded in $a)$ sparse representation of signals and $b)$ measurement matrices of low coherence. We calculate a related quantity, mutual coherence (MC), as a theoretical indicator of arrays' suitability for OMP based on the recovery error bounds in (Donoho et al. 2006). OMP reconstructions of both point and extended o...
Accelerate Single-shot Data Acquisitions Using Compressed Sensing and FRONSAC Imaging
Wang, Haifeng; Galiana, Gigi
2015-01-01
Nonlinear spatial encoding magnetic (SEM) fields have been studied to complement multichannel RF encoding and accelerate MRI scans. Published schemes include PatLoc, O-Space, Null Space, 4D-RIO, and others, but the large variety of possible approaches to exploiting nonlinear SEMs remains mostly unexplored. Before, we have presented a new approach, Fast ROtary Nonlinear Spatial ACquisition (FRONSAC) imaging, where the nonlinear fields provide a small rotating perturbation to standard linear trajectories. While FRONSAC encoding greatly improves image quality, at the highest accelerations or weakest FRONSAC fields, some undersampling artifacts remain. However, the under-sampling artifacts that occur with FRONSAC encoding are relatively incoherent and well suited to the compressed sensing (CS) reconstruction. CS provides a sparsity-promoting convex strategy to reconstruct images from highly undersampled datasets. The work presented here combines the benefits of FRONSAC and CS. Simulations illustrate that this com...
Si, Weijian; Qu, Xinggen; Liu, Lutao
2014-01-01
A novel direction of arrival (DOA) estimation method in compressed sensing (CS) is presented, in which DOA estimation is considered as the joint sparse recovery from multiple measurement vectors (MMV). The proposed method is obtained by minimizing the modified-based covariance matching criterion, which is acquired by adding penalties according to the regularization method. This minimization problem is shown to be a semidefinite program (SDP) and transformed into a constrained quadratic programming problem for reducing computational complexity which can be solved by the augmented Lagrange method. The proposed method can significantly improve the performance especially in the scenarios with low signal to noise ratio (SNR), small number of snapshots, and closely spaced correlated sources. In addition, the Cramér-Rao bound (CRB) of the proposed method is developed and the performance guarantee is given according to a version of the restricted isometry property (RIP). The effectiveness and satisfactory performance of the proposed method are illustrated by simulation results. PMID:24678272
Off-Grid DOA Estimation Using Alternating Block Coordinate Descent in Compressed Sensing
Si, Weijian; Qu, Xinggen; Qu, Zhiyu
2015-01-01
This paper presents a novel off-grid direction of arrival (DOA) estimation method to achieve the superior performance in compressed sensing (CS), in which DOA estimation problem is cast as a sparse reconstruction. By minimizing the mixed k-l norm, the proposed method can reconstruct the sparse source and estimate grid error caused by mismatch. An iterative process that minimizes the mixed k-l norm alternately over two sparse vectors is employed so that the nonconvex problem is solved by alternating convex optimization. In order to yield the better reconstruction properties, the block sparse source is exploited for off-grid DOA estimation. A block selection criterion is engaged to reduce the computational complexity. In addition, the proposed method is proved to have the global convergence. Simulation results show that the proposed method has the superior performance in comparisons to existing methods. PMID:26343658
Compressive sensing based spinning mode detections by in-duct microphone arrays
Yu, Wenjun; Huang, Xun
2016-05-01
This paper presents a compressive sensing based experimental method for detecting spinning modes of sound waves propagating inside a cylindrical duct system. This method requires fewer dynamic pressure sensors than the number required by the Shannon–Nyquist sampling theorem so long as the incident waves are sparse in spinning modes. In this work, the proposed new method is firstly validated by preparing some of the numerical simulations with representative set-ups. Then, a duct acoustic testing rig with a spinning mode synthesiser and an in-duct microphone array is built to experimentally demonstrate the new approach. Both the numerical simulations and the experiment results are satisfactory, even when the practical issue of the background noise pollution is taken into account. The approach is beneficial for sensory array tests of silent aeroengines in particular and some other engineering systems with duct acoustics in general.
A CT reconstruction algorithm based on non-aliasing Contourlet transform and compressive sensing.
Deng, Lu-zhen; Feng, Peng; Chen, Mian-yi; He, Peng; Vo, Quang-sang; Wei, Biao
2014-01-01
Compressive sensing (CS) theory has great potential for reconstructing CT images from sparse-views projection data. Currently, total variation (TV-) based CT reconstruction method is a hot research point in medical CT field, which uses the gradient operator as the sparse representation approach during the iteration process. However, the images reconstructed by this method often suffer the smoothing problem; to improve the quality of reconstructed images, this paper proposed a hybrid reconstruction method combining TV and non-aliasing Contourlet transform (NACT) and using the Split-Bregman method to solve the optimization problem. Finally, the simulation results show that the proposed algorithm can reconstruct high-quality CT images from few-views projection using less iteration numbers, which is more effective in suppressing noise and artefacts than algebraic reconstruction technique (ART) and TV-based reconstruction method. PMID:25101142
Weijian Si
2015-01-01
Full Text Available A novel direction of arrival (DOA estimation method in compressed sensing (CS is proposed, in which the DOA estimation problem is cast as the joint sparse reconstruction from multiple measurement vectors (MMV. The proposed method is derived through transforming quadratically constrained linear programming (QCLP into unconstrained convex optimization which overcomes the drawback that l1-norm is nondifferentiable when sparse sources are reconstructed by minimizing l1-norm. The convergence rate and estimation performance of the proposed method can be significantly improved, since the steepest descent step and Barzilai-Borwein step are alternately used as the search step in the unconstrained convex optimization. The proposed method can obtain satisfactory performance especially in these scenarios with low signal to noise ratio (SNR, small number of snapshots, or coherent sources. Simulation results show the superior performance of the proposed method as compared with existing methods.
Jørgensen, Jakob H; Pan, Xiaochuan
2011-01-01
Discrete-to-discrete imaging models for computed tomography (CT) are becoming increasingly ubiquitous as the interest in iterative image reconstruction algorithms has heightened. Despite this trend, all the intuition for algorithm and system design derives from analysis of continuous-to-continuous models such as the X-ray and Radon transform. While the similarity between these models justifies some crossover, questions such as what are sufficient sampling conditions can be quite different for the two models. This sampling issue is addressed extensively in the first half of the article using singular value decomposition analysis for determining sufficient number of views and detector bins. The question of full sampling for CT is particularly relevant to current attempts to adapt compressive sensing (CS) motivated methods to application in CT image reconstruction. The second half goes in depth on this subject and discusses the link between object sparsity and sufficient sampling for accurate reconstruction. Par...
Ahmed M. Khedr
2015-10-01
Full Text Available In designing wireless sensor networks (WSNs, it is important to reduce energy dissipation and prolong network lifetime. Clustering of nodes is one of the most effective approaches for conserving energy in WSNs. Cluster formation protocols generally consider the heterogeneity of sensor nodes in terms of energy difference of nodes but ignore the different transmission ranges of them. In this paper, we propose an effective data acquisition clustered protocol using compressive sensing (EDACP-CS for heterogeneous WSNs that aims to conserve the energy of sensor nodes in the presence of energy and transmission range heterogeneity. In EDACP-CS, cluster heads are selected based on the distance from the base station and sensor residual energy. Simulation results show that our protocol offers a much better performance than the existing protocols in terms of energy consumption, stability, network lifetime, and throughput.
An adaptive fusion approach for infrared and visible images based on NSCT and compressed sensing
Zhang, Qiong; Maldague, Xavier
2016-01-01
A novel nonsubsampled contourlet transform (NSCT) based image fusion approach, implementing an adaptive-Gaussian (AG) fuzzy membership method, compressed sensing (CS) technique, total variation (TV) based gradient descent reconstruction algorithm, is proposed for the fusion computation of infrared and visible images. Compared with wavelet, contourlet, or any other multi-resolution analysis method, NSCT has many evident advantages, such as multi-scale, multi-direction, and translation invariance. As is known, a fuzzy set is characterized by its membership function (MF), while the commonly known Gaussian fuzzy membership degree can be introduced to establish an adaptive control of the fusion processing. The compressed sensing technique can sparsely sample the image information in a certain sampling rate, and the sparse signal can be recovered by solving a convex problem employing gradient descent based iterative algorithm(s). In the proposed fusion process, the pre-enhanced infrared image and the visible image are decomposed into low-frequency subbands and high-frequency subbands, respectively, via the NSCT method as a first step. The low-frequency coefficients are fused using the adaptive regional average energy rule; the highest-frequency coefficients are fused using the maximum absolute selection rule; the other high-frequency coefficients are sparsely sampled, fused using the adaptive-Gaussian regional standard deviation rule, and then recovered by employing the total variation based gradient descent recovery algorithm. Experimental results and human visual perception illustrate the effectiveness and advantages of the proposed fusion approach. The efficiency and robustness are also analyzed and discussed through different evaluation methods, such as the standard deviation, Shannon entropy, root-mean-square error, mutual information and edge-based similarity index.
Chang, Chen-Ming; Grant, Alexander M.; Lee, Brian J.; Kim, Ealgoo; Hong, KeyJo; Levin, Craig S.
2015-08-01
In the field of information theory, compressed sensing (CS) had been developed to recover signals at a lower sampling rate than suggested by the Nyquist-Shannon theorem, provided the signals have a sparse representation with respect to some base. CS has recently emerged as a method to multiplex PET detector readouts thanks to the sparse nature of 511 keV photon interactions in a typical PET study. We have shown in our previous numerical studies that, at the same multiplexing ratio, CS achieves higher signal-to-noise ratio (SNR) compared to Anger and cross-strip multiplexing. In addition, unlike Anger logic, multiplexing by CS preserves the capability to resolve multi-hit events, in which multiple pixels are triggered within the resolving time of the detector. In this work, we characterized the time, energy and intrinsic spatial resolution of two CS detectors and a data acquisition system we have developed for a PET insert system for simultaneous PET/MRI. The CS detector comprises a 2× 4 mosaic of 4× 4 arrays of 3.2× 3.2× 20 mm3 lutetium-yttrium orthosilicate crystals coupled one-to-one to eight 4× 4 silicon photomultiplier arrays. The total number of 128 pixels is multiplexed down to 16 readout channels by CS. The energy, coincidence time and intrinsic spatial resolution achieved by two CS detectors were 15.4+/- 0.1 % FWHM at 511 keV, 4.5 ns FWHM and 2.3 mm FWHM, respectively. A series of experiments were conducted to measure the sources of time jitter that limit the time resolution of the current system, which provides guidance for potential system design improvements. These findings demonstrate the feasibility of compressed sensing as a promising multiplexing method for PET detectors.
Detection and Tracking of Moving Targets Behind Cluttered Environments Using Compressive Sensing
Dang, Vinh Quang
Detection and tracking of moving targets (target's motion, vibration, etc.) in cluttered environments have been receiving much attention in numerous applications, such as disaster search-and-rescue, law enforcement, urban warfare, etc. One of the popular techniques is the use of stepped frequency continuous wave radar due to its low cost and complexity. However, the stepped frequency radar suffers from long data acquisition time. This dissertation focuses on detection and tracking of moving targets and vibration rates of stationary targets behind cluttered medium such as wall using stepped frequency radar enhanced by compressive sensing. The application of compressive sensing enables the reconstruction of the target space using fewer random frequencies, which decreases the acquisition time. Hardware-accelerated parallelization on GPU is investigated for the Orthogonal Matching Pursuit reconstruction algorithm. For simulation purpose, two hybrid methods have been developed to calculate the scattered fields from the targets through the wall approaching the antenna system, and to convert the incoming fields into voltage signals at terminals of the receive antenna. The first method is developed based on the plane wave spectrum approach for calculating the scattered fields of targets behind the wall. The method uses Fast Multiple Method (FMM) to calculate scattered fields on a particular source plane, decomposes them into plane wave components, and propagates the plane wave spectrum through the wall by integrating wall transmission coefficients before constructing the fields on a desired observation plane. The second method allows one to calculate the complex output voltage at terminals of a receiving antenna which fully takes into account the antenna effects. This method adopts the concept of complex antenna factor in Electromagnetic Compatibility (EMC) community for its calculation.
Chang, Chen-Ming; Grant, Alexander M; Lee, Brian J; Kim, Ealgoo; Hong, KeyJo; Levin, Craig S
2015-08-21
In the field of information theory, compressed sensing (CS) had been developed to recover signals at a lower sampling rate than suggested by the Nyquist-Shannon theorem, provided the signals have a sparse representation with respect to some base. CS has recently emerged as a method to multiplex PET detector readouts thanks to the sparse nature of 511 keV photon interactions in a typical PET study. We have shown in our previous numerical studies that, at the same multiplexing ratio, CS achieves higher signal-to-noise ratio (SNR) compared to Anger and cross-strip multiplexing. In addition, unlike Anger logic, multiplexing by CS preserves the capability to resolve multi-hit events, in which multiple pixels are triggered within the resolving time of the detector. In this work, we characterized the time, energy and intrinsic spatial resolution of two CS detectors and a data acquisition system we have developed for a PET insert system for simultaneous PET/MRI. The CS detector comprises a 2 x 4 mosaic of 4 x 4 arrays of 3.2 x 3.2 x 20 mm(3) lutetium-yttrium orthosilicate crystals coupled one-to-one to eight 4 x 4 silicon photomultiplier arrays. The total number of 128 pixels is multiplexed down to 16 readout channels by CS. The energy, coincidence time and intrinsic spatial resolution achieved by two CS detectors were 15.4±0.1% FWHM at 511 keV, 4.5 ns FWHM and 2.3 mm FWHM, respectively. A series of experiments were conducted to measure the sources of time jitter that limit the time resolution of the current system, which provides guidance for potential system design improvements. These findings demonstrate the feasibility of compressed sensing as a promising multiplexing method for PET detectors. PMID:26237671
Wang, Yishan; Doleschel, Sammy; Wunderlich, Ralf; Heinen, Stefan
2016-07-01
In this paper, a wearable and wireless ECG system is firstly designed with Bluetooth Low Energy (BLE). It can detect 3-lead ECG signals and is completely wireless. Secondly the digital Compressed Sensing (CS) is implemented to increase the energy efficiency of wireless ECG sensor. Different sparsifying basis, various compression ratio (CR) and several reconstruction algorithms are simulated and discussed. Finally the reconstruction is done by the android application (App) on smartphone to display the signal in real time. The power efficiency is measured and compared with the system without CS. The optimum satisfying basis built by 3-level decomposed db4 wavelet coefficients, 1-bit Bernoulli random matrix and the most suitable reconstruction algorithm are selected by the simulations and applied on the sensor node and App. The signal is successfully reconstructed and displayed on the App of smartphone. Battery life of sensor node is extended from 55 h to 67 h. The presented wireless ECG system with CS can significantly extend the battery life by 22 %. With the compact characteristic and long term working time, the system provides a feasible solution for the long term homecare utilization. PMID:27240841
Robust Watermarking Using Compressed Sensing Framework with Application to MP3 Audio
Mohamed Waleed Fakhr
2012-12-01
Full Text Available In this paper a watermark embedding and recovery technique is proposed based on the compressed sensing framework. Both the watermark and the host signal are sparse, each in its own domain. In recovery, the L1-minimization is used to recover the watermark and the host signal almost perfectly in clean conditions. The proposed technique is tested on MP3 audio compression-decompression attack and additive noise attack. Bit error rates are compared with standard spread spectrum embedding. The proposed technique is implemented for both time domain and frequency domain embedding with a unified approach. The WalshHadamard transform (WHT, the discrete cosine transform (DCT and the Karhunen-Loeve transform (KLT are compared in the host signal sparsifying process. Significant performance improvements in all tested conditions are achieved against the spread spectrum embedding. A payload as high as 172bps in additive noise attacks, 86bps in 128kbps MP3 attacks and 11bps in 64kbps MP3 attacks are achieved at small bit error rates and acceptable MP3 audio signal quality.
Performance assessment of a single-pixel compressive sensing imaging system
Du Bosq, Todd W.; Preece, Bradley L.
2016-05-01
Conventional electro-optical and infrared (EO/IR) systems capture an image by measuring the light incident at each of the millions of pixels in a focal plane array. Compressive sensing (CS) involves capturing a smaller number of unconventional measurements from the scene, and then using a companion process known as sparse reconstruction to recover the image as if a fully populated array that satisfies the Nyquist criteria was used. Therefore, CS operates under the assumption that signal acquisition and data compression can be accomplished simultaneously. CS has the potential to acquire an image with equivalent information content to a large format array while using smaller, cheaper, and lower bandwidth components. However, the benefits of CS do not come without compromise. The CS architecture chosen must effectively balance between physical considerations (SWaP-C), reconstruction accuracy, and reconstruction speed to meet operational requirements. To properly assess the value of such systems, it is necessary to fully characterize the image quality, including artifacts and sensitivity to noise. Imagery of the two-handheld object target set at range was collected using a passive SWIR single-pixel CS camera for various ranges, mirror resolution, and number of processed measurements. Human perception experiments were performed to determine the identification performance within the trade space. The performance of the nonlinear CS camera was modeled with the Night Vision Integrated Performance Model (NV-IPM) by mapping the nonlinear degradations to an equivalent linear shift invariant model. Finally, the limitations of CS modeling techniques will be discussed.
Estimation of multipath parameters in wireless communications using multi-way compressive sensing
Fangqing Wen; Gong Zhang; De Ben
2015-01-01
This paper addresses the problem of joint angle and delay estimation (JADE) in a multipath communication scenario. A low-complexity multi-way compressive sensing (MCS) estimation algorithm is proposed. The received data are firstly stacked up to a trilinear tensor model. To reduce the computational complexity, three random compression matrices are individual y used to re-duce each tensor to a much smal er one. JADE then is linked to a low-dimensional trilinear model. Our algorithm has an estima-tion performance very close to that of the paral el factor analysis (PARAFAC) algorithm and automatic pairing of the two parameter sets. Compared with other methods, such as multiple signal classi-fication (MUSIC), the estimation of signal parameters via rotational invariance techniques (ESPRIT), the MCS algorithm requires nei-ther eigenvalue decomposition of the received signal covariance matrix nor spectral peak searching. It also does not require the channel fading information, which means the proposed algorithm is blind and robust, therefore it has a higher working efficiency. Simulation results indicate the proposed algorithm have a bright future in wireless communications.
Lei You
2013-05-01
Full Text Available The lifetime of the emerging Wireless visual sensor network (WVSN is seriously dependent on the energy shored in the battery of its sensor nodes as well as the compression and resource allocation scheme. In this paper, the energy harvesting technology was adopted to provide almost perpetual operation of the WVSN and compressed-sensing-based encoding was used to decrease the power consumption of acquiring visual information at the front-end sensors. A Dynamic Source and Transmission Control Algorithm (DSTCA was proposed to jointly determine source rate, source energy consumption, and the allocation of transmission energy and available bandwidth under energy harvesting and queue stability constraints. A virtual energy queue was introduced to control the resource allocation and the measurement rate in each time slot. The algorithm can guarantee the stability of the visual data queues in all sensors and achieve near-optimal performance. The distributed implementation of the proposed algorithm was discussed and the achievable performance theorem was also given.
On the Feedback Reduction of Relay Multiuser Networks using Compressive Sensing
Elkhalil, Khalil
2016-01-29
This paper presents a comprehensive performance analysis of full-duplex multiuser relay networks employing opportunistic scheduling with noisy and compressive feedback. Specifically, two feedback techniques based on compressive sensing (CS) theory are introduced and their effect on the system performance is analyzed. The problem of joint user identity and signal-tonoise ratio (SNR) estimation at the base-station is casted as a block sparse signal recovery problem in CS. Using existing CS block recovery algorithms, the identity of the strong users is obtained and their corresponding SNRs are estimated using the best linear unbiased estimator (BLUE). To minimize the effect of feedback noise on the estimated SNRs, a back-off strategy that optimally backs-off on the noisy estimated SNRs is introduced, and the error covariance matrix of the noise after CS recovery is derived. Finally, closed-form expressions for the end-to-end SNRs of the system are derived. Numerical results show that the proposed techniques drastically reduce the feedback air-time and achieve a rate close to that obtained by scheduling techniques that require dedicated error-free feedback from all network users. Key findings of this paper suggest that the choice of half-duplex or full-duplex SNR feedback is dependent on the channel coherence interval, and on low coherence intervals, full-duplex feedback is superior to the interference-free half-duplex feedback.
Optical image encryption via photon-counting imaging and compressive sensing based ptychography
Rawat, Nitin; Hwang, In-Chul; Shi, Yishi; Lee, Byung-Geun
2015-06-01
In this study, we investigate the integration of compressive sensing (CS) and photon-counting imaging (PCI) techniques with a ptychography-based optical image encryption system. Primarily, the plaintext real-valued image is optically encrypted and recorded via a classical ptychography technique. Further, the sparse-based representations of the original encrypted complex data can be produced by combining CS and PCI techniques with the primary encrypted image. Such a combination takes an advantage of reduced encrypted samples (i.e., linearly projected random compressive complex samples and photon-counted complex samples) that can be exploited to realize optical decryption, which inherently serves as a secret key (i.e., independent to encryption phase keys) and makes an intruder attack futile. In addition to this, recording fewer encrypted samples provides a substantial bandwidth reduction in online transmission. We demonstrate that the fewer sparse-based complex samples have adequate information to realize decryption. To the best of our knowledge, this is the first report on integrating CS and PCI with conventional ptychography-based optical image encryption.
3d-3d correspondence revisited
Chung, Hee-Joong; Dimofte, Tudor; Gukov, Sergei; Sułkowski, Piotr
2016-04-01
In fivebrane compactifications on 3-manifolds, we point out the importance of all flat connections in the proper definition of the effective 3d {N}=2 theory. The Lagrangians of some theories with the desired properties can be constructed with the help of homological knot invariants that categorify colored Jones polynomials. Higgsing the full 3d theories constructed this way recovers theories found previously by Dimofte-Gaiotto-Gukov. We also consider the cutting and gluing of 3-manifolds along smooth boundaries and the role played by all flat connections in this operation.
Mahrous, Hesham; Ward, Rabab
2016-01-01
This paper proposes a compressive sensing (CS) method for multi-channel electroencephalogram (EEG) signals in Wireless Body Area Network (WBAN) applications, where the battery life of sensors is limited. For the single EEG channel case, known as the single measurement vector (SMV) problem, the Block Sparse Bayesian Learning-BO (BSBL-BO) method has been shown to yield good results. This method exploits the block sparsity and the intra-correlation (i.e., the linear dependency) within the measurement vector of a single channel. For the multichannel case, known as the multi-measurement vector (MMV) problem, the Spatio-Temporal Sparse Bayesian Learning (STSBL-EM) method has been proposed. This method learns the joint correlation structure in the multichannel signals by whitening the model in the temporal and the spatial domains. Our proposed method represents the multi-channels signal data as a vector that is constructed in a specific way, so that it has a better block sparsity structure than the conventional representation obtained by stacking the measurement vectors of the different channels. To reconstruct the multichannel EEG signals, we modify the parameters of the BSBL-BO algorithm, so that it can exploit not only the linear but also the non-linear dependency structures in a vector. The modified BSBL-BO is then applied on the vector with the better sparsity structure. The proposed method is shown to significantly outperform existing SMV and also MMV methods. It also shows significant lower compression errors even at high compression ratios such as 10:1 on three different datasets. PMID:26861335
Hesham Mahrous
2016-02-01
Full Text Available This paper proposes a compressive sensing (CS method for multi-channel electroencephalogram (EEG signals in Wireless Body Area Network (WBAN applications, where the battery life of sensors is limited. For the single EEG channel case, known as the single measurement vector (SMV problem, the Block Sparse Bayesian Learning-BO (BSBL-BO method has been shown to yield good results. This method exploits the block sparsity and the intra-correlation (i.e., the linear dependency within the measurement vector of a single channel. For the multichannel case, known as the multi-measurement vector (MMV problem, the Spatio-Temporal Sparse Bayesian Learning (STSBL-EM method has been proposed. This method learns the joint correlation structure in the multichannel signals by whitening the model in the temporal and the spatial domains. Our proposed method represents the multi-channels signal data as a vector that is constructed in a specific way, so that it has a better block sparsity structure than the conventional representation obtained by stacking the measurement vectors of the different channels. To reconstruct the multichannel EEG signals, we modify the parameters of the BSBL-BO algorithm, so that it can exploit not only the linear but also the non-linear dependency structures in a vector. The modified BSBL-BO is then applied on the vector with the better sparsity structure. The proposed method is shown to significantly outperform existing SMV and also MMV methods. It also shows significant lower compression errors even at high compression ratios such as 10:1 on three different datasets.
The spatial aliasing of seismic data is usually serious because of the sub-sampling rate of the acquisition system. It induces amplitude artifacts or blurs the migration result when the spatial aliasing is not removed before migration. The compressed sensing (CS) method has been proven to be an effective tool to restore a sub-sampled signal which is compressible in another domain. Since the wave-fronts of seismic data are sparse and linear in a local spatiotemporal window, they can be significantly compressed by linear Radon transform or Fourier transform. Therefore, seismic data interpolation can be considered as a CS problem. The approximate solution of a CS problem using L0-norm can be achieved by matching pursuit (MP) algorithm. MP becomes intractable due to the high computing cost induced by the increasing dimension of the problem. In order to tackle this issue, a variant of MP—weighted matching pursuit (WMP)—is presented in this paper. Since there is little spatial aliasing in the data of low frequency and the events are supposed to be linear, the linear Radon spectrogram of the interpolated data of low frequency can be used to predict the energy distribution of data of high frequency in a frequency-wavenumber (FK) domain. The predicted energy distribution is then utilized to form the weighted factor of WMP. With this factor, WMP possesses the ability to distinguish the linear events from the spatial aliasing in the FK domain. WMP is also proven to be an efficient algorithm. Since projection onto convex sets (POCS) is another common sparsity-based method, we use Fourier POCS and WMP to realize high-dimension interpolation in numerical examples. The numerical examples show that the interpolation result of WMP significantly improves the quality of seismic data, and the quality of the migration result is also improved by the interpolation. (paper)
Brdnik, Lovro
2015-01-01
Diplomsko delo analizira trenutno stanje 3D tiskalnikov na trgu. Prikazan je razvoj in principi delovanja 3D tiskalnikov. Predstavljeni so tipi 3D tiskalnikov, njihove prednosti in slabosti. Podrobneje je predstavljena zgradba in delovanje koračnih motorjev. Opravljene so meritve koračnih motorjev. Opisana je programska oprema za rokovanje s 3D tiskalniki in komponente, ki jih potrebujemo za izdelavo. Diploma se oklepa vprašanja, ali je izdelava 3D tiskalnika bolj ekonomična kot pa naložba v ...
Tongfeng Zhang
2016-01-01
Full Text Available A one-dimensional (1D hybrid chaotic system is constructed by three different 1D chaotic maps in parallel-then-cascade fashion. The proposed chaotic map has larger key space and exhibits better uniform distribution property in some parametric range compared with existing 1D chaotic map. Meanwhile, with the combination of compressive sensing (CS and Fibonacci-Lucas transform (FLT, a novel image compression and encryption scheme is proposed with the advantages of the 1D hybrid chaotic map. The whole encryption procedure includes compression by compressed sensing (CS, scrambling with FLT, and diffusion after linear scaling. Bernoulli measurement matrix in CS is generated by the proposed 1D hybrid chaotic map due to its excellent uniform distribution. To enhance the security and complexity, transform kernel of FLT varies in each permutation round according to the generated chaotic sequences. Further, the key streams used in the diffusion process depend on the chaotic map as well as plain image, which could resist chosen plaintext attack (CPA. Experimental results and security analyses demonstrate the validity of our scheme in terms of high security and robustness against noise attack and cropping attack.
Generalized Block Sparse Constraint Based Compressive Wideband Spectrum Sensing for Cognitive Radio
Liu, Yipeng
2010-01-01
Wideband spectrum sensing detects the unused spectrum holes for dynamic spectrum access of cognitive radios. However the too high sampling rate is the challenge for application. As the survey shows that the monitoring primary signal has sparse representation in frequency domain, compressive sensing can be used to transfer the sampling burden to the digital signal processor. An analog to information converter can randomly sample the received signal with sub-Nyquist rate to obtain the random measurements. In the spectrum recovery, to match the practical situation, an improved block sparse signal model can be formulated in that the static frequency spectrum allocation of primary radios means the bounds between di?erent primary radios is known. The whole monitoring spectrum is divided sections of di?erent length in accordance with di?erent allocated primary radios. The minimization of the l2 norm can encourage the dense distribution locally, while the l1 norm of the l2 norms can give the sparse distribution of th...
Li, Jia; Wang, Qiang; Yan, Wenjie; Shen, Yi
2015-12-01
Cooperative spectrum sensing exploits the spatial diversity to improve the detection of occupied channels in cognitive radio networks (CRNs). Cooperative compressive spectrum sensing (CCSS) utilizing the sparsity of channel occupancy further improves the efficiency by reducing the number of reports without degrading detection performance. In this paper, we firstly and mainly propose the referred multi-candidate orthogonal matrix matching pursuit (MOMMP) algorithms to efficiently and effectively detect occupied channels at fusion center (FC), where multi-candidate identification and orthogonal projection are utilized to respectively reduce the number of required iterations and improve the probability of exact identification. Secondly, two common but different approaches based on threshold and Gaussian distribution are introduced to realize the multi-candidate identification. Moreover, to improve the detection accuracy and energy efficiency, we propose the matrix construction based on shrinkage and gradient descent (MCSGD) algorithm to provide a deterministic filter coefficient matrix of low t-average coherence. Finally, several numerical simulations validate that our proposals provide satisfactory performance with higher probability of detection, lower probability of false alarm and less detection time.
Ji Yunyun; Yang Zhen; Xu Qian
2012-01-01
Structural and statistical characteristics of signals can improve the performance of Compressed Sensing (CS).Two kinds of features of Discrete Cosine Transform (DCT) coefficients of voiced speech signals are discussed in this paper.The first one is the block sparsity of DCT coefficients of voiced speech formulated from two different aspects which are the distribution of the DCT coefficients of voiced speech and the comparison of reconstruction performance between the mixed l2 /l1 program and Basis Pursuit (BP).The block sparsity of DCT coefficients of voiced speech means that some algorithms of block-sparse CS can be used to improve the recovery performance of speech signals.It is proved by the simulation results of the l2 / reweighted l1 mixed program which is an improved version of the mixed l2 /l1 program.The second one is the well known large DCT coefficients of voiced speech focus on low frequency.In line with this feature,a special Gaussian and Partial Identity Joint (GPIJ)matrix is constructed as the sensing matrix for voiced speech signals.Simulation results show that the GPIJ matrix outperforms the classical Gaussian matrix for speech signals of male and female adults.
Wow! 3D Content Awakens the Classroom
Gordon, Dan
2010-01-01
From her first encounter with stereoscopic 3D technology designed for classroom instruction, Megan Timme, principal at Hamilton Park Pacesetter Magnet School in Dallas, sensed it could be transformative. Last spring, when she began pilot-testing 3D content in her third-, fourth- and fifth-grade classrooms, Timme wasn't disappointed. Students…
S. Schneiderwind
2015-09-01
Full Text Available Two normal faults on the Island of Crete and mainland Greece were studied to create and test an innovative workflow to make palaeoseismic trench logging more objective, and visualise the sedimentary architecture within the trench wall in 3-D. This is achieved by combining classical palaeoseismic trenching techniques with multispectral approaches. A conventional trench log was firstly compared to results of iso cluster analysis of a true colour photomosaic representing the spectrum of visible light. Passive data collection disadvantages (e.g. illumination were addressed by complementing the dataset with active near-infrared backscatter signal image from t-LiDAR measurements. The multispectral analysis shows that distinct layers can be identified and it compares well with the conventional trench log. According to this, a distinction of adjacent stratigraphic units was enabled by their particular multispectral composition signature. Based on the trench log, a 3-D-interpretation of GPR data collected on the vertical trench wall was then possible. This is highly beneficial for measuring representative layer thicknesses, displacements and geometries at depth within the trench wall. Thus, misinterpretation due to cutting effects is minimised. Sedimentary feature geometries related to earthquake magnitude can be used to improve the accuracy of seismic hazard assessments. Therefore, this manuscript combines multiparametric approaches and shows: (i how a 3-D visualisation of palaeoseismic trench stratigraphy and logging can be accomplished by combining t-LiDAR and GRP techniques, and (ii how a multispectral digital analysis can offer additional advantages and a higher objectivity in the interpretation of palaeoseismic and stratigraphic information. The multispectral datasets are stored allowing unbiased input for future (re-investigations.
严实; 李冬华; 泮世东; 冯吉才
2013-01-01
基于声发射(AE)技术研究了不同编织角度的三维四向炭/环氧编织复合材料在压缩载荷作用下的破坏过程.分析了累积声发射能量,事件率,幅值和波形经过快速傅里叶变换后的峰值频率.同时,结合载荷-位移曲线,把破坏过程分成不同的阶段来深入理解编织复合材料的破坏机理.用光学显微镜观测试件的破坏表面.结果表明AE参数能很好地描述三维编织复合材料的破坏过程,而且破坏机理也可用AE特性来识别.%The fracture process of 3D four-directional carbon/epoxy braided composites with different braiding angles under the monotonic compressive loading were investigated by the acoustic emission (AE) technique. The cumulative AE energy, event rate, amplitude, and the peak frequency of a dominant frequency band after treated with the fast Fourier transform (FFT) were analyzed. At the same time, combining with the load-displacement curve varying feature, the fracture processes were divided into different stages to deeply understand the damaged mechanisms of the textile composites. Furthermore, the fracture surfaces of the specimens were observed under optical microscopy. Results reveal that the behavior of AE parameters described well the fracture process in the 3D braided composites with different braiding angles, and the damage mechanisms of the composites can be successfully identified by AE characteristics.
Meulien Ohlmann, Odile
2013-02-01
Today the industry offers a chain of 3D products. Learning to "read" and to "create in 3D" becomes an issue of education of primary importance. 25 years professional experience in France, the United States and Germany, Odile Meulien set up a personal method of initiation to 3D creation that entails the spatial/temporal experience of the holographic visual. She will present some different tools and techniques used for this learning, their advantages and disadvantages, programs and issues of educational policies, constraints and expectations related to the development of new techniques for 3D imaging. Although the creation of display holograms is very much reduced compared to the creation of the 90ies, the holographic concept is spreading in all scientific, social, and artistic activities of our present time. She will also raise many questions: What means 3D? Is it communication? Is it perception? How the seeing and none seeing is interferes? What else has to be taken in consideration to communicate in 3D? How to handle the non visible relations of moving objects with subjects? Does this transform our model of exchange with others? What kind of interaction this has with our everyday life? Then come more practical questions: How to learn creating 3D visualization, to learn 3D grammar, 3D language, 3D thinking? What for? At what level? In which matter? for whom?
Feasibility and performances of compressed-sensing and sparse map-making with Herschel/PACS data
Barbey, Nicolas; Starck, Jean-Luc; Ottensamer, Roland; Chanial, Pierre
2010-01-01
The Herschel Space Observatory of ESA was launched in May 2009 and is in operation since. From its distant orbit around L2 it needs to transmit a huge quantity of information through a very limited bandwidth. This is especially true for the PACS imaging camera which needs to compress its data far more than what can be achieved with lossless compression. This is currently solved by including lossy averaging and rounding steps on board. Recently, a new theory called compressed-sensing emerged from the statistics community. This theory makes use of the sparsity of natural (or astrophysical) images to optimize the acquisition scheme of the data needed to estimate those images. Thus, it can lead to high compression factors. A previous article by Bobin et al. (2008) showed how the new theory could be applied to simulated Herschel/PACS data to solve the compression requirement of the instrument. In this article, we show that compressed-sensing theory can indeed be successfully applied to actual Herschel/PACS data an...
Tang, Jie; Nett, Brian E.; Chen, Guang-Hong
2009-10-01
Of all available reconstruction methods, statistical iterative reconstruction algorithms appear particularly promising since they enable accurate physical noise modeling. The newly developed compressive sampling/compressed sensing (CS) algorithm has shown the potential to accurately reconstruct images from highly undersampled data. The CS algorithm can be implemented in the statistical reconstruction framework as well. In this study, we compared the performance of two standard statistical reconstruction algorithms (penalized weighted least squares and q-GGMRF) to the CS algorithm. In assessing the image quality using these iterative reconstructions, it is critical to utilize realistic background anatomy as the reconstruction results are object dependent. A cadaver head was scanned on a Varian Trilogy system at different dose levels. Several figures of merit including the relative root mean square error and a quality factor which accounts for the noise performance and the spatial resolution were introduced to objectively evaluate reconstruction performance. A comparison is presented between the three algorithms for a constant undersampling factor comparing different algorithms at several dose levels. To facilitate this comparison, the original CS method was formulated in the framework of the statistical image reconstruction algorithms. Important conclusions of the measurements from our studies are that (1) for realistic neuro-anatomy, over 100 projections are required to avoid streak artifacts in the reconstructed images even with CS reconstruction, (2) regardless of the algorithm employed, it is beneficial to distribute the total dose to more views as long as each view remains quantum noise limited and (3) the total variation-based CS method is not appropriate for very low dose levels because while it can mitigate streaking artifacts, the images exhibit patchy behavior, which is potentially harmful for medical diagnosis.
Of all available reconstruction methods, statistical iterative reconstruction algorithms appear particularly promising since they enable accurate physical noise modeling. The newly developed compressive sampling/compressed sensing (CS) algorithm has shown the potential to accurately reconstruct images from highly undersampled data. The CS algorithm can be implemented in the statistical reconstruction framework as well. In this study, we compared the performance of two standard statistical reconstruction algorithms (penalized weighted least squares and q-GGMRF) to the CS algorithm. In assessing the image quality using these iterative reconstructions, it is critical to utilize realistic background anatomy as the reconstruction results are object dependent. A cadaver head was scanned on a Varian Trilogy system at different dose levels. Several figures of merit including the relative root mean square error and a quality factor which accounts for the noise performance and the spatial resolution were introduced to objectively evaluate reconstruction performance. A comparison is presented between the three algorithms for a constant undersampling factor comparing different algorithms at several dose levels. To facilitate this comparison, the original CS method was formulated in the framework of the statistical image reconstruction algorithms. Important conclusions of the measurements from our studies are that (1) for realistic neuro-anatomy, over 100 projections are required to avoid streak artifacts in the reconstructed images even with CS reconstruction, (2) regardless of the algorithm employed, it is beneficial to distribute the total dose to more views as long as each view remains quantum noise limited and (3) the total variation-based CS method is not appropriate for very low dose levels because while it can mitigate streaking artifacts, the images exhibit patchy behavior, which is potentially harmful for medical diagnosis.
Compressed histogram attribute profiles for the classification of VHR remote sensing images
Battiti, Romano; Demir, Begüm; Bruzzone, Lorenzo
2015-10-01
This paper presents a novel compressed histogram attribute profile (CHAP) for classification of very high resolution remote sensing images. The CHAP characterizes the marginal local distribution of attribute filter responses to model the texture information of each sample with a small number of image features. This is achieved based on a three steps algorithm. The first step is devoted to provide a complete characterization of spatial properties of objects in a scene. To this end, the attribute profile (AP) is initially built by the sequential application of attribute filters to the considered image. Then, to capture complete spatial characteristics of the structures in the scene a local histogram is calculated for each sample of each image in the AP. The local histograms of the same pixel location can contain redundant information since: i) adjacent histogram bins can provide similar information; and ii) the attributes obtained with similar attribute filter threshold values lead to redundant features. In the second step, to point out the redundancies the local histograms of the same pixel locations in the AP are organized into a 2D matrix representation, where columns are associated to the local histograms and rows represents a specific bin in all histograms of the considered sequence of filtered attributes in the profile. This representation results in the characterization of the texture information of each sample through a 2D texture descriptor. In the final step, a novel compression approach based on a uniform 2D quantization strategy is applied to remove the redundancy of the 2D texture descriptors. Finally the CHAP is classified by a Support Vector Machine classifier with histogram intersection kernel that is very effective for high dimensional histogram-based feature representations. Experimental results confirm the effectiveness of the proposed CHAP in terms of computational complexity, storage requirements and classification accuracy when compared to the
Effective Low-Power Wearable Wireless Surface EMG Sensor Design Based on Analog-Compressed Sensing
Mohammadreza Balouchestani
2014-12-01
Full Text Available Surface Electromyography (sEMG is a non-invasive measurement process that does not involve tools and instruments to break the skin or physically enter the body to investigate and evaluate the muscular activities produced by skeletal muscles. The main drawbacks of existing sEMG systems are: (1 they are not able to provide real-time monitoring; (2 they suffer from long processing time and low speed; (3 they are not effective for wireless healthcare systems because they consume huge power. In this work, we present an analog-based Compressed Sensing (CS architecture, which consists of three novel algorithms for design and implementation of wearable wireless sEMG bio-sensor. At the transmitter side, two new algorithms are presented in order to apply the analog-CS theory before Analog to Digital Converter (ADC. At the receiver side, a robust reconstruction algorithm based on a combination of ℓ1-ℓ1-optimization and Block Sparse Bayesian Learning (BSBL framework is presented to reconstruct the original bio-signals from the compressed bio-signals. The proposed architecture allows reducing the sampling rate to 25% of Nyquist Rate (NR. In addition, the proposed architecture reduces the power consumption to 40%, Percentage Residual Difference (PRD to 24%, Root Mean Squared Error (RMSE to 2%, and the computation time from 22 s to 9.01 s, which provide good background for establishing wearable wireless healthcare systems. The proposed architecture achieves robust performance in low Signal-to-Noise Ratio (SNR for the reconstruction process.
Effective low-power wearable wireless surface EMG sensor design based on analog-compressed sensing.
Balouchestani, Mohammadreza; Krishnan, Sridhar
2014-01-01
Surface Electromyography (sEMG) is a non-invasive measurement process that does not involve tools and instruments to break the skin or physically enter the body to investigate and evaluate the muscular activities produced by skeletal muscles. The main drawbacks of existing sEMG systems are: (1) they are not able to provide real-time monitoring; (2) they suffer from long processing time and low speed; (3) they are not effective for wireless healthcare systems because they consume huge power. In this work, we present an analog-based Compressed Sensing (CS) architecture, which consists of three novel algorithms for design and implementation of wearable wireless sEMG bio-sensor. At the transmitter side, two new algorithms are presented in order to apply the analog-CS theory before Analog to Digital Converter (ADC). At the receiver side, a robust reconstruction algorithm based on a combination of ℓ1-ℓ1-optimization and Block Sparse Bayesian Learning (BSBL) framework is presented to reconstruct the original bio-signals from the compressed bio-signals. The proposed architecture allows reducing the sampling rate to 25% of Nyquist Rate (NR). In addition, the proposed architecture reduces the power consumption to 40%, Percentage Residual Difference (PRD) to 24%, Root Mean Squared Error (RMSE) to 2%, and the computation time from 22 s to 9.01 s, which provide good background for establishing wearable wireless healthcare systems. The proposed architecture achieves robust performance in low Signal-to-Noise Ratio (SNR) for the reconstruction process. PMID:25526357
The reduction of dose in cone beam computer tomography (CBCT) arises from the decrease of the tube current for each projection as well as from the reduction of the number of projections. In order to maintain good image quality, sophisticated image reconstruction techniques are required. The Prior Image Constrained Compressed Sensing (PICCS) incorporates prior images into the reconstruction algorithm and outperforms the widespread used Feldkamp-Davis-Kress-algorithm (FDK) when the number of projections is reduced. However, prior images that contain major variations are not appropriately considered so far in PICCS. We therefore propose the partial-PICCS (pPICCS) algorithm. This framework is a problem-specific extension of PICCS and enables the incorporation of the reliability of the prior images additionally. We assumed that the prior images are composed of areas with large and small deviations. Accordingly, a weighting matrix considered the assigned areas in the objective function. We applied our algorithm to the problem of image reconstruction from few views by simulations with a computer phantom as well as on clinical CBCT projections from a head-and-neck case. All prior images contained large local variations. The reconstructed images were compared to the reconstruction results by the FDK-algorithm, by Compressed Sensing (CS) and by PICCS. To show the gain of image quality we compared image details with the reference image and used quantitative metrics (root-mean-square error (RMSE), contrast-to-noise-ratio (CNR)). The pPICCS reconstruction framework yield images with substantially improved quality even when the number of projections was very small. The images contained less streaking, blurring and inaccurately reconstructed structures compared to the images reconstructed by FDK, CS and conventional PICCS. The increased image quality is also reflected in large RMSE differences. We proposed a modification of the original PICCS algorithm. The pPICCS algorithm
Vaegler, Sven; Sauer, Otto [Wuerzburg Univ. (Germany). Dept. of Radiation Oncology; Stsepankou, Dzmitry; Hesser, Juergen [University Medical Center Mannheim, Mannheim (Germany). Dept. of Experimental Radiation Oncology
2015-07-01
The reduction of dose in cone beam computer tomography (CBCT) arises from the decrease of the tube current for each projection as well as from the reduction of the number of projections. In order to maintain good image quality, sophisticated image reconstruction techniques are required. The Prior Image Constrained Compressed Sensing (PICCS) incorporates prior images into the reconstruction algorithm and outperforms the widespread used Feldkamp-Davis-Kress-algorithm (FDK) when the number of projections is reduced. However, prior images that contain major variations are not appropriately considered so far in PICCS. We therefore propose the partial-PICCS (pPICCS) algorithm. This framework is a problem-specific extension of PICCS and enables the incorporation of the reliability of the prior images additionally. We assumed that the prior images are composed of areas with large and small deviations. Accordingly, a weighting matrix considered the assigned areas in the objective function. We applied our algorithm to the problem of image reconstruction from few views by simulations with a computer phantom as well as on clinical CBCT projections from a head-and-neck case. All prior images contained large local variations. The reconstructed images were compared to the reconstruction results by the FDK-algorithm, by Compressed Sensing (CS) and by PICCS. To show the gain of image quality we compared image details with the reference image and used quantitative metrics (root-mean-square error (RMSE), contrast-to-noise-ratio (CNR)). The pPICCS reconstruction framework yield images with substantially improved quality even when the number of projections was very small. The images contained less streaking, blurring and inaccurately reconstructed structures compared to the images reconstructed by FDK, CS and conventional PICCS. The increased image quality is also reflected in large RMSE differences. We proposed a modification of the original PICCS algorithm. The pPICCS algorithm
Tournay, Bruno; Rüdiger, Bjarne
2006-01-01
3d digital model af Arkitektskolens gård med virtuel udstilling af afgangsprojekter fra afgangen sommer 2006. 10 s.......3d digital model af Arkitektskolens gård med virtuel udstilling af afgangsprojekter fra afgangen sommer 2006. 10 s....
Sampling of finite elements for sparse recovery in large scale 3D electrical impedance tomography
This study proposes a method to improve performance of sparse recovery inverse solvers in 3D electrical impedance tomography (3D EIT), especially when the volume under study contains small-sized inclusions, e.g. 3D imaging of breast tumours. Initially, a quadratic regularized inverse solver is applied in a fast manner with a stopping threshold much greater than the optimum. Based on assuming a fixed level of sparsity for the conductivity field, finite elements are then sampled via applying a compressive sensing (CS) algorithm to the rough blurred estimation previously made by the quadratic solver. Finally, a sparse inverse solver is applied solely to the sampled finite elements, with the solution to the CS as its initial guess. The results show the great potential of the proposed CS-based sparse recovery in improving accuracy of sparse solution to the large-size 3D EIT. (paper)
Roberto Rinaldi
2014-12-01
Full Text Available After an experimental phase of many years, 3D filming is now effective and successful. Improvements are still possible, but the film industry achieved memorable success on 3D movie’s box offices due to the overall quality of its products. Special environments such as space (“Gravity” and the underwater realm look perfect to be reproduced in 3D. “Filming in space” was possible in “Gravity” using special effects and computer graphic. The underwater realm is still difficult to be handled. Underwater filming in 3D was not that easy and effective as filming in 2D, since not long ago. After almost 3 years of research, a French, Austrian and Italian team realized a perfect tool to film underwater, in 3D, without any constrains. This allows filmmakers to bring the audience deep inside an environment where they most probably will never have the chance to be.
Buck, Janina; Noeske, Jonas; Wöhnert, Jens; Schwalbe, Harald
2010-01-01
Long-range tertiary interactions determine the three-dimensional structure of a number of metabolite-binding riboswitch RNA elements and were found to be important for their regulatory function. For the guanine-sensing riboswitch of the Bacillus subtilis xpt-pbuX operon, our previous NMR-spectroscopic studies indicated pre-formation of long-range tertiary contacts in the ligand-free state of its aptamer domain. Loss of the structural pre-organization in a mutant of this RNA (G37A/C61U) result...
We have successfully observed the development of three-dimensional (3D) face-centered-cubic ZnSnO3 into two-dimensional (2D) orthorhombic ZnSnO3 nanosheets, which is the first observation of 2D ZnSnO3 nanostructures to date. The synthesis from 3D to 2D nanostructures is realized by the dual-hydrolysis-assisted liquid precipitation reaction and subsequent hydrothermal treatment. The time-dependent morphology indicates the transformation via a ‘dissolution–recrystallization’ mechanism, accompanied by a ‘further growth’ process. Furthermore, the 2D ZnSnO3 nanosheets consist of smaller sized nanoflakes. This further increases the special specific surface area and facilitates their application in gas sensing. The 2D ZnSnO3 nanosheets exhibit excellent gas sensing properties, especially through their ultra-fast response and recovery. When exposed to ethanol and acetone, the response rate is as fast as 0.26 s and 0.18 s, respectively, and the concentration limit can reach as low as 50 ppb of ethanol. All these results are much better than those reported so far. Our experimental results indicate an efficient approach to realize high-performance gas sensors. (paper)
Compressed sensing with gradient total variation for low-dose CBCT reconstruction
This paper describes the improvement of convergence speed with gradient total variation (GTV) in compressed sensing (CS) for low-dose cone–beam computed tomography (CBCT) reconstruction. We derive a fast algorithm for the constrained total variation (TV)-based a minimum number of noisy projections. To achieve this task we combine the GTV with a TV-norm regularization term to promote an accelerated sparsity in the X-ray attenuation characteristics of the human body. The GTV is derived from a TV and enforces more efficient computationally and faster in convergence until a desired solution is achieved. The numerical algorithm is simple and derives relatively fast convergence. We apply a gradient projection algorithm that seeks a solution iteratively in the direction of the projected gradient while enforcing a non-negatively of the found solution. In comparison with the Feldkamp, Davis, and Kress (FDK) and conventional TV algorithms, the proposed GTV algorithm showed convergence in ≤18 iterations, whereas the original TV algorithm needs at least 34 iterations in reducing 50% of the projections compared with the FDK algorithm in order to reconstruct the chest phantom images. Future investigation includes improving imaging quality, particularly regarding X-ray cone–beam scatter, and motion artifacts of CBCT reconstruction
Interpolated compressed sensing for 2D multiple slice fast MR imaging.
Yong Pang
Full Text Available Sparse MRI has been introduced to reduce the acquisition time and raw data size by undersampling the k-space data. However, the image quality, particularly the contrast to noise ratio (CNR, decreases with the undersampling rate. In this work, we proposed an interpolated Compressed Sensing (iCS method to further enhance the imaging speed or reduce data size without significant sacrifice of image quality and CNR for multi-slice two-dimensional sparse MR imaging in humans. This method utilizes the k-space data of the neighboring slice in the multi-slice acquisition. The missing k-space data of a highly undersampled slice are estimated by using the raw data of its neighboring slice multiplied by a weighting function generated from low resolution full k-space reference images. In-vivo MR imaging in human feet has been used to investigate the feasibility and the performance of the proposed iCS method. The results show that by using the proposed iCS reconstruction method, the average image error can be reduced and the average CNR can be improved, compared with the conventional sparse MRI reconstruction at the same undersampling rate.
Comment on "Benchmarking Compressed Sensing, Super-Resolution, and Filter Diagonalization"
Mandelshtam, Vladimir A
2016-01-01
In a recent paper [Int. J. Quant. Chem. (2016) DOI: 10.1002/qua.25144, arXiv:1502.06579] Markovich, Blau, Sanders, and Aspuru-Guzik presented a numerical evaluation and comparison of three methods, Compressed Sensing (CS), Super-Resolution (SR), and Filter Diagonalization (FDM), on their ability of "recovering information" from time signals, concluding that CS and RS outperform FDM. We argue that this comparison is invalid for the following reasons. FDM is a well established method designed for solving the harmonic inversion problem or, similarly, for the problem of spectral estimation, and as such should be applied only to problems of this kind. The authors incorrectly assume that the problem of data fitting is equivalent to the spectral estimation problem, regardless of what parametric form is used, and, consequently, in all five numerical examples FDM is applied to the wrong problem. Moreover, the authors' implementation of FDM turned out to be incorrect, leading to extremely bad results, caused by numeric...
Fast and low-dose computed laminography using compressive sensing based technique
Abbas, Sajid, E-mail: scho@kaist.ac.kr; Park, Miran, E-mail: scho@kaist.ac.kr; Cho, Seungryong, E-mail: scho@kaist.ac.kr [Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 305-701 (Korea, Republic of)
2015-03-31
Computed laminography (CL) is well known for inspecting microstructures in the materials, weldments and soldering defects in high density packed components or multilayer printed circuit boards. The overload problem on x-ray tube and gross failure of the radio-sensitive electronics devices during a scan are among important issues in CL which needs to be addressed. The sparse-view CL can be one of the viable option to overcome such issues. In this work a numerical aluminum welding phantom was simulated to collect sparsely sampled projection data at only 40 views using a conventional CL scanning scheme i.e. oblique scan. A compressive-sensing inspired total-variation (TV) minimization algorithm was utilized to reconstruct the images. It is found that the images reconstructed using sparse view data are visually comparable with the images reconstructed using full scan data set i.e. at 360 views on regular interval. We have quantitatively confirmed that tiny structures such as copper and tungsten slags, and copper flakes in the reconstructed images from sparsely sampled data are comparable with the corresponding structure present in the fully sampled data case. A blurring effect can be seen near the edges of few pores at the bottom of the reconstructed images from sparsely sampled data, despite the overall image quality is reasonable for fast and low-dose NDT.
Zhao, Shengmei; Wang, Le; Liang, Wenqiang; Cheng, Weiwen; Gong, Longyan
2015-10-01
In this paper, we propose a high performance optical encryption (OE) scheme based on computational ghost imaging (GI) with QR code and compressive sensing (CS) technique, named QR-CGI-OE scheme. N random phase screens, generated by Alice, is a secret key and be shared with its authorized user, Bob. The information is first encoded by Alice with QR code, and the QR-coded image is then encrypted with the aid of computational ghost imaging optical system. Here, measurement results from the GI optical system's bucket detector are the encrypted information and be transmitted to Bob. With the key, Bob decrypts the encrypted information to obtain the QR-coded image with GI and CS techniques, and further recovers the information by QR decoding. The experimental and numerical simulated results show that the authorized users can recover completely the original image, whereas the eavesdroppers can not acquire any information about the image even the eavesdropping ratio (ER) is up to 60% at the given measurement times. For the proposed scheme, the number of bits sent from Alice to Bob are reduced considerably and the robustness is enhanced significantly. Meantime, the measurement times in GI system is reduced and the quality of the reconstructed QR-coded image is improved.
Peak reduction and clipping mitigation in OFDM by augmented compressive sensing
Al-Safadi, Ebrahim B.
2012-07-01
This work establishes the design, analysis, and fine-tuning of a peak-to-average-power-ratio (PAPR) reducing system, based on compressed sensing (CS) at the receiver of a peak-reducing sparse clipper applied to an orthogonal frequency-division multiplexing (OFDM) signal at the transmitter. By exploiting the sparsity of clipping events in the time domain relative to a predefined clipping threshold, the method depends on partially observing the frequency content of the clipping distortion over reserved tones to estimate the remaining distortion. The approach has the advantage of eliminating the computational complexity at the transmitter and reducing the overall complexity of the system compared to previous methods which incorporate pilots to cancel nonlinear distortion. Data-based augmented CS methods are also proposed that draw upon available phase and support information from data tones for enhanced estimation and cancelation of clipping noise. This enables signal recovery under more severe clipping scenarios and hence lower PAPR can be achieved compared to conventional CS techniques. © 2012 IEEE.
Huang, Jianping; Wang, Lihui; Chu, Chunyu; Zhang, Yanli; Liu, Wanyu; Zhu, Yuemin
2016-04-29
Diffusion tensor magnetic resonance (DTMR) imaging and diffusion tensor imaging (DTI) have been widely used to probe noninvasively biological tissue structures. However, DTI suffers from long acquisition times, which limit its practical and clinical applications. This paper proposes a new Compressed Sensing (CS) reconstruction method that employs joint sparsity and rank deficiency to reconstruct cardiac DTMR images from undersampled k-space data. Diffusion-weighted images acquired in different diffusion directions were firstly stacked as columns to form the matrix. The matrix was row sparse in the transform domain and had a low rank. These two properties were then incorporated into the CS reconstruction framework. The underlying constrained optimization problem was finally solved by the first-order fast method. Experiments were carried out on both simulation and real human cardiac DTMR images. The results demonstrated that the proposed approach had lower reconstruction errors for DTI indices, including fractional anisotropy (FA) and mean diffusivities (MD), compared to the existing CS-DTMR image reconstruction techniques. PMID:27163322
FPGA Implementation of Real-Time Compressive Sensing with Partial Fourier Dictionary
Yinghui Quan
2016-01-01
Full Text Available This paper presents a novel real-time compressive sensing (CS reconstruction which employs high density field-programmable gate array (FPGA for hardware acceleration. Traditionally, CS can be implemented using a high-level computer language in a personal computer (PC or multicore platforms, such as graphics processing units (GPUs and Digital Signal Processors (DSPs. However, reconstruction algorithms are computing demanding and software implementation of these algorithms is extremely slow and power consuming. In this paper, the orthogonal matching pursuit (OMP algorithm is refined to solve the sparse decomposition optimization for partial Fourier dictionary, which is always adopted in radar imaging and detection application. OMP reconstruction can be divided into two main stages: optimization which finds the closely correlated vectors and least square problem. For large scale dictionary, the implementation of correlation is time consuming since it often requires a large number of matrix multiplications. Also solving the least square problem always needs a scalable matrix decomposition operation. To solve these problems efficiently, the correlation optimization is implemented by fast Fourier transform (FFT and the large scale least square problem is implemented by Conjugate Gradient (CG technique, respectively. The proposed method is verified by FPGA (Xilinx Virtex-7 XC7VX690T realization, revealing its effectiveness in real-time applications.
Compressed sensing reconstruction of cardiac cine MRI using golden angle spiral trajectories
Tolouee, Azar; Alirezaie, Javad; Babyn, Paul
2015-11-01
In dynamic cardiac cine Magnetic Resonance Imaging (MRI), the spatiotemporal resolution is limited by the low imaging speed. Compressed sensing (CS) theory has been applied to improve the imaging speed and thus the spatiotemporal resolution. The purpose of this paper is to improve CS reconstruction of under sampled data by exploiting spatiotemporal sparsity and efficient spiral trajectories. We extend k-t sparse algorithm to spiral trajectories to achieve high spatio temporal resolutions in cardiac cine imaging. We have exploited spatiotemporal sparsity of cardiac cine MRI by applying a 2D + time wavelet-Fourier transform. For efficient coverage of k-space, we have used a modified version of multi shot (interleaved) spirals trajectories. In order to reduce incoherent aliasing artifact, we use different random undersampling pattern for each temporal frame. Finally, we have used nonuniform fast Fourier transform (NUFFT) algorithm to reconstruct the image from the non-uniformly acquired samples. The proposed approach was tested in simulated and cardiac cine MRI data. Results show that higher acceleration factors with improved image quality can be obtained with the proposed approach in comparison to the existing state-of-the-art method. The flexibility of the introduced method should allow it to be used not only for the challenging case of cardiac imaging, but also for other patient motion where the patient moves or breathes during acquisition.
Detection of Defective Sensors in Phased Array Using Compressed Sensing and Hybrid Genetic Algorithm
Shafqat Ullah Khan
2016-01-01
Full Text Available A compressed sensing based array diagnosis technique has been presented. This technique starts from collecting the measurements of the far-field pattern. The system linking the difference between the field measured using the healthy reference array and the field radiated by the array under test is solved using a genetic algorithm (GA, parallel coordinate descent (PCD algorithm, and then a hybridized GA with PCD algorithm. These algorithms are applied for fully and partially defective antenna arrays. The simulation results indicate that the proposed hybrid algorithm outperforms in terms of localization of element failure with a small number of measurements. In the proposed algorithm, the slow and early convergence of GA has been avoided by combining it with PCD algorithm. It has been shown that the hybrid GA-PCD algorithm provides an accurate diagnosis of fully and partially defective sensors as compared to GA or PCD alone. Different simulations have been provided to validate the performance of the designed algorithms in diversified scenarios.
Compressed sensing with gradient total variation for low-dose CBCT reconstruction
Seo, Chang-Woo [Department of Radiation Convergence Engineering, College of Health Science, Yonsei University, Wonju (Korea, Republic of); Cha, Bo Kyung [Advanced Medical Device Research Center, Korea Electrotechnology Research Institute, Ansan (Korea, Republic of); Jeon, Seongchae, E-mail: sarim@keri.re.kr [Advanced Medical Device Research Center, Korea Electrotechnology Research Institute, Ansan (Korea, Republic of); Huh, Young [Advanced Medical Device Research Center, Korea Electrotechnology Research Institute, Ansan (Korea, Republic of); Park, Justin C. [Department of Radiation Oncology, University of Florida, Gainesville, FL (United States); Lee, Byeonghun [School of Information and Communication on Engineering, Inha University, Incheon (Korea, Republic of); Baek, Junghee; Kim, Eunyoung [School of the Global Media, Soongsil University, Seoul (Korea, Republic of)
2015-06-01
This paper describes the improvement of convergence speed with gradient total variation (GTV) in compressed sensing (CS) for low-dose cone–beam computed tomography (CBCT) reconstruction. We derive a fast algorithm for the constrained total variation (TV)-based a minimum number of noisy projections. To achieve this task we combine the GTV with a TV-norm regularization term to promote an accelerated sparsity in the X-ray attenuation characteristics of the human body. The GTV is derived from a TV and enforces more efficient computationally and faster in convergence until a desired solution is achieved. The numerical algorithm is simple and derives relatively fast convergence. We apply a gradient projection algorithm that seeks a solution iteratively in the direction of the projected gradient while enforcing a non-negatively of the found solution. In comparison with the Feldkamp, Davis, and Kress (FDK) and conventional TV algorithms, the proposed GTV algorithm showed convergence in ≤18 iterations, whereas the original TV algorithm needs at least 34 iterations in reducing 50% of the projections compared with the FDK algorithm in order to reconstruct the chest phantom images. Future investigation includes improving imaging quality, particularly regarding X-ray cone–beam scatter, and motion artifacts of CBCT reconstruction.
Method for Multiple Targets Tracking in Cognitive Radar Based on Compressed Sensing
Yang Jun
2016-02-01
Full Text Available A multiple targets cognitive radar tracking method based on Compressed Sensing (CS is proposed. In this method, the theory of CS is introduced to the case of cognitive radar tracking process in multiple targets scenario. The echo signal is sparsely expressed. The designs of sparse matrix and measurement matrix are accomplished by expressing the echo signal sparsely, and subsequently, the restruction of measurement signal under the down-sampling condition is realized. On the receiving end, after considering that the problems that traditional particle filter suffers from degeneracy, and require a large number of particles, the particle swarm optimization particle filter is used to track the targets. On the transmitting end, the Posterior Cramér-Rao Bounds (PCRB of the tracking accuracy is deduced, and the radar waveform parameters are further cognitively designed using PCRB. Simulation results show that the proposed method can not only reduce the data quantity, but also provide a better tracking performance compared with traditional method.
Comparison and analysis of nonlinear algorithms for compressed sensing in MRI.
Yu, Yeyang; Hong, Mingjian; Liu, Feng; Wang, Hua; Crozier, Stuart
2010-01-01
Compressed sensing (CS) theory has been recently applied in Magnetic Resonance Imaging (MRI) to accelerate the overall imaging process. In the CS implementation, various algorithms have been used to solve the nonlinear equation system for better image quality and reconstruction speed. However, there are no explicit criteria for an optimal CS algorithm selection in the practical MRI application. A systematic and comparative study of those commonly used algorithms is therefore essential for the implementation of CS in MRI. In this work, three typical algorithms, namely, the Gradient Projection For Sparse Reconstruction (GPSR) algorithm, Interior-point algorithm (l(1)_ls), and the Stagewise Orthogonal Matching Pursuit (StOMP) algorithm are compared and investigated in three different imaging scenarios, brain, angiogram and phantom imaging. The algorithms' performances are characterized in terms of image quality and reconstruction speed. The theoretical results show that the performance of the CS algorithms is case sensitive; overall, the StOMP algorithm offers the best solution in imaging quality, while the GPSR algorithm is the most efficient one among the three methods. In the next step, the algorithm performances and characteristics will be experimentally explored. It is hoped that this research will further support the applications of CS in MRI. PMID:21097312
A compressive sensing-based approach for Preisach hysteresis model identification
Zhang, Jun; Torres, David; Sepúlveda, Nelson; Tan, Xiaobo
2016-07-01
The Preisach hysteresis model has been adopted extensively in magnetic and smart material-based systems. Fidelity of the model hinges on accurate identification of the Preisach density function. Existing work on the identification of the density function usually involves applying an input that provides sufficient excitation and measuring a large set of output data. In this paper, we propose a novel compressive sensing-based approach for Preisach model identification that requires fewer measurements. The proposed approach adopts the discrete cosine transform of the output data to obtain a sparse vector, where the order of all the output data is assumed to be known. The model parameters can be efficiently reconstructed using the proposed scheme. For comparison purposes, a constrained least-squares scheme using the same number of measurements is also considered. The root-mean-square error is adopted to examine the model identification performance. The proposed identification approach is shown to have better performance than the least-squares scheme through both simulation and experiments involving a vanadium dioxide ({{VO}}2)-integrated microactuator. This work was supported by the National Science Foundation (CMMI 1301243).
Sana, Furrukh
2015-07-26
Recovering information on subsurface geological features, such as flow channels, holds significant importance for optimizing the productivity of oil reservoirs. The flow channels exhibit high permeability in contrast to low permeability rock formations in their surroundings, enabling formulation of a sparse field recovery problem. The Ensemble Kalman filter (EnKF) is a widely used technique for the estimation of subsurface parameters, such as permeability. However, the EnKF often fails to recover and preserve the channel structures during the estimation process. Compressed Sensing (CS) has shown to significantly improve the reconstruction quality when dealing with such problems. We propose a new scheme based on CS principles to enhance the reconstruction of subsurface geological features by transforming the EnKF estimation process to a sparse domain representing diverse geological structures. Numerical experiments suggest that the proposed scheme provides an efficient mechanism to incorporate and preserve structural information in the estimation process and results in significant enhancement in the recovery of flow channel structures.
Nguyen ThanhSon; Guo Shuxu; Chen Haipeng
2013-01-01
Multipath arrivals in an Ultra-WideBand (UWB) channel have a long time intervals between clusters and rays where the signal takes on zero or negligible values.It is precisely the signal sparsity of the impulse response of the UWB channel that is exploited in this work aiming at UWB channel estimation based on Compressed Sensing (CS).However,these multipath arrivals mainly depend on the channel environments that generate different sparse levels (low-sparse or high-sparse) of the UWB channels.According to this basis,we have analyzed the two most basic recovery algorithms,one based on linear programming Basis Pursuit (BP),another using greedy method Orthogonal Matching Pursuit (OMP),and chosen the best recovery algorithm which are suitable to the sparse level for each type of channel environment.Besides,the results of this work is an open topic for further research aimed at creating a optimal algorithm specially for application of CS based UWB systems.
Inverse transport problem solvers based on regularized and compressive sensing techniques
According to the direct exposure measurements from flash radiographic image, regularized-based method and compressive sensing (CS)-based method for inverse transport equation are presented. The linear absorption coefficients and interface locations of objects are reconstructed directly at the same time. With a large number of measurements, least-square method is utilized to complete the reconstruction. Owing to the ill-posedness of the inverse problems, regularized algorithm is employed. Tikhonov method is applied with an appropriate posterior regularization parameter to get a meaningful solution. However, it's always very costly to obtain enough measurements. With limited measurements, CS sparse reconstruction technique Orthogonal Matching Pursuit (OMP) is applied to obtain the sparse coefficients by solving an optimization problem. This paper constructs and takes the forward projection matrix rather than Gauss matrix as measurement matrix. In the CS-based algorithm, Fourier expansion and wavelet expansion are adopted to convert an underdetermined system to a well-posed system. Simulations and numerical results of regularized method with appropriate regularization parameter and that of CS-based agree well with the reference value, furthermore, both methods avoid amplifying the noise. (authors)
Fast and low-dose computed laminography using compressive sensing based technique
Abbas, Sajid; Park, Miran; Cho, Seungryong
2015-03-01
Computed laminography (CL) is well known for inspecting microstructures in the materials, weldments and soldering defects in high density packed components or multilayer printed circuit boards. The overload problem on x-ray tube and gross failure of the radio-sensitive electronics devices during a scan are among important issues in CL which needs to be addressed. The sparse-view CL can be one of the viable option to overcome such issues. In this work a numerical aluminum welding phantom was simulated to collect sparsely sampled projection data at only 40 views using a conventional CL scanning scheme i.e. oblique scan. A compressive-sensing inspired total-variation (TV) minimization algorithm was utilized to reconstruct the images. It is found that the images reconstructed using sparse view data are visually comparable with the images reconstructed using full scan data set i.e. at 360 views on regular interval. We have quantitatively confirmed that tiny structures such as copper and tungsten slags, and copper flakes in the reconstructed images from sparsely sampled data are comparable with the corresponding structure present in the fully sampled data case. A blurring effect can be seen near the edges of few pores at the bottom of the reconstructed images from sparsely sampled data, despite the overall image quality is reasonable for fast and low-dose NDT.
Fast and low-dose computed laminography using compressive sensing based technique
Computed laminography (CL) is well known for inspecting microstructures in the materials, weldments and soldering defects in high density packed components or multilayer printed circuit boards. The overload problem on x-ray tube and gross failure of the radio-sensitive electronics devices during a scan are among important issues in CL which needs to be addressed. The sparse-view CL can be one of the viable option to overcome such issues. In this work a numerical aluminum welding phantom was simulated to collect sparsely sampled projection data at only 40 views using a conventional CL scanning scheme i.e. oblique scan. A compressive-sensing inspired total-variation (TV) minimization algorithm was utilized to reconstruct the images. It is found that the images reconstructed using sparse view data are visually comparable with the images reconstructed using full scan data set i.e. at 360 views on regular interval. We have quantitatively confirmed that tiny structures such as copper and tungsten slags, and copper flakes in the reconstructed images from sparsely sampled data are comparable with the corresponding structure present in the fully sampled data case. A blurring effect can be seen near the edges of few pores at the bottom of the reconstructed images from sparsely sampled data, despite the overall image quality is reasonable for fast and low-dose NDT