Sensing and compressing 3-D models
Krumm, J.
1998-02-01
The goal of this research project was to create a passive and robust computer vision system for producing 3-D computer models of arbitrary scenes. Although the authors were unsuccessful in achieving the overall goal, several components of this research have shown significant potential. Of particular interest is the application of parametric eigenspace methods for planar pose measurement of partially occluded objects in gray-level images. The techniques presented provide a simple, accurate, and robust solution to the planar pose measurement problem. In addition, the representational efficiency of eigenspace methods used with gray-level features were successfully extended to binary features, which are less sensitive to illumination changes. The results of this research are presented in two papers that were written during the course of this project. The papers are included in sections 2 and 3. The first section of this report summarizes the 3-D modeling efforts.
Modeling 3D faces from samplings via compressive sensing
NASA Astrophysics Data System (ADS)
Sun, Qi; Tang, Yanlong; Hu, Ping
2013-07-01
3D data is easier to acquire for family entertainment purpose today because of the mass-production, cheapness and portability of domestic RGBD sensors, e.g., Microsoft Kinect. However, the accuracy of facial modeling is affected by the roughness and instability of the raw input data from such sensors. To overcome this problem, we introduce compressive sensing (CS) method to build a novel 3D super-resolution scheme to reconstruct high-resolution facial models from rough samples captured by Kinect. Unlike the simple frame fusion super-resolution method, this approach aims to acquire compressed samples for storage before a high-resolution image is produced. In this scheme, depth frames are firstly captured and then each of them is measured into compressed samples using sparse coding. Next, the samples are fused to produce an optimal one and finally a high-resolution image is recovered from the fused sample. This framework is able to recover 3D facial model of a given user from compressed simples and this can reducing storage space as well as measurement cost in future devices e.g., single-pixel depth cameras. Hence, this work can potentially be applied into future applications, such as access control system using face recognition, and smart phones with depth cameras, which need high resolution and little measure time.
3D multifocus astigmatism and compressed sensing (3D MACS) based superresolution reconstruction
Huang, Jiaqing; Sun, Mingzhai; Gumpper, Kristyn; Chi, Yuejie; Ma, Jianjie
2015-01-01
Single molecule based superresolution techniques (STORM/PALM) achieve nanometer spatial resolution by integrating the temporal information of the switching dynamics of fluorophores (emitters). When emitter density is low for each frame, they are located to the nanometer resolution. However, when the emitter density rises, causing significant overlapping, it becomes increasingly difficult to accurately locate individual emitters. This is particularly apparent in three dimensional (3D) localization because of the large effective volume of the 3D point spread function (PSF). The inability to precisely locate the emitters at a high density causes poor temporal resolution of localization-based superresolution technique and significantly limits its application in 3D live cell imaging. To address this problem, we developed a 3D high-density superresolution imaging platform that allows us to precisely locate the positions of emitters, even when they are significantly overlapped in three dimensional space. Our platform involves a multi-focus system in combination with astigmatic optics and an ℓ1-Homotopy optimization procedure. To reduce the intrinsic bias introduced by the discrete formulation of compressed sensing, we introduced a debiasing step followed by a 3D weighted centroid procedure, which not only increases the localization accuracy, but also increases the computation speed of image reconstruction. We implemented our algorithms on a graphic processing unit (GPU), which speeds up processing 10 times compared with central processing unit (CPU) implementation. We tested our method with both simulated data and experimental data of fluorescently labeled microtubules and were able to reconstruct a 3D microtubule image with 1000 frames (512×512) acquired within 20 seconds. PMID:25798314
A new combined prior based reconstruction method for compressed sensing in 3D ultrasound imaging
NASA Astrophysics Data System (ADS)
Uddin, Muhammad S.; Islam, Rafiqul; Tahtali, Murat; Lambert, Andrew J.; Pickering, Mark R.
2015-03-01
Ultrasound (US) imaging is one of the most popular medical imaging modalities, with 3D US imaging gaining popularity recently due to its considerable advantages over 2D US imaging. However, as it is limited by long acquisition times and the huge amount of data processing it requires, methods for reducing these factors have attracted considerable research interest. Compressed sensing (CS) is one of the best candidates for accelerating the acquisition rate and reducing the data processing time without degrading image quality. However, CS is prone to introduce noise-like artefacts due to random under-sampling. To address this issue, we propose a combined prior-based reconstruction method for 3D US imaging. A Laplacian mixture model (LMM) constraint in the wavelet domain is combined with a total variation (TV) constraint to create a new regularization regularization prior. An experimental evaluation conducted to validate our method using synthetic 3D US images shows that it performs better than other approaches in terms of both qualitative and quantitative measures.
High-Performance 3D Compressive Sensing MRI Reconstruction Using Many-Core Architectures
Kim, Daehyun; Trzasko, Joshua; Smelyanskiy, Mikhail; Haider, Clifton; Dubey, Pradeep; Manduca, Armando
2011-01-01
Compressive sensing (CS) describes how sparse signals can be accurately reconstructed from many fewer samples than required by the Nyquist criterion. Since MRI scan duration is proportional to the number of acquired samples, CS has been gaining significant attention in MRI. However, the computationally intensive nature of CS reconstructions has precluded their use in routine clinical practice. In this work, we investigate how different throughput-oriented architectures can benefit one CS algorithm and what levels of acceleration are feasible on different modern platforms. We demonstrate that a CUDA-based code running on an NVIDIA Tesla C2050 GPU can reconstruct a 256 × 160 × 80 volume from an 8-channel acquisition in 19 seconds, which is in itself a significant improvement over the state of the art. We then show that Intel's Knights Ferry can perform the same 3D MRI reconstruction in only 12 seconds, bringing CS methods even closer to clinical viability. PMID:21922017
Real-time 3D video utilizing a compressed sensing time-of-flight single-pixel camera
NASA Astrophysics Data System (ADS)
Edgar, Matthew P.; Sun, Ming-Jie; Gibson, Graham M.; Spalding, Gabriel C.; Phillips, David B.; Padgett, Miles J.
2016-09-01
Time-of-flight 3D imaging is an important tool for applications such as remote sensing, machine vision and autonomous navigation. Conventional time-of-flight three-dimensional imaging systems that utilize a raster scanned laser to measure the range of each pixel in the scene sequentially, inherently have acquisition times that scale directly with the resolution. Here we show a modified time-of-flight 3D camera employing structured illumination, which uses a visible camera to enable a novel compressed sensing technique, minimising the acquisition time as well as providing a high-resolution reflectivity map for image overlay. Furthermore, a quantitative assessment of the 3D imaging performance is provided.
Nam, Seunghoon; Akçakaya, Mehmet; Basha, Tamer; Stehning, Christian; Manning, Warren J; Tarokh, Vahid; Nezafat, Reza
2013-01-01
A disadvantage of three-dimensional (3D) isotropic acquisition in whole-heart coronary MRI is the prolonged data acquisition time. Isotropic 3D radial trajectories allow undersampling of k-space data in all three spatial dimensions, enabling accelerated acquisition of the volumetric data. Compressed sensing (CS) reconstruction can provide further acceleration in the acquisition by removing the incoherent artifacts due to undersampling and improving the image quality. However, the heavy computational overhead of the CS reconstruction has been a limiting factor for its application. In this article, a parallelized implementation of an iterative CS reconstruction method for 3D radial acquisitions using a commercial graphics processing unit is presented. The execution time of the graphics processing unit-implemented CS reconstruction was compared with that of the C++ implementation, and the efficacy of the undersampled 3D radial acquisition with CS reconstruction was investigated in both phantom and whole-heart coronary data sets. Subsequently, the efficacy of CS in suppressing streaking artifacts in 3D whole-heart coronary MRI with 3D radial imaging and its convergence properties were studied. The CS reconstruction provides improved image quality (in terms of vessel sharpness and suppression of noise-like artifacts) compared with the conventional 3D gridding algorithm, and the graphics processing unit implementation greatly reduces the execution time of CS reconstruction yielding 34-54 times speed-up compared with C++ implementation.
Bevacqua, Martina T; Scapaticci, Rosa
2016-02-01
In microwave breast cancer imaging magnetic nanoparticles have been recently proposed as contrast agent. Due to the non-magnetic nature of human tissues, magnetic nanoparticles make possible the overcoming of some limitations of conventional microwave imaging techniques, thus providing reliable and specific diagnosis of breast cancer. In this paper, a Compressive Sensing inspired inversion technique is introduced for the reconstruction of the magnetic contrast induced within the tumor. The applicability of Compressive Sensing theory is guaranteed by the fact that the underlying inverse scattering problem is linear and the searched magnetic perturbation is sparse. From the numerical analysis, performed in realistic conditions in 3D geometry, it has been pointed out that the adoption of this new tool allows improving resolution and accuracy of the reconstructions, as well as reducing the number of required measurements.
ICER-3D Hyperspectral Image Compression Software
NASA Technical Reports Server (NTRS)
Xie, Hua; Kiely, Aaron; Klimesh, matthew; Aranki, Nazeeh
2010-01-01
Software has been developed to implement the ICER-3D algorithm. ICER-3D effects progressive, three-dimensional (3D), wavelet-based compression of hyperspectral images. If a compressed data stream is truncated, the progressive nature of the algorithm enables reconstruction of hyperspectral data at fidelity commensurate with the given data volume. The ICER-3D software is capable of providing either lossless or lossy compression, and incorporates an error-containment scheme to limit the effects of data loss during transmission. The compression algorithm, which was derived from the ICER image compression algorithm, includes wavelet-transform, context-modeling, and entropy coding subalgorithms. The 3D wavelet decomposition structure used by ICER-3D exploits correlations in all three dimensions of sets of hyperspectral image data, while facilitating elimination of spectral ringing artifacts, using a technique summarized in "Improving 3D Wavelet-Based Compression of Spectral Images" (NPO-41381), NASA Tech Briefs, Vol. 33, No. 3 (March 2009), page 7a. Correlation is further exploited by a context-modeling subalgorithm, which exploits spectral dependencies in the wavelet-transformed hyperspectral data, using an algorithm that is summarized in "Context Modeler for Wavelet Compression of Hyperspectral Images" (NPO-43239), which follows this article. An important feature of ICER-3D is a scheme for limiting the adverse effects of loss of data during transmission. In this scheme, as in the similar scheme used by ICER, the spatial-frequency domain is partitioned into rectangular error-containment regions. In ICER-3D, the partitions extend through all the wavelength bands. The data in each partition are compressed independently of those in the other partitions, so that loss or corruption of data from any partition does not affect the other partitions. Furthermore, because compression is progressive within each partition, when data are lost, any data from that partition received
3D microstructure modeling of compressed fiber-based materials
NASA Astrophysics Data System (ADS)
Gaiselmann, Gerd; Tötzke, Christian; Manke, Ingo; Lehnert, Werner; Schmidt, Volker
2014-07-01
A novel parametrized model that describes the 3D microstructure of compressed fiber-based materials is introduced. It allows to virtually generate the microstructure of realistically compressed gas-diffusion layers (GDL). Given the input of a 3D microstructure of some fiber-based material, the model compresses the system of fibers in a uniaxial direction for arbitrary compression rates. The basic idea is to translate the fibers in the direction of compression according to a vector field which depends on the rate of compression and on the locations of fibers within the material. In order to apply the model to experimental 3D image data of fiber-based materials given for several compression states, an optimal vector field is estimated by simulated annealing. The model is applied to 3D image data of non-woven GDL in PEMFC gained by synchrotron tomography for different compression rates. The compression model is validated by comparing structural characteristics computed for experimentally compressed and virtually compressed microstructures, where two kinds of compression - using a flat stamp and a stamp with a flow-field profile - are applied. For both stamps types, a good agreement is found. Furthermore, the compression model is combined with a stochastic 3D microstructure model for uncompressed fiber-based materials. This allows to efficiently generate compressed fiber-based microstructures in arbitrary volumes.
An efficient and robust 3D mesh compression based on 3D watermarking and wavelet transform
NASA Astrophysics Data System (ADS)
Zagrouba, Ezzeddine; Ben Jabra, Saoussen; Didi, Yosra
2011-06-01
The compression and watermarking of 3D meshes are very important in many areas of activity including digital cinematography, virtual reality as well as CAD design. However, most studies on 3D watermarking and 3D compression are done independently. To verify a good trade-off between protection and a fast transfer of 3D meshes, this paper proposes a new approach which combines 3D mesh compression with mesh watermarking. This combination is based on a wavelet transformation. In fact, the used compression method is decomposed to two stages: geometric encoding and topologic encoding. The proposed approach consists to insert a signature between these two stages. First, the wavelet transformation is applied to the original mesh to obtain two components: wavelets coefficients and a coarse mesh. Then, the geometric encoding is done on these two components. The obtained coarse mesh will be marked using a robust mesh watermarking scheme. This insertion into coarse mesh allows obtaining high robustness to several attacks. Finally, the topologic encoding is applied to the marked coarse mesh to obtain the compressed mesh. The combination of compression and watermarking permits to detect the presence of signature after a compression of the marked mesh. In plus, it allows transferring protected 3D meshes with the minimum size. The experiments and evaluations show that the proposed approach presents efficient results in terms of compression gain, invisibility and robustness of the signature against of many attacks.
DCT and DST Based Image Compression for 3D Reconstruction
NASA Astrophysics Data System (ADS)
Siddeq, Mohammed M.; Rodrigues, Marcos A.
2017-03-01
This paper introduces a new method for 2D image compression whose quality is demonstrated through accurate 3D reconstruction using structured light techniques and 3D reconstruction from multiple viewpoints. The method is based on two discrete transforms: (1) A one-dimensional Discrete Cosine Transform (DCT) is applied to each row of the image. (2) The output from the previous step is transformed again by a one-dimensional Discrete Sine Transform (DST), which is applied to each column of data generating new sets of high-frequency components followed by quantization of the higher frequencies. The output is then divided into two parts where the low-frequency components are compressed by arithmetic coding and the high frequency ones by an efficient minimization encoding algorithm. At decompression stage, a binary search algorithm is used to recover the original high frequency components. The technique is demonstrated by compressing 2D images up to 99% compression ratio. The decompressed images, which include images with structured light patterns for 3D reconstruction and from multiple viewpoints, are of high perceptual quality yielding accurate 3D reconstruction. Perceptual assessment and objective quality of compression are compared with JPEG and JPEG2000 through 2D and 3D RMSE. Results show that the proposed compression method is superior to both JPEG and JPEG2000 concerning 3D reconstruction, and with equivalent perceptual quality to JPEG2000.
Li, Fangmin; Liu, Guo; Liu, Jian; Chen, Xiaochuang; Ma, Xiaolin
2016-01-01
Most location-based services are based on a global positioning system (GPS), which only works well in outdoor environments. Compared to outdoor environments, indoor localization has created more buzz in recent years as people spent most of their time indoors working at offices and shopping at malls, etc. Existing solutions mainly rely on inertial sensors (i.e., accelerometer and gyroscope) embedded in mobile devices, which are usually not accurate enough to be useful due to the mobile devices’ random movements while people are walking. In this paper, we propose the use of shoe sensing (i.e., sensors attached to shoes) to achieve 3D indoor positioning. Specifically, a short-time energy-based approach is used to extract the gait pattern. Moreover, in order to improve the accuracy of vertical distance estimation while the person is climbing upstairs, a state classification is designed to distinguish the walking status including plane motion (i.e., normal walking and jogging horizontally), walking upstairs, and walking downstairs. Furthermore, we also provide a mechanism to reduce the vertical distance accumulation error. Experimental results show that we can achieve nearly 100% accuracy when extracting gait patterns from walking/jogging with a low-cost shoe sensor, and can also achieve 3D indoor real-time positioning with high accuracy. PMID:27801839
Real-time 3D video compression for tele-immersive environments
NASA Astrophysics Data System (ADS)
Yang, Zhenyu; Cui, Yi; Anwar, Zahid; Bocchino, Robert; Kiyanclar, Nadir; Nahrstedt, Klara; Campbell, Roy H.; Yurcik, William
2006-01-01
Tele-immersive systems can improve productivity and aid communication by allowing distributed parties to exchange information via a shared immersive experience. The TEEVE research project at the University of Illinois at Urbana-Champaign and the University of California at Berkeley seeks to foster the development and use of tele-immersive environments by a holistic integration of existing components that capture, transmit, and render three-dimensional (3D) scenes in real time to convey a sense of immersive space. However, the transmission of 3D video poses significant challenges. First, it is bandwidth-intensive, as it requires the transmission of multiple large-volume 3D video streams. Second, existing schemes for 2D color video compression such as MPEG, JPEG, and H.263 cannot be applied directly because the 3D video data contains depth as well as color information. Our goal is to explore from a different angle of the 3D compression space with factors including complexity, compression ratio, quality, and real-time performance. To investigate these trade-offs, we present and evaluate two simple 3D compression schemes. For the first scheme, we use color reduction to compress the color information, which we then compress along with the depth information using zlib. For the second scheme, we use motion JPEG to compress the color information and run-length encoding followed by Huffman coding to compress the depth information. We apply both schemes to 3D videos captured from a real tele-immersive environment. Our experimental results show that: (1) the compressed data preserves enough information to communicate the 3D images effectively (min. PSNR > 40) and (2) even without inter-frame motion estimation, very high compression ratios (avg. > 15) are achievable at speeds sufficient to allow real-time communication (avg. ~ 13 ms per 3D video frame).
Novel 3D Compression Methods for Geometry, Connectivity and Texture
NASA Astrophysics Data System (ADS)
Siddeq, M. M.; Rodrigues, M. A.
2016-06-01
A large number of applications in medical visualization, games, engineering design, entertainment, heritage, e-commerce and so on require the transmission of 3D models over the Internet or over local networks. 3D data compression is an important requirement for fast data storage, access and transmission within bandwidth limitations. The Wavefront OBJ (object) file format is commonly used to share models due to its clear simple design. Normally each OBJ file contains a large amount of data (e.g. vertices and triangulated faces, normals, texture coordinates and other parameters) describing the mesh surface. In this paper we introduce a new method to compress geometry, connectivity and texture coordinates by a novel Geometry Minimization Algorithm (GM-Algorithm) in connection with arithmetic coding. First, each vertex ( x, y, z) coordinates are encoded to a single value by the GM-Algorithm. Second, triangle faces are encoded by computing the differences between two adjacent vertex locations, which are compressed by arithmetic coding together with texture coordinates. We demonstrate the method on large data sets achieving compression ratios between 87 and 99 % without reduction in the number of reconstructed vertices and triangle faces. The decompression step is based on a Parallel Fast Matching Search Algorithm (Parallel-FMS) to recover the structure of the 3D mesh. A comparative analysis of compression ratios is provided with a number of commonly used 3D file formats such as VRML, OpenCTM and STL highlighting the performance and effectiveness of the proposed method.
Highly compressible 3D periodic graphene aerogel microlattices
Zhu, Cheng; Han, T. Yong-Jin; Duoss, Eric B.; Golobic, Alexandra M.; Kuntz, Joshua D.; Spadaccini, Christopher M.; Worsley, Marcus A.
2015-04-22
Graphene is a two-dimensional material that offers a unique combination of low density, exceptional mechanical properties, large surface area and excellent electrical conductivity. Recent progress has produced bulk 3D assemblies of graphene, such as graphene aerogels, but they possess purely stochastic porous networks, which limit their performance compared with the potential of an engineered architecture. Here we report the fabrication of periodic graphene aerogel microlattices, possessing an engineered architecture via a 3D printing technique known as direct ink writing. The 3D printed graphene aerogels are lightweight, highly conductive and exhibit supercompressibility (up to 90% compressive strain). Moreover, the Young’s moduli of the 3D printed graphene aerogels show an order of magnitude improvement over bulk graphene materials with comparable geometric density and possess large surface areas. Ultimately, adapting the 3D printing technique to graphene aerogels realizes the possibility of fabricating a myriad of complex aerogel architectures for a broad range of applications.
Highly compressible 3D periodic graphene aerogel microlattices
NASA Astrophysics Data System (ADS)
Zhu, Cheng; Han, T. Yong-Jin; Duoss, Eric B.; Golobic, Alexandra M.; Kuntz, Joshua D.; Spadaccini, Christopher M.; Worsley, Marcus A.
2015-04-01
Graphene is a two-dimensional material that offers a unique combination of low density, exceptional mechanical properties, large surface area and excellent electrical conductivity. Recent progress has produced bulk 3D assemblies of graphene, such as graphene aerogels, but they possess purely stochastic porous networks, which limit their performance compared with the potential of an engineered architecture. Here we report the fabrication of periodic graphene aerogel microlattices, possessing an engineered architecture via a 3D printing technique known as direct ink writing. The 3D printed graphene aerogels are lightweight, highly conductive and exhibit supercompressibility (up to 90% compressive strain). Moreover, the Young's moduli of the 3D printed graphene aerogels show an order of magnitude improvement over bulk graphene materials with comparable geometric density and possess large surface areas. Adapting the 3D printing technique to graphene aerogels realizes the possibility of fabricating a myriad of complex aerogel architectures for a broad range of applications.
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
Spectrally Adaptable Compressive Sensing Imaging System
2014-05-01
2D coded projections. The underlying spectral 3D data cube is then recovered using compressed sensing (CS) reconstruction algorithms which assume...introduced in [?], is a remarkable imaging architecture that allows capturing spectral imaging information of a 3D cube with just a single 2D mea...allows capturing spectral imaging information of a 3D cube with just a single 2D measurement of the coded and spectrally dispersed source field
Lossless compression of 3D seismic data using a horizon displacement compensated 3D lifting scheme
NASA Astrophysics Data System (ADS)
Meftah, Anis; Antonini, Marc; Ben Amar, Chokri
2010-01-01
In this paper we present a method to optimize the computation of the wavelet transform for the 3D seismic data while reducing the energy of coefficients to the minimum. This allow us to reduce the entropy of the signal and so increase the compression ratios. The proposed method exploits the geometrical information contained in the seismic 3D data to optimize the computation of the wavelet transform. Indeed, the classic filtering is replaced by a filtering following the horizons contained in the 3D seismic images. Applying this approach in two dimensions permits us to obtain wavelets coefficients with lowest energy. The experiments show that our method permits to save extra 8% of the size of the object compared to the classic wavelet transform.
A skinning prediction scheme for dynamic 3D mesh compression
NASA Astrophysics Data System (ADS)
Mamou, Khaled; Zaharia, Titus; Prêteux, Françoise
2006-08-01
This paper presents a new prediction-based compression technique for dynamic 3D meshes with constant connectivity and time-varying geometry. The core of the proposed algorithm is a skinning model used for motion compensation. The mesh is first partitioned within vertex clusters that can be described by a single affine motion model. The proposed segmentation technique automatically determines the number of clusters and relays on a decimation strategy privileging the simplification of vertices exhibiting the same affine motion over the whole animation sequence. The residual prediction errors are finally compressed using a temporal-DCT representation. The performances of our encoder are objectively evaluated on a data set of eight animation sequences with various sizes, geometries and topologies, and exhibiting both rigid and elastic motions. The experimental evaluation shows that the proposed compression scheme outperforms state of the art techniques such as MPEG-4/AFX, Dynapack, RT, GV, MCGV, TDCT, PCA and RT compression schemes.
Postprocessing of compressed 3D graphic data by using subdivision
NASA Astrophysics Data System (ADS)
Cheang, Ka Man; Li, Jiankun; Kuo, C.-C. Jay
1998-10-01
In this work, we present a postprocessing technique applied to a 3D graphic model of a lower resolution to obtain a visually more pleasant representation. Our method is an improved version of the Butterfly subdivision scheme developed by Zorin et al. Our main contribution is to exploit the flatness information of local areas of a 3D graphic model for adaptive refinement. Consequently, we can avoid unnecessary subdivision in regions which are relatively flat. The proposed new algorithm not only reduces the computational complexity but also saves the storage space. With the hierarchical mesh compression method developed by Li and Kuo as the baseline coding method, we show that the postprocessing technique can greatly improve the visual quality of the decoded 3D graphic model.
Highly compressible 3D periodic graphene aerogel microlattices
Zhu, Cheng; Han, T. Yong-Jin; Duoss, Eric B.; ...
2015-04-22
Graphene is a two-dimensional material that offers a unique combination of low density, exceptional mechanical properties, large surface area and excellent electrical conductivity. Recent progress has produced bulk 3D assemblies of graphene, such as graphene aerogels, but they possess purely stochastic porous networks, which limit their performance compared with the potential of an engineered architecture. Here we report the fabrication of periodic graphene aerogel microlattices, possessing an engineered architecture via a 3D printing technique known as direct ink writing. The 3D printed graphene aerogels are lightweight, highly conductive and exhibit supercompressibility (up to 90% compressive strain). Moreover, the Young’s modulimore » of the 3D printed graphene aerogels show an order of magnitude improvement over bulk graphene materials with comparable geometric density and possess large surface areas. Ultimately, adapting the 3D printing technique to graphene aerogels realizes the possibility of fabricating a myriad of complex aerogel architectures for a broad range of applications.« less
NASA Astrophysics Data System (ADS)
Je, U. K.; Lee, M. S.; Cho, H. S.; Hong, D. K.; Park, Y. O.; Park, C. K.; Cho, H. M.; Choi, S. I.; Woo, T. H.
2015-06-01
In practical applications of three-dimensional (3D) tomographic imaging, there are often challenges for image reconstruction from insufficient sampling data. In computed tomography (CT), for example, image reconstruction from sparse views and/or limited-angle (<360°) views would enable fast scanning with reduced imaging doses to the patient. In this study, we investigated and implemented a reconstruction algorithm based on the compressed-sensing (CS) theory, which exploits the sparseness of the gradient image with substantially high accuracy, for potential applications to low-dose, high-accurate dental cone-beam CT (CBCT). We performed systematic simulation works to investigate the image characteristics and also performed experimental works by applying the algorithm to a commercially-available dental CBCT system to demonstrate its effectiveness for image reconstruction in insufficient sampling problems. We successfully reconstructed CBCT images of superior accuracy from insufficient sampling data and evaluated the reconstruction quality quantitatively. Both simulation and experimental demonstrations of the CS-based reconstruction from insufficient data indicate that the CS-based algorithm can be applied directly to current dental CBCT systems for reducing the imaging doses and further improving the image quality.
Beamforming Using Compressive Sensing
2011-10-01
Am. 130 (4), October 2011 VC 2011 Acoustical Society of America G. F. Edelmann and C. F. Gaumond: JASA Express Letters [DOI: 10.1121/1.3632046...arbitrarily spaced array, the rank of A may be insufficient, G. F. Edelmann and C. F. Gaumond: JASA Express Letters [DOI: 10.1121/1.3632046] Published Online...09 September 2011 J. Acoust. Soc. Am. 130 (4), October 2011 G. F. Edelmann and C. F. Gaumond: Beamforming using compressive sensing EL233 Downloaded
Improving 3D Wavelet-Based Compression of Hyperspectral Images
NASA Technical Reports Server (NTRS)
Klimesh, Matthew; Kiely, Aaron; Xie, Hua; Aranki, Nazeeh
2009-01-01
Two methods of increasing the effectiveness of three-dimensional (3D) wavelet-based compression of hyperspectral images have been developed. (As used here, images signifies both images and digital data representing images.) The methods are oriented toward reducing or eliminating detrimental effects of a phenomenon, referred to as spectral ringing, that is described below. In 3D wavelet-based compression, an image is represented by a multiresolution wavelet decomposition consisting of several subbands obtained by applying wavelet transforms in the two spatial dimensions corresponding to the two spatial coordinate axes of the image plane, and by applying wavelet transforms in the spectral dimension. Spectral ringing is named after the more familiar spatial ringing (spurious spatial oscillations) that can be seen parallel to and near edges in ordinary images reconstructed from compressed data. These ringing phenomena are attributable to effects of quantization. In hyperspectral data, the individual spectral bands play the role of edges, causing spurious oscillations to occur in the spectral dimension. In the absence of such corrective measures as the present two methods, spectral ringing can manifest itself as systematic biases in some reconstructed spectral bands and can reduce the effectiveness of compression of spatially-low-pass subbands. One of the two methods is denoted mean subtraction. The basic idea of this method is to subtract mean values from spatial planes of spatially low-pass subbands prior to encoding, because (a) such spatial planes often have mean values that are far from zero and (b) zero-mean data are better suited for compression by methods that are effective for subbands of two-dimensional (2D) images. In this method, after the 3D wavelet decomposition is performed, mean values are computed for and subtracted from each spatial plane of each spatially-low-pass subband. The resulting data are converted to sign-magnitude form and compressed in a
Stevens, Andrew J.; Kovarik, Libor; Abellan, Patricia; Yuan, Xin; Carin, Lawrence; Browning, Nigel D.
2015-08-02
One of the main limitations of imaging at high spatial and temporal resolution during in-situ TEM experiments is the frame rate of the camera being used to image the dynamic process. While the recent development of direct detectors has provided the hardware to achieve frame rates approaching 0.1ms, the cameras are expensive and must replace existing detectors. In this paper, we examine the use of coded aperture compressive sensing methods [1, 2, 3, 4] to increase the framerate of any camera with simple, low-cost hardware modifications. The coded aperture approach allows multiple sub-frames to be coded and integrated into a single camera frame during the acquisition process, and then extracted upon readout using statistical compressive sensing inversion. Our simulations show that it should be possible to increase the speed of any camera by at least an order of magnitude. Compressive Sensing (CS) combines sensing and compression in one operation, and thus provides an approach that could further improve the temporal resolution while correspondingly reducing the electron dose rate. Because the signal is measured in a compressive manner, fewer total measurements are required. When applied to TEM video capture, compressive imaging couled improve acquisition speed and reduce the electron dose rate. CS is a recent concept, and has come to the forefront due the seminal work of Candès [5]. Since the publication of Candès, there has been enormous growth in the application of CS and development of CS variants. For electron microscopy applications, the concept of CS has also been recently applied to electron tomography [6], and reduction of electron dose in scanning transmission electron microscopy (STEM) imaging [7]. To demonstrate the applicability of coded aperture CS video reconstruction for atomic level imaging, we simulate compressive sensing on observations of Pd nanoparticles and Ag nanoparticles during exposure to high temperatures and other environmental
Finite element solver for 3-D compressible viscous flows
NASA Technical Reports Server (NTRS)
Reddy, K. C.; Reddy, J. N.
1986-01-01
The space shuttle main engine (SSME) has extremely complex internal flow structure. The geometry of the flow domain is three-dimensional with complicated topology. The flow is compressible, viscous, and turbulent with large gradients in flow quantities and regions of recirculations. The analysis of the flow field in SSME involves several tedious steps. One is the geometrical modeling of the particular zone of the SSME being studied. Accessing the geometry definition, digitalizing it, and developing surface interpolations suitable for an interior grid generator require considerable amount of manual labor. There are several types of grid generators available with some general-purpose finite element programs. An efficient and robust computational scheme for solving 3D Navier-Stokes equations has to be implemented. Post processing software has to be adapted to visualize and analyze the computed 3D flow field. The progress made in a project to develop software for the analysis of the flow is discussed. The technical approach to the development of the finite element scheme and the relaxation procedure are discussed. The three dimensional finite element code for the compressible Navier-Stokes equations is listed.
Beamforming using compressive sensing.
Edelmann, Geoffrey F; Gaumond, Charles F
2011-10-01
Compressive sensing (CS) is compared with conventional beamforming using horizontal beamforming of at-sea, towed-array data. They are compared qualitatively using bearing time records and quantitatively using signal-to-interference ratio. Qualitatively, CS exhibits lower levels of background interference than conventional beamforming. Furthermore, bearing time records show increasing, but tolerable, levels of background interference when the number of elements is decreased. For the full array, CS generates signal-to-interference ratio of 12 dB, but conventional beamforming only 8 dB. The superiority of CS over conventional beamforming is much more pronounced with undersampling.
High-speed optical 3D sensing and its applications
NASA Astrophysics Data System (ADS)
Watanabe, Yoshihiro
2016-12-01
This paper reviews high-speed optical 3D sensing technologies for obtaining the 3D shape of a target using a camera. The focusing speed is from 100 to 1000 fps, exceeding normal camera frame rates, which are typically 30 fps. In particular, contactless, active, and real-time systems are introduced. Also, three example applications of this type of sensing technology are introduced, including surface reconstruction from time-sequential depth images, high-speed 3D user interaction, and high-speed digital archiving.
Compressive sensing in medical imaging
Graff, Christian G.; Sidky, Emil Y.
2015-01-01
The promise of compressive sensing, exploitation of compressibility to achieve high quality image reconstructions with less data, has attracted a great deal of attention in the medical imaging community. At the Compressed Sensing Incubator meeting held in April 2014 at OSA Headquarters in Washington, DC, presentations were given summarizing some of the research efforts ongoing in compressive sensing for x-ray computed tomography and magnetic resonance imaging systems. This article provides an expanded version of these presentations. Sparsity-exploiting reconstruction algorithms that have gained popularity in the medical imaging community are studied, and examples of clinical applications that could benefit from compressive sensing ideas are provided. The current and potential future impact of compressive sensing on the medical imaging field is discussed. PMID:25968400
A finite element solver for 3-D compressible viscous flows
NASA Technical Reports Server (NTRS)
Reddy, K. C.; Reddy, J. N.; Nayani, S.
1990-01-01
Computation of the flow field inside a space shuttle main engine (SSME) requires the application of state of the art computational fluid dynamic (CFD) technology. Several computer codes are under development to solve 3-D flow through the hot gas manifold. Some algorithms were designed to solve the unsteady compressible Navier-Stokes equations, either by implicit or explicit factorization methods, using several hundred or thousands of time steps to reach a steady state solution. A new iterative algorithm is being developed for the solution of the implicit finite element equations without assembling global matrices. It is an efficient iteration scheme based on a modified nonlinear Gauss-Seidel iteration with symmetric sweeps. The algorithm is analyzed for a model equation and is shown to be unconditionally stable. Results from a series of test problems are presented. The finite element code was tested for couette flow, which is flow under a pressure gradient between two parallel plates in relative motion. Another problem that was solved is viscous laminar flow over a flat plate. The general 3-D finite element code was used to compute the flow in an axisymmetric turnaround duct at low Mach numbers.
Compressively sensed complex networks.
Dunlavy, Daniel M.; Ray, Jaideep; Pinar, Ali
2010-07-01
The aim of this project is to develop low dimension parametric (deterministic) models of complex networks, to use compressive sensing (CS) and multiscale analysis to do so and to exploit the structure of complex networks (some are self-similar under coarsening). CS provides a new way of sampling and reconstructing networks. The approach is based on multiresolution decomposition of the adjacency matrix and its efficient sampling. It requires preprocessing of the adjacency matrix to make it 'blocky' which is the biggest (combinatorial) algorithm challenge. Current CS reconstruction algorithm makes no use of the structure of a graph, its very general (and so not very efficient/customized). Other model-based CS techniques exist, but not yet adapted to networks. Obvious starting point for future work is to increase the efficiency of reconstruction.
MSV3d: database of human MisSense Variants mapped to 3D protein structure.
Luu, Tien-Dao; Rusu, Alin-Mihai; Walter, Vincent; Ripp, Raymond; Moulinier, Luc; Muller, Jean; Toursel, Thierry; Thompson, Julie D; Poch, Olivier; Nguyen, Hoan
2012-01-01
The elucidation of the complex relationships linking genotypic and phenotypic variations to protein structure is a major challenge in the post-genomic era. We present MSV3d (Database of human MisSense Variants mapped to 3D protein structure), a new database that contains detailed annotation of missense variants of all human proteins (20 199 proteins). The multi-level characterization includes details of the physico-chemical changes induced by amino acid modification, as well as information related to the conservation of the mutated residue and its position relative to functional features in the available or predicted 3D model. Major releases of the database are automatically generated and updated regularly in line with the dbSNP (database of Single Nucleotide Polymorphism) and SwissVar releases, by exploiting the extensive Décrypthon computational grid resources. The database (http://decrypthon.igbmc.fr/msv3d) is easily accessible through a simple web interface coupled to a powerful query engine and a standard web service. The content is completely or partially downloadable in XML or flat file formats. Database URL: http://decrypthon.igbmc.fr/msv3d.
Advanced 3D Sensing and Visualization System for Unattended Monitoring
Carlson, J.J.; Little, C.Q.; Nelson, C.L.
1999-01-01
The purpose of this project was to create a reliable, 3D sensing and visualization system for unattended monitoring. The system provides benefits for several of Sandia's initiatives including nonproliferation, treaty verification, national security and critical infrastructure surety. The robust qualities of the system make it suitable for both interior and exterior monitoring applications. The 3D sensing system combines two existing sensor technologies in a new way to continuously maintain accurate 3D models of both static and dynamic components of monitored areas (e.g., portions of buildings, roads, and secured perimeters in addition to real-time estimates of the shape, location, and motion of humans and moving objects). A key strength of this system is the ability to monitor simultaneous activities on a continuous basis, such as several humans working independently within a controlled workspace, while also detecting unauthorized entry into the workspace. Data from the sensing system is used to identi~ activities or conditions that can signi~ potential surety (safety, security, and reliability) threats. The system could alert a security operator of potential threats or could be used to cue other detection, inspection or warning systems. An interactive, Web-based, 3D visualization capability was also developed using the Virtual Reality Modeling Language (VRML). The intex%ace allows remote, interactive inspection of a monitored area (via the Internet or Satellite Links) using a 3D computer model of the area that is rendered from actual sensor data.
3-D Adaptive Sparsity Based Image Compression with Applications to Optical Coherence Tomography
Fang, Leyuan; Li, Shutao; Kang, Xudong; Izatt, Joseph A.; Farsiu, Sina
2015-01-01
We present a novel general-purpose compression method for tomographic images, termed 3D adaptive sparse representation based compression (3D-ASRC). In this paper, we focus on applications of 3D-ASRC for the compression of ophthalmic 3D optical coherence tomography (OCT) images. The 3D-ASRC algorithm exploits correlations among adjacent OCT images to improve compression performance, yet is sensitive to preserving their differences. Due to the inherent denoising mechanism of the sparsity based 3D-ASRC, the quality of the compressed images are often better than the raw images they are based on. Experiments on clinical-grade retinal OCT images demonstrate the superiority of the proposed 3D-ASRC over other well-known compression methods. PMID:25561591
Designing experiments through compressed sensing.
Young, Joseph G.; Ridzal, Denis
2013-06-01
In the following paper, we discuss how to design an ensemble of experiments through the use of compressed sensing. Specifically, we show how to conduct a small number of physical experiments and then use compressed sensing to reconstruct a larger set of data. In order to accomplish this, we organize our results into four sections. We begin by extending the theory of compressed sensing to a finite product of Hilbert spaces. Then, we show how these results apply to experiment design. Next, we develop an efficient reconstruction algorithm that allows us to reconstruct experimental data projected onto a finite element basis. Finally, we verify our approach with two computational experiments.
Compressive Sensing for Quantum Imaging
NASA Astrophysics Data System (ADS)
Howland, Gregory A.
This thesis describes the application of compressive sensing to several challenging problems in quantum imaging with practical and fundamental implications. Compressive sensing is a measurement technique that compresses a signal during measurement such that it can be dramatically undersampled. Compressive sensing has been shown to be an extremely efficient measurement technique for imaging, particularly when detector arrays are not available. The thesis first reviews compressive sensing through the lens of quantum imaging and quantum measurement. Four important applications and their corresponding experiments are then described in detail. The first application is a compressive sensing, photon-counting lidar system. A novel depth mapping technique that uses standard, linear compressive sensing is described. Depth maps up to 256 x 256 pixel transverse resolution are recovered with depth resolution less than 2.54 cm. The first three-dimensional, photon counting video is recorded at 32 x 32 pixel resolution and 14 frames-per-second. The second application is the use of compressive sensing for complementary imaging---simultaneously imaging the transverse-position and transverse-momentum distributions of optical photons. This is accomplished by taking random, partial projections of position followed by imaging the momentum distribution on a cooled CCD camera. The projections are shown to not significantly perturb the photons' momenta while allowing high resolution position images to be reconstructed using compressive sensing. A variety of objects and their diffraction patterns are imaged including the double slit, triple slit, alphanumeric characters, and the University of Rochester logo. The third application is the use of compressive sensing to characterize spatial entanglement of photon pairs produced by spontaneous parametric downconversion. The technique gives a theoretical speedup N2/log N for N-dimensional entanglement over the standard raster scanning technique
Hyperspectral image compression: adapting SPIHT and EZW to anisotropic 3-D wavelet coding.
Christophe, Emmanuel; Mailhes, Corinne; Duhamel, Pierre
2008-12-01
Hyperspectral images present some specific characteristics that should be used by an efficient compression system. In compression, wavelets have shown a good adaptability to a wide range of data, while being of reasonable complexity. Some wavelet-based compression algorithms have been successfully used for some hyperspectral space missions. This paper focuses on the optimization of a full wavelet compression system for hyperspectral images. Each step of the compression algorithm is studied and optimized. First, an algorithm to find the optimal 3-D wavelet decomposition in a rate-distortion sense is defined. Then, it is shown that a specific fixed decomposition has almost the same performance, while being more useful in terms of complexity issues. It is shown that this decomposition significantly improves the classical isotropic decomposition. One of the most useful properties of this fixed decomposition is that it allows the use of zero tree algorithms. Various tree structures, creating a relationship between coefficients, are compared. Two efficient compression methods based on zerotree coding (EZW and SPIHT) are adapted on this near-optimal decomposition with the best tree structure found. Performances are compared with the adaptation of JPEG 2000 for hyperspectral images on six different areas presenting different statistical properties.
NASA Astrophysics Data System (ADS)
Mekuria, Rufael; Cesar, Pablo; Doumanis, Ioannis; Frisiello, Antonella
2015-09-01
Compression of 3D object based video is relevant for 3D Immersive applications. Nevertheless, the perceptual aspects of the degradation introduced by codecs for meshes and point clouds are not well understood. In this paper we evaluate the subjective and objective degradations introduced by such codecs in a state of art 3D immersive virtual room. In the 3D immersive virtual room, users are captured with multiple cameras, and their surfaces are reconstructed as photorealistic colored/textured 3D meshes or point clouds. To test the perceptual effect of compression and transmission, we render degraded versions with different frame rates in different contexts (near/far) in the scene. A quantitative subjective study with 16 users shows that negligible distortion of decoded surfaces compared to the original reconstructions can be achieved in the 3D virtual room. In addition, a qualitative task based analysis in a full prototype field trial shows increased presence, emotion, user and state recognition of the reconstructed 3D Human representation compared to animated computer avatars.
3D temperature field reconstruction using ultrasound sensing system
NASA Astrophysics Data System (ADS)
Liu, Yuqian; Ma, Tong; Cao, Chengyu; Wang, Xingwei
2016-04-01
3D temperature field reconstruction is of practical interest to the power, transportation and aviation industries and it also opens up opportunities for real time control or optimization of high temperature fluid or combustion process. In our paper, a new distributed optical fiber sensing system consisting of a series of elements will be used to generate and receive acoustic signals. This system is the first active temperature field sensing system that features the advantages of the optical fiber sensors (distributed sensing capability) and the acoustic sensors (non-contact measurement). Signals along multiple paths will be measured simultaneously enabled by a code division multiple access (CDMA) technique. Then a proposed Gaussian Radial Basis Functions (GRBF)-based approach can approximate the temperature field as a finite summation of space-dependent basis functions and time-dependent coefficients. The travel time of the acoustic signals depends on the temperature of the media. On this basis, the Gaussian functions are integrated along a number of paths which are determined by the number and distribution of sensors. The inversion problem to estimate the unknown parameters of the Gaussian functions can be solved with the measured times-of-flight (ToF) of acoustic waves and the length of propagation paths using the recursive least square method (RLS). The simulation results show an approximation error less than 2% in 2D and 5% in 3D respectively. It demonstrates the availability and efficiency of our proposed 3D temperature field reconstruction mechanism.
3-D wavelet compression and progressive inverse wavelet synthesis rendering of concentric mosaic.
Luo, Lin; Wu, Yunnan; Li, Jin; Zhang, Ya-Qin
2002-01-01
Using an array of photo shots, the concentric mosaic offers a quick way to capture and model a realistic three-dimensional (3-D) environment. We compress the concentric mosaic image array with a 3-D wavelet transform and coding scheme. Our compression algorithm and bitstream syntax are designed to ensure that a local view rendering of the environment requires only a partial bitstream, thereby eliminating the need to decompress the entire compressed bitstream before rendering. By exploiting the ladder-like structure of the wavelet lifting scheme, the progressive inverse wavelet synthesis (PIWS) algorithm is proposed to maximally reduce the computational cost of selective data accesses on such wavelet compressed datasets. Experimental results show that the 3-D wavelet coder achieves high-compression performance. With the PIWS algorithm, a 3-D environment can be rendered in real time from a compressed dataset.
Compressed sensing for phase retrieval.
Newton, Marcus C
2012-05-01
To date there are several iterative techniques that enjoy moderate success when reconstructing phase information, where only intensity measurements are made. There remains, however, a number of cases in which conventional approaches are unsuccessful. In the last decade, the theory of compressed sensing has emerged and provides a route to solving convex optimisation problems exactly via ℓ(1)-norm minimization. Here the application of compressed sensing to phase retrieval in a nonconvex setting is reported. An algorithm is presented that applies reweighted ℓ(1)-norm minimization to yield accurate reconstruction where conventional methods fail.
Compressive Sensing with Optical Chaos
NASA Astrophysics Data System (ADS)
Rontani, D.; Choi, D.; Chang, C.-Y.; Locquet, A.; Citrin, D. S.
2016-12-01
Compressive sensing (CS) is a technique to sample a sparse signal below the Nyquist-Shannon limit, yet still enabling its reconstruction. As such, CS permits an extremely parsimonious way to store and transmit large and important classes of signals and images that would be far more data intensive should they be sampled following the prescription of the Nyquist-Shannon theorem. CS has found applications as diverse as seismology and biomedical imaging. In this work, we use actual optical signals generated from temporal intensity chaos from external-cavity semiconductor lasers (ECSL) to construct the sensing matrix that is employed to compress a sparse signal. The chaotic time series produced having their relevant dynamics on the 100 ps timescale, our results open the way to ultrahigh-speed compression of sparse signals.
Compressive Sensing with Optical Chaos
Rontani, D.; Choi, D.; Chang, C.-Y.; Locquet, A.; Citrin, D. S.
2016-01-01
Compressive sensing (CS) is a technique to sample a sparse signal below the Nyquist-Shannon limit, yet still enabling its reconstruction. As such, CS permits an extremely parsimonious way to store and transmit large and important classes of signals and images that would be far more data intensive should they be sampled following the prescription of the Nyquist-Shannon theorem. CS has found applications as diverse as seismology and biomedical imaging. In this work, we use actual optical signals generated from temporal intensity chaos from external-cavity semiconductor lasers (ECSL) to construct the sensing matrix that is employed to compress a sparse signal. The chaotic time series produced having their relevant dynamics on the 100 ps timescale, our results open the way to ultrahigh-speed compression of sparse signals. PMID:27910863
Volumetric medical image compression using 3D listless embedded block partitioning.
Senapati, Ranjan K; Prasad, P M K; Swain, Gandharba; Shankar, T N
2016-01-01
This paper presents a listless variant of a modified three-dimensional (3D)-block coding algorithm suitable for medical image compression. A higher degree of correlation is achieved by using a 3D hybrid transform. The 3D hybrid transform is performed by a wavelet transform in the spatial dimension and a Karhunen-Loueve transform in the spectral dimension. The 3D transformed coefficients are arranged in a one-dimensional (1D) fashion, as in the hierarchical nature of the wavelet-coefficient distribution strategy. A novel listless block coding algorithm is applied to the mapped 1D coefficients which encode in an ordered-bit-plane fashion. The algorithm originates from the most significant bit plane and terminates at the least significant bit plane to generate an embedded bit stream, as in 3D-SPIHT. The proposed algorithm is called 3D hierarchical listless block (3D-HLCK), which exhibits better compression performance than that exhibited by 3D-SPIHT. Further, it is highly competitive with some of the state-of-the-art 3D wavelet coders for a wide range of bit rates for magnetic resonance, digital imaging and communication in medicine and angiogram images. 3D-HLCK provides rate and resolution scalability similar to those provided by 3D-SPIHT and 3D-SPECK. In addition, a significant memory reduction is achieved owing to the listless nature of 3D-HLCK.
A Cartesian scheme for compressible multimaterial models in 3D
NASA Astrophysics Data System (ADS)
de Brauer, Alexia; Iollo, Angelo; Milcent, Thomas
2016-05-01
We model the three-dimensional interaction of compressible materials separated by sharp interfaces. We simulate fluid and hyperelastic solid flows in a fully Eulerian framework. The scheme is the same for all materials and can handle large deformations and frictionless contacts. Necessary conditions for hyperbolicity of the hyperelastic neohookean model in three dimensions are proved thanks to an explicit computation of the characteristic speeds. We present stiff multimaterial interactions including air-helium and water-air shock interactions, projectile-shield impacts in air and rebounds.
Towards an efficient compression of 3D coordinates of macromolecular structures
Valasatava, Yana; Bradley, Anthony R.; Rose, Alexander S.; Duarte, Jose M.; Prlić, Andreas
2017-01-01
The size and complexity of 3D macromolecular structures available in the Protein Data Bank is constantly growing. Current tools and file formats have reached limits of scalability. New compression approaches are required to support the visualization of large molecular complexes and enable new and scalable means for data analysis. We evaluated a series of compression techniques for coordinates of 3D macromolecular structures and identified the best performing approaches. By balancing compression efficiency in terms of the decompression speed and compression ratio, and code complexity, our results provide the foundation for a novel standard to represent macromolecular coordinates in a compact and useful file format. PMID:28362865
Towards an efficient compression of 3D coordinates of macromolecular structures.
Valasatava, Yana; Bradley, Anthony R; Rose, Alexander S; Duarte, Jose M; Prlić, Andreas; Rose, Peter W
2017-01-01
The size and complexity of 3D macromolecular structures available in the Protein Data Bank is constantly growing. Current tools and file formats have reached limits of scalability. New compression approaches are required to support the visualization of large molecular complexes and enable new and scalable means for data analysis. We evaluated a series of compression techniques for coordinates of 3D macromolecular structures and identified the best performing approaches. By balancing compression efficiency in terms of the decompression speed and compression ratio, and code complexity, our results provide the foundation for a novel standard to represent macromolecular coordinates in a compact and useful file format.
Introduction to the special section on 3D representation, compression, and rendering.
Vetro, Anthony; Frossard, Pascal; Lee, Sanghoon; Mueller, Karsten; Ohm, Jens-Rainer; Sullivan, Gary
2013-09-01
A new set of three-dimensional (3D) data formats and associated compression technologies are emerging with the aim to achieve more flexible representation and higher compression of 3D and multiview video content. These new tools will facilitate the generation of multiview output (e.g., as needed for multiview auto-stereoscopic displays), provide richer immersive multimedia experiences, and allow new interactive applications. This special section includes a timely set of papers covering the most recent technical developments in this area with papers covering topics in the different aspects of 3D systems, from representation and compression algorithms to rendering techniques and quality assessment. This special section includes a good balance on topics that are of interest to academic, industrial, and standardization communities. We believe that this collection of papers represent the most recent advances in representation, compression, rendering, and quality assessment of 3D scenes.
Deterministic sensing matrices in compressive sensing: a survey.
Nguyen, Thu L N; Shin, Yoan
2013-01-01
Compressive sensing is a sampling method which provides a new approach to efficient signal compression and recovery by exploiting the fact that a sparse signal can be suitably reconstructed from very few measurements. One of the most concerns in compressive sensing is the construction of the sensing matrices. While random sensing matrices have been widely studied, only a few deterministic sensing matrices have been considered. These matrices are highly desirable on structure which allows fast implementation with reduced storage requirements. In this paper, a survey of deterministic sensing matrices for compressive sensing is presented. We introduce a basic problem in compressive sensing and some disadvantage of the random sensing matrices. Some recent results on construction of the deterministic sensing matrices are discussed.
Investigation of out of plane compressive strength of 3D printed sandwich composites
NASA Astrophysics Data System (ADS)
Dikshit, V.; Yap, Y. L.; Goh, G. D.; Yang, H.; Lim, J. C.; Qi, X.; Yeong, W. Y.; Wei, J.
2016-07-01
In this study, the 3D printing technique was utilized to manufacture the sandwich composites. Composite filament fabrication based 3D printer was used to print the face-sheet, and inkjet 3D printer was used to print the sandwich core structure. This work aims to study the compressive failure of the sandwich structure manufactured by using these two manufacturing techniques. Two different types of core structures were investigated with the same type of face-sheet configuration. The core structures were printed using photopolymer, while the face-sheet was made using nylon/glass. The out-of-plane compressive strength of the 3D printed sandwich composite structure has been examined in accordance with ASTM standards C365/C365-M and presented in this paper.
Tension and compression fatigue response of unnotched 3D braided composites
NASA Technical Reports Server (NTRS)
Portanova, M. A.
1992-01-01
The unnotched compression and tension fatigue response of a 3-D braided composite was measured. Both gross compressive stress and tensile stress were plotted against cycles to failure to evaluate the fatigue life of these materials. Damage initiation and growth was monitored visually and by tracking compliance change during cycle loading. The intent was to establish by what means the strength of a 3-D architecture will start to degrade, at what point will it degrade beyond an acceptable level, and how this material will typically fail.
First application of the 3D-MHB on dynamic compressive behavior of UHPC
NASA Astrophysics Data System (ADS)
Cadoni, Ezio; Dotta, Matteo; Forni, Daniele; Riganti, Gianmario; Albertini, Carlo
2015-09-01
In order to study the dynamic behaviour of material in confined conditions a new machine was conceived and called 3D-Modified Hopkinson Bar (3D-MHB). It is a Modified Hopkinson Bar apparatus designed to apply dynamic loading in materials having a tri-axial stress state. It consists of a pulse generator system (with pre-tensioned bar and brittle joint), 1 input bar, and 5 output bars. The first results obtained on Ultra High Performance Concrete in compression with three different mono-axial compression states are presented. The results show how the pre-stress states minimize the boundary condition and a more uniform response is obtained.
A Novel Image Compression Algorithm for High Resolution 3D Reconstruction
NASA Astrophysics Data System (ADS)
Siddeq, M. M.; Rodrigues, M. A.
2014-06-01
This research presents a novel algorithm to compress high-resolution images for accurate structured light 3D reconstruction. Structured light images contain a pattern of light and shadows projected on the surface of the object, which are captured by the sensor at very high resolutions. Our algorithm is concerned with compressing such images to a high degree with minimum loss without adversely affecting 3D reconstruction. The Compression Algorithm starts with a single level discrete wavelet transform (DWT) for decomposing an image into four sub-bands. The sub-band LL is transformed by DCT yielding a DC-matrix and an AC-matrix. The Minimize-Matrix-Size Algorithm is used to compress the AC-matrix while a DWT is applied again to the DC-matrix resulting in LL2, HL2, LH2 and HH2 sub-bands. The LL2 sub-band is transformed by DCT, while the Minimize-Matrix-Size Algorithm is applied to the other sub-bands. The proposed algorithm has been tested with images of different sizes within a 3D reconstruction scenario. The algorithm is demonstrated to be more effective than JPEG2000 and JPEG concerning higher compression rates with equivalent perceived quality and the ability to more accurately reconstruct the 3D models.
Compressive Behavior of 3D Woven Composite Stiffened Panels: Experimental and Numerical Study
NASA Astrophysics Data System (ADS)
Zhou, Guangming; Pan, Ruqin; Li, Chao; Cai, Deng'an; Wang, Xiaopei
2016-10-01
The structural behavior and damage propagation of 3D woven composite stiffened panels with different woven patterns under axial-compression are here investigated. The panel is 2.5D interlock woven composites (2.5DIWC), while the straight-stiffeners are 3D woven orthogonal composites (3DWOC). They are coupled together with the Z-fibers from the stiffener passing straight thought the thickness of the panel. A "T-shape" model, in which the fiber bundle structure and resin matrix are drawn out to simulate the real situation of the connection area, is established to predict elastic constants and strength of the connection region. Based on Hashin failure criterion, a progressive damage model is carried out to simulate the compressive behavior of the stiffened panel. The 3D woven composite stiffened panels are manufactured using RTM process and then tested. A good agreement between experimental results and numerical predicted values for the compressive failure load is obtained. From initial damage to final collapse, the panel and stiffeners will not separate each other in the connection region. The main failure mode of 3D woven composite stiffened panels is compressive failure of fiber near the loading end corner.
JP3D compression of solar data-cubes: Photospheric imaging and spectropolarimetry
NASA Astrophysics Data System (ADS)
Del Moro, Dario; Giovannelli, Luca; Pietropaolo, Ermanno; Berrilli, Francesco
2017-02-01
Hyperspectral imaging is an ubiquitous technique in solar physics observations and the recent advances in solar instrumentation enabled us to acquire and record data at an unprecedented rate. The huge amount of data which will be archived in the upcoming solar observatories press us to compress the data in order to reduce the storage space and transfer times. The correlation present over all dimensions, spatial, temporal and spectral, of solar data-sets suggests the use of a 3D base wavelet decomposition, to achieve higher compression rates. In this work, we evaluate the performance of the recent JPEG2000 Part 10 standard, known as JP3D, for the lossless compression of several types of solar data-cubes. We explore the differences in: a) The compressibility of broad-band or narrow-band time-sequence; I or V Stokes profiles in spectropolarimetric data-sets; b) Compressing data in [x,y, λ] packages at different times or data in [x,y,t] packages of different wavelength; c) Compressing a single large data-cube or several smaller data-cubes; d) Compressing data which is under-sampled or super-sampled with respect to the diffraction cut-off.
Accurate compressed look up table method for CGH in 3D holographic display.
Gao, Chuan; Liu, Juan; Li, Xin; Xue, Gaolei; Jia, Jia; Wang, Yongtian
2015-12-28
Computer generated hologram (CGH) should be obtained with high accuracy and high speed in 3D holographic display, and most researches focus on the high speed. In this paper, a simple and effective computation method for CGH is proposed based on Fresnel diffraction theory and look up table. Numerical simulations and optical experiments are performed to demonstrate its feasibility. The proposed method can obtain more accurate reconstructed images with lower memory usage compared with split look up table method and compressed look up table method without sacrificing the computational speed in holograms generation, so it is called accurate compressed look up table method (AC-LUT). It is believed that AC-LUT method is an effective method to calculate the CGH of 3D objects for real-time 3D holographic display where the huge information data is required, and it could provide fast and accurate digital transmission in various dynamic optical fields in the future.
3D hydrodynamic focusing microfluidics for emerging sensing technologies.
Daniele, Michael A; Boyd, Darryl A; Mott, David R; Ligler, Frances S
2015-05-15
While the physics behind laminar flows has been studied for 200 years, understanding of how to use parallel flows to augment the capabilities of microfluidic systems has been a subject of study primarily over the last decade. The use of one flow to focus another within a microfluidic channel has graduated from a two-dimensional to a three-dimensional process and the design principles are only now becoming established. This review explores the underlying principles for hydrodynamic focusing in three dimensions (3D) using miscible fluids and the application of these principles for creation of biosensors, separation of cells and particles for sample manipulation, and fabrication of materials that could be used for biosensors. Where sufficient information is available, the practicality of devices implementing fluid flows directed in 3D is evaluated and the advantages and limitations of 3D hydrodynamic focusing for the particular application are highlighted.
Subjective evaluation of mobile 3D video content: depth range versus compression artifacts
NASA Astrophysics Data System (ADS)
Jumisko-Pyykkö, Satu; Haustola, Tomi; Boev, Atanas; Gotchev, Atanas
2011-02-01
Mobile 3D television is a new form of media experience, which combines the freedom of mobility with the greater realism of presenting visual scenes in 3D. Achieving this combination is a challenging task as greater viewing experience has to be achieved with the limited resources of the mobile delivery channel such as limited bandwidth and power constrained handheld player. This challenge sets need for tight optimization of the overall mobile 3DTV system. Presence of depth and compression artifacts in the played 3D video are two major factors that influence viewer's subjective quality of experience and satisfaction. The primary goal of this study has been to examine the influence of varying depth and compression artifacts on the subjective quality of experience for mobile 3D video content. In addition, the influence of the studied variables on simulator sickness symptoms has been studied and vocabulary-based descriptive quality of experience has been conducted for a sub-set of variables in order to understand the perceptual characteristics in detail. In the experiment, 30 participants have evaluated the overall quality of different 3D video contents with varying depth ranges and compressed with varying quantization parameters. The test video content has been presented on a portable autostereoscopic LCD display with horizontal double density pixel arrangement. The results of the psychometric study indicate that compression artifacts are a dominant factor determining the quality of experience compared to varying depth range. More specifically, contents with strong compression has been rejected by the viewers and deemed unacceptable. The results of descriptive study confirm the dominance of visible spatial artifacts along the added value of depth for artifact-free content. The level of visual discomfort has been determined as not offending.
JP3D compressed-domain watermarking of volumetric medical data sets
NASA Astrophysics Data System (ADS)
Ouled Zaid, Azza; Makhloufi, Achraf; Olivier, Christian
2010-01-01
Increasing transmission of medical data across multiple user systems raises concerns for medical image watermarking. Additionaly, the use of volumetric images triggers the need for efficient compression techniques in picture archiving and communication systems (PACS), or telemedicine applications. This paper describes an hybrid data hiding/compression system, adapted to volumetric medical imaging. The central contribution is to integrate blind watermarking, based on turbo trellis-coded quantization (TCQ), to JP3D encoder. Results of our method applied to Magnetic Resonance (MR) and Computed Tomography (CT) medical images have shown that our watermarking scheme is robust to JP3D compression attacks and can provide relative high data embedding rate whereas keep a relative lower distortion.
Photon counting passive 3D image sensing for automatic target recognition.
Yeom, Seokwon; Javidi, Bahram; Watson, Edward
2005-11-14
In this paper, we propose photon counting three-dimensional (3D) passive sensing and object recognition using integral imaging. The application of this approach to 3D automatic target recognition (ATR) is investigated using both linear and nonlinear matched filters. We find there is significant potential of the proposed system for 3D sensing and recognition with a low number of photons. The discrimination capability of the proposed system is quantified in terms of discrimination ratio, Fisher ratio, and receiver operating characteristic (ROC) curves. To the best of our knowledge, this is the first report on photon counting 3D passive sensing and ATR with integral imaging.
Spatial Compressive Sensing for Strain Data Reconstruction from Sparse Sensors
2014-10-01
the novel theory of compressive sensing and principles of continuum mechanics. Compressive sensing , also known as compressed sensing , refers to the...asserts that certain signals or images can be recovered from what was previously believed to be a highly incomplete measurement. Compressed sensing ...matrix completion problem is quite similar to compressive sensing , as a similar heuristic approach , convex relaxation, is used to recover
New Theory and Algorithms for Compressive Sensing
2009-03-06
measurement device has limited computational resources (as in a sensor network ). Fortunately, over the past two years a new theory of Compressive Sensing... neural circuits,” Neural Computation, vol. 20, pp. 2526–2563. S. Sarvotham, D. Baron, and R. Baraniuk, “ Measurements vs. bits: Compressed sensing meets... measurements that corresponds to the problem structure, rather than bandwidth. Second, we improved on previous work in distributed compressive
Lossy compression of hyperspectral images using shearlet transform and 3D SPECK
NASA Astrophysics Data System (ADS)
Karami, A.
2015-10-01
In this paper, a new lossy compression method for hyperspectral images (HSI) is introduced. HSI are considered as a 3D dataset with two dimensions in the spatial and one dimension in the spectral domain. In the proposed method, first 3D multidirectional anisotropic shearlet transform is applied to the HSI. Because, unlike traditional wavelets, shearlets are theoretically optimal in representing images with edges and other geometrical features. Second, soft thresholding method is applied to the shearlet transform coefficients and finally the modified coefficients are encoded using Three Dimensional- Set Partitioned Embedded bloCK (3D SPECK). Our simulation results show that the proposed method, in comparison with well-known approaches such as 3D SPECK (using 3D wavelet) and combined PCA and JPEG2000 algorithms, provides a higher SNR (signal to noise ratio) for any given compression ratio (CR). It is noteworthy to mention that the superiority of proposed method is distinguishable as the value of CR grows. In addition, the effect of proposed method on the spectral unmixing analysis is also evaluated.
Compressive sensing for nuclear security.
Gestner, Brian Joseph
2013-12-01
Special nuclear material (SNM) detection has applications in nuclear material control, treaty verification, and national security. The neutron and gamma-ray radiation signature of SNMs can be indirectly observed in scintillator materials, which fluoresce when exposed to this radiation. A photomultiplier tube (PMT) coupled to the scintillator material is often used to convert this weak fluorescence to an electrical output signal. The fluorescence produced by a neutron interaction event differs from that of a gamma-ray interaction event, leading to a slightly different pulse in the PMT output signal. The ability to distinguish between these pulse types, i.e., pulse shape discrimination (PSD), has enabled applications such as neutron spectroscopy, neutron scatter cameras, and dual-mode neutron/gamma-ray imagers. In this research, we explore the use of compressive sensing to guide the development of novel mixed-signal hardware for PMT output signal acquisition. Effectively, we explore smart digitizers that extract sufficient information for PSD while requiring a considerably lower sample rate than conventional digitizers. Given that we determine the feasibility of realizing these designs in custom low-power analog integrated circuits, this research enables the incorporation of SNM detection into wireless sensor networks.
Sensing and 3D Mapping of Soil Compaction
Tekin, Yücel; Kul, Basri; Okursoy, Rasim
2008-01-01
Soil compaction is an important physical limiting factor for the root growth and plant emergence and is one of the major causes for reduced crop yield worldwide. The objective of this study was to generate 2D/3D soil compaction maps for different depth layers of the soil. To do so, a soil penetrometer was designed, which was mounted on the three-point hitch of an agricultural tractor, consisting of a mechanical system, data acquisition system (DAS), and 2D/3D imaging and analysis software. The system was successfully tested in field conditions, measuring soil penetration resistances as a function of depth from 0 to 40 cm at 1 cm intervals. The software allows user to either tabulate the measured quantities or generate maps as soon as data collection has been terminated. The system may also incorporate GPS data to create geo-referenced soil maps. The software enables the user to graph penetration resistances at a specified coordinate. Alternately, soil compaction maps could be generated using data collected from multiple coordinates. The data could be automatically stratified to determine soil compaction distribution at different layers of 5, 10,.…, 40 cm depths. It was concluded that the system tested in this study could be used to assess the soil compaction at topsoil and the randomly distributed hardpan formations just below the common tillage depths, enabling visualization of spatial variability through the imaging software. PMID:27879888
Mechanical response of 3D Insert(®) PCL to compression.
Brunelli, M; Perrault, C M; Lacroix, D
2017-01-01
3D polymeric scaffolds are increasingly used for in vitro experiments aiming to mimic the environment found in vivo, to support for cellular growth and to induce differentiation through the application of external mechanical cues. In research, experimental results must be shown to be reproducible to be claimed as valid and the first clause to ensure consistency is to provide identical initial experimental conditions between trials. As a matter of fact, 3D structures fabricated in batch are supposed to present a highly reproducible geometry and consequently, to give the same bulk response to mechanical forces. This study aims to measure the overall mechanical response to compression of commercially available 3D Insert PCL scaffolds (3D PCL) fabricated in series by fuse deposition and evaluate how small changes in the architecture of scaffolds affect the mechanical response. The apparent elastic modulus (Ea) was evaluated by performing quasi-static mechanical tests at various temperatures showing a decrease in material stiffness from 5MPa at 25°C to 2.2MPa at 37°C. Then, a variability analysis revealed variations in Ea related to the repositioning of the sample into the testing machine, but also consistent differences comparing different scaffolds. To clarify the source of the differences measured in the mechanical response, the same scaffolds previously undergoing compression, were scanned by micro computed tomography (μCT) to identify any architectural difference. Eventually, to clarify the contribution given by differences in the architecture to the standard deviation of Ea, their mechanical response was qualitatively compared to a compact reference material such as polydimethylsiloxane (PDMS). This study links the geometry, architecture and mechanical response to compression of 3D PCL scaffolds and shows the importance of controlling such parameters in the manufacturing process to obtain scaffolds that can be used in vitro or in vivo under reproducible
NASA Astrophysics Data System (ADS)
Yang, L. M.; Shu, C.; Wang, Y.; Sun, Y.
2016-08-01
The sphere function-based gas kinetic scheme (GKS), which was presented by Shu and his coworkers [23] for simulation of inviscid compressible flows, is extended to simulate 3D viscous incompressible and compressible flows in this work. Firstly, we use certain discrete points to represent the spherical surface in the phase velocity space. Then, integrals along the spherical surface for conservation forms of moments, which are needed to recover 3D Navier-Stokes equations, are approximated by integral quadrature. The basic requirement is that these conservation forms of moments can be exactly satisfied by weighted summation of distribution functions at discrete points. It was found that the integral quadrature by eight discrete points on the spherical surface, which forms the D3Q8 discrete velocity model, can exactly match the integral. In this way, the conservative variables and numerical fluxes can be computed by weighted summation of distribution functions at eight discrete points. That is, the application of complicated formulations resultant from integrals can be replaced by a simple solution process. Several numerical examples including laminar flat plate boundary layer, 3D lid-driven cavity flow, steady flow through a 90° bending square duct, transonic flow around DPW-W1 wing and supersonic flow around NACA0012 airfoil are chosen to validate the proposed scheme. Numerical results demonstrate that the present scheme can provide reasonable numerical results for 3D viscous flows.
Application of Compressive Sensing to Digital Holography
2015-05-01
AFRL-RY-WP-TR-2015-0071 APPLICATION OF COMPRESSIVE SENSING TO DIGITAL HOLOGRAPHY Mark Neifeld University of Arizona...From - To) May 2015 Final 3 September 2013 – 27 February 2015 4. TITLE AND SUBTITLE APPLICATION OF COMPRESSIVE SENSING TO DIGITAL HOLOGRAPHY 5a...from under- sampled data. This work presents a new reconstruction algorithm for use with under-sampled digital holography measurements and yields
NASA Astrophysics Data System (ADS)
Huang, Bormin; Huang, Hung-Lung; Chen, Hao; Ahuja, Alok; Baggett, Kevin; Schmit, Timothy J.; Heymann, Roger W.
2004-02-01
The next-generation NOAA/NESDIS GOES-R hyperspectral sounder, now referred to as the HES (Hyperspectral Environmental Suite), will have hyperspectral resolution (over one thousand channels with spectral widths on the order of 0.5 wavenumber) and high spatial resolution (less than 10 km). Hyperspectral sounder data is a particular class of data requiring high accuracy for useful retrieval of atmospheric temperature and moisture profiles, surface characteristics, cloud properties, and trace gas information. Hence compression of these data sets is better to be lossless or near lossless. Given the large volume of three-dimensional hyperspectral sounder data that will be generated by the HES instrument, the use of robust data compression techniques will be beneficial to data transfer and archive. In this paper, we study lossless data compression for the HES using 3D integer wavelet transforms via the lifting schemes. The wavelet coefficients are processed with the 3D set partitioning in hierarchical trees (SPIHT) scheme followed by context-based arithmetic coding. SPIHT provides better coding efficiency than Shapiro's original embedded zerotree wavelet (EZW) algorithm. We extend the 3D SPIHT scheme to take on any size of 3D satellite data, each of whose dimensions need not be divisible by 2N, where N is the levels of the wavelet decomposition being performed. The compression ratios of various kinds of wavelet transforms are presented along with a comparison with the JPEG2000 codec.
NASA Astrophysics Data System (ADS)
Huang, Bormin; Huang, Hung-Lung; Chen, Hao; Ahuja, Alok; Baggett, Kevin; Schmit, Timothy J.; Heymann, Roger W.
2003-09-01
Hyperspectral sounder data is a particular class of data that requires high accuracy for useful retrieval of atmospheric temperature and moisture profiles, surface characteristics, cloud properties, and trace gas information. Therefore compression of these data sets is better to be lossless or near lossless. The next-generation NOAA/NESDIS GOES-R hyperspectral sounder, now referred to as the HES (Hyperspectral Environmental Suite), will have hyperspectral resolution (over one thousand channels with spectral widths on the order of 0.5 wavenumber) and high spatial resolution (less than 10 km). Given the large volume of three-dimensional hyperspectral sounder data that will be generated by the HES instrument, the use of robust data compression techniques will be beneficial to data transfer and archive. In this paper, we study lossless data compression for the HES using 3D integer wavelet transforms via the lifting schemes. The wavelet coefficients are then processed with the 3D embedded zerotree wavelet (EZW) algorithm followed by context-based arithmetic coding. We extend the 3D EZW scheme to take on any size of 3D satellite data, each of whose dimensions need not be divisible by 2N, where N is the levels of the wavelet decomposition being performed. The compression ratios of various kinds of wavelet transforms are presented along with a comparison with the JPEG2000 codec.
ROI-preserving 3D video compression method utilizing depth information
NASA Astrophysics Data System (ADS)
Ti, Chunli; Xu, Guodong; Guan, Yudong; Teng, Yidan
2015-09-01
Efficiently transmitting the extra information of three dimensional (3D) video is becoming a key issue of the development of 3DTV. 2D plus depth format not only occupies the smaller bandwidth and is compatible transmission under the condition of the existing channel, but also can provide technique support for advanced 3D video compression in some extend. This paper proposes an ROI-preserving compression scheme to further improve the visual quality at a limited bit rate. According to the connection between the focus of Human Visual System (HVS) and depth information, region of interest (ROI) can be automatically selected via depth map progressing. The main improvement from common method is that a meanshift based segmentation is executed to the depth map before foreground ROI selection to keep the integrity of scene. Besides, the sensitive areas along the edges are also protected. The Spatio-temporal filtering adapting to H.264 is used to the non-ROI of both 2D video and depth map before compression. Experiments indicate that, the ROI extracted by this method is more undamaged and according with subjective feeling, and the proposed method can keep the key high-frequency information more effectively while the bit rate is reduced.
NASA Astrophysics Data System (ADS)
Wei, Shih-Chieh; Huang, Bormin
2004-10-01
Hyperspectral sounder data is used for retrieval of useful geophysical parameters which promise better weather prediction. It features two characteristics. First it is huge in size with 2D spatial coverage and high spectral resolution in the infrared region. Second it allows low tolerance of noise and error in retrieving the geophysical parameters where a mathematically ill-posed problem is involved. Therefore compression is better to be lossless or near lossless for data transfer and archive. Meanwhile medical data from X-ray computerized tomography (CT) or magnetic resonance imaging (MRI) techniques also possesses similar characteristics. It provides motivation to apply lossless compression schemes for medical data to the hyperspectral sounder data. In this paper, we explore the use of a wavelet-based lossless data compression scheme for the 3D hyperspectral data which uses in sequence a forward difference scheme, an integer wavelet transform, a Burrows-Wheeler transform and an arithmetic coder. Compared to previous work, our approach is shown to outperform the CALIC and 3D EZW schemes.
Compressive sensing exploiting wavelet-domain dependencies for ECG compression
NASA Astrophysics Data System (ADS)
Polania, Luisa F.; Carrillo, Rafael E.; Blanco-Velasco, Manuel; Barner, Kenneth E.
2012-06-01
Compressive sensing (CS) is an emerging signal processing paradigm that enables sub-Nyquist sampling of sparse signals. Extensive previous work has exploited the sparse representation of ECG signals in compression applications. In this paper, we propose the use of wavelet domain dependencies to further reduce the number of samples in compressive sensing-based ECG compression while decreasing the computational complexity. R wave events manifest themselves as chains of large coefficients propagating across scales to form a connected subtree of the wavelet coefficient tree. We show that the incorporation of this connectedness as additional prior information into a modified version of the CoSaMP algorithm can significantly reduce the required number of samples to achieve good quality in the reconstruction. This approach also allows more control over the ECG signal reconstruction, in particular, the QRS complex, which is typically distorted when prior information is not included in the recovery. The compression algorithm was tested upon records selected from the MIT-BIH arrhythmia database. Simulation results show that the proposed algorithm leads to high compression ratios associated with low distortion levels relative to state-of-the-art compression algorithms.
Free boundary value problem to 3D spherically symmetric compressible Navier-Stokes-Poisson equations
NASA Astrophysics Data System (ADS)
Kong, Huihui; Li, Hai-Liang
2017-02-01
In the paper, we consider the free boundary value problem to 3D spherically symmetric compressible isentropic Navier-Stokes-Poisson equations for self-gravitating gaseous stars with γ -law pressure density function for 6/5 <γ ≤ 4/3. For stress-free boundary condition and zero flow density continuously across the free boundary, the global existence of spherically symmetric weak solutions is shown, and the regularity and long time behavior of global solution are investigated for spherically symmetric initial data with the total mass smaller than a critical mass.
Compressible magma/mantle dynamics: 3-D, adaptive simulations in ASPECT
NASA Astrophysics Data System (ADS)
Dannberg, Juliane; Heister, Timo
2016-12-01
Melt generation and migration are an important link between surface processes and the thermal and chemical evolution of the Earth's interior. However, their vastly different timescales make it difficult to study mantle convection and melt migration in a unified framework, especially for 3-D global models. And although experiments suggest an increase in melt volume of up to 20 per cent from the depth of melt generation to the surface, previous computations have neglected the individual compressibilities of the solid and the fluid phase. Here, we describe our extension of the finite element mantle convection code ASPECT that adds melt generation and migration. We use the original compressible formulation of the McKenzie equations, augmented by an equation for the conservation of energy. Applying adaptive mesh refinement to this type of problems is particularly advantageous, as the resolution can be increased in areas where melt is present and viscosity gradients are high, whereas a lower resolution is sufficient in regions without melt. Together with a high-performance, massively parallel implementation, this allows for high-resolution, 3-D, compressible, global mantle convection simulations coupled with melt migration. We evaluate the functionality and potential of this method using a series of benchmarks and model setups, compare results of the compressible and incompressible formulation, and show the effectiveness of adaptive mesh refinement when applied to melt migration. Our model of magma dynamics provides a framework for modelling processes on different scales and investigating links between processes occurring in the deep mantle and melt generation and migration. This approach could prove particularly useful applied to modelling the generation of komatiites or other melts originating in greater depths. The implementation is available in the Open Source ASPECT repository.
Expanding Window Compressed Sensing for Non-Uniform Compressible Signals
Liu, Yu; Zhu, Xuqi; Zhang, Lin; Cho, Sung Ho
2012-01-01
Many practical compressible signals like image signals or the networked data in wireless sensor networks have non-uniform support distribution in their sparse representation domain. Utilizing this prior information, a novel compressed sensing (CS) scheme with unequal protection capability is proposed in this paper by introducing a windowing strategy called expanding window compressed sensing (EW-CS). According to the importance of different parts of the signal, the signal is divided into several nested subsets, i.e., the expanding windows. Each window generates its own measurements using a random sensing matrix. The more significant elements are contained by more windows, so they are captured by more measurements. This design makes the EW-CS scheme have more convenient implementation and better overall recovery quality for non-uniform compressible signals than ordinary CS schemes. These advantages are theoretically analyzed and experimentally confirmed. Moreover, the EW-CS scheme is applied to the compressed acquisition of image signals and networked data where it also has superior performance than ordinary CS and the existing unequal protection CS schemes. PMID:23201984
Compressed sensing for bioelectric signals: a review.
Craven, Darren; McGinley, Brian; Kilmartin, Liam; Glavin, Martin; Jones, Edward
2015-03-01
This paper provides a comprehensive review of compressed sensing or compressive sampling (CS) in bioelectric signal compression applications. The aim is to provide a detailed analysis of the current trends in CS, focusing on the advantages and disadvantages in compressing different biosignals and its suitability for deployment in embedded hardware. Performance metrics such as percent root-mean-squared difference (PRD), signal-to-noise ratio (SNR), and power consumption are used to objectively quantify the capabilities of CS. Furthermore, CS is compared to state-of-the-art compression algorithms in compressing electrocardiogram (ECG) and electroencephalography (EEG) as examples of typical biosignals. The main technical challenges associated with CS are discussed along with the predicted future trends.
Compression of 3D Point Clouds Using a Region-Adaptive Hierarchical Transform.
De Queiroz, Ricardo; Chou, Philip A
2016-06-01
In free-viewpoint video, there is a recent trend to represent scene objects as solids rather than using multiple depth maps. Point clouds have been used in computer graphics for a long time and with the recent possibility of real time capturing and rendering, point clouds have been favored over meshes in order to save computation. Each point in the cloud is associated with its 3D position and its color. We devise a method to compress the colors in point clouds which is based on a hierarchical transform and arithmetic coding. The transform is a hierarchical sub-band transform that resembles an adaptive variation of a Haar wavelet. The arithmetic encoding of the coefficients assumes Laplace distributions, one per sub-band. The Laplace parameter for each distribution is transmitted to the decoder using a custom method. The geometry of the point cloud is encoded using the well-established octtree scanning. Results show that the proposed solution performs comparably to the current state-of-the-art, in many occasions outperforming it, while being much more computationally efficient. We believe this work represents the state-of-the-art in intra-frame compression of point clouds for real-time 3D video.
Shale nanopore reconstruction with compressive sensing
NASA Astrophysics Data System (ADS)
Guo, Long; Xiao, Lizhi
2017-03-01
With increasing global demand for energy resources, shale gas has been paid considerable attention in recent years. Nanopore geometry is the basis for all microscopic rock physics and petrophysical numerical experiments for shale. At present, nano digital cores can be acquired via thin section reconstruction, nanometer-scale x-ray computed tomography (nano-CT), and focused ion beam and scanning electron microscopy (FIB-SEM). FIB-SEM detects nanoscale pores in the xy-plane with a resolution of up to 0.8 nm voxel‑1, and it is usually provides higher resolution than nano-CT. The main workload associated with FIB-SEM is the need to recut the sample many times and scan every section, with these then being overlaid to create a three-dimensional (3D) pore model. Each cutting distance can be ascertained, but this cannot be controlled precisely because of the fundamental limits of focused ion beams. Many interpolation methods can be used to fit the anisotropy resolution. However, these methods can also alter the geometry of the pores. Nanopores that are close to the limiting resolution are particularly susceptible to stretching. Linear interpolation is likely to lengthen the pores in the low-resolution direction. The subsequent calculation of sensitive physical attributes will be affected by geometric alterations. Through foundational work in the compressive sensing (CS) method, we present a reconstruction workflow for maintaining the pore shape using prior knowledge and reliable information. The images are reassembled with equal distance, so the nanoscale structures can have a resolution of unity in three dimensions.
Fast electron microscopy via compressive sensing
Larson, Kurt W; Anderson, Hyrum S; Wheeler, Jason W
2014-12-09
Various technologies described herein pertain to compressive sensing electron microscopy. A compressive sensing electron microscope includes a multi-beam generator and a detector. The multi-beam generator emits a sequence of electron patterns over time. Each of the electron patterns can include a plurality of electron beams, where the plurality of electron beams is configured to impart a spatially varying electron density on a sample. Further, the spatially varying electron density varies between each of the electron patterns in the sequence. Moreover, the detector collects signals respectively corresponding to interactions between the sample and each of the electron patterns in the sequence.
High-resolution three-dimensional imaging with compress sensing
NASA Astrophysics Data System (ADS)
Wang, Jingyi; Ke, Jun
2016-10-01
LIDAR three-dimensional imaging technology have been used in many fields, such as military detection. However, LIDAR require extremely fast data acquisition speed. This makes the manufacture of detector array for LIDAR system is very difficult. To solve this problem, we consider using compress sensing which can greatly decrease the data acquisition and relax the requirement of a detection device. To use the compressive sensing idea, a spatial light modulator will be used to modulate the pulsed light source. Then a photodetector is used to receive the reflected light. A convex optimization problem is solved to reconstruct the 2D depth map of the object. To improve the resolution in transversal direction, we use multiframe image restoration technology. For each 2D piecewise-planar scene, we move the SLM half-pixel each time. Then the position where the modulated light illuminates will changed accordingly. We repeat moving the SLM to four different directions. Then we can get four low-resolution depth maps with different details of the same plane scene. If we use all of the measurements obtained by the subpixel movements, we can reconstruct a high-resolution depth map of the sense. A linear minimum-mean-square error algorithm is used for the reconstruction. By combining compress sensing and multiframe image restoration technology, we reduce the burden on data analyze and improve the efficiency of detection. More importantly, we obtain high-resolution depth maps of a 3D scene.
Compressed Sensing Meets Wave Chaology
NASA Astrophysics Data System (ADS)
Pinto, Innocenzo M.; Addesso, Paolo; Principe, Maria
2015-03-01
The Wigner distribution is an important tool in the study of high-frequency wave-packet dynamics in ray-chaotic enclosures. Smoothing the Wigner distribution helps improving its readability, by suppressing nonlinear artifacts, but spoils its resolution. Adding a sparsity constraint to smoothing, in the spirit of the compressed coding paradigm, restores resolution while still avoiding artifacts. The result is particularly valuable in the perspective of complexity gauging via Renyi-Wehrl entropy measures. Representative numerical experiments are presented to substantiate such clues.
Yamauchi, Takahiro; Kitai, Ryuhei; Neishi, Hiroyuki; Tsunetoshi, Kenzo; Matsuda, Ken; Arishima, Hidetaka; Kodera, Toshiaki; Arai, Yoshikazu; Takeuchi, Hiroaki; Kikuta, Ken-ichiro
2014-02-01
We report the usefulness of 3D-FIESTA magnetic resonance imaging(MRI)for the detection of oculomotor nerve palsy in a case of pituitary apoplexy. A 69-year-old man with diabetes mellitus presented with complete left-side blepharoptosis. Computed tomography of the brain showed an intrasellar mass with hemorrhage. MRI demonstrated a pituitary adenoma with a cyst toward the left cavernous sinus, which was diagnosed as pituitary apoplexy. 3D-FIESTA revealed that the left oculomotor nerve was compressed by the cyst. He underwent trans-sphenoid tumor resection at 5 days after his hospitalization. Post-operative 3D-FIESTA MRI revealed decrease in compression of the left oculomotor nerve by the cyst. His left oculomotor palsy recovered completely within a few months. Oculomotor nerve palsy can occur due to various diseases, and 3D-FIESTA MRI is useful for detection of oculomotor nerve compression, especially in the field of parasellar lesions.
Inductively Driven, 3D Liner Compression of a Magnetized Plasma to Megabar Energy Densities
Slough, John
2015-02-01
modules. The additional energy and switching capability proposed will thus provide for optimal utilization of the liner energy. The following tasks were outlined for the three year effort: (1) Design and assemble the foil liner compression test structure and chamber including the compression bank and test foils [Year 1]. (2) Perform foil liner compression experiments and obtain performance data over a range on liner dimensions and bank parameters [Year 2]. (3) Carry out compression experiments of the FRC plasma to Megagauss fields and measure key fusion parameters [Year 3]. (4) Develop numerical codes and analyze experimental results, and determine the physics and scaling for future work [Year 1-3]. The principle task of the project was to design and assemble the foil liner FRC formation chamber, the full compression test structure and chamber including the compression bank. This task was completed successfully. The second task was to test foils in the test facility constructed in year one and characterize the performance obtained from liner compression. These experimental measurements were then compared with analytical predictions, and numerical code results. The liner testing was completed and compared with both the analytical results as well as the code work performed with the 3D structural dynamics package of ANSYS Metaphysics®. This code is capable of modeling the dynamic behavior of materials well into the non-linear regime (e.g. a bullet hit plate glass). The liner dynamic behavior was found to be remarkably close to that predicted by the 3D structural dynamics results. Incorporating a code that can also include the magnetics and plasma physics has also made significant progress at the UW. The remaining test bed construction and assembly task is was completed, and the FRC formation and merging experiments were carried out as planned. The liner compression of the FRC to Megagauss fields was not performed due to not obtaining a sufficiently long lived FRC during the
Mechano-sensing and cell migration: a 3D model approach.
Borau, C; Kamm, R D; García-Aznar, J M
2011-12-01
Cell migration is essential for tissue development in different physiological and pathological conditions. It is a complex process orchestrated by chemistry, biological factors, microstructure and surrounding mechanical properties. Focusing on the mechanical interactions, cells do not only exert forces on the matrix that surrounds them, but they also sense and react to mechanical cues in a process called mechano-sensing. Here, we hypothesize the involvement of mechano-sensing in the regulation of directional cell migration through a three-dimensional (3D) matrix. For this purpose, we develop a 3D numerical model of individual cell migration, which incorporates the mechano-sensing process of the cell as the main mechanism regulating its movement. Consistent with this hypothesis, we found that factors, such as substrate stiffness, boundary conditions and external forces, regulate specific and distinct cell movements.
M-OTDR sensing system based on 3D encoded microstructures
Sun, Qizhen; Ai, Fan; Liu, Deming; Cheng, Jianwei; Luo, Hongbo; Peng, Kuan; Luo, Yiyang; Yan, Zhijun; Shum, Perry Ping
2017-01-01
In this work, a quasi-distributed sensing scheme named as microstructured OTDR (M-OTDR) by introducing ultra-weak microstructures along the fiber is proposed. Owing to its relative higher reflectivity compared with the backscattered coefficient in fiber and three dimensional (3D) i.e. wavelength/frequency/time encoded property, the M-OTDR system exhibits the superiorities of high signal to noise ratio (SNR), high spatial resolution of millimeter level and high multiplexing capacity up to several ten thousands theoretically. A proof-of-concept system consisting of 64 sensing units is constructed to demonstrate the feasibility and sensing performance. With the help of the demodulation method based on 3D analysis and spectrum reconstruction of the signal light, quasi-distributed temperature sensing with a spatial resolution of 20 cm as well as a measurement resolution of 0.1 °C is realized. PMID:28106132
Data compression in remote sensing applications
NASA Technical Reports Server (NTRS)
Sayood, Khalid
1992-01-01
A survey of current data compression techniques which are being used to reduce the amount of data in remote sensing applications is provided. The survey aspect is far from complete, reflecting the substantial activity in this area. The purpose of the survey is more to exemplify the different approaches being taken rather than to provide an exhaustive list of the various proposed approaches.
Whole brain susceptibility mapping using compressed sensing.
Wu, Bing; Li, Wei; Guidon, Arnaud; Liu, Chunlei
2012-01-01
The derivation of susceptibility from image phase is hampered by the ill-conditioned filter inversion in certain k-space regions. In this article, compressed sensing is used to compensate for the k-space regions where direct filter inversion is unstable. A significantly lower level of streaking artifacts is produced in the resulting susceptibility maps for both simulated and in vivo data sets compared to outcomes obtained using the direct threshold method. It is also demonstrated that the compressed sensing based method outperforms regularization based methods. The key difference between the regularized inversions and compressed sensing compensated inversions is that, in the former case, the entire k-space spectrum estimation is affected by the ill-conditioned filter inversion in certain k-space regions, whereas in the compressed sensing based method only the ill-conditioned k-space regions are estimated. In the susceptibility map calculated from the phase measurement obtained using a 3T scanner, not only are the iron-rich regions well depicted, but good contrast between white and gray matter interfaces that feature a low level of susceptibility variations are also obtained. The correlation between the iron content and the susceptibility levels in iron-rich deep nucleus regions is studied, and strong linear relationships are observed which agree with previous findings.
Compressive optical remote sensing via fractal classification
NASA Astrophysics Data System (ADS)
Sun, Quan-sen; Liu, Ji-xin
2015-11-01
High resolution and large field of view are two major development trends in optical remote sensing imaging. But these trends will cause the difficult problem of mass data processing and remote sensor design under the limitation of conventional sampling method. Therefore, we will propose a novel optical remote sensing imaging method based on compressed sensing theory and fractal feature extraction in this study. We could utilize the result of fractal classification to realize the selectable partitioned image recovery with undersampling measurement. The two experiments illustrate the availability and feasibility of this new method.
NASA Astrophysics Data System (ADS)
Kimpe, T.; Bruylants, T.; Sneyders, Y.; Deklerck, R.; Schelkens, P.
2007-03-01
The size of medical data has increased significantly over the last few years. This poses severe problems for the rapid transmission of medical data across the hospital network resulting into longer access times of the images. Also longterm storage of data becomes more and more a problem. In an attempt to overcome the increasing data size often lossless or lossy compression algorithms are being used. This paper compares the existing JPEG2000 compression algorithm and the new emerging JP3D standard for compression of volumetric datasets. The main benefit of JP3D is that this algorithm truly is a 3D compression algorithm that exploits correlation not only within but also in between slices of a dataset. We evaluate both lossless and lossy modes of these algorithms. As a first step we perform an objective evaluation. Using RMSE and PSNR metrics we determine which compression algorithm performs best and this for multiple compression ratios and for several clinically relevant medical datasets. It is well known that RMSE and PSNR often do not correlate well with subjectively perceived image quality. Therefore we also perform a psycho visual analysis by means of a numerical observer. With this observer model we analyze how compression artifacts actually are perceived by a human observer. Results show superior performance of the new JP3D algorithm compared to the existing JPEG2000 algorithm.
Chandrasekaran, S.; Liebig, W. V.; Mecklenberg, M.; ...
2015-11-04
Aerographite (AG) is a mechanically robust, lightweight synthetic cellular material, which consists of a 3D interconnected network of tubular carbon [1]. The presence of open channels in AG aids to infiltrate them with polymer matrices, thereby yielding an electrical conducting and lightweight composite. Aerographite produced with densities in the range of 7–15 mg/cm3 was infiltrated with a low viscous epoxy resin by means of vacuum infiltration technique. Detailed morphological and structural investigations on synthesized AG and AG/epoxy composite were performed by scanning electron microscopic techniques. Our present study investigates the fracture and failure of AG/epoxy composites and its energy absorptionmore » capacity under compression. The composites displayed an extended plateau region when uni-axially compressed, which led to an increase in energy absorption of ~133% per unit volume for 1.5 wt% of AG, when compared to pure epoxy. Preliminary results on fracture toughness showed an enhancement of ~19% in KIC for AG/epoxy composites with 0.45 wt% of AG. Furthermore, our observations of fractured surfaces under scanning electron microscope gives evidence of pull-out of arms of AG tetrapod, interface and inter-graphite failure as the dominating mechanism for the toughness improvement in these composites. These observations were consistent with the results obtained from photoelasticity experiments on a thin film AG/epoxy model composite.« less
Chandrasekaran, S.; Liebig, W. V.; Mecklenberg, M.; Fiedler, B.; Smazna, D.; Adelung, R.; Schulte, K.
2015-11-04
Aerographite (AG) is a mechanically robust, lightweight synthetic cellular material, which consists of a 3D interconnected network of tubular carbon [1]. The presence of open channels in AG aids to infiltrate them with polymer matrices, thereby yielding an electrical conducting and lightweight composite. Aerographite produced with densities in the range of 7–15 mg/cm^{3} was infiltrated with a low viscous epoxy resin by means of vacuum infiltration technique. Detailed morphological and structural investigations on synthesized AG and AG/epoxy composite were performed by scanning electron microscopic techniques. Our present study investigates the fracture and failure of AG/epoxy composites and its energy absorption capacity under compression. The composites displayed an extended plateau region when uni-axially compressed, which led to an increase in energy absorption of ~133% per unit volume for 1.5 wt% of AG, when compared to pure epoxy. Preliminary results on fracture toughness showed an enhancement of ~19% in K_{IC} for AG/epoxy composites with 0.45 wt% of AG. Furthermore, our observations of fractured surfaces under scanning electron microscope gives evidence of pull-out of arms of AG tetrapod, interface and inter-graphite failure as the dominating mechanism for the toughness improvement in these composites. These observations were consistent with the results obtained from photoelasticity experiments on a thin film AG/epoxy model composite.
Coding Strategies and Implementations of Compressive Sensing
NASA Astrophysics Data System (ADS)
Tsai, Tsung-Han
This dissertation studies the coding strategies of computational imaging to overcome the limitation of conventional sensing techniques. The information capacity of conventional sensing is limited by the physical properties of optics, such as aperture size, detector pixels, quantum efficiency, and sampling rate. These parameters determine the spatial, depth, spectral, temporal, and polarization sensitivity of each imager. To increase sensitivity in any dimension can significantly compromise the others. This research implements various coding strategies subject to optical multidimensional imaging and acoustic sensing in order to extend their sensing abilities. The proposed coding strategies combine hardware modification and signal processing to exploiting bandwidth and sensitivity from conventional sensors. We discuss the hardware architecture, compression strategies, sensing process modeling, and reconstruction algorithm of each sensing system. Optical multidimensional imaging measures three or more dimensional information of the optical signal. Traditional multidimensional imagers acquire extra dimensional information at the cost of degrading temporal or spatial resolution. Compressive multidimensional imaging multiplexes the transverse spatial, spectral, temporal, and polarization information on a two-dimensional (2D) detector. The corresponding spectral, temporal and polarization coding strategies adapt optics, electronic devices, and designed modulation techniques for multiplex measurement. This computational imaging technique provides multispectral, temporal super-resolution, and polarization imaging abilities with minimal loss in spatial resolution and noise level while maintaining or gaining higher temporal resolution. The experimental results prove that the appropriate coding strategies may improve hundreds times more sensing capacity. Human auditory system has the astonishing ability in localizing, tracking, and filtering the selected sound sources or
Feasibility Study of Compressive Sensing Underwater Imaging Lidar
2014-03-28
Compressive Sensing Underwater Imaging Lidar 5a. CONTRACT NUMBER 5b. GRANT NUMBER N00014-12-1-0921 5c. PROGRAM ELEMENT NUMBER 6...Feasibility study of Compressive Sensing Underwater Imaging Lidar Bing Ouyang phone: (772) 242-2288 fax : (772) 242-2257 email: bouvang@hboi.fau.edu...study of the frame based Compressive Sensing concept. ■ Another related project "Airborne Compressive Sensing Topographic Lidar " is being
Close-range environmental remote sensing with 3D hyperspectral technologies
NASA Astrophysics Data System (ADS)
Nevalainen, O.; Honkavaara, E.; Hakala, T.; Kaasalainen, Sanna; Viljanen, N.; Rosnell, T.; Khoramshahi, E.; Näsi, R.
2016-10-01
Estimation of the essential climate variables (ECVs), such as photosynthetically active radiation (FAPAR) and the leaf area index (LAI), is largely based on satellite-based remote sensing and the subsequent inversion of radiative transfer (RT) models. In order to build models that accurately describe the radiative transfer within and below the canopy, detailed 3D structural (geometrical) and spectral (radiometrical) information of the canopy is needed. Close-range remote sensing, such as terrestrial remote sensing and UAV-based 3D spectral measurements, offers significant opportunity to improve the RT modelling and ECV estimation of forests. Finnish Geospatial Research Institute (FGI) has been developing active and passive high resolution 3D hyperspectral measurement technologies that provide reflectance, anisotropy and 3D structure information of forests (i.e. hyperspectral point clouds). Technologies include hyperspectral imaging from unmanned airborne vehicle (UAV), terrestrial hyperspectral lidar (HSL) and terrestrial hyperspectral stereoscopic imaging. A measurement campaign to demonstrate these technologies in ECV estimation with uncertainty propagation was carried out in the Wytham Woods, Oxford, UK, in June 2015. Our objective is to develop traceable processing procedures for generating hyperspectral point clouds with geometric and radiometric uncertainty propagation using hyperspectral aerial and terrestrial imaging and hyperspectral terrestrial laser scanning. The article and presentation will present the methodology, instrumentation and first results of our study.
3D-Web-GIS RFID location sensing system for construction objects.
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.
Compressive line sensing underwater imaging system
NASA Astrophysics Data System (ADS)
Ouyang, Bing; Dalgleish, Fraser R.; Caimi, Frank M.; Giddings, Thomas E.; Britton, Walter; Vuorenkoski, Anni K.; Nootz, Gero
2014-05-01
Compressive sensing (CS) theory has drawn great interest and led to new imaging techniques in many different fields. Over the last few years, the authors have conducted extensive research on CS-based active electro-optical imaging in a scattering medium, such as the underwater environment. This paper proposes a compressive line sensing underwater imaging system that is more compatible with conventional underwater survey operations. This new imaging system builds on our frame-based CS underwater laser imager concept, which is more advantageous for hover capable platforms. We contrast features of CS underwater imaging with those of traditional underwater electro-optical imaging and highlight some advantages of the CS approach. Simulation and initial underwater validation test results are also presented.
Restricted isometry properties and nonconvex compressive sensing
NASA Astrophysics Data System (ADS)
Chartrand, Rick; Staneva, Valentina
2008-06-01
The recently emerged field known as compressive sensing has produced powerful results showing the ability to recover sparse signals from surprisingly few linear measurements, using ell1 minimization. In previous work, numerical experiments showed that ellp minimization with 0 < p < 1 recovers sparse signals from fewer linear measurements than does ell1 minimization. It was also shown that a weaker restricted isometry property is sufficient to guarantee perfect recovery in the ellp case. In this work, we generalize this result to an ellp variant of the restricted isometry property, and then determine how many random, Gaussian measurements are sufficient for the condition to hold with high probability. The resulting sufficient condition is met by fewer measurements for smaller p. This adds to the theoretical justification for the methods already being applied to replacing high-dose CT scans with a small number of x-rays and reducing MRI scanning time. The potential benefits extend to any application of compressive sensing.
Compressive line sensing underwater imaging system
NASA Astrophysics Data System (ADS)
Ouyang, B.; Dalgleish, F. R.; Vuorenkoski, A. K.; Caimi, F. M.; Britton, W.
2013-05-01
Compressive sensing (CS) theory has drawn great interest and led to new imaging techniques in many different fields. In recent years, the FAU/HBOI OVOL has conducted extensive research to study the CS based active electro-optical imaging system in the scattering medium such as the underwater environment. The unique features of such system in comparison with the traditional underwater electro-optical imaging system are discussed. Building upon the knowledge from the previous work on a frame based CS underwater laser imager concept, more advantageous for hover-capable platforms such as the Hovering Autonomous Underwater Vehicle (HAUV), a compressive line sensing underwater imaging (CLSUI) system that is more compatible with the conventional underwater platforms where images are formed in whiskbroom fashion, is proposed in this paper. Simulation results are discussed.
Compressive Sensing Image Sensors-Hardware Implementation
Dadkhah, Mohammadreza; Deen, M. Jamal; Shirani, Shahram
2013-01-01
The compressive sensing (CS) paradigm uses simultaneous sensing and compression to provide an efficient image acquisition technique. The main advantages of the CS method include high resolution imaging using low resolution sensor arrays and faster image acquisition. Since the imaging philosophy in CS imagers is different from conventional imaging systems, new physical structures have been developed for cameras that use the CS technique. In this paper, a review of different hardware implementations of CS encoding in optical and electrical domains is presented. Considering the recent advances in CMOS (complementary metal–oxide–semiconductor) technologies and the feasibility of performing on-chip signal processing, important practical issues in the implementation of CS in CMOS sensors are emphasized. In addition, the CS coding for video capture is discussed. PMID:23584123
Sparsity optimized compressed sensing image recovery
NASA Astrophysics Data System (ADS)
Wang, Sha; Chen, Yueting; Feng, Huajun; Xu, Zhihai; Li, Qi
2014-05-01
Training over-complete dictionaries which facilitate a sparse representation of the image leads to state-of-the-art results in compressed sensing image restoration. The training sparsity should be specified when training, while the recovering sparsity should also be set when image recovery. We find that the recovering sparsity has significant effects on the image reconstruction properties. To further improve the compressed sensing image recover accuracy, in this paper, we proposed a method by optimal estimation of the recovering sparsity according to the training sparsity to control the reconstruction method, and better reconstruction results can be achieved successfully. The method mainly includes three procedures. Firstly, forecasting the possible sparsity range by analyzing a large test data set to obtain a possible sparsity set. We find that the possible sparsity is always 3~5 times the training sparsity. Secondly, to precisely estimate the optimal recovering sparsity, we choose only several samples randomly from the compressed sensing measurements and using the sparsity candidates in the possible sparsity set to reconstruct the original image patches. Thirdly, choosing the sparsity corresponding to the best recovered result as the optimal recovering sparsity to be used in image reconstruction. The estimation computational cost is relatively small and the reconstruction result can be much better than the traditional method. The experimental results show that, the PSNR of the recovered images adopting our estimation method can be higher up to 4dB compared to the traditional method without the sparsity estimation.
Biomedical sensor design using analog compressed sensing
NASA Astrophysics Data System (ADS)
Balouchestani, Mohammadreza; Krishnan, Sridhar
2015-05-01
The main drawback of current healthcare systems is the location-specific nature of the system due to the use of fixed/wired biomedical sensors. Since biomedical sensors are usually driven by a battery, power consumption is the most important factor determining the life of a biomedical sensor. They are also restricted by size, cost, and transmission capacity. Therefore, it is important to reduce the load of sampling by merging the sampling and compression steps to reduce the storage usage, transmission times, and power consumption in order to expand the current healthcare systems to Wireless Healthcare Systems (WHSs). In this work, we present an implementation of a low-power biomedical sensor using analog Compressed Sensing (CS) framework for sparse biomedical signals that addresses both the energy and telemetry bandwidth constraints of wearable and wireless Body-Area Networks (BANs). This architecture enables continuous data acquisition and compression of biomedical signals that are suitable for a variety of diagnostic and treatment purposes. At the transmitter side, an analog-CS framework is applied at the sensing step before Analog to Digital Converter (ADC) in order to generate the compressed version of the input analog bio-signal. At the receiver side, a reconstruction algorithm based on Restricted Isometry Property (RIP) condition is applied in order to reconstruct the original bio-signals form the compressed bio-signals with high probability and enough accuracy. We examine the proposed algorithm with healthy and neuropathy surface Electromyography (sEMG) signals. The proposed algorithm achieves a good level for Average Recognition Rate (ARR) at 93% and reconstruction accuracy at 98.9%. In addition, The proposed architecture reduces total computation time from 32 to 11.5 seconds at sampling-rate=29 % of Nyquist rate, Percentage Residual Difference (PRD)=26 %, Root Mean Squared Error (RMSE)=3 %.
Parallel hyperspectral compressive sensing method on GPU
NASA Astrophysics Data System (ADS)
Bernabé, Sergio; Martín, Gabriel; Nascimento, José M. P.
2015-10-01
Remote hyperspectral sensors collect large amounts of data per flight usually with low spatial resolution. It is known that the bandwidth connection between the satellite/airborne platform and the ground station is reduced, thus a compression onboard method is desirable to reduce the amount of data to be transmitted. This paper presents a parallel implementation of an compressive sensing method, called parallel hyperspectral coded aperture (P-HYCA), for graphics processing units (GPU) using the compute unified device architecture (CUDA). This method takes into account two main properties of hyperspectral dataset, namely the high correlation existing among the spectral bands and the generally low number of endmembers needed to explain the data, which largely reduces the number of measurements necessary to correctly reconstruct the original data. Experimental results conducted using synthetic and real hyperspectral datasets on two different GPU architectures by NVIDIA: GeForce GTX 590 and GeForce GTX TITAN, reveal that the use of GPUs can provide real-time compressive sensing performance. The achieved speedup is up to 20 times when compared with the processing time of HYCA running on one core of the Intel i7-2600 CPU (3.4GHz), with 16 Gbyte memory.
NASA Astrophysics Data System (ADS)
Cheng, Kai-jen; Dill, Jeffrey
2013-05-01
In this paper, a lossless to lossy transform based image compression of hyperspectral images based on Integer Karhunen-Loève Transform (IKLT) and Integer Discrete Wavelet Transform (IDWT) is proposed. Integer transforms are used to accomplish reversibility. The IKLT is used as a spectral decorrelator and the 2D-IDWT is used as a spatial decorrelator. The three-dimensional Binary Embedded Zerotree Wavelet (3D-BEZW) algorithm efficiently encodes hyperspectral volumetric image by implementing progressive bitplane coding. The signs and magnitudes of transform coefficients are encoded separately. Lossy and lossless compressions of signs are implemented by conventional EZW algorithm and arithmetic coding respectively. The efficient 3D-BEZW algorithm is applied to code magnitudes. Further compression can be achieved using arithmetic coding. The lossless and lossy compression performance is compared with other state of the art predictive and transform based image compression methods on Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) images. Results show that the 3D-BEZW performance is comparable to predictive algorithms. However, its computational cost is comparable to transform- based algorithms.
Embedding silica and polymer fibre Bragg gratings (FBG) in plastic 3D-printed sensing patches
NASA Astrophysics Data System (ADS)
Zubel, Michal G.; Sugden, Kate; Webb, David J.; Sáez-Rodríguez, David; Nielsen, Kristian; Bang, Ole
2016-04-01
This paper reports the first demonstration of a silica fibre Bragg grating (SOFBG) embedded in an FDM 3-D printed housing to yield a dual grating temperature-compensated strain sensor. We also report the first ever integration of polymer fibre Bragg grating (POFBG) within a 3-D printed sensing patch for strain or temperature sensing. The cyclic strain performance and temperature characteristics of both devices are examined and discussed. The strain sensitivities of the sensing patches were 0.40 and 0.95 pm/μɛ for SOFBG embedded in ABS, 0.38 pm/μɛ for POFBG in PLA, and 0.15 pm/μɛ for POFBG in ABS. The strain response was linear above a threshold and repeatable. The temperature sensitivity of the SOFBG sensing patch was found to be up to 169 pm/°C, which was up to 17 times higher than for an unembedded silica grating. Unstable temperature response POFBG embedded in PLA was reported, with temperature sensitivity values varying between 30 and 40 pm/°C.
LIBS spectroscopic classification relative to compressive sensing
NASA Astrophysics Data System (ADS)
Griffin, Steven T.; Jacobs, Eddie; Furxhi, Orges
2011-05-01
Laser Induced Breakdown Spectroscopy (LIBS) utilizes a diversity of standard spectroscopic techniques for classification of materials present in the sample. Pre-excitation processing sometimes limits the analyte to a short list of candidates. Prior art demonstrates that sparsity is present in the data. This is sometimes characterized as identification by components. Traditionally, spectroscopic identification has been accomplished by an expert reader in a manner typical for MRI images in the medicine. In an effort to automate this process, more recent art has emphasized the use of customized variations to standard classification algorithms. In addition, formal mathematical proofs for compressive sensing have been advanced. Recently the University of Memphis has been contracted by the Spectroscopic Materials Identification Center to advance and characterize the sensor research and development related to LIBS. Applications include portable standoff sensing for improvised explosive device detection and related law enforcement and military applications. Reduction of the mass, power consumption and other portability parameters is seen as dependent on classification choices for a LIBS system. This paper presents results for the comparison of standard LIBS classification techniques to those implied by Compressive Sensing mathematics. Optimization results and implications for portable LIBS design are presented.
Self-calibration and biconvex compressive sensing
NASA Astrophysics Data System (ADS)
Ling, Shuyang; Strohmer, Thomas
2015-11-01
The design of high-precision sensing devises becomes ever more difficult and expensive. At the same time, the need for precise calibration of these devices (ranging from tiny sensors to space telescopes) manifests itself as a major roadblock in many scientific and technological endeavors. To achieve optimal performance of advanced high-performance sensors one must carefully calibrate them, which is often difficult or even impossible to do in practice. In this work we bring together three seemingly unrelated concepts, namely self-calibration, compressive sensing, and biconvex optimization. The idea behind self-calibration is to equip a hardware device with a smart algorithm that can compensate automatically for the lack of calibration. We show how several self-calibration problems can be treated efficiently within the framework of biconvex compressive sensing via a new method called SparseLift. More specifically, we consider a linear system of equations {\\boldsymbol{y}}={\\boldsymbol{D}}{\\boldsymbol{A}}{\\boldsymbol{x}}, where both {\\boldsymbol{x}} and the diagonal matrix {\\boldsymbol{D}} (which models the calibration error) are unknown. By ‘lifting’ this biconvex inverse problem we arrive at a convex optimization problem. By exploiting sparsity in the signal model, we derive explicit theoretical guarantees under which both {\\boldsymbol{x}} and {\\boldsymbol{D}} can be recovered exactly, robustly, and numerically efficiently via linear programming. Applications in array calibration and wireless communications are discussed and numerical simulations are presented, confirming and complementing our theoretical analysis.
Cochlea-inspired sensing node for compressive sensing
NASA Astrophysics Data System (ADS)
Peckens, Courtney A.; Lynch, Jerome P.
2013-04-01
While sensing technologies for structural monitoring applications have made significant advances over the last several decades, there is still room for improvement in terms of computational efficiency, as well as overall energy consumption. The biological nervous system can offer a potential solution to address these current deficiencies. The nervous system is capable of sensing and aggregating information about the external environment through very crude processing units known as neurons. Neurons effectively communicate in an extremely condensed format by encoding information into binary electrical spike trains, thereby reducing the amount of raw information sent throughout a neural network. Due to its unique signal processing capabilities, the mammalian cochlea and its interaction with the biological nervous system is of particular interest for devising compressive sensing strategies for dynamic engineered systems. The cochlea uses a novel method of place theory and frequency decomposition, thereby allowing for rapid signal processing within the nervous system. In this study, a low-power sensing node is proposed that draws inspiration from the mechanisms employed by the cochlea and the biological nervous system. As such, the sensor is able to perceive and transmit a compressed representation of the external stimulus with minimal distortion. Each sensor represents a basic building block, with function similar to the neuron, and can form a network with other sensors, thus enabling a system that can convey input stimulus in an extremely condensed format. The proposed sensor is validated through a structural monitoring application of a single degree of freedom structure excited by seismic ground motion.
An optimal sensing strategy for recognition and localization of 3-D natural quadric objects
NASA Technical Reports Server (NTRS)
Lee, Sukhan; Hahn, Hernsoo
1991-01-01
An optimal sensing strategy for an optical proximity sensor system engaged in the recognition and localization of 3-D natural quadric objects is presented. The optimal sensing strategy consists of the selection of an optimal beam orientation and the determination of an optimal probing plane that compose an optimal data collection operation known as an optimal probing. The decision of an optimal probing is based on the measure of discrimination power of a cluster of surfaces on a multiple interpretation image (MII), where the measure of discrimination power is defined in terms of a utility function computing the expected number of interpretations that can be pruned out by a probing. An object representation suitable for active sensing based on a surface description vector (SDV) distribution graph and hierarchical tables is presented. Experimental results are shown.
Applying Mean-Shift - Clustering for 3D object detection in remote sensing data
NASA Astrophysics Data System (ADS)
Simon, Jürgen-Lorenz; Diederich, Malte; Troemel, Silke
2013-04-01
The timely warning and forecasting of high-impact weather events is crucial for life, safety and economy. Therefore, the development and improvement of methods for detection and nowcasting / short-term forecasting of these events is an ongoing research question. A new 3D object detection and tracking algorithm is presented. Within the project "object-based analysis and seamless predictin (OASE)" we address a better understanding and forecasting of convective events based on the synergetic use of remotely sensed data and new methods for detection, nowcasting, validation and assimilation. In order to gain advanced insight into the lifecycle of convective cells, we perform an object-detection on a new high-resolution 3D radar- and satellite based composite and plan to track the detected objects over time, providing us with a model of the lifecycle. The insights in the lifecycle will be used in order to improve prediction of convective events in the nowcasting time scale, as well as a new type of data to be assimilated into numerical weather models, thus seamlessly bridging the gap between nowcasting and NWP.. The object identification (or clustering) is performed using a technique borrowed from computer vision, called mean-shift clustering. Mean-Shift clustering works without many of the parameterizations or rigid threshold schemes employed by many existing schemes (e. g. KONRAD, TITAN, Trace-3D), which limit the tracking to fully matured, convective cells of significant size and/or strength. Mean-Shift performs without such limiting definitions, providing a wider scope for studying larger classes of phenomena and providing a vehicle for research into the object definition itself. Since the mean-shift clustering technique could be applied on many types of remote-sensing and model data for object detection, it is of general interest to the remote sensing and modeling community. The focus of the presentation is the introduction of this technique and the results of its
Symmetric Toeplitz-Structured Compressed Sensing Matrices
NASA Astrophysics Data System (ADS)
Huang, Tao; Fan, Yi-Zheng; Zhu, Ming
2015-11-01
How to construct a suitable measurement matrix is an important topic in compressed sensing. A significant part of the recent work is that the measurement matrices are not completely random on the entries but exhibit some considerable structures. In this paper, we proved that a symmetric Toeplitz matrix and its variant can be used as measurement matrices and recovery signal with high probability. Compared with random matrices (e.g. Gaussian and Bernoulli matrices) and some structured matrices (e.g. Toeplitz and circulant matrices), we need to generate fewer independent entries to obtain the measurement matrix while the effectiveness of the recovery keeps good.
Compressive Sensing via Nonlocal Smoothed Rank Function
Fan, Ya-Ru; Liu, Jun; Zhao, Xi-Le
2016-01-01
Compressive sensing (CS) theory asserts that we can reconstruct signals and images with only a small number of samples or measurements. Recent works exploiting the nonlocal similarity have led to better results in various CS studies. To better exploit the nonlocal similarity, in this paper, we propose a non-convex smoothed rank function based model for CS image reconstruction. We also propose an efficient alternating minimization method to solve the proposed model, which reduces a difficult and coupled problem to two tractable subproblems. Experimental results have shown that the proposed method performs better than several existing state-of-the-art CS methods for image reconstruction. PMID:27583683
Compressive Sensing via Nonlocal Smoothed Rank Function.
Fan, Ya-Ru; Huang, Ting-Zhu; Liu, Jun; Zhao, Xi-Le
2016-01-01
Compressive sensing (CS) theory asserts that we can reconstruct signals and images with only a small number of samples or measurements. Recent works exploiting the nonlocal similarity have led to better results in various CS studies. To better exploit the nonlocal similarity, in this paper, we propose a non-convex smoothed rank function based model for CS image reconstruction. We also propose an efficient alternating minimization method to solve the proposed model, which reduces a difficult and coupled problem to two tractable subproblems. Experimental results have shown that the proposed method performs better than several existing state-of-the-art CS methods for image reconstruction.
Enablement of scientific remote sensing missions with in-space 3D printing
NASA Astrophysics Data System (ADS)
Hirsch, Michael; McGuire, Thomas; Parsons, Michael; Leake, Skye; Straub, Jeremy
2016-05-01
This paper provides an overview of the capability of a 3D printer to successfully operate in-space to create structures and equipment useful in the field of scientific remote sensing. Applications of this printer involve oceanography, weather tracking, as well as space exploration sensing. The design for the 3D printer includes a parabolic array to collect and focus thermal energy. This thermal energy then be used to heat the extrusion head, allowing for the successful extrusion of the print material. Print material can range from plastics to metals, with the hope of being able to extrude aluminum for its low-mass structural integrity and its conductive properties. The printer will be able to print structures as well as electrical components. The current process of creating and launching a remote sensor into space is constrained by many factors such as gravity on earth, the forces of launch, the size of the launch vehicle, and the number of available launches. The design intent of the in-space 3D printer is to ease or eliminate these constraints, making space-based scientific remote sensors a more readily available resource.
On the Estimation of Forest Resources Using 3D Remote Sensing Techniques and Point Cloud Data
NASA Astrophysics Data System (ADS)
Karjalainen, Mika; Karila, Kirsi; Liang, Xinlian; Yu, Xiaowei; Huang, Guoman; Lu, Lijun
2016-08-01
In recent years, 3D capable remote sensing techniques have shown great potential in forest biomass estimation because of their ability to measure the forest canopy structure, tree height and density. The objective of the Dragon3 forest resources research project (ID 10667) and the supporting ESA young scientist project (ESA contract NO. 4000109483/13/I-BG) was to study the use of satellite based 3D techniques in forest tree height estimation, and consequently in forest biomass and biomass change estimation, by combining satellite data with terrestrial measurements. Results from airborne 3D techniques were also used in the project. Even though, forest tree height can be estimated from 3D satellite SAR data to some extent, there is need for field reference plots. For this reason, we have also been developing automated field plot measurement techniques based on Terrestrial Laser Scanning data, which can be used to train and calibrate satellite based estimation models. In this paper, results of canopy height models created from TerraSAR-X stereo and TanDEM-X INSAR data are shown as well as preliminary results from TLS field plot measurement system. Also, results from the airborne CASMSAR system to measure forest canopy height from P- and X- band INSAR are presented.
Wang, Lin; Acosta, Miguel A; Leach, Jennie B; Carrier, Rebecca L
2013-04-21
Capability of measuring and monitoring local oxygen concentration at the single cell level (tens of microns scale) is often desirable but difficult to achieve in cell culture. In this study, biocompatible oxygen sensing beads were prepared and tested for their potential for real-time monitoring and mapping of local oxygen concentration in 3D micro-patterned cell culture systems. Each oxygen sensing bead is composed of a silica core loaded with both an oxygen sensitive Ru(Ph2phen3)Cl2 dye and oxygen insensitive Nile blue reference dye, and a poly-dimethylsiloxane (PDMS) shell rendering biocompatibility. Human intestinal epithelial Caco-2 cells were cultivated on a series of PDMS and type I collagen based substrates patterned with micro-well arrays for 3 or 7 days, and then brought into contact with oxygen sensing beads. Using an image analysis algorithm to convert florescence intensity of beads to partial oxygen pressure in the culture system, tens of microns-size oxygen sensing beads enabled the spatial measurement of local oxygen concentration in the microfabricated system. Results generally indicated lower oxygen level inside wells than on top of wells, and local oxygen level dependence on structural features of cell culture surfaces. Interestingly, chemical composition of cell culture substrates also appeared to affect oxygen level, with type-I collagen based cell culture systems having lower oxygen concentration compared to PDMS based cell culture systems. In general, results suggest that oxygen sensing beads can be utilized to achieve real-time and local monitoring of micro-environment oxygen level in 3D microfabricated cell culture systems.
Region-based compression of remote sensing stereo image pairs
NASA Astrophysics Data System (ADS)
Yan, Ruomei; Li, Yunsong; Wu, Chengke; Wang, Keyan; Li, Shizhong
2009-08-01
According to the data characteristics of remote sensing stereo image pairs, a novel compression algorithm based on the combination of feature-based image matching (FBM), area-based image matching (ABM), and region-based disparity estimation is proposed. First, the Scale Invariant Feature Transform (SIFT) and the Sobel operator are carried out for texture classification. Second, an improved ABM is used in the area with flat terrain (flat area), while the disparity estimation, a combination of quadtree decomposition and FBM, is used in the area with alpine terrain (alpine area). Furthermore, the radiation compensation is applied in every area. Finally, the disparities, the residual image, and the reference image are compressed by JPEG2000 together. The new algorithm provides a reasonable prediction in different areas according to characteristics of image textures, which improves the precision of the sensed image. The experimental results show that the PSNR of the proposed algorithm can obtain up to about 3dB's gain compared with the traditional algorithm at low or medium bitrates, and the subjective quality is obviously enhanced.
Adaptive compression of remote sensing stereo image pairs
NASA Astrophysics Data System (ADS)
Li, Yunsong; Yan, Ruomei; Wu, Chengke; Wang, Keyan; Li, Shizhong; Wang, Yu
2010-09-01
According to the data characteristics of remote sensing stereo image pairs, a novel adaptive compression algorithm based on the combination of feature-based image matching (FBM), area-based image matching (ABM), and region-based disparity estimation is proposed. First, the Scale Invariant Feature Transform (SIFT) and the Sobel operator are carried out for texture classification. Second, an improved ABM is used in the flat area, while the disparity estimation is used in the alpine area. The radiation compensation is applied to further improve the performance. Finally, the residual image and the reference image are compressed by JPEG2000 independently. The new algorithm provides a reasonable prediction in different areas according to the image textures, which improves the precision of the sensed image. The experimental results show that the PSNR of the proposed algorithm can obtain up to about 3dB's gain compared with the traditional algorithm at low or medium bitrates, and the DTM and subjective quality is also obviously enhanced.
Frequency extrapolation by nonconvex compressive sensing
Chartrand, Rick; Sidky, Emil Y; Pan, Xiaochaun
2010-12-03
Tomographic imaging modalities sample subjects with a discrete, finite set of measurements, while the underlying object function is continuous. Because of this, inversion of the imaging model, even under ideal conditions, necessarily entails approximation. The error incurred by this approximation can be important when there is rapid variation in the object function or when the objects of interest are small. In this work, we investigate this issue with the Fourier transform (FT), which can be taken as the imaging model for magnetic resonance imaging (MRl) or some forms of wave imaging. Compressive sensing has been successful for inverting this data model when only a sparse set of samples are available. We apply the compressive sensing principle to a somewhat related problem of frequency extrapolation, where the object function is represented by a super-resolution grid with many more pixels than FT measurements. The image on the super-resolution grid is obtained through nonconvex minimization. The method fully utilizes the available FT samples, while controlling aliasing and ringing. The algorithm is demonstrated with continuous FT samples of the Shepp-Logan phantom with additional small, high-contrast objects.
Phase diagram of matrix compressed sensing
NASA Astrophysics Data System (ADS)
Schülke, Christophe; Schniter, Philip; Zdeborová, Lenka
2016-12-01
In the problem of matrix compressed sensing, we aim to recover a low-rank matrix from a few noisy linear measurements. In this contribution, we analyze the asymptotic performance of a Bayes-optimal inference procedure for a model where the matrix to be recovered is a product of random matrices. The results that we obtain using the replica method describe the state evolution of the Parametric Bilinear Generalized Approximate Message Passing (P-BiG-AMP) algorithm, recently introduced in J. T. Parker and P. Schniter [IEEE J. Select. Top. Signal Process. 10, 795 (2016), 10.1109/JSTSP.2016.2539123]. We show the existence of two different types of phase transition and their implications for the solvability of the problem, and we compare the results of our theoretical analysis to the numerical performance reached by P-BiG-AMP. Remarkably, the asymptotic replica equations for matrix compressed sensing are the same as those for a related but formally different problem of matrix factorization.
Distributed Compressive Sensing: A Deep Learning Approach
NASA Astrophysics Data System (ADS)
Palangi, Hamid; Ward, Rabab; Deng, Li
2016-09-01
Various studies that address the compressed sensing problem with Multiple Measurement Vectors (MMVs) have been recently carried. These studies assume the vectors of the different channels to be jointly sparse. In this paper, we relax this condition. Instead we assume that these sparse vectors depend on each other but that this dependency is unknown. We capture this dependency by computing the conditional probability of each entry in each vector being non-zero, given the "residuals" of all previous vectors. To estimate these probabilities, we propose the use of the Long Short-Term Memory (LSTM)[1], a data driven model for sequence modelling that is deep in time. To calculate the model parameters, we minimize a cross entropy cost function. To reconstruct the sparse vectors at the decoder, we propose a greedy solver that uses the above model to estimate the conditional probabilities. By performing extensive experiments on two real world datasets, we show that the proposed method significantly outperforms the general MMV solver (the Simultaneous Orthogonal Matching Pursuit (SOMP)) and a number of the model-based Bayesian methods. The proposed method does not add any complexity to the general compressive sensing encoder. The trained model is used just at the decoder. As the proposed method is a data driven method, it is only applicable when training data is available. In many applications however, training data is indeed available, e.g. in recorded images and videos.
A simple approach for the fabrication of 3D microelectrodes for impedimetric sensing
NASA Astrophysics Data System (ADS)
Tahsin Guler, Mustafa; Bilican, Ismail; Agan, Sedat; Elbuken, Caglar
2015-09-01
In this paper, we present a very simple method to fabricate three-dimensional (3D) microelectrodes integrated with microfluidic devices. We form the electrodes by etching a microwire placed across a microchannel. For precise control of the electrode spacing, we employ a hydrodynamic focusing microfluidic device and control the width of the etching solution stream. The focused widths of the etchant solution and the etching time determine the gap formed between the electrodes. Using the same microfluidic device, we can fabricate integrated 3D electrodes with different electrode gaps. We have demonstrated the functionality of these electrodes using an impedimetric particle counting setup. Using 3D microelectrodes with a diameter of 25 μm, we have detected 6 μm-diameter polystyrene beads in a buffer solution as well as erythrocytes in a PBS solution. We study the effect of electrode spacing on the signal-to-noise ratio of the impedance signal and we demonstrate that the smaller the electrode spacing the higher the signal obtained from a single microparticle. The sample stream is introduced to the system using the same hydrodynamic focusing device, which ensures the alignment of the sample in between the electrodes. Utilising a 3D hydrodynamic focusing approach, we force all the particles to go through the sensing region of the electrodes. This fabrication scheme not only provides a very low-cost and easy method for rapid prototyping, but which can also be used for applications requiring 3D electric field focused through a narrow section of the microchannel.
NASA Astrophysics Data System (ADS)
Chen, Luoyang; Liu, Jiansheng; cheng, Jiangtao; Liu, Haitao; Zhou, Hongwen
2017-03-01
3D optical coherence tomography imaging (OCT) combined with compressive sensing (CS) has been proved to be an attractive and effective tool in a variety of fields, such as medicine and biology. To achieve high quality imaging while using as less CS sampling rate as possible is the goal of this approach. Here we present an innovative single step fully 3D CS-OCT volumetric image recovery method, in which 3D OCT volumetric image of the object is compressively sampled via our proposed CS coding strategies in all three dimensions while its sparsity is simultaneously taken into consideration in every direction. The object can be directly recovered as the whole volume reconstruction via our advanced full 3D CS reconstruction algorithm. The numerical simulations of a human retina OCT volumetric image reconstruction by our method demonstrate a PSNR of as high as 38dB at a sampling rate of less than 10%.
Compressive Sensing for DoD Sensor Systems
2012-11-01
the mathematical problem f = Mx where f is the vector of frequencies that are measured in the different pools. The theory of compressed sensing ... compressed sensing can be applied, particularly to radar and optical systems. These efforts should include applying new sparse reconstruction...can and should play a major role in exploring where and how compressed sensing can be applied, particularly to radar and optical systems. These
Modeling the Impact of Drizzle and 3D Cloud Structure on Remote Sensing of Effective Radius
NASA Technical Reports Server (NTRS)
Platnick, Steven; Zinner, Tobias; Ackerman, S.
2008-01-01
Remote sensing of cloud particle size with passive sensors like MODIS is an important tool for cloud microphysical studies. As a measure of the radiatively relevant droplet size, effective radius can be retrieved with different combinations of visible through shortwave infrared channels. MODIS observations sometimes show significantly larger effective radii in marine boundary layer cloud fields derived from the 1.6 and 2.1 pm channel observations than for 3.7 pm retrievals. Possible explanations range from 3D radiative transport effects and sub-pixel cloud inhomogeneity to the impact of drizzle formation on the droplet distribution. To investigate the potential influence of these factors, we use LES boundary layer cloud simulations in combination with 3D Monte Carlo simulations of MODIS observations. LES simulations of warm cloud spectral microphysics for cases of marine stratus and broken stratocumulus, each for two different values of cloud condensation nuclei density, produce cloud structures comprising droplet size distributions with and without drizzle size drops. In this study, synthetic MODIS observations generated from 3D radiative transport simulations that consider the full droplet size distribution will be generated for each scene. The operational MODIS effective radius retrievals will then be applied to the simulated reflectances and the results compared with the LES microphysics.
ShrinkWrap: 3D model abstraction for remote sensing simulation
Pope, Paul A
2009-01-01
Remote sensing simulations often require the use of 3D models of objects of interest. There are a multitude of these models available from various commercial sources. There are image processing, computational, database storage, and . data access advantages to having a regularized, encapsulating, triangular mesh representing the surface of a 3D object model. However, this is usually not how these models are stored. They can have too much detail in some areas, and not enough detail in others. They can have a mix of planar geometric primitives (triangles, quadrilaterals, n-sided polygons) representing not only the surface of the model, but also interior features. And the exterior mesh is usually not regularized nor encapsulating. This paper presents a method called SHRlNKWRAP which can be used to process 3D object models to achieve output models having the aforementioned desirable traits. The method works by collapsing an encapsulating sphere, which has a regularized triangular mesh on its surface, onto the surface of the model. A GUI has been developed to make it easy to leverage this capability. The SHRlNKWRAP processing chain and use of the GUI are described and illustrated.
Lithographic VCSEL array multimode and single mode sources for sensing and 3D imaging
NASA Astrophysics Data System (ADS)
Leshin, J.; Li, M.; Beadsworth, J.; Yang, X.; Zhang, Y.; Tucker, F.; Eifert, L.; Deppe, D. G.
2016-05-01
Sensing applications along with free space data links can benefit from advanced laser sources that produce novel radiation patterns and tight spectral control for optical filtering. Vertical-cavity surface-emitting lasers (VCSELs) are being developed for these applications. While oxide VCSELs are being produced by most companies, a new type of oxide-free VCSEL is demonstrating many advantages in beam pattern, spectral control, and reliability. These lithographic VCSELs offer increased power density from a given aperture size, and enable dense integration of high efficiency and single mode elements that improve beam pattern. In this paper we present results for lithographic VCSELs and describes integration into military systems for very low cost pulsed applications, as well as continuouswave applications in novel sensing applications. The VCSELs are being developed for U.S. Army for soldier weapon engagement simulation training to improve beam pattern and spectral control. Wavelengths in the 904 nm to 990 nm ranges are being developed with the spectral control designed to eliminate unwanted water absorption bands from the data links. Multiple beams and radiation patterns based on highly compact packages are being investigated for improved target sensing and transmission fidelity in free space data links. These novel features based on the new VCSEL sources are also expected to find applications in 3-D imaging, proximity sensing and motion control, as well as single mode sensors such as atomic clocks and high speed data transmission.
Compressed sensing recovery via nonconvex shrinkage penalties
NASA Astrophysics Data System (ADS)
Woodworth, Joseph; Chartrand, Rick
2016-07-01
The {{\\ell }}0 minimization of compressed sensing is often relaxed to {{\\ell }}1, which yields easy computation using the shrinkage mapping known as soft thresholding, and can be shown to recover the original solution under certain hypotheses. Recent work has derived a general class of shrinkages and associated nonconvex penalties that better approximate the original {{\\ell }}0 penalty and empirically can recover the original solution from fewer measurements. We specifically examine p-shrinkage and firm thresholding. In this work, we prove that given data and a measurement matrix from a broad class of matrices, one can choose parameters for these classes of shrinkages to guarantee exact recovery of the sparsest solution. We further prove convergence of the algorithm iterative p-shrinkage (IPS) for solving one such relaxed problem.
Multichannel Compressive Sensing MRI Using Noiselet Encoding
Pawar, Kamlesh; Egan, Gary; Zhang, Jingxin
2015-01-01
The incoherence between measurement and sparsifying transform matrices and the restricted isometry property (RIP) of measurement matrix are two of the key factors in determining the performance of compressive sensing (CS). In CS-MRI, the randomly under-sampled Fourier matrix is used as the measurement matrix and the wavelet transform is usually used as sparsifying transform matrix. However, the incoherence between the randomly under-sampled Fourier matrix and the wavelet matrix is not optimal, which can deteriorate the performance of CS-MRI. Using the mathematical result that noiselets are maximally incoherent with wavelets, this paper introduces the noiselet unitary bases as the measurement matrix to improve the incoherence and RIP in CS-MRI. Based on an empirical RIP analysis that compares the multichannel noiselet and multichannel Fourier measurement matrices in CS-MRI, we propose a multichannel compressive sensing (MCS) framework to take the advantage of multichannel data acquisition used in MRI scanners. Simulations are presented in the MCS framework to compare the performance of noiselet encoding reconstructions and Fourier encoding reconstructions at different acceleration factors. The comparisons indicate that multichannel noiselet measurement matrix has better RIP than that of its Fourier counterpart, and that noiselet encoded MCS-MRI outperforms Fourier encoded MCS-MRI in preserving image resolution and can achieve higher acceleration factors. To demonstrate the feasibility of the proposed noiselet encoding scheme, a pulse sequences with tailored spatially selective RF excitation pulses was designed and implemented on a 3T scanner to acquire the data in the noiselet domain from a phantom and a human brain. The results indicate that noislet encoding preserves image resolution better than Fouirer encoding. PMID:25965548
Multichannel compressive sensing MRI using noiselet encoding.
Pawar, Kamlesh; Egan, Gary; Zhang, Jingxin
2015-01-01
The incoherence between measurement and sparsifying transform matrices and the restricted isometry property (RIP) of measurement matrix are two of the key factors in determining the performance of compressive sensing (CS). In CS-MRI, the randomly under-sampled Fourier matrix is used as the measurement matrix and the wavelet transform is usually used as sparsifying transform matrix. However, the incoherence between the randomly under-sampled Fourier matrix and the wavelet matrix is not optimal, which can deteriorate the performance of CS-MRI. Using the mathematical result that noiselets are maximally incoherent with wavelets, this paper introduces the noiselet unitary bases as the measurement matrix to improve the incoherence and RIP in CS-MRI. Based on an empirical RIP analysis that compares the multichannel noiselet and multichannel Fourier measurement matrices in CS-MRI, we propose a multichannel compressive sensing (MCS) framework to take the advantage of multichannel data acquisition used in MRI scanners. Simulations are presented in the MCS framework to compare the performance of noiselet encoding reconstructions and Fourier encoding reconstructions at different acceleration factors. The comparisons indicate that multichannel noiselet measurement matrix has better RIP than that of its Fourier counterpart, and that noiselet encoded MCS-MRI outperforms Fourier encoded MCS-MRI in preserving image resolution and can achieve higher acceleration factors. To demonstrate the feasibility of the proposed noiselet encoding scheme, a pulse sequences with tailored spatially selective RF excitation pulses was designed and implemented on a 3T scanner to acquire the data in the noiselet domain from a phantom and a human brain. The results indicate that noislet encoding preserves image resolution better than Fouirer encoding.
Evaluation of a combined pre-processing and H.264-compression scheme for 3D integral images
NASA Astrophysics Data System (ADS)
Olsson, Roger; Sjöström, Mårten; Xu, Youzhi
2007-01-01
To provide sufficient 3D-depth fidelity, integral imaging (II) requires an increase in spatial resolution of several orders of magnitude from today's 2D images. We have recently proposed a pre-processing and compression scheme for still II-frames based on forming a pseudo video sequence (PVS) from sub images (SI), which is later coded using the H.264/MPEG-4 AVC video coding standard. The scheme has shown good performance on a set of reference images. In this paper we first investigate and present how five different ways to select the SIs when forming the PVS affect the schemes compression efficiency. We also study how the II-frame structure relates to the performance of a PVS coding scheme. Finally we examine the nature of the coding artifacts which are specific to the evaluated PVS-schemes. We can conclude that for all except the most complex reference image, all evaluated SI selection orders significantly outperforms JPEG 2000 where compression ratios of up to 342:1, while still keeping PSNR > 30 dB, is achieved. We can also confirm that when selecting PVS-scheme, the scheme which results in a higher PVS-picture resolution should be preferred to maximize compression efficiency. Our study of the coded II-frames also indicates that the SI-based PVS, contrary to other PVS schemes, tends to distribute its coding artifacts more homogenously over all 3D-scene depths.
FPGA Implementation of Optimal 3D-Integer DCT Structure for Video Compression
Jacob, J. Augustin; Kumar, N. Senthil
2015-01-01
A novel optimal structure for implementing 3D-integer discrete cosine transform (DCT) is presented by analyzing various integer approximation methods. The integer set with reduced mean squared error (MSE) and high coding efficiency are considered for implementation in FPGA. The proposed method proves that the least resources are utilized for the integer set that has shorter bit values. Optimal 3D-integer DCT structure is determined by analyzing the MSE, power dissipation, coding efficiency, and hardware complexity of different integer sets. The experimental results reveal that direct method of computing the 3D-integer DCT using the integer set [10, 9, 6, 2, 3, 1, 1] performs better when compared to other integer sets in terms of resource utilization and power dissipation. PMID:26601120
FPGA Implementation of Optimal 3D-Integer DCT Structure for Video Compression.
Jacob, J Augustin; Kumar, N Senthil
2015-01-01
A novel optimal structure for implementing 3D-integer discrete cosine transform (DCT) is presented by analyzing various integer approximation methods. The integer set with reduced mean squared error (MSE) and high coding efficiency are considered for implementation in FPGA. The proposed method proves that the least resources are utilized for the integer set that has shorter bit values. Optimal 3D-integer DCT structure is determined by analyzing the MSE, power dissipation, coding efficiency, and hardware complexity of different integer sets. The experimental results reveal that direct method of computing the 3D-integer DCT using the integer set [10, 9, 6, 2, 3, 1, 1] performs better when compared to other integer sets in terms of resource utilization and power dissipation.
Hyperspectral images lossless compression using the 3D binary EZW algorithm
NASA Astrophysics Data System (ADS)
Cheng, Kai-jen; Dill, Jeffrey
2013-02-01
This paper presents a transform based lossless compression for hyperspectral images which is inspired by Shapiro (1993)'s EZW algorithm. The proposed compression method uses a hybrid transform which includes an integer Karhunrn-Loeve transform (KLT) and integer discrete wavelet transform (DWT). The integer KLT is employed to eliminate the presence of correlations among the bands of the hyperspectral image. The integer 2D discrete wavelet transform (DWT) is applied to eliminate the correlations in the spatial dimensions and produce wavelet coefficients. These coefficients are then coded by a proposed binary EZW algorithm. The binary EZW eliminates the subordinate pass of conventional EZW by coding residual values, and produces binary sequences. The binary EZW algorithm combines the merits of well-known EZW and SPIHT algorithms, and it is computationally simpler for lossless compression. The proposed method was applied to AVIRIS images and compared to other state-of-the-art image compression techniques. The results show that the proposed lossless image compression is more efficient and it also has higher compression ratio than other algorithms.
Compressed Sensing Electron Tomography for Determining Biological Structure
NASA Astrophysics Data System (ADS)
Guay, Matthew D.; Czaja, Wojciech; Aronova, Maria A.; Leapman, Richard D.
2016-06-01
There has been growing interest in applying compressed sensing (CS) theory and practice to reconstruct 3D volumes at the nanoscale from electron tomography datasets of inorganic materials, based on known sparsity in the structure of interest. Here we explore the application of CS for visualizing the 3D structure of biological specimens from tomographic tilt series acquired in the scanning transmission electron microscope (STEM). CS-ET reconstructions match or outperform commonly used alternative methods in full and undersampled tomogram recovery, but with less significant performance gains than observed for the imaging of inorganic materials. We propose that this disparity stems from the increased structural complexity of biological systems, as supported by theoretical CS sampling considerations and numerical results in simulated phantom datasets. A detailed analysis of the efficacy of CS-ET for undersampled recovery is therefore complicated by the structure of the object being imaged. The numerical nonlinear decoding process of CS shares strong connections with popular regularized least-squares methods, and the use of such numerical recovery techniques for mitigating artifacts and denoising in reconstructions of fully sampled datasets remains advantageous. This article provides a link to the software that has been developed for CS-ET reconstruction of electron tomographic data sets.
Laser speckle reduction based on compressive sensing and edge detection
NASA Astrophysics Data System (ADS)
Wen, Dong-hai; Jiang, Yue-song; Hua, Hou-qiang; Yu, Rong; Gao, Qian; Zhang, Yan-zhong
2013-09-01
Polarization active imager technology obtains images encoded by parameters different than just the reflectivity and therefore provides new information on the image. So polarization active imager systems represent a very powerful observation tool. However, automatic interpretation of the information contained in the reflected intensity of the polarization active image data is extremely difficult because of the speckle phenomenon. An approach for speckle reduction of polarization active image based on the concepts of compressive sensing (CS) theory and edge detection. First, A Canny operator is first utilized to detect and remove edges from the polarization active image. Then, a dictionary learning algorithm which is applied to sparse image representation. The dictionary learning problem is expressed as a box-constrained quadratic program and a fast projected gradient method is introduced to solve it. The Gradient Projection for Square Reconstruction (GPSR) algorithm for solving bound constrained quadratic programming to reduce the speckle noise in the polarization active images. The block-matching 3-D (BM3D) algorithm is used to reduce speckle nosie, it works in two steps: The first one uses hard thresholding to build a relatively clean image for estimating statistics, while the second one performs the actual denoising through empirical Wiener filtering in the transform domain. Finally, the removed edges are added to the reconstructed image. Experimental results show that the visual quality and evaluation indexes outperform the other methods with no edge preservation. The proposed algorithm effectively realizes both despeckling and edge preservation and reaches the state-of-the-art performance.
Compressed Sensing Electron Tomography for Determining Biological Structure
Guay, Matthew D.; Czaja, Wojciech; Aronova, Maria A.; Leapman, Richard D.
2016-01-01
There has been growing interest in applying compressed sensing (CS) theory and practice to reconstruct 3D volumes at the nanoscale from electron tomography datasets of inorganic materials, based on known sparsity in the structure of interest. Here we explore the application of CS for visualizing the 3D structure of biological specimens from tomographic tilt series acquired in the scanning transmission electron microscope (STEM). CS-ET reconstructions match or outperform commonly used alternative methods in full and undersampled tomogram recovery, but with less significant performance gains than observed for the imaging of inorganic materials. We propose that this disparity stems from the increased structural complexity of biological systems, as supported by theoretical CS sampling considerations and numerical results in simulated phantom datasets. A detailed analysis of the efficacy of CS-ET for undersampled recovery is therefore complicated by the structure of the object being imaged. The numerical nonlinear decoding process of CS shares strong connections with popular regularized least-squares methods, and the use of such numerical recovery techniques for mitigating artifacts and denoising in reconstructions of fully sampled datasets remains advantageous. This article provides a link to the software that has been developed for CS-ET reconstruction of electron tomographic data sets. PMID:27291259
Polarimetric and Indoor Imaging Fusion Based on Compressive Sensing
2013-04-01
Signal Process., vol. 57, no. 6, pp. 2275-2284, 2009. [20] A. Gurbuz, J. McClellan, and W. Scott, Jr., "Compressive sensing for subsurface imaging using...SciTech Publishing, 2010, pp. 922- 938. [45] A. C. Gurbuz, J. H. McClellan, and W. R. Scott, Jr., "Compressive sensing for subsurface imaging using
Application of Compressive Sensing to Gravitational Microlensing Experiments
NASA Technical Reports Server (NTRS)
Korde-Patel, Asmita; Barry, Richard K.; Mohsenin, Tinoosh
2016-01-01
Compressive Sensing is an emerging technology for data compression and simultaneous data acquisition. This is an enabling technique for significant reduction in data bandwidth, and transmission power and hence, can greatly benefit spaceflight instruments. We apply this process to detect exoplanets via gravitational microlensing. We experiment with various impact parameters that describe microlensing curves to determine the effectiveness and uncertainty caused by Compressive Sensing. Finally, we describe implications for spaceflight missions.
3D polypyrrole structures as a sensing material for glucose detection
NASA Astrophysics Data System (ADS)
Cysewska, Karolina; Szymańska, Magdalena; Jasiński, Piotr
2016-11-01
In this work, 3D polypyrrole (PPy) structures as material for glucose detection is proposed. Polypyrrole was electrochemically polymerized on platinum screen-printed electrode from an aqueous solution of lithium perchlorate and pyrrole. The growth mechanism of such PPy structures was studied by ex-situ scanning electron microscopy. Preliminary studies show that studied here PPy film is a good candidate as a sensing material for glucose biosensor. It exhibits very high sensitivity (28.5 mA·mM-1·cm-2) and can work without any additional dopants, mediators or enzymes. It was also shown that glucose detection depends on the PPy morphology. The same PPy material was immobilized with the glucose oxidase enzyme. Such material exhibited higher signal response, however it lost its stability very fast.
NASA Astrophysics Data System (ADS)
Kim, Min Woo; Choi, Jiyoung; Yu, Liu; Lee, Kyung Eun; Han, Sung-Sik; Ye, Jong Chul
2007-02-01
Sparse object supports are often encountered in many imaging problems. For such sparse objects, recent theory of compressed sensing tells us that accurate reconstruction of objects are possible even from highly limited number of measurements drastically smaller than the Nyquist sampling limit by solving L I minimization problem. This paper employs the compressed sensing theory for cryo-electron microscopy (cryo-EM) single particle reconstruction of virus particles. Cryo-EM single particle reconstruction is a nice application of the compressed sensing theory because of the following reasons: 1) in some cases, due to the difficulty in sample collection, each experiment can obtain micrographs with limited number of virus samples, providing undersampled projection data, and 2) the nucleic acid of a viron is enclosed within capsid composed of a few proteins; hence the support of capsid in 3-D real space is quite sparse. In order to minimize the L I cost function derived from compressed sensing, we develop a novel L I minimization method based on the sliding mode control theory. Experimental results using synthetic and real virus data confirm that the our algorithm provides superior reconstructions of 3-D viral structures compared to the conventional reconstruction algorithms.
Real-time sensing of mouth 3-D position and orientation
NASA Astrophysics Data System (ADS)
Burdea, Grigore C.; Dunn, Stanley M.; Mallik, Matsumita; Jun, Heesung
1990-07-01
A key problem in using digital subtraction radiography in dentistry is the ability to reposition the X-ray source and patient so as to reproduce an identical imaging geometry. In this paper we describe an approach to solving this problem based on real time sensing of the 3-D position and orientation of the patient's mouth. The research described here is part of a program which has a long term goal to develop an automated digital subtraction radiography system. This will allow the patient and X-ray source to be accurately repositioned without the mechanical fixtures that are presently used to preserve the imaging geometry. If we can measure the position and orientation of the mouth, then the desired position of the source can be computed as the product of the transformation matrices describing the desired imaging geometry and the position vector of the targeted tooth. Position and orientation of the mouth is measured by a real time sensing device using low-frequency magnetic field technology. We first present the problem of repositioning the patient and source and then outline our analytic solution. Then we describe an experimental setup to measure the accuracy, reproducibility and resolution of the sensor and present results of preliminary experiments.
Pope, Paul A; Ranken, Doug M
2010-01-01
A method for abstracting a 3D model by shrinking a triangular mesh, defined upon a best fitting ellipsoid surrounding the model, onto the model's surface has been previously described. This ''shrinkwrap'' process enables a semi-regular mesh to be defined upon an object's surface. This creates a useful data structure for conducting remote sensing simulations and image processing. However, using a best fitting ellipsoid having a graticule-based tessellation to seed the shrinkwrap process suffers from a mesh which is too dense at the poles. To achieve a more regular mesh, the use of a best fitting, subdivided icosahedron was tested. By subdividing each of the twenty facets of the icosahedron into regular triangles of a predetermined size, arbitrarily dense, highly-regular starting meshes can be created. Comparisons of the meshes resulting from these two seed surfaces are described. Use of a best fitting icosahedron-based mesh as the seed surface in the shrinkwrap process is preferable to using a best fitting ellipsoid. The impacts to remote sensing simulations, specifically generation of synthetic imagery, is illustrated.
NASA Astrophysics Data System (ADS)
Yu, Haibo; Zhao, Junning
2017-01-01
In this paper, we study the global existence for classical solutions to the 3D isentropic compressible Navier-Stokes equations in a cuboid domain. Compared to the Cauchy problem studied in Hoff (1995 J. Differ. Equ. 120 215-54), Hoff (2005 J. Math. Fluid Mech. 7 315-38), Huang et al (2012 Commun. Pure Appl. Math. 65 549-85), some new thoughts are applied to obtain upper bounds for density. Precisely, through piecewise estimation and some time-depending a priori estimates, we establish time-uniform upper bounds for density under the assumption that the initial energy is small. The initial vacuum is allowed.
Whole Brain Susceptibility Mapping Using Compressed Sensing
Wu, Bing; Li, Wei; Guidon, Arnaud; Liu, Chunlei
2011-01-01
The derivation of susceptibility from image phase is hampered by the ill-conditioned filter inversion in certain k-space regions. In this paper, compressed sensing (CS) is used to compensate for the k-space regions where direct filter inversion is unstable. A significantly lower level of streaking artifacts is produced in the resulting susceptibility maps for both simulated and in vivo data sets compared to outcomes obtained using the direct threshold method. It is also demonstrated that the CS based method outperforms regularization based methods. The key difference between the regularized inversions and CS compensated inversions is that, in the former case, the entire k-space spectrum estimation is affected by the ill-conditioned filter inversion in certain k-space regions, whereas in the CS based method only the ill-conditioned k-space regions are estimated. In the susceptibility map calculated from the phase measurement obtained using a 3T scanner, not only are the iron-rich regions well depicted, but good contrast between white and gray matter interfaces that feature a low level of susceptibility variations are also obtained. The correlation between the iron content and the susceptibility levels in iron-rich deep nucleus regions is studied, and strong linear relationships are observed which agree with previous findings. PMID:21671269
Bacterial community reconstruction using compressed sensing.
Amir, Amnon; Zuk, Or
2011-11-01
Bacteria are the unseen majority on our planet, with millions of species and comprising most of the living protoplasm. We propose a novel approach for reconstruction of the composition of an unknown mixture of bacteria using a single Sanger-sequencing reaction of the mixture. Our method is based on compressive sensing theory, which deals with reconstruction of a sparse signal using a small number of measurements. Utilizing the fact that in many cases each bacterial community is comprised of a small subset of all known bacterial species, we show the feasibility of this approach for determining the composition of a bacterial mixture. Using simulations, we show that sequencing a few hundred base-pairs of the 16S rRNA gene sequence may provide enough information for reconstruction of mixtures containing tens of species, out of tens of thousands, even in the presence of realistic measurement noise. Finally, we show initial promising results when applying our method for the reconstruction of a toy experimental mixture with five species. Our approach may have a potential for a simple and efficient way for identifying bacterial species compositions in biological samples. All supplementary data and the MATLAB code are available at www.broadinstitute.org/?orzuk/publications/BCS/.
Energy Preserved Sampling for Compressed Sensing MRI
Peterson, Bradley S.; Ji, Genlin; Dong, Zhengchao
2014-01-01
The sampling patterns, cost functions, and reconstruction algorithms play important roles in optimizing compressed sensing magnetic resonance imaging (CS-MRI). Simple random sampling patterns did not take into account the energy distribution in k-space and resulted in suboptimal reconstruction of MR images. Therefore, a variety of variable density (VD) based samplings patterns had been developed. To further improve it, we propose a novel energy preserving sampling (ePRESS) method. Besides, we improve the cost function by introducing phase correction and region of support matrix, and we propose iterative thresholding algorithm (ITA) to solve the improved cost function. We evaluate the proposed ePRESS sampling method, improved cost function, and ITA reconstruction algorithm by 2D digital phantom and 2D in vivo MR brains of healthy volunteers. These assessments demonstrate that the proposed ePRESS method performs better than VD, POWER, and BKO; the improved cost function can achieve better reconstruction quality than conventional cost function; and the ITA is faster than SISTA and is competitive with FISTA in terms of computation time. PMID:24971155
Compressed sensing MRI exploiting complementary dual decomposition.
Park, Suhyung; Park, Jaeseok
2014-04-01
Compressed sensing (CS) MRI exploits the sparsity of an image in a transform domain to reconstruct the image from incoherently under-sampled k-space data. However, it has been shown that CS suffers particularly from loss of low-contrast image features with increasing reduction factors. To retain image details in such degraded experimental conditions, in this work we introduce a novel CS reconstruction method exploiting feature-based complementary dual decomposition with joint estimation of local scale mixture (LSM) model and images. Images are decomposed into dual block sparse components: total variation for piecewise smooth parts and wavelets for residuals. The LSM model parameters of residuals in the wavelet domain are estimated and then employed as a regional constraint in spatially adaptive reconstruction of high frequency subbands to restore image details missing in piecewise smooth parts. Alternating minimization of the dual image components subject to data consistency is performed to extract image details from residuals and add them back to their complementary counterparts while the LSM model parameters and images are jointly estimated in a sequential fashion. Simulations and experiments demonstrate the superior performance of the proposed method in preserving low-contrast image features even at high reduction factors.
Xia, Yidong; Luo, Hong; Frisbey, Megan; Nourgaliev, Robert
2014-07-01
A set of implicit methods are proposed for a third-order hierarchical WENO reconstructed discontinuous Galerkin method for compressible flows on 3D hybrid grids. An attractive feature in these methods are the application of the Jacobian matrix based on the P1 element approximation, resulting in a huge reduction of memory requirement compared with DG (P2). Also, three approaches -- analytical derivation, divided differencing, and automatic differentiation (AD) are presented to construct the Jacobian matrix respectively, where the AD approach shows the best robustness. A variety of compressible flow problems are computed to demonstrate the fast convergence property of the implemented flow solver. Furthermore, an SPMD (single program, multiple data) programming paradigm based on MPI is proposed to achieve parallelism. The numerical results on complex geometries indicate that this low-storage implicit method can provide a viable and attractive DG solution for complicated flows of practical importance.
Xia, Yidong; Luo, Hong; Frisbey, Megan; ...
2014-07-01
A set of implicit methods are proposed for a third-order hierarchical WENO reconstructed discontinuous Galerkin method for compressible flows on 3D hybrid grids. An attractive feature in these methods are the application of the Jacobian matrix based on the P1 element approximation, resulting in a huge reduction of memory requirement compared with DG (P2). Also, three approaches -- analytical derivation, divided differencing, and automatic differentiation (AD) are presented to construct the Jacobian matrix respectively, where the AD approach shows the best robustness. A variety of compressible flow problems are computed to demonstrate the fast convergence property of the implemented flowmore » solver. Furthermore, an SPMD (single program, multiple data) programming paradigm based on MPI is proposed to achieve parallelism. The numerical results on complex geometries indicate that this low-storage implicit method can provide a viable and attractive DG solution for complicated flows of practical importance.« less
Energy-Efficient Sensing in Wireless Sensor Networks Using Compressed Sensing
Razzaque, Mohammad Abdur; Dobson, Simon
2014-01-01
Sensing of the application environment is the main purpose of a wireless sensor network. Most existing energy management strategies and compression techniques assume that the sensing operation consumes significantly less energy than radio transmission and reception. This assumption does not hold in a number of practical applications. Sensing energy consumption in these applications may be comparable to, or even greater than, that of the radio. In this work, we support this claim by a quantitative analysis of the main operational energy costs of popular sensors, radios and sensor motes. In light of the importance of sensing level energy costs, especially for power hungry sensors, we consider compressed sensing and distributed compressed sensing as potential approaches to provide energy efficient sensing in wireless sensor networks. Numerical experiments investigating the effectiveness of compressed sensing and distributed compressed sensing using real datasets show their potential for efficient utilization of sensing and overall energy costs in wireless sensor networks. It is shown that, for some applications, compressed sensing and distributed compressed sensing can provide greater energy efficiency than transform coding and model-based adaptive sensing in wireless sensor networks. PMID:24526302
Energy-efficient sensing in wireless sensor networks using compressed sensing.
Razzaque, Mohammad Abdur; Dobson, Simon
2014-02-12
Sensing of the application environment is the main purpose of a wireless sensor network. Most existing energy management strategies and compression techniques assume that the sensing operation consumes significantly less energy than radio transmission and reception. This assumption does not hold in a number of practical applications. Sensing energy consumption in these applications may be comparable to, or even greater than, that of the radio. In this work, we support this claim by a quantitative analysis of the main operational energy costs of popular sensors, radios and sensor motes. In light of the importance of sensing level energy costs, especially for power hungry sensors, we consider compressed sensing and distributed compressed sensing as potential approaches to provide energy efficient sensing in wireless sensor networks. Numerical experiments investigating the effectiveness of compressed sensing and distributed compressed sensing using real datasets show their potential for efficient utilization of sensing and overall energy costs in wireless sensor networks. It is shown that, for some applications, compressed sensing and distributed compressed sensing can provide greater energy efficiency than transform coding and model-based adaptive sensing in wireless sensor networks.
Gaul, C; Hastreiter, P; Duncker, A; Naraghi, R
2011-10-01
Glossopharyngeal neuralgia is a rare condition with neuralgic sharp pain in the pharyngeal and auricular region. Classical glossopharyngeal neuralgia is caused by neurovascular compression at the root entry zone of the nerve. Regarding the rare occurrence of glossopharyngeal neuralgia, we report clinical data and magnetic resonance imaging (MRI) findings in a case series of 19 patients, of whom 18 underwent surgery. Two patients additionally suffered from trigeminal neuralgia and three from additional symptomatic vagal nerve compression. In all patients, ipsilateral neurovascular compression syndrome of the IX cranial nerve could be shown by high-resolution MRI and image processing, which was confirmed intraoperatively. Additional neurovascular compression of the V cranial nerve was shown in patients suffering from trigeminal neuralgia. Vagal nerve neurovascular compression could be seen in all patients during surgery. Sixteen patients were completely pain free after surgery without need of anticonvulsant treatment. As a consequence of the operation, two patients suffered from transient cerebrospinal fluid hypersecretion as a reaction to Teflon implants. One patient suffered postoperatively from deep vein thrombosis and pulmonary embolism. Six patients showed transient cranial nerve dysfunctions (difficulties in swallowing, vocal cord paresis), but all recovered within 1 week. One patient complained of a gnawing and burning pain in the cervical area. Microvascular decompression is a second-line treatment after failure of standard medical treatment with high success in glossopharyngeal neuralgia. High-resolution MRI and 3D visualization of the brainstem and accompanying vessels as well as the cranial nerves is helpful in identifying neurovascular compression before microvascular decompression procedure.
Bar-Kochba, Eyal; Scimone, Mark T; Estrada, Jonathan B; Franck, Christian
2016-08-02
In the United States over 1.7 million cases of traumatic brain injury are reported yearly, but predictive correlation of cellular injury to impact tissue strain is still lacking, particularly for neuronal injury resulting from compression. Given the prevalence of compressive deformations in most blunt head trauma, this information is critically important for the development of future mitigation and diagnosis strategies. Using a 3D in vitro neuronal compression model, we investigated the role of impact strain and strain rate on neuronal lifetime, viability, and pathomorphology. We find that strain magnitude and rate have profound, yet distinctively different effects on the injury pathology. While strain magnitude affects the time of neuronal death, strain rate influences the pathomorphology and extent of population injury. Cellular injury is not initiated through localized deformation of the cytoskeleton but rather driven by excess strain on the entire cell. Furthermore we find that, mechanoporation, one of the key pathological trigger mechanisms in stretch and shear neuronal injuries, was not observed under compression.
NASA Astrophysics Data System (ADS)
Bar-Kochba, Eyal; Scimone, Mark T.; Estrada, Jonathan B.; Franck, Christian
2016-08-01
In the United States over 1.7 million cases of traumatic brain injury are reported yearly, but predictive correlation of cellular injury to impact tissue strain is still lacking, particularly for neuronal injury resulting from compression. Given the prevalence of compressive deformations in most blunt head trauma, this information is critically important for the development of future mitigation and diagnosis strategies. Using a 3D in vitro neuronal compression model, we investigated the role of impact strain and strain rate on neuronal lifetime, viability, and pathomorphology. We find that strain magnitude and rate have profound, yet distinctively different effects on the injury pathology. While strain magnitude affects the time of neuronal death, strain rate influences the pathomorphology and extent of population injury. Cellular injury is not initiated through localized deformation of the cytoskeleton but rather driven by excess strain on the entire cell. Furthermore we find that, mechanoporation, one of the key pathological trigger mechanisms in stretch and shear neuronal injuries, was not observed under compression.
Bar-Kochba, Eyal; Scimone, Mark T.; Estrada, Jonathan B.; Franck, Christian
2016-01-01
In the United States over 1.7 million cases of traumatic brain injury are reported yearly, but predictive correlation of cellular injury to impact tissue strain is still lacking, particularly for neuronal injury resulting from compression. Given the prevalence of compressive deformations in most blunt head trauma, this information is critically important for the development of future mitigation and diagnosis strategies. Using a 3D in vitro neuronal compression model, we investigated the role of impact strain and strain rate on neuronal lifetime, viability, and pathomorphology. We find that strain magnitude and rate have profound, yet distinctively different effects on the injury pathology. While strain magnitude affects the time of neuronal death, strain rate influences the pathomorphology and extent of population injury. Cellular injury is not initiated through localized deformation of the cytoskeleton but rather driven by excess strain on the entire cell. Furthermore we find that, mechanoporation, one of the key pathological trigger mechanisms in stretch and shear neuronal injuries, was not observed under compression. PMID:27480807
NASA Astrophysics Data System (ADS)
Javidi, Bahram; Yeom, Seokwon; Moon, Inkyu; Daneshpanah, Mehdi
2006-05-01
In this paper, we present an overview of three-dimensional (3D) optical imaging techniques for real-time automated sensing, visualization, and recognition of dynamic biological microorganisms. Real time sensing and 3D reconstruction of the dynamic biological microscopic objects can be performed by single-exposure on-line (SEOL) digital holographic microscopy. A coherent 3D microscope-based interferometer is constructed to record digital holograms of dynamic micro biological events. Complex amplitude 3D images of the biological microorganisms are computationally reconstructed at different depths by digital signal processing. Bayesian segmentation algorithms are applied to identify regions of interest for further processing. A number of pattern recognition approaches are addressed to identify and recognize the microorganisms. One uses 3D morphology of the microorganisms by analyzing 3D geometrical shapes which is composed of magnitude and phase. Segmentation, feature extraction, graph matching, feature selection, and training and decision rules are used to recognize the biological microorganisms. In a different approach, 3D technique is used that are tolerant to the varying shapes of the non-rigid biological microorganisms. After segmentation, a number of sampling patches are arbitrarily extracted from the complex amplitudes of the reconstructed 3D biological microorganism. These patches are processed using a number of cost functions and statistical inference theory for the equality of means and equality of variances between the sampling segments. Also, we discuss the possibility of employing computational integral imaging for 3D sensing, visualization, and recognition of biological microorganisms illuminated under incoherent light. Experimental results with several biological microorganisms are presented to illustrate detection, segmentation, and identification of micro biological events.
Finite element methods of analysis for 3D inviscid compressible flows
NASA Technical Reports Server (NTRS)
Peraire, Jaime
1990-01-01
The applicants have developed a finite element based approach for the solution of three-dimensional compressible flows. The procedure enables flow solutions to be obtained on tetrahedral discretizations of computational domains of complex form. A further development was the incorporation of a solution adaptive mesh strategy in which the adaptivity is achieved by complete remeshing of the solution domain. During the previous year, the applicants were working with the Advanced Aerodynamics Concepts Branch at NASA Ames Research Center with an implementation of the basic meshing and solution procedure. The objective of the work to be performed over this twelve month period was the transfer of the adaptive mesh technology and also the undertaking of basic research into alternative flow algorithms for the Euler equations on unstructured meshes.
3D change detection in staggered voxels model for robotic sensing and navigation
NASA Astrophysics Data System (ADS)
Liu, Ruixu; Hampshire, Brandon; Asari, Vijayan K.
2016-05-01
3D scene change detection is a challenging problem in robotic sensing and navigation. There are several unpredictable aspects in performing scene change detection. A change detection method which can support various applications in varying environmental conditions is proposed. Point cloud models are acquired from a RGB-D sensor, which provides the required color and depth information. Change detection is performed on robot view point cloud model. A bilateral filter smooths the surface and fills the holes as well as keeps the edge details on depth image. Registration of the point cloud model is implemented by using Random Sample Consensus (RANSAC) algorithm. It uses surface normal as the previous stage for the ground and wall estimate. After preprocessing the data, we create a point voxel model which defines voxel as surface or free space. Then we create a color model which defines each voxel that has a color by the mean of all points' color value in this voxel. The preliminary change detection is detected by XOR subtract on the point voxel model. Next, the eight neighbors for this center voxel are defined. If they are neither all `changed' voxels nor all `no changed' voxels, a histogram of location and hue channel color is estimated. The experimental evaluations performed to evaluate the capability of our algorithm show promising results for novel change detection that indicate all the changing objects with very limited false alarm rate.
Reducing Disk Storage of Full-3D Seismic Waveform Tomography (F3DT) Through Lossy Online Compression
Lindstrom, Peter; Chen, Po; Lee, En-Jui
2016-05-05
Full-3D seismic waveform tomography (F3DT) is the latest seismic tomography technique that can assimilate broadband, multi-component seismic waveform observations into high-resolution 3D subsurface seismic structure models. The main drawback in the current F3DT implementation, in particular the scattering-integral implementation (F3DT-SI), is the high disk storage cost and the associated I/O overhead of archiving the 4D space-time wavefields of the receiver- or source-side strain tensors. The strain tensor fields are needed for computing the data sensitivity kernels, which are used for constructing the Jacobian matrix in the Gauss-Newton optimization algorithm. In this study, we have successfully integrated a lossy compression algorithm into our F3DT SI workflow to significantly reduce the disk space for storing the strain tensor fields. The compressor supports a user-specified tolerance for bounding the error, and can be integrated into our finite-difference wave-propagation simulation code used for computing the strain fields. The decompressor can be integrated into the kernel calculation code that reads the strain fields from the disk and compute the data sensitivity kernels. During the wave-propagation simulations, we compress the strain fields before writing them to the disk. To compute the data sensitivity kernels, we read the compressed strain fields from the disk and decompress them before using them in kernel calculations. Experiments using a realistic dataset in our California statewide F3DT project have shown that we can reduce the strain-field disk storage by at least an order of magnitude with acceptable loss, and also improve the overall I/O performance of the entire F3DT-SI workflow significantly. The integration of the lossy online compressor may potentially open up the possibilities of the wide adoption of F3DT-SI in routine seismic tomography practices in the near future.
Reducing disk storage of full-3D seismic waveform tomography (F3DT) through lossy online compression
NASA Astrophysics Data System (ADS)
Lindstrom, Peter; Chen, Po; Lee, En-Jui
2016-08-01
Full-3D seismic waveform tomography (F3DT) is the latest seismic tomography technique that can assimilate broadband, multi-component seismic waveform observations into high-resolution 3D subsurface seismic structure models. The main drawback in the current F3DT implementation, in particular the scattering-integral implementation (F3DT-SI), is the high disk storage cost and the associated I/O overhead of archiving the 4D space-time wavefields of the receiver- or source-side strain tensors. The strain tensor fields are needed for computing the data sensitivity kernels, which are used for constructing the Jacobian matrix in the Gauss-Newton optimization algorithm. In this study, we have successfully integrated a lossy compression algorithm into our F3DT-SI workflow to significantly reduce the disk space for storing the strain tensor fields. The compressor supports a user-specified tolerance for bounding the error, and can be integrated into our finite-difference wave-propagation simulation code used for computing the strain fields. The decompressor can be integrated into the kernel calculation code that reads the strain fields from the disk and compute the data sensitivity kernels. During the wave-propagation simulations, we compress the strain fields before writing them to the disk. To compute the data sensitivity kernels, we read the compressed strain fields from the disk and decompress them before using them in kernel calculations. Experiments using a realistic dataset in our California statewide F3DT project have shown that we can reduce the strain-field disk storage by at least an order of magnitude with acceptable loss, and also improve the overall I/O performance of the entire F3DT-SI workflow significantly. The integration of the lossy online compressor may potentially open up the possibilities of the wide adoption of F3DT-SI in routine seismic tomography practices in the near future.
Accelerated high-resolution photoacoustic tomography via compressed sensing
NASA Astrophysics Data System (ADS)
Arridge, Simon; Beard, Paul; Betcke, Marta; Cox, Ben; Huynh, Nam; Lucka, Felix; Ogunlade, Olumide; Zhang, Edward
2016-12-01
Current 3D photoacoustic tomography (PAT) systems offer either high image quality or high frame rates but are not able to deliver high spatial and temporal resolution simultaneously, which limits their ability to image dynamic processes in living tissue (4D PAT). A particular example is the planar Fabry-Pérot (FP) photoacoustic scanner, which yields high-resolution 3D images but takes several minutes to sequentially map the incident photoacoustic field on the 2D sensor plane, point-by-point. However, as the spatio-temporal complexity of many absorbing tissue structures is rather low, the data recorded in such a conventional, regularly sampled fashion is often highly redundant. We demonstrate that combining model-based, variational image reconstruction methods using spatial sparsity constraints with the development of novel PAT acquisition systems capable of sub-sampling the acoustic wave field can dramatically increase the acquisition speed while maintaining a good spatial resolution: first, we describe and model two general spatial sub-sampling schemes. Then, we discuss how to implement them using the FP interferometer and demonstrate the potential of these novel compressed sensing PAT devices through simulated data from a realistic numerical phantom and through measured data from a dynamic experimental phantom as well as from in vivo experiments. Our results show that images with good spatial resolution and contrast can be obtained from highly sub-sampled PAT data if variational image reconstruction techniques that describe the tissues structures with suitable sparsity-constraints are used. In particular, we examine the use of total variation (TV) regularization enhanced by Bregman iterations. These novel reconstruction strategies offer new opportunities to dramatically increase the acquisition speed of photoacoustic scanners that employ point-by-point sequential scanning as well as reducing the channel count of parallelized schemes that use detector arrays.
Depth-image-based rendering (DIBR), compression, and transmission for a new approach on 3D-TV
NASA Astrophysics Data System (ADS)
Fehn, Christoph
2004-05-01
This paper presents details of a system that allows for an evolutionary introduction of depth perception into the existing 2D digital TV framework. The work is part of the European Information Society Technologies (IST) project "Advanced Three-Dimensional Television System Technologies" (ATTEST), an activity, where industries, research centers and universities have joined forces to design a backwards-compatible, flexible and modular broadcast 3D-TV system. At the very heart of the described new concept is the generation and distribution of a novel data representation format, which consists of monoscopic color video and associated per-pixel depth information. From these data, one or more "virtual" views of a real-world scene can be synthesized in real-time at the receiver side (i.e. a 3D-TV set-top box) by means of so-called depth-image-based rendering (DIBR) techniques. This publication will provide: (1) a detailed description of the fundamentals of this new approach on 3D-TV; (2) a comparison with the classical approach of "stereoscopic" video; (3) a short introduction to DIBR techniques in general; (4) the development of a specific DIBR algorithm that can be used for the efficient generation of high-quality "virtual" stereoscopic views; (5) a number of implementation details that are specific to the current state of the development; (6) research on the backwards-compatible compression and transmission of 3D imagery using state-of-the-art MPEG (Moving Pictures Expert Group) tools.
NASA Astrophysics Data System (ADS)
Jinesh, Mathew; MacPherson, William N.; Hand, Duncan P.; Maier, Robert R. J.
2016-05-01
A smart metal component having the potential for high temperature strain sensing capability is reported. The stainless steel (SS316) structure is made by selective laser melting (SLM). A fiber Bragg grating (FBG) is embedded in to a 3D printed U-groove by high temperature brazing using a silver based alloy, achieving an axial FBG compression of 13 millistrain at room temperature. Initial results shows that the test component can be used for up to 700°C for sensing applications.
The 3D Navier-Stokes analysis of a Mach 2.68 bifurcated rectangular mixed-compression inlet
NASA Technical Reports Server (NTRS)
Mizukami, M.; Saunders, J. D.
1995-01-01
The supersonic diffuser of a Mach 2.68 bifurcated, rectangular, mixed-compression inlet was analyzed using a three-dimensional (3D) Navier-Stokes flow solver. A two-equation turbulence model, and a porous bleed model based on unchoked bleed hole discharge coefficients were used. Comparisons were made with experimental data, inviscid theory, and two-dimensional Navier-Stokes analyses. The main objective was to gain insight into the inlet fluid dynamics. Examination of the computational results along with the experimental data suggest that the cowl shock-sidewall boundary layer interaction near the leading edge caused a substantial separation in the wind tunnel inlet model. As a result, the inlet performance may have been compromised by increased spillage and higher bleed mass flow requirements. The internal flow contained substantial waves that were not in the original inviscid design. 3D effects were fairly minor for this inlet at on-design conditions. Navier-Stokes analysis appears to be an useful tool for gaining insight into the inlet fluid dynamics. It provides a higher fidelity simulation of the flowfield than the original inviscid design, by taking into account boundary layers, porous bleed, and their interactions with shock waves.
Tan, Yu Jun; Tan, Xipeng; Yeong, Wai Yee; Tor, Shu Beng
2016-01-01
A hybrid 3D bioprinting approach using porous microscaffolds and extrusion-based printing method is presented. Bioink constitutes of cell-laden poly(D,L-lactic-co-glycolic acid) (PLGA) porous microspheres with thin encapsulation of agarose-collagen composite hydrogel (AC hydrogel). Highly porous microspheres enable cells to adhere and proliferate before printing. Meanwhile, AC hydrogel allows a smooth delivery of cell-laden microspheres (CLMs), with immediate gelation of construct upon printing on cold build platform. Collagen fibrils were formed in the AC hydrogel during culture at body temperature, improving the cell affinity and spreading compared to pure agarose hydrogel. Cells were proven to proliferate in the bioink and the bioprinted construct. High cell viability up to 14 days was observed. The compressive strength of the bioink is more than 100 times superior to those of pure AC hydrogel. A potential alternative in tissue engineering of tissue replacements and biological models is made possible by combining the advantages of the conventional solid scaffolds with the new 3D bioprinting technology. PMID:27966623
Reducing Disk Storage of Full-3D Seismic Waveform Tomography (F3DT) Through Lossy Online Compression
Lindstrom, Peter; Chen, Po; Lee, En-Jui
2016-05-05
Full-3D seismic waveform tomography (F3DT) is the latest seismic tomography technique that can assimilate broadband, multi-component seismic waveform observations into high-resolution 3D subsurface seismic structure models. The main drawback in the current F3DT implementation, in particular the scattering-integral implementation (F3DT-SI), is the high disk storage cost and the associated I/O overhead of archiving the 4D space-time wavefields of the receiver- or source-side strain tensors. The strain tensor fields are needed for computing the data sensitivity kernels, which are used for constructing the Jacobian matrix in the Gauss-Newton optimization algorithm. In this study, we have successfully integrated a lossy compression algorithmmore » into our F3DT SI workflow to significantly reduce the disk space for storing the strain tensor fields. The compressor supports a user-specified tolerance for bounding the error, and can be integrated into our finite-difference wave-propagation simulation code used for computing the strain fields. The decompressor can be integrated into the kernel calculation code that reads the strain fields from the disk and compute the data sensitivity kernels. During the wave-propagation simulations, we compress the strain fields before writing them to the disk. To compute the data sensitivity kernels, we read the compressed strain fields from the disk and decompress them before using them in kernel calculations. Experiments using a realistic dataset in our California statewide F3DT project have shown that we can reduce the strain-field disk storage by at least an order of magnitude with acceptable loss, and also improve the overall I/O performance of the entire F3DT-SI workflow significantly. The integration of the lossy online compressor may potentially open up the possibilities of the wide adoption of F3DT-SI in routine seismic tomography practices in the near future.« less
Characterization of 3D printing output using an optical sensing system
NASA Astrophysics Data System (ADS)
Straub, Jeremy
2015-05-01
This paper presents the experimental design and initial testing of a system to characterize the progress and performance of a 3D printer. The system is based on five Raspberry Pi single-board computers. It collects images of the 3D printed object, which are compared to an ideal model. The system, while suitable for printers of all sizes, can potentially be produced at a sufficiently low cost to allow its incorporation into consumer-grade printers. The efficacy and accuracy of this system is presented and discussed. The paper concludes with a discussion of the benefits of being able to characterize 3D printer performance.
Interactive video compression for remote sensing
NASA Astrophysics Data System (ADS)
Maleh, Ray; Boyle, Frank A.; Deignan, Paul B.; Yancey, Jerry W.
2011-05-01
Modern day remote video cameras enjoy the ability of producing quality video streams at extremely high resolutions. Unfortunately, the benefit of such technology cannot be realized when the channel between the sensor and the operator restricts the bit-rate of incoming data. In order to cram more information into the available bandwidth, video technologies typically employ compression schemes (e.g. H.264/MPEG 4 standard) which exploit spatial and temporal redundancies. We present an alternative method utilizing region of interest (ROI) based compression. Each region in the incoming scene is assigned a score measuring importance to the operator. Scores may be determined based on the manual selection of one or more objects which are then automatically tracked by the system; or alternatively, listeners may be pre-assigned to various areas that trigger high scores upon the occurrence of customizable events. A multi-resolution wavelet expansion is then used to optimally transmit important regions at higher resolutions and frame rates than less interesting peripheral background objects subject to bandwidth constraints. We show that our methodology makes it possible to obtain high compression ratios while ensuring no loss in overall situational awareness. If combined with modules from traditional video codecs, compression ratios of 100:1 to 1000:1, depending on ROI size, can easily be achieved.
Study of 3D remote sensing system based on optical scanning holography
NASA Astrophysics Data System (ADS)
Zhao, Shihu; Yan, Lei
2009-06-01
High-precision and real-time remote sensing imaging system is an important part of remote sensing development. Holography is a method of wave front record and recovery which was presented by Dennis Gabor. As a new kind of holography techniques, Optical scanning holography (OSH) and remote sensing imaging are intended to be combined together and applied in acquisition and interference measurement of remote sensing. The key principles and applicability of OSH are studied and the mathematic relation between Fresnel Zone Plate number, numerical aperture and object distance was deduced, which are proved to be feasible for OSH to apply in large scale remote sensing. At last, a new three-dimensional reflected OSH remote sensing imaging system is designed with the combination of scanning technique to record hologram patterns of large scale remote sensing scenes. This scheme is helpful for expanding OSH technique to remote sensing in future.
Imaging industry expectations for compressed sensing in MRI
NASA Astrophysics Data System (ADS)
King, Kevin F.; Kanwischer, Adriana; Peters, Rob
2015-09-01
Compressed sensing requires compressible data, incoherent acquisition and a nonlinear reconstruction algorithm to force creation of a compressible image consistent with the acquired data. MRI images are compressible using various transforms (commonly total variation or wavelets). Incoherent acquisition of MRI data by appropriate selection of pseudo-random or non-Cartesian locations in k-space is straightforward. Increasingly, commercial scanners are sold with enough computing power to enable iterative reconstruction in reasonable times. Therefore integration of compressed sensing into commercial MRI products and clinical practice is beginning. MRI frequently requires the tradeoff of spatial resolution, temporal resolution and volume of spatial coverage to obtain reasonable scan times. Compressed sensing improves scan efficiency and reduces the need for this tradeoff. Benefits to the user will include shorter scans, greater patient comfort, better image quality, more contrast types per patient slot, the enabling of previously impractical applications, and higher throughput. Challenges to vendors include deciding which applications to prioritize, guaranteeing diagnostic image quality, maintaining acceptable usability and workflow, and acquisition and reconstruction algorithm details. Application choice depends on which customer needs the vendor wants to address. The changing healthcare environment is putting cost and productivity pressure on healthcare providers. The improved scan efficiency of compressed sensing can help alleviate some of this pressure. Image quality is strongly influenced by image compressibility and acceleration factor, which must be appropriately limited. Usability and workflow concerns include reconstruction time and user interface friendliness and response. Reconstruction times are limited to about one minute for acceptable workflow. The user interface should be designed to optimize workflow and minimize additional customer training. Algorithm
NASA Astrophysics Data System (ADS)
Roberts, M. A.; Graymer, R. W.; McPhee, D.
2015-12-01
During the late Miocene, a small change in the relative motion of the Pacific plate resulted in compressive as well as translational deformation along the central San Andreas Fault (SAF), creating thrust faults and folds throughout this region of California. We constructed a 3D model of an upper crustal volume between Pinnacles National Park and Gold Hill by assembling geologic map data and cross sections, geophysical data, and petroleum well logs in MoveTm, software which has the ability to forward and reverse model movement along faults and folds. For this study, we chose a blind thrust fault west of the SAF near Parkfield to compare deformation produced by MoveTm's forward modeling algorithm with that observed. We chose various synclines east of the SAF to explore the software's ability to unfold (reverse model) units. For the initial round of modeling, strike-slip movement has been omitted as the fault algorithm was designed primarily for extensional or compressional environments. Preliminary forward modeling of originally undeformed strata along the blind thrust produced geometries similar to those in the present-day 3D geologic model. The modeled amount of folding produced in hanging wall strata was less severe, suggesting these units were slightly folded before displacement. Based on these results, the algorithm shows potential in predicting deformation related to blind thrusts. Contraction in the region varies with fold axis location and orientation. MoveTm's unfolding algorithm can allow researchers to measure the amount of contraction a fold represents, and compare that amount across the modeled area as a way of observing regional stress patterns. The unfolding algorithm also allows for passive deformation of strata unconformably underlying the fold; one example reveals a steeper orientation of Cretaceous units prior to late Miocene deformation. Such modeling capabilities can allow for a better understanding of the structural history of the region.
Optical frequency comb interference profilometry using compressive sensing.
Pham, Quang Duc; Hayasaki, Yoshio
2013-08-12
We describe a new optical system using an ultra-stable mode-locked frequency comb femtosecond laser and compressive sensing to measure an object's surface profile. The ultra-stable frequency comb laser was used to precisely measure an object with a large depth, over a wide dynamic range. The compressive sensing technique was able to obtain the spatial information of the object with two single-pixel fast photo-receivers, with no mechanical scanning and fewer measurements than the number of sampling points. An optical experiment was performed to verify the advantages of the proposed method.
Robust compressive sensing of sparse signals: a review
NASA Astrophysics Data System (ADS)
Carrillo, Rafael E.; Ramirez, Ana B.; Arce, Gonzalo R.; Barner, Kenneth E.; Sadler, Brian M.
2016-12-01
Compressive sensing generally relies on the ℓ 2 norm for data fidelity, whereas in many applications, robust estimators are needed. Among the scenarios in which robust performance is required, applications where the sampling process is performed in the presence of impulsive noise, i.e., measurements are corrupted by outliers, are of particular importance. This article overviews robust nonlinear reconstruction strategies for sparse signals based on replacing the commonly used ℓ 2 norm by M-estimators as data fidelity functions. The derived methods outperform existing compressed sensing techniques in impulsive environments, while achieving good performance in light-tailed environments, thus offering a robust framework for CS.
2D imaging and 3D sensing data acquisition and mutual registration for painting conservation
NASA Astrophysics Data System (ADS)
Fontana, Raffaella; Gambino, Maria Chiara; Greco, Marinella; Marras, Luciano; Pampaloni, Enrico M.; Pelagotti, Anna; Pezzati, Luca; Poggi, Pasquale
2004-12-01
We describe the application of 2D and 3D data acquisition and mutual registration to the conservation of paintings. RGB color image acquisition, IR and UV fluorescence imaging, together with the more recent hyperspectral imaging (32 bands) are among the most useful techniques in this field. They generally are meant to provide information on the painting materials, on the employed techniques and on the object state of conservation. However, only when the various images are perfectly registered on each other and on the 3D model, no ambiguity is possible and safe conclusions may be drawn. We present the integration of 2D and 3D measurements carried out on two different paintings: "Madonna of the Yarnwinder" by Leonardo da Vinci, and "Portrait of Lionello d'Este", by Pisanello, both painted in the XV century.
2D imaging and 3D sensing data acquisition and mutual registration for painting conservation
NASA Astrophysics Data System (ADS)
Fontana, Raffaella; Gambino, Maria Chiara; Greco, Marinella; Marras, Luciano; Pampaloni, Enrico M.; Pelagotti, Anna; Pezzati, Luca; Poggi, Pasquale
2005-01-01
We describe the application of 2D and 3D data acquisition and mutual registration to the conservation of paintings. RGB color image acquisition, IR and UV fluorescence imaging, together with the more recent hyperspectral imaging (32 bands) are among the most useful techniques in this field. They generally are meant to provide information on the painting materials, on the employed techniques and on the object state of conservation. However, only when the various images are perfectly registered on each other and on the 3D model, no ambiguity is possible and safe conclusions may be drawn. We present the integration of 2D and 3D measurements carried out on two different paintings: "Madonna of the Yarnwinder" by Leonardo da Vinci, and "Portrait of Lionello d'Este", by Pisanello, both painted in the XV century.
NASA Astrophysics Data System (ADS)
Tu, Jihui; Sui, Haigang; Feng, Wenqing; Song, Zhina
2016-06-01
In this paper, a novel approach of building damaged detection is proposed using high resolution remote sensing images and 3D GIS-Model data. Traditional building damage detection method considers to detect damaged building due to earthquake, but little attention has been paid to analyze various building damaged types(e.g., trivial damaged, severely damaged and totally collapsed.) Therefore, we want to detect the different building damaged type using 2D and 3D feature of scenes because the real world we live in is a 3D space. The proposed method generalizes that the image geometric correction method firstly corrects the post-disasters remote sensing image using the 3D GIS model or RPC parameters, then detects the different building damaged types using the change of the height and area between the pre- and post-disasters and the texture feature of post-disasters. The results, evaluated on a selected study site of the Beichuan earthquake ruins, Sichuan, show that this method is feasible and effective in building damage detection. It has also shown that the proposed method is easily applicable and well suited for rapid damage assessment after natural disasters.
Sparsifying transformations of photoacoustic signals enabling compressed sensing algorithms
NASA Astrophysics Data System (ADS)
Burgholzer, P.; Sandbichler, M.; Krahmer, F.; Berer, T.; Haltmeier, M.
2016-03-01
Compressed sensing allows performing much fewer measurements than advised by the Shannon sampling theory. This is surprising because it requires the solution of a system of equations with much fewer equations than unknowns. This is possible if one can assume sparsity of the solution, which means that only a few components of the solution are significantly different from zero. An important ingredient for compressed sensing is the restricted isometry property (RIP) of the sensing matrix, which is satisfied for certain types of random measurement ensembles. Then a sparse solution can be found by minimizing the ℓ1-norm. Using standard approaches, photoacoustic imaging generally neither satisfies sparsity of the data nor the RIP. Therefore, no theoretical recovery guarantees could be given. Despite ℓ1- minimization has been used for photoacoustic image reconstruction, only marginal improvements in comparison to classical photoacoustic reconstruction have been observed. We propose the application of a sparsifying temporal transformation to the detected pressure signals, which allows obtaining theoretical recovery guarantees for our compressed sensing scheme. Such a sparsifying transform can be found because spatial and temporal evolution of the pressure wave are not independent, but connected by the wave equation. We give an example of a sparsifying transform and apply our compressed sensing scheme to reconstruct images from simulated data.
Compact and robust hyperspectral camera based on compressed sensing
NASA Astrophysics Data System (ADS)
Žídek, K.; Denk, O.; Hlubuček, J.; Václavík, J.
2016-11-01
Spectrum of light which is emitted or reflected by an object carries immense amount of information about the object. A simple piece of evidence is the importance of color sensing for human vision. Combining an image acquisition with efficient measurement of light spectra for each detected pixel is therefore one of the important issues in imaging, referred as hyperspectral imaging. We demonstrate a construction of a compact and robust hyperspectral camera for the visible and near-IR spectral region. The camera was designed vastly based on off-shelf optics, yet an extensive optimization and addition of three customized parts enabled construction of the camera featuring a low f-number (F/3.9) and fully concentric optics. We employ a novel approach of compressed sensing (namely coded aperture snapshot spectral imaging, abbrev. CASSI). The compressed sensing enables to computationally extract an encoded hyperspectral information from a single camera exposition. Owing to the technique the camera lacks any moving or scanning part, while it can record the full image and spectral information in a single snapshot. Moreover, unlike the commonly used compressed sensing table-top apparatuses, the camera represents a portable device able to work outside a lab. We demonstrate the spectro-temporal reconstruction of recorded scenes based on 90×90 random matrix encoding. Finally, we discuss potential of the compressed sensing in hyperspectral camera.
NASA Astrophysics Data System (ADS)
Yu, Hengyong; Wang, Ge
2009-07-01
The authors would like to add some missing references. On page 2799, lines 15 and 16 from the bottom should read 'Specifically, the algorithm can be summarized in the following pseudo-code (Candes and Romberg 2005, Candes et al 2006, Sidky et al 2006, Chen et al 2008, Sidky and Pan 2008)'. References Candes E J and Romberg J 2005 Signal recovery from random projections Computational Imaging III; Proc. SPIE 5764 76-86 Candes E J, Romberg J and Tao T 2006 Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information IEEE Trans. Inf. Theory 52 489-509 Chen G H, Tang J and Leng S 2008 Prior image constrained compressed sensing (PICCS): a method to accurately reconstruct dynamic CT images from highly undersampled projection data sets Med. Phys. 35 660-3 Sidky E Y, Kao C M and Pan X C 2006 Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT J. X-ray Sci. Technol. 14 119-39 Sidky E Y and Pan X C 2008 Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization Phys. Med. Biol. 53 4777-807
Compressive sensing scalp EEG signals: implementations and practical performance.
Abdulghani, Amir M; Casson, Alexander J; Rodriguez-Villegas, Esther
2012-11-01
Highly miniaturised, wearable computing and communication systems allow unobtrusive, convenient and long term monitoring of a range of physiological parameters. For long term operation from the physically smallest batteries, the average power consumption of a wearable device must be very low. It is well known that the overall power consumption of these devices can be reduced by the inclusion of low power consumption, real-time compression of the raw physiological data in the wearable device itself. Compressive sensing is a new paradigm for providing data compression: it has shown significant promise in fields such as MRI; and is potentially suitable for use in wearable computing systems as the compression process required in the wearable device has a low computational complexity. However, the practical performance very much depends on the characteristics of the signal being sensed. As such the utility of the technique cannot be extrapolated from one application to another. Long term electroencephalography (EEG) is a fundamental tool for the investigation of neurological disorders and is increasingly used in many non-medical applications, such as brain-computer interfaces. This article investigates in detail the practical performance of different implementations of the compressive sensing theory when applied to scalp EEG signals.
NASA Astrophysics Data System (ADS)
Altschuler, Bruce R.; Oliver, William R.; Altschuler, Martin D.
1996-02-01
We describe a system for rapid and convenient video data acquisition and 3-D numerical coordinate data calculation able to provide precise 3-D topographical maps and 3-D archival data sufficient to reconstruct a 3-D virtual reality display of a crime scene or mass disaster area. Under a joint U.S. army/U.S. Air Force project with collateral U.S. Navy support, to create a 3-D surgical robotic inspection device -- a mobile, multi-sensor robotic surgical assistant to aid the surgeon in diagnosis, continual surveillance of patient condition, and robotic surgical telemedicine of combat casualties -- the technology is being perfected for remote, non-destructive, quantitative 3-D mapping of objects of varied sizes. This technology is being advanced with hyper-speed parallel video technology and compact, very fast laser electro-optics, such that the acquisition of 3-D surface map data will shortly be acquired within the time frame of conventional 2-D video. With simple field-capable calibration, and mobile or portable platforms, the crime scene investigator could set up and survey the entire crime scene, or portions of it at high resolution, with almost the simplicity and speed of video or still photography. The survey apparatus would record relative position, location, and instantly archive thousands of artifacts at the site with 3-D data points capable of creating unbiased virtual reality reconstructions, or actual physical replicas, for the investigators, prosecutors, and jury.
NASA Astrophysics Data System (ADS)
Iftekhar, Ahmed Tashfin; Ho, Jenny Che-Ting; Mellinger, Axel; Kaya, Tolga
2017-03-01
Sweat-based physiological monitoring has been intensively explored in the last decade with the hopes of developing real-time hydration monitoring devices. Although the content of sweat (electrolytes, lactate, urea, etc.) provides significant information about the physiology, it is also very important to know the rate of sweat at the time of sweat content measurements because the sweat rate is known to alter the concentrations of sweat compounds. We developed a calorimetric based flow rate sensor using PolydimethylSiloxane that is suitable for sweat rate applications. Our simple approach on using temperature-based flow rate detection can easily be adapted to multiple sweat collection and analysis devices. Moreover, we have developed a 3D finite element analysis model of the device using COMSOL Multiphysics™ and verified the flow rate measurements. The experiment investigated flow rate values from 0.3 μl/min up to 2.1 ml/min, which covers the human sweat rate range (0.5 μl/min-10 μl/min). The 3D model simulations and analytical model calculations covered an even wider range in order to understand the main physical mechanisms of the device. With a verified 3D model, different environmental heat conditions could be further studied to shed light on the physiology of the sweat rate.
Polymer optical fibers integrated directly into 3D orthogonal woven composites for sensing
NASA Astrophysics Data System (ADS)
Hamouda, Tamer; Seyam, Abdel-Fattah M.; Peters, Kara
2015-02-01
This study demonstrates that standard polymer optical fibers (POF) can be directly integrated into composites from 3D orthogonal woven preforms during the weaving process and then serve as in-situ sensors to detect damage due to bending or impact loads. Different composite samples with embedded POF were fabricated of 3D orthogonal woven composites with different parameters namely number of y-/x-layers and x-yarn density. The signal of POF was not affected significantly by the preform structure. During application of resin using VARTM technique, significant drop in backscattering level was observed due to pressure caused by vacuum on the embedded POF. Measurements of POF signal while in the final composites after resin cure indicated that the backscattering level almost returned to the original level of un-embedded POF. The POF responded to application of bending and impact loads to the composite with a reduction in the backscattering level. The backscattering level almost returned back to its original level after removing the bending load until damage was present in the composite. Similar behavior occurred due to impact events. As the POF itself is used as the sensor and can be integrated throughout the composite, large sections of future 3D woven composite structures could be monitored without the need for specialized sensors or complex instrumentation.
3D Analysis of Remote-Sensed Heliospheric Data for Space Weather Forecasting
NASA Astrophysics Data System (ADS)
Yu, H. S.; Jackson, B. V.; Hick, P. P.; Buffington, A.; Bisi, M. M.; Odstrcil, D.; Hong, S.; Kim, J.; Yi, J.; Tokumaru, M.; Gonzalez-Esparza, A.
2015-12-01
The University of California, San Diego (UCSD) time-dependent iterative kinematic reconstruction technique has been used and expanded upon for over two decades. It currently provides some of the most accurate predictions and three-dimensional (3D) analyses of heliospheric solar-wind parameters now available using interplanetary scintillation (IPS) data. The parameters provided include reconstructions of velocity, density, and magnetic fields. Precise time-dependent results are obtained at any solar distance in the inner heliosphere using current Solar-Terrestrial Environment Laboratory (STELab), Nagoya University, Japan IPS data sets, but the reconstruction technique can also incorporate data from other IPS systems from around the world. With access using world IPS data systems, not only can predictions using the reconstruction technique be made without observation dead times due to poor longitude coverage or system outages, but the program can itself be used to standardize observations of IPS. Additionally, these analyses are now being exploited as inner-boundary values to drive an ENLIL 3D-MHD heliospheric model in real time. A major potential of this is that it will use the more realistic physics of 3D-MHD modeling to provide an automatic forecast of CMEs and corotating structures up to several days in advance of the event/features arriving at Earth, with or without involving coronagraph imagery or the necessity of magnetic fields being used to provide the background solar wind speeds.
Autonomous Real-Time Interventional Scan Plane Control With a 3-D Shape-Sensing Needle
Plata, Juan Camilo; Holbrook, Andrew B.; Park, Yong-Lae; Pauly, Kim Butts; Daniel, Bruce L.; Cutkosky, Mark R.
2016-01-01
This study demonstrates real-time scan plane control dependent on three-dimensional needle bending, as measured from magnetic resonance imaging (MRI)-compatible optical strain sensors. A biopsy needle with embedded fiber Bragg grating (FBG) sensors to measure surface strains is used to estimate its full 3-D shape and control the imaging plane of an MR scanner in real-time, based on the needle’s estimated profile. The needle and scanner coordinate frames are registered to each other via miniature radio-frequency (RF) tracking coils, and the scan planes autonomously track the needle as it is deflected, keeping its tip in view. A 3-D needle annotation is superimposed over MR-images presented in a 3-D environment with the scanner’s frame of reference. Scan planes calculated based on the FBG sensors successfully follow the tip of the needle. Experiments using the FBG sensors and RF coils to track the needle shape and location in real-time had an average root mean square error of 4.2 mm when comparing the estimated shape to the needle profile as seen in high resolution MR images. This positional variance is less than the image artifact caused by the needle in high resolution SPGR (spoiled gradient recalled) images. Optical fiber strain sensors can estimate a needle’s profile in real-time and be used for MRI scan plane control to potentially enable faster and more accurate physician response. PMID:24968093
Autonomous real-time interventional scan plane control with a 3-D shape-sensing needle.
Elayaperumal, Santhi; Plata, Juan Camilo; Holbrook, Andrew B; Park, Yong-Lae; Pauly, Kim Butts; Daniel, Bruce L; Cutkosky, Mark R
2014-11-01
This study demonstrates real-time scan plane control dependent on three-dimensional needle bending, as measured from magnetic resonance imaging (MRI)-compatible optical strain sensors. A biopsy needle with embedded fiber Bragg grating (FBG) sensors to measure surface strains is used to estimate its full 3-D shape and control the imaging plane of an MR scanner in real-time, based on the needle's estimated profile. The needle and scanner coordinate frames are registered to each other via miniature radio-frequency (RF) tracking coils, and the scan planes autonomously track the needle as it is deflected, keeping its tip in view. A 3-D needle annotation is superimposed over MR-images presented in a 3-D environment with the scanner's frame of reference. Scan planes calculated based on the FBG sensors successfully follow the tip of the needle. Experiments using the FBG sensors and RF coils to track the needle shape and location in real-time had an average root mean square error of 4.2 mm when comparing the estimated shape to the needle profile as seen in high resolution MR images. This positional variance is less than the image artifact caused by the needle in high resolution SPGR (spoiled gradient recalled) images. Optical fiber strain sensors can estimate a needle's profile in real-time and be used for MRI scan plane control to potentially enable faster and more accurate physician response.
3-D Numerical Simulations of Biofilm Dynamics with Quorum Sensing in a Flow Cell
2014-01-01
Especially, the phenomenon of quorum sensing regulating expopolysacharride production during biofilm formation has been reported widely in the...of, even if it is produced by other bacteria. However, how quorum sensing regulation affect the EPS production is not clearly resolved. Besides, in...some bacterial systems, like Vibrio cholerae, the EPS production is suppressed when the density of autoinducer reaches its threshold value [?]; whereas
Canali, C; Mazzoni, C; Larsen, L B; Heiskanen, A; Martinsen, Ø G; Wolff, A; Dufva, M; Emnéus, J
2015-09-07
We present the characterisation and validation of multiplexed 4-terminal (4T) impedance measurements as a method for sensing the spatial location of cell aggregates within large three-dimensional (3D) gelatin scaffolds. The measurements were performed using an array of four rectangular chambers, each having eight platinum needle electrodes for parallel analysis. The electrode positions for current injection and voltage measurements were optimised by means of finite element simulations to maximise the sensitivity field distribution and spatial resolution. Eight different 4T combinations were experimentally tested in terms of the spatial sensitivity. The simulated sensitivity fields were validated using objects (phantoms) with different conductivity and size placed in different positions inside the chamber. This provided the detection limit (volume sensitivity) of 16.5%, i.e. the smallest detectable volume with respect to the size of the measurement chamber. Furthermore, the possibility for quick single frequency analysis was demonstrated by finding a common frequency of 250 kHz for all the presented electrode combinations. As final proof of concept, a high density of human hepatoblastoma (HepG2) cells were encapsulated in gelatin to form artificial 3D cell constructs and detected when placed in different positions inside large gelatin scaffolds. Taken together, these results open new perspectives for impedance-based sensing technologies for non-invasive monitoring in tissue engineering applications providing spatial information of constructs within biologically relevant 3D environments.
Microfluidic pressure sensing using trapped air compression.
Srivastava, Nimisha; Burns, Mark A
2007-05-01
We have developed a microfluidic method for measuring the fluid pressure head experienced at any location inside a microchannel. The principal component is a microfabricated sealed chamber with a single inlet and no exit; the entrance to the single inlet is positioned at the location where pressure is to be measured. The pressure measurement is then based on monitoring the movement of a liquid-air interface as it compresses air trapped inside the microfabricated sealed chamber and calculating the pressure using the ideal gas law. The method has been used to measure the pressure of the air stream and continuous liquid flow inside microfluidic channels (d approximately 50 microm). Further, a pressure drop has also been measured using multiple microfabricated sealed chambers. For air pressure, a resolution of 700 Pa within a full-scale range of 700-100 kPa was obtained. For liquids, pressure drops as low as 70 Pa were obtained in an operating range from 70 Pa to 10 kPa. Since the method primarily uses a microfluidic sealed chamber, it does not require additional fabrication steps and may easily be incorporated in several lab-on-a-chip fluidic applications for laminar as well as turbulent flow conditions.
Cladding waveguide gratings in standard single-mode fiber for 3D shape sensing.
Waltermann, Christian; Doering, Alexander; Köhring, Michael; Angelmahr, Martin; Schade, Wolfgang
2015-07-01
Femtosecond laser pulses were used for the direct point-by-point inscription of waveguides into the cladding of standard single-mode fibers. Homogeneous S-shaped waveguides have been processed as a bundle of overlapping lines without damaging the surrounding material. Within these structures, FBGs have been successfully inscribed and characterized. A sensor device to measure the bending direction of a fiber was created by two perpendicular inscribed cladding waveguides with FBG. Finally, a complete 3D shape sensor consisting of several bending sensor planes, capable of detecting bending radii even below 2.5 cm is demonstrated.
NASA Astrophysics Data System (ADS)
Chen, Tinghuan; Zhang, Meng; Wu, Jianhui; Yuen, Chau; Tong, You
2016-10-01
Because of simple encryption and compression procedure in single step, compressed sensing (CS) is utilized to encrypt and compress an image. Difference of sparsity levels among blocks of the sparsely transformed image degrades compression performance. In this paper, motivated by this difference of sparsity levels, we propose an encryption and compression approach combining Kronecker CS (KCS) with elementary cellular automata (ECA). In the first stage of encryption, ECA is adopted to scramble the sparsely transformed image in order to uniformize sparsity levels. A simple approximate evaluation method is introduced to test the sparsity uniformity. Due to low computational complexity and storage, in the second stage of encryption, KCS is adopted to encrypt and compress the scrambled and sparsely transformed image, where the measurement matrix with a small size is constructed from the piece-wise linear chaotic map. Theoretical analysis and experimental results show that our proposed scrambling method based on ECA has great performance in terms of scrambling and uniformity of sparsity levels. And the proposed encryption and compression method can achieve better secrecy, compression performance and flexibility.
Compressed sensing for real-time energy-efficient ECG compression on wireless body sensor nodes.
Mamaghanian, Hossein; Khaled, Nadia; Atienza, David; Vandergheynst, Pierre
2011-09-01
Wireless body sensor networks (WBSN) hold the promise to be a key enabling information and communications technology for next-generation patient-centric telecardiology or mobile cardiology solutions. Through enabling continuous remote cardiac monitoring, they have the potential to achieve improved personalization and quality of care, increased ability of prevention and early diagnosis, and enhanced patient autonomy, mobility, and safety. However, state-of-the-art WBSN-enabled ECG monitors still fall short of the required functionality, miniaturization, and energy efficiency. Among others, energy efficiency can be improved through embedded ECG compression, in order to reduce airtime over energy-hungry wireless links. In this paper, we quantify the potential of the emerging compressed sensing (CS) signal acquisition/compression paradigm for low-complexity energy-efficient ECG compression on the state-of-the-art Shimmer WBSN mote. Interestingly, our results show that CS represents a competitive alternative to state-of-the-art digital wavelet transform (DWT)-based ECG compression solutions in the context of WBSN-based ECG monitoring systems. More specifically, while expectedly exhibiting inferior compression performance than its DWT-based counterpart for a given reconstructed signal quality, its substantially lower complexity and CPU execution time enables it to ultimately outperform DWT-based ECG compression in terms of overall energy efficiency. CS-based ECG compression is accordingly shown to achieve a 37.1% extension in node lifetime relative to its DWT-based counterpart for "good" reconstruction quality.
Effects of 3D geometries on cellular gradient sensing and polarization
NASA Astrophysics Data System (ADS)
Spill, Fabian; Andasari, Vivi; Mak, Michael; Kamm, Roger D.; Zaman, Muhammad H.
2016-06-01
During cell migration, cells become polarized, change their shape, and move in response to various internal and external cues. Cell polarization is defined through the spatio-temporal organization of molecules such as PI3K or small GTPases, and is determined by intracellular signaling networks. It results in directional forces through actin polymerization and myosin contractions. Many existing mathematical models of cell polarization are formulated in terms of reaction-diffusion systems of interacting molecules, and are often defined in one or two spatial dimensions. In this paper, we introduce a 3D reaction-diffusion model of interacting molecules in a single cell, and find that cell geometry has an important role affecting the capability of a cell to polarize, or change polarization when an external signal changes direction. Our results suggest a geometrical argument why more roundish cells can repolarize more effectively than cells which are elongated along the direction of the original stimulus, and thus enable roundish cells to turn faster, as has been observed in experiments. On the other hand, elongated cells preferentially polarize along their main axis even when a gradient stimulus appears from another direction. Furthermore, our 3D model can accurately capture the effect of binding and unbinding of important regulators of cell polarization to and from the cell membrane. This spatial separation of membrane and cytosol, not possible to capture in 1D or 2D models, leads to marked differences of our model from comparable lower-dimensional models.
Investigation of inclined dual-fiber optical tweezers for 3D manipulation and force sensing.
Liu, Yuxiang; Yu, Miao
2009-08-03
Optical tweezers provide a versatile tool in biological and physical researches. Optical tweezers based on optical fibers are more flexible and ready to be integrated when compared with those based on microscope objectives. In this paper, the three-dimensional (3D) trapping ability of an inclined dual-fiber optical tweezers is demonstrated. The trapping efficiency with respect to displacement is experimentally calibrated along two dimensions. The system is studied numerically using a modified ray-optics model. The spring constants obtained in the experiment are predicted by simulations. It is found both experimentally and numerically that there is a critical value for the fiber inclination angle to retain the 3D trapping ability. The inclined dual-fiber optical tweezers are demonstrated to be more robust to z-axis misalignment than the counter-propagating fiber optical tweezers, which is a special case of th former when the fiber inclination angle is 90 masculine. This inclined dual-fiber optical tweezers can serve as both a manipulator and a force sensor in integrated systems, such as microfluidic systems and lab-on-a-chip systems.
Spatial Sense and Perspective: A 3-D Model of the Orion Constellation
NASA Astrophysics Data System (ADS)
Heyer, I.; Slater, T. F.; Slater, S. J.
2012-08-01
Building a scale model of the Orion constellation provides spatial perspective for students studying astronomy. For this activity, students read a passage from literature that refers to stars being strange when seen from a different point of view. From a data set of the seven major stars of Orion they construct a 3-D distance scale model. This involves the subject areas of astronomy, mathematics, literature and art, as well as the skill areas of perspective, relative distances, line-of-sight, and basic algebra. This model will appear from one side exactly the way we see it from Earth. But when looking at it from any other angle the familiar constellation will look very alien. Students are encouraged to come up with their own names and stories to go with these new constellations. This activity has been used for K-12 teacher professional development classes, and would be most suitable for grades 6-12.
Image compression-encryption scheme based on hyper-chaotic system and 2D compressive sensing
NASA Astrophysics Data System (ADS)
Zhou, Nanrun; Pan, Shumin; Cheng, Shan; Zhou, Zhihong
2016-08-01
Most image encryption algorithms based on low-dimensional chaos systems bear security risks and suffer encryption data expansion when adopting nonlinear transformation directly. To overcome these weaknesses and reduce the possible transmission burden, an efficient image compression-encryption scheme based on hyper-chaotic system and 2D compressive sensing is proposed. The original image is measured by the measurement matrices in two directions to achieve compression and encryption simultaneously, and then the resulting image is re-encrypted by the cycle shift operation controlled by a hyper-chaotic system. Cycle shift operation can change the values of the pixels efficiently. The proposed cryptosystem decreases the volume of data to be transmitted and simplifies the keys distribution simultaneously as a nonlinear encryption system. Simulation results verify the validity and the reliability of the proposed algorithm with acceptable compression and security performance.
Force sensing using 3D displacement measurements in linear elastic bodies
NASA Astrophysics Data System (ADS)
Feng, Xinzeng; Hui, Chung-Yuen
2016-07-01
In cell traction microscopy, the mechanical forces exerted by a cell on its environment is usually determined from experimentally measured displacement by solving an inverse problem in elasticity. In this paper, an innovative numerical method is proposed which finds the "optimal" traction to the inverse problem. When sufficient regularization is applied, we demonstrate that the proposed method significantly improves the widely used approach using Green's functions. Motivated by real cell experiments, the equilibrium condition of a slowly migrating cell is imposed as a set of equality constraints on the unknown traction. Our validation benchmarks demonstrate that the numeric solution to the constrained inverse problem well recovers the actual traction when the optimal regularization parameter is used. The proposed method can thus be applied to study general force sensing problems, which utilize displacement measurements to sense inaccessible forces in linear elastic bodies with a priori constraints.
Compressed Sensing Based Fingerprint Identification for Wireless Transmitters
Zhao, Caidan; Wu, Xiongpeng; Huang, Lianfen; Yao, Yan; Chang, Yao-Chung
2014-01-01
Most of the existing fingerprint identification techniques are unable to distinguish different wireless transmitters, whose emitted signals are highly attenuated, long-distance propagating, and of strong similarity to their transient waveforms. Therefore, this paper proposes a new method to identify different wireless transmitters based on compressed sensing. A data acquisition system is designed to capture the wireless transmitter signals. Complex analytical wavelet transform is used to obtain the envelope of the transient signal, and the corresponding features are extracted by using the compressed sensing theory. Feature selection utilizing minimum redundancy maximum relevance (mRMR) is employed to obtain the optimal feature subsets for identification. The results show that the proposed method is more efficient for the identification of wireless transmitters with similar transient waveforms. PMID:24892053
Learning physical descriptors for materials science by compressed sensing
NASA Astrophysics Data System (ADS)
Ghiringhelli, Luca M.; Vybiral, Jan; Ahmetcik, Emre; Ouyang, Runhai; Levchenko, Sergey V.; Draxl, Claudia; Scheffler, Matthias
2017-02-01
The availability of big data in materials science offers new routes for analyzing materials properties and functions and achieving scientific understanding. Finding structure in these data that is not directly visible by standard tools and exploitation of the scientific information requires new and dedicated methodology based on approaches from statistical learning, compressed sensing, and other recent methods from applied mathematics, computer science, statistics, signal processing, and information science. In this paper, we explain and demonstrate a compressed-sensing based methodology for feature selection, specifically for discovering physical descriptors, i.e., physical parameters that describe the material and its properties of interest, and associated equations that explicitly and quantitatively describe those relevant properties. As showcase application and proof of concept, we describe how to build a physical model for the quantitative prediction of the crystal structure of binary compound semiconductors.
Connell, Jodi L; Kim, Jiyeon; Shear, Jason B; Bard, Allen J; Whiteley, Marvin
2014-12-23
Microbes frequently live in nature as small, densely packed aggregates containing ∼10(1)-10(5) cells. These aggregates not only display distinct phenotypes, including resistance to antibiotics, but also, serve as building blocks for larger biofilm communities. Aggregates within these larger communities display nonrandom spatial organization, and recent evidence indicates that this spatial organization is critical for fitness. Studying single aggregates as well as spatially organized aggregates remains challenging because of the technical difficulties associated with manipulating small populations. Micro-3D printing is a lithographic technique capable of creating aggregates in situ by printing protein-based walls around individual cells or small populations. This 3D-printing strategy can organize bacteria in complex arrangements to investigate how spatial and environmental parameters influence social behaviors. Here, we combined micro-3D printing and scanning electrochemical microscopy (SECM) to probe quorum sensing (QS)-mediated communication in the bacterium Pseudomonas aeruginosa. Our results reveal that QS-dependent behaviors are observed within aggregates as small as 500 cells; however, aggregates larger than 2,000 bacteria are required to stimulate QS in neighboring aggregates positioned 8 μm away. These studies provide a powerful system to analyze the impact of spatial organization and aggregate size on microbial behaviors.
NASA Astrophysics Data System (ADS)
Weng, Jiawen; Clark, David C.; Kim, Myung K.
2016-05-01
A numerical reconstruction method based on compressive sensing (CS) for self-interference incoherent digital holography (SIDH) is proposed to achieve sectional imaging by single-shot in-line self-interference incoherent hologram. The sensing operator is built up based on the physical mechanism of SIDH according to CS theory, and a recovery algorithm is employed for image restoration. Numerical simulation and experimental studies employing LEDs as discrete point-sources and resolution targets as extended sources are performed to demonstrate the feasibility and validity of the method. The intensity distribution and the axial resolution along the propagation direction of SIDH by angular spectrum method (ASM) and by CS are discussed. The analysis result shows that compared to ASM the reconstruction by CS can improve the axial resolution of SIDH, and achieve sectional imaging. The proposed method may be useful to 3D analysis of dynamic systems.
3D subsurface geological modeling using GIS, remote sensing, and boreholes data
NASA Astrophysics Data System (ADS)
Kavoura, Katerina; Konstantopoulou, Maria; Kyriou, Aggeliki; Nikolakopoulos, Konstantinos G.; Sabatakakis, Nikolaos; Depountis, Nikolaos
2016-08-01
The current paper presents the combined use of geological-geotechnical insitu data, remote sensing data and GIS techniques for the evaluation of a subsurface geological model. High accuracy Digital Surface Model (DSM), airphotos mosaic and satellite data, with a spatial resolution of 0.5m were used for an othophoto base map compilation of the study area. Geological - geotechnical data obtained from exploratory boreholes and the 1:5000 engineering geological maps were digitized and implemented in a GIS platform for a three - dimensional subsurface model evaluation. The study is located at the North part of Peloponnese along the new national road.
New non-Doppler remote sensing technique for 3D wind field mapping
NASA Astrophysics Data System (ADS)
Belen'kii, Mikhail S.; Gimmestad, Gary G.; Gurvich, Alexander V.
1994-06-01
A new approach to the statistical analysis of fluctuating, photon-limited signals that permits us to accumulate and process the lidar returns without averaging of the reflected energy fluctuations is developed. This approach requires recording the photocounts for each pulse in a series of pulses and then determining photocount statistics. Based on the semiclassical theory of photodetection and Mandel's formula, a relationship has been obtained between the time-space cross correlation function and the cross spectrum of the lidar returns and corresponding photocount statistics. It is shown that the relative uncertainties of measuring the cross correlation or the cross spectrum of the lidar returns is determined by the general number of photocounts, but not by their mean value. A fast-scanning lidar system, which is based on a new photocounting analysis approach, is described for 3D wind field mapping in the atmosphere at altitudes up to 5 km. A program for the experimental verification of the new approach is presented.
Alenezi, Mohammad R; Henley, Simon J; Emerson, Neil G; Silva, S Ravi P
2014-01-07
Facile and low cost hydrothermal routes are developed to fabricate three-dimensional (3D) hierarchical ZnO structures with high surface-to-volume ratios and an increased fraction of (0001) polar surfaces. Hierarchical ZnO nanowires (ZNWs) and nanodisks (ZNDs) assembled from initial ZnO nanostructures are prepared from sequential nucleation and growth following a hydrothermal process. These hierarchical ZnO structures display an enhancement of gas sensing performance and exhibit significantly improved sensitivity and fast response to acetone in comparison to other mono-morphological ZnO, such as nanoparticles, NWs, or NDs. In addition to the high surface-to-volume ratio due to its small size, the nanowire building blocks show the enhanced gas sensing properties mainly ascribed to the increased proportion of exposed active (0001) planes, and the formation of many nanojunctions at the interface between the initial ZnO nanostructure and secondary NWs. This work provides the route for structure induced enhancement of gas sensing performance by designing a desirable nanostructure, which could also be extended to synthesize other metal oxide nanostructures with superior gas sensing performance.
Huang, Jianfei; Zhu, Yihua; Yang, Xiaoling; Chen, Wei; Zhou, Ying; Li, Chunzhong
2015-01-14
Convenient determination of glucose in a sensitive, reliable and cost-effective way has aroused sustained research passion, bringing along assiduous investigation of high-performance electroactive nanomaterials to build enzymeless sensors. In addition to the intrinsic electrocatalytic capability of the sensing materials, electrode architecture at the microscale is also crucial for fully enhancing the performance. In this work, free-standing porous CuO nanowire (NW) was taken as a model sensing material to illustrate this point, where an in situ formed 3D CuO nanowire array (NWA) and CuO nanowires pile (NWP) immobilized with polymer binder by conventional drop-casting technique were both studied for enzymeless glucose sensing. The NWA electrode exhibited greatly promoted electrochemistry characterized by decreased overpotential for electro-oxidation of glucose and over 5-fold higher sensitivity compared to the NWP counterpart, benefiting from the binder-free nanoarray structure. Besides, its sensing performance was also satisfying in terms of rapidness, selectivity and durability. Further, the CuO NWA was utilized to fabricate a flexible sensor which showed excellent performance stability against mechanical bending. Thanks to its favorable electrode architecture, the CuO NWA is believed to offer opportunities for building high-efficiency flexible electrochemical devices.
Application of joint orthogonal bases in compressive sensing ghost image
NASA Astrophysics Data System (ADS)
Fan, Xiang; Chen, Yi; Cheng, Zheng-dong; Liang, Zheng-yu; Zhu, Bin
2016-11-01
Sparse decomposition is one of the core issue of compressive sensing ghost image. At this stage, traditional methods still have the problems of poor sparsity and low reconstruction accuracy, such as discrete fourier transform and discrete cosine transform. In order to solve these problems, joint orthogonal bases transform is proposed to optimize ghost imaging. First, introduce the principle of compressive sensing ghost imaging and point out that sparsity is related to the minimum sample data required for imaging. Then, analyze the development and principle of joint orthogonal bases in detail and find out it can use less nonzero coefficients to reach the same identification effect as other methods. So, joint orthogonal bases transform is able to provide the sparsest representation. Finally, the experimental setup is built in order to verify simulation results. Experimental results indicate that the PSNR of joint orthogonal bases is much higher than traditional methods by using same sample data in compressive sensing ghost image.Therefore, joint orthogonal bases transform can realize better imaging quality under less sample data, which can satisfy the system requirements of convenience and rapid speed in ghost image.
Quantum tomography protocols with positivity are compressed sensing protocols
NASA Astrophysics Data System (ADS)
Kalev, Amir; Kosut, Robert L.; Deutsch, Ivan H.
2015-12-01
Characterising complex quantum systems is a vital task in quantum information science. Quantum tomography, the standard tool used for this purpose, uses a well-designed measurement record to reconstruct quantum states and processes. It is, however, notoriously inefficient. Recently, the classical signal reconstruction technique known as ‘compressed sensing’ has been ported to quantum information science to overcome this challenge: accurate tomography can be achieved with substantially fewer measurement settings, thereby greatly enhancing the efficiency of quantum tomography. Here we show that compressed sensing tomography of quantum systems is essentially guaranteed by a special property of quantum mechanics itself—that the mathematical objects that describe the system in quantum mechanics are matrices with non-negative eigenvalues. This result has an impact on the way quantum tomography is understood and implemented. In particular, it implies that the information obtained about a quantum system through compressed sensing methods exhibits a new sense of ‘informational completeness.’ This has important consequences on the efficiency of the data taking for quantum tomography, and enables us to construct informationally complete measurements that are robust to noise and modelling errors. Moreover, our result shows that one can expand the numerical tool-box used in quantum tomography and employ highly efficient algorithms developed to handle large dimensional matrices on a large dimensional Hilbert space. Although we mainly present our results in the context of quantum tomography, they apply to the general case of positive semidefinite matrix recovery.
Remote sensing solution using 3-D flash LADAR for automated control of aircraft
NASA Astrophysics Data System (ADS)
Neff, Brian J.; Fuka, Jennifer A.; Burwell, Alan C.; Gray, Stephen W.; Hubbard, Mason J.; Schenkel, Joseph W.
2015-09-01
The majority of image quality studies in the field of remote sensing have been performed on systems with conventional aperture functions. These systems have well-understood image quality tradeoffs, characterized by the General Image Quality Equation (GIQE). Advanced, next-generation imaging systems present challenges to both post-processing and image quality prediction. Examples include sparse apertures, synthetic apertures, coded apertures and phase elements. As a result of the non-conventional point spread functions of these systems, post-processing becomes a critical step in the imaging process and artifacts arise that are more complicated than simple edge overshoot. Previous research at the Rochester Institute of Technology's Digital Imaging and Remote Sensing Laboratory has resulted in a modeling methodology for sparse and segmented aperture systems, the validation of which will be the focus of this work. This methodology has predicted some unique post-processing artifacts that arise when a sparse aperture system with wavefront error is used over a large (panchromatic) spectral bandpass. Since these artifacts are unique to sparse aperture systems, they have not yet been observed in any real-world data. In this work, a laboratory setup and initial results for a model validation study will be described. Initial results will focus on the validation of spatial frequency response predictions and verification of post-processing artifacts. The goal of this study is to validate the artifact and spatial frequency response predictions of this model. This will allow model predictions to be used in image quality studies, such as aperture design optimization, and the signal-to-noise vs. post-processing artifact tradeoff resulting from choosing a panchromatic vs. multispectral system.
NASA Astrophysics Data System (ADS)
Wu, Guochun
2017-01-01
In this paper, we investigate the global existence and uniqueness of strong solutions to the initial boundary value problem for the 3D compressible Navier-Stokes equations without heat conductivity in a bounded domain with slip boundary. The global existence and uniqueness of strong solutions are obtained when the initial data is near its equilibrium in H2 (Ω). Furthermore, the exponential convergence rates of the pressure and velocity are also proved by delicate energy methods.
NASA Astrophysics Data System (ADS)
Zhou, Nanrun; Li, Haolin; Wang, Di; Pan, Shumin; Zhou, Zhihong
2015-05-01
Most of the existing image encryption techniques bear security risks for taking linear transform or suffer encryption data expansion for adopting nonlinear transformation directly. To overcome these difficulties, a novel image compression-encryption scheme is proposed by combining 2D compressive sensing with nonlinear fractional Mellin transform. In this scheme, the original image is measured by measurement matrices in two directions to achieve compression and encryption simultaneously, and then the resulting image is re-encrypted by the nonlinear fractional Mellin transform. The measurement matrices are controlled by chaos map. The Newton Smoothed l0 Norm (NSL0) algorithm is adopted to obtain the decryption image. Simulation results verify the validity and the reliability of this scheme.
Texture-based medical image retrieval in compressed domain using compressive sensing.
Yadav, Kuldeep; Srivastava, Avi; Mittal, Ankush; Ansari, M A
2014-01-01
Content-based image retrieval has gained considerable attention in today's scenario as a useful tool in many applications; texture is one of them. In this paper, we focus on texture-based image retrieval in compressed domain using compressive sensing with the help of DC coefficients. Medical imaging is one of the fields which have been affected most, as there had been huge size of image database and getting out the concerned image had been a daunting task. Considering this, in this paper we propose a new model of image retrieval process using compressive sampling, since it allows accurate recovery of image from far fewer samples of unknowns and it does not require a close relation of matching between sampling pattern and characteristic image structure with increase acquisition speed and enhanced image quality.
Perceptual rate-distortion optimized image compression based on block compressive sensing
NASA Astrophysics Data System (ADS)
Xu, Jin; Qiao, Yuansong; Wen, Quan; Fu, Zhizhong
2016-09-01
The emerging compressive sensing (CS) theory provides a paradigm for image compression. Most current efforts in CS-based image compression have been focused on enhancing the objective coding efficiency. In order to achieve a maximal perceptual quality under the measurements budget constraint, we propose a perceptual rate-distortion optimized (RDO) CS-based image codec in this paper. By incorporating both the human visual system characteristics and the signal sparsity into a RDO model designed for the block compressive sensing framework, the measurements allocation for each block is formulated as an optimization problem, which can be efficiently solved by the Lagrangian relaxation method. After the optimal measurement number is determined, each block is adaptively sampled using an image-dependent measurement matrix. To make our proposed codec applicable to different scenarios, we also propose two solutions to implement the perceptual RDO measurements allocation technique: one at the encoder side and the other at the decoder side. The experimental results show that our codec outperforms the other existing CS-based image codecs in terms of both objective and subjective performances. In particular, our codec can also achieve a low complexity encoder by adopting the decoder-based solution for the perceptual RDO measurements allocation.
Basha, Tamer A; Akçakaya, Mehmet; Goddu, Beth; Berg, Sophie; Nezafat, Reza
2015-01-01
The aim of this study was to implement and evaluate an accelerated three-dimensional (3D) cine phase contrast MRI sequence by combining a randomly sampled 3D k-space acquisition sequence with an echo planar imaging (EPI) readout. An accelerated 3D cine phase contrast MRI sequence was implemented by combining EPI readout with randomly undersampled 3D k-space data suitable for compressed sensing (CS) reconstruction. The undersampled data were then reconstructed using low-dimensional structural self-learning and thresholding (LOST). 3D phase contrast MRI was acquired in 11 healthy adults using an overall acceleration of 7 (EPI factor of 3 and CS rate of 3). For comparison, a single two-dimensional (2D) cine phase contrast scan was also performed with sensitivity encoding (SENSE) rate 2 and approximately at the level of the pulmonary artery bifurcation. The stroke volume and mean velocity in both the ascending and descending aorta were measured and compared between two sequences using Bland-Altman plots. An average scan time of 3 min and 30 s, corresponding to an acceleration rate of 7, was achieved for 3D cine phase contrast scan with one direction flow encoding, voxel size of 2 × 2 × 3 mm(3) , foot-head coverage of 6 cm and temporal resolution of 30 ms. The mean velocity and stroke volume in both the ascending and descending aorta were statistically equivalent between the proposed 3D sequence and the standard 2D cine phase contrast sequence. The combination of EPI with a randomly undersampled 3D k-space sampling sequence using LOST reconstruction allows a seven-fold reduction in scan time of 3D cine phase contrast MRI without compromising blood flow quantification.
Adam, Clayton; Izatt, Maree; Askin, Geoffrey
2010-01-01
Magnetic Resonance Imaging (MRI) offers a valuable research tool for the assessment of 3D spinal deformity in AIS, however the horizontal patient position imposed by conventional scanners removes the axial compressive loading on the spine. The objective of this study was to design, construct and test an MRI compatible compression device for research into the effect of axial loading on spinal deformity using supine MRI scans. The device was evaluated by performing unloaded and loaded supine MRI scans on a series of 10 AIS patients. The patient group had a mean initial (unloaded) major Cobb angle of 43+/-7 degrees, which increased to 50+/-9 degrees on application of the compressive load. The 7 degrees increase in mean Cobb angle is consistent with that reported by a previous study comparing standing versus supine posture in scoliosis patients (Torell et al, 1985. Spine 10:425-7).
Exploiting prior knowledge in compressed sensing wireless ECG systems.
Polanía, Luisa F; Carrillo, Rafael E; Blanco-Velasco, Manuel; Barner, Kenneth E
2015-03-01
Recent results in telecardiology show that compressed sensing (CS) is a promising tool to lower energy consumption in wireless body area networks for electrocardiogram (ECG) monitoring. However, the performance of current CS-based algorithms, in terms of compression rate and reconstruction quality of the ECG, still falls short of the performance attained by state-of-the-art wavelet-based algorithms. In this paper, we propose to exploit the structure of the wavelet representation of the ECG signal to boost the performance of CS-based methods for compression and reconstruction of ECG signals. More precisely, we incorporate prior information about the wavelet dependencies across scales into the reconstruction algorithms and exploit the high fraction of common support of the wavelet coefficients of consecutive ECG segments. Experimental results utilizing the MIT-BIH Arrhythmia Database show that significant performance gains, in terms of compression rate and reconstruction quality, can be obtained by the proposed algorithms compared to current CS-based methods.
Podoleanu, Adrian Gh; Bradu, Adrian
2013-08-12
Conventional spectral domain interferometry (SDI) methods suffer from the need of data linearization. When applied to optical coherence tomography (OCT), conventional SDI methods are limited in their 3D capability, as they cannot deliver direct en-face cuts. Here we introduce a novel SDI method, which eliminates these disadvantages. We denote this method as Master - Slave Interferometry (MSI), because a signal is acquired by a slave interferometer for an optical path difference (OPD) value determined by a master interferometer. The MSI method radically changes the main building block of an SDI sensor and of a spectral domain OCT set-up. The serially provided signal in conventional technology is replaced by multiple signals, a signal for each OPD point in the object investigated. This opens novel avenues in parallel sensing and in parallelization of signal processing in 3D-OCT, with applications in high- resolution medical imaging and microscopy investigation of biosamples. Eliminating the need of linearization leads to lower cost OCT systems and opens potential avenues in increasing the speed of production of en-face OCT images in comparison with conventional SDI.
Applications of compressed sensing to coherent radar imaging
NASA Astrophysics Data System (ADS)
Zhu, Qian
Although meteoroids fragmentation has been observed and studied in the optical meteor community since the 1950s, no definitive fragmentation mechanisms for the relatively small meteoroids (mass .10.4 kg) have been proposed. This is in part due to the lack of observations to constrain physical mechanisms of the fragmentation process. While it is challenging to record fragmentation in faint optical meteors, observing meteors using HPLA (High-Power, Large- Aperture) radars can yield considerable information especially when employing coherent radar imaging (CRI). CRI can potentially resolve the fragmentation process in three spatial dimensions by monitoring the evolution of the plasma in the meteor head-echo, flare-echo, and trail-echo regions. On the other hand, the emerging field of compressed sensing (CS) provides a novel paradigm for signal acquisition and processing. Furthermore, it has been, and continues to be, applied with great success in radar systems, offering various benefits such as better resolution compared to traditional techniques, reduced resource requirements, and so forth. In this dissertation, we examine how CS can be incorporated to improve the performance of CRI using HPLA radars. We propose a single CS-based formalism that enables the threedimensions (3D).the range, Doppler frequency, and cross range (represented by the direction cosines) domain.coherent imaging. We show that the CS-based CRI can not only reduce the system costs and decrease the needed number of baselines by spatial sparse sampling, which can be much less than the number required by Nyquist-Shannon sampling criterion, but also achieve high resolution for target detection. We implement the CS-based CRI for meteor studies with observations conducted at the Jicamarca Radio Observatory (JRO) in Peru. We present the unprecedented resolved details of meteoroids fragmentation, including both along and transverse to the trajectory spreading of the developing plasma, apparently caused by
MAXAD distortion minimization for wavelet compression of remote sensing data
NASA Astrophysics Data System (ADS)
Alecu, Alin; Munteanu, Adrian; Schelkens, Peter; Cornelis, Jan P.; Dewitte, Steven
2001-12-01
In the context of compression of high resolution multi-spectral satellite image data consisting of radiances and top-of-the-atmosphere fluxes, it is vital that image calibration characteristics (luminance, radiance) must be preserved within certain limits in lossy image compression. Though existing compression schemes (SPIHT, JPEG2000, SQP) give good results as far as minimization of the global PSNR error is concerned, they fail to guarantee a maximum local error. With respect to this, we introduce a new image compression scheme, which guarantees a MAXAD distortion, defined as the maximum absolute difference between original pixel values and reconstructed pixel values. In terms of defining the Lagrangian optimization problem, this reflects in minimization of the rate given the MAXAD distortion. Our approach thus uses the l-infinite distortion measure, which is applied to the lifting scheme implementation of the 9-7 floating point Cohen-Daubechies-Feauveau (CDF) filter. Scalar quantizers, optimal in the D-R sense, are derived for every subband, by solving a global optimization problem that guarantees a user-defined MAXAD. The optimization problem has been defined and solved for the case of the 9-7 filter, and we show that our approach is valid and may be applied to any finite wavelet filters synthesized via lifting. The experimental assessment of our codec shows that our technique provides excellent results in applications such as those for remote sensing, in which reconstruction of image calibration characteristics within a tolerable local error (MAXAD) is perceived as being of crucial importance compared to obtaining an acceptable global error (PSNR), as is the case of existing quantizer design techniques.
Multifrequency Bayesian compressive sensing methods for microwave imaging.
Poli, Lorenzo; Oliveri, Giacomo; Ding, Ping Ping; Moriyama, Toshifumi; Massa, Andrea
2014-11-01
The Bayesian retrieval of sparse scatterers under multifrequency transverse magnetic illuminations is addressed. Two innovative imaging strategies are formulated to process the spectral content of microwave scattering data according to either a frequency-hopping multistep scheme or a multifrequency one-shot scheme. To solve the associated inverse problems, customized implementations of single-task and multitask Bayesian compressive sensing are introduced. A set of representative numerical results is discussed to assess the effectiveness and the robustness against the noise of the proposed techniques also in comparison with some state-of-the-art deterministic strategies.
A high resolution spectrum reconstruction algorithm using compressive sensing theory
NASA Astrophysics Data System (ADS)
Zheng, Zhaoyu; Liang, Dakai; Liu, Shulin; Feng, Shuqing
2015-07-01
This paper proposes a quick spectrum scanning and reconstruction method using compressive sensing in composite structure. The strain field of corrugated structure is simulated by finite element analysis. Then the reflect spectrum is calculated using an improved transfer matrix algorithm. The K-means singular value decomposition sparse dictionary is trained . In the test the spectrum with limited sample points can be obtained and the high resolution spectrum is reconstructed by solving sparse representation equation. Compared with the other conventional basis, the effect of this method is better. The match rate of the recovered spectrum and the original spectrum is over 95%.
Instance Optimal Decoding by Thresholding in Compressed Sensing
2008-11-01
Λ ‖v − Φz‖ (5.12) has a unique solution given by the Moore - Penrose pseudo inverse û(Λ) = [Φ∗ΛΦΛ] −1Φ∗Λv. (5.13) By (5.10) the solution can be...reasonable computational time. 1.2 Objectives In the present paper , we shall be interested in which practical decoders can be used with a general random...compressed sensing, as well as for more general inverse problems, is a very active area of research. In addition to `1-minimization and its efficient
Object detection oriented video reconstruction using compressed sensing
NASA Astrophysics Data System (ADS)
Kang, Bin; Zhu, Wei-Ping; Yan, Jun
2015-12-01
Moving object detection plays a key role in video surveillance. A number of object detection methods have been proposed in the spatial domain. In this paper, we propose a compressed sensing (CS)-based algorithm for the detection of moving object in video sequences. First, we propose an object detection model to simultaneously reconstruct the foreground, background, and video sequence using the sampled measurement. Then, we use the reconstructed video sequence to estimate a confidence map to improve the foreground reconstruction result. Experimental results show that the proposed moving object detection algorithm outperforms the state-of-the-art approaches and is robust to the movement turbulence and sudden illumination changes.
Rate and power efficient image compressed sensing and transmission
NASA Astrophysics Data System (ADS)
Olanigan, Saheed; Cao, Lei; Viswanathan, Ramanarayanan
2016-01-01
This paper presents a suboptimal quantization and transmission scheme for multiscale block-based compressed sensing images over wireless channels. The proposed method includes two stages: dealing with quantization distortion and transmission errors. First, given the total transmission bit rate, the optimal number of quantization bits is assigned to the sensed measurements in different wavelet sub-bands so that the total quantization distortion is minimized. Second, given the total transmission power, the energy is allocated to different quantization bit layers based on their different error sensitivities. The method of Lagrange multipliers with Karush-Kuhn-Tucker conditions is used to solve both optimization problems, for which the first problem can be solved with relaxation and the second problem can be solved completely. The effectiveness of the scheme is illustrated through simulation results, which have shown up to 10 dB improvement over the method without the rate and power optimization in medium and low signal-to-noise ratio cases.
Accelerated Whole-Brain Multi-Parameter Mapping using Blind Compressed Sensing
Bhave, Sampada; Lingala, Sajan Goud; Johnson, Casey P.; Magnotta, Vincent A.; Jacob, Mathews
2015-01-01
Purpose To introduce a blind compressed sensing (BCS) framework to accelerate multi-parameter MR mapping, and demonstrate its feasibility in high-resolution, whole-brain T1ρ and T2 mapping. Methods BCS models the evolution of magnetization at every pixel as a sparse linear combination of bases in a dictionary. Unlike compressed sensing (CS), the dictionary and the sparse coefficients are jointly estimated from under-sampled data. Large number of non-orthogonal bases in BCS accounts for more complex signals than low rank representations. The low degree of freedom of BCS, attributed to sparse coefficients, translates to fewer artifacts at high acceleration factors(R). Results From 2D retrospective under-sampling experiments, the mean square errors in T1ρ and T2 maps were observed to be within 0.1% up to R=10. BCS was observed to be more robust to patient-specific motion as compared to other CS schemes and resulted in minimal degradation of parameter maps in the presence of motion. Our results suggested that BCS can provide an acceleration factor of 8 in prospective 3D imaging with reasonable reconstructions. Conclusion BCS considerably reduces scan time for multi-parameter mapping of the whole brain with minimal artifacts, and is more robust to motion-induced signal changes compared to current CS and PCA based techniques. PMID:25850952
Optical scanning holography based on compressive sensing using a digital micro-mirror device
NASA Astrophysics Data System (ADS)
A-qian, Sun; Ding-fu, Zhou; Sheng, Yuan; You-jun, Hu; Peng, Zhang; Jian-ming, Yue; xin, Zhou
2017-02-01
Optical scanning holography (OSH) is a distinct digital holography technique, which uses a single two-dimensional (2D) scanning process to record the hologram of a three-dimensional (3D) object. Usually, these 2D scanning processes are in the form of mechanical scanning, and the quality of recorded hologram may be affected due to the limitation of mechanical scanning accuracy and unavoidable vibration of stepper motor's start-stop. In this paper, we propose a new framework, which replaces the 2D mechanical scanning mirrors with a Digital Micro-mirror Device (DMD) to modulate the scanning light field, and we call it OSH based on Compressive Sensing (CS) using a digital micro-mirror device (CS-OSH). CS-OSH can reconstruct the hologram of an object through the use of compressive sensing theory, and then restore the image of object itself. Numerical simulation results confirm this new type OSH can get a reconstructed image with favorable visual quality even under the condition of a low sample rate.
Compressed sensing sodium MRI of cartilage at 7T: Preliminary study
NASA Astrophysics Data System (ADS)
Madelin, Guillaume; Chang, Gregory; Otazo, Ricardo; Jerschow, Alexej; Regatte, Ravinder R.
2012-01-01
Sodium MRI has been shown to be highly specific for glycosaminoglycan (GAG) content in articular cartilage, the loss of which is an early sign of osteoarthritis (OA). Quantitative sodium MRI techniques are therefore under development in order to detect and assess early biochemical degradation of cartilage, but due to low sodium NMR sensitivity and its low concentration, sodium images need long acquisition times (15-25 min) even at high magnetic fields and are typically of low resolution. In this preliminary study, we show that compressed sensing can be applied to reduce the acquisition time by a factor of 2 at 7T without losing sodium quantification accuracy. Alternatively, the nonlinear reconstruction technique can be used to denoise fully-sampled images. We expect to even further reduce this acquisition time by using parallel imaging techniques combined with SNR-improved 3D sequences at 3T and 7T.
High-Resolution Mesoscopic Fluorescence Molecular Tomography Based on Compressive Sensing
Yang, Fugang; Ozturk, Mehmet S.; Zhao, Lingling; Cong, Wenxiang; Wang, Ge
2017-01-01
Mesoscopic fluorescence molecular tomography (MFMT) is new imaging modality aiming at 3-D imaging of molecular probes in a few millimeter thick biological samples with high-spatial resolution. In this paper, we develop a compressive sensing-based reconstruction method with l1-norm regularization for MFMT with the goal of improving spatial resolution and stability of the optical inverse problem. Three-dimensional numerical simulations of anatomically accurate microvasculature and real data obtained from phantom experiments are employed to evaluate the merits of the proposed method. Experimental results show that the proposed method can achieve 80 μm spatial resolution for a biological sample of 3 mm thickness and more accurate quantifications of concentrations and locations for the fluorophore distribution than those of the conventional methods. PMID:25137718
High dynamic range coherent imaging using compressed sensing.
He, Kuan; Sharma, Manoj Kumar; Cossairt, Oliver
2015-11-30
In both lensless Fourier transform holography (FTH) and coherent diffraction imaging (CDI), a beamstop is used to block strong intensities which exceed the limited dynamic range of the sensor, causing a loss in low-frequency information, making high quality reconstructions difficult or even impossible. In this paper, we show that an image can be recovered from high-frequencies alone, thereby overcoming the beamstop problem in both FTH and CDI. The only requirement is that the object is sparse in a known basis, a common property of most natural and manmade signals. The reconstruction method relies on compressed sensing (CS) techniques, which ensure signal recovery from incomplete measurements. Specifically, in FTH, we perform compressed sensing (CS) reconstruction of captured holograms and show that this method is applicable not only to standard FTH, but also multiple or extended reference FTH. For CDI, we propose a new phase retrieval procedure, which combines Fienup's hybrid input-output (HIO) method and CS. Both numerical simulations and proof-of-principle experiments are shown to demonstrate the effectiveness and robustness of the proposed CS-based reconstructions in dealing with missing data in both FTH and CDI.
A novel image fusion approach based on compressive sensing
NASA Astrophysics Data System (ADS)
Yin, Hongpeng; Liu, Zhaodong; Fang, Bin; Li, Yanxia
2015-11-01
Image fusion can integrate complementary and relevant information of source images captured by multiple sensors into a unitary synthetic image. The compressive sensing-based (CS) fusion approach can greatly reduce the processing speed and guarantee the quality of the fused image by integrating fewer non-zero coefficients. However, there are two main limitations in the conventional CS-based fusion approach. Firstly, directly fusing sensing measurements may bring greater uncertain results with high reconstruction error. Secondly, using single fusion rule may result in the problems of blocking artifacts and poor fidelity. In this paper, a novel image fusion approach based on CS is proposed to solve those problems. The non-subsampled contourlet transform (NSCT) method is utilized to decompose the source images. The dual-layer Pulse Coupled Neural Network (PCNN) model is used to integrate low-pass subbands; while an edge-retention based fusion rule is proposed to fuse high-pass subbands. The sparse coefficients are fused before being measured by Gaussian matrix. The fused image is accurately reconstructed by Compressive Sampling Matched Pursuit algorithm (CoSaMP). Experimental results demonstrate that the fused image contains abundant detailed contents and preserves the saliency structure. These also indicate that our proposed method achieves better visual quality than the current state-of-the-art methods.
Statistical mechanics analysis of thresholding 1-bit compressed sensing
NASA Astrophysics Data System (ADS)
Xu, Yingying; Kabashima, Yoshiyuki
2016-08-01
The one-bit compressed sensing framework aims to reconstruct a sparse signal by only using the sign information of its linear measurements. To compensate for the loss of scale information, past studies in the area have proposed recovering the signal by imposing an additional constraint on the l 2-norm of the signal. Recently, an alternative strategy that captures scale information by introducing a threshold parameter to the quantization process was advanced. In this paper, we analyze the typical behavior of thresholding 1-bit compressed sensing utilizing the replica method of statistical mechanics, so as to gain an insight for properly setting the threshold value. Our result shows that fixing the threshold at a constant value yields better performance than varying it randomly when the constant is optimally tuned, statistically. Unfortunately, the optimal threshold value depends on the statistical properties of the target signal, which may not be known in advance. In order to handle this inconvenience, we develop a heuristic that adaptively tunes the threshold parameter based on the frequency of positive (or negative) values in the binary outputs. Numerical experiments show that the heuristic exhibits satisfactory performance while incurring low computational cost.
Coded strobing photography: compressive sensing of high speed periodic videos.
Veeraraghavan, Ashok; Reddy, Dikpal; Raskar, Ramesh
2011-04-01
We show that, via temporal modulation, one can observe and capture a high-speed periodic video well beyond the abilities of a low-frame-rate camera. By strobing the exposure with unique sequences within the integration time of each frame, we take coded projections of dynamic events. From a sequence of such frames, we reconstruct a high-speed video of the high-frequency periodic process. Strobing is used in entertainment, medical imaging, and industrial inspection to generate lower beat frequencies. But this is limited to scenes with a detectable single dominant frequency and requires high-intensity lighting. In this paper, we address the problem of sub-Nyquist sampling of periodic signals and show designs to capture and reconstruct such signals. The key result is that for such signals, the Nyquist rate constraint can be imposed on the strobe rate rather than the sensor rate. The technique is based on intentional aliasing of the frequency components of the periodic signal while the reconstruction algorithm exploits recent advances in sparse representations and compressive sensing. We exploit the sparsity of periodic signals in the Fourier domain to develop reconstruction algorithms that are inspired by compressive sensing.
NASA Astrophysics Data System (ADS)
Liu, Xingbin; Mei, Wenbo; Du, Huiqian
2016-05-01
In this paper, a novel approach based on compressive sensing and chaos is proposed for simultaneously compressing, fusing and encrypting multi-modal images. The sparsely represented source images are firstly measured with the key-controlled pseudo-random measurement matrix constructed using logistic map, which reduces the data to be processed and realizes the initial encryption. Then the obtained measurements are fused by the proposed adaptive weighted fusion rule. The fused measurement is further encrypted into the ciphertext through an iterative procedure including improved random pixel exchanging technique and fractional Fourier transform. The fused image can be reconstructed by decrypting the ciphertext and using a recovery algorithm. The proposed algorithm not only reduces data volume but also simplifies keys, which improves the efficiency of transmitting data and distributing keys. Numerical results demonstrate the feasibility and security of the proposed scheme.
Modelling compression sensing in ionic polymer metal composites
NASA Astrophysics Data System (ADS)
Volpini, Valentina; Bardella, Lorenzo; Rodella, Andrea; Cha, Youngsu; Porfiri, Maurizio
2017-03-01
Ionic polymer metal composites (IPMCs) consist of an ionomeric membrane, including mobile counterions, sandwiched between two thin noble metal electrodes. IPMCs find application as sensors and actuators, where an imposed mechanical loading generates a voltage across the electrodes, and, vice versa, an imposed electric field causes deformation. Here, we present a predictive modelling approach to elucidate the dynamic sensing response of IPMCs subject to a time-varying through-the-thickness compression (‘compression sensing’). The model relies on the continuum theory recently developed by Porfiri and co-workers, which couples finite deformations to the modified Poisson–Nernst–Planck (PNP) system governing the IPMC electrochemistry. For the ‘compression sensing’ problem we establish a perturbative closed-form solution along with a finite element (FE) solution. The systematic comparison between these two solutions is a central contribution of this study, offering insight on accuracy and mathematical complexity. The method of matched asymptotic expansions is employed to find the analytical solution. To this end, we uncouple the force balance from the modified PNP system and separately linearise the PNP equations in the ionomer bulk and in the boundary layers at the ionomer–electrode interfaces. Comparison with FE results for the fully coupled nonlinear system demonstrates the accuracy of the analytical solution to describe IPMC sensing for moderate deformation levels. We finally demonstrate the potential of the modelling scheme to accurately reproduce experimental results from the literature. The proposed model is expected to aid in the design of IPMC sensors, contribute to an improved understanding of IPMC electrochemomechanical response, and offer insight into the role of nonlinear phenomena across mechanics and electrochemistry.
Quantum Tomography via Compressed Sensing: Error Bounds, Sample Complexity and Efficient Estimators
2012-09-27
REPORT Quantum tomography via compressed sensing : error bounds, sample complexity and efficient estimators 14. ABSTRACT 16. SECURITY CLASSIFICATION OF...Box 12211 Research Triangle Park, NC 27709-2211 15. SUBJECT TERMS quantum tomography, compressed sensing Steven T Flammia, David Gross, Yi-Kai Liu... compressed sensing : error bounds, sample complexity and efficient estimators Report Title ABSTRACT Intuitively, if a density operator has small rank, then
NASA Astrophysics Data System (ADS)
Li, P.; Turk, J.; Vu, Q.; Knosp, B.; Hristova-Veleva, S. M.; Lambrigtsen, B.; Poulsen, W. L.; Licata, S.
2009-12-01
NASA is planning a new field experiment, the Genesis and Rapid Intensification Processes (GRIP), in the summer of 2010 to better understand how tropical storms form and develop into major hurricanes. The DC-8 aircraft and the Global Hawk Unmanned Airborne System (UAS) will be deployed loaded with instruments for measurements including lightning, temperature, 3D wind, precipitation, liquid and ice water contents, aerosol and cloud profiles. During the field campaign, both the spaceborne and the airborne observations will be collected in real-time and integrated with the hurricane forecast models. This observation-model integration will help the campaign achieve its science goals by allowing team members to effectively plan the mission with current forecasts. To support the GRIP experiment, JPL developed a website for interactive visualization of all related remote-sensing observations in the GRIP’s geographical domain using the new Google Earth API. All the observations are collected in near real-time (NRT) with 2 to 5 hour latency. The observations include a 1KM blended Sea Surface Temperature (SST) map from GHRSST L2P products; 6-hour composite images of GOES IR; stability indices, temperature and vapor profiles from AIRS and AMSU-B; microwave brightness temperature and rain index maps from AMSR-E, SSMI and TRMM-TMI; ocean surface wind vectors, vorticity and divergence of the wind from QuikSCAT; the 3D precipitation structure from TRMM-PR and vertical profiles of cloud and precipitation from CloudSAT. All the NRT observations are collected from the data centers and science facilities at NASA and NOAA, subsetted, re-projected, and composited into hourly or daily data products depending on the frequency of the observation. The data products are then displayed on the 3D Google Earth plug-in at the JPL Tropical Cyclone Information System (TCIS) website. The data products offered by the TCIS in the Google Earth display include image overlays, wind vectors, clickable
NASA Astrophysics Data System (ADS)
Zinner, T.; Wind, G.; Platnick, S.; Ackerman, A. S.
2010-10-01
Remote sensing of cloud effective particle size with passive sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) is an important tool for cloud microphysical studies. As a measure of the radiatively relevant droplet size, effective radius can be retrieved with different combinations of visible through shortwave and midwave infrared channels. In practice, retrieved effective radii from these combinations can be quite different. This difference is perhaps indicative of different penetration depths and path lengths for the spectral reflectances used. In addition, operational liquid water cloud retrievals are based on the assumption of a relatively narrow distribution of droplet sizes; the role of larger precipitation particles in these distributions is neglected. Therefore, possible explanations for the discrepancy in some MODIS spectral size retrievals could include 3-D radiative transport effects, including sub-pixel cloud inhomogeneity, and/or the impact of drizzle formation. For three cloud cases the possible factors of influence are isolated and investigated in detail by the use of simulated cloud scenes and synthetic satellite data: marine boundary layer cloud scenes from large eddy simulations (LES) with detailed microphysics are combined with Monte Carlo radiative transfer calculations that explicitly account for the detailed droplet size distributions as well as 3-D radiative transfer to simulate MODIS observations. The operational MODIS optical thickness and effective radius retrieval algorithm is applied to these and the results are compared to the given LES microphysics. We investigate two types of marine cloud situations each with and without drizzle from LES simulations: (1) a typical daytime stratocumulus deck at two times in the diurnal cycle and (2) one scene with scattered cumulus. Only small impact of drizzle formation on the retrieved domain average and on the differences between the three effective radius retrievals is noticed
NASA Astrophysics Data System (ADS)
Zhou, Nanrun; Zhang, Aidi; Zheng, Fen; Gong, Lihua
2014-10-01
The existing ways to encrypt images based on compressive sensing usually treat the whole measurement matrix as the key, which renders the key too large to distribute and memorize or store. To solve this problem, a new image compression-encryption hybrid algorithm is proposed to realize compression and encryption simultaneously, where the key is easily distributed, stored or memorized. The input image is divided into 4 blocks to compress and encrypt, then the pixels of the two adjacent blocks are exchanged randomly by random matrices. The measurement matrices in compressive sensing are constructed by utilizing the circulant matrices and controlling the original row vectors of the circulant matrices with logistic map. And the random matrices used in random pixel exchanging are bound with the measurement matrices. Simulation results verify the effectiveness, security of the proposed algorithm and the acceptable compression performance.
Learning-based compressed sensing for infrared image super resolution
NASA Astrophysics Data System (ADS)
Zhao, Yao; Sui, Xiubao; Chen, Qian; Wu, Shaochi
2016-05-01
This paper presents an infrared image super-resolution method based on compressed sensing (CS). First, the reconstruction model under the CS framework is established and a Toeplitz matrix is selected as the sensing matrix. Compared with traditional learning-based methods, the proposed method uses a set of sub-dictionaries instead of two coupled dictionaries to recover high resolution (HR) images. And Toeplitz sensing matrix allows the proposed method time-efficient. Second, all training samples are divided into several feature spaces by using the proposed adaptive k-means classification method, which is more accurate than the standard k-means method. On the basis of this approach, a complex nonlinear mapping from the HR space to low resolution (LR) space can be converted into several compact linear mappings. Finally, the relationships between HR and LR image patches can be obtained by multi-sub-dictionaries and HR infrared images are reconstructed by the input LR images and multi-sub-dictionaries. The experimental results show that the proposed method is quantitatively and qualitatively more effective than other state-of-the-art methods.
NASA Technical Reports Server (NTRS)
Harris, Julius E.; Iyer, Venkit; Radwan, Samir
1987-01-01
The application of stability theory in Laminar Flow Control (LFC) research requires that density and velocity profiles be specified throughout the viscous flow field of interest. These profile values must be as numerically accurate as possible and free of any numerically induced oscillations. Guidelines for the present research project are presented: develop an efficient and accurate procedure for solving the 3-D boundary layer equation for aerospace configurations; develop an interface program to couple selected 3-D inviscid programs that span the subsonic to hypersonic Mach number range; and document and release software to the LFC community. The interface program was found to be a dependable approach for developing a user friendly procedure for generating the boundary-layer grid and transforming an inviscid solution from a relatively coarse grid to a sufficiently fine boundary-layer grid. The boundary-layer program was shown to be fourth-order accurate in the direction normal to the wall boundary and second-order accurate in planes parallel to the boundary. The fourth-order accuracy allows accurate calculations with as few as one-fifth the number of grid points required for conventional second-order schemes.
NASA Astrophysics Data System (ADS)
Zhang, Lisha
We present fast and robust numerical algorithms for 3-D scattering from perfectly electrical conducting (PEC) and dielectric random rough surfaces in microwave remote sensing. The Coifman wavelets or Coiflets are employed to implement Galerkin's procedure in the method of moments (MoM). Due to the high-precision one-point quadrature, the Coiflets yield fast evaluations of the most off-diagonal entries, reducing the matrix fill effort from O(N2) to O( N). The orthogonality and Riesz basis of the Coiflets generate well conditioned impedance matrix, with rapid convergence for the conjugate gradient solver. The resulting impedance matrix is further sparsified by the matrix-formed standard fast wavelet transform (SFWT). By properly selecting multiresolution levels of the total transformation matrix, the solution precision can be enhanced while matrix sparsity and memory consumption have not been noticeably sacrificed. The unified fast scattering algorithm for dielectric random rough surfaces can asymptotically reduce to the PEC case when the loss tangent grows extremely large. Numerical results demonstrate that the reduced PEC model does not suffer from ill-posed problems. Compared with previous publications and laboratory measurements, good agreement is observed.
NASA Astrophysics Data System (ADS)
Petrov, Mikhail A.; Kosatchyov, Nikolay V.; Petrov, Pavel A.
2016-10-01
The paper represents the results of the study concerning the investigation of the influence of the filling grade (material density) on the force characteristic during the uniaxial compression test of the cylindrical polymer probes produced by additive technology based on FDM. The authors have shown that increasing of the filling grate follows to the increase of the deformation forces. However, the dependency is not a linear function and characterized by soft-elastic model of material behaviour, which is typical for polymers partly crystallized structure.
Solwnd: A 3D Compressible MHD Code for Solar Wind Studies. Version 1.0: Cartesian Coordinates
NASA Technical Reports Server (NTRS)
Deane, Anil E.
1996-01-01
Solwnd 1.0 is a three-dimensional compressible MHD code written in Fortran for studying the solar wind. Time-dependent boundary conditions are available. The computational algorithm is based on Flux Corrected Transport and the code is based on the existing code of Zalesak and Spicer. The flow considered is that of shear flow with incoming flow that perturbs this base flow. Several test cases corresponding to pressure balanced magnetic structures with velocity shear flow and various inflows including Alfven waves are presented. Version 1.0 of solwnd considers a rectangular Cartesian geometry. Future versions of solwnd will consider a spherical geometry. Some discussions of this issue is presented.
Underwater Acoustic Matched Field Imaging Based on Compressed Sensing
Yan, Huichen; Xu, Jia; Long, Teng; Zhang, Xudong
2015-01-01
Matched field processing (MFP) is an effective method for underwater target imaging and localizing, but its performance is not guaranteed due to the nonuniqueness and instability problems caused by the underdetermined essence of MFP. By exploiting the sparsity of the targets in an imaging area, this paper proposes a compressive sensing MFP (CS-MFP) model from wave propagation theory by using randomly deployed sensors. In addition, the model’s recovery performance is investigated by exploring the lower bounds of the coherence parameter of the CS dictionary. Furthermore, this paper analyzes the robustness of CS-MFP with respect to the displacement of the sensors. Subsequently, a coherence-excluding coherence optimized orthogonal matching pursuit (CCOOMP) algorithm is proposed to overcome the high coherent dictionary problem in special cases. Finally, some numerical experiments are provided to demonstrate the effectiveness of the proposed CS-MFP method. PMID:26457708
Radial velocity data analysis with compressed sensing techniques
NASA Astrophysics Data System (ADS)
Hara, Nathan C.; Boué, G.; Laskar, J.; Correia, A. C. M.
2017-01-01
We present a novel approach for analysing radial velocity data that combines two features: all the planets are searched at once and the algorithm is fast. This is achieved by utilizing compressed sensing techniques, which are modified to be compatible with the Gaussian process framework. The resulting tool can be used like a Lomb-Scargle periodogram and has the same aspect but with much fewer peaks due to aliasing. The method is applied to five systems with published radial velocity data sets: HD 69830, HD 10180, 55 Cnc, GJ 876 and a simulated very active star. The results are fully compatible with previous analysis, though obtained more straightforwardly. We further show that 55 Cnc e and f could have been respectively detected and suspected in early measurements from the Lick Observatory and Hobby-Eberly Telescope available in 2004, and that frequencies due to dynamical interactions in GJ 876 can be seen.
Separate Magnitude and Phase Regularization via Compressed Sensing
Noll, Douglas C.; Nielsen, Jon-Fredrik; Fessler, Jeffrey A.
2012-01-01
Compressed sensing (CS) has been used for accelerating magnetic resonance imaging (MRI) acquisitions, but its use in applications with rapid spatial phase variations is challenging, e.g., proton resonance frequency shift (PRF-shift) thermometry and velocity mapping. Previously, an iterative MRI reconstruction with separate magnitude and phase regularization was proposed for applications where magnitude and phase maps are both of interest, but it requires fully sampled data and unwrapped phase maps. In this paper, CS is combined into this framework to reconstruct magnitude and phase images accurately from undersampled data. Moreover, new phase regularization terms are proposed to accommodate phase wrapping and to reconstruct images with encoded phase variations, e.g., PRF-shift thermometry and velocity mapping. The proposed method is demonstrated with simulated thermometry data and in-vivo velocity mapping data and compared to conventional phase corrected CS. PMID:22552571
LED-based digital hologram reconstruction by compressive sensing
NASA Astrophysics Data System (ADS)
Weng, Jiawen; Yang, Chuping; Qin, Yi; Li, Hai
2015-10-01
LED-based digital hologram, considered as low-coherence digital hologram, is confined to in-line holography because the interference fringes could be observed only when the angle between the object and reference wave is small enough. So, phase-shifting technique is usually employed. But it is not fit for dynamic analysis for demanding more than one hologram. A numerical reconstruction method based on compressive sensing theory for single LED-based digital hologram is proposed to achieve dynamic analysis. By this method, the out-of-focus twin image and the coherent noise can be inhibited to some extent. The theory is presented in detail, and experimental result on LED-based digital holography with USAF pattern as test target, is performed to demonstrate the feasibility and validity of the method.
Causal MRI reconstruction via Kalman prediction and compressed sensing correction.
Majumdar, Angshul
2017-02-04
This technical note addresses the problem of causal online reconstruction of dynamic MRI, i.e. given the reconstructed frames till the previous time instant, we reconstruct the frame at the current instant. Our work follows a prediction-correction framework. Given the previous frames, the current frame is predicted based on a Kalman estimate. The difference between the estimate and the current frame is then corrected based on the k-space samples of the current frame; this reconstruction assumes that the difference is sparse. The method is compared against prior Kalman filtering based techniques and Compressed Sensing based techniques. Experimental results show that the proposed method is more accurate than these and considerably faster.
Estimation of many-body quantum Hamiltonians via compressive sensing
Shabani, A.; Rabitz, H.; Mohseni, M.; Lloyd, S.; Kosut, R. L.
2011-07-15
We develop an efficient and robust approach for quantum measurement of nearly sparse many-body quantum Hamiltonians based on the method of compressive sensing. This work demonstrates that with only O(sln(d)) experimental configurations, consisting of random local preparations and measurements, one can estimate the Hamiltonian of a d-dimensional system, provided that the Hamiltonian is nearly s sparse in a known basis. The classical postprocessing is a convex optimization problem on the total Hilbert space which is generally not scalable. We numerically simulate the performance of this algorithm for three- and four-body interactions in spin-coupled quantum dots and atoms in optical lattices. Furthermore, we apply the algorithm to characterize Hamiltonian fine structure and unknown system-bath interactions.
Compressed-sensed-domain L1-PCA video surveillance
NASA Astrophysics Data System (ADS)
Liu, Ying; Pados, Dimitris A.
2015-05-01
We consider the problem of foreground and background extraction from compressed-sensed (CS) surveillance video. We propose, for the first time in the literature, a principal component analysis (PCA) approach that computes the low-rank subspace of the background scene directly in the CS domain. Rather than computing the conventional L2-norm-based principal components, which are simply the dominant left singular vectors of the CS measurement matrix, we compute the principal components under an L1-norm maximization criterion. The background scene is then obtained by projecting the CS measurement vector onto the L1 principal components followed by total-variation (TV) minimization image recovery. The proposed L1-norm procedure directly carries out low-rank background representation without reconstructing the video sequence and, at the same time, exhibits significant robustness against outliers in CS measurements compared to L2-norm PCA.
Compressed sensing MRI: a review of the clinical literature
Jaspan, Oren N; Fleysher, Roman
2015-01-01
MRI is one of the most dynamic and safe imaging techniques available in the clinic today. However, MRI acquisitions tend to be slow, limiting patient throughput and limiting potential indications for use while driving up costs. Compressed sensing (CS) is a method for accelerating MRI acquisition by acquiring less data through undersampling of k-space. This has the potential to mitigate the time-intensiveness of MRI. The limited body of research evaluating the effects of CS on MR images has been mostly positive with regards to its potential as a clinical tool. Studies have successfully accelerated MRI with this technology, with varying degrees of success. However, more must be performed before its diagnostic efficacy and benefits are clear. Studies involving a greater number radiologists and images must be completed, rating CS based on its diagnostic efficacy. Also, standardized methods for determining optimal imaging parameters must be developed. PMID:26402216
Recovering network topologies via Taylor expansion and compressive sensing
Li, Guangjun; Liu, Juan E-mail: liujuanjp@163.com; Wu, Xiaoqun E-mail: liujuanjp@163.com; Lu, Jun-an; Guo, Chi
2015-04-15
Gaining knowledge of the intrinsic topology of a complex dynamical network is the precondition to understand its evolutionary mechanisms and to control its dynamical and functional behaviors. In this article, a general framework is developed to recover topologies of complex networks with completely unknown node dynamics based on Taylor expansion and compressive sensing. Numerical simulations illustrate the feasibility and effectiveness of the proposed method. Moreover, this method is found to have good robustness to weak stochastic perturbations. Finally, the impact of two major factors on the topology identification performance is evaluated. This method provides a natural and direct point to reconstruct network topologies from measurable data, which is likely to have potential applicability in a wide range of fields.
Resolving intravoxel fiber architecture using nonconvex regularized blind compressed sensing
NASA Astrophysics Data System (ADS)
Chu, C. Y.; Huang, J. P.; Sun, C. Y.; Liu, W. Y.; Zhu, Y. M.
2015-03-01
In diffusion magnetic resonance imaging, accurate and reliable estimation of intravoxel fiber architectures is a major prerequisite for tractography algorithms or any other derived statistical analysis. Several methods have been proposed that estimate intravoxel fiber architectures using low angular resolution acquisitions owing to their shorter acquisition time and relatively low b-values. But these methods are highly sensitive to noise. In this work, we propose a nonconvex regularized blind compressed sensing approach to estimate intravoxel fiber architectures in low angular resolution acquisitions. The method models diffusion-weighted (DW) signals as a sparse linear combination of unfixed reconstruction basis functions and introduces a nonconvex regularizer to enhance the noise immunity. We present a general solving framework to simultaneously estimate the sparse coefficients and the reconstruction basis. Experiments on synthetic, phantom, and real human brain DW images demonstrate the superiority of the proposed approach.
Underwater Acoustic Matched Field Imaging Based on Compressed Sensing.
Yan, Huichen; Xu, Jia; Long, Teng; Zhang, Xudong
2015-10-07
Matched field processing (MFP) is an effective method for underwater target imaging and localizing, but its performance is not guaranteed due to the nonuniqueness and instability problems caused by the underdetermined essence of MFP. By exploiting the sparsity of the targets in an imaging area, this paper proposes a compressive sensing MFP (CS-MFP) model from wave propagation theory by using randomly deployed sensors. In addition, the model's recovery performance is investigated by exploring the lower bounds of the coherence parameter of the CS dictionary. Furthermore, this paper analyzes the robustness of CS-MFP with respect to the displacement of the sensors. Subsequently, a coherence-excluding coherence optimized orthogonal matching pursuit (CCOOMP) algorithm is proposed to overcome the high coherent dictionary problem in special cases. Finally, some numerical experiments are provided to demonstrate the effectiveness of the proposed CS-MFP method.
Compressive sensing optical coherence tomography using randomly accessible lasers
NASA Astrophysics Data System (ADS)
Harfouche, Mark; Satyan, Naresh; Vasilyev, Arseny; Yariv, Amnon
2014-05-01
We propose and demonstrate a novel a compressive sensing swept source optical coherence tomography (SSOCT) system that enables high speed images to be taken while maintaining the high resolution offered from a large bandwidth sweep. Conventional SSOCT systems sweep the optical frequency of a laser ω(t) to determine the depth of the reflectors at a given lateral location. A scatterer located at delay τ appears as a sinusoid cos (ω(t)τ ) at the photodetector. The finite optical chirp rate and the speed of analog to digital and digital to analog converters limit the acquisition rate of an axial scan. The proposed acquisition modality enables much faster image acquisition rates by interrogating the beat signal at randomly selected optical frequencies while preserving resolution and depth of field. The system utilizes a randomly accessible laser, a modulated grating Y-branch laser, to sample the interference pattern from a scene at randomly selected optical frequencies over an optical bandwidth of 5 THz , corresponding to a resolution of 30 μm in air. The depth profile is then reconstructed using an l1 minimization algorithm with a LASSO constraint. Signal-dependent noise sources, shot noise and phase noise, are analyzed and taken into consideration during the recovery. Redundant dictionaries are used to improve the reconstruction of the depth profile. A compression by a factor of 10 for sparse targets up to a depth of 15 mm in noisy environments is shown.
Experimental Investigations on Airborne Gravimetry Based on Compressed Sensing
Yang, Yapeng; Wu, Meiping; Wang, Jinling; Zhang, Kaidong; Cao, Juliang; Cai, Shaokun
2014-01-01
Gravity surveys are an important research topic in geophysics and geodynamics. This paper investigates a method for high accuracy large scale gravity anomaly data reconstruction. Based on the airborne gravimetry technology, a flight test was carried out in China with the strap-down airborne gravimeter (SGA-WZ) developed by the Laboratory of Inertial Technology of the National University of Defense Technology. Taking into account the sparsity of airborne gravimetry by the discrete Fourier transform (DFT), this paper proposes a method for gravity anomaly data reconstruction using the theory of compressed sensing (CS). The gravity anomaly data reconstruction is an ill-posed inverse problem, which can be transformed into a sparse optimization problem. This paper uses the zero-norm as the objective function and presents a greedy algorithm called Orthogonal Matching Pursuit (OMP) to solve the corresponding minimization problem. The test results have revealed that the compressed sampling rate is approximately 14%, the standard deviation of the reconstruction error by OMP is 0.03 mGal and the signal-to-noise ratio (SNR) is 56.48 dB. In contrast, the standard deviation of the reconstruction error by the existing nearest-interpolation method (NIPM) is 0.15 mGal and the SNR is 42.29 dB. These results have shown that the OMP algorithm can reconstruct the gravity anomaly data with higher accuracy and fewer measurements. PMID:24647125
Digital orthogonal receiver for wideband radar based on compressed sensing
NASA Astrophysics Data System (ADS)
Hou, Qingkai; Liu, Yang; Chen, Zengping; Su, Shaoying
2014-10-01
Digital orthogonal receiver is one of the key techniques in digital receiver of soft radar, and compressed sensing is attracting more and more attention in radar signal processing. In this paper, we propose a CS digital orthogonal receiver for wideband radar which utilizes compressed sampling in the acquisition of radar raw data. In order to reconstruct complex signal from sub-sampled raw data, a novel sparse dictionary is proposed to represent the real-valued radar raw signal sparsely. Using our dictionary and CS algorithm, we can reconstruct the complex-valued radar signal from sub-sampled echoes. Compared with conventional digital orthogonal radar receiver, the architecture of receiver in this paper is more simplified and the sampling frequency of ADC is reduced sharply. At the same time, the range profile can be obtained during the reconstruction, so the matched filtering can be eliminated in the receiver. Some experiments on ISAR imaging based on simulated data prove that the phase information of radar echoes is well reserved in our orthogonal receiver and the whole design is effective for wideband radar.
Efficient 2D MRI relaxometry using compressed sensing
NASA Astrophysics Data System (ADS)
Bai, Ruiliang; Cloninger, Alexander; Czaja, Wojciech; Basser, Peter J.
2015-06-01
Potential applications of 2D relaxation spectrum NMR and MRI to characterize complex water dynamics (e.g., compartmental exchange) in biology and other disciplines have increased in recent years. However, the large amount of data and long MR acquisition times required for conventional 2D MR relaxometry limits its applicability for in vivo preclinical and clinical MRI. We present a new MR pipeline for 2D relaxometry that incorporates compressed sensing (CS) as a means to vastly reduce the amount of 2D relaxation data needed for material and tissue characterization without compromising data quality. Unlike the conventional CS reconstruction in the Fourier space (k-space), the proposed CS algorithm is directly applied onto the Laplace space (the joint 2D relaxation data) without compressing k-space to reduce the amount of data required for 2D relaxation spectra. This framework is validated using synthetic data, with NMR data acquired in a well-characterized urea/water phantom, and on fixed porcine spinal cord tissue. The quality of the CS-reconstructed spectra was comparable to that of the conventional 2D relaxation spectra, as assessed using global correlation, local contrast between peaks, peak amplitude and relaxation parameters, etc. This result brings this important type of contrast closer to being realized in preclinical, clinical, and other applications.
Motion-compensated compressed sensing for dynamic imaging
NASA Astrophysics Data System (ADS)
Sundaresan, Rajagopalan; Kim, Yookyung; Nadar, Mariappan S.; Bilgin, Ali
2010-08-01
The recently introduced Compressed Sensing (CS) theory explains how sparse or compressible signals can be reconstructed from far fewer samples than what was previously believed possible. The CS theory has attracted significant attention for applications such as Magnetic Resonance Imaging (MRI) where long acquisition times have been problematic. This is especially true for dynamic MRI applications where high spatio-temporal resolution is needed. For example, in cardiac cine MRI, it is desirable to acquire the whole cardiac volume within a single breath-hold in order to avoid artifacts due to respiratory motion. Conventional MRI techniques do not allow reconstruction of high resolution image sequences from such limited amount of data. Vaswani et al. recently proposed an extension of the CS framework to problems with partially known support (i.e. sparsity pattern). In their work, the problem of recursive reconstruction of time sequences of sparse signals was considered. Under the assumption that the support of the signal changes slowly over time, they proposed using the support of the previous frame as the "known" part of the support for the current frame. While this approach works well for image sequences with little or no motion, motion causes significant change in support between adjacent frames. In this paper, we illustrate how motion estimation and compensation techniques can be used to reconstruct more accurate estimates of support for image sequences with substantial motion (such as cardiac MRI). Experimental results using phantoms as well as real MRI data sets illustrate the improved performance of the proposed technique.
NASA Technical Reports Server (NTRS)
Donohue, James M.; Victor, Kenneth G.; Mcdaniel, James C., Jr.
1993-01-01
A computer-controlled technique, using planar laser-induced iodine fluorescence, for measuring complex compressible flowfields is presented. A new laser permits the use of a planar two-line temperature technique so that all parameters can be measured with the laser operated narrowband. Pressure and temperature measurements in a step flowfield show agreement within 10 percent of a CFD model except in regions close to walls. Deviation of near wall temperature measurements from the model was decreased from 21 percent to 12 percent compared to broadband planar temperature measurements. Computer-control of the experiment has been implemented, except for the frequency tuning of the laser. Image data storage and processing has been improved by integrating a workstation into the experimental setup reducing the data reduction time by a factor of 50.
RZA-NLMF algorithm-based adaptive sparse sensing for realizing compressive sensing
NASA Astrophysics Data System (ADS)
Gui, Guan; Xu, Li; Adachi, Fumiyuki
2014-12-01
Nonlinear sparse sensing (NSS) techniques have been adopted for realizing compressive sensing in many applications such as radar imaging. Unlike the NSS, in this paper, we propose an adaptive sparse sensing (ASS) approach using the reweighted zero-attracting normalized least mean fourth (RZA-NLMF) algorithm which depends on several given parameters, i.e., reweighted factor, regularization parameter, and initial step size. First, based on the independent assumption, Cramer-Rao lower bound (CRLB) is derived as for the performance comparisons. In addition, reweighted factor selection method is proposed for achieving robust estimation performance. Finally, to verify the algorithm, Monte Carlo-based computer simulations are given to show that the ASS achieves much better mean square error (MSE) performance than the NSS.
NASA Astrophysics Data System (ADS)
He, Jinping; Ruan, Ningjuan; Zhao, Haibo; Liu, Yuchen
2016-10-01
Remote sensing features are varied and complicated. There is no comprehensive coverage dictionary for reconstruction. The reconstruction precision is not guaranteed. Aiming at the above problems, a novel reconstruction method with multiple compressed sensing data based on energy compensation is proposed in this paper. The multiple measured data and multiple coding matrices compose the reconstruction equation. It is locally solved through the Orthogonal Matching Pursuit (OMP) algorithm. Then the initial reconstruction image is obtained. Further assuming the local image patches have the same compensation gray value, the mathematical model of compensation value is constructed by minimizing the error of multiple estimated measured values and actual measured values. After solving the minimization, the compensation values are added to the initial reconstruction image. Then the final energy compensation image is obtained. The experiments prove that the energy compensation method is superior to those without compensation. Our method is more suitable for remote sensing features.
Visualization of Astronomical Nebulae via Distributed Multi-GPU Compressed Sensing Tomography.
Wenger, S; Ament, M; Guthe, S; Lorenz, D; Tillmann, A; Weiskopf, D; Magnor, M
2012-12-01
The 3D visualization of astronomical nebulae is a challenging problem since only a single 2D projection is observable from our fixed vantage point on Earth. We attempt to generate plausible and realistic looking volumetric visualizations via a tomographic approach that exploits the spherical or axial symmetry prevalent in some relevant types of nebulae. Different types of symmetry can be implemented by using different randomized distributions of virtual cameras. Our approach is based on an iterative compressed sensing reconstruction algorithm that we extend with support for position-dependent volumetric regularization and linear equality constraints. We present a distributed multi-GPU implementation that is capable of reconstructing high-resolution datasets from arbitrary projections. Its robustness and scalability are demonstrated for astronomical imagery from the Hubble Space Telescope. The resulting volumetric data is visualized using direct volume rendering. Compared to previous approaches, our method preserves a much higher amount of detail and visual variety in the 3D visualization, especially for objects with only approximate symmetry.
Compressed Sensing Techniques Applied to Ultrasonic Imaging of Cargo Containers.
López, Yuri Álvarez; Lorenzo, José Ángel Martínez
2017-01-15
One of the key issues in the fight against the smuggling of goods has been the development of scanners for cargo inspection. X-ray-based radiographic system scanners are the most developed sensing modality. However, they are costly and use bulky sources that emit hazardous, ionizing radiation. Aiming to improve the probability of threat detection, an ultrasonic-based technique, capable of detecting the footprint of metallic containers or compartments concealed within the metallic structure of the inspected cargo, has been proposed. The system consists of an array of acoustic transceivers that is attached to the metallic structure-under-inspection, creating a guided acoustic Lamb wave. Reflections due to discontinuities are detected in the images, provided by an imaging algorithm. Taking into consideration that the majority of those images are sparse, this contribution analyzes the application of Compressed Sensing (CS) techniques in order to reduce the amount of measurements needed, thus achieving faster scanning, without compromising the detection capabilities of the system. A parametric study of the image quality, as a function of the samples needed in spatial and frequency domains, is presented, as well as the dependence on the sampling pattern. For this purpose, realistic cargo inspection scenarios have been simulated.
Enhanced compressive wideband frequency spectrum sensing for dynamic spectrum access
NASA Astrophysics Data System (ADS)
Liu, Yipeng; Wan, Qun
2012-12-01
Wideband spectrum sensing detects the unused spectrum holes for dynamic spectrum access (DSA). Too high sampling rate is the main challenge. Compressive sensing (CS) can reconstruct sparse signal with much fewer randomized samples than Nyquist sampling with high probability. Since survey shows that the monitored signal is sparse in frequency domain, CS can deal with the sampling burden. Random samples can be obtained by the analog-to-information converter. Signal recovery can be formulated as the combination of an L0 norm minimization and a linear measurement fitting constraint. In DSA, the static spectrum allocation of primary radios means the bounds between different types of primary radios are known in advance. To incorporate this a priori information, we divide the whole spectrum into sections according to the spectrum allocation policy. In the new optimization model, the minimization of the L2 norm of each section is used to encourage the cluster distribution locally, while the L0 norm of the L2 norms is minimized to give sparse distribution globally. Because the L2/L0 optimization is not convex, an iteratively re-weighted L2/L1 optimization is proposed to approximate it. Simulations demonstrate the proposed method outperforms others in accuracy, denoising ability, etc.
Compressed Sensing Techniques Applied to Ultrasonic Imaging of Cargo Containers
Álvarez López, Yuri; Martínez Lorenzo, José Ángel
2017-01-01
One of the key issues in the fight against the smuggling of goods has been the development of scanners for cargo inspection. X-ray-based radiographic system scanners are the most developed sensing modality. However, they are costly and use bulky sources that emit hazardous, ionizing radiation. Aiming to improve the probability of threat detection, an ultrasonic-based technique, capable of detecting the footprint of metallic containers or compartments concealed within the metallic structure of the inspected cargo, has been proposed. The system consists of an array of acoustic transceivers that is attached to the metallic structure-under-inspection, creating a guided acoustic Lamb wave. Reflections due to discontinuities are detected in the images, provided by an imaging algorithm. Taking into consideration that the majority of those images are sparse, this contribution analyzes the application of Compressed Sensing (CS) techniques in order to reduce the amount of measurements needed, thus achieving faster scanning, without compromising the detection capabilities of the system. A parametric study of the image quality, as a function of the samples needed in spatial and frequency domains, is presented, as well as the dependence on the sampling pattern. For this purpose, realistic cargo inspection scenarios have been simulated. PMID:28098841
Vibration-based monitoring and diagnostics using compressive sensing
NASA Astrophysics Data System (ADS)
Ganesan, Vaahini; Das, Tuhin; Rahnavard, Nazanin; Kauffman, Jeffrey L.
2017-04-01
Vibration data from mechanical systems carry important information that is useful for characterization and diagnosis. Standard approaches rely on continually streaming data at a fixed sampling frequency. For applications involving continuous monitoring, such as Structural Health Monitoring (SHM), such approaches result in high volume data and rely on sensors being powered for prolonged durations. Furthermore, for spatial resolution, structures are instrumented with a large array of sensors. This paper shows that both volume of data and number of sensors can be reduced significantly by applying Compressive Sensing (CS) in vibration monitoring applications. The reduction is achieved by using random sampling and capitalizing on the sparsity of vibration signals in the frequency domain. Preliminary experimental results validating CS-based frequency recovery are also provided. By exploiting the sparsity of mode shapes, CS can also enable efficient spatial reconstruction using fewer spatially distributed sensors. CS can thereby reduce the cost and power requirement of sensing as well as streamline data storage and processing in monitoring applications. In well-instrumented structures, CS can enable continued monitoring in case of sensor or computational failures.
Resolution enhancement for ISAR imaging via improved statistical compressive sensing
NASA Astrophysics Data System (ADS)
Zhang, Lei; Wang, Hongxian; Qiao, Zhi-jun
2016-12-01
Developing compressed sensing (CS) theory reveals that optimal reconstruction of an unknown signal can be achieved from very limited observations by utilizing signal sparsity. For inverse synthetic aperture radar (ISAR), the image of an interesting target is generally constructed by limited strong scattering centers, representing strong spatial sparsity. Such prior sparsity intrinsically paves a way to improved ISAR imaging performance. In this paper, we develop a super-resolution algorithm for forming ISAR images from limited observations. When the amplitude of the target scattered field follows an identical Laplace probability distribution, the approach converts super-resolution imaging into sparsity-driven optimization in the Bayesian statistics sense. We show that improved performance is achievable by taking advantage of the meaningful spatial structure of the scattered field. Further, we use the nonidentical Laplace distribution with small scale on strong signal components and large scale on noise to discriminate strong scattering centers from noise. A maximum likelihood estimator combined with a bandwidth extrapolation technique is also developed to estimate the scale parameters. Real measured data processing indicates the proposal can reconstruct the high-resolution image though only limited pulses even with low SNR, which shows advantages over current super-resolution imaging methods.
2016-02-01
AFRL-RY-WP-TR-2016-0006 SENSITIVITY SIMULATION OF COMPRESSED SENSING BASED ELECTRONIC WARFARE RECEIVER USING ORTHOGONAL MATCHING PURSUIT...TITLE AND SUBTITLE SENSITIVITY SIMULATION OF COMPRESSED SENSING BASED ELECTRONIC WARFARE RECEIVER USING ORTHOGONAL MATCHING PURSUIT ALGORITHM 5a...August 2014. Report contains color. 14. ABSTRACT The wideband coverage of the traditional fast Fourier transform (FFT)-based electronic warfare
A mosaic approach for unmanned airship remote sensing images based on compressive sensing
NASA Astrophysics Data System (ADS)
Yang, Jilian; Zhang, Aiwu; Sun, Weidong
2011-12-01
The recently-emerged compressive sensing (CS) theory goes against the Nyquist-Shannon (NS) sampling theory and shows that signals can be recovered from far fewer samples than what the NS sampling theorem states. In this paper, to solve the problems in image fusion step of the full-scene image mosaic for the multiple images acquired by a low-altitude unmanned airship, a novel information mutual complement (IMC) model based on CS theory is proposed. IMC model rests on a similar concept that was termed as the joint sparsity models (JSMs) in distributed compressive sensing (DCS) theory, but the measurement matrix in our IMC model is rearranged in order for the multiple images to be reconstructed as one combination. The experimental results of the BP and TSW-CS algorithm with our IMC model certified the effectiveness and adaptability of this proposed approach, and demonstrated that it is possible to substantially reduce the measurement rates of the signal ensemble with good performance in the compressive domain.
Spatially Regularized Compressed Sensing for High Angular Resolution Diffusion Imaging
Rathi, Yogesh; Dolui, Sudipto
2013-01-01
Despite the relative recency of its inception, the theory of compressive sampling (aka compressed sensing) (CS) has already revolutionized multiple areas of applied sciences, a particularly important instance of which is medical imaging. Specifically, the theory has provided a different perspective on the important problem of optimal sampling in magnetic resonance imaging (MRI), with an ever-increasing body of works reporting stable and accurate reconstruction of MRI scans from the number of spectral measurements which would have been deemed unacceptably small as recently as five years ago. In this paper, the theory of CS is employed to palliate the problem of long acquisition times, which is known to be a major impediment to the clinical application of high angular resolution diffusion imaging (HARDI). Specifically, we demonstrate that a substantial reduction in data acquisition times is possible through minimization of the number of diffusion encoding gradients required for reliable reconstruction of HARDI scans. The success of such a minimization is primarily due to the availability of spherical ridgelet transformation, which excels in sparsifying HARDI signals. What makes the resulting reconstruction procedure even more accurate is a combination of the sparsity constraints in the diffusion domain with additional constraints imposed on the estimated diffusion field in the spatial domain. Accordingly, the present paper describes an original way to combine the diffusion-and spatial-domain constraints to achieve a maximal reduction in the number of diffusion measurements, while sacrificing little in terms of reconstruction accuracy. Finally, details are provided on an efficient numerical scheme which can be used to solve the aforementioned reconstruction problem by means of standard and readily available estimation tools. The paper is concluded with experimental results which support the practical value of the proposed reconstruction methodology. PMID:21536524
Online sparse representation for remote sensing compressed-sensed video sampling
NASA Astrophysics Data System (ADS)
Wang, Jie; Liu, Kun; Li, Sheng-liang; Zhang, Li
2014-11-01
Most recently, an emerging Compressed Sensing (CS) theory has brought a major breakthrough for data acquisition and recovery. It asserts that a signal, which is highly compressible in a known basis, can be reconstructed with high probability through sampling frequency which is well below Nyquist Sampling Frequency. When applying CS to Remote Sensing (RS) Video imaging, it can directly and efficiently acquire compressed image data by randomly projecting original data to obtain linear and non-adaptive measurements. In this paper, with the help of distributed video coding scheme which is a low-complexity technique for resource limited sensors, the frames of a RS video sequence are divided into Key frames (K frames) and Non-Key frames (CS frames). In other words, the input video sequence consists of many groups of pictures (GOPs) and each GOP consists of one K frame followed by several CS frames. Both of them are measured based on block, but at different sampling rates. In this way, the major encoding computation burden will be shifted to the decoder. At the decoder, the Side Information (SI) is generated for the CS frames using traditional Motion-Compensated Interpolation (MCI) technique according to the reconstructed key frames. The over-complete dictionary is trained by dictionary learning methods based on SI. These learning methods include ICA-like, PCA, K-SVD, MOD, etc. Using these dictionaries, the CS frames could be reconstructed according to sparse-land model. In the numerical experiments, the reconstruction performance of ICA algorithm, which is often evaluated by Peak Signal-to-Noise Ratio (PSNR), has been made compared with other online sparse representation algorithms. The simulation results show its advantages in reducing reconstruction time and robustness in reconstruction performance when applying ICA algorithm to remote sensing video reconstruction.
COxSwAIN: Compressive Sensing for Advanced Imaging and Navigation
NASA Technical Reports Server (NTRS)
Kurwitz, Richard; Pulley, Marina; LaFerney, Nathan; Munoz, Carlos
2015-01-01
The COxSwAIN project focuses on building an image and video compression scheme that can be implemented in a small or low-power satellite. To do this, we used Compressive Sensing, where the compression is performed by matrix multiplications on the satellite and reconstructed on the ground. Our paper explains our methodology and demonstrates the results of the scheme, being able to achieve high quality image compression that is robust to noise and corruption.
A Computational model for compressed sensing RNAi cellular screening
2012-01-01
Background RNA interference (RNAi) becomes an increasingly important and effective genetic tool to study the function of target genes by suppressing specific genes of interest. This system approach helps identify signaling pathways and cellular phase types by tracking intensity and/or morphological changes of cells. The traditional RNAi screening scheme, in which one siRNA is designed to knockdown one specific mRNA target, needs a large library of siRNAs and turns out to be time-consuming and expensive. Results In this paper, we propose a conceptual model, called compressed sensing RNAi (csRNAi), which employs a unique combination of group of small interfering RNAs (siRNAs) to knockdown a much larger size of genes. This strategy is based on the fact that one gene can be partially bound with several small interfering RNAs (siRNAs) and conversely, one siRNA can bind to a few genes with distinct binding affinity. This model constructs a multi-to-multi correspondence between siRNAs and their targets, with siRNAs much fewer than mRNA targets, compared with the conventional scheme. Mathematically this problem involves an underdetermined system of equations (linear or nonlinear), which is ill-posed in general. However, the recently developed compressed sensing (CS) theory can solve this problem. We present a mathematical model to describe the csRNAi system based on both CS theory and biological concerns. To build this model, we first search nucleotide motifs in a target gene set. Then we propose a machine learning based method to find the effective siRNAs with novel features, such as image features and speech features to describe an siRNA sequence. Numerical simulations show that we can reduce the siRNA library to one third of that in the conventional scheme. In addition, the features to describe siRNAs outperform the existing ones substantially. Conclusions This csRNAi system is very promising in saving both time and cost for large-scale RNAi screening experiments which
Jones, Stacy; Sinha, Sudarson Sekhar; Pramanik, Avijit; Ray, Paresh Chandra
2016-11-03
Drug resistant superbug infection is one of the foremost threats to human health. Plasmonic nanoparticles can be used for ultrasensitive bio-imaging and photothermal killing by amplification of electromagnetic fields at nanoscale "hot spots". One of the main challenges to plasmonic imaging and photothermal killing is design of a plasmonic substrate with a large number of "hot spots". Driven by this need, this article reports design of a three-dimensional (3D) plasmonic "hot spot"-based substrate using gold nanoparticle attached hybrid graphene oxide (GO), free from the traditional 2D limitations. Experimental results show that the 3D substrate has capability for highly sensitive label-free sensing and generates high photothermal heat. Reported data using p-aminothiophenol conjugated 3D substrate show that the surface enhanced Raman spectroscopy (SERS) enhancement factor for the 3D "hot spot"-based substrate is more than two orders of magnitude greater than that for the two-dimensional (2D) substrate and five orders of magnitude greater than that for the zero-dimensional (0D) p-aminothiophenol conjugated gold nanoparticle. 3D-Finite-Difference Time-Domain (3D-FDTD) simulation calculations indicate that the SERS enhancement factor can be greater than 10(4) because of the bent assembly structure in the 3D substrate. Results demonstrate that the 3D-substrate-based SERS can be used for fingerprint identification of several multi-drug resistant superbugs with detection limits of 5 colony forming units per mL. Experimental data show that 785 nm near infrared (NIR) light generates around two times more photothermal heat for the 3D substrate with respect to the 2D substrate, and allows rapid and effective killing of 100% of the multi-drug resistant superbugs within 5 minutes.
NASA Astrophysics Data System (ADS)
Castaldo, Raffaele; De Novellis, Vincenzo; Lollino, Piernicola; Manunta, Michele; Tizzani, Pietro
2015-04-01
The new challenge that the research in slopes instabilities phenomena is going to tackle is the effective integration and joint exploitation of remote sensing measurements with in situ data and observations to study and understand the sub-surface interactions, the triggering causes, and, in general, the long term behaviour of the investigated landslide phenomenon. In this context, a very promising approach is represented by Finite Element (FE) techniques, which allow us to consider the intrinsic complexity of the mass movement phenomena and to effectively benefit from multi source observations and data. In this context, we perform a three dimensional (3D) numerical model of the Ivancich (Assisi, Central Italy) instability phenomenon. In particular, we apply an inverse FE method based on a Genetic Algorithm optimization procedure, benefitting from advanced DInSAR measurements, retrieved through the full resolution Small Baseline Subset (SBAS) technique, and an inclinometric array distribution. To this purpose we consider the SAR images acquired from descending orbit by the COSMO-SkyMed (CSK) X-band radar constellation, from December 2009 to February 2012. Moreover the optimization input dataset is completed by an array of eleven inclinometer measurements, from 1999 to 2006, distributed along the unstable mass. The landslide body is formed of debris material sliding on a arenaceous marl substratum, with a thin shear band detected using borehole and inclinometric data, at depth ranging from 20 to 60 m. Specifically, we consider the active role of this shear band in the control of the landslide evolution process. A large field monitoring dataset of the landslide process, including at-depth piezometric and geological borehole observations, were available. The integration of these datasets allows us to develop a 3D structural geological model of the considered slope. To investigate the dynamic evolution of a landslide, various physical approaches can be considered
Choi, Kihwan; Fahimian, Benjamin P; Li, Tianfang; Suh, Tae-Suk; Lei, Xing
2013-01-01
In four-dimensional (4D) cone-beam computed tomography (CBCT), there is a spatio-temporal tradeoff that currently limits the accuracy. The aim of this study is to develop a Bregman iteration based formalism for high quality 4D CBCT image reconstruction from a limited number of low-dose projections. The 4D CBCT problem is first divided into multiple 3D CBCT subproblems by grouping the projection images corresponding to the phases. To maximally utilize the information from the under-sampled projection data, a compressed sensing (CS) method with Bregman iterations is employed for solving each subproblem. We formulate an unconstrained optimization problem based on least-square criterion regularized by total-variation. The least-square criterion reflects the inconsistency between the measured and the estimated line integrals. Furthermore, the unconstrained problem is updated and solved repeatedly by Bregman iterations. The performance of the proposed algorithm is demonstrated through a series of simulation studies and phantom experiments, and the results are compared to those of previously implemented compressed sensing technique using other gradient-based methods as well as conventional filtered back-projection (FBP) results. The simulation and experimental studies have shown that artifact suppressed images can be obtained with as small as 41 projections per phase, which is adequate for clinical 4D CBCT reconstruction. With such small number of projections, the conventional FDK failed to yield meaningful 4D CBCT images, and CS technique using conjugate gradient was not able to recover sharp edges. The proposed method significantly reduces the radiation dose and scanning time to achieve the high quality images compared to the 4D CBCT imaging based on the conventional FDK technique and the existing CS techniques.
A Secure LFSR Based Random Measurement Matrix for Compressive Sensing
NASA Astrophysics Data System (ADS)
George, Sudhish N.; Pattathil, Deepthi P.
2014-11-01
In this paper, a novel approach for generating the secure measurement matrix for compressive sensing (CS) based on linear feedback shift register (LFSR) is presented. The basic idea is to select the different states of LFSR as the random entries of the measurement matrix and normalize these values to get independent and identically distributed (i.i.d.) random variables with zero mean and variance , where N is the number of input samples. The initial seed for the LFSR system act as the key to the user to provide security. Since the measurement matrix is generated from the LFSR system, and memory overload to store the measurement matrix is avoided in the proposed system. Moreover, the proposed system can provide security maintaining the robustness to noise of the CS system. The proposed system is validated through different block-based CS techniques of images. To enhance security, the different blocks of images are measured with different measurement matrices so that the proposed encryption system can withstand known plaintext attack. A modulo division circuit is used to reseed the LFSR system to generate multiple random measurement matrices, whereby after each fundamental period of LFSR, the feedback polynomial of the modulo circuit is modified in terms of a chaotic value. The proposed secure robust CS paradigm for images is subjected to several forms of attacks and is proven to be resistant against the same. From experimental analysis, it is proven that the proposed system provides better performance than its counterparts.
Compressed Sensing MR Image Reconstruction Exploiting TGV and Wavelet Sparsity
Du, Huiqian; Han, Yu; Mei, Wenbo
2014-01-01
Compressed sensing (CS) based methods make it possible to reconstruct magnetic resonance (MR) images from undersampled measurements, which is known as CS-MRI. The reference-driven CS-MRI reconstruction schemes can further decrease the sampling ratio by exploiting the sparsity of the difference image between the target and the reference MR images in pixel domain. Unfortunately existing methods do not work well given that contrast changes are incorrectly estimated or motion compensation is inaccurate. In this paper, we propose to reconstruct MR images by utilizing the sparsity of the difference image between the target and the motion-compensated reference images in wavelet transform and gradient domains. The idea is attractive because it requires neither the estimation of the contrast changes nor multiple times motion compensations. In addition, we apply total generalized variation (TGV) regularization to eliminate the staircasing artifacts caused by conventional total variation (TV). Fast composite splitting algorithm (FCSA) is used to solve the proposed reconstruction problem in order to improve computational efficiency. Experimental results demonstrate that the proposed method can not only reduce the computational cost but also decrease sampling ratio or improve the reconstruction quality alternatively. PMID:25371704
Compressed Sensing Doppler Ultrasound Reconstruction Using Block Sparse Bayesian Learning.
Lorintiu, Oana; Liebgott, Herve; Friboulet, Denis
2016-04-01
In this paper we propose a framework for using duplex Doppler ultrasound systems. These type of systems need to interleave the acquisition and display of a B-mode image and of the pulsed Doppler spectrogram. In a recent study (Richy , 2013), we have shown that compressed sensing-based reconstruction of Doppler signal allowed reducing the number of Doppler emissions and yielded better results than traditional interpolation and at least equivalent or even better depending on the configuration than the study estimating the signal from sparse data sets given in Jensen, 2006. We propose here to improve over this study by using a novel framework for randomly interleaving Doppler and US emissions. The proposed method reconstructs the Doppler signal segment by segment using a block sparse Bayesian learning (BSBL) algorithm based CS reconstruction. The interest of using such framework in the context of duplex Doppler is linked to the unique ability of BSBL to exploit block-correlated signals and to recover non-sparse signals. The performance of the technique is evaluated from simulated data as well as experimental in vivo data and compared to the recent results in Richy , 2013.
Improved Compressive Sensing of Natural Scenes Using Localized Random Sampling
NASA Astrophysics Data System (ADS)
Barranca, Victor J.; Kovačič, Gregor; Zhou, Douglas; Cai, David
2016-08-01
Compressive sensing (CS) theory demonstrates that by using uniformly-random sampling, rather than uniformly-spaced sampling, higher quality image reconstructions are often achievable. Considering that the structure of sampling protocols has such a profound impact on the quality of image reconstructions, we formulate a new sampling scheme motivated by physiological receptive field structure, localized random sampling, which yields significantly improved CS image reconstructions. For each set of localized image measurements, our sampling method first randomly selects an image pixel and then measures its nearby pixels with probability depending on their distance from the initially selected pixel. We compare the uniformly-random and localized random sampling methods over a large space of sampling parameters, and show that, for the optimal parameter choices, higher quality image reconstructions can be consistently obtained by using localized random sampling. In addition, we argue that the localized random CS optimal parameter choice is stable with respect to diverse natural images, and scales with the number of samples used for reconstruction. We expect that the localized random sampling protocol helps to explain the evolutionarily advantageous nature of receptive field structure in visual systems and suggests several future research areas in CS theory and its application to brain imaging.
The fast algorithm of spark in compressive sensing
NASA Astrophysics Data System (ADS)
Xie, Meihua; Yan, Fengxia
2017-01-01
Compressed Sensing (CS) is an advanced theory on signal sampling and reconstruction. In CS theory, the reconstruction condition of signal is an important theory problem, and spark is a good index to study this problem. But the computation of spark is NP hard. In this paper, we study the problem of computing spark. For some special matrixes, for example, the Gaussian random matrix and 0-1 random matrix, we obtain some conclusions. Furthermore, for Gaussian random matrix with fewer rows than columns, we prove that its spark equals to the number of its rows plus one with probability 1. For general matrix, two methods are given to compute its spark. One is the method of directly searching and the other is the method of dual-tree searching. By simulating 24 Gaussian random matrixes and 18 0-1 random matrixes, we tested the computation time of these two methods. Numerical results showed that the dual-tree searching method had higher efficiency than directly searching, especially for those matrixes which has as much as rows and columns.
Compressive sensing for direct millimeter-wave holographic imaging.
Qiao, Lingbo; Wang, Yingxin; Shen, Zongjun; Zhao, Ziran; Chen, Zhiqiang
2015-04-10
Direct millimeter-wave (MMW) holographic imaging, which provides both the amplitude and phase information by using the heterodyne mixing technique, is considered a powerful tool for personnel security surveillance. However, MWW imaging systems usually suffer from the problem of high cost or relatively long data acquisition periods for array or single-pixel systems. In this paper, compressive sensing (CS), which aims at sparse sampling, is extended to direct MMW holographic imaging for reducing the number of antenna units or the data acquisition time. First, following the scalar diffraction theory, an exact derivation of the direct MMW holographic reconstruction is presented. Then, CS reconstruction strategies for complex-valued MMW images are introduced based on the derived reconstruction formula. To pursue the applicability for near-field MMW imaging and more complicated imaging targets, three sparsity bases, including total variance, wavelet, and curvelet, are evaluated for the CS reconstruction of MMW images. We also discuss different sampling patterns for single-pixel, linear array and two-dimensional array MMW imaging systems. Both simulations and experiments demonstrate the feasibility of recovering MMW images from measurements at 1/2 or even 1/4 of the Nyquist rate.
Compressive-Sensing-Based Structure Identification for Multilayer Networks.
Mei, Guofeng; Wu, Xiaoqun; Wang, Yingfei; Hu, Mi; Lu, Jun-An; Chen, Guanrong
2017-02-13
The coexistence of multiple types of interactions within social, technological, and biological networks has motivated the study of the multilayer nature of real-world networks. Meanwhile, identifying network structures from dynamical observations is an essential issue pervading over the current research on complex networks. This paper addresses the problem of structure identification for multilayer networks, which is an important topic but involves a challenging inverse problem. To clearly reveal the formalism, the simplest two-layer network model is considered and a new approach to identifying the structure of one layer is proposed. Specifically, if the interested layer is sparsely connected and the node behaviors of the other layer are observable at a few time points, then a theoretical framework is established based on compressive sensing and regularization. Some numerical examples illustrate the effectiveness of the identification scheme, its requirement of a relatively small number of observations, as well as its robustness against small noise. It is noteworthy that the framework can be straightforwardly extended to multilayer networks, thus applicable to a variety of real-world complex systems.
Pitfalls in compressed sensing reconstruction and how to avoid them.
Shchukina, Alexandra; Kasprzak, Paweł; Dass, Rupashree; Nowakowski, Michał; Kazimierczuk, Krzysztof
2016-11-11
Multidimensional NMR can provide unmatched spectral resolution, which is crucial when dealing with samples of biological macromolecules. The resolution, however, comes at the high price of long experimental time. Non-uniform sampling (NUS) of the evolution time domain allows to suppress this limitation by sampling only a small fraction of the data, but requires sophisticated algorithms to reconstruct omitted data points. A significant group of such algorithms known as compressed sensing (CS) is based on the assumption of sparsity of a reconstructed spectrum. Several papers on the application of CS in multidimensional NMR have been published in the last years, and the developed methods have been implemented in most spectral processing software. However, the publications rarely show the cases when NUS reconstruction does not work perfectly or explain how to solve the problem. On the other hand, every-day users of NUS develop their rules-of-thumb, which help to set up the processing in an optimal way, but often without a deeper insight. In this paper, we discuss several sources of problems faced in CS reconstructions: low sampling level, missassumption of spectral sparsity, wrong stopping criterion and attempts to extrapolate the signal too much. As an appendix, we provide MATLAB codes of several CS algorithms used in NMR. We hope that this work will explain the mechanism of NUS reconstructions and help readers to set up acquisition and processing parameters. Also, we believe that it might be helpful for algorithm developers.
Single-snapshot DOA estimation by using Compressed Sensing
NASA Astrophysics Data System (ADS)
Fortunati, Stefano; Grasso, Raffaele; Gini, Fulvio; Greco, Maria S.; LePage, Kevin
2014-12-01
This paper deals with the problem of estimating the directions of arrival (DOA) of multiple source signals from a single observation vector of an array data. In particular, four estimation algorithms based on the theory of compressed sensing (CS), i.e., the classical ℓ 1 minimization (or Least Absolute Shrinkage and Selection Operator, LASSO), the fast smooth ℓ 0 minimization, and the Sparse Iterative Covariance-Based Estimator, SPICE and the Iterative Adaptive Approach for Amplitude and Phase Estimation, IAA-APES algorithms, are analyzed, and their statistical properties are investigated and compared with the classical Fourier beamformer (FB) in different simulated scenarios. We show that unlike the classical FB, a CS-based beamformer (CSB) has some desirable properties typical of the adaptive algorithms (e.g., Capon and MUSIC) even in the single snapshot case. Particular attention is devoted to the super-resolution property. Theoretical arguments and simulation analysis provide evidence that a CS-based beamformer can achieve resolution beyond the classical Rayleigh limit. Finally, the theoretical findings are validated by processing a real sonar dataset.
Genetic optical design for a compressive sensing task
NASA Astrophysics Data System (ADS)
Horisaki, Ryoichi; Niihara, Takahiro; Tanida, Jun
2016-10-01
We present a sophisticated optical design method for reducing the number of photodetectors for a specific sensing task. The chosen design parameter is the point spread function, and the selected task is object recognition. The point spread function is optimized iteratively with a genetic algorithm for object recognition based on a neural network. In the experimental demonstration, binary classification of face and non-face datasets was performed with a single measurement using two photodetectors. A spatial light modulator operating in the amplitude modulation mode was provided in the imaging optics and was used to modulate the point spread function. In each generation of the genetic algorithm, the classification accuracy with a pattern displayed on the spatial light modulator was fed-back to the next generation to find better patterns. The proposed method increased the accuracy by about 30 % compared with a conventional imaging system in which the point spread function was the delta function. This approach is practically useful for compressing the cost, size, and observation time of optical sensors in specific applications, and robust for imperfections in optical elements.
Sparse radar imaging using 2D compressed sensing
NASA Astrophysics Data System (ADS)
Hou, Qingkai; Liu, Yang; Chen, Zengping; Su, Shaoying
2014-10-01
Radar imaging is an ill-posed linear inverse problem and compressed sensing (CS) has been proved to have tremendous potential in this field. This paper surveys the theory of radar imaging and a conclusion is drawn that the processing of ISAR imaging can be denoted mathematically as a problem of 2D sparse decomposition. Based on CS, we propose a novel measuring strategy for ISAR imaging radar and utilize random sub-sampling in both range and azimuth dimensions, which will reduce the amount of sampling data tremendously. In order to handle 2D reconstructing problem, the ordinary solution is converting the 2D problem into 1D by Kronecker product, which will increase the size of dictionary and computational cost sharply. In this paper, we introduce the 2D-SL0 algorithm into the reconstruction of imaging. It is proved that 2D-SL0 can achieve equivalent result as other 1D reconstructing methods, but the computational complexity and memory usage is reduced significantly. Moreover, we will state the results of simulating experiments and prove the effectiveness and feasibility of our method.
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
NASA Astrophysics Data System (ADS)
Tan, Elliot; Yang, Ming; Ma, Lixin; Zheng, Yahong Rosa
2014-03-01
This paper presents a practical implementation of Compressive Sensing (CS) for a preclinical MRI machine to acquire randomly undersampled k-space data in cardiac function imaging applications. First, random undersampling masks were generated based on Gaussian, Cauchy, wrapped Cauchy and von Mises probability distribution functions by the inverse transform method. The best masks for undersampling ratios of 0.3, 0.4 and 0.5 were chosen for animal experimentation, and were programmed into a Bruker Avance III BioSpec 7.0T MRI system through method programming in ParaVision. Three undersampled mouse heart datasets were obtained using a fast low angle shot (FLASH) sequence, along with a control undersampled phantom dataset. ECG and respiratory gating was used to obtain high quality images. After CS reconstructions were applied to all acquired data, resulting images were quantitatively analyzed using the performance metrics of reconstruction error and Structural Similarity Index (SSIM). The comparative analysis indicated that CS reconstructed images from MRI machine undersampled data were indeed comparable to CS reconstructed images from retrospective undersampled data, and that CS techniques are practical in a preclinical setting. The implementation achieved 2 to 4 times acceleration for image acquisition and satisfactory quality of image reconstruction.
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.
NASA Astrophysics Data System (ADS)
Lim, Se Hoon
Compressive holography estimates images from incomplete data by using sparsity priors. Compressive holography combines digital holography and compressive sensing. Digital holography consists of computational image estimation from data captured by an electronic focal plane array. Compressive sensing enables accurate data reconstruction by prior knowledge on desired signal. Computational and optical co-design optimally supports compressive holography in the joint computational and optical domain. This dissertation explores two examples of compressive holography: estimation of 3D tomographic images from 2D data and estimation of images from under sampled apertures. Compressive holography achieves single shot holographic tomography using decompressive inference. In general, 3D image reconstruction suffers from underdetermined measurements with a 2D detector. Specifically, single shot holographic tomography shows the uniqueness problem in the axial direction because the inversion is ill-posed. Compressive sensing alleviates the ill-posed problem by enforcing some sparsity constraints. Holographic tomography is applied for video-rate microscopic imaging and diffuse object imaging. In diffuse object imaging, sparsity priors are not valid in coherent image basis due to speckle. So incoherent image estimation is designed to hold the sparsity in incoherent image basis by support of multiple speckle realizations. High pixel count holography achieves high resolution and wide field-of-view imaging. Coherent aperture synthesis can be one method to increase the aperture size of a detector. Scanning-based synthetic aperture confronts a multivariable global optimization problem due to time-space measurement errors. A hierarchical estimation strategy divides the global problem into multiple local problems with support of computational and optical co-design. Compressive sparse aperture holography can be another method. Compressive sparse sampling collects most of significant field
Lee, M; Suh, T; Han, B; Xing, L; Jenkins, C
2015-06-15
Purpose: To develop and validate an innovative method of using depth sensing cameras and 3D printing techniques for Total Body Irradiation (TBI) treatment planning and compensator fabrication. Methods: A tablet with motion tracking cameras and integrated depth sensing was used to scan a RANDOTM phantom arranged in a TBI treatment booth to detect and store the 3D surface in a point cloud (PC) format. The accuracy of the detected surface was evaluated by comparison to extracted measurements from CT scan images. The thickness, source to surface distance and off-axis distance of the phantom at different body section was measured for TBI treatment planning. A 2D map containing a detailed compensator design was calculated to achieve uniform dose distribution throughout the phantom. The compensator was fabricated using a 3D printer, silicone molding and tungsten powder. In vivo dosimetry measurements were performed using optically stimulated luminescent detectors (OSLDs). Results: The whole scan of the anthropomorphic phantom took approximately 30 seconds. The mean error for thickness measurements at each section of phantom compare to CT was 0.44 ± 0.268 cm. These errors resulted in approximately 2% dose error calculation and 0.4 mm tungsten thickness deviation for the compensator design. The accuracy of 3D compensator printing was within 0.2 mm. In vivo measurements for an end-to-end test showed the overall dose difference was within 3%. Conclusion: Motion cameras and depth sensing techniques proved to be an accurate and efficient tool for TBI patient measurement and treatment planning. 3D printing technique improved the efficiency and accuracy of the compensator production and ensured a more accurate treatment delivery.
Accelerated 3D catheter visualization from triplanar MR projection images.
Schirra, Carsten Oliver; Weiss, Steffen; Krueger, Sascha; Caulfield, Denis; Pedersen, Steen F; Razavi, Reza; Kozerke, Sebastian; Schaeffter, Tobias
2010-07-01
One major obstacle for MR-guided catheterizations is long acquisition times associated with visualizing interventional devices. Therefore, most techniques presented hitherto rely on single-plane imaging to visualize the catheter. Recently, accelerated three-dimensional (3D) imaging based on compressed sensing has been proposed to reduce acquisition times. However, frame rates with this technique remain low, and the 3D reconstruction problem yields a considerable computational load. In X-ray angiography, it is well understood that the shape of interventional devices can be derived in 3D space from a limited number of projection images. In this work, this fact is exploited to develop a method for 3D visualization of active catheters from multiplanar two-dimensional (2D) projection MR images. This is favorable to 3D MRI as the overall number of acquired profiles, and consequently the acquisition time, is reduced. To further reduce measurement times, compressed sensing is employed. Furthermore, a novel single-channel catheter design is presented that combines a solenoidal tip coil in series with a single-loop antenna, enabling simultaneous tip tracking and shape visualization. The tracked tip and catheter properties provide constraints for compressed sensing reconstruction and subsequent 2D/3D curve fitting. The feasibility of the method is demonstrated in phantoms and in an in vivo pig experiment.
NASA Astrophysics Data System (ADS)
Liang, Jinyang; Gao, Liang; Hai, Pengfei; Li, Chiye; Wang, Lihong V.
2016-02-01
We applied compressed ultrafast photography (CUP), a computational imaging technique, to acquire three-dimensional (3D) images. The approach unites image encryption, compression, and acquisition in a single measurement, thereby allowing efficient and secure data transmission. By leveraging the time-of-flight (ToF) information of pulsed light reflected by the object, we can reconstruct a volumetric image (150 mm×150 mm×1050 mm, x × y × z) from a single camera snapshot. Furthermore, we demonstrated high-speed 3D videography of a moving object at 75 frames per second using the ToF-CUP camera.
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.
Rapid MR spectroscopic imaging of lactate using compressed sensing
NASA Astrophysics Data System (ADS)
Vidya Shankar, Rohini; Agarwal, Shubhangi; Geethanath, Sairam; Kodibagkar, Vikram D.
2015-03-01
Imaging lactate metabolism in vivo may improve cancer targeting and therapeutics due to its key role in the development, maintenance, and metastasis of cancer. The long acquisition times associated with magnetic resonance spectroscopic imaging (MRSI), which is a useful technique for assessing metabolic concentrations, are a deterrent to its routine clinical use. The objective of this study was to combine spectral editing and prospective compressed sensing (CS) acquisitions to enable precise and high-speed imaging of the lactate resonance. A MRSI pulse sequence with two key modifications was developed: (1) spectral editing components for selective detection of lactate, and (2) a variable density sampling mask for pseudo-random under-sampling of the k-space `on the fly'. The developed sequence was tested on phantoms and in vivo in rodent models of cancer. Datasets corresponding to the 1X (fully-sampled), 2X, 3X, 4X, 5X, and 10X accelerations were acquired. The under-sampled datasets were reconstructed using a custom-built algorithm in MatlabTM, and the fidelity of the CS reconstructions was assessed in terms of the peak amplitudes, SNR, and total acquisition time. The accelerated reconstructions demonstrate a reduction in the scan time by up to 90% in vitro and up to 80% in vivo, with negligible loss of information when compared with the fully-sampled dataset. The proposed unique combination of spectral editing and CS facilitated rapid mapping of the spatial distribution of lactate at high temporal resolution. This technique could potentially be translated to the clinic for the routine assessment of lactate changes in solid tumors.
Compressive Sensing Based Bio-Inspired Shape Feature Detection CMOS Imager
NASA Technical Reports Server (NTRS)
Duong, Tuan A. (Inventor)
2015-01-01
A CMOS imager integrated circuit using compressive sensing and bio-inspired detection is presented which integrates novel functions and algorithms within a novel hardware architecture enabling efficient on-chip implementation.
Chappard, Daniel; Terranova, Lisa; Mallet, Romain; Mercier, Philippe
2015-01-01
The 3D arrangement of porous granular biomaterials usable to fill bone defects has received little study. Granular biomaterials occupy 3D space when packed together in a manner that creates a porosity suitable for the invasion of vascular and bone cells. Granules of beta-tricalcium phosphate (β-TCP) were prepared with either 12.5 or 25 g of β-TCP powder in the same volume of slurry. When the granules were placed in a test tube, this produced 3D stacks with a high (HP) or low porosity (LP), respectively. Stacks of granules mimic the filling of a bone defect by a surgeon. The aim of this study was to compare the porosity of stacks of β-TCP granules with that of cores of trabecular bone. Biomechanical compression tests were done on the granules stacks. Bone cylinders were prepared from calf tibia plateau, constituted high-density (HD) blocks. Low-density (LD) blocks were harvested from aged cadaver tibias. Microcomputed tomography was used on the β-TCP granule stacks and the trabecular bone cores to determine porosity and specific surface. A vector-projection algorithm was used to image porosity employing a frontal plane image, which was constructed line by line from all images of a microCT stack. Stacks of HP granules had porosity (75.3 ± 0.4%) and fractal lacunarity (0.043 ± 0.007) intermediate between that of HD (respectively 69.1 ± 6.4%, p < 0.05 and 0.087 ± 0.045, p < 0.05) and LD bones (respectively 88.8 ± 1.57% and 0.037 ± 0.014), but exhibited a higher surface density (5.56 ± 0.11 mm2/mm3 vs. 2.06 ± 0.26 for LD, p < 0.05). LP granular arrangements created large pores coexisting with dense areas of material. Frontal plane analysis evidenced a more regular arrangement of β-TCP granules than bone trabecule. Stacks of HP granules represent a scaffold that resembles trabecular bone in its porous microarchitecture. PMID:26528240
Chappard, Daniel; Terranova, Lisa; Mallet, Romain; Mercier, Philippe
2015-01-01
The 3D arrangement of porous granular biomaterials usable to fill bone defects has received little study. Granular biomaterials occupy 3D space when packed together in a manner that creates a porosity suitable for the invasion of vascular and bone cells. Granules of beta-tricalcium phosphate (β-TCP) were prepared with either 12.5 or 25 g of β-TCP powder in the same volume of slurry. When the granules were placed in a test tube, this produced 3D stacks with a high (HP) or low porosity (LP), respectively. Stacks of granules mimic the filling of a bone defect by a surgeon. The aim of this study was to compare the porosity of stacks of β-TCP granules with that of cores of trabecular bone. Biomechanical compression tests were done on the granules stacks. Bone cylinders were prepared from calf tibia plateau, constituted high-density (HD) blocks. Low-density (LD) blocks were harvested from aged cadaver tibias. Microcomputed tomography was used on the β-TCP granule stacks and the trabecular bone cores to determine porosity and specific surface. A vector-projection algorithm was used to image porosity employing a frontal plane image, which was constructed line by line from all images of a microCT stack. Stacks of HP granules had porosity (75.3 ± 0.4%) and fractal lacunarity (0.043 ± 0.007) intermediate between that of HD (respectively 69.1 ± 6.4%, p < 0.05 and 0.087 ± 0.045, p < 0.05) and LD bones (respectively 88.8 ± 1.57% and 0.037 ± 0.014), but exhibited a higher surface density (5.56 ± 0.11 mm(2)/mm(3) vs. 2.06 ± 0.26 for LD, p < 0.05). LP granular arrangements created large pores coexisting with dense areas of material. Frontal plane analysis evidenced a more regular arrangement of β-TCP granules than bone trabecule. Stacks of HP granules represent a scaffold that resembles trabecular bone in its porous microarchitecture.
Elastic-Waveform Inversion with Compressive Sensing for Sparse Seismic Data
Lin, Youzuo; Huang, Lianjie
2015-01-28
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 models produced with our new method using a sparse source array are almost as accurate as those obtained using a dense source array.
van den Noort, Josien C.; Kortier, Henk G.; van Beek, Nathalie; Veeger, DirkJan H. E. J.; Veltink, Peter H.
2016-01-01
Objective analysis of hand and finger kinematics is important to increase understanding of hand function and to quantify motor symptoms for clinical diagnosis. The aim of this paper is to compare a new 3D measurement system containing multiple miniature inertial sensors (PowerGlove) with an opto-electronic marker system during specific finger tasks in three healthy subjects. Various finger movements tasks were performed: flexion, fast flexion, tapping, hand open/closing, ab/adduction and circular pointing. 3D joint angles of the index finger joints and position of the thumb and index were compared between systems. Median root mean square differences of the main joint angles of interest ranged between 3.3 and 8.4deg. Largest differences were found in fast and circular pointing tasks, mainly in range of motion. Smallest differences for all 3D joint angles were observed in the flexion tasks. For fast finger tapping, the thumb/index amplitude showed a median difference of 15.8mm. Differences could be explained by skin movement artifacts caused by relative marker movements of the marker system, particularly during fast tasks; large movement accelerations and angular velocities which exceeded the range of the inertial sensors; and by differences in segment calibrations between systems. The PowerGlove is a system that can be of value to measure 3D hand and finger kinematics and positions in an ambulatory setting. The reported differences need to be taken into account when applying the system in studies understanding the hand function and quantifying hand motor symptoms in clinical practice. PMID:27812139
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.
Otazo, Ricardo; Kim, Daniel; Axel, Leon; Sodickson, Daniel K.
2010-01-01
First-pass cardiac perfusion MRI is a natural candidate for compressed sensing acceleration since its representation in the combined temporal Fourier and spatial domain is sparse and the required incoherence can be effectively accomplished by k-t random undersampling. However, the required number of samples in practice (three to five times the number of sparse coefficients) limits the acceleration for compressed sensing alone. Parallel imaging may also be used to accelerate cardiac perfusion MRI, with acceleration factors ultimately limited by noise amplification. In this work, compressed sensing and parallel imaging are combined by merging the k-t SPARSE technique with SENSE reconstruction to substantially increase the acceleration rate for perfusion imaging. We also present a new theoretical framework for understanding the combination of k-t SPARSE with SENSE based on distributed compressed sensing theory. This framework, which identifies parallel imaging as a distributed multisensor implementation of compressed sensing, enables an estimate of feasible acceleration for the combined approach. We demonstrate feasibility of 8-fold acceleration in vivo with whole-heart coverage and high spatial and temporal resolution using standard coil arrays. The method is relatively insensitive to respiratory motion artifacts and presents similar temporal fidelity and image quality when compared to GRAPPA with 2-fold acceleration. PMID:20535813
NASA Astrophysics Data System (ADS)
Fan, J.; Peng, L.; Li, K. H.; Tan, C. S.
2012-10-01
Low-temperature wafer-level Cu-to-Cu thermo-compression bonding and its reliability for hermetic sealing application have been investigated in this work. The volume of the encapsulated cavities is about 1.4×10-3 cm3 in accordance with the MIL-STD-883E standard prescribed for microelectronics packaging hermeticity measurement. The samples under test are bonded at 300 °C under a bonding force of 5500 N for 1 h in vacuum (˜2.5 × 10-4 mbar) with a 300 nm thick Cu diffusion layer and 50 nm thick Ti barrier layer which are deposited in an e-beam evaporator. The reliability test is accomplished through a temperature cycling test (TCT) from -40 to 125 °C up to 1000 cycles and a humidity test based on IPC/JEDEC J-STD-020 standard. In addition, an immersion in acid/base solution is applied to verify the corrosion resistance of the Cu frame for hermetic application. Excellent helium leak rate which is smaller than the reject limit defined by the MIL-STD-883E standard (method 1014.10) is detected for all the samples. These excellent helium leak rates show an outstanding bonding quality against harsh environment for hermetic encapsulation in 3D integration applications.
NASA Astrophysics Data System (ADS)
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.
The MUSIC algorithm for sparse objects: a compressed sensing analysis
NASA Astrophysics Data System (ADS)
Fannjiang, Albert C.
2011-03-01
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}(s^2) 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
Il Yong Chun; Adcock, Ben; Talavage, Thomas M
2014-01-01
Magnetic resonance imaging (MRI) is considered a key modality for the future as it offers several advantages, including the use of non-ionizing radiation and having no known side effects on the human body, and has recently begun to serve as a key component of multi-modal neuroimaging. However, two major intrinsic problems exist: slow acquisition and intrusive acoustic noise. Parallel MRI (pMRI) techniques accelerate acquisition by reducing the duration and coverage of conventional gradient encoding. The under-sampled k-space data is detected with several receiver coils surrounding the object, using distinct spatial encoding information for each coil element to reconstruct the image. However, this scanning remains slow compared to typical clinical imaging (e.g. X-ray CT). Compressed Sensing (CS), a sampling theory based on random sub-sampling, has potential to further reduce the sampling used in pMRI, accelerating acquisition further. In this work, we propose a new CS SENSE pMRI reconstruction model promoting joint sparsity across channels and enhancing mutual incoherence to improve reconstruction accuracy from limited k-space data. For fast image reconstruction and fair comparisons, all reconstructions are computed with split-Bregman and variable splitting techniques. Numerical results show that, with the introduced methods, reconstruction performance can be crucially improved with limited amount of k-space data.
NASA Astrophysics Data System (ADS)
Schneiderwind, Sascha; Mason, Jack; Wiatr, Thomas; Papanikolaou, Ioannis; Reicherter, Klaus
2016-03-01
Two normal faults on the island of Crete and mainland Greece were studied to test an innovative workflow with the goal of obtaining a more objective palaeoseismic trench log, and a 3-D view of the sedimentary architecture within the trench walls. Sedimentary feature geometries in palaeoseismic trenches are related to palaeoearthquake magnitudes which are used in seismic hazard assessments. If the geometry of these sedimentary features can be more representatively measured, seismic hazard assessments can be improved. In this study more representative measurements of sedimentary features are achieved by combining classical palaeoseismic trenching techniques with multispectral approaches. A conventional trench log was firstly compared to results of ISO (iterative self-organising) cluster analysis of a true colour photomosaic representing the spectrum of visible light. Photomosaic acquisition disadvantages (e.g. illumination) were addressed by complementing the data set with active near-infrared backscatter signal image from t-LiDAR measurements. The multispectral analysis shows that distinct layers can be identified and it compares well with the conventional trench log. According to this, a distinction of adjacent stratigraphic units was enabled by their particular multispectral composition signature. Based on the trench log, a 3-D interpretation of attached 2-D ground-penetrating radar (GPR) profiles collected on the vertical trench wall was then possible. This is highly beneficial for measuring representative layer thicknesses, displacements, and geometries at depth within the trench wall. Thus, misinterpretation due to cutting effects is minimised. This manuscript combines multiparametric approaches and shows (i) how a 3-D visualisation of palaeoseismic trench stratigraphy and logging can be accomplished by combining t-LiDAR and GPR techniques, and (ii) how a multispectral digital analysis can offer additional advantages to interpret palaeoseismic and stratigraphic
Compressive sensing beamforming based on covariance for acoustic imaging with noisy measurements.
Zhong, Siyang; Wei, Qingkai; Huang, Xun
2013-11-01
Compressive sensing, a newly emerging method from information technology, is applied to array beamforming and associated acoustic applications. A compressive sensing beamforming method (CSB-II) is developed based on sampling covariance matrix, assuming spatially sparse and incoherent signals, and then examined using both simulations and aeroacoustic measurements. The simulation results clearly show that the proposed CSB-II method is robust to sensing noise. In addition, aeroacoustic tests of a landing gear model demonstrate the good performance in terms of resolution and sidelobe rejection.
NASA Astrophysics Data System (ADS)
Baumberger, R.; Wehrens, Ph.; Herwegh, M.
2012-04-01
Allowing deep insight into the formation history of a rock complex, shear zones, faults and joint systems represent important sources of geological information. The granitic rocks of the Haslital valley (Switzerland) show very good outcrop conditions to study these mechanical anisotropies. Furthermore, they permit a quantitative characterisation of the above-mentioned deformation structures on the large-scale, in terms of their 3D orientation, 3D spatial distribution, kinematics and evolution in 3D. A key problem while developing valid geological 3D models is the three-dimensional spatial distribution of geological structures, particularly with increasing distance from the surface. That is especially true in regions, where only little or even no "hard" underground data (e.g. bore holes, tunnel mappings and seismics) is available. In the study area, many subsurface data are available (e.g. cross sections, tunnel and pipeline mappings, bore holes etc.). Therefore, two methods dealing with the problems mentioned are developed: (1) A data acquisition, processing and visualisation method, (2) A methodology to improve the reliability of 3D models regarding the spatial trend of geological structures with increasing depth: 1) Using aerial photographs and a high-resolution digital elevation model, a GIS-based remote-sensing structural map of large-scale structural elements (shear zones, faults) of the study area was elaborated. Based on that lineament map, (i) a shear zone map was derived and (ii) a geostatistical analysis was applied to identify sub regions applicable for serving as field areas to test the methodology presented above. During fieldwork, the shear zone map was evaluated by verifying the occurrence and spatial distribution of the structures designated by remote sensing. Additionally, the geometry of the structures (e.g. 3D orientation, width, kinematics) was characterised and parameterised accordingly. These tasks were partially done using a GPS based Slate
Feasibility and performances of compressed sensing and sparse map-making with Herschel/PACS data
NASA Astrophysics Data System (ADS)
Barbey, N.; Sauvage, M.; Starck, J.-L.; Ottensamer, R.; Chanial, P.
2011-03-01
The Herschel Space Observatory of ESA was launched in May 2009 and has been in operation ever 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 onboard. Recently, a new theory called compressed sensing has 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, IEEE J. Selected Topics Signal Process., 2, 718) has shown 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 and significantly improves over the standard pipeline. To fully use the redundancy present in the data, we perform a full sky-map estimation and decompression at the same time, which cannot be done in most other compression methods. We also demonstrate that the various artifacts affecting the data (pink noise and glitches, whose behavior is a priori not very compatible with compressed sensing) can also be handled in this new framework. Finally, we compare the methods from the compressed sensing scheme and data acquired with the standard compression scheme. We discuss improvements that can be made on Earth for the creation of sky maps from the data.
Wall Sensing for an Autonomous Robot With a Three-Dimensional Time-of-Flight (3-D TOF) Camera
2011-02-01
one frame to another. Another problem commonly occurs when the elevation of the sensor causes it to cast its single sensing beam on a cluttered...the scene, shown in figure 6, where drawers or walls separated by a door may or may not be classified as belonging to a common plane. 17 Figure
NASA Astrophysics Data System (ADS)
LIU, Yiping; XU, Qing; ZhANG, Heng; LV, Liang; LU, Wanjie; WANG, Dandi
2016-11-01
The purpose of this paper is to solve the problems of the traditional single system for interpretation and draughting such as inconsistent standards, single function, dependence on plug-ins, closed system and low integration level. On the basis of the comprehensive analysis of the target elements composition, map representation and similar system features, a 3D interpretation and draughting integrated service platform for multi-source, multi-scale and multi-resolution geospatial objects is established based on HTML5 and WebGL, which not only integrates object recognition, access, retrieval, three-dimensional display and test evaluation but also achieves collection, transfer, storage, refreshing and maintenance of data about Geospatial Objects and shows value in certain prospects and potential for growth.
Compressed sensing for super-resolution spatial and temporal laser detection and ranging
NASA Astrophysics Data System (ADS)
Laurenzis, Martin; Schertzer, Stephane; Christnacher, Frank
2016-10-01
In the past decades, laser aided electro-optical sensing has reached high maturity and several commercial systems are available at the market for various but specific applications. These systems can be used for detection i.e. imaging as well as ranging. They cover laser scanning devices like LiDAR and staring full frame imaging systems like laser gated viewing or LADAR. The sensing capabilities of these systems is limited by physical parameter (like FPA array size, temporal band width, scanning rate, sampling rate) and is adapted to specific applications. Change of system parameter like an increase of spatial resolution implies the setup of a new sensing device with high development cost or the purchase and installation of a complete new sensor unit. Computational imaging approaches can help to setup sensor devices with flexible or adaptable sensing capabilities. Especially, compressed sensing is an emerging computational method which is a promising candidate to realize super-resolution sensing with the possibility to adapt its performance to various sensing tasks. It is possible to increase sensing capabilities with compressed sensing to gain either higher spatial and/or temporal resolution. Then, the sensing capabilities depend no longer only on the physical performance of the device but also on the computational effort and can be adapted to the application. In this paper, we demonstrate and discuss laser aided imaging using CS for super-resolution tempo-spatial imaging and ranging.
Otazo, Ricardo; Tsai, Shang-Yueh; Lin, Fa-Hsuan; Posse, Stefan
2007-12-01
MR spectroscopic imaging (MRSI) with whole brain coverage in clinically feasible acquisition times still remains a major challenge. A combination of MRSI with parallel imaging has shown promise to reduce the long encoding times and 2D acceleration with a large array coil is expected to provide high acceleration capability. In this work a very high-speed method for 3D-MRSI based on the combination of proton echo planar spectroscopic imaging (PEPSI) with regularized 2D-SENSE reconstruction is developed. Regularization was performed by constraining the singular value decomposition of the encoding matrix to reduce the effect of low-value and overlapped coil sensitivities. The effects of spectral heterogeneity and discontinuities in coil sensitivity across the spectroscopic voxels were minimized by unaliasing the point spread function. As a result the contamination from extracranial lipids was reduced 1.6-fold on average compared to standard SENSE. We show that the acquisition of short-TE (15 ms) 3D-PEPSI at 3 T with a 32 x 32 x 8 spatial matrix using a 32-channel array coil can be accelerated 8-fold (R = 4 x 2) along y-z to achieve a minimum acquisition time of 1 min. Maps of the concentrations of N-acetyl-aspartate, creatine, choline, and glutamate were obtained with moderate reduction in spatial-spectral quality. The short acquisition time makes the method suitable for volumetric metabolite mapping in clinical studies.
NASA Astrophysics Data System (ADS)
Brook, Anna; Cristofani, Edison; Becquaert, Mathias; Lauwens, Ben; Jonuscheit, Joachim; Vandewal, Marijke
2013-04-01
The quality control of composite multilayered materials and structures using nondestructive tests is of high interest for numerous applications in the aerospace and aeronautics industry. One of the established nondestructive methods uses microwaves to reveal defects inside a three-dimensional (3-D) object. Recently, there has been a tendency to extrapolate this method to higher frequencies (going to the subterahertz spectrum) which could lead to higher resolutions in the obtained 3-D images. Working at higher frequencies reveals challenges to deal with the increased data rate and to efficiently and effectively process and evaluate the obtained 3-D imagery for defect detection and recognition. To deal with these two challenges, we combine compressive sensing (for data rate reduction) with a dedicated image processing methodology for a fast, accurate, and robust quality evaluation of the object under test. We describe in detail the used methodology and evaluate the obtained results using subterahertz data acquired of two calibration samples with a frequency modulated continuous wave system. The applicability of compressive sensing within this context is discussed as well as the quality of the image processing methodology dealing with the reconstructed images.
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.
High capacity image steganography method based on framelet and compressive sensing
NASA Astrophysics Data System (ADS)
Xiao, Moyan; He, Zhibiao
2015-12-01
To improve the capacity and imperceptibility of image steganography, a novel high capacity and imperceptibility image steganography method based on a combination of framelet and compressive sensing (CS) is put forward. Firstly, SVD (Singular Value Decomposition) transform to measurement values obtained by compressive sensing technique to the secret data. Then the singular values in turn embed into the low frequency coarse subbands of framelet transform to the blocks of the cover image which is divided into non-overlapping blocks. Finally, use inverse framelet transforms and combine to obtain the stego image. The experimental results show that the proposed steganography method has a good performance in hiding capacity, security and imperceptibility.
An Adaptive Data Collection Algorithm Based on a Bayesian Compressed Sensing Framework
Liu, Zhi; Zhang, Mengmeng; Cui, Jian
2014-01-01
For Wireless Sensor Networks, energy efficiency is always a key consideration in system design. Compressed sensing is a new theory which has promising prospects in WSNs. However, how to construct a sparse projection matrix is a problem. In this paper, based on a Bayesian compressed sensing framework, a new adaptive algorithm which can integrate routing and data collection is proposed. By introducing new target node selection metrics, embedding the routing structure and maximizing the differential entropy for each collection round, an adaptive projection vector is constructed. Simulations show that compared to reference algorithms, the proposed algorithm can decrease computation complexity and improve energy efficiency. PMID:24818659
NASA Astrophysics Data System (ADS)
Wu, Huijuan; Liu, Jun; Xu, Jiwei; Zhang, Linqiang; Li, Hanyu; Zhang, Weili; Rao, Yunjiang
2015-07-01
With the growing demand for monitoring length and channel number of the fully distributed optical fiber sensors (DOFSs), the amount of sensing data is increasing rapidly, and there will be a heavy pressure for the massive data storage and transmission. In this paper, two lossless compression algorithms of Lempel-Ziv-Welch (LZW) and Huffman are comparatively studied to effectively compress the huge amount of data of typical DOFSs, e.g. Φ -OTDR, POTDR, and BOTDA systems. The comparison results show that the LZW based on dictionary has a better performance in the consuming time and compression ratio for the DOFS data.
NASA Astrophysics Data System (ADS)
Cashmore, Matt. T.; Koutsourakis, George; Gottschalg, Ralph; Hall, Simon. R. G.
2016-04-01
Compressive sensing has been widely used in image compression and signal recovery techniques in recent years; however, it has received limited attention in the field of optical measurement. This paper describes the use of compressive sensing for measurements of photovoltaic (PV) solar cells, using fully random sensing matrices, rather than mapping an orthogonal basis set directly. Existing compressive sensing systems optically image the surface of the object under test, this contrasts with the method described, where illumination patterns defined by precalculated sensing matrices, probe PV devices. We discuss the use of spatially modulated light fields to probe a PV sample to produce a photocurrent map of the optical response. This allows for faster measurements than would be possible using traditional translational laser beam induced current techniques. Results produced to a 90% correlation to raster scanned measurements, which can be achieved with under 25% of the conventionally required number of data points. In addition, both crack and spot type defects are detected at resolutions comparable to electroluminescence techniques, with 50% of the number of measurements required for a conventional scan.
Application of Compressed Sensing to 2-D Ultrasonic Propagation Imaging System data
Mascarenas, David D.; Farrar, Charles R.; Chong, See Yenn; Lee, J.R.; Park, Gyu Hae; Flynn, Eric B.
2012-06-29
The Ultrasonic Propagation Imaging (UPI) System is a unique, non-contact, laser-based ultrasonic excitation and measurement system developed for structural health monitoring applications. The UPI system imparts laser-induced ultrasonic excitations at user-defined locations on a structure of interest. The response of these excitations is then measured by piezoelectric transducers. By using appropriate data reconstruction techniques, a time-evolving image of the response can be generated. A representative measurement of a plate might contain 800x800 spatial data measurement locations and each measurement location might be sampled at 500 instances in time. The result is a total of 640,000 measurement locations and 320,000,000 unique measurements. This is clearly a very large set of data to collect, store in memory and process. The value of these ultrasonic response images for structural health monitoring applications makes tackling these challenges worthwhile. Recently compressed sensing has presented itself as a candidate solution for directly collecting relevant information from sparse, high-dimensional measurements. The main idea behind compressed sensing is that by directly collecting a relatively small number of coefficients it is possible to reconstruct the original measurement. The coefficients are obtained from linear combinations of (what would have been the original direct) measurements. Often compressed sensing research is simulated by generating compressed coefficients from conventionally collected measurements. The simulation approach is necessary because the direct collection of compressed coefficients often requires compressed sensing analog front-ends that are currently not commercially available. The ability of the UPI system to make measurements at user-defined locations presents a unique capability on which compressed measurement techniques may be directly applied. The application of compressed sensing techniques on this data holds the potential to
Split Bregman's optimization method for image construction in compressive sensing
NASA Astrophysics Data System (ADS)
Skinner, D.; Foo, S.; Meyer-Bäse, A.
2014-05-01
The theory of compressive sampling (CS) was reintroduced by Candes, Romberg and Tao, and D. Donoho in 2006. Using a priori knowledge that a signal is sparse, it has been mathematically proven that CS can defY Nyquist sampling theorem. Theoretically, reconstruction of a CS image relies on the minimization and optimization techniques to solve this complex almost NP-complete problem. There are many paths to consider when compressing and reconstructing an image but these methods have remained untested and unclear on natural images, such as underwater sonar images. The goal of this research is to perfectly reconstruct the original sonar image from a sparse signal while maintaining pertinent information, such as mine-like object, in Side-scan sonar (SSS) images. Goldstein and Osher have shown how to use an iterative method to reconstruct the original image through a method called Split Bregman's iteration. This method "decouples" the energies using portions of the energy from both the !1 and !2 norm. Once the energies are split, Bregman iteration is used to solve the unconstrained optimization problem by recursively solving the problems simultaneously. The faster these two steps or energies can be solved then the faster the overall method becomes. While the majority of CS research is still focused on the medical field, this paper will demonstrate the effectiveness of the Split Bregman's methods on sonar images.
A deterministic compressive sensing model for bat biosonar.
Hague, David A; Buck, John R; Bilik, Igal
2012-12-01
The big brown bat (Eptesicus fuscus) uses frequency modulated (FM) echolocation calls to accurately estimate range and resolve closely spaced objects in clutter and noise. They resolve glints spaced down to 2 μs in time delay which surpasses what traditional signal processing techniques can achieve using the same echolocation call. The Matched Filter (MF) attains 10-12 μs resolution while the Inverse Filter (IF) achieves higher resolution at the cost of significantly degraded detection performance. Recent work by Fontaine and Peremans [J. Acoustic. Soc. Am. 125, 3052-3059 (2009)] demonstrated that a sparse representation of bat echolocation calls coupled with a decimating sensing method facilitates distinguishing closely spaced objects over realistic SNRs. Their work raises the intriguing question of whether sensing approaches structured more like a mammalian auditory system contains the necessary information for the hyper-resolution observed in behavioral tests. This research estimates sparse echo signatures using a gammatone filterbank decimation sensing method which loosely models the processing of the bat's auditory system. The decimated filterbank outputs are processed with [script-l](1) minimization. Simulations demonstrate that this model maintains higher resolution than the MF and significantly better detection performance than the IF for SNRs of 5-45 dB while undersampling the return signal by a factor of six.
A joint image encryption and watermarking algorithm based on compressive sensing and chaotic map
NASA Astrophysics Data System (ADS)
Xiao, Di; Cai, Hong-Kun; Zheng, Hong-Ying
2015-06-01
In this paper, a compressive sensing (CS) and chaotic map-based joint image encryption and watermarking algorithm is proposed. The transform domain coefficients of the original image are scrambled by Arnold map firstly. Then the watermark is adhered to the scrambled data. By compressive sensing, a set of watermarked measurements is obtained as the watermarked cipher image. In this algorithm, watermark embedding and data compression can be performed without knowing the original image; similarly, watermark extraction will not interfere with decryption. Due to the characteristics of CS, this algorithm features compressible cipher image size, flexible watermark capacity, and lossless watermark extraction from the compressed cipher image as well as robustness against packet loss. Simulation results and analyses show that the algorithm achieves good performance in the sense of security, watermark capacity, extraction accuracy, reconstruction, robustness, etc. Project supported by the Open Research Fund of Chongqing Key Laboratory of Emergency Communications, China (Grant No. CQKLEC, 20140504), the National Natural Science Foundation of China (Grant Nos. 61173178, 61302161, and 61472464), and the Fundamental Research Funds for the Central Universities, China (Grant Nos. 106112013CDJZR180005 and 106112014CDJZR185501).
Curvelet-based compressive sensing for InSAR raw data
NASA Astrophysics Data System (ADS)
Costa, Marcello G.; da Silva Pinho, Marcelo; Fernandes, David
2015-10-01
The aim of this work is to evaluate the compression performance of SAR raw data for interferometry applications collected by airborne from BRADAR (Brazilian SAR System operating in X and P bands) using the new approach based on compressive sensing (CS) to achieve an effective recovery with a good phase preserving. For this framework is desirable a real-time capability, where the collected data can be compressed to reduce onboard storage and bandwidth required for transmission. In the CS theory, a sparse unknown signals can be recovered from a small number of random or pseudo-random measurements by sparsity-promoting nonlinear recovery algorithms. Therefore, the original signal can be significantly reduced. To achieve the sparse representation of SAR signal, was done a curvelet transform. The curvelets constitute a directional frame, which allows an optimal sparse representation of objects with discontinuities along smooth curves as observed in raw data and provides an advanced denoising optimization. For the tests were made available a scene of 8192 x 2048 samples in range and azimuth in X-band with 2 m of resolution. The sparse representation was compressed using low dimension measurements matrices in each curvelet subband. Thus, an iterative CS reconstruction method based on IST (iterative soft/shrinkage threshold) was adjusted to recover the curvelets coefficients and then the original signal. To evaluate the compression performance were computed the compression ratio (CR), signal to noise ratio (SNR), and because the interferometry applications require more reconstruction accuracy the phase parameters like the standard deviation of the phase (PSD) and the mean phase error (MPE) were also computed. Moreover, in the image domain, a single-look complex image was generated to evaluate the compression effects. All results were computed in terms of sparsity analysis to provides an efficient compression and quality recovering appropriated for inSAR applications
Block Hadamard measurement matrix with arbitrary dimension in compressed sensing
NASA Astrophysics Data System (ADS)
Liu, Shaoqiang; Yan, Xiaoyan; Fan, Xiaoping; Li, Fei; Xu, Wen
2017-01-01
As Hadamard measurement matrix cannot be used for compressing signals with dimension of a non-integral power-of-2, this paper proposes a construction method of block Hadamard measurement matrix with arbitrary dimension. According to the dimension N of signals to be measured, firstly, construct a set of Hadamard sub matrixes with different dimensions and make the sum of these dimensions equals to N. Then, arrange the Hadamard sub matrixes in a certain order to form a block diagonal matrix. Finally, take the former M rows of the block diagonal matrix as the measurement matrix. The proposed measurement matrix which retains the orthogonality of Hadamard matrix and sparsity of block diagonal matrix has highly sparse structure, simple hardware implements and general applicability. Simulation results show that the performance of our measurement matrix is better than Gaussian matrix, Logistic chaotic matrix, and Toeplitz matrix.
Remotely sensed image compression based on wavelet transform
NASA Technical Reports Server (NTRS)
Kim, Seong W.; Lee, Heung K.; Kim, Kyung S.; Choi, Soon D.
1995-01-01
In this paper, we present an image compression algorithm that is capable of significantly reducing the vast amount of information contained in multispectral images. The developed algorithm exploits the spectral and spatial correlations found in multispectral images. The scheme encodes the difference between images after contrast/brightness equalization to remove the spectral redundancy, and utilizes a two-dimensional wavelet transform to remove the spatial redundancy. the transformed images are then encoded by Hilbert-curve scanning and run-length-encoding, followed by Huffman coding. We also present the performance of the proposed algorithm with the LANDSAT MultiSpectral Scanner data. The loss of information is evaluated by PSNR (peak signal to noise ratio) and classification capability.
2015-06-01
Wide- Band Antennas from Sparse Measurements by Patrick Debroux and Berenice Verdin Approved for public release...Army Research Laboratory The Use of Compressive Sensing to Reconstruct Radiation Characteristics of Wide- Band Antennas from Sparse Measurements...Compressive Sensing to Reconstruct Radiation Characteristics of Wide-Band Antennas from Sparse Measurements 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c
Three-Dimensional Inverse Transport Solver Based on Compressive Sensing Technique
NASA Astrophysics Data System (ADS)
Cheng, Yuxiong; Wu, Hongchun; Cao, Liangzhi; Zheng, Youqi
2013-09-01
According to the direct exposure measurements from flash radiographic image, a compressive sensing-based method for three-dimensional inverse transport problem is presented. The linear absorption coefficients and interface locations of objects are reconstructed directly at the same time. It is always very expensive to obtain enough measurements. With limited measurements, compressive sensing sparse reconstruction technique orthogonal matching pursuit is applied to obtain the sparse coefficients by solving an optimization problem. A three-dimensional inverse transport solver is developed based on a compressive sensing-based technique. There are three features in this solver: (1) AutoCAD is employed as a geometry preprocessor due to its powerful capacity in graphic. (2) The forward projection matrix rather than Gauss matrix is constructed by the visualization tool generator. (3) Fourier transform and Daubechies wavelet transform are adopted to convert an underdetermined system to a well-posed system in the algorithm. Simulations are performed and numerical results in pseudo-sine absorption problem, two-cube problem and two-cylinder problem when using compressive sensing-based solver agree well with the reference value.
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
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.
Energy and Quality Evaluation for Compressive Sensing of Fetal Electrocardiogram Signals
Da Poian, Giulia; Brandalise, Denis; Bernardini, Riccardo; Rinaldo, Roberto
2016-01-01
This manuscript addresses the problem of non-invasive fetal Electrocardiogram (ECG) signal acquisition with low power/low complexity sensors. A sensor architecture using the Compressive Sensing (CS) paradigm is compared to a standard compression scheme using wavelets in terms of energy consumption vs. reconstruction quality, and, more importantly, vs. performance of fetal heart beat detection in the reconstructed signals. We show in this paper that a CS scheme based on reconstruction with an over-complete dictionary has similar reconstruction quality to one based on wavelet compression. We also consider, as a more important figure of merit, the accuracy of fetal beat detection after reconstruction as a function of the sensor power consumption. Experimental results with an actual implementation in a commercial device show that CS allows significant reduction of energy consumption in the sensor node, and that the detection performance is comparable to that obtained from original signals for compression ratios up to about 75%. PMID:28025510
NASA Astrophysics Data System (ADS)
Akoguz, A.; Bozkurt, S.; Gozutok, A. A.; Alp, G.; Turan, E. G.; Bogaz, M.; Kent, S.
2016-06-01
High resolution level in satellite imagery came with its fundamental problem as big amount of telemetry data which is to be stored after the downlink operation. Moreover, later the post-processing and image enhancement steps after the image is acquired, the file sizes increase even more and then it gets a lot harder to store and consume much more time to transmit the data from one source to another; hence, it should be taken into account that to save even more space with file compression of the raw and various levels of processed data is a necessity for archiving stations to save more space. Lossless data compression algorithms that will be examined in this study aim to provide compression without any loss of data holding spectral information. Within this objective, well-known open source programs supporting related compression algorithms have been implemented on processed GeoTIFF images of Airbus Defence & Spaces SPOT 6 & 7 satellites having 1.5 m. of GSD, which were acquired and stored by ITU Center for Satellite Communications and Remote Sensing (ITU CSCRS), with the algorithms Lempel-Ziv-Welch (LZW), Lempel-Ziv-Markov chain Algorithm (LZMA & LZMA2), Lempel-Ziv-Oberhumer (LZO), Deflate & Deflate 64, Prediction by Partial Matching (PPMd or PPM2), Burrows-Wheeler Transform (BWT) in order to observe compression performances of these algorithms over sample datasets in terms of how much of the image data can be compressed by ensuring lossless compression.
Compressive sensing for efficient health monitoring and effective damage detection of structures
NASA Astrophysics Data System (ADS)
Jayawardhana, Madhuka; Zhu, Xinqun; Liyanapathirana, Ranjith; Gunawardana, Upul
2017-02-01
Real world Structural Health Monitoring (SHM) systems consist of sensors in the scale of hundreds, each sensor generating extremely large amounts of data, often arousing the issue of the cost associated with data transfer and storage. Sensor energy is a major component included in this cost factor, especially in Wireless Sensor Networks (WSN). Data compression is one of the techniques that is being explored to mitigate the effects of these issues. In contrast to traditional data compression techniques, Compressive Sensing (CS) - a very recent development - introduces the means of accurately reproducing a signal by acquiring much less number of samples than that defined by Nyquist's theorem. CS achieves this task by exploiting the sparsity of the signal. By the reduced amount of data samples, CS may help reduce the energy consumption and storage costs associated with SHM systems. This paper investigates CS based data acquisition in SHM, in particular, the implications of CS on damage detection and localization. CS is implemented in a simulation environment to compress structural response data from a Reinforced Concrete (RC) structure. Promising results were obtained from the compressed data reconstruction process as well as the subsequent damage identification process using the reconstructed data. A reconstruction accuracy of 99% could be achieved at a Compression Ratio (CR) of 2.48 using the experimental data. Further analysis using the reconstructed signals provided accurate damage detection and localization results using two damage detection algorithms, showing that CS has not compromised the crucial information on structural damages during the compression process.
A new application of compressive sensing in MRI
NASA Astrophysics Data System (ADS)
Baselice, Fabio; Ferraioli, Giampaolo; Lenti, Flavia; Pascazio, Vito
2014-03-01
Image formation in Magnetic Resonance Imaging (MRI) is the procedure which allows the generation of the image starting from data acquired in the so called k-space. At the present, many image formation techniques have been presented, working with different k-space filling strategies. Recently, Compressive Sampling (CS) has been successfully used for image formation from non fully sampled k-space acquisitions, due to its interesting property of reconstructing signal from highly undetermined linear systems. The main advantage consists in greatly reducing the acquisition time. Within this manuscript, a novel application of CS to MRI field is presented, named Intra Voxel Analysis (IVA). The idea is to achieve the so-called super resolution, i.e. the possibility of distinguish anatomical structures smaller than the spatial resolution of the image. For this aim, multiple Spin Echo images acquired with different Echo Times are required. The output of the algorithm is the estimation of the number of contributions present in the same pixel, i.e. the number of tissues inside the same voxel, and their spin-spin relaxation times. This allows us not only to identify the number of involved tissues, but also to discriminate them. At the present, simulated case studies have been considered, obtaining interesting and promising results. In particular, a study on the required number of images, on the estimation noise and on the regularization parameter of different CS algorithms has been conducted. As future work, the method will be applied to real clinical datasets, in order to validate the estimations.
YAMPA: Yet Another Matching Pursuit Algorithm for compressive sensing
NASA Astrophysics Data System (ADS)
Lodhi, Muhammad A.; Voronin, Sergey; Bajwa, Waheed U.
2016-05-01
State-of-the-art sparse recovery methods often rely on the restricted isometry property for their theoretical guarantees. However, they cannot explicitly incorporate metrics such as restricted isometry constants within their recovery procedures due to the computational intractability of calculating such metrics. This paper formulates an iterative algorithm, termed yet another matching pursuit algorithm (YAMPA), for recovery of sparse signals from compressive measurements. YAMPA differs from other pursuit algorithms in that: (i) it adapts to the measurement matrix using a threshold that is explicitly dependent on two computable coherence metrics of the matrix, and (ii) it does not require knowledge of the signal sparsity. Performance comparisons of YAMPA against other matching pursuit and approximate message passing algorithms are made for several types of measurement matrices. These results show that while state-of-the-art approximate message passing algorithms outperform other algorithms (including YAMPA) in the case of well-conditioned random matrices, they completely break down in the case of ill-conditioned measurement matrices. On the other hand, YAMPA and comparable pursuit algorithms not only result in reasonable performance for well-conditioned matrices, but their performance also degrades gracefully for ill-conditioned matrices. The paper also shows that YAMPA uniformly outperforms other pursuit algorithms for the case of thresholding parameters chosen in a clairvoyant fashion. Further, when combined with a simple and fast technique for selecting thresholding parameters in the case of ill-conditioned matrices, YAMPA outperforms other pursuit algorithms in the regime of low undersampling, although some of these algorithms can outperform YAMPA in the regime of high undersampling in this setting.
WSNs Data Acquisition by Combining Hierarchical Routing Method and Compressive Sensing
Zou, Zhiqiang; Hu, Cunchen; Zhang, Fei; Zhao, Hao; Shen, Shu
2014-01-01
We address the problem of data acquisition in large distributed wireless sensor networks (WSNs). We propose a method for data acquisition using the hierarchical routing method and compressive sensing for WSNs. Only a few samples are needed to recover the original signal with high probability since sparse representation technology is exploited to capture the similarities and differences of the original signal. To collect samples effectively in WSNs, a framework for the use of the hierarchical routing method and compressive sensing is proposed, using a randomized rotation of cluster-heads to evenly distribute the energy load among the sensors in the network. Furthermore, L1-minimization and Bayesian compressed sensing are used to approximate the recovery of the original signal from the smaller number of samples with a lower signal reconstruction error. We also give an extensive validation regarding coherence, compression rate, and lifetime, based on an analysis of the theory and experiments in the environment with real world signals. The results show that our solution is effective in a large distributed network, especially for energy constrained WSNs. PMID:25207873
Utilizing the cochlea as a bio-inspired compressive sensing technique
NASA Astrophysics Data System (ADS)
Peckens, C. A.; Lynch, J. P.
2013-10-01
Structural monitoring for civil infrastructure is a rapidly developing field that has made significant advancements over the last decade. However, a number of performance bottlenecks remain including challenges with cost-effectively scaling monitoring systems up to large nodal counts. Due to the many parallels between biological sensory systems and engineered sensing systems, the biological nervous system can offer potential solutions to the current deficiencies of structural monitoring systems. The nervous system is capable of real-time processing and data transmission of external stimuli through an extremely condensed format with very basic processing units. This study explores the mammalian auditory system for inspiration because it achieves efficient data acquisition processes that outperform existing engineered sensing systems. Specifically, the auditory system realizes this through three steps: (1) real-time decomposition of a convoluted time-based signal into frequency components, (2) information compression for each component, and (3) efficient high-speed data transmission to the auditory cortex. In this paper, these three main mechanisms are explored and a bio-inspired structural monitoring system is proposed. The functionality of the proposed system is compared to traditional data compression techniques (wavelet transforms and compressed sensing) on various vibratory signals. While the wavelet transform is able to outperform the proposed sensor by minimizing signal reconstruction errors, the proposed bio-inspired sensor achieves similar compression rates but, unlike the others, does so using real-time processing.
Tang, Gang; Hou, Wei; Wang, Huaqing; Luo, Ganggang; Ma, Jianwei
2015-10-09
The Shannon sampling principle requires substantial amounts of data to ensure the accuracy of on-line monitoring of roller bearing fault signals. Challenges are often encountered as a result of the cumbersome data monitoring, thus a novel method focused on compressed vibration signals for detecting roller bearing faults is developed in this study. Considering that harmonics often represent the fault characteristic frequencies in vibration signals, a compressive sensing frame of characteristic harmonics is proposed to detect bearing faults. A compressed vibration signal is first acquired from a sensing matrix with information preserved through a well-designed sampling strategy. A reconstruction process of the under-sampled vibration signal is then pursued as attempts are conducted to detect the characteristic harmonics from sparse measurements through a compressive matching pursuit strategy. In the proposed method bearing fault features depend on the existence of characteristic harmonics, as typically detected directly from compressed data far before reconstruction completion. The process of sampling and detection may then be performed simultaneously without complete recovery of the under-sampled signals. The effectiveness of the proposed method is validated by simulations and experiments.
A Comparison of Compressed Sensing and Sparse Recovery Algorithms Applied to Simulation Data
Fan, Ya Ju; Kamath, Chandrika
2016-09-01
The move toward exascale computing for scientific simulations is placing new demands on compression techniques. It is expected that the I/O system will not be able to support the volume of data that is expected to be written out. To enable quantitative analysis and scientific discovery, we are interested in techniques that compress high-dimensional simulation data and can provide perfect or near-perfect reconstruction. In this paper, we explore the use of compressed sensing (CS) techniques to reduce the size of the data before they are written out. Using large-scale simulation data, we investigate how the sufficient sparsity condition and themore » contrast in the data affect the quality of reconstruction and the degree of compression. Also, we provide suggestions for the practical implementation of CS techniques and compare them with other sparse recovery methods. Finally, our results show that despite longer times for reconstruction, compressed sensing techniques can provide near perfect reconstruction over a range of data with varying sparsity.« less
Energy-efficient multi-mode compressed sensing system for implantable neural recordings.
Suo, Yuanming; Zhang, Jie; Xiong, Tao; Chin, Peter S; Etienne-Cummings, Ralph; Tran, Trac D
2014-10-01
Widely utilized in the field of Neuroscience, implantable neural recording devices could capture neuron activities with an acquisition rate on the order of megabytes per second. In order to efficiently transmit neural signals through wireless channels, these devices require compression methods that reduce power consumption. Although recent Compressed Sensing (CS) approaches have successfully demonstrated their power, their full potential is yet to be explored. Built upon our previous on-chip CS implementation, we propose an energy efficient multi-mode CS framework that focuses on improving the off-chip components, including (i) a two-stage sensing strategy, (ii) a sparsifying dictionary directly using data, (iii) enhanced compression performance from Full Signal CS mode and Spike Restoration mode to Spike CS + Restoration mode and; (iv) extension of our framework to the Tetrode CS recovery using joint sparsity. This new framework achieves energy efficiency, implementation simplicity and system flexibility simultaneously. Extensive experiments are performed on simulation and real datasets. For our Spike CS + Restoration mode, we achieve a compression ratio of 6% with a reconstruction SNDR > 10 dB and a classification accuracy > 95% for synthetic datasets. For real datasets, we get a 10% compression ratio with ∼ 10 dB for Spike CS + Restoration mode.
A Comparison of Compressed Sensing and Sparse Recovery Algorithms Applied to Simulation Data
Fan, Ya Ju; Kamath, Chandrika
2016-09-01
The move toward exascale computing for scientific simulations is placing new demands on compression techniques. It is expected that the I/O system will not be able to support the volume of data that is expected to be written out. To enable quantitative analysis and scientific discovery, we are interested in techniques that compress high-dimensional simulation data and can provide perfect or near-perfect reconstruction. In this paper, we explore the use of compressed sensing (CS) techniques to reduce the size of the data before they are written out. Using large-scale simulation data, we investigate how the sufficient sparsity condition and the contrast in the data affect the quality of reconstruction and the degree of compression. Also, we provide suggestions for the practical implementation of CS techniques and compare them with other sparse recovery methods. Finally, our results show that despite longer times for reconstruction, compressed sensing techniques can provide near perfect reconstruction over a range of data with varying sparsity.
NASA Technical Reports Server (NTRS)
Korde-Patel, Asmita (Inventor); Barry, Richard K.; Mohsenin, Tinoosh
2016-01-01
Compressive Sensing is a technique for simultaneous acquisition and compression of data that is sparse or can be made sparse in some domain. It is currently under intense development and has been profitably employed for industrial and medical applications. We here describe the use of this technique for the processing of astronomical data. We outline the procedure as applied to exoplanet gravitational microlensing and analyze measurement results and uncertainty values. We describe implications for on-spacecraft data processing for space observatories. Our findings suggest that application of these techniques may yield significant, enabling benefits especially for power and volume-limited space applications such as miniaturized or micro-constellation satellites.
Detection of Modified Matrix Encoding Using Machine Learning and Compressed Sensing
Allen, Josef D
2011-01-01
In recent years, driven by the development of steganalysis methods, steganographic algorithms have been evolved rapidly with the ultimate goal of an unbreakable embedding procedure, resulting in recent steganographic algorithms with minimum distortions, exemplified by the recent family of Modified Matrix Encoding (MME) algorithms, which has shown to be most difficult to be detected. In this paper we propose a compressed sensing based on approach for intrinsic steganalysis to detect MME stego messages. Compressed sensing is a recently proposed mathematical framework to represent an image (in general, a signal) using a sparse representation relative to an overcomplete dictionary by minimizing the l1-norm of resulting coefficients. Here we first learn a dictionary from a training set so that the performance will be optimized using the KSVD algorithm; since JPEG images are processed by 8x8 blocks, the training examples are 8x8 patches, rather than the entire images and this increases the generalization of compressed sensing. For each 8x8 block, we compute its sparse representation using OMP (orthogonal matching pursuit) algorithm. Using computed sparse representations, we train a support vector machine (SVM) to classify 8x8 blocks into stego and non-stego classes. Then given an input image, we first divide it into 8x8 blocks. For each 8x8 block, we compute its sparse representation and classify it using the trained SVM. After all the 8x8 blocks are classified, the entire image is classified based on the majority rule of 8x8 block classification results. This allows us to achieve a robust decision even when 8x8 blocks can be classified only with relatively low accuracy. We have tested the proposed algorithm on two datasets (Corel-1000 dataset and a remote sensing image dataset) and have achieved 100% accuracy on classifying images, even though the accuracy of classifying 8x8 blocks is only 80.89%. Key Words Compressed Sensing, Sparcity, Data Dictionary, Steganography
Chen, Yongyao; Liu, Haijun; Reilly, Michael; Bae, Hyungdae; Yu, Miao
2014-10-15
Acoustic sensors play an important role in many areas, such as homeland security, navigation, communication, health care and industry. However, the fundamental pressure detection limit hinders the performance of current acoustic sensing technologies. Here, through analytical, numerical and experimental studies, we show that anisotropic acoustic metamaterials can be designed to have strong wave compression effect that renders direct amplification of pressure fields in metamaterials. This enables a sensing mechanism that can help overcome the detection limit of conventional acoustic sensing systems. We further demonstrate a metamaterial-enhanced acoustic sensing system that achieves more than 20 dB signal-to-noise enhancement (over an order of magnitude enhancement in detection limit). With this system, weak acoustic pulse signals overwhelmed by the noise are successfully recovered. This work opens up new vistas for the development of metamaterial-based acoustic sensors with improved performance and functionalities that are highly desirable for many applications.
NASA Astrophysics Data System (ADS)
Sidorova, Elena
2013-04-01
the recognition of linear and ring features. The features of geological interest detected during the interpretation process were digitized using raster based GIS software. As results of collaboration between GIS and RS data analysis the new prospect areas were extracted from the study areas. Were revealed the geological structures in 3-D model, associated with mineralization, lineaments and ring structures. The complex analysis of model allowed proposing new potential ore areas for statement of prospecting work.
Bhave, Sampada; Lingala, Sajan Goud; Jacob, Mathews
2014-01-01
Recent work on blind compressed sensing (BCS) has shown that exploiting sparsity in dictionaries that are learnt directly from the data at hand can outperform compressed sensing (CS) that uses fixed dictionaries. A challenge with BCS however is the large computational complexity during its optimization, which limits its practical use in several MRI applications. In this paper, we propose a novel optimization algorithm that utilize variable splitting strategies to significantly improve the convergence speed of the BCS optimization. The splitting allows us to efficiently decouple the sparse coefficient, and dictionary update steps from the data fidelity term, resulting in subproblems that take closed form analytical solutions, which otherwise require slower iterative conjugate gradient algorithms. Through experiments on multi coil parametric MRI data, we demonstrate the superior performance of BCS over conventional CS schemes, while achieving convergence speed up factors of over 10 fold over the previously proposed implementation of the BCS algorithm.
Effects of ADC Nonlinearity on the Spurious Dynamic Range Performance of Compressed Sensing
Tian, Pengwu; Yu, Hongyi
2014-01-01
Analog-to-information converter (AIC) plays an important role in the compressed sensing system; it has the potential to significantly extend the capabilities of conventional analog-to-digital converter. This paper evaluates the impact of AIC nonlinearity on the dynamic performance in practical compressed sensing system, which included the nonlinearity introduced by quantization as well as the circuit non-ideality. It presents intuitive yet quantitative insights into the harmonics of quantization output of AIC, and the effect of other AIC nonlinearity on the spurious dynamic range (SFDR) performance is also analyzed. The analysis and simulation results demonstrated that, compared with conventional ADC-based system, the measurement process decorrelates the input signal and the quantization error and alleviate the effect of other decorrelates of AIC, which results in a dramatic increase in spurious free dynamic range (SFDR). PMID:24895645
Zhou, Fei; Nielson, Weston; Xia, Yi; Ozolins, Vidvuds
2014-10-27
First-principles prediction of lattice thermal conductivity K_{L} of strongly anharmonic crystals is a long-standing challenge in solid state physics. Using recent advances in information science, we propose a systematic and rigorous approach to this problem, compressive sensing lattice dynamics (CSLD). Compressive sensing is used to select the physically important terms in the lattice dynamics model and determine their values in one shot. Non-intuitively, high accuracy is achieved when the model is trained on first-principles forces in quasi-random atomic configurations. The method is demonstrated for Si, NaCl, and Cu_{12}Sb_{4}S_{13}, an earth-abundant thermoelectric with strong phononphonon interactions that limit the room-temperature K_{L} to values near the amorphous limit.
Zhou, Fei; Nielson, Weston; Xia, Yi; ...
2014-10-27
First-principles prediction of lattice thermal conductivity KL of strongly anharmonic crystals is a long-standing challenge in solid state physics. Using recent advances in information science, we propose a systematic and rigorous approach to this problem, compressive sensing lattice dynamics (CSLD). Compressive sensing is used to select the physically important terms in the lattice dynamics model and determine their values in one shot. Non-intuitively, high accuracy is achieved when the model is trained on first-principles forces in quasi-random atomic configurations. The method is demonstrated for Si, NaCl, and Cu12Sb4S13, an earth-abundant thermoelectric with strong phononphonon interactions that limit the room-temperature KLmore » to values near the amorphous limit.« less
Zhou, Fei; Nielson, Weston; Xia, Yi; Ozoliņš, Vidvuds
2014-10-01
First-principles prediction of lattice thermal conductivity κ_{L} of strongly anharmonic crystals is a long-standing challenge in solid-state physics. Making use of recent advances in information science, we propose a systematic and rigorous approach to this problem, compressive sensing lattice dynamics. Compressive sensing is used to select the physically important terms in the lattice dynamics model and determine their values in one shot. Nonintuitively, high accuracy is achieved when the model is trained on first-principles forces in quasirandom atomic configurations. The method is demonstrated for Si, NaCl, and Cu_{12}Sb_{4}S_{13}, an earth-abundant thermoelectric with strong phonon-phonon interactions that limit the room-temperature κ_{L} to values near the amorphous limit.
Guided compressive sensing single-pixel imaging technique based on hierarchical model
NASA Astrophysics Data System (ADS)
Peng, Yang; Liu, Yu; Ren, Weiya; Tan, Shuren; Zhang, Maojun
2016-04-01
Single-pixel imaging has emerged a decade ago as an imaging technique that exploits the theory of compressive sensing. In this research, the problem of optimizing the measurement matrix in compressive sensing framework was addressed. Thus far, random measurement matrices are widely used because they provide small coherence. However, recent reports claim that measurement matrix can be optimized, thereby improving its performance. Based on such proposition, this study proposed an alternative approach of optimizing the measurement matrix in a hierarchical model. In particular, this study constructed the hierarchical model based on an increasing resolution grade by exploiting the guided information and the adaptive step size method. An image with a demanded resolution was then obtained using the l1-norm method. Subsequently, the performance of the introduced method was verified and compared with those of existing approaches via several experiments. Results of the tests indicated that the reconstruction quality of optimizing the measurement matrix was improved when the proposed method was used.
Monitoring and diagnosis of Alzheimer's disease using noninvasive compressive sensing EEG
NASA Astrophysics Data System (ADS)
Morabito, F. C.; Labate, D.; Morabito, G.; Palamara, I.; Szu, H.
2013-05-01
The majority of elderly with Alzheimer's Disease (AD) receive care at home from caregivers. In contrast to standard tethered clinical settings, a wireless, real-time, body-area smartphone-based remote monitoring of electroencephalogram (EEG) can be extremely advantageous for home care of those patients. Such wearable tools pave the way to personalized medicine, for example giving the opportunity to control the progression of the disease and the effect of drugs. By applying Compressive Sensing (CS) techniques it is in principle possible to overcome the difficulty raised by smartphones spatial-temporal throughput rate bottleneck. Unfortunately, EEG and other physiological signals are often non-sparse. In this paper, it is instead shown that the EEG of AD patients becomes actually more compressible with the progression of the disease. EEG of Mild Cognitive Impaired (MCI) subjects is also showing clear tendency to enhanced compressibility. This feature favor the use of CS techniques and ultimately the use of telemonitoring with wearable sensors.
NASA Astrophysics Data System (ADS)
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.
2015-03-26
medical imaging , e.g., magnetic resonance imaging (MRI). Since the early 1980s, MRI has granted doctors the ability to distinguish between healthy tissue...chemical composition of a star. Conventional hyperspectral cameras are slow. Different methods of hyperspectral imaging either require time to process ...Recent Advances in Compressed Sensing: Discrete Uncertainty Principles and Fast Hyperspectral Imaging THESIS MARCH 2015 Megan E. Lewis, Second
NASA Astrophysics Data System (ADS)
Mejia, Yuri H.; Arguello, Henry
2016-05-01
Compressive sensing state-of-the-art proposes random Gaussian and Bernoulli as measurement matrices. Nev- ertheless, often the design of the measurement matrix is subject to physical constraints, and therefore it is frequently not possible that the matrix follows a Gaussian or Bernoulli distribution. Examples of these lim- itations are the structured and sparse matrices of the compressive X-Ray, and compressive spectral imaging systems. A standard algorithm for recovering sparse signals consists in minimizing an objective function that includes a quadratic error term combined with a sparsity-inducing regularization term. This problem can be solved using the iterative algorithms for solving linear inverse problems. This class of methods, which can be viewed as an extension of the classical gradient algorithm, is attractive due to its simplicity. However, current algorithms are slow for getting a high quality image reconstruction because they do not exploit the structured and sparsity characteristics of the compressive measurement matrices. This paper proposes the development of a gradient-based algorithm for compressive sensing reconstruction by including a filtering step that yields improved quality using less iterations. This algorithm modifies the iterative solution such that it forces to converge to a filtered version of the residual AT y, where y is the measurement vector and A is the compressive measurement matrix. We show that the algorithm including the filtering step converges faster than the unfiltered version. We design various filters that are motivated by the structure of AT y. Extensive simulation results using various sparse and structured matrices highlight the relative performance gain over the existing iterative process.
NASA Astrophysics Data System (ADS)
Moré, G.; Pesquer, L.; Blanes, I.; Serra-Sagristà, J.; Pons, X.
2012-12-01
World coverage Digital Elevation Models (DEM) have progressively increased their spatial resolution (e.g., ETOPO, SRTM, or Aster GDEM) and, consequently, their storage requirements. On the other hand, lossy data compression facilitates accessing, sharing and transmitting large spatial datasets in environments with limited storage. However, since lossy compression modifies the original information, rigorous studies are needed to understand its effects and consequences. The present work analyzes the influence of DEM quality -modified by lossy compression-, on the radiometric correction of remote sensing imagery, and the eventual propagation of the uncertainty in the resulting land cover classification. Radiometric correction is usually composed of two parts: atmospheric correction and topographical correction. For topographical correction, DEM provides the altimetry information that allows modeling the incidence radiation on terrain surface (cast shadows, self shadows, etc). To quantify the effects of the DEM lossy compression on the radiometric correction, we use radiometrically corrected images for classification purposes, and compare the accuracy of two standard coding techniques for a wide range of compression ratios. The DEM has been obtained by resampling the DEM v.2 of Catalonia (ICC), originally having 15 m resolution, to the Landsat TM resolution. The Aster DEM has been used to fill the gaps beyond the administrative limits of Catalonia. The DEM has been lossy compressed with two coding standards at compression ratios 5:1, 10:1, 20:1, 100:1 and 200:1. The employed coding standards have been JPEG2000 and CCSDS-IDC; the former is an international ISO/ITU-T standard for almost any type of images, while the latter is a recommendation of the CCSDS consortium for mono-component remote sensing images. Both techniques are wavelet-based followed by an entropy-coding stage. Also, for large compression ratios, both techniques need a post processing for correctly
NASA Astrophysics Data System (ADS)
Wachter, Paul; Höppner, Kathrin; Jacobeit, Jucundus; Diedrich, Erhard
2015-04-01
West Antarctica and the Antarctic Peninsula are in the focus of current studies on a changing environment and climate of the polar regions. A recently founded Junior Researchers Group at the German Aerospace Center (DLR) is studying changing processes in cryosphere and atmosphere above the Antarctic Peninsula. It is the aim of the group to make use of long-term remote sensing data sets of the land and ice surfaces and the atmosphere in order to characterize environmental changes in this highly sensitive region. One of the PhD projects focuses on the investigation of the 3D temperature distribution patterns above the Antarctic Peninsula. Temperature data sets ranging from MODIS land surface temperatures up to middle atmosphere data of AURA/MLS will be evaluated over the last approx. 12 years. This 3-dimensional view allows comprehensive investigations of the thermal structure and spatio-temporal characteristics of the southern polar atmosphere. Tropospheric data sets will be analyzed by multivariate statistical methods and will allow the identification of dominant atmospheric circulation patterns as well as their temporal variability. An overview of the data sets and first results will be presented.
Efficient Reconstruction of Heterogeneous Networks from Time Series via Compressed Sensing
Ma, Long; Han, Xiao; Shen, Zhesi; Wang, Wen-Xu; Di, Zengru
2015-01-01
Recent years have witnessed a rapid development of network reconstruction approaches, especially for a series of methods based on compressed sensing. Although compressed-sensing based methods require much less data than conventional approaches, the compressed sensing for reconstructing heterogeneous networks has not been fully exploited because of hubs. Hub neighbors require much more data to be inferred than small-degree nodes, inducing a cask effect for the reconstruction of heterogeneous networks. Here, a conflict-based method is proposed to overcome the cast effect to considerably reduce data amounts for achieving accurate reconstruction. Moreover, an element elimination method is presented to use the partially available structural information to reduce data requirements. The integration of both methods can further improve the reconstruction performance than separately using each technique. These methods are validated by exploring two evolutionary games taking place in scale-free networks, where individual information is accessible and an attempt to decode the network structure from measurable data is made. The results demonstrate that for all of the cases, much data are saved compared to that in the absence of these two methods. Due to the prevalence of heterogeneous networks in nature and society and the high cost of data acquisition in large-scale networks, these approaches have wide applications in many fields and are valuable for understanding and controlling the collective dynamics of a variety of heterogeneous networked systems. PMID:26588832
McClymont, Darryl; Teh, Irvin; Whittington, Hannah J.; Grau, Vicente
2015-01-01
Purpose Diffusion MRI requires acquisition of multiple diffusion‐weighted images, resulting in long scan times. Here, we investigate combining compressed sensing and a fast imaging sequence to dramatically reduce acquisition times in cardiac diffusion MRI. Methods Fully sampled and prospectively undersampled diffusion tensor imaging data were acquired in five rat hearts at acceleration factors of between two and six using a fast spin echo (FSE) sequence. Images were reconstructed using a compressed sensing framework, enforcing sparsity by means of decomposition by adaptive dictionaries. A tensor was fit to the reconstructed images and fiber tractography was performed. Results Acceleration factors of up to six were achieved, with a modest increase in root mean square error of mean apparent diffusion coefficient (ADC), fractional anisotropy (FA), and helix angle. At an acceleration factor of six, mean values of ADC and FA were within 2.5% and 5% of the ground truth, respectively. Marginal differences were observed in the fiber tracts. Conclusion We developed a new k‐space sampling strategy for acquiring prospectively undersampled diffusion‐weighted data, and validated a novel compressed sensing reconstruction algorithm based on adaptive dictionaries. The k‐space undersampling and FSE acquisition each reduced acquisition times by up to 6× and 8×, respectively, as compared to fully sampled spin echo imaging. Magn Reson Med 76:248–258, 2016. © 2015 Wiley Periodicals, Inc. PMID:26302363
Channel estimation for OFDM system in atmospheric optical communication based on compressive sensing
NASA Astrophysics Data System (ADS)
Zhao, Qingsong; Hao, Shiqi; Geng, Hongjian; Sun, Han
2015-10-01
Orthogonal frequency division multiplexing (OFDM) technique applied to the atmospheric optical communication can improve data transmission rate, restrain pulse interference, and reduce effect of multipath caused by atmospheric scattering. Channel estimation, as one of the important modules in OFDM, has been investigated thoroughly and widely with great progress. In atmospheric optical communication system, channel estimation methods based on pilot are common approaches, such as traditional least-squares (LS) algorithm and minimum mean square error (MMSE) algorithm. However, sensitivity of the noise effects and high complexity of computation are shortcomings of LS algorithm and MMSE algorithm, respectively. Here, a new method based on compressive sensing is proposed to estimate the channel state information of atmospheric optical communication OFDM system, especially when the condition is closely associated with turbulence. Firstly, time-varying channel model is established under the condition of turbulence. Then, in consideration of multipath effect, sparse channel model is available for compressive sensing. And, the pilot signal is reconstructed with orthogonal matching tracking (OMP) algorithm, which is used for reconstruction. By contrast, the work of channel estimation is completed by LS algorithm as well. After that, simulations are conducted respectively in two different indexes -signal error rate (SER) and mean square error (MSE). Finally, result shows that compared with LS algorithm, the application of compressive sensing can improve the performance of SER and MSE. Theoretical analysis and simulation results show that the proposed method is reasonable and efficient.
Efficient Reconstruction of Heterogeneous Networks from Time Series via Compressed Sensing.
Ma, Long; Han, Xiao; Shen, Zhesi; Wang, Wen-Xu; Di, Zengru
2015-01-01
Recent years have witnessed a rapid development of network reconstruction approaches, especially for a series of methods based on compressed sensing. Although compressed-sensing based methods require much less data than conventional approaches, the compressed sensing for reconstructing heterogeneous networks has not been fully exploited because of hubs. Hub neighbors require much more data to be inferred than small-degree nodes, inducing a cask effect for the reconstruction of heterogeneous networks. Here, a conflict-based method is proposed to overcome the cast effect to considerably reduce data amounts for achieving accurate reconstruction. Moreover, an element elimination method is presented to use the partially available structural information to reduce data requirements. The integration of both methods can further improve the reconstruction performance than separately using each technique. These methods are validated by exploring two evolutionary games taking place in scale-free networks, where individual information is accessible and an attempt to decode the network structure from measurable data is made. The results demonstrate that for all of the cases, much data are saved compared to that in the absence of these two methods. Due to the prevalence of heterogeneous networks in nature and society and the high cost of data acquisition in large-scale networks, these approaches have wide applications in many fields and are valuable for understanding and controlling the collective dynamics of a variety of heterogeneous networked systems.
NASA Astrophysics Data System (ADS)
Zhao, Hui; Li, Minghui; Wang, Ruyan; Liu, Yuanni; Song, Daiping
2014-09-01
Due to the spare multipath property of the channel, a channel estimation method, which is based on partial superimposed training sequence and compressed sensing theory, is proposed for line of sight optical orthogonal frequency division multiplexing communication systems. First, a continuous training sequence is added at variable power ratio to the cyclic prefix of orthogonal frequency division multiplexing symbols at the transmitter prior to transmission. Then the observation matrix of compressed sensing theory is structured by the use of the training symbols at receiver. Finally, channel state information is estimated using sparse signal reconstruction algorithm. Compared to traditional training sequences, the proposed partial superimposed training sequence not only improves the spectral efficiency, but also reduces the influence to information symbols. In addition, compared with classical least squares and linear minimum mean square error methods, the proposed compressed sensing theory based channel estimation method can improve both the estimation accuracy and the system performance. Simulation results are given to demonstrate the performance of the proposed method.
Accelerated high-frame-rate mouse heart cine-MRI using compressed sensing reconstruction.
Motaal, Abdallah G; Coolen, Bram F; Abdurrachim, Desiree; Castro, Rui M; Prompers, Jeanine J; Florack, Luc M J; Nicolay, Klaas; Strijkers, Gustav J
2013-04-01
We introduce a new protocol to obtain very high-frame-rate cinematographic (Cine) MRI movies of the beating mouse heart within a reasonable measurement time. The method is based on a self-gated accelerated fast low-angle shot (FLASH) acquisition and compressed sensing reconstruction. Key to our approach is that we exploit the stochastic nature of the retrospective triggering acquisition scheme to produce an undersampled and random k-t space filling that allows for compressed sensing reconstruction and acceleration. As a standard, a self-gated FLASH sequence with a total acquisition time of 10 min was used to produce single-slice Cine movies of seven mouse hearts with 90 frames per cardiac cycle. Two times (2×) and three times (3×) k-t space undersampled Cine movies were produced from 2.5- and 1.5-min data acquisitions, respectively. The accelerated 90-frame Cine movies of mouse hearts were successfully reconstructed with a compressed sensing algorithm. The movies had high image quality and the undersampling artifacts were effectively removed. Left ventricular functional parameters, i.e. end-systolic and end-diastolic lumen surface areas and early-to-late filling rate ratio as a parameter to evaluate diastolic function, derived from the standard and accelerated Cine movies, were nearly identical.
NASA Technical Reports Server (NTRS)
Smith, Brandon; Jan, Darrell Leslie; Venkatapathy, Ethiraj
2015-01-01
Vehicles re-entering Earth's atmosphere require protection from the heat of atmospheric friction. The Orion Multi-Purpose Crew Vehicle (MPCV) has more demanding thermal protection system (TPS) requirements than the Low Earth Orbit (LEO) missions, especially in regions where the structural load passes through. The use of 2-dimensional laminate materials along with a metal insert, used in EFT1 flight test for the compression pad region, are deemed adequate but cannot be extended for Lunar return missions.
NASA Astrophysics Data System (ADS)
Puy, Gilles; Vandergheynst, Pierre; Gribonval, Rémi; Wiaux, Yves
2012-12-01
We advocate a compressed sensing strategy that consists of multiplying the signal of interest by a wide bandwidth modulation before projection onto randomly selected vectors of an orthonormal basis. First, in a digital setting with random modulation, considering a whole class of sensing bases including the Fourier basis, we prove that the technique is universal in the sense that the required number of measurements for accurate recovery is optimal and independent of the sparsity basis. This universality stems from a drastic decrease of coherence between the sparsity and the sensing bases, which for a Fourier sensing basis relates to a spread of the original signal spectrum by the modulation (hence the name "spread spectrum"). The approach is also efficient as sensing matrices with fast matrix multiplication algorithms can be used, in particular in the case of Fourier measurements. Second, these results are confirmed by a numerical analysis of the phase transition of the ℓ1-minimization problem. Finally, we show that the spread spectrum technique remains effective in an analog setting with chirp modulation for application to realistic Fourier imaging. We illustrate these findings in the context of radio interferometry and magnetic resonance imaging.
NASA Astrophysics Data System (ADS)
Kim, Kyuseok; Park, Yeonok; Cho, Heemoon; Cho, Hyosung; Je, Uikyu; Park, Chulkyu; Lim, Hyunwoo; Park, Soyoung; Woo, Taeho; Choi, Sungil
2016-10-01
In this work, we investigated a compressed-sensing (CS)-based deblurring scheme incorporated with the total-variation (TV) regularization penalty for image deblurring of high accuracy and adopted it into the image reconstruction in conventional digital breast tomosynthesis (DBT). We implemented the proposed algorithm and performed a systematic simulation to demonstrate its viability for improving the image performance in DBT as well as two-dimensional (2D) mammography. In the simulation, blurred noisy projection images of a 3D numerical breast phantom were generated by convolving their original (or exact) version by a designed 2D Gaussian filter kernel (standard deviation=2 in pixel unit, kernel size=11×11), followed by adding Gaussian noise (mean=0, variance=0.05), and deblurred by using the algorithm before performing the DBT reconstruction procedure. Here the projection images were taken with a half tomographic angle of θ=20° and an angle step of Δθ=2°. We investigated the image performance of the reconstructed DBT images quantitatively in terms of the modulation and the slice-sensitive profile (SSP).
Efficient Sparse Signal Transmission over a Lossy Link Using Compressive Sensing
Wu, Liantao; Yu, Kai; Cao, Dongyu; Hu, Yuhen; Wang, Zhi
2015-01-01
Reliable data transmission over lossy communication link is expensive due to overheads for error protection. For signals that have inherent sparse structures, compressive sensing (CS) is applied to facilitate efficient sparse signal transmissions over lossy communication links without data compression or error protection. The natural packet loss in the lossy link is modeled as a random sampling process of the transmitted data, and the original signal will be reconstructed from the lossy transmission results using the CS-based reconstruction method at the receiving end. The impacts of packet lengths on transmission efficiency under different channel conditions have been discussed, and interleaving is incorporated to mitigate the impact of burst data loss. Extensive simulations and experiments have been conducted and compared to the traditional automatic repeat request (ARQ) interpolation technique, and very favorable results have been observed in terms of both accuracy of the reconstructed signals and the transmission energy consumption. Furthermore, the packet length effect provides useful insights for using compressed sensing for efficient sparse signal transmission via lossy links. PMID:26287195
NASA Astrophysics Data System (ADS)
Atubga, David; Wu, Huijuan; Lu, Lidong; Sun, Xiaoyan
2017-02-01
Typical fully distributed optical fiber sensors (DOFS) with dozens of kilometers are equivalent to tens of thousands of point sensors along the whole monitoring line, which means tens of thousands of data will be generated for one pulse launching period. Therefore, in an all-day nonstop monitoring, large volumes of data are created thereby triggering the demand for large storage space and high speed for data transmission. In addition, when the monitoring length and channel numbers increase, the data also increase extensively. The task of mitigating large volumes of data accumulation, large storage capacity, and high-speed data transmission is, therefore, the aim of this paper. To demonstrate our idea, we carried out a comparative study of two lossless methods, Huffman and Lempel Ziv Welch (LZW), with a lossy data compression algorithm, fast wavelet transform (FWT) based on three distinctive DOFS sensing data, such as Φ-OTDR, P-OTDR, and B-OTDA. Our results demonstrated that FWT yielded the best compression ratio with good consumption time, irrespective of errors in signal construction of the three DOFS data. Our outcomes indicate the promising potentials of FWT which makes it more suitable, reliable, and convenient for real-time compression of the DOFS data. Finally, it was observed that differences in the DOFS data structure have some influence on both the compression ratio and computational cost.
Optical image encryption based on compressive sensing and chaos in the fractional Fourier domain
NASA Astrophysics Data System (ADS)
Liu, Xingbin; Mei, Wenbo; Du, Huiqian
2014-11-01
We propose a novel image encryption algorithm based on compressive sensing (CS) and chaos in the fractional Fourier domain. The original image is dimensionality reduction measured using CS. The measured values are then encrypted using chaotic-based double-random-phase encoding technique in the fractional Fourier transform domain. The measurement matrix and the random-phase masks used in the encryption process are formed from pseudo-random sequences generated by the chaotic map. In this proposed algorithm, the final result is compressed and encrypted. The proposed cryptosystem decreases the volume of data to be transmitted and simplifies the keys for distribution simultaneously. Numerical experiments verify the validity and security of the proposed algorithm.
Spencer, Austin P.; Spokoyny, Boris; Ray, Supratim; Sarvari, Fahad; Harel, Elad
2016-01-01
Compressive sensing allows signals to be efficiently captured by exploiting their inherent sparsity. Here we implement sparse sampling to capture the electronic structure and ultrafast dynamics of molecular systems using phase-resolved 2D coherent spectroscopy. Until now, 2D spectroscopy has been hampered by its reliance on array detectors that operate in limited spectral regions. Combining spatial encoding of the nonlinear optical response and rapid signal modulation allows retrieval of state-resolved correlation maps in a photosynthetic protein and carbocyanine dye. We report complete Hadamard reconstruction of the signals and compression factors as high as 10, in good agreement with array-detected spectra. Single-point array reconstruction by spatial encoding (SPARSE) Spectroscopy reduces acquisition times by about an order of magnitude, with further speed improvements enabled by fast scanning of a digital micromirror device. We envision unprecedented applications for coherent spectroscopy using frequency combs and super-continua in diverse spectral regions. PMID:26804546
NASA Astrophysics Data System (ADS)
Li, Jiaosheng; Zhong, Liyun; Zhang, Qinnan; Zhou, Yunfei; Xiong, Jiaxiang; Tian, Jindong; Lu, Xiaoxu
2017-01-01
We propose an optical image hiding method based on dual-channel simultaneous phase-shifting interferometry (DCSPSI) and compressive sensing (CS) in all-optical domain. In the DCSPSI architecture, a secret image is firstly embedded in the host image without destroying the original host's form, and a pair of interferograms with the phase shifts of π/2 is simultaneously generated by the polarization components and captured by two CCDs. Then, the holograms are further compressed sampling to the less data by CS. The proposed strategy will provide a useful solution for the real-time optical image security transmission and largely reducing data volume of interferogram. The experimental result demonstrates the validity and feasibility of the proposed method.
Model-based compressive sensing for damage localization in Lamb wave inspection.
Perelli, Alessandro; Di Ianni, Tommaso; Marzani, Alessandro; De Marchi, Luca; Masetti, Guido
2013-10-01
Compressive sensing (CS) has emerged as a potentially viable technique for the efficient compression and analysis of high-resolution signals that have a sparse representation in a fixed basis. In this work, we have developed a CS approach for ultrasonic signal decomposition suitable to achieve high performance in Lamb-wave-based defect detection procedures. In the proposed approach, a CS algorithm based on an alternating minimization (AM) procedure is adopted to extract the information about both the system impulse response and the reflectivity function. The implemented tool exploits the dispersion compensation properties of the warped frequency transform as a means to generate the sparsifying basis for the signal representation. The effectiveness of the decomposition task is demonstrated on synthetic signals and successfully tested on experimental Lamb waves propagating in an aluminum plate. Compared with available strategies, the proposed approach provides an improvement in the accuracy of wave propagation path length estimation, a fundamental step in defect localization procedures.
Photonic compressive sensing with a micro-ring-resonator-based microwave photonic filter
NASA Astrophysics Data System (ADS)
Chen, Ying; Ding, Yunhong; Zhu, Zhijing; Chi, Hao; Zheng, Shilie; Zhang, Xianmin; Jin, Xiaofeng; Galili, Michael; Yu, Xianbin
2016-08-01
A novel approach to realize photonic compressive sensing (CS) with a multi-tap microwave photonic filter is proposed and demonstrated. The system takes both advantages of CS and photonics to capture wideband sparse signals with sub-Nyquist sampling rate. The low-pass filtering function required in the CS is realized in a photonic way by using a frequency comb and a dispersive element. The frequency comb is realized by shaping an amplified spontaneous emission (ASE) source with an on-chip micro-ring resonator, which is beneficial to the integration of photonic CS. A proof-of-concept experiment for a two-tone signal acquisition with frequencies of 350 MHz and 1.25 GHz is experimentally demonstrated with a compression factor up to 16.
NASA Astrophysics Data System (ADS)
Wang, Zihao; Spinoulas, Leonidas; He, Kuan; Tian, Lei; Cossairt, Oliver; Katsaggelos, Aggelos K.; Chen, Huaijin
2017-01-01
Compressed sensing has been discussed separately in spatial and temporal domains. Compressive holography has been introduced as a method that allows 3D tomographic reconstruction at different depths from a single 2D image. Coded exposure is a temporal compressed sensing method for high speed video acquisition. In this work, we combine compressive holography and coded exposure techniques and extend the discussion to 4D reconstruction in space and time from one coded captured image. In our prototype, digital in-line holography was used for imaging macroscopic, fast moving objects. The pixel-wise temporal modulation was implemented by a digital micromirror device. In this paper we demonstrate $10\\times$ temporal super resolution with multiple depths recovery from a single image. Two examples are presented for the purpose of recording subtle vibrations and tracking small particles within 5 ms.
Compressive holographic video.
Wang, Zihao; Spinoulas, Leonidas; He, Kuan; Tian, Lei; Cossairt, Oliver; Katsaggelos, Aggelos K; Chen, Huaijin
2017-01-09
Compressed sensing has been discussed separately in spatial and temporal domains. Compressive holography has been introduced as a method that allows 3D tomographic reconstruction at different depths from a single 2D image. Coded exposure is a temporal compressed sensing method for high speed video acquisition. In this work, we combine compressive holography and coded exposure techniques and extend the discussion to 4D reconstruction in space and time from one coded captured image. In our prototype, digital in-line holography was used for imaging macroscopic, fast moving objects. The pixel-wise temporal modulation was implemented by a digital micromirror device. In this paper we demonstrate 10× temporal super resolution with multiple depths recovery from a single image. Two examples are presented for the purpose of recording subtle vibrations and tracking small particles within 5 ms.
Compressed sensing techniques for arbitrary frequency-sparse signals in structural health monitoring
NASA Astrophysics Data System (ADS)
Duan, Zhongdong; Kang, Jie
2014-03-01
Structural health monitoring requires collection of large number sample data and sometimes high frequent vibration data for detecting the damage of structures. The expensive cost for collecting the data is a big challenge. The recent proposed Compressive Sensing method enables a potentially large reduction in the sampling, and it is a way to meet the challenge. The Compressed Sensing theory requires sparse signal, meaning that the signals can be well-approximated as a linear combination of just a few elements from a known discrete basis or dictionary. The signal of structure vibration can be decomposed into a few sinusoid linear combinations in the DFT domain. Unfortunately, in most cases, the frequencies of decomposed sinusoid are arbitrary in that domain, which may not lie precisely on the discrete DFT basis or dictionary. In this case, the signal will lost its sparsity, and that makes recovery performance degrades significantly. One way to improve the sparsity of the signal is to increase the size of the dictionary, but there exists a tradeoff: the closely-spaced DFT dictionary will increase the coherence between the elements in the dictionary, which in turn decreases recovery performance. In this work we introduce three approaches for arbitrary frequency signals recovery. The first approach is the continuous basis pursuit (CBP), which reconstructs a continuous basis by introducing interpolation steps. The second approach is a semidefinite programming (SDP), which searches the sparest signal on continuous basis without establish any dictionary, enabling a very high recovery precision. The third approach is spectral iterative hard threshold (SIHT), which is based on redundant DFT dictionary and a restricted union-of-subspaces signal model, inhibiting closely spaced sinusoids. The three approaches are studied by numerical simulation. Structure vibration signal is simulated by a finite element model, and compressed measurements of the signal are taken to perform
Fast, accurate 2D-MR relaxation exchange spectroscopy (REXSY): Beyond compressed sensing
NASA Astrophysics Data System (ADS)
Bai, Ruiliang; Benjamini, Dan; Cheng, Jian; Basser, Peter J.
2016-10-01
Previously, we showed that compressive or compressed sensing (CS) can be used to reduce significantly the data required to obtain 2D-NMR relaxation and diffusion spectra when they are sparse or well localized. In some cases, an order of magnitude fewer uniformly sampled data were required to reconstruct 2D-MR spectra of comparable quality. Nonetheless, this acceleration may still not be sufficient to make 2D-MR spectroscopy practicable for many important applications, such as studying time-varying exchange processes in swelling gels or drying paints, in living tissue in response to various biological or biochemical challenges, and particularly for in vivo MRI applications. A recently introduced framework, marginal distributions constrained optimization (MADCO), tremendously accelerates such 2D acquisitions by using a priori obtained 1D marginal distribution as powerful constraints when 2D spectra are reconstructed. Here we exploit one important intrinsic property of the 2D-MR relaxation exchange spectra: the fact that the 1D marginal distributions of each 2D-MR relaxation exchange spectrum in both dimensions are equal and can be rapidly estimated from a single Carr-Purcell-Meiboom-Gill (CPMG) or inversion recovery prepared CPMG measurement. We extend the MADCO framework by further proposing to use the 1D marginal distributions to inform the subsequent 2D data-sampling scheme, concentrating measurements where spectral peaks are present and reducing them where they are not. In this way we achieve compression or acceleration that is an order of magnitude greater than that in our previous CS method while providing data in reconstructed 2D-MR spectral maps of comparable quality, demonstrated using several simulated and real 2D T2 - T2 experimental data. This method, which can be called "informed compressed sensing," is extendable to other 2D- and even ND-MR exchange spectroscopy.
NASA Astrophysics Data System (ADS)
Arias, Fernando X.; Sierra, Heidy; Rajadhyaksha, Milind; Arzuaga, Emmanuel
2016-03-01
Compressive Sensing (CS)-based technologies have shown potential to improve the efficiency of acquisition, manipulation, analysis and storage processes on signals and imagery with slight discernible loss in data performance. The CS framework relies on the reconstruction of signals that are presumed sparse in some domain, from a significantly small data collection of linear projections of the signal of interest. As a result, a solution to the underdetermined linear system resulting from this paradigm makes it possible to estimate the original signal with high accuracy. One common approach to solve the linear system is based on methods that minimize the L1-norm. Several fast algorithms have been developed for this purpose. This paper presents a study on the use of CS in high-resolution reflectance confocal microscopy (RCM) images of the skin. RCM offers a cell resolution level similar to that used in histology to identify cellular patterns for diagnosis of skin diseases. However, imaging of large areas (required for effective clinical evaluation) at such high-resolution can turn image capturing, processing and storage processes into a time consuming procedure, which may pose a limitation for use in clinical settings. We present an analysis on the compression ratio that may allow for a simpler capturing approach while reconstructing the required cellular resolution for clinical use. We provide a comparative study in compressive sensing and estimate its effectiveness in terms of compression ratio vs. image reconstruction accuracy. Preliminary results show that by using as little as 25% of the original number of samples, cellular resolution may be reconstructed with high accuracy.
Domingos, M; Intranuovo, F; Russo, T; De Santis, R; Gloria, A; Ambrosio, L; Ciurana, J; Bartolo, P
2013-12-01
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.
Tang, Wenlong; Cao, Hongbao; Zhang, Ji-Gang; Duan, Junbo; Lin, Dongdong; Wang, Yu-Ping
2013-01-01
It is realized that a combined analysis of different types of genomic measurements tends to give more reliable classification results. However, how to efficiently combine data with different resolutions is challenging. We propose a novel compressed sensing based approach for the combined analysis of gene expression and copy number variants data for the purpose of subtyping six types of Gliomas. Experimental results show that the proposed combined approach can substantially improve the classification accuracy compared to that of using either of individual data type. The proposed approach can be applicable to many other types of genomic data. PMID:25267935
High-quality correspondence imaging based on sorting and compressive sensing technique
NASA Astrophysics Data System (ADS)
Wu, Heng; Zhang, Xianmin; Gan, Jinqiang; Luo, Chunling; Ge, Peng
2016-11-01
We propose a high-quality imaging method based on correspondence imaging (CI) using a sorting and compressive sensing (CS) technique. Unlike the traditional CI, the positive and negative (PN) subsets are created by a sorting method, and the image of an object is then recovered from the PN subsets using a CS technique. We compare the performance of the proposed method with different ghost imaging (GI) algorithms using the data from a single-detector computational GI system. The results demonstrate that our method enjoys excellent imaging and anti-interference capabilities, and can further reduce the measurement numbers compared with the direct use of CS in GI.
NASA Astrophysics Data System (ADS)
Wan, Yuhong; Man, Tianlong; Wu, Fan; Kim, Myung K.; Wang, Dayong
2016-11-01
We present a new self-interference digital holographic approach that allows single-shot capturing three-dimensional intensity distribution of the spatially incoherent objects. The Fresnel incoherent correlation holographic microscopy is combined with parallel phase-shifting technique to instantaneously obtain spatially multiplexed phase-shifting holograms. The compressive-sensing-based reconstruction algorithm is implemented to reconstruct the original object from the under sampled demultiplexed holograms. The scheme is verified with simulations. The validity of the proposed method is experimentally demonstrated in an indirectly way by simulating the use of specific parallel phase-shifting recording device.
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.
A novel reconstruction algorithm for bioluminescent tomography based on Bayesian compressive sensing
NASA Astrophysics Data System (ADS)
Wang, Yaqi; Feng, Jinchao; Jia, Kebin; Sun, Zhonghua; Wei, Huijun
2016-03-01
Bioluminescence tomography (BLT) is becoming a promising tool because it can resolve the biodistribution of bioluminescent reporters associated with cellular and subcellular function through several millimeters with to centimeters of tissues in vivo. However, BLT reconstruction is an ill-posed problem. By incorporating sparse a priori information about bioluminescent source, enhanced image quality is obtained for sparsity based reconstruction algorithm. Therefore, sparsity based BLT reconstruction algorithm has a great potential. Here, we proposed a novel reconstruction method based on Bayesian compressive sensing and investigated its feasibility and effectiveness with a heterogeneous phantom. The results demonstrate the potential and merits of the proposed algorithm.
DLA based compressed sensing for high resolution MR microscopy of neuronal tissue
NASA Astrophysics Data System (ADS)
Nguyen, Khieu-Van; Li, Jing-Rebecca; Radecki, Guillaume; Ciobanu, Luisa
2015-10-01
In this work we present the implementation of compressed sensing (CS) on a high field preclinical scanner (17.2 T) using an undersampling trajectory based on the diffusion limited aggregation (DLA) random growth model. When applied to a library of images this approach performs better than the traditional undersampling based on the polynomial probability density function. In addition, we show that the method is applicable to imaging live neuronal tissues, allowing significantly shorter acquisition times while maintaining the image quality necessary for identifying the majority of neurons via an automatic cell segmentation algorithm.
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
Fast algorithms for nonconvex compression sensing: MRI reconstruction from very few data
Chartrand, Rick
2009-01-01
Compressive sensing is the reconstruction of sparse images or signals from very few samples, by means of solving a tractable optimization problem. In the context of MRI, this can allow reconstruction from many fewer k-space samples, thereby reducing scanning time. Previous work has shown that nonconvex optimization reduces still further the number of samples required for reconstruction, while still being tractable. In this work, we extend recent Fourier-based algorithms for convex optimization to the nonconvex setting, and obtain methods that combine the reconstruction abilities of previous nonconvex approaches with the computational speed of state-of-the-art convex methods.
New Compressed Sensing ISAR Imaging Algorithm Based on Log-Sum Minimization
NASA Astrophysics Data System (ADS)
Ping, Cheng; Jiaqun, Zhao
2016-12-01
To improve the performance of inverse synthetic aperture radar (ISAR) imaging based on compressed sensing (CS), a new algorithm based on log-sum minimization is proposed. A new interpretation of the algorithm is also provided. Compared with the conventional algorithm, the new algorithm can recover signals based on fewer measurements, in looser sparsity condition, with smaller recovery error, and it has obtained better sinusoidal signal spectrum and imaging result for real ISAR data. Therefore, the proposed algorithm is a promising imaging algorithm in CS ISAR.
NASA Astrophysics Data System (ADS)
Huang, H.; Hu, J.; Huang, S.; Huang, C.
2010-12-01
The Taiwan orogenic belt is resulted from the convergence between Philippine Sea plate and Eurasian plate. Serious earthquakes occurred in west and northwest flanks of main mountain belt of the island in 1935 and 1999, caused more than 5000 deaths in total. In addition, Hsinchu Science and Industrial Park (HSIP) located in northwest Taiwan is one of the world's most important areas for semiconductor manufacturing. There are more than 400 technology companies in this park, and accounted for 10% of Taiwan's GDP. Consequently, active Hsincheng and Hsinchu faults in study area become the major threat of the industrial park, thus the understanding of complex subsurface seismogenic structures are crucial issue of earthquake hazard assessment and mitigation in Hsinchu area. Several geological cross sections have been constructed and discussed to suggest possible deep structures of these two major faults in previous study. However, how subsurface fault system and folding intersect still remains unclear and the evolution of fault and fold geometry in Hsinchu area is not fully understood. The main purpose of this study is to clarify the spatial linkage between the major thrust faults, folds, and adjacent transverse structures. In this study, we first construct the NW-SE trending cross-section which is sub-parallel to the regional shortening direction, and then balance this cross section to derive the structure evolution in Hsinchu area. We also incorporate several cross-sections and relocated seismicity to get detail 3D fault geometry for the numerical modeling in order to assess the interseismic strain accumulation and seismic potential based on geodetic measurements.
Robust group compressive sensing for DOA estimation with partially distorted observations
NASA Astrophysics Data System (ADS)
Wang, Ben; Zhang, Yimin D.; Wang, Wei
2016-12-01
In this paper, we propose a robust direction-of-arrival (DOA) estimation algorithm based on group sparse reconstruction algorithm utilizing signals observed at multiple frequencies. The group sparse reconstruction scheme for DOA estimation is solved through the complex multitask Bayesian compressive sensing algorithm by exploiting the group sparse property of the received multi-frequency signals. Then, we propose a robust reconstruction algorithm in the presence of distorted signals. In particular, we consider a problem where the observed data in some frequencies are distorted due to, e.g., interference contamination. In this case, the residual error will follow the impulsive Gaussian mixture distribution instead of the Gaussian distribution due to the fact that some of the estimation errors significantly depart from the mean value of the estimation error distribution. Thus, the minimum least square restriction used in the conventional sparse reconstruction algorithm may lead to a failed reconstruction result. By exploiting the maximum correntropy criterion which is inherently insensitive to the impulsive noise, a weighting vector is derived to automatically mitigate the effect of the distorted narrowband signals, and a robust group compressive sensing approach is developed to achieve reliable DOA estimation. The robustness and effectiveness of the proposed algorithm are verified using simulation results.
A DMD-based hyperspectral imaging system using compressive sensing method
NASA Astrophysics Data System (ADS)
Sun, Zhongqiu; Chen, Bo; Cheng, Chengqi
2014-11-01
Hyperspectral Imaging Systems (HIS) are widely applied in many fields. However, in the traditional design of HIS, it is much time-consuming to acquire an integrated hyperspectral image. Compressive sensing is an efficient method to process sparse data, and a single-pixel camera which used the digital micromirror device (DMD) for accomplishing the CS algorithms had been developed. Nowadays, DMD achieved great development. The size of mirror array is increasing while switch speed of a single mirror becomes very fast. Consequently, researchers make efforts to design a HIS using CS method. CS method is a method to scale down the spatial information but the hyperspectral datacubes are still huge because of the thousands of bands. In this paper, we design a DMD-based spectrometer architecture using the method of compressed sensing principle, combined with DMD's spectral filter characteristics. In the new architecture, there are two DMDs. One is used for implementing the CS pattern, the other for filtering the bands. It has spectral simply adjustable advantages. With this new technology, we can reduce the amount of information which needs to be transmitted and processed in both spatial and spectral domain. We also present some simulation results of implementation procedures.
First-principles interatomic potentials for ten elemental metals via compressed sensing
NASA Astrophysics Data System (ADS)
Seko, Atsuto; Takahashi, Akira; Tanaka, Isao
2015-08-01
Interatomic potentials have been widely used in atomistic simulations such as molecular dynamics. Recently, frameworks to construct accurate interatomic potentials that combine a set of density functional theory (DFT) calculations with machine learning techniques have been proposed. One of these methods is to use compressed sensing to derive a sparse representation for the interatomic potential. This facilitates the control of the accuracy of interatomic potentials. In this study, we demonstrate the applicability of compressed sensing to deriving the interatomic potential of ten elemental metals, namely, Ag, Al, Au, Ca, Cu, Ga, In, K, Li, and Zn. For each elemental metal, the interatomic potential is obtained from DFT calculations using elastic net regression. The interatomic potentials are found to have prediction errors of less than 3.5 meV/atom, 0.03 eV/Å, and 0.15 GPa for the energy, force, and the stress tensor, respectively, which enable the accurate prediction of physical properties such as lattice constants and the phonon dispersion relationship.
Noor, Amina; Serpedin, Erchin; Nounou, Mohamed; Nounou, Hazem
2013-01-01
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. PMID:23737768
Secure biometric image sensor and authentication scheme based on compressed sensing.
Suzuki, Hiroyuki; Suzuki, Masamichi; Urabe, Takuya; Obi, Takashi; Yamaguchi, Masahiro; Ohyama, Nagaaki
2013-11-20
It is important to ensure the security of biometric authentication information, because its leakage causes serious risks, such as replay attacks using the stolen biometric data, and also because it is almost impossible to replace raw biometric information. In this paper, we propose a secure biometric authentication scheme that protects such information by employing an optical data ciphering technique based on compressed sensing. The proposed scheme is based on two-factor authentication, the biometric information being supplemented by secret information that is used as a random seed for a cipher key. In this scheme, a biometric image is optically encrypted at the time of image capture, and a pair of restored biometric images for enrollment and verification are verified in the authentication server. If any of the biometric information is exposed to risk, it can be reenrolled by changing the secret information. Through numerical experiments, we confirm that finger vein images can be restored from the compressed sensing measurement data. We also present results that verify the accuracy of the scheme.
A COMPRESSED SENSING METHOD WITH ANALYTICAL RESULTS FOR LIDAR FEATURE CLASSIFICATION
Allen, Josef D
2011-01-01
We present an innovative way to autonomously classify LiDAR points into bare earth, building, vegetation, and other categories. One desirable product of LiDAR data is the automatic classification of the points in the scene. Our algorithm automatically classifies scene points using Compressed Sensing Methods via Orthogonal Matching Pursuit algorithms utilizing a generalized K-Means clustering algorithm to extract buildings and foliage from a Digital Surface Models (DSM). This technology reduces manual editing while being cost effective for large scale automated global scene modeling. Quantitative analyses are provided using Receiver Operating Characteristics (ROC) curves to show Probability of Detection and False Alarm of buildings vs. vegetation classification. Histograms are shown with sample size metrics. Our inpainting algorithms then fill the voids where buildings and vegetation were removed, utilizing Computational Fluid Dynamics (CFD) techniques and Partial Differential Equations (PDE) to create an accurate Digital Terrain Model (DTM) [6]. Inpainting preserves building height contour consistency and edge sharpness of identified inpainted regions. Qualitative results illustrate other benefits such as Terrain Inpainting s unique ability to minimize or eliminate undesirable terrain data artifacts. Keywords: Compressed Sensing, Sparsity, Data Dictionary, LiDAR, ROC, K-Means, Clustering, K-SVD, Orthogonal Matching Pursuit
Continuous Compressed Sensing for Surface Dynamical Processes with Helium Atom Scattering
Jones, Alex; Tamtögl, Anton; Calvo-Almazán, Irene; Hansen, Anders
2016-01-01
Compressed Sensing (CS) techniques are used to measure and reconstruct surface dynamical processes with a helium spin-echo spectrometer for the first time. Helium atom scattering is a well established method for examining the surface structure and dynamics of materials at atomic sized resolution and the spin-echo technique opens up the possibility of compressing the data acquisition process. CS methods demonstrating the compressibility of spin-echo spectra are presented for several measurements. Recent developments on structured multilevel sampling that are empirically and theoretically shown to substantially improve upon the state of the art CS techniques are implemented. In addition, wavelet based CS approximations, founded on a new continuous CS approach, are used to construct continuous spectra. In order to measure both surface diffusion and surface phonons, which appear usually on different energy scales, standard CS techniques are not sufficient. However, the new continuous CS wavelet approach allows simultaneous analysis of surface phonons and molecular diffusion while reducing acquisition times substantially. The developed methodology is not exclusive to Helium atom scattering and can also be applied to other scattering frameworks such as neutron spin-echo and Raman spectroscopy. PMID:27301423
Compressed sensing of ECG signal for wireless system with new fast iterative method.
Tawfic, Israa; Kayhan, Sema
2015-12-01
Recent experiments in wireless body area network (WBAN) show that compressive sensing (CS) is a promising tool to compress the Electrocardiogram signal ECG signal. The performance of CS is based on algorithms use to reconstruct exactly or approximately the original signal. In this paper, we present two methods work with absence and presence of noise, these methods are Least Support Orthogonal Matching Pursuit (LS-OMP) and Least Support Denoising-Orthogonal Matching Pursuit (LSD-OMP). The algorithms achieve correct support recovery without requiring sparsity knowledge. We derive an improved restricted isometry property (RIP) based conditions over the best known results. The basic procedures are done by observational and analytical of a different Electrocardiogram signal downloaded them from PhysioBankATM. Experimental results show that significant performance in term of reconstruction quality and compression rate can be obtained by these two new proposed algorithms, and help the specialist gathering the necessary information from the patient in less time if we use Magnetic Resonance Imaging (MRI) application, or reconstructed the patient data after sending it through the network.
Continuous Compressed Sensing for Surface Dynamical Processes with Helium Atom Scattering
NASA Astrophysics Data System (ADS)
Jones, Alex; Tamtögl, Anton; Calvo-Almazán, Irene; Hansen, Anders
2016-06-01
Compressed Sensing (CS) techniques are used to measure and reconstruct surface dynamical processes with a helium spin-echo spectrometer for the first time. Helium atom scattering is a well established method for examining the surface structure and dynamics of materials at atomic sized resolution and the spin-echo technique opens up the possibility of compressing the data acquisition process. CS methods demonstrating the compressibility of spin-echo spectra are presented for several measurements. Recent developments on structured multilevel sampling that are empirically and theoretically shown to substantially improve upon the state of the art CS techniques are implemented. In addition, wavelet based CS approximations, founded on a new continuous CS approach, are used to construct continuous spectra. In order to measure both surface diffusion and surface phonons, which appear usually on different energy scales, standard CS techniques are not sufficient. However, the new continuous CS wavelet approach allows simultaneous analysis of surface phonons and molecular diffusion while reducing acquisition times substantially. The developed methodology is not exclusive to Helium atom scattering and can also be applied to other scattering frameworks such as neutron spin-echo and Raman spectroscopy.
A Compressed Sensing-Based Wearable Sensor Network for Quantitative Assessment of Stroke Patients.
Yu, Lei; Xiong, Daxi; Guo, Liquan; Wang, Jiping
2016-02-05
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.
Continuous Compressed Sensing for Surface Dynamical Processes with Helium Atom Scattering.
Jones, Alex; Tamtögl, Anton; Calvo-Almazán, Irene; Hansen, Anders
2016-06-15
Compressed Sensing (CS) techniques are used to measure and reconstruct surface dynamical processes with a helium spin-echo spectrometer for the first time. Helium atom scattering is a well established method for examining the surface structure and dynamics of materials at atomic sized resolution and the spin-echo technique opens up the possibility of compressing the data acquisition process. CS methods demonstrating the compressibility of spin-echo spectra are presented for several measurements. Recent developments on structured multilevel sampling that are empirically and theoretically shown to substantially improve upon the state of the art CS techniques are implemented. In addition, wavelet based CS approximations, founded on a new continuous CS approach, are used to construct continuous spectra. In order to measure both surface diffusion and surface phonons, which appear usually on different energy scales, standard CS techniques are not sufficient. However, the new continuous CS wavelet approach allows simultaneous analysis of surface phonons and molecular diffusion while reducing acquisition times substantially. The developed methodology is not exclusive to Helium atom scattering and can also be applied to other scattering frameworks such as neutron spin-echo and Raman spectroscopy.
A Compressed Sensing-Based Wearable Sensor Network for Quantitative Assessment of Stroke Patients
Yu, Lei; Xiong, Daxi; Guo, Liquan; Wang, Jiping
2016-01-01
Clinical rehabilitation assessment is an important part of the therapy process because it is the premise for prescribing suitable rehabilitation interventions. However, the commonly used assessment scales have the following two drawbacks: (1) they are susceptible to subjective factors; (2) they only have several rating levels and are influenced by a ceiling effect, making it impossible to exactly detect any further improvement in the movement. Meanwhile, energy constraints are a primary design consideration in wearable sensor network systems since they are often battery-operated. Traditionally, for wearable sensor network systems that follow the Shannon/Nyquist sampling theorem, there are many data that need to be sampled and transmitted. This paper proposes a novel wearable sensor network system to monitor and quantitatively assess the upper limb motion function, based on compressed sensing technology. With the sparse representation model, less data is transmitted to the computer than with traditional systems. The experimental results show that the accelerometer signals of Bobath handshake and shoulder touch exercises can be compressed, and the length of the compressed signal is less than 1/3 of the raw signal length. More importantly, the reconstruction errors have no influence on the predictive accuracy of the Brunnstrom stage classification model. It also indicated that the proposed system can not only reduce the amount of data during the sampling and transmission processes, but also, the reconstructed accelerometer signals can be used for quantitative assessment without any loss of useful information. PMID:26861337
Can Compressed Sensing Be Applied To Dual-Polarimetric Weather Radars?
NASA Astrophysics Data System (ADS)
Mishra, K.; Kruger, A.; Krajewski, W. F.
2013-12-01
The recovery of sparsely-sampled signals has long attracted considerable research interest in various fields such as reflection seismology, microscopy, and astronomy. Recently, such recovery techniques have been formalized as a sampling method called compressed sensing (CS) which uses few linear and non-adaptive measurements to reconstruct a signal that is sparse in a known domain. Many radar and remote sensing applications require efficient and rapid data acquisition. CS techniques have, therefore, enormous potential in dramatically changing the way the radar samples and processes data. A number of recent studies have investigated CS for radar applications with emphasis on point target radars, and synthetic aperture radar (SAR) imaging. CS radar holds the promise of compressing-while-sampling, and may yield simpler receiver hardware which uses low-rate ADCs and eliminates pulse compression/matched filter. The need of fewer measurements also implies that a CS radar may need smaller dwell times without significant loss of information. Finally, CS radar data could be used for improving the quality of low-resolution radar observations. In this study, we explore the feasibility of using CS for dual-polarimetric weather radars. In order to recover a signal in CS framework, two conditions must be satisfied: sparsity and incoherence. The sparsity of weather radar measurements can be modeled in several domains such as time, frequency, joint time-frequency domain, or polarimetric measurement domains. The condition of incoherence relates to the measurement process which, in a radar scenario, would imply designing an incoherent transmit waveform or an equivalent scanning strategy with an existing waveform. In this study, we formulate a sparse signal model for precipitation targets as observed by a polarimetric weather radar. The applicability of CS for such a signal model is then examined through simulations of incoherent measurements along with real weather data obtained
NASA Astrophysics Data System (ADS)
Pletinckx, D.
2011-09-01
The current 3D hype creates a lot of interest in 3D. People go to 3D movies, but are we ready to use 3D in our homes, in our offices, in our communication? Are we ready to deliver real 3D to a general public and use interactive 3D in a meaningful way to enjoy, learn, communicate? The CARARE project is realising this for the moment in the domain of monuments and archaeology, so that real 3D of archaeological sites and European monuments will be available to the general public by 2012. There are several aspects to this endeavour. First of all is the technical aspect of flawlessly delivering 3D content over all platforms and operating systems, without installing software. We have currently a working solution in PDF, but HTML5 will probably be the future. Secondly, there is still little knowledge on how to create 3D learning objects, 3D tourist information or 3D scholarly communication. We are still in a prototype phase when it comes to integrate 3D objects in physical or virtual museums. Nevertheless, Europeana has a tremendous potential as a multi-facetted virtual museum. Finally, 3D has a large potential to act as a hub of information, linking to related 2D imagery, texts, video, sound. We describe how to create such rich, explorable 3D objects that can be used intuitively by the generic Europeana user and what metadata is needed to support the semantic linking.
Minh-Chinh, Truong; Tran-Duc, Tan; Linh-Trung, Nguyen; Luong, Marie; Do, Minh N
2012-01-01
Sweep imaging Fourier transform (SWIFT) is an efficient (fast and quiet) specialized magnetic resonance imaging (MRI) method for imaging tissues or organs that give only short-lived signals due to fast spin-spin relaxation rates. Based on the idea of compressed sensing, this paper proposes a novel method for further enhancing SWIFT using chaotic compressed sensing (CCS-SWIFT). With reduced number of measurements, CCS-SWIFT effectively faster than SWIFT. In comparison with a recently proposed chaotic compressed sensing method for standard MRI (CCS-MRI), simulation results showed that CCS-SWIFT outperforms CCS-MRI in terms of the normalized relative error in the image reconstruction and the probability of exact reconstruction.
Balamurugan, Jayaraman; Thanh, Tran Duy; Karthikeyan, Gopalsamy; Kim, Nam Hoon; Lee, Joong Hee
2017-03-15
A novel 3D nanocomposite of nitrogen doped Co-CNTs over graphene sheets (3D N-Co-CNT@NG) have been successfully fabricated via a simple, scalable and one-step thermal decomposition method. This 3D hierarchical nanostructure provides an admirable conductive network for effective charge transfer and avoids the agglomeration of NG matrices, which examine direct as well as non-enzymatic responses to glucose oxidation and H2O2 reduction at a low potential. The novel electrode showed excellent electrochemical performance towards glucose oxidation, with high sensitivity of 9.05μAcm(-2)mM(-1), a wide linear range from 0.025 to 10.83mM, and a detection limit of 100nM with a fast response time of less than 3s. Furthermore, non-enzymatic H2O2 sensors based on the 3D N-Co-CNT@NG electrode exhibited high sensitivity (28.66μAmM(-1)cm(-2)), wide linear range (2.0-7449μM), low detection limit of 2.0μM (S/N=3), excellent selectivity, decent reproducibility and long term stability. Such outstanding electrochemical performance can be endorsed to the large electroactive surface area, unique porous architecture, highly conductive networks, and synergistic interaction between N-Co-CNTs and nitrogen doped graphene (NG) in the novel 3D nanocomposite. This facile, cost-effective, sensitive, and selective glucose as well as H2O2 sensors are also proven to be appropriate for the detection of glucose as well as H2O2 in human serum.
Quantitative Inspection of Remanence of Broken Wire Rope Based on Compressed Sensing
Zhang, Juwei; Tan, Xiaojiang
2016-01-01
Most traditional strong magnetic inspection equipment has disadvantages such as big excitation devices, high weight, low detection precision, and inconvenient operation. This paper presents the design of a giant magneto-resistance (GMR) sensor array collection system. The remanence signal is collected to acquire two-dimensional magnetic flux leakage (MFL) data on the surface of wire ropes. Through the use of compressed sensing wavelet filtering (CSWF), the image expression of wire ropes MFL on the surface was obtained. Then this was taken as the input of the designed back propagation (BP) neural network to extract three kinds of MFL image geometry features and seven invariant moments of defect images. Good results were obtained. The experimental results show that nondestructive inspection through the use of remanence has higher accuracy and reliability compared with traditional inspection devices, along with smaller volume, lighter weight and higher precision. PMID:27571077
Quantitative Inspection of Remanence of Broken Wire Rope Based on Compressed Sensing.
Zhang, Juwei; Tan, Xiaojiang
2016-08-25
Most traditional strong magnetic inspection equipment has disadvantages such as big excitation devices, high weight, low detection precision, and inconvenient operation. This paper presents the design of a giant magneto-resistance (GMR) sensor array collection system. The remanence signal is collected to acquire two-dimensional magnetic flux leakage (MFL) data on the surface of wire ropes. Through the use of compressed sensing wavelet filtering (CSWF), the image expression of wire ropes MFL on the surface was obtained. Then this was taken as the input of the designed back propagation (BP) neural network to extract three kinds of MFL image geometry features and seven invariant moments of defect images. Good results were obtained. The experimental results show that nondestructive inspection through the use of remanence has higher accuracy and reliability compared with traditional inspection devices, along with smaller volume, lighter weight and higher precision.
Compressed sensing of hyperspectral images based on scrambled block Hadamard ensemble
NASA Astrophysics Data System (ADS)
Wang, Li; Feng, Yan
2016-11-01
A fast measurement matrix based on scrambled block Hadamard ensemble for compressed sensing (CS) of hyperspectral images (HSI) is investigated. The proposed measurement matrix offers several attractive features. First, the proposed measurement matrix possesses Gaussian behavior, which illustrates that the matrix is universal and requires a near-optimal number of samples for exact reconstruction. In addition, it could be easily implemented in the optical domain due to its integer-valued elements. More importantly, the measurement matrix only needs small memory for storage in the sampling process. Experimental results on HSIs reveal that the reconstruction performance of the proposed measurement matrix is comparable or better than Gaussian matrix and Bernoulli matrix using different reconstruction algorithms while consuming less computational time. The proposed matrix could be used in CS of HSI, which would save the storage memory on board, improve the sampling efficiency, and ameliorate the reconstruction quality.
Multi-static passive SAR imaging based on Bayesian compressive sensing
NASA Astrophysics Data System (ADS)
Wu, Qisong; Zhang, Yimin D.; Amin, Moeness G.; Himed, Braham
2014-05-01
Passive radar systems, which utilize broadcast and navigation signals as sources of opportunity, have attracted significant interests in recent years due to their low cost, covertness, and the availability of different illuminator sources. In this paper, we propose a novel method for synthetic aperture imaging in multi-static passive radar systems based on a group sparse Bayesian learning technique. In particular, the problem of imaging sparse targets is formulated as a group sparse signal reconstruction problem, which is solved using a complex multi- task Bayesian compressive sensing (CMT-BCS) method to achieve a high resolution. The proposed approach significantly improves the imaging resolution beyond the range resolution. Compared to the other group sparse signal reconstruction methods, such as the block orthogonal matching pursuit (BOMP) and group Lasso, the CMT-BCS provides significant performance improvement for the reconstruction of sparse targets in the redundant dictionary with high coherence. Simulations results demonstrate the superior performance of the proposed approach.
A compressed-sensing approach for super-resolution reconstruction of diffusion MRI
Ning, Lipeng; Setsompop, Kawin; Michailovich, Oleg; Makris, Nikos; Westin, Carl-Fredrik; Rathi, Yogesh
2015-01-01
We present an innovative framework for reconstructing high-spatial-resolution diffusion magnetic resonance imaging (dMRI) from multiple low-resolution (LR) images. Our approach combines the twin concepts of compressed sensing (CS) and classical super-resolution to reduce acquisition time while increasing spatial resolution. We use sub-pixel-shifted LR images with down-sampled and non-overlapping diffusion directions to reduce acquisition time. The diffusion signal in the high resolution (HR) image is represented in a sparsifying basis of spherical ridgelets to model complex fiber orientations with reduced number of measurements. The HR image is obtained as the solution of a convex optimization problem which can be solved using the proposed algorithm based on the alternating direction method of multipliers (ADMM). We qualitatively and quantitatively evaluate the performance of our method on two sets of in-vivo human brain data and show its effectiveness in accurately recovering very high resolution diffusion images. PMID:26221667
An infrared image super-resolution reconstruction method based on compressive sensing
NASA Astrophysics Data System (ADS)
Mao, Yuxing; Wang, Yan; Zhou, Jintao; Jia, Haiwei
2016-05-01
Limited by the properties of infrared detector and camera lens, infrared images are often detail missing and indistinct in vision. The spatial resolution needs to be improved to satisfy the requirements of practical application. Based on compressive sensing (CS) theory, this thesis presents a single image super-resolution reconstruction (SRR) method. With synthetically adopting image degradation model, difference operation-based sparse transformation method and orthogonal matching pursuit (OMP) algorithm, the image SRR problem is transformed into a sparse signal reconstruction issue in CS theory. In our work, the sparse transformation matrix is obtained through difference operation to image, and, the measurement matrix is achieved analytically from the imaging principle of infrared camera. Therefore, the time consumption can be decreased compared with the redundant dictionary obtained by sample training such as K-SVD. The experimental results show that our method can achieve favorable performance and good stability with low algorithm complexity.
Image reconstruction of compressed sensing MRI using graph-based redundant wavelet transform.
Lai, Zongying; Qu, Xiaobo; Liu, Yunsong; Guo, Di; Ye, Jing; Zhan, Zhifang; Chen, Zhong
2016-01-01
Compressed sensing magnetic resonance imaging has shown great capacity for accelerating magnetic resonance imaging if an image can be sparsely represented. How the image is sparsified seriously affects its reconstruction quality. In the present study, a graph-based redundant wavelet transform is introduced to sparsely represent magnetic resonance images in iterative image reconstructions. With this transform, image patches is viewed as vertices and their differences as edges, and the shortest path on the graph minimizes the total difference of all image patches. Using the l1 norm regularized formulation of the problem solved by an alternating-direction minimization with continuation algorithm, the experimental results demonstrate that the proposed method outperforms several state-of-the-art reconstruction methods in removing artifacts and achieves fewer reconstruction errors on the tested datasets.
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
Sparse-view ultrasound diffraction tomography using compressed sensing with nonuniform FFT.
Hua, Shaoyan; Ding, Mingyue; Yuchi, Ming
2014-01-01
Accurate reconstruction of the object from sparse-view sampling data is an appealing issue for ultrasound diffraction tomography (UDT). In this paper, we present a reconstruction method based on compressed sensing framework for sparse-view UDT. Due to the piecewise uniform characteristics of anatomy structures, the total variation is introduced into the cost function to find a more faithful sparse representation of the object. The inverse problem of UDT is iteratively resolved by conjugate gradient with nonuniform fast Fourier transform. Simulation results show the effectiveness of the proposed method that the main characteristics of the object can be properly presented with only 16 views. Compared to interpolation and multiband method, the proposed method can provide higher resolution and lower artifacts with the same view number. The robustness to noise and the computation complexity are also discussed.
Compressive sensing based spinning mode detections by in-duct microphone arrays
NASA Astrophysics Data System (ADS)
Yu, Wenjun; Huang, Xun
2016-05-01
This paper presents a compressive sensing based experimental method for detecting spinning modes of sound waves propagating inside a cylindrical duct system. This method requires fewer dynamic pressure sensors than the number required by the Shannon-Nyquist sampling theorem so long as the incident waves are sparse in spinning modes. In this work, the proposed new method is firstly validated by preparing some of the numerical simulations with representative set-ups. Then, a duct acoustic testing rig with a spinning mode synthesiser and an in-duct microphone array is built to experimentally demonstrate the new approach. Both the numerical simulations and the experiment results are satisfactory, even when the practical issue of the background noise pollution is taken into account. The approach is beneficial for sensory array tests of silent aeroengines in particular and some other engineering systems with duct acoustics in general.
NASA Astrophysics Data System (ADS)
Aharchaou, M.; Levander, A.
2016-11-01
We propose a new approach to the linear Radon transform (LRT) based on compressive sensing (CS) theory. This method can be used to extract signals of interest embedded in teleseismic measurements recorded by regional seismic arrays. We pose the problem of enhancing the resolution of the LRT as an inverse problem formulated in the frequency domain and solved according to a CS framework. We show how irregularity in the measurements along with sparsity constraints can be used to reach very compact and meaningful representations in the Radon domain, offering a benefit for both signal isolation and spatial interpolation during data reconstruction. We demonstrate the effectiveness of our approach and its benefits on both synthetic and USArray seismograms. This CS-based version of the LRT presents a valuable tool relevant for both global and exploration seismic processing, and which can be used as a basis for signal enhancement techniques exploiting irregularly sampled data.
Chen, Minhua; Silva, Jorge; Paisley, John; Wang, Chunping; Dunson, David; Carin, Lawrence
2013-01-01
Nonparametric Bayesian methods are employed to constitute a mixture of low-rank Gaussians, for data x ∈ ℝN that are of high dimension N but are constrained to reside in a low-dimensional subregion of ℝN. The number of mixture components and their rank are inferred automatically from the data. The resulting algorithm can be used for learning manifolds and for reconstructing signals from manifolds, based on compressive sensing (CS) projection measurements. The statistical CS inversion is performed analytically. We derive the required number of CS random measurements needed for successful reconstruction, based on easily-computed quantities, drawing on block-sparsity properties. The proposed methodology is validated on several synthetic and real datasets. PMID:23894225
Biological application of Compressed Sensing Tomography in the Scanning Electron Microscope
NASA Astrophysics Data System (ADS)
Ferroni, Matteo; Signoroni, Alberto; Sanzogni, Andrea; Masini, Luca; Migliori, Andrea; Ortolani, Luca; Pezza, Alessandro; Morandi, Vittorio
2016-09-01
The three-dimensional tomographic reconstruction of a biological sample, namely collagen fibrils in human dermal tissue, was obtained from a set of projection-images acquired in the Scanning Electron Microscope. A tailored strategy for the transmission imaging mode was implemented in the microscope and proved effective in acquiring the projections needed for the tomographic reconstruction. Suitable projection alignment and Compressed Sensing formulation were used to overcome the limitations arising from the experimental acquisition strategy and to improve the reconstruction of the sample. The undetermined problem of structure reconstruction from a set of projections, limited in number and angular range, was indeed supported by exploiting the sparsity of the object projected in the electron microscopy images. In particular, the proposed system was able to preserve the reconstruction accuracy even in presence of a significant reduction of experimental projections.
Biological application of Compressed Sensing Tomography in the Scanning Electron Microscope
Ferroni, Matteo; Signoroni, Alberto; Sanzogni, Andrea; Masini, Luca; Migliori, Andrea; Ortolani, Luca; Pezza, Alessandro; Morandi, Vittorio
2016-01-01
The three-dimensional tomographic reconstruction of a biological sample, namely collagen fibrils in human dermal tissue, was obtained from a set of projection-images acquired in the Scanning Electron Microscope. A tailored strategy for the transmission imaging mode was implemented in the microscope and proved effective in acquiring the projections needed for the tomographic reconstruction. Suitable projection alignment and Compressed Sensing formulation were used to overcome the limitations arising from the experimental acquisition strategy and to improve the reconstruction of the sample. The undetermined problem of structure reconstruction from a set of projections, limited in number and angular range, was indeed supported by exploiting the sparsity of the object projected in the electron microscopy images. In particular, the proposed system was able to preserve the reconstruction accuracy even in presence of a significant reduction of experimental projections. PMID:27646194
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.
An adaptive fusion approach for infrared and visible images based on NSCT and compressed sensing
NASA Astrophysics Data System (ADS)
Zhang, Qiong; Maldague, Xavier
2016-01-01
A novel nonsubsampled contourlet transform (NSCT) based image fusion approach, implementing an adaptive-Gaussian (AG) fuzzy membership method, compressed sensing (CS) technique, total variation (TV) based gradient descent reconstruction algorithm, is proposed for the fusion computation of infrared and visible images. Compared with wavelet, contourlet, or any other multi-resolution analysis method, NSCT has many evident advantages, such as multi-scale, multi-direction, and translation invariance. As is known, a fuzzy set is characterized by its membership function (MF), while the commonly known Gaussian fuzzy membership degree can be introduced to establish an adaptive control of the fusion processing. The compressed sensing technique can sparsely sample the image information in a certain sampling rate, and the sparse signal can be recovered by solving a convex problem employing gradient descent based iterative algorithm(s). In the proposed fusion process, the pre-enhanced infrared image and the visible image are decomposed into low-frequency subbands and high-frequency subbands, respectively, via the NSCT method as a first step. The low-frequency coefficients are fused using the adaptive regional average energy rule; the highest-frequency coefficients are fused using the maximum absolute selection rule; the other high-frequency coefficients are sparsely sampled, fused using the adaptive-Gaussian regional standard deviation rule, and then recovered by employing the total variation based gradient descent recovery algorithm. Experimental results and human visual perception illustrate the effectiveness and advantages of the proposed fusion approach. The efficiency and robustness are also analyzed and discussed through different evaluation methods, such as the standard deviation, Shannon entropy, root-mean-square error, mutual information and edge-based similarity index.
Detection and Tracking of Moving Targets Behind Cluttered Environments Using Compressive Sensing
NASA Astrophysics Data System (ADS)
Dang, Vinh Quang
Detection and tracking of moving targets (target's motion, vibration, etc.) in cluttered environments have been receiving much attention in numerous applications, such as disaster search-and-rescue, law enforcement, urban warfare, etc. One of the popular techniques is the use of stepped frequency continuous wave radar due to its low cost and complexity. However, the stepped frequency radar suffers from long data acquisition time. This dissertation focuses on detection and tracking of moving targets and vibration rates of stationary targets behind cluttered medium such as wall using stepped frequency radar enhanced by compressive sensing. The application of compressive sensing enables the reconstruction of the target space using fewer random frequencies, which decreases the acquisition time. Hardware-accelerated parallelization on GPU is investigated for the Orthogonal Matching Pursuit reconstruction algorithm. For simulation purpose, two hybrid methods have been developed to calculate the scattered fields from the targets through the wall approaching the antenna system, and to convert the incoming fields into voltage signals at terminals of the receive antenna. The first method is developed based on the plane wave spectrum approach for calculating the scattered fields of targets behind the wall. The method uses Fast Multiple Method (FMM) to calculate scattered fields on a particular source plane, decomposes them into plane wave components, and propagates the plane wave spectrum through the wall by integrating wall transmission coefficients before constructing the fields on a desired observation plane. The second method allows one to calculate the complex output voltage at terminals of a receiving antenna which fully takes into account the antenna effects. This method adopts the concept of complex antenna factor in Electromagnetic Compatibility (EMC) community for its calculation.
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
3d-3d correspondence revisited
Chung, Hee -Joong; Dimofte, Tudor; Gukov, Sergei; ...
2016-04-21
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. As a result, we also consider the cutting and gluing of 3-manifolds along smooth boundaries and the role played by all flat connections in this operation.
Performance assessment of a single-pixel compressive sensing imaging system
NASA Astrophysics Data System (ADS)
Du Bosq, Todd W.; Preece, Bradley L.
2016-05-01
Conventional electro-optical and infrared (EO/IR) systems capture an image by measuring the light incident at each of the millions of pixels in a focal plane array. Compressive sensing (CS) involves capturing a smaller number of unconventional measurements from the scene, and then using a companion process known as sparse reconstruction to recover the image as if a fully populated array that satisfies the Nyquist criteria was used. Therefore, CS operates under the assumption that signal acquisition and data compression can be accomplished simultaneously. CS has the potential to acquire an image with equivalent information content to a large format array while using smaller, cheaper, and lower bandwidth components. However, the benefits of CS do not come without compromise. The CS architecture chosen must effectively balance between physical considerations (SWaP-C), reconstruction accuracy, and reconstruction speed to meet operational requirements. To properly assess the value of such systems, it is necessary to fully characterize the image quality, including artifacts and sensitivity to noise. Imagery of the two-handheld object target set at range was collected using a passive SWIR single-pixel CS camera for various ranges, mirror resolution, and number of processed measurements. Human perception experiments were performed to determine the identification performance within the trade space. The performance of the nonlinear CS camera was modeled with the Night Vision Integrated Performance Model (NV-IPM) by mapping the nonlinear degradations to an equivalent linear shift invariant model. Finally, the limitations of CS modeling techniques will be discussed.
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.
Huang, Wei; Xiao, Liang; Liu, Hongyi; Wei, Zhihui
2015-01-01
Due to the instrumental and imaging optics limitations, it is difficult to acquire high spatial resolution hyperspectral imagery (HSI). Super-resolution (SR) imagery aims at inferring high quality images of a given scene from degraded versions of the same scene. This paper proposes a novel hyperspectral imagery super-resolution (HSI-SR) method via dictionary learning and spatial-spectral regularization. The main contributions of this paper are twofold. First, inspired by the compressive sensing (CS) framework, for learning the high resolution dictionary, we encourage stronger sparsity on image patches and promote smaller coherence between the learned dictionary and sensing matrix. Thus, a sparsity and incoherence restricted dictionary learning method is proposed to achieve higher efficiency sparse representation. Second, a variational regularization model combing a spatial sparsity regularization term and a new local spectral similarity preserving term is proposed to integrate the spectral and spatial-contextual information of the HSI. Experimental results show that the proposed method can effectively recover spatial information and better preserve spectral information. The high spatial resolution HSI reconstructed by the proposed method outperforms reconstructed results by other well-known methods in terms of both objective measurements and visual evaluation. PMID:25608212
Tam, Leo K.; Galiana, Gigi; Stockmann, Jason P.; Tagare, Hemant; Peters, Dana C.; Constable, R. Todd
2014-01-01
Purpose Nonlinear spatial encoding magnetic (SEM) field strategies such as O-space imaging have previously reported dispersed artifacts during accelerated scans. Compressed sensing (CS) has shown a sparsity-promoting convex program allows image reconstruction from a reduced data set when using the appropriate sampling. The development of a pseudo-random center placement (CP) O-space CS approach optimizes incoherence through SEM field modulation to reconstruct an image with reduced error. Theory and Methods The incoherence parameter determines the sparsity levels for which CS is valid and the related transform point spread function measures the maximum interference for a single point. The O-space acquisition is optimized for CS by perturbing the Z2 strength within 30% of the nominal value and demonstrated on a human 3T scanner. Results Pseudo-random CP O-space imaging is shown to improve incoherence between the sensing and sparse domains. Images indicate pseudo-random CP O-space has reduced mean squared error compared with a typical linear SEM field acquisition method. Conclusion Pseudo-random CP O-space imaging, with a nonlinear SEM field designed for CS, is shown to reduce mean squared error of images at high acceleration over linear encoding methods for a 2D slice when using an eight channel circumferential receiver array for parallel imaging. PMID:25042143
Digital holography and 3-D imaging.
Banerjee, Partha; Barbastathis, George; Kim, Myung; Kukhtarev, Nickolai
2011-03-01
This feature issue on Digital Holography and 3-D Imaging comprises 15 papers on digital holographic techniques and applications, computer-generated holography and encryption techniques, and 3-D display. It is hoped that future work in the area leads to innovative applications of digital holography and 3-D imaging to biology and sensing, and to the development of novel nonlinear dynamic digital holographic techniques.
NASA Astrophysics Data System (ADS)
Meulien Ohlmann, Odile
2013-02-01
Today the industry offers a chain of 3D products. Learning to "read" and to "create in 3D" becomes an issue of education of primary importance. 25 years professional experience in France, the United States and Germany, Odile Meulien set up a personal method of initiation to 3D creation that entails the spatial/temporal experience of the holographic visual. She will present some different tools and techniques used for this learning, their advantages and disadvantages, programs and issues of educational policies, constraints and expectations related to the development of new techniques for 3D imaging. Although the creation of display holograms is very much reduced compared to the creation of the 90ies, the holographic concept is spreading in all scientific, social, and artistic activities of our present time. She will also raise many questions: What means 3D? Is it communication? Is it perception? How the seeing and none seeing is interferes? What else has to be taken in consideration to communicate in 3D? How to handle the non visible relations of moving objects with subjects? Does this transform our model of exchange with others? What kind of interaction this has with our everyday life? Then come more practical questions: How to learn creating 3D visualization, to learn 3D grammar, 3D language, 3D thinking? What for? At what level? In which matter? for whom?
Wow! 3D Content Awakens the Classroom
ERIC Educational Resources Information Center
Gordon, Dan
2010-01-01
From her first encounter with stereoscopic 3D technology designed for classroom instruction, Megan Timme, principal at Hamilton Park Pacesetter Magnet School in Dallas, sensed it could be transformative. Last spring, when she began pilot-testing 3D content in her third-, fourth- and fifth-grade classrooms, Timme wasn't disappointed. Students…
NASA Astrophysics Data System (ADS)
Schneiderwind, S.; Mason, J.; Wiatr, T.; Papanikolaou, I.; Reicherter, K.
2015-09-01
Two normal faults on the Island of Crete and mainland Greece were studied to create and test an innovative workflow to make palaeoseismic trench logging more objective, and visualise the sedimentary architecture within the trench wall in 3-D. This is achieved by combining classical palaeoseismic trenching techniques with multispectral approaches. A conventional trench log was firstly compared to results of iso cluster analysis of a true colour photomosaic representing the spectrum of visible light. Passive data collection disadvantages (e.g. illumination) were addressed by complementing the dataset with active near-infrared backscatter signal image from t-LiDAR measurements. The multispectral analysis shows that distinct layers can be identified and it compares well with the conventional trench log. According to this, a distinction of adjacent stratigraphic units was enabled by their particular multispectral composition signature. Based on the trench log, a 3-D-interpretation of GPR data collected on the vertical trench wall was then possible. This is highly beneficial for measuring representative layer thicknesses, displacements and geometries at depth within the trench wall. Thus, misinterpretation due to cutting effects is minimised. Sedimentary feature geometries related to earthquake magnitude can be used to improve the accuracy of seismic hazard assessments. Therefore, this manuscript combines multiparametric approaches and shows: (i) how a 3-D visualisation of palaeoseismic trench stratigraphy and logging can be accomplished by combining t-LiDAR and GRP techniques, and (ii) how a multispectral digital analysis can offer additional advantages and a higher objectivity in the interpretation of palaeoseismic and stratigraphic information. The multispectral datasets are stored allowing unbiased input for future (re-)investigations.
A review of 3D first-pass, whole-heart, myocardial perfusion cardiovascular magnetic resonance.
Fair, Merlin J; Gatehouse, Peter D; DiBella, Edward V R; Firmin, David N
2015-08-01
A comprehensive review is undertaken of the methods available for 3D whole-heart first-pass perfusion (FPP) and their application to date, with particular focus on possible acceleration techniques. Following a summary of the parameters typically desired of 3D FPP methods, the review explains the mechanisms of key acceleration techniques and their potential use in FPP for attaining 3D acquisitions. The mechanisms include rapid sequences, non-Cartesian k-space trajectories, reduced k-space acquisitions, parallel imaging reconstructions and compressed sensing. An attempt is made to explain, rather than simply state, the varying methods with the hope that it will give an appreciation of the different components making up a 3D FPP protocol. Basic estimates demonstrating the required total acceleration factors in typical 3D FPP cases are included, providing context for the extent that each acceleration method can contribute to the required imaging speed, as well as potential limitations in present 3D FPP literature. Although many 3D FPP methods are too early in development for the type of clinical trials required to show any clear benefit over current 2D FPP methods, the review includes the small but growing quantity of clinical research work already using 3D FPP, alongside the more technical work. Broader challenges concerning FPP such as quantitative analysis are not covered, but challenges with particular impact on 3D FPP methods, particularly with regards to motion effects, are discussed along with anticipated future work in the field.
NASA Astrophysics Data System (ADS)
Ting, Samuel T.
other dynamic and static imaging techniques based on cardiac magnetic resonance. Conventional segmented techniques for cardiac cine imaging require breath-holding as well as regular cardiac rhythm, and can be time-consuming to acquire. Inadequate breath-holding or irregular cardiac rhythm can result in completely non-diagnostic images, limiting the utility of these techniques in a significant patient population. Real-time single-shot cardiac cine imaging enables free-breathing acquisition with significantly shortened imaging time and promises to significantly improve the utility of cine imaging for diagnosis and evaluation of cardiovascular disease. However, utility of real-time cine images depends heavily on the successful reconstruction of final cine images from undersampled data. Successful reconstruction of images from more highly undersampled data results directly in images exhibiting finer spatial and temporal resolution provided that image quality is sufficient. This work focuses primarily on the development, validation, and deployment of practical techniques for enabling the reconstruction of real-time cardiac cine images at the spatial and temporal resolutions and image quality needed for diagnostic utility. Particular emphasis is placed on the development of reconstruction approaches resulting in with short computation times that can be used in the clinical environment. Specifically, the use of compressed sensing signal recovery techniques is considered; such techniques show great promise in allowing successful reconstruction of highly undersampled data. The scope of this work concerns two primary topics related to signal recovery using compressed sensing: (1) long reconstruction times of these techniques, and (2) improved sparsity models for signal recovery from more highly undersampled data. Both of these aspects are relevant to the practical application of compressed sensing techniques in the context of improving image reconstruction of real-time cardiac
Effective Low-Power Wearable Wireless Surface EMG Sensor Design Based on Analog-Compressed Sensing
Balouchestani, Mohammadreza; Krishnan, Sridhar
2014-01-01
Surface Electromyography (sEMG) is a non-invasive measurement process that does not involve tools and instruments to break the skin or physically enter the body to investigate and evaluate the muscular activities produced by skeletal muscles. The main drawbacks of existing sEMG systems are: (1) they are not able to provide real-time monitoring; (2) they suffer from long processing time and low speed; (3) they are not effective for wireless healthcare systems because they consume huge power. In this work, we present an analog-based Compressed Sensing (CS) architecture, which consists of three novel algorithms for design and implementation of wearable wireless sEMG bio-sensor. At the transmitter side, two new algorithms are presented in order to apply the analog-CS theory before Analog to Digital Converter (ADC). At the receiver side, a robust reconstruction algorithm based on a combination of ℓ1-ℓ1-optimization and Block Sparse Bayesian Learning (BSBL) framework is presented to reconstruct the original bio-signals from the compressed bio-signals. The proposed architecture allows reducing the sampling rate to 25% of Nyquist Rate (NR). In addition, the proposed architecture reduces the power consumption to 40%, Percentage Residual Difference (PRD) to 24%, Root Mean Squared Error (RMSE) to 2%, and the computation time from 22 s to 9.01 s, which provide good background for establishing wearable wireless healthcare systems. The proposed architecture achieves robust performance in low Signal-to-Noise Ratio (SNR) for the reconstruction process. PMID:25526357
Effective low-power wearable wireless surface EMG sensor design based on analog-compressed sensing.
Balouchestani, Mohammadreza; Krishnan, Sridhar
2014-12-17
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.
NASA Astrophysics Data System (ADS)
Qin, W.; Yin, J.; Yao, H.
2013-12-01
On May 24th 2013 a Mw 8.3 normal faulting earthquake occurred at a depth of approximately 600 km beneath the sea of Okhotsk, Russia. It is a rare mega earthquake that ever occurred at such a great depth. We use the time-domain iterative backprojection (IBP) method [1] and also the frequency-domain compressive sensing (CS) technique[2] to investigate the rupture process and energy radiation of this mega earthquake. We currently use the teleseismic P-wave data from about 350 stations of USArray. IBP is an improved method of the traditional backprojection method, which more accurately locates subevents (energy burst) during earthquake rupture and determines the rupture speeds. The total rupture duration of this earthquake is about 35 s with a nearly N-S rupture direction. We find that the rupture is bilateral in the beginning 15 seconds with slow rupture speeds: about 2.5km/s for the northward rupture and about 2 km/s for the southward rupture. After that, the northward rupture stopped while the rupture towards south continued. The average southward rupture speed between 20-35 s is approximately 5 km/s, lower than the shear wave speed (about 5.5 km/s) at the hypocenter depth. The total rupture length is about 140km, in a nearly N-S direction, with a southward rupture length about 100 km and a northward rupture length about 40 km. We also use the CS method, a sparse source inversion technique, to study the frequency-dependent seismic radiation of this mega earthquake. We observe clear along-strike frequency dependence of the spatial and temporal distribution of seismic radiation and rupture process. The results from both methods are generally similar. In the next step, we'll use data from dense arrays in southwest China and also global stations for further analysis in order to more comprehensively study the rupture process of this deep mega earthquake. Reference [1] Yao H, Shearer P M, Gerstoft P. Subevent location and rupture imaging using iterative backprojection for
Improved compressed sensing-based cone-beam CT reconstruction using adaptive prior image constraints
NASA Astrophysics Data System (ADS)
Lee, Ho; Xing, Lei; Davidi, Ran; Li, Ruijiang; Qian, Jianguo; Lee, Rena
2012-04-01
Volumetric cone-beam CT (CBCT) images are acquired repeatedly during a course of radiation therapy and a natural question to ask is whether CBCT images obtained earlier in the process can be utilized as prior knowledge to reduce patient imaging dose in subsequent scans. The purpose of this work is to develop an adaptive prior image constrained compressed sensing (APICCS) method to solve this problem. Reconstructed images using full projections are taken on the first day of radiation therapy treatment and are used as prior images. The subsequent scans are acquired using a protocol of sparse projections. In the proposed APICCS algorithm, the prior images are utilized as an initial guess and are incorporated into the objective function in the compressed sensing (CS)-based iterative reconstruction process. Furthermore, the prior information is employed to detect any possible mismatched regions between the prior and current images for improved reconstruction. For this purpose, the prior images and the reconstructed images are classified into three anatomical regions: air, soft tissue and bone. Mismatched regions are identified by local differences of the corresponding groups in the two classified sets of images. A distance transformation is then introduced to convert the information into an adaptive voxel-dependent relaxation map. In constructing the relaxation map, the matched regions (unchanged anatomy) between the prior and current images are assigned with smaller weight values, which are translated into less influence on the CS iterative reconstruction process. On the other hand, the mismatched regions (changed anatomy) are associated with larger values and the regions are updated more by the new projection data, thus avoiding any possible adverse effects of prior images. The APICCS approach was systematically assessed by using patient data acquired under standard and low-dose protocols for qualitative and quantitative comparisons. The APICCS method provides an
Hall, Rick S; Desmoulin, Geoffrey T; Milner, Theodore E
2008-12-05
Miniature sensors that could measure forces applied by the fingers and hand without interfering with manual dexterity or range of motion would have considerable practical value in ergonomics and rehabilitation. In this study, techniques have been developed to use inexpensive pressure-sensing resistors (FSRs) to accurately measure compression force. The FSRs are converted from pressure-sensing to force-sensing devices. The effects of nonlinear response properties and dependence on loading history are compensated by signal conditioning and calibration. A fourth-order polynomial relating the applied force to the current voltage output and a linearly weighted sum of prior outputs corrects for sensor hysteresis and drift. It was found that prolonged (>20h) shear force loading caused sensor gain to change by approximately 100%. Shear loading also had the effect of eliminating shear force effects on sensor output, albeit only in the direction of shear loading. By applying prolonged shear loading in two orthogonal directions, the sensors were converted into pure compression sensors. Such preloading of the sensor is, therefore, required prior to calibration. The error in compression force after prolonged shear loading and calibration was consistently <5% from 0 to 30N and <10% from 30 to 40N. This novel method of calibrating FSRs for measuring compression force provides an inexpensive tool for biomedical and industrial design applications where measurements of finger and hand force are needed.
Wiscombe, Warren; Marshak, Alexander; Knyazikhin, Yuri; Chiu, Christine
2007-05-04
We have basically completed all the goals stated in the previous proposal and published or submitted journal papers thereon, the only exception being First-Principles Monte Carlo which has taken more time than expected. We finally finished the comprehensive book on 3D cloud radiative transfer (edited by Marshak and Davis and published by Springer), with many contributions by ARM scientists; this book was highlighted in the 2005 ARM Annual Report. We have also completed (for now) our pioneering work on new models of cloud drop clustering based on ARM aircraft FSSP data, with applications both to radiative transfer and to rainfall. This clustering work was highlighted in the FY07 “Our Changing Planet” (annual report of the US Climate Change Science Program). Our group published 22 papers, one book, and 5 chapters in that book, during this proposal period. All are listed at the end of this section. Below, we give brief highlights of some of those papers.
ERIC Educational Resources Information Center
Hastings, S. K.
2002-01-01
Discusses 3 D imaging as it relates to digital representations in virtual library collections. Highlights include X-ray computed tomography (X-ray CT); the National Science Foundation (NSF) Digital Library Initiatives; output peripherals; image retrieval systems, including metadata; and applications of 3 D imaging for libraries and museums. (LRW)
Machineni, Lakshmi; Rajapantul, Anil; Nandamuri, Vandana; Pawar, Parag D
2017-03-01
The resistance of bacterial biofilms to antibiotic treatment has been attributed to the emergence of structurally heterogeneous microenvironments containing metabolically inactive cell populations. In this study, we use a three-dimensional individual-based cellular automata model to investigate the influence of nutrient availability and quorum sensing on microbial heterogeneity in growing biofilms. Mature biofilms exhibited at least three structurally distinct strata: a high-volume, homogeneous region sandwiched between two compact sections of high heterogeneity. Cell death occurred preferentially in layers in close proximity to the substratum, resulting in increased heterogeneity in this section of the biofilm; the thickness and heterogeneity of this lowermost layer increased with time, ultimately leading to sloughing. The model predicted the formation of metabolically dormant cellular microniches embedded within faster-growing cell clusters. Biofilms utilizing quorum sensing were more heterogeneous compared to their non-quorum sensing counterparts, and resisted sloughing, featuring a cell-devoid layer of EPS atop the substratum upon which the remainder of the biofilm developed. Overall, our study provides a computational framework to analyze metabolic diversity and heterogeneity of biofilm-associated microorganisms and may pave the way toward gaining further insights into the biophysical mechanisms of antibiotic resistance.
Jin, An; Yazici, Birsen; Ntziachristos, Vasilis
2014-06-01
Fluorescence diffuse optical tomography (FDOT) is an emerging molecular imaging modality that uses near infrared light to excite the fluorophore injected into tissue; and to reconstruct the fluorophore concentration from boundary measurements. The FDOT image reconstruction is a highly ill-posed inverse problem due to a large number of unknowns and limited number of measurements. However, the fluorophore distribution is often very sparse in the imaging domain since fluorophores are typically designed to accumulate in relatively small regions. In this paper, we use compressive sensing (CS) framework to design light illumination and detection patterns to improve the reconstruction of sparse fluorophore concentration. Unlike the conventional FDOT imaging where spatially distributed light sources illuminate the imaging domain one at a time and the corresponding boundary measurements are used for image reconstruction, we assume that the light sources illuminate the imaging domain simultaneously several times and the corresponding boundary measurements are linearly filtered prior to image reconstruction. We design a set of optical intensities (illumination patterns) and a linear filter (detection pattern) applied to the boundary measurements to improve the reconstruction of sparse fluorophore concentration maps. We show that the FDOT sensing matrix can be expressed as a columnwise Kronecker product of two matrices determined by the excitation and emission light fields. We derive relationships between the incoherence of the FDOT forward matrix and these two matrices, and use these results to reduce the incoherence of the FDOT forward matrix. We present extensive numerical simulation and the results of a real phantom experiment to demonstrate the improvements in image reconstruction due to the CS-based light illumination and detection patterns in conjunction with relaxation and greedy-type reconstruction algorithms.
Sampling of finite elements for sparse recovery in large scale 3D electrical impedance tomography.
Javaherian, Ashkan; Soleimani, Manuchehr; Moeller, Knut
2015-01-01
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.
3D change detection - Approaches and applications
NASA Astrophysics Data System (ADS)
Qin, Rongjun; Tian, Jiaojiao; Reinartz, Peter
2016-12-01
Due to the unprecedented technology development of sensors, platforms and algorithms for 3D data acquisition and generation, 3D spaceborne, airborne and close-range data, in the form of image based, Light Detection and Ranging (LiDAR) based point clouds, Digital Elevation Models (DEM) and 3D city models, become more accessible than ever before. Change detection (CD) or time-series data analysis in 3D has gained great attention due to its capability of providing volumetric dynamics to facilitate more applications and provide more accurate results. The state-of-the-art CD reviews aim to provide a comprehensive synthesis and to simplify the taxonomy of the traditional remote sensing CD techniques, which mainly sit within the boundary of 2D image/spectrum analysis, largely ignoring the particularities of 3D aspects of the data. The inclusion of 3D data for change detection (termed 3D CD), not only provides a source with different modality for analysis, but also transcends the border of traditional top-view 2D pixel/object-based analysis to highly detailed, oblique view or voxel-based geometric analysis. This paper reviews the recent developments and applications of 3D CD using remote sensing and close-range data, in support of both academia and industry researchers who seek for solutions in detecting and analyzing 3D dynamics of various objects of interest. We first describe the general considerations of 3D CD problems in different processing stages and identify CD types based on the information used, being the geometric comparison and geometric-spectral analysis. We then summarize relevant works and practices in urban, environment, ecology and civil applications, etc. Given the broad spectrum of applications and different types of 3D data, we discuss important issues in 3D CD methods. Finally, we present concluding remarks in algorithmic aspects of 3D CD.
Compressed sensing reconstruction of cardiac cine MRI using golden angle spiral trajectories
NASA Astrophysics Data System (ADS)
Tolouee, Azar; Alirezaie, Javad; Babyn, Paul
2015-11-01
In dynamic cardiac cine Magnetic Resonance Imaging (MRI), the spatiotemporal resolution is limited by the low imaging speed. Compressed sensing (CS) theory has been applied to improve the imaging speed and thus the spatiotemporal resolution. The purpose of this paper is to improve CS reconstruction of under sampled data by exploiting spatiotemporal sparsity and efficient spiral trajectories. We extend k-t sparse algorithm to spiral trajectories to achieve high spatio temporal resolutions in cardiac cine imaging. We have exploited spatiotemporal sparsity of cardiac cine MRI by applying a 2D + time wavelet-Fourier transform. For efficient coverage of k-space, we have used a modified version of multi shot (interleaved) spirals trajectories. In order to reduce incoherent aliasing artifact, we use different random undersampling pattern for each temporal frame. Finally, we have used nonuniform fast Fourier transform (NUFFT) algorithm to reconstruct the image from the non-uniformly acquired samples. The proposed approach was tested in simulated and cardiac cine MRI data. Results show that higher acceleration factors with improved image quality can be obtained with the proposed approach in comparison to the existing state-of-the-art method. The flexibility of the introduced method should allow it to be used not only for the challenging case of cardiac imaging, but also for other patient motion where the patient moves or breathes during acquisition.
An efficient compressive sensing based PS-DInSAR method for surface deformation estimation
NASA Astrophysics Data System (ADS)
Li, J. T.; Xu, H. P.; Shan, L.; Liu, W.; Chen, G. Z.
2016-11-01
Permanent scatterers differential interferometric synthetic aperture radar (PS-DInSAR) is a technique for detecting surface micro-deformation, with an accuracy at the centimeter to millimeter level. However, its performance is limited by the number of SAR images available (normally more than 20 are needed). Compressive sensing (CS) has been proven to be an effective signal recovery method with only a very limited number of measurements. Applying CS to PS-DInSAR, a novel CS-PS-DInSAR method is proposed to estimate the deformation with fewer SAR images. By analyzing the PS-DInSAR process in detail, first the sparsity representation of deformation velocity difference is obtained; then, the mathematical model of CS-PS-DInSAR is derived and the restricted isometry property (RIP) of the measurement matrix is discussed to validate the proposed CS-PS-DInSAR in theory. The implementation of CS-PS-DInSAR is achieved by employing basis pursuit algorithms to estimate the deformation velocity. With the proposed method, DInSAR deformation estimation can be achieved by a much smaller number of SAR images, as demonstrated by simulation results.
NASA Astrophysics Data System (ADS)
Gan, Shuwei; Wang, Shoudong; Chen, Yangkang; Chen, Xiaohong; Huang, Weiling; Chen, Hanming
2016-07-01
According to the compressive sensing (CS) theory in the signal-processing field, we proposed a new CS approach based on a fast projection onto convex sets (POCS) algorithm with sparsity constraint in the seislet transform domain. The seislet transform appears to be the sparest among the state-of-the-art sparse transforms. The FPOCS can obtain much faster convergence than conventional POCS (about two thirds of conventional iterations can be saved), while maintaining the same recovery performance. The FPOCS can obtain faster and better performance than FISTA for relatively cleaner data but will get slower and worse performance than FISTA, which becomes a reference to decide which algorithm to use in practice according the noise level in the seismic data. The seislet transform based CS approach can achieve obviously better data recovery results than f - k transform based scenarios, considering both signal-to-noise ratio (SNR), local similarity comparison, and visual observation, because of a much sparser structure in the seislet transform domain. We have used both synthetic and field data examples to demonstrate the superior performance of the proposed seislet-based FPOCS approach.
Worst configurations (instantons) for compressed sensing over reals: a channel coding approach
Chertkov, Michael; Chilappagari, Shashi K; Vasic, Bane
2010-01-01
We consider Linear Programming (LP) solution of a Compressed Sensing (CS) problem over reals, also known as the Basis Pursuit (BasP) algorithm. The BasP allows interpretation as a channel-coding problem, and it guarantees the error-free reconstruction over reals for properly chosen measurement matrix and sufficiently sparse error vectors. In this manuscript, we examine how the BasP performs on a given measurement matrix and develop a technique to discover sparse vectors for which the BasP fails. The resulting algorithm is a generalization of our previous results on finding the most probable error-patterns, so called instantons, degrading performance of a finite size Low-Density Parity-Check (LDPC) code in the error-floor regime. The BasP fails when its output is different from the actual error-pattern. We design CS-Instanton Search Algorithm (ISA) generating a sparse vector, called CS-instanton, such that the BasP fails on the instanton, while its action on any modification of the CS-instanton decreasing a properly defined norm is successful. We also prove that, given a sufficiently dense random input for the error-vector, the CS-ISA converges to an instanton in a small finite number of steps. Performance of the CS-ISA is tested on example of a randomly generated 512 * 120 matrix, that outputs the shortest instanton (error vector) pattern of length 11.
Use of optical speckle patterns for compressive sensing of RF signals in the GHz band
NASA Astrophysics Data System (ADS)
Valley, George C.; Sefler, George A.; Shaw, T. Justin
2016-02-01
We demonstrate that speckle patterns at the output of multimode optical waveguides can be used for a compressive sensing (CS) measurement matrix (MM) to measure sparse RF signals in the GHz band (1-100 GHz). In our system mode-locked femtosecond laser pulses are stretched to a width on the order of the interpulse time, modulated by the RF, and injected into a multimode waveguide. The speckle pattern out of the guide is imaged onto an array of photodiodes whose output is digitized by a bank of ADCs. We have measured the CS MM for multimode fibers and used these MMs to demonstrate that sparse RF signals (sparsity K) modulated on a chirped optical carrier can be recovered from M measurements (the number of photodiodes) consistent with the CS relation M ~ K log(N/K) (N is the number of samples needed for Nyquist rate sampling). We demonstrate experimentally that speckle sampling gives comparable results to the photonic WDM sampling system used previously for periodic undersampling (multi-coset sampling) of RF chirp pulses. We have also calculated MMs for both multimode fibers and planar waveguides using their respective mode solutions to determine optimal waveguide parameters for a CS system. Our results suggest a path to a CS system for GHz band RF signals that can be completely constructed using photonic integrated circuit (PIC) technology.
Network dynamics for optimal compressive-sensing input-signal recovery.
Barranca, Victor J; Kovačič, Gregor; Zhou, Douglas; Cai, David
2014-10-01
By using compressive sensing (CS) theory, a broad class of static signals can be reconstructed through a sequence of very few measurements in the framework of a linear system. For networks with nonlinear and time-evolving dynamics, is it similarly possible to recover an unknown input signal from only a small number of network output measurements? We address this question for pulse-coupled networks and investigate the network dynamics necessary for successful input signal recovery. Determining the specific network characteristics that correspond to a minimal input reconstruction error, we are able to achieve high-quality signal reconstructions with few measurements of network output. Using various measures to characterize dynamical properties of network output, we determine that networks with highly variable and aperiodic output can successfully encode network input information with high fidelity and achieve the most accurate CS input reconstructions. For time-varying inputs, we also find that high-quality reconstructions are achievable by measuring network output over a relatively short time window. Even when network inputs change with time, the same optimal choice of network characteristics and corresponding dynamics apply as in the case of static inputs.
Network dynamics for optimal compressive-sensing input-signal recovery
NASA Astrophysics Data System (ADS)
Barranca, Victor J.; Kovačič, Gregor; Zhou, Douglas; Cai, David
2014-10-01
By using compressive sensing (CS) theory, a broad class of static signals can be reconstructed through a sequence of very few measurements in the framework of a linear system. For networks with nonlinear and time-evolving dynamics, is it similarly possible to recover an unknown input signal from only a small number of network output measurements? We address this question for pulse-coupled networks and investigate the network dynamics necessary for successful input signal recovery. Determining the specific network characteristics that correspond to a minimal input reconstruction error, we are able to achieve high-quality signal reconstructions with few measurements of network output. Using various measures to characterize dynamical properties of network output, we determine that networks with highly variable and aperiodic output can successfully encode network input information with high fidelity and achieve the most accurate CS input reconstructions. For time-varying inputs, we also find that high-quality reconstructions are achievable by measuring network output over a relatively short time window. Even when network inputs change with time, the same optimal choice of network characteristics and corresponding dynamics apply as in the case of static inputs.
Compact opto-electronic engine for high-speed compressive sensing
NASA Astrophysics Data System (ADS)
Tidman, James; Weston, Tyler; Hewitt, Donna; Herman, Matthew A.; McMackin, Lenore
2013-09-01
The measurement efficiency of Compressive Sensing (CS) enables the computational construction of images from far fewer measurements than what is usually considered necessary by the Nyquist- Shannon sampling theorem. There is now a vast literature around CS mathematics and applications since the development of its theoretical principles about a decade ago. Applications include quantum information to optical microscopy to seismic and hyper-spectral imaging. In the application of shortwave infrared imaging, InView has developed cameras based on the CS single-pixel camera architecture. This architecture is comprised of an objective lens to image the scene onto a Texas Instruments DLP® Micromirror Device (DMD), which by using its individually controllable mirrors, modulates the image with a selected basis set. The intensity of the modulated image is then recorded by a single detector. While the design of a CS camera is straightforward conceptually, its commercial implementation requires significant development effort in optics, electronics, hardware and software, particularly if high efficiency and high-speed operation are required. In this paper, we describe the development of a high-speed CS engine as implemented in a lab-ready workstation. In this engine, configurable measurement patterns are loaded into the DMD at speeds up to 31.5 kHz. The engine supports custom reconstruction algorithms that can be quickly implemented. Our work includes optical path design, Field programmable Gate Arrays for DMD pattern generation, and circuit boards for front end data acquisition, ADC and system control, all packaged in a compact workstation.
Three-dimensional acoustic imaging with planar microphone arrays and compressive sensing
NASA Astrophysics Data System (ADS)
Ning, Fangli; Wei, Jingang; Qiu, Lianfang; Shi, Hongbing; Li, Xiaofan
2016-10-01
For obtaining super-resolution source maps, we extend compressive sensing (CS) to three-dimensional acoustic imaging. Source maps are simulated with a planar microphone array and a CS algorithm. Comparing the source maps of the CS algorithm with those of the conventional beamformer (CBF) and Tikhonov Regularization (TIKR), we find that the CS algorithm is computationally more effective and can obtain much higher resolution source maps than the CBF and TIKR. The effectiveness of the CS algorithm is analyzed. The CS algorithm can locate the sound sources exactly when the frequency is above 4000 Hz and the signal-to-noise ratio (SNR) is above 12 dB. The location error of the CS algorithm increases as the frequency drops below the threshold, and the errors in location and power increase as SNR decreases. The further from the array the source is, the larger the location error is. The lateral resolution of the CS algorithm is much better than the range resolution. Finally, experimental measurements are conducted in a semi-anechoic room. Two mobile phones are served as sound sources. The results show that the CS algorithm can reconstruct two sound sources near the bottom of the two mobile phones where the speakers are located. The feasibility of the CS algorithm is also validated with the experiment.
Long-term surface EMG monitoring using K-means clustering and compressive sensing
NASA Astrophysics Data System (ADS)
Balouchestani, Mohammadreza; Krishnan, Sridhar
2015-05-01
In this work, we present an advanced K-means clustering algorithm based on Compressed Sensing theory (CS) in combination with the K-Singular Value Decomposition (K-SVD) method for Clustering of long-term recording of surface Electromyography (sEMG) signals. The long-term monitoring of sEMG signals aims at recording of the electrical activity produced by muscles which are very useful procedure for treatment and diagnostic purposes as well as for detection of various pathologies. The proposed algorithm is examined for three scenarios of sEMG signals including healthy person (sEMG-Healthy), a patient with myopathy (sEMG-Myopathy), and a patient with neuropathy (sEMG-Neuropathr), respectively. The proposed algorithm can easily scan large sEMG datasets of long-term sEMG recording. We test the proposed algorithm with Principal Component Analysis (PCA) and Linear Correlation Coefficient (LCC) dimensionality reduction methods. Then, the output of the proposed algorithm is fed to K-Nearest Neighbours (K-NN) and Probabilistic Neural Network (PNN) classifiers in order to calclute the clustering performance. The proposed algorithm achieves a classification accuracy of 99.22%. This ability allows reducing 17% of Average Classification Error (ACE), 9% of Training Error (TE), and 18% of Root Mean Square Error (RMSE). The proposed algorithm also reduces 14% clustering energy consumption compared to the existing K-Means clustering algorithm.
NASA Astrophysics Data System (ADS)
Zhao, Shengmei; Wang, Le; Liang, Wenqiang; Cheng, Weiwen; Gong, Longyan
2015-10-01
In this paper, we propose a high performance optical encryption (OE) scheme based on computational ghost imaging (GI) with QR code and compressive sensing (CS) technique, named QR-CGI-OE scheme. N random phase screens, generated by Alice, is a secret key and be shared with its authorized user, Bob. The information is first encoded by Alice with QR code, and the QR-coded image is then encrypted with the aid of computational ghost imaging optical system. Here, measurement results from the GI optical system's bucket detector are the encrypted information and be transmitted to Bob. With the key, Bob decrypts the encrypted information to obtain the QR-coded image with GI and CS techniques, and further recovers the information by QR decoding. The experimental and numerical simulated results show that the authorized users can recover completely the original image, whereas the eavesdroppers can not acquire any information about the image even the eavesdropping ratio (ER) is up to 60% at the given measurement times. For the proposed scheme, the number of bits sent from Alice to Bob are reduced considerably and the robustness is enhanced significantly. Meantime, the measurement times in GI system is reduced and the quality of the reconstructed QR-coded image is improved.
Phase Error Correction for Approximated Observation-Based Compressed Sensing Radar Imaging
Li, Bo; Liu, Falin; Zhou, Chongbin; Lv, Yuanhao; Hu, Jingqiu
2017-01-01
Defocus of the reconstructed image of synthetic aperture radar (SAR) occurs in the presence of the phase error. In this work, a phase error correction method is proposed for compressed sensing (CS) radar imaging based on approximated observation. The proposed method has better image focusing ability with much less memory cost, compared to the conventional approaches, due to the inherent low memory requirement of the approximated observation operator. The one-dimensional (1D) phase error correction for approximated observation-based CS-SAR imaging is first carried out and it can be conveniently applied to the cases of random-frequency waveform and linear frequency modulated (LFM) waveform without any a priori knowledge. The approximated observation operators are obtained by calculating the inverse of Omega-K and chirp scaling algorithms for random-frequency and LFM waveforms, respectively. Furthermore, the 1D phase error model is modified by incorporating a priori knowledge and then a weighted 1D phase error model is proposed, which is capable of correcting two-dimensional (2D) phase error in some cases, where the estimation can be simplified to a 1D problem. Simulation and experimental results validate the effectiveness of the proposed method in the presence of 1D phase error or weighted 1D phase error. PMID:28304353
Compressed sensing with gradient total variation for low-dose CBCT reconstruction
NASA Astrophysics Data System (ADS)
Seo, Chang-Woo; Cha, Bo Kyung; Jeon, Seongchae; Huh, Young; Park, Justin C.; Lee, Byeonghun; Baek, Junghee; Kim, Eunyoung
2015-06-01
This paper describes the improvement of convergence speed with gradient total variation (GTV) in compressed sensing (CS) for low-dose cone-beam computed tomography (CBCT) reconstruction. We derive a fast algorithm for the constrained total variation (TV)-based a minimum number of noisy projections. To achieve this task we combine the GTV with a TV-norm regularization term to promote an accelerated sparsity in the X-ray attenuation characteristics of the human body. The GTV is derived from a TV and enforces more efficient computationally and faster in convergence until a desired solution is achieved. The numerical algorithm is simple and derives relatively fast convergence. We apply a gradient projection algorithm that seeks a solution iteratively in the direction of the projected gradient while enforcing a non-negatively of the found solution. In comparison with the Feldkamp, Davis, and Kress (FDK) and conventional TV algorithms, the proposed GTV algorithm showed convergence in ≤18 iterations, whereas the original TV algorithm needs at least 34 iterations in reducing 50% of the projections compared with the FDK algorithm in order to reconstruct the chest phantom images. Future investigation includes improving imaging quality, particularly regarding X-ray cone-beam scatter, and motion artifacts of CBCT reconstruction.
NASA Astrophysics Data System (ADS)
Ali, Hussain; Ahmed, Sajid; Al-Naffouri, Tareq Y.; Sharawi, Mohammad S.; Alouini, Mohamed-S.
2017-01-01
Conventional algorithms used for parameter estimation in colocated multiple-input-multiple-output (MIMO) radars require the inversion of the covariance matrix of the received spatial samples. In these algorithms, the number of received snapshots should be at least equal to the size of the covariance matrix. For large size MIMO antenna arrays, the inversion of the covariance matrix becomes computationally very expensive. Compressive sensing (CS) algorithms which do not require the inversion of the complete covariance matrix can be used for parameter estimation with fewer number of received snapshots. In this work, it is shown that the spatial formulation is best suitable for large MIMO arrays when CS algorithms are used. A temporal formulation is proposed which fits the CS algorithms framework, especially for small size MIMO arrays. A recently proposed low-complexity CS algorithm named support agnostic Bayesian matching pursuit (SABMP) is used to estimate target parameters for both spatial and temporal formulations for the unknown number of targets. The simulation results show the advantage of SABMP algorithm utilizing low number of snapshots and better parameter estimation for both small and large number of antenna elements. Moreover, it is shown by simulations that SABMP is more effective than other existing algorithms at high signal-to-noise ratio.
NASA Astrophysics Data System (ADS)
Yu, Zhicong; Leng, Shuai; Li, Zhoubo; McCollough, Cynthia H.
2016-09-01
Photon-counting computed tomography (PCCT) is an emerging imaging technique that enables multi-energy imaging with only a single scan acquisition. To enable multi-energy imaging, the detected photons corresponding to the full x-ray spectrum are divided into several subgroups of bin data that correspond to narrower energy windows. Consequently, noise in each energy bin increases compared to the full-spectrum data. This work proposes an iterative reconstruction algorithm for noise suppression in the narrower energy bins used in PCCT imaging. The algorithm is based on the framework of prior image constrained compressed sensing (PICCS) and is called spectral PICCS; it uses the full-spectrum image reconstructed using conventional filtered back-projection as the prior image. The spectral PICCS algorithm is implemented using a constrained optimization scheme with adaptive iterative step sizes such that only two tuning parameters are required in most cases. The algorithm was first evaluated using computer simulations, and then validated by both physical phantoms and in vivo swine studies using a research PCCT system. Results from both computer-simulation and experimental studies showed substantial image noise reduction in narrow energy bins (43-73%) without sacrificing CT number accuracy or spatial resolution.
Two-Layer Tight Frame Sparsifying Model for Compressed Sensing Magnetic Resonance Imaging
Peng, Xi; Dong, Pei
2016-01-01
Compressed sensing magnetic resonance imaging (CSMRI) employs image sparsity to reconstruct MR images from incoherently undersampled K-space data. Existing CSMRI approaches have exploited analysis transform, synthesis dictionary, and their variants to trigger image sparsity. Nevertheless, the accuracy, efficiency, or acceleration rate of existing CSMRI methods can still be improved due to either lack of adaptability, high complexity of the training, or insufficient sparsity promotion. To properly balance the three factors, this paper proposes a two-layer tight frame sparsifying (TRIMS) model for CSMRI by sparsifying the image with a product of a fixed tight frame and an adaptively learned tight frame. The two-layer sparsifying and adaptive learning nature of TRIMS has enabled accurate MR reconstruction from highly undersampled data with efficiency. To solve the reconstruction problem, a three-level Bregman numerical algorithm is developed. The proposed approach has been compared to three state-of-the-art methods over scanned physical phantom and in vivo MR datasets and encouraging performances have been achieved. PMID:27747226
Xu, Jason; Minin, Vladimir N.
2016-01-01
Branching processes are a class of continuous-time Markov chains (CTMCs) with ubiquitous applications. A general difficulty in statistical inference under partially observed CTMC models arises in computing transition probabilities when the discrete state space is large or uncountable. Classical methods such as matrix exponentiation are infeasible for large or countably infinite state spaces, and sampling-based alternatives are computationally intensive, requiring integration over all possible hidden events. Recent work has successfully applied generating function techniques to computing transition probabilities for linear multi-type branching processes. While these techniques often require significantly fewer computations than matrix exponentiation, they also become prohibitive in applications with large populations. We propose a compressed sensing framework that significantly accelerates the generating function method, decreasing computational cost up to a logarithmic factor by only assuming the probability mass of transitions is sparse. We demonstrate accurate and efficient transition probability computations in branching process models for blood cell formation and evolution of self-replicating transposable elements in bacterial genomes. PMID:26949377
Inverse transport problem solvers based on regularized and compressive sensing techniques
Cheng, Y.; Cao, L.; Wu, H.; Zhang, H.
2012-07-01
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
2015-03-31
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.
Multidimensional Compressed Sensing MRI Using Tensor Decomposition-Based Sparsifying Transform
Yu, Yeyang; Jin, Jin; Liu, Feng; Crozier, Stuart
2014-01-01
Compressed Sensing (CS) has been applied in dynamic Magnetic Resonance Imaging (MRI) to accelerate the data acquisition without noticeably degrading the spatial-temporal resolution. A suitable sparsity basis is one of the key components to successful CS applications. Conventionally, a multidimensional dataset in dynamic MRI is treated as a series of two-dimensional matrices, and then various matrix/vector transforms are used to explore the image sparsity. Traditional methods typically sparsify the spatial and temporal information independently. In this work, we propose a novel concept of tensor sparsity for the application of CS in dynamic MRI, and present the Higher-order Singular Value Decomposition (HOSVD) as a practical example. Applications presented in the three- and four-dimensional MRI data demonstrate that HOSVD simultaneously exploited the correlations within spatial and temporal dimensions. Validations based on cardiac datasets indicate that the proposed method achieved comparable reconstruction accuracy with the low-rank matrix recovery methods and, outperformed the conventional sparse recovery methods. PMID:24901331
Non-reference quality assessment of infrared images reconstructed by compressive sensing
NASA Astrophysics Data System (ADS)
Ospina-Borras, J. E.; Benitez-Restrepo, H. D.
2015-01-01
Infrared (IR) images are representations of the world and have natural features like images in the visible spectrum. As such, natural features from infrared images support image quality assessment (IQA).1 In this work, we compare the quality of a set of indoor and outdoor IR images reconstructed from measurement functions formed by linear combination of their pixels. The reconstruction methods are: linear discrete cosine transform (DCT) acquisition, DCT augmented with total variation minimization, and compressive sensing scheme. Peak Signal to Noise Ratio (PSNR), three full-reference (FR), and four no-reference (NR) IQA measures compute the qualities of each reconstruction: multi-scale structural similarity (MSSIM), visual information fidelity (VIF), information fidelity criterion (IFC), sharpness identification based on local phase coherence (LPC-SI), blind/referenceless image spatial quality evaluator (BRISQUE), naturalness image quality evaluator (NIQE) and gradient singular value decomposition (GSVD), respectively. Each measure is compared to human scores that were obtained by differential mean opinion score (DMOS) test. We observe that GSVD has the highest correlation coefficients of all NR measures, but all FR have better performance. We use MSSIM to compare the reconstruction methods and we find that CS scheme produces a good-quality IR image, using only 30000 random sub-samples and 1000 DCT coefficients (2%). In contrast, linear DCT provides higher correlation coefficients than CS scheme by using all the pixels of the image and 31000 DCT (47%) coefficients.
Applying compressive sensing to TEM video: A substantial frame rate increase on any camera
Stevens, Andrew; Kovarik, Libor; Abellan, Patricia; Yuan, Xin; Carin, Lawrence; Browning, Nigel D.
2015-08-13
One of the main limitations of imaging at high spatial and temporal resolution during in-situ transmission electron microscopy (TEM) experiments is the frame rate of the camera being used to image the dynamic process. While the recent development of direct detectors has provided the hardware to achieve frame rates approaching 0.1 ms, the cameras are expensive and must replace existing detectors. In this paper, we examine the use of coded aperture compressive sensing (CS) methods to increase the frame rate of any camera with simple, low-cost hardware modifications. The coded aperture approach allows multiple sub-frames to be coded and integrated into a single camera frame during the acquisition process, and then extracted upon readout using statistical CS inversion. Here we describe the background of CS and statistical methods in depth and simulate the frame rates and efficiencies for in-situ TEM experiments. Depending on the resolution and signal/noise of the image, it should be possible to increase the speed of any camera by more than an order of magnitude using this approach.
Applying compressive sensing to TEM video: A substantial frame rate increase on any camera
Stevens, Andrew; Kovarik, Libor; Abellan, Patricia; ...
2015-08-13
One of the main limitations of imaging at high spatial and temporal resolution during in-situ transmission electron microscopy (TEM) experiments is the frame rate of the camera being used to image the dynamic process. While the recent development of direct detectors has provided the hardware to achieve frame rates approaching 0.1 ms, the cameras are expensive and must replace existing detectors. In this paper, we examine the use of coded aperture compressive sensing (CS) methods to increase the frame rate of any camera with simple, low-cost hardware modifications. The coded aperture approach allows multiple sub-frames to be coded and integratedmore » into a single camera frame during the acquisition process, and then extracted upon readout using statistical CS inversion. Here we describe the background of CS and statistical methods in depth and simulate the frame rates and efficiencies for in-situ TEM experiments. Depending on the resolution and signal/noise of the image, it should be possible to increase the speed of any camera by more than an order of magnitude using this approach.« less
Emad, Amin; Milenkovic, Olgica
2014-01-01
We introduce a novel algorithm for inference of causal gene interactions, termed CaSPIAN (Causal Subspace Pursuit for Inference and Analysis of Networks), which is based on coupling compressive sensing and Granger causality techniques. The core of the approach is to discover sparse linear dependencies between shifted time series of gene expressions using a sequential list-version of the subspace pursuit reconstruction algorithm and to estimate the direction of gene interactions via Granger-type elimination. The method is conceptually simple and computationally efficient, and it allows for dealing with noisy measurements. Its performance as a stand-alone platform without biological side-information was tested on simulated networks, on the synthetic IRMA network in Saccharomyces cerevisiae, and on data pertaining to the human HeLa cell network and the SOS network in E. coli. The results produced by CaSPIAN are compared to the results of several related algorithms, demonstrating significant improvements in inference accuracy of documented interactions. These findings highlight the importance of Granger causality techniques for reducing the number of false-positives, as well as the influence of noise and sampling period on the accuracy of the estimates. In addition, the performance of the method was tested in conjunction with biological side information of the form of sparse “scaffold networks”, to which new edges were added using available RNA-seq or microarray data. These biological priors aid in increasing the sensitivity and precision of the algorithm in the small sample regime. PMID:24622336
Fast and low-dose computed laminography using compressive sensing based technique
NASA Astrophysics Data System (ADS)
Abbas, Sajid; Park, Miran; Cho, Seungryong
2015-03-01
Computed laminography (CL) is well known for inspecting microstructures in the materials, weldments and soldering defects in high density packed components or multilayer printed circuit boards. The overload problem on x-ray tube and gross failure of the radio-sensitive electronics devices during a scan are among important issues in CL which needs to be addressed. The sparse-view CL can be one of the viable option to overcome such issues. In this work a numerical aluminum welding phantom was simulated to collect sparsely sampled projection data at only 40 views using a conventional CL scanning scheme i.e. oblique scan. A compressive-sensing inspired total-variation (TV) minimization algorithm was utilized to reconstruct the images. It is found that the images reconstructed using sparse view data are visually comparable with the images reconstructed using full scan data set i.e. at 360 views on regular interval. We have quantitatively confirmed that tiny structures such as copper and tungsten slags, and copper flakes in the reconstructed images from sparsely sampled data are comparable with the corresponding structure present in the fully sampled data case. A blurring effect can be seen near the edges of few pores at the bottom of the reconstructed images from sparsely sampled data, despite the overall image quality is reasonable for fast and low-dose NDT.
Compressed Sensing for Millimeter-wave Ground Based SAR/ISAR Imaging
NASA Astrophysics Data System (ADS)
Yiğit, Enes
2014-11-01
Millimeter-wave (MMW) ground based (GB) synthetic aperture radar (SAR) and inverse SAR (ISAR) imaging are the powerful tools for the detection of foreign object debris (FOD) and concealed objects that requires wide bandwidths and highly frequent samplings in both slow-time and fast-time domains according to Shannon/Nyquist sampling theorem. However, thanks to the compressive sensing (CS) theory GB-SAR/ISAR data can be reconstructed by much fewer random samples than the Nyquist rate. In this paper, the impact of both random frequency sampling and random spatial domain data collection of a SAR/ISAR sensor on reconstruction quality of a scene of interest was studied. To investigate the feasibility of using proposed CS framework, different experiments for various FOD-like and concealed object-like targets were carried out at the Ka and W band frequencies of the MMW. The robustness and effectiveness of the recommend CS-based reconstruction configurations were verified through a comparison among each other by using integrated side lobe ratios (ISLR) of the images.
Phase Error Correction for Approximated Observation-Based Compressed Sensing Radar Imaging.
Li, Bo; Liu, Falin; Zhou, Chongbin; Lv, Yuanhao; Hu, Jingqiu
2017-03-17
Defocus of the reconstructed image of synthetic aperture radar (SAR) occurs in the presence of the phase error. In this work, a phase error correction method is proposed for compressed sensing (CS) radar imaging based on approximated observation. The proposed method has better image focusing ability with much less memory cost, compared to the conventional approaches, due to the inherent low memory requirement of the approximated observation operator. The one-dimensional (1D) phase error correction for approximated observation-based CS-SAR imaging is first carried out and it can be conveniently applied to the cases of random-frequency waveform and linear frequency modulated (LFM) waveform without any a priori knowledge. The approximated observation operators are obtained by calculating the inverse of Omega-K and chirp scaling algorithms for random-frequency and LFM waveforms, respectively. Furthermore, the 1D phase error model is modified by incorporating a priori knowledge and then a weighted 1D phase error model is proposed, which is capable of correcting two-dimensional (2D) phase error in some cases, where the estimation can be simplified to a 1D problem. Simulation and experimental results validate the effectiveness of the proposed method in the presence of 1D phase error or weighted 1D phase error.
Multichannel and Wide-Angle SAR Imaging Based on Compressed Sensing
Sun, Chao; Wang, Baoping; Fang, Yang; Song, Zuxun; Wang, Shuzhen
2017-01-01
The multichannel or wide-angle imaging performance of synthetic aperture radar (SAR) can be improved by applying the compressed sensing (CS) theory to each channel or sub-aperture image formation independently. However, this not only neglects the complementary information between signals of each channel or sub-aperture, but also may lead to failure in guaranteeing the consistency of the position of a scatterer in different channel or sub-aperture images which will make the extraction of some scattering information become difficult. By exploiting the joint sparsity of the signal ensemble, this paper proposes a novel CS-based method for joint sparse recovery of all channel or sub-aperture images. Solving the joint sparse recovery problem with a modified orthogonal matching pursuit algorithm, the recovery precision of scatterers is effectively improved and the scattering information is also preserved during the image formation process. Finally, the simulation and real data is used for verifying the effectiveness of the proposed method. Compared with single channel or sub-aperture independent CS processing, the proposed method can not only obtain better imaging performance with fewer measurements, but also preserve more valuable scattering information for target recognition. PMID:28165433
A compressed sensing method with analytical results for lidar feature classification
NASA Astrophysics Data System (ADS)
Allen, Josef D.; Yuan, Jiangbo; Liu, Xiuwen; Rahmes, Mark
2011-04-01
We present an innovative way to autonomously classify LiDAR points into bare earth, building, vegetation, and other categories. One desirable product of LiDAR data is the automatic classification of the points in the scene. Our algorithm automatically classifies scene points using Compressed Sensing Methods via Orthogonal Matching Pursuit algorithms utilizing a generalized K-Means clustering algorithm to extract buildings and foliage from a Digital Surface Models (DSM). This technology reduces manual editing while being cost effective for large scale automated global scene modeling. Quantitative analyses are provided using Receiver Operating Characteristics (ROC) curves to show Probability of Detection and False Alarm of buildings vs. vegetation classification. Histograms are shown with sample size metrics. Our inpainting algorithms then fill the voids where buildings and vegetation were removed, utilizing Computational Fluid Dynamics (CFD) techniques and Partial Differential Equations (PDE) to create an accurate Digital Terrain Model (DTM) [6]. Inpainting preserves building height contour consistency and edge sharpness of identified inpainted regions. Qualitative results illustrate other benefits such as Terrain Inpainting's unique ability to minimize or eliminate undesirable terrain data artifacts.
NASA Astrophysics Data System (ADS)
Zhang, Yushu; Zhou, Jiantao; Chen, Fei; Zhang, Leo Yu; Xiao, Di; Chen, Bin; Liao, Xiaofeng
The existing Block Compressive Sensing (BCS) based image ciphers adopted the same sampling rate for all the blocks, which may lead to the desirable result that after subsampling, significant blocks lose some more-useful information while insignificant blocks still retain some less-useful information. Motivated by this observation, we propose a scalable encryption framework (SEF) based on BCS together with a Sobel Edge Detector and Cascade Chaotic Maps. Our work is firstly dedicated to the design of two new fusion techniques, chaos-based structurally random matrices and chaos-based random convolution and subsampling. The basic idea is to divide an image into some blocks with an equal size and then diagnose their respective significance with the help of the Sobel Edge Detector. For significant block encryption, chaos-based structurally random matrix is applied to significant blocks whereas chaos-based random convolution and subsampling are responsible for the remaining insignificant ones. In comparison with the BCS based image ciphers, the SEF takes lightweight subsampling and severe sensitivity encryption for the significant blocks and severe subsampling and lightweight robustness encryption for the insignificant ones in parallel, thus better protecting significant image regions.
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.
Stevens, Andrew J.; Yang, Hao; Carin, Lawrence; Arslan, Ilke; Browning, Nigel D.
2014-02-11
The use of high resolution imaging methods in the scanning transmission electron microscope (STEM) is limited in many cases by the sensitivity of the sample to the beam and the onset of electron beam damage (for example in the study of organic systems, in tomography and during in-situ experiments). To demonstrate that alternative strategies for image acquisition can help alleviate this beam damage issue, here we apply compressive sensing via Bayesian dictionary learning to high resolution STEM images. These experiments successively reduce the number of pixels in the image (thereby reducing the overall dose while maintaining the high resolution information) and show promising results for reconstructing images from this reduced set of randomly collected measurements. We show that this approach is valid for both atomic resolution images and nanometer resolution studies, such as those that might be used in tomography datasets, by applying the method to images of strontium titanate and zeolites. As STEM images are acquired pixel by pixel while the beam is scanned over the surface of the sample, these post acquisition manipulations of the images can, in principle, be directly implemented as a low-dose acquisition method with no change in the electron optics or alignment of the microscope itself.
Motion adaptive patch-based low-rank approach for compressed sensing cardiac cine MRI.
Yoon, Huisu; Kim, Kyung Sang; Kim, Daniel; Bresler, Yoram; Ye, Jong Chul
2014-11-01
One of the technical challenges in cine magnetic resonance imaging (MRI) is to reduce the acquisition time to enable the high s