Design of a multi-spectral imager built using the compressive sensing single-pixel camera architecture

NASA Astrophysics Data System (ADS)

McMackin, Lenore; Herman, Matthew A.; Weston, Tyler

2016-02-01

We present the design of a multi-spectral imager built using the architecture of the single-pixel camera. The architecture is enabled by the novel sampling theory of compressive sensing implemented optically using the Texas Instruments DLP™ micro-mirror array. The array not only implements spatial modulation necessary for compressive imaging but also provides unique diffractive spectral features that result in a multi-spectral, high-spatial resolution imager design. The new camera design provides multi-spectral imagery in a wavelength range that extends from the visible to the shortwave infrared without reduction in spatial resolution. In addition to the compressive imaging spectrometer design, we present a diffractive model of the architecture that allows us to predict a variety of detailed functional spatial and spectral design features. We present modeling results, architectural design and experimental results that prove the concept.

Compressed digital holography: from micro towards macro

NASA Astrophysics Data System (ADS)

Schretter, Colas; Bettens, Stijn; Blinder, David; Pesquet-Popescu, Béatrice; Cagnazzo, Marco; Dufaux, Frédéric; Schelkens, Peter

2016-09-01

signal processing methods from software-driven computer engineering and applied mathematics. The compressed sensing theory in particular established a practical framework for reconstructing the scene content using few linear combinations of complex measurements and a sparse prior for regularizing the solution. Compressed sensing found direct applications in digital holography for microscopy. Indeed, the wave propagation phenomenon in free space mixes in a natural way the spatial distribution of point sources from the 3-dimensional scene. As the 3-dimensional scene is mapped to a 2-dimensional hologram, the hologram samples form a compressed representation of the scene as well. This overview paper discusses contributions in the field of compressed digital holography at the micro scale. Then, an outreach on future extensions towards the real-size macro scale is discussed. Thanks to advances in sensor technologies, increasing computing power and the recent improvements in sparse digital signal processing, holographic modalities are on the verge of practical high-quality visualization at a macroscopic scale where much higher resolution holograms must be acquired and processed on the computer.

Compressed-sensing-based content-driven hierarchical reconstruction: Theory and application to C-arm cone-beam tomography

DOE Office of Scientific and Technical Information (OSTI.GOV)

Langet, Hélène; Laboratoire des Signaux et Systèmes, CentraleSupélec, Gif-sur-Yvette F-91192; Center for Visual Computing, CentraleSupélec, Châtenay-Malabry F-92295

2015-09-15

Purpose: This paper addresses the reconstruction of x-ray cone-beam computed tomography (CBCT) for interventional C-arm systems. Subsampling of CBCT is a significant issue with C-arms due to their slow rotation and to the low frame rate of their flat panel x-ray detectors. The aim of this work is to propose a novel method able to handle the subsampling artifacts generally observed with analytical reconstruction, through a content-driven hierarchical reconstruction based on compressed sensing. Methods: The central idea is to proceed with a hierarchical method where the most salient features (high intensities or gradients) are reconstructed first to reduce the artifactsmore » these features induce. These artifacts are addressed first because their presence contaminates less salient features. Several hierarchical schemes aiming at streak artifacts reduction are introduced for C-arm CBCT: the empirical orthogonal matching pursuit approach with the ℓ{sub 0} pseudonorm for reconstructing sparse vessels; a convex variant using homotopy with the ℓ{sub 1}-norm constraint of compressed sensing, for reconstructing sparse vessels over a nonsparse background; homotopy with total variation (TV); and a novel empirical extension to nonlinear diffusion (NLD). Such principles are implemented with penalized iterative filtered backprojection algorithms. For soft-tissue imaging, the authors compare the use of TV and NLD filters as sparsity constraints, both optimized with the alternating direction method of multipliers, using a threshold for TV and a nonlinear weighting for NLD. Results: The authors show on simulated data that their approach provides fast convergence to good approximations of the solution of the TV-constrained minimization problem introduced by the compressed sensing theory. Using C-arm CBCT clinical data, the authors show that both TV and NLD can deliver improved image quality by reducing streaks. Conclusions: A flexible compressed-sensing-based algorithmic approach is proposed that is able to accommodate for a wide range of constraints. It is successfully applied to C-arm CBCT images that may not be so well approximated by piecewise constant functions.« less

Compressive Sensing via Nonlocal Smoothed Rank Function

PubMed Central

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

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.

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 information from a noisy environment. Using engineering efforts to accomplish the same task usually requires multiple detectors, advanced computational algorithms, or artificial intelligence systems. Compressive acoustic sensing incorporates acoustic metamaterials in compressive sensing theory to emulate the abilities of sound localization and selective attention. This research investigates and optimizes the sensing capacity and the spatial sensitivity of the acoustic sensor. The well-modeled acoustic sensor allows localizing multiple speakers in both stationary and dynamic auditory scene; and distinguishing mixed conversations from independent sources with high audio recognition rate.

On Compressible Vortex Sheets

NASA Astrophysics Data System (ADS)

Secchi, Paolo

2005-05-01

We introduce the main known results of the theory of incompressible and compressible vortex sheets. Moreover, we present recent results obtained by the author with J. F. Coulombel about supersonic compressible vortex sheets in two space dimensions. The problem is a nonlinear free boundary hyperbolic problem with two difficulties: the free boundary is characteristic and the Lopatinski condition holds only in a weak sense, yielding losses of derivatives. Under a supersonic condition that precludes violent instabilities, we prove an energy estimate for the boundary value problem obtained by linearization around an unsteady piecewise solution.

Sensitivity Analysis in RIPless Compressed Sensing

DTIC Science & Technology

2014-10-01

SECURITY CLASSIFICATION OF: The compressive sensing framework finds a wide range of applications in signal processing and analysis. Within this...Analysis of Compressive Sensing Solutions Report Title The compressive sensing framework finds a wide range of applications in signal processing and...compressed sensing. More specifically, we show that in a noiseless and RIP-less setting [11], the recovery process of a compressed sensing framework is

Energy-efficient sensing in wireless sensor networks using compressed sensing.

PubMed

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.

Research on compressive sensing reconstruction algorithm based on total variation model

NASA Astrophysics Data System (ADS)

Gao, Yu-xuan; Sun, Huayan; Zhang, Tinghua; Du, Lin

2017-12-01

Compressed sensing for breakthrough Nyquist sampling theorem provides a strong theoretical , making compressive sampling for image signals be carried out simultaneously. In traditional imaging procedures using compressed sensing theory, not only can it reduces the storage space, but also can reduce the demand for detector resolution greatly. Using the sparsity of image signal, by solving the mathematical model of inverse reconfiguration, realize the super-resolution imaging. Reconstruction algorithm is the most critical part of compression perception, to a large extent determine the accuracy of the reconstruction of the image.The reconstruction algorithm based on the total variation (TV) model is more suitable for the compression reconstruction of the two-dimensional image, and the better edge information can be obtained. In order to verify the performance of the algorithm, Simulation Analysis the reconstruction result in different coding mode of the reconstruction algorithm based on the TV reconstruction algorithm. The reconstruction effect of the reconfigurable algorithm based on TV based on the different coding methods is analyzed to verify the stability of the algorithm. This paper compares and analyzes the typical reconstruction algorithm in the same coding mode. On the basis of the minimum total variation algorithm, the Augmented Lagrangian function term is added and the optimal value is solved by the alternating direction method.Experimental results show that the reconstruction algorithm is compared with the traditional classical algorithm based on TV has great advantages, under the low measurement rate can be quickly and accurately recovers target image.

First Principles Studies for Lithium Intercalation and Diffusion Behaviors in MoS2 treated with the Compressive Sensing Cluster Expansion

NASA Astrophysics Data System (ADS)

Liu, Chi-Ping; Zhou, Fei; Ozolins, Vidvuds

2014-03-01

Molybdenum disulfide (MoS2) is a good candidate electrode material for high capacity energy storage applications, such as lithium ion batteries and supercapacitors. In this work, we investigate lithium intercalation and diffusion kinetics in MoS2 by using first-principles density-functional theory (DFT) calculations. Two different lithium intercalation sites (1-H and 2-T) in MoS2 are found to be stable for lithium intercalation at different van der Waals' (vdW) gap distances. It is found that both thermodynamic and kinetic properties are highly related to the interlayer vdW gap distance, and that the optimal gap distance leads to effective solid-state diffusion in MoS2. Additionally, through the use of compressive sensing, we build accurate cluster expansion models to study the thermodynamic properties of MoS2 at high lithium content by truncating the higher order effective clusters with significant contributions. The results show that compressive sensing cluster expansion is a rigorous and powerful tool for model construction for advanced electrochemical applications in the future.

DOA Estimation for Underwater Wideband Weak Targets Based on Coherent Signal Subspace and Compressed Sensing.

PubMed

Li, Jun; Lin, Qiu-Hua; Kang, Chun-Yu; Wang, Kai; Yang, Xiu-Ting

2018-03-18

Direction of arrival (DOA) estimation is the basis for underwater target localization and tracking using towed line array sonar devices. A method of DOA estimation for underwater wideband weak targets based on coherent signal subspace (CSS) processing and compressed sensing (CS) theory is proposed. Under the CSS processing framework, wideband frequency focusing is accompanied by a two-sided correlation transformation, allowing the DOA of underwater wideband targets to be estimated based on the spatial sparsity of the targets and the compressed sensing reconstruction algorithm. Through analysis and processing of simulation data and marine trial data, it is shown that this method can accomplish the DOA estimation of underwater wideband weak targets. Results also show that this method can considerably improve the spatial spectrum of weak target signals, enhancing the ability to detect them. It can solve the problems of low directional resolution and unreliable weak-target detection in traditional beamforming technology. Compared with the conventional minimum variance distortionless response beamformers (MVDR), this method has many advantages, such as higher directional resolution, wider detection range, fewer required snapshots and more accurate detection for weak targets.

Informational analysis for compressive sampling in radar imaging.

PubMed

Zhang, Jingxiong; Yang, Ke

2015-03-24

Compressive sampling or compressed sensing (CS) works on the assumption of the sparsity or compressibility of the underlying signal, relies on the trans-informational capability of the measurement matrix employed and the resultant measurements, operates with optimization-based algorithms for signal reconstruction and is thus able to complete data compression, while acquiring data, leading to sub-Nyquist sampling strategies that promote efficiency in data acquisition, while ensuring certain accuracy criteria. Information theory provides a framework complementary to classic CS theory for analyzing information mechanisms and for determining the necessary number of measurements in a CS environment, such as CS-radar, a radar sensor conceptualized or designed with CS principles and techniques. Despite increasing awareness of information-theoretic perspectives on CS-radar, reported research has been rare. This paper seeks to bridge the gap in the interdisciplinary area of CS, radar and information theory by analyzing information flows in CS-radar from sparse scenes to measurements and determining sub-Nyquist sampling rates necessary for scene reconstruction within certain distortion thresholds, given differing scene sparsity and average per-sample signal-to-noise ratios (SNRs). Simulated studies were performed to complement and validate the information-theoretic analysis. The combined strategy proposed in this paper is valuable for information-theoretic orientated CS-radar system analysis and performance evaluation.

Compressive sensing sectional imaging for single-shot in-line self-interference incoherent holography

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.

Terahertz imaging with compressed sensing and phase retrieval.

PubMed

Chan, Wai Lam; Moravec, Matthew L; Baraniuk, Richard G; Mittleman, Daniel M

2008-05-01

We describe a novel, high-speed pulsed terahertz (THz) Fourier imaging system based on compressed sensing (CS), a new signal processing theory, which allows image reconstruction with fewer samples than traditionally required. Using CS, we successfully reconstruct a 64 x 64 image of an object with pixel size 1.4 mm using a randomly chosen subset of the 4096 pixels, which defines the image in the Fourier plane, and observe improved reconstruction quality when we apply phase correction. For our chosen image, only about 12% of the pixels are required for reassembling the image. In combination with phase retrieval, our system has the capability to reconstruct images with only a small subset of Fourier amplitude measurements and thus has potential application in THz imaging with cw sources.

Application of wavefield compressive sensing in surface wave tomography

NASA Astrophysics Data System (ADS)

Zhan, Zhongwen; Li, Qingyang; Huang, Jianping

2018-06-01

Dense arrays allow sampling of seismic wavefield without significant aliasing, and surface wave tomography has benefitted from exploiting wavefield coherence among neighbouring stations. However, explicit or implicit assumptions about wavefield, irregular station spacing and noise still limit the applicability and resolution of current surface wave methods. Here, we propose to apply the theory of compressive sensing (CS) to seek a sparse representation of the surface wavefield using a plane-wave basis. Then we reconstruct the continuous surface wavefield on a dense regular grid before applying any tomographic methods. Synthetic tests demonstrate that wavefield CS improves robustness and resolution of Helmholtz tomography and wavefield gradiometry, especially when traditional approaches have difficulties due to sub-Nyquist sampling or complexities in wavefield.

Compressed sensing based missing nodes prediction in temporal communication network

NASA Astrophysics Data System (ADS)

Cheng, Guangquan; Ma, Yang; Liu, Zhong; Xie, Fuli

2018-02-01

The reconstruction of complex network topology is of great theoretical and practical significance. Most research so far focuses on the prediction of missing links. There are many mature algorithms for link prediction which have achieved good results, but research on the prediction of missing nodes has just begun. In this paper, we propose an algorithm for missing node prediction in complex networks. We detect the position of missing nodes based on their neighbor nodes under the theory of compressed sensing, and extend the algorithm to the case of multiple missing nodes using spectral clustering. Experiments on real public network datasets and simulated datasets show that our algorithm can detect the locations of hidden nodes effectively with high precision.

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.

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, therefore, providing a feasibility for compressive sensing application.

Compressive sensing in medical imaging

PubMed Central

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

DOA Estimation for Underwater Wideband Weak Targets Based on Coherent Signal Subspace and Compressed Sensing

PubMed Central

2018-01-01

Direction of arrival (DOA) estimation is the basis for underwater target localization and tracking using towed line array sonar devices. A method of DOA estimation for underwater wideband weak targets based on coherent signal subspace (CSS) processing and compressed sensing (CS) theory is proposed. Under the CSS processing framework, wideband frequency focusing is accompanied by a two-sided correlation transformation, allowing the DOA of underwater wideband targets to be estimated based on the spatial sparsity of the targets and the compressed sensing reconstruction algorithm. Through analysis and processing of simulation data and marine trial data, it is shown that this method can accomplish the DOA estimation of underwater wideband weak targets. Results also show that this method can considerably improve the spatial spectrum of weak target signals, enhancing the ability to detect them. It can solve the problems of low directional resolution and unreliable weak-target detection in traditional beamforming technology. Compared with the conventional minimum variance distortionless response beamformers (MVDR), this method has many advantages, such as higher directional resolution, wider detection range, fewer required snapshots and more accurate detection for weak targets. PMID:29562642

Elastic MCF Rubber with Photovoltaics and Sensing on Hybrid Skin (H-Skin) for Artificial Skin by Utilizing Natural Rubber: Third Report on Electric Charge and Storage under Tension and Compression †.

PubMed

Shimada, Kunio

2018-06-06

In the series of studies on new types of elastic and compressible artificial skins with hybrid sensing functions, photovoltaics, and battery, we have proposed a hybrid skin (H-Skin) by utilizing an electrolytically polymerized magnetic compound fluid (MCF) made of natural rubber latex (NR-latex). By using the experimental results in the first and second reports, we have clarified the feasibility of electric charge at irradiation, and that without illumination under compression and elongation. The former was explained in a wet-type MCF rubber solar cell by developing a tunneling theory together with an equivalent electric circuit model. The latter corresponds to the battery rather than to the solar cell. As for the MCF rubber battery, depending on the selected agent type, we can make the MCF rubber have higher electricity and lighter weight. Therefore, the MCF rubber has an electric charge and storage whether at irradiation or not.

Feasibility of high temporal resolution breast DCE-MRI using compressed sensing theory.

PubMed

Wang, Haoyu; Miao, Yanwei; Zhou, Kun; Yu, Yanming; Bao, Shanglian; He, Qiang; Dai, Yongming; Xuan, Stephanie Y; Tarabishy, Bisher; Ye, Yongquan; Hu, Jiani

2010-09-01

To investigate the feasibility of high temporal resolution breast DCE-MRI using compressed sensing theory. Two experiments were designed to investigate the feasibility of using reference image based compressed sensing (RICS) technique in DCE-MRI of the breast. The first experiment examined the capability of RICS to faithfully reconstruct uptake curves using undersampled data sets extracted from fully sampled clinical breast DCE-MRI data. An average approach and an approach using motion estimation and motion compensation (ME/MC) were implemented to obtain reference images and to evaluate their efficacy in reducing motion related effects. The second experiment, an in vitro phantom study, tested the feasibility of RICS for improving temporal resolution without degrading the spatial resolution. For the uptake-curve reconstruction experiment, there was a high correlation between uptake curves reconstructed from fully sampled data by Fourier transform and from undersampled data by RICS, indicating high similarity between them. The mean Pearson correlation coefficients for RICS with the ME/MC approach and RICS with the average approach were 0.977 +/- 0.023 and 0.953 +/- 0.031, respectively. The comparisons of final reconstruction results between RICS with the average approach and RICS with the ME/MC approach suggested that the latter was superior to the former in reducing motion related effects. For the in vitro experiment, compared to the fully sampled method, RICS improved the temporal resolution by an acceleration factor of 10 without degrading the spatial resolution. The preliminary study demonstrates the feasibility of RICS for faithfully reconstructing uptake curves and improving temporal resolution of breast DCE-MRI without degrading the spatial resolution.

A Sparsity-Promoted Decomposition for Compressed Fault Diagnosis of Roller Bearings

PubMed Central

Wang, Huaqing; Ke, Yanliang; Song, Liuyang; Tang, Gang; Chen, Peng

2016-01-01

The traditional approaches for condition monitoring of roller bearings are almost always achieved under Shannon sampling theorem conditions, leading to a big-data problem. The compressed sensing (CS) theory provides a new solution to the big-data problem. However, the vibration signals are insufficiently sparse and it is difficult to achieve sparsity using the conventional techniques, which impedes the application of CS theory. Therefore, it is of great significance to promote the sparsity when applying the CS theory to fault diagnosis of roller bearings. To increase the sparsity of vibration signals, a sparsity-promoted method called the tunable Q-factor wavelet transform based on decomposing the analyzed signals into transient impact components and high oscillation components is utilized in this work. The former become sparser than the raw signals with noise eliminated, whereas the latter include noise. Thus, the decomposed transient impact components replace the original signals for analysis. The CS theory is applied to extract the fault features without complete reconstruction, which means that the reconstruction can be completed when the components with interested frequencies are detected and the fault diagnosis can be achieved during the reconstruction procedure. The application cases prove that the CS theory assisted by the tunable Q-factor wavelet transform can successfully extract the fault features from the compressed samples. PMID:27657063

Energy Efficient GNSS Signal Acquisition Using Singular Value Decomposition (SVD).

PubMed

Bermúdez Ordoñez, Juan Carlos; Arnaldo Valdés, Rosa María; Gómez Comendador, Fernando

2018-05-16

A significant challenge in global navigation satellite system (GNSS) signal processing is a requirement for a very high sampling rate. The recently-emerging compressed sensing (CS) theory makes processing GNSS signals at a low sampling rate possible if the signal has a sparse representation in a certain space. Based on CS and SVD theories, an algorithm for sampling GNSS signals at a rate much lower than the Nyquist rate and reconstructing the compressed signal is proposed in this research, which is validated after the output from that process still performs signal detection using the standard fast Fourier transform (FFT) parallel frequency space search acquisition. The sparse representation of the GNSS signal is the most important precondition for CS, by constructing a rectangular Toeplitz matrix (TZ) of the transmitted signal, calculating the left singular vectors using SVD from the TZ, to achieve sparse signal representation. Next, obtaining the M-dimensional observation vectors based on the left singular vectors of the SVD, which are equivalent to the sampler operator in standard compressive sensing theory, the signal can be sampled below the Nyquist rate, and can still be reconstructed via ℓ 1 minimization with accuracy using convex optimization. As an added value, there is a GNSS signal acquisition enhancement effect by retaining the useful signal and filtering out noise by projecting the signal into the most significant proper orthogonal modes (PODs) which are the optimal distributions of signal power. The algorithm is validated with real recorded signals, and the results show that the proposed method is effective for sampling, reconstructing intermediate frequency (IF) GNSS signals in the time discrete domain.

Energy Efficient GNSS Signal Acquisition Using Singular Value Decomposition (SVD)

PubMed Central

Arnaldo Valdés, Rosa María; Gómez Comendador, Fernando

2018-01-01

A significant challenge in global navigation satellite system (GNSS) signal processing is a requirement for a very high sampling rate. The recently-emerging compressed sensing (CS) theory makes processing GNSS signals at a low sampling rate possible if the signal has a sparse representation in a certain space. Based on CS and SVD theories, an algorithm for sampling GNSS signals at a rate much lower than the Nyquist rate and reconstructing the compressed signal is proposed in this research, which is validated after the output from that process still performs signal detection using the standard fast Fourier transform (FFT) parallel frequency space search acquisition. The sparse representation of the GNSS signal is the most important precondition for CS, by constructing a rectangular Toeplitz matrix (TZ) of the transmitted signal, calculating the left singular vectors using SVD from the TZ, to achieve sparse signal representation. Next, obtaining the M-dimensional observation vectors based on the left singular vectors of the SVD, which are equivalent to the sampler operator in standard compressive sensing theory, the signal can be sampled below the Nyquist rate, and can still be reconstructed via ℓ1 minimization with accuracy using convex optimization. As an added value, there is a GNSS signal acquisition enhancement effect by retaining the useful signal and filtering out noise by projecting the signal into the most significant proper orthogonal modes (PODs) which are the optimal distributions of signal power. The algorithm is validated with real recorded signals, and the results show that the proposed method is effective for sampling, reconstructing intermediate frequency (IF) GNSS signals in the time discrete domain. PMID:29772731

Compressed Sensing for Body MRI

PubMed Central

Feng, Li; Benkert, Thomas; Block, Kai Tobias; Sodickson, Daniel K; Otazo, Ricardo; Chandarana, Hersh

2016-01-01

The introduction of compressed sensing for increasing imaging speed in MRI has raised significant interest among researchers and clinicians, and has initiated a large body of research across multiple clinical applications over the last decade. Compressed sensing aims to reconstruct unaliased images from fewer measurements than that are traditionally required in MRI by exploiting image compressibility or sparsity. Moreover, appropriate combinations of compressed sensing with previously introduced fast imaging approaches, such as parallel imaging, have demonstrated further improved performance. The advent of compressed sensing marks the prelude to a new era of rapid MRI, where the focus of data acquisition has changed from sampling based on the nominal number of voxels and/or frames to sampling based on the desired information content. This paper presents a brief overview of the application of compressed sensing techniques in body MRI, where imaging speed is crucial due to the presence of respiratory motion along with stringent constraints on spatial and temporal resolution. The first section provides an overview of the basic compressed sensing methodology, including the notion of sparsity, incoherence, and non-linear reconstruction. The second section reviews state-of-the-art compressed sensing techniques that have been demonstrated for various clinical body MRI applications. In the final section, the paper discusses current challenges and future opportunities. PMID:27981664

Accurate reconstruction of hyperspectral images from compressive sensing measurements

NASA Astrophysics Data System (ADS)

Greer, John B.; Flake, J. C.

2013-05-01

The emerging field of Compressive Sensing (CS) provides a new way to capture data by shifting the heaviest burden of data collection from the sensor to the computer on the user-end. This new means of sensing requires fewer measurements for a given amount of information than traditional sensors. We investigate the efficacy of CS for capturing HyperSpectral Imagery (HSI) remotely. We also introduce a new family of algorithms for constructing HSI from CS measurements with Split Bregman Iteration [Goldstein and Osher,2009]. These algorithms combine spatial Total Variation (TV) with smoothing in the spectral dimension. We examine models for three different CS sensors: the Coded Aperture Snapshot Spectral Imager-Single Disperser (CASSI-SD) [Wagadarikar et al.,2008] and Dual Disperser (CASSI-DD) [Gehm et al.,2007] cameras, and a hypothetical random sensing model closer to CS theory, but not necessarily implementable with existing technology. We simulate the capture of remotely sensed images by applying the sensor forward models to well-known HSI scenes - an AVIRIS image of Cuprite, Nevada and the HYMAP Urban image. To measure accuracy of the CS models, we compare the scenes constructed with our new algorithm to the original AVIRIS and HYMAP cubes. The results demonstrate the possibility of accurately sensing HSI remotely with significantly fewer measurements than standard hyperspectral cameras.

Low dose reconstruction algorithm for differential phase contrast imaging.

PubMed

Wang, Zhentian; Huang, Zhifeng; Zhang, Li; Chen, Zhiqiang; Kang, Kejun; Yin, Hongxia; Wang, Zhenchang; Marco, Stampanoni

2011-01-01

Differential phase contrast imaging computed tomography (DPCI-CT) is a novel x-ray inspection method to reconstruct the distribution of refraction index rather than the attenuation coefficient in weakly absorbing samples. In this paper, we propose an iterative reconstruction algorithm for DPCI-CT which benefits from the new compressed sensing theory. We first realize a differential algebraic reconstruction technique (DART) by discretizing the projection process of the differential phase contrast imaging into a linear partial derivative matrix. In this way the compressed sensing reconstruction problem of DPCI reconstruction can be transformed to a resolved problem in the transmission imaging CT. Our algorithm has the potential to reconstruct the refraction index distribution of the sample from highly undersampled projection data. Thus it can significantly reduce the dose and inspection time. The proposed algorithm has been validated by numerical simulations and actual experiments.

An Energy-Efficient Compressive Image Coding for Green Internet of Things (IoT).

PubMed

Li, Ran; Duan, Xiaomeng; Li, Xu; He, Wei; Li, Yanling

2018-04-17

Aimed at a low-energy consumption of Green Internet of Things (IoT), this paper presents an energy-efficient compressive image coding scheme, which provides compressive encoder and real-time decoder according to Compressive Sensing (CS) theory. The compressive encoder adaptively measures each image block based on the block-based gradient field, which models the distribution of block sparse degree, and the real-time decoder linearly reconstructs each image block through a projection matrix, which is learned by Minimum Mean Square Error (MMSE) criterion. Both the encoder and decoder have a low computational complexity, so that they only consume a small amount of energy. Experimental results show that the proposed scheme not only has a low encoding and decoding complexity when compared with traditional methods, but it also provides good objective and subjective reconstruction qualities. In particular, it presents better time-distortion performance than JPEG. Therefore, the proposed compressive image coding is a potential energy-efficient scheme for Green IoT.

Compressed Sensing for Metrics Development

NASA Astrophysics Data System (ADS)

McGraw, R. L.; Giangrande, S. E.; Liu, Y.

2012-12-01

Models by their very nature tend to be sparse in the sense that they are designed, with a few optimally selected key parameters, to provide simple yet faithful representations of a complex observational dataset or computer simulation output. This paper seeks to apply methods from compressed sensing (CS), a new area of applied mathematics currently undergoing a very rapid development (see for example Candes et al., 2006), to FASTER needs for new approaches to model evaluation and metrics development. The CS approach will be illustrated for a time series generated using a few-parameter (i.e. sparse) model. A seemingly incomplete set of measurements, taken at a just few random sampling times, is then used to recover the hidden model parameters. Remarkably there is a sharp transition in the number of required measurements, beyond which both the model parameters and time series are recovered exactly. Applications to data compression, data sampling/collection strategies, and to the development of metrics for model evaluation by comparison with observation (e.g. evaluation of model predictions of cloud fraction using cloud radar observations) are presented and discussed in context of the CS approach. Cited reference: Candes, E. J., Romberg, J., and Tao, T. (2006), Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information, IEEE Transactions on Information Theory, 52, 489-509.

Distributed Sensing and Processing for Multi-Camera Networks

NASA Astrophysics Data System (ADS)

Sankaranarayanan, Aswin C.; Chellappa, Rama; Baraniuk, Richard G.

Sensor networks with large numbers of cameras are becoming increasingly prevalent in a wide range of applications, including video conferencing, motion capture, surveillance, and clinical diagnostics. In this chapter, we identify some of the fundamental challenges in designing such systems: robust statistical inference, computationally efficiency, and opportunistic and parsimonious sensing. We show that the geometric constraints induced by the imaging process are extremely useful for identifying and designing optimal estimators for object detection and tracking tasks. We also derive pipelined and parallelized implementations of popular tools used for statistical inference in non-linear systems, of which multi-camera systems are examples. Finally, we highlight the use of the emerging theory of compressive sensing in reducing the amount of data sensed and communicated by a camera network.

Simple relationship between the virial-route hypernetted-chain and the compressibility-route Percus-Yevick values of the fourth virial coefficient.

PubMed

Santos, Andrés; Manzano, Gema

2010-04-14

As is well known, approximate integral equations for liquids, such as the hypernetted chain (HNC) and Percus-Yevick (PY) theories, are in general thermodynamically inconsistent in the sense that the macroscopic properties obtained from the spatial correlation functions depend on the route followed. In particular, the values of the fourth virial coefficient B(4) predicted by the HNC and PY approximations via the virial route differ from those obtained via the compressibility route. Despite this, it is shown in this paper that the value of B(4) obtained from the virial route in the HNC theory is exactly three halves the value obtained from the compressibility route in the PY theory, irrespective of the interaction potential (whether isotropic or not), the number of components, and the dimensionality of the system. This simple relationship is confirmed in one-component systems by analytical results for the one-dimensional penetrable-square-well model and the three-dimensional penetrable-sphere model, as well as by numerical results for the one-dimensional Lennard-Jones model, the one-dimensional Gaussian core model, and the three-dimensional square-well model.

ICON: 3D reconstruction with 'missing-information' restoration in biological electron tomography.

PubMed

Deng, Yuchen; Chen, Yu; Zhang, Yan; Wang, Shengliu; Zhang, Fa; Sun, Fei

2016-07-01

Electron tomography (ET) plays an important role in revealing biological structures, ranging from macromolecular to subcellular scale. Due to limited tilt angles, ET reconstruction always suffers from the 'missing wedge' artifacts, thus severely weakens the further biological interpretation. In this work, we developed an algorithm called Iterative Compressed-sensing Optimized Non-uniform fast Fourier transform reconstruction (ICON) based on the theory of compressed-sensing and the assumption of sparsity of biological specimens. ICON can significantly restore the missing information in comparison with other reconstruction algorithms. More importantly, we used the leave-one-out method to verify the validity of restored information for both simulated and experimental data. The significant improvement in sub-tomogram averaging by ICON indicates its great potential in the future application of high-resolution structural determination of macromolecules in situ. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Dental cone-beam CT reconstruction from limited-angle view data based on compressed-sensing (CS) theory for fast, low-dose X-ray imaging

NASA Astrophysics Data System (ADS)

Je, Uikyu; Cho, Hyosung; Lee, Minsik; Oh, Jieun; Park, Yeonok; Hong, Daeki; Park, Cheulkyu; Cho, Heemoon; Choi, Sungil; Koo, Yangseo

2014-06-01

Recently, reducing radiation doses has become an issue of critical importance in the broader radiological community. As a possible technical approach, especially, in dental cone-beam computed tomography (CBCT), reconstruction from limited-angle view data (< 360°) would enable fast scanning with reduced doses to the patient. In this study, we investigated and implemented an efficient reconstruction algorithm based on compressed-sensing (CS) theory for the scan geometry and performed systematic simulation works to investigate the image characteristics. We also performed experimental works by applying the algorithm to a commercially-available dental CBCT system to demonstrate its effectiveness for image reconstruction in incomplete data problems. We successfully reconstructed CBCT images with incomplete projections acquired at selected scan angles of 120, 150, 180, and 200° with a fixed angle step of 1.2° and evaluated the reconstruction quality quantitatively. Both simulation and experimental demonstrations of the CS-based reconstruction from limited-angle view data show that the algorithm can be applied directly to current dental CBCT systems for reducing the imaging doses and further improving the image quality.

Improved Compressive Sensing of Natural Scenes Using Localized Random Sampling

PubMed Central

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

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.

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.

Compressive Sensing Cluster Expansion Studies of Lithium Intercalation and Phase Transformation in MoS2 for Energy Storage

NASA Astrophysics Data System (ADS)

Liu, Chi-Ping; Zhou, Fei; Ozolins, Vidvuds; University of California, Los Angeles Collaboration; Lawrence livermore national laboratory Collaboration

2015-03-01

Bulk molybdenum disulfide (MoS2) is a good electrode material candidate for energy storage applications, such as lithium ion batteries and supercapacitors due to its high theoretical energy and power density. First-principles density-functional theory (DFT) calculations combined with cluster expansion are an effective method to study thermodynamic and kinetic properties of electrode materials. In order to construct accurate models for cluster expansion, it is important to effectively choose clusters with significant contributions. In this work, we employ a compressive sensing based technique to select relevant clusters in order to build an accurate Hamiltonian for cluster expansion, enabling the study of Li intercalation in MoS2. We find that the 2H MoS2 structure is only stable at low Li content while 1T MoS2 is the preferred phase at high Li content. The results show that the 2H MoS2 phase transforms into the disordered 1T phase and the disordered 1T structure remains after the first Li insertion/deinsertion cycle suggesting that disordered 1T MoS2 is stable even at dilute Li content. This work also highlights that cluster expansion treated with compressive sensing is an effective and powerful tool for model construction and can be applied to advanced battery and supercapacitor electrode materials.

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.

Compressive Video Recovery Using Block Match Multi-Frame Motion Estimation Based on Single Pixel Cameras

PubMed Central

Bi, Sheng; Zeng, Xiao; Tang, Xin; Qin, Shujia; Lai, King Wai Chiu

2016-01-01

Compressive sensing (CS) theory has opened up new paths for the development of signal processing applications. Based on this theory, a novel single pixel camera architecture has been introduced to overcome the current limitations and challenges of traditional focal plane arrays. However, video quality based on this method is limited by existing acquisition and recovery methods, and the method also suffers from being time-consuming. In this paper, a multi-frame motion estimation algorithm is proposed in CS video to enhance the video quality. The proposed algorithm uses multiple frames to implement motion estimation. Experimental results show that using multi-frame motion estimation can improve the quality of recovered videos. To further reduce the motion estimation time, a block match algorithm is used to process motion estimation. Experiments demonstrate that using the block match algorithm can reduce motion estimation time by 30%. PMID:26950127

Polarimetric and Indoor Imaging Fusion Based on Compressive Sensing

DTIC Science & Technology

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 Astrophysics Data System (ADS)

Korde-Patel, Asmita; Barry, Richard K.; Mohsenin, Tinoosh

2017-06-01

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 space-flight 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 space-flight missions.

Efficient two-dimensional compressive sensing in MIMO radar

NASA Astrophysics Data System (ADS)

Shahbazi, Nafiseh; Abbasfar, Aliazam; Jabbarian-Jahromi, Mohammad

2017-12-01

Compressive sensing (CS) has been a way to lower sampling rate leading to data reduction for processing in multiple-input multiple-output (MIMO) radar systems. In this paper, we further reduce the computational complexity of a pulse-Doppler collocated MIMO radar by introducing a two-dimensional (2D) compressive sensing. To do so, we first introduce a new 2D formulation for the compressed received signals and then we propose a new measurement matrix design for our 2D compressive sensing model that is based on minimizing the coherence of sensing matrix using gradient descent algorithm. The simulation results show that our proposed 2D measurement matrix design using gradient decent algorithm (2D-MMDGD) has much lower computational complexity compared to one-dimensional (1D) methods while having better performance in comparison with conventional methods such as Gaussian random measurement matrix.

TEM Video Compressive Sensing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stevens, Andrew; Kovarik, Libor; Abellan, Patricia

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 amore » 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 conditions. Figure 1 highlights the results from the Pd nanoparticle experiment. On the left, 10 frames are reconstructed from a single coded frame—the original frames are shown for comparison. On the right a selection of three frames are shown from reconstructions at compression levels 10,20,30. The reconstructions, which are not post-processed, are true to the original and degrade in a straightforward manner. The final choice of compression level will obviously depend on both the temporal and spatial resolution required for a specific imaging task, but the results indicate that an increase in speed of better than an order of magnitude should be possible for all experiments. References: [1] P Llull, X Liao, X Yuan et al. Optics express 21(9), (2013), p. 10526. [2] J Yang, X Yuan, X Liao et al. Image Processing, IEEE Trans 23(11), (2014), p. 4863. [3] X Yuan, J Yang, P Llull et al. In ICIP 2013 (IEEE), p. 14. [4] X Yuan, P Llull, X Liao et al. In CVPR 2014. p. 3318. [5] EJ Candès, J Romberg and T Tao. Information Theory, IEEE Trans 52(2), (2006), p. 489. [6] P Binev, W Dahmen, R DeVore et al. In Modeling Nanoscale Imaging in Electron Microscopy, eds. T Vogt, W Dahmen and P Binev (Springer US), Nanostructure Science and Technology (2012). p. 73. [7] A Stevens, H Yang, L Carin et al. Microscopy 63(1), (2014), pp. 41.« less

Simulation study on compressive laminar optical tomography for cardiac action potential propagation

PubMed Central

Harada, Takumi; Tomii, Naoki; Manago, Shota; Kobayashi, Etsuko; Sakuma, Ichiro

2017-01-01

To measure the activity of tissue at the microscopic level, laminar optical tomography (LOT), which is a microscopic form of diffuse optical tomography, has been developed. However, obtaining sufficient recording speed to determine rapidly changing dynamic activity remains major challenges. For a high frame rate of the reconstructed data, we here propose a new LOT method using compressed sensing theory, called compressive laminar optical tomography (CLOT), in which novel digital micromirror device-based illumination and data reduction in a single reconstruction are applied. In the simulation experiments, the reconstructed volumetric images of the action potentials that were acquired from 5 measured images with random pattern featured a wave border at least to a depth of 2.5 mm. Consequently, it was shown that CLOT has potential for over 200 fps required for the cardiac electrophysiological phenomena. PMID:28736675

Continuous diffusion signal, EAP and ODF estimation via Compressive Sensing in diffusion MRI.

PubMed

Merlet, Sylvain L; Deriche, Rachid

2013-07-01

In this paper, we exploit the ability of Compressed Sensing (CS) to recover the whole 3D Diffusion MRI (dMRI) signal from a limited number of samples while efficiently recovering important diffusion features such as the Ensemble Average Propagator (EAP) and the Orientation Distribution Function (ODF). Some attempts to use CS in estimating diffusion signals have been done recently. However, this was mainly an experimental insight of CS capabilities in dMRI and the CS theory has not been fully exploited. In this work, we also propose to study the impact of the sparsity, the incoherence and the RIP property on the reconstruction of diffusion signals. We show that an efficient use of the CS theory enables to drastically reduce the number of measurements commonly used in dMRI acquisitions. Only 20-30 measurements, optimally spread on several b-value shells, are shown to be necessary, which is less than previous attempts to recover the diffusion signal using CS. This opens an attractive perspective to measure the diffusion signals in white matter within a reduced acquisition time and shows that CS holds great promise and opens new and exciting perspectives in diffusion MRI (dMRI). Copyright © 2013 Elsevier B.V. All rights reserved.

Efficient Imaging and Real-Time Display of Scanning Ion Conductance Microscopy Based on Block Compressive Sensing

NASA Astrophysics Data System (ADS)

Li, Gongxin; Li, Peng; Wang, Yuechao; Wang, Wenxue; Xi, Ning; Liu, Lianqing

2014-07-01

Scanning Ion Conductance Microscopy (SICM) is one kind of Scanning Probe Microscopies (SPMs), and it is widely used in imaging soft samples for many distinctive advantages. However, the scanning speed of SICM is much slower than other SPMs. Compressive sensing (CS) could improve scanning speed tremendously by breaking through the Shannon sampling theorem, but it still requires too much time in image reconstruction. Block compressive sensing can be applied to SICM imaging to further reduce the reconstruction time of sparse signals, and it has another unique application that it can achieve the function of image real-time display in SICM imaging. In this article, a new method of dividing blocks and a new matrix arithmetic operation were proposed to build the block compressive sensing model, and several experiments were carried out to verify the superiority of block compressive sensing in reducing imaging time and real-time display in SICM imaging.

Development of a compressive sampling hyperspectral imager prototype

NASA Astrophysics Data System (ADS)

Barducci, Alessandro; Guzzi, Donatella; Lastri, Cinzia; Nardino, Vanni; Marcoionni, Paolo; Pippi, Ivan

2013-10-01

Compressive sensing (CS) is a new technology that investigates the chance to sample signals at a lower rate than the traditional sampling theory. The main advantage of CS is that compression takes place during the sampling phase, making possible significant savings in terms of the ADC, data storage memory, down-link bandwidth, and electrical power absorption. The CS technology could have primary importance for spaceborne missions and technology, paving the way to noteworthy reductions of payload mass, volume, and cost. On the contrary, the main CS disadvantage is made by the intensive off-line data processing necessary to obtain the desired source estimation. In this paper we summarize the CS architecture and its possible implementations for Earth observation, giving evidence of possible bottlenecks hindering this technology. CS necessarily employs a multiplexing scheme, which should produce some SNR disadvantage. Moreover, this approach would necessitate optical light modulators and 2-dim detector arrays of high frame rate. This paper describes the development of a sensor prototype at laboratory level that will be utilized for the experimental assessment of CS performance and the related reconstruction errors. The experimental test-bed adopts a push-broom imaging spectrometer, a liquid crystal plate, a standard CCD camera and a Silicon PhotoMultiplier (SiPM) matrix. The prototype is being developed within the framework of the ESA ITI-B Project titled "Hyperspectral Passive Satellite Imaging via Compressive Sensing".

Integrating dynamic and distributed compressive sensing techniques to enhance image quality of the compressive line sensing system for unmanned aerial vehicles application

NASA Astrophysics Data System (ADS)

Ouyang, Bing; Hou, Weilin; Caimi, Frank M.; Dalgleish, Fraser R.; Vuorenkoski, Anni K.; Gong, Cuiling

2017-07-01

The compressive line sensing imaging system adopts distributed compressive sensing (CS) to acquire data and reconstruct images. Dynamic CS uses Bayesian inference to capture the correlated nature of the adjacent lines. An image reconstruction technique that incorporates dynamic CS in the distributed CS framework was developed to improve the quality of reconstructed images. The effectiveness of the technique was validated using experimental data acquired in an underwater imaging test facility. Results that demonstrate contrast and resolution improvements will be presented. The improved efficiency is desirable for unmanned aerial vehicles conducting long-duration missions.

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.

Optical encryption of multiple three-dimensional objects based on multiple interferences and single-pixel digital holography

NASA Astrophysics Data System (ADS)

Wang, Ying; Liu, Qi; Wang, Jun; Wang, Qiong-Hua

2018-03-01

We present an optical encryption method of multiple three-dimensional objects based on multiple interferences and single-pixel digital holography. By modifying the Mach–Zehnder interferometer, the interference of the multiple objects beams and the one reference beam is used to simultaneously encrypt multiple objects into a ciphertext. During decryption, each three-dimensional object can be decrypted independently without having to decrypt other objects. Since the single-pixel digital holography based on compressive sensing theory is introduced, the encrypted data of this method is effectively reduced. In addition, recording fewer encrypted data can greatly reduce the bandwidth of network transmission. Moreover, the compressive sensing essentially serves as a secret key that makes an intruder attack invalid, which means that the system is more secure than the conventional encryption method. Simulation results demonstrate the feasibility of the proposed method and show that the system has good security performance. Project supported by the National Natural Science Foundation of China (Grant Nos. 61405130 and 61320106015).

Exploiting the wavelet structure in compressed sensing MRI.

PubMed

Chen, Chen; Huang, Junzhou

2014-12-01

Sparsity has been widely utilized in magnetic resonance imaging (MRI) to reduce k-space sampling. According to structured sparsity theories, fewer measurements are required for tree sparse data than the data only with standard sparsity. Intuitively, more accurate image reconstruction can be achieved with the same number of measurements by exploiting the wavelet tree structure in MRI. A novel algorithm is proposed in this article to reconstruct MR images from undersampled k-space data. In contrast to conventional compressed sensing MRI (CS-MRI) that only relies on the sparsity of MR images in wavelet or gradient domain, we exploit the wavelet tree structure to improve CS-MRI. This tree-based CS-MRI problem is decomposed into three simpler subproblems then each of the subproblems can be efficiently solved by an iterative scheme. Simulations and in vivo experiments demonstrate the significant improvement of the proposed method compared to conventional CS-MRI algorithms, and the feasibleness on MR data compared to existing tree-based imaging algorithms. Copyright © 2014 Elsevier Inc. All rights reserved.

Turbulent Friction in the Boundary Layer of a Flat Plate in a Two-Dimensional Compressible Flow at High Speeds

NASA Technical Reports Server (NTRS)

Frankl, F.; Voishel, V.

1943-01-01

In the present report an investigation is made on a flat plate in a two-dimensional compressible flow of the effect of compressibility and heating on the turbulent frictional drag coefficient in the boundary layer of an airfoil or wing radiator. The analysis is based on the Prandtl-Karman theory of the turbulent boundary later and the Stodola-Crocco, theorem on the linear relation between the total energy of the flow and its velocity. Formulas are obtained for the velocity distribution and the frictional drag law in a turbulent boundary later with the compressibility effect and heat transfer taken into account. It is found that with increase of compressibility and temperature at full retardation of the flow (the temperature when the velocity of the flow at a given point is reduced to zero in case of an adiabatic process in the gas) at a constant R (sub x), the frictional drag coefficient C (sub f) decreased, both of these factors acting in the same sense.

Novel image compression-encryption hybrid algorithm based on key-controlled measurement matrix in compressive sensing

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.

The Restricted Isometry Property for Time-Frequency Structured Random Matrices

DTIC Science & Technology

2011-06-16

tests illustrating the use of Ψg for compressive sensing are presented in [41]. They illustrate that empirically Ψg performs very similarly to a...E.J., J., Tao, T., Romberg , J.: Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans...Inform. Theory 52(2), 489–509 (2006) [12] Candès, E.J., Romberg , J., Tao, T.: Stable signal recovery from incomplete and inaccurate mea- surements. Comm

Compressed learning and its applications to subcellular localization.

PubMed

Zheng, Zhong-Long; Guo, Li; Jia, Jiong; Xie, Chen-Mao; Zeng, Wen-Cai; Yang, Jie

2011-09-01

One of the main challenges faced by biological applications is to predict protein subcellular localization in automatic fashion accurately. To achieve this in these applications, a wide variety of machine learning methods have been proposed in recent years. Most of them focus on finding the optimal classification scheme and less of them take the simplifying the complexity of biological systems into account. Traditionally, such bio-data are analyzed by first performing a feature selection before classification. Motivated by CS (Compressed Sensing) theory, we propose the methodology which performs compressed learning with a sparseness criterion such that feature selection and dimension reduction are merged into one analysis. The proposed methodology decreases the complexity of biological system, while increases protein subcellular localization accuracy. Experimental results are quite encouraging, indicating that the aforementioned sparse methods are quite promising in dealing with complicated biological problems, such as predicting the subcellular localization of Gram-negative bacterial proteins.

A new hyperspectral image compression paradigm based on fusion

NASA Astrophysics Data System (ADS)

Guerra, Raúl; Melián, José; López, Sebastián.; Sarmiento, Roberto

2016-10-01

The on-board compression of remote sensed hyperspectral images is an important task nowadays. One of the main difficulties is that the compression of these images must be performed in the satellite which carries the hyperspectral sensor. Hence, this process must be performed by space qualified hardware, having area, power and speed limitations. Moreover, it is important to achieve high compression ratios without compromising the quality of the decompress image. In this manuscript we proposed a new methodology for compressing hyperspectral images based on hyperspectral image fusion concepts. The proposed compression process has two independent steps. The first one is to spatially degrade the remote sensed hyperspectral image to obtain a low resolution hyperspectral image. The second step is to spectrally degrade the remote sensed hyperspectral image to obtain a high resolution multispectral image. These two degraded images are then send to the earth surface, where they must be fused using a fusion algorithm for hyperspectral and multispectral image, in order to recover the remote sensed hyperspectral image. The main advantage of the proposed methodology for compressing remote sensed hyperspectral images is that the compression process, which must be performed on-board, becomes very simple, being the fusion process used to reconstruct image the more complex one. An extra advantage is that the compression ratio can be fixed in advanced. Many simulations have been performed using different fusion algorithms and different methodologies for degrading the hyperspectral image. The results obtained in the simulations performed corroborate the benefits of the proposed methodology.

Echo-Planar Imaging-Based, J-Resolved Spectroscopic Imaging for Improved Metabolite Detection in Prostate Cancer

DTIC Science & Technology

2016-12-01

tiple dimensions (20). Hu et al. employed pseudo-random phase-encoding blips during the EPSI readout to create nonuniform sampling along the spatial...resolved MRSI with Nonuniform Undersampling and Compressed Sensing 514 30.5 Prior-knowledge Fitting for Metabolite Quantitation 515 30.6 Future Directions... NONUNIFORM UNDERSAMPLING AND COMPRESSED SENSING Nonuniform undersampling (NUS) of k-space and subsequent reconstruction using compressed sensing (CS

On-Chip Neural Data Compression Based On Compressed Sensing With Sparse Sensing Matrices.

PubMed

Zhao, Wenfeng; Sun, Biao; Wu, Tong; Yang, Zhi

2018-02-01

On-chip neural data compression is an enabling technique for wireless neural interfaces that suffer from insufficient bandwidth and power budgets to transmit the raw data. The data compression algorithm and its implementation should be power and area efficient and functionally reliable over different datasets. Compressed sensing is an emerging technique that has been applied to compress various neurophysiological data. However, the state-of-the-art compressed sensing (CS) encoders leverage random but dense binary measurement matrices, which incur substantial implementation costs on both power and area that could offset the benefits from the reduced wireless data rate. In this paper, we propose two CS encoder designs based on sparse measurement matrices that could lead to efficient hardware implementation. Specifically, two different approaches for the construction of sparse measurement matrices, i.e., the deterministic quasi-cyclic array code (QCAC) matrix and -sparse random binary matrix [-SRBM] are exploited. We demonstrate that the proposed CS encoders lead to comparable recovery performance. And efficient VLSI architecture designs are proposed for QCAC-CS and -SRBM encoders with reduced area and total power consumption.

Direct current force sensing device based on compressive spring, permanent magnet, and coil-wound magnetostrictive/piezoelectric laminate.

PubMed

Leung, Chung Ming; Or, Siu Wing; Ho, S L

2013-12-01

A force sensing device capable of sensing dc (or static) compressive forces is developed based on a NAS106N stainless steel compressive spring, a sintered NdFeB permanent magnet, and a coil-wound Tb(0.3)Dy(0.7)Fe(1.92)/Pb(Zr, Ti)O3 magnetostrictive∕piezoelectric laminate. The dc compressive force sensing in the device is evaluated theoretically and experimentally and is found to originate from a unique force-induced, position-dependent, current-driven dc magnetoelectric effect. The sensitivity of the device can be increased by increasing the spring constant of the compressive spring, the size of the permanent magnet, and/or the driving current for the coil-wound laminate. Devices of low-force (20 N) and high-force (200 N) types, showing high output voltages of 262 and 128 mV peak, respectively, are demonstrated at a low driving current of 100 mA peak by using different combinations of compressive spring and permanent magnet.

COxSwAIN: Compressive Sensing for Advanced Imaging and Navigation

NASA Technical Reports Server (NTRS)

Kurwitz, Richard; Pulley, Marina; LaFerney, Nathan; Munoz, Carlos

2015-01-01

The COxSwAIN project focuses on building an image and video compression scheme that can be implemented in a small or low-power satellite. To do this, we used Compressive Sensing, where the compression is performed by matrix multiplications on the satellite and reconstructed on the ground. Our paper explains our methodology and demonstrates the results of the scheme, being able to achieve high quality image compression that is robust to noise and corruption.

Compressed sensing for high-resolution nonlipid suppressed 1 H FID MRSI of the human brain at 9.4T.

PubMed

Nassirpour, Sahar; Chang, Paul; Avdievitch, Nikolai; Henning, Anke

2018-04-29

The aim of this study was to apply compressed sensing to accelerate the acquisition of high resolution metabolite maps of the human brain using a nonlipid suppressed ultra-short TR and TE 1 H FID MRSI sequence at 9.4T. X-t sparse compressed sensing reconstruction was optimized for nonlipid suppressed 1 H FID MRSI data. Coil-by-coil x-t sparse reconstruction was compared with SENSE x-t sparse and low rank reconstruction. The effect of matrix size and spatial resolution on the achievable acceleration factor was studied. Finally, in vivo metabolite maps with different acceleration factors of 2, 4, 5, and 10 were acquired and compared. Coil-by-coil x-t sparse compressed sensing reconstruction was not able to reliably recover the nonlipid suppressed data, rather a combination of parallel and sparse reconstruction was necessary (SENSE x-t sparse). For acceleration factors of up to 5, both the low-rank and the compressed sensing methods were able to reconstruct the data comparably well (root mean squared errors [RMSEs] ≤ 10.5% for Cre). However, the reconstruction time of the low rank algorithm was drastically longer than compressed sensing. Using the optimized compressed sensing reconstruction, acceleration factors of 4 or 5 could be reached for the MRSI data with a matrix size of 64 × 64. For lower spatial resolutions, an acceleration factor of up to R∼4 was successfully achieved. By tailoring the reconstruction scheme to the nonlipid suppressed data through parameter optimization and performance evaluation, we present high resolution (97 µL voxel size) accelerated in vivo metabolite maps of the human brain acquired at 9.4T within scan times of 3 to 3.75 min. © 2018 International Society for Magnetic Resonance in Medicine.

Less is More: Bigger Data from Compressive Measurements

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stevens, Andrew; Browning, Nigel D.

Compressive sensing approaches are beginning to take hold in (scanning) transmission electron microscopy (S/TEM) [1,2,3]. Compressive sensing is a mathematical theory about acquiring signals in a compressed form (measurements) and the probability of recovering the original signal by solving an inverse problem [4]. The inverse problem is underdetermined (more unknowns than measurements), so it is not obvious that recovery is possible. Compression is achieved by taking inner products of the signal with measurement weight vectors. Both Gaussian random weights and Bernoulli (0,1) random weights form a large class of measurement vectors for which recovery is possible. The measurements can alsomore » be designed through an optimization process. The key insight for electron microscopists is that compressive sensing can be used to increase acquisition speed and reduce dose. Building on work initially developed for optical cameras, this new paradigm will allow electron microscopists to solve more problems in the engineering and life sciences. We will be collecting orders of magnitude more data than previously possible. The reason that we will have more data is because we will have increased temporal/spatial/spectral sampling rates, and we will be able ability to interrogate larger classes of samples that were previously too beam sensitive to survive the experiment. For example consider an in-situ experiment that takes 1 minute. With traditional sensing, we might collect 5 images per second for a total of 300 images. With compressive sensing, each of those 300 images can be expanded into 10 more images, making the collection rate 50 images per second, and the decompressed data a total of 3000 images [3]. But, what are the implications, in terms of data, for this new methodology? Acquisition of compressed data will require downstream reconstruction to be useful. The reconstructed data will be much larger than traditional data, we will need space to store the reconstructions during analysis, and the computational demands for analysis will be higher. Moreover, there will be time costs associated with reconstruction. Deep learning [5] is an approach to address these problems. Deep learning is a hierarchical approach to find useful (for a particular task) representations of data. Each layer of the hierarchy is intended to represent higher levels of abstraction. For example, a deep model of faces might have sinusoids, edges and gradients in the first layer; eyes, noses, and mouths in the second layer, and faces in the third layer. There has been significant effort recently in deep learning algorithms for tasks beyond image classification such as compressive reconstruction [6] and image segmentation [7]. A drawback of deep learning, however, is that training the model requires large datasets and dedicated computational resources (to reduce training time to a few days). A second issue is that deep learning is not user-friendly and the meaning behind the results is usually not interpretable. We have shown it is possible to reduce the data set size while maintaining model quality [8] and developed interpretable models for image classification [9], but the demands are still significant. The key to addressing these problems is to NOT reconstruct the data. Instead, we should design computational sensors that give answers to specific problems. A simple version of this idea is compressive classification [10], where the goal is to classify signal type from a small number of compressed measurements. Classification is a much simpler problem than reconstruction, so 1) much fewer measurements will be necessary, and 2) these measurements will probably not be useful for reconstruction. Other simple examples of computational sensing include determining object volume or the number of objects present in the field of view [11].« less

Image quality enhancement in low-light-level ghost imaging using modified compressive sensing method

NASA Astrophysics Data System (ADS)

Shi, Xiaohui; Huang, Xianwei; Nan, Suqin; Li, Hengxing; Bai, Yanfeng; Fu, Xiquan

2018-04-01

Detector noise has a significantly negative impact on ghost imaging at low light levels, especially for existing recovery algorithm. Based on the characteristics of the additive detector noise, a method named modified compressive sensing ghost imaging is proposed to reduce the background imposed by the randomly distributed detector noise at signal path. Experimental results show that, with an appropriate choice of threshold value, modified compressive sensing ghost imaging algorithm can dramatically enhance the contrast-to-noise ratio of the object reconstruction significantly compared with traditional ghost imaging and compressive sensing ghost imaging methods. The relationship between the contrast-to-noise ratio of the reconstruction image and the intensity ratio (namely, the average signal intensity to average noise intensity ratio) for the three reconstruction algorithms are also discussed. This noise suppression imaging technique will have great applications in remote-sensing and security areas.

Effectiveness of compressed sensing and transmission in wireless sensor networks for structural health monitoring

NASA Astrophysics Data System (ADS)

Fujiwara, Takahiro; Uchiito, Haruki; Tokairin, Tomoya; Kawai, Hiroyuki

2017-04-01

Regarding Structural Health Monitoring (SHM) for seismic acceleration, Wireless Sensor Networks (WSN) is a promising tool for low-cost monitoring. Compressed sensing and transmission schemes have been drawing attention to achieve effective data collection in WSN. Especially, SHM systems installing massive nodes of WSN require efficient data transmission due to restricted communications capability. The dominant frequency band of seismic acceleration is occupied within 100 Hz or less. In addition, the response motions on upper floors of a structure are activated at a natural frequency, resulting in induced shaking at the specified narrow band. Focusing on the vibration characteristics of structures, we introduce data compression techniques for seismic acceleration monitoring in order to reduce the amount of transmission data. We carry out a compressed sensing and transmission scheme by band pass filtering for seismic acceleration data. The algorithm executes the discrete Fourier transform for the frequency domain and band path filtering for the compressed transmission. Assuming that the compressed data is transmitted through computer networks, restoration of the data is performed by the inverse Fourier transform in the receiving node. This paper discusses the evaluation of the compressed sensing for seismic acceleration by way of an average error. The results present the average error was 0.06 or less for the horizontal acceleration, in conditions where the acceleration was compressed into 1/32. Especially, the average error on the 4th floor achieved a small error of 0.02. Those results indicate that compressed sensing and transmission technique is effective to reduce the amount of data with maintaining the small average error.

Implementation of a compressive sampling scheme for wireless sensors to achieve energy efficiency in a structural health monitoring system

NASA Astrophysics Data System (ADS)

O'Connor, Sean M.; Lynch, Jerome P.; Gilbert, Anna C.

2013-04-01

Wireless sensors have emerged to offer low-cost sensors with impressive functionality (e.g., data acquisition, computing, and communication) and modular installations. Such advantages enable higher nodal densities than tethered systems resulting in increased spatial resolution of the monitoring system. However, high nodal density comes at a cost as huge amounts of data are generated, weighing heavy on power sources, transmission bandwidth, and data management requirements, often making data compression necessary. The traditional compression paradigm consists of high rate (>Nyquist) uniform sampling and storage of the entire target signal followed by some desired compression scheme prior to transmission. The recently proposed compressed sensing (CS) framework combines the acquisition and compression stage together, thus removing the need for storage and operation of the full target signal prior to transmission. The effectiveness of the CS approach hinges on the presence of a sparse representation of the target signal in a known basis, similarly exploited by several traditional compressive sensing applications today (e.g., imaging, MRI). Field implementations of CS schemes in wireless SHM systems have been challenging due to the lack of commercially available sensing units capable of sampling methods (e.g., random) consistent with the compressed sensing framework, often moving evaluation of CS techniques to simulation and post-processing. The research presented here describes implementation of a CS sampling scheme to the Narada wireless sensing node and the energy efficiencies observed in the deployed sensors. Of interest in this study is the compressibility of acceleration response signals collected from a multi-girder steel-concrete composite bridge. The study shows the benefit of CS in reducing data requirements while ensuring data analysis on compressed data remain accurate.

Spatial-Temporal Data Collection with Compressive Sensing in Mobile Sensor Networks

PubMed Central

Li, Jiayin; Guo, Wenzhong; Chen, Zhonghui; Xiong, Neal

2017-01-01

Compressive sensing (CS) provides an energy-efficient paradigm for data gathering in wireless sensor networks (WSNs). However, the existing work on spatial-temporal data gathering using compressive sensing only considers either multi-hop relaying based or multiple random walks based approaches. In this paper, we exploit the mobility pattern for spatial-temporal data collection and propose a novel mobile data gathering scheme by employing the Metropolis-Hastings algorithm with delayed acceptance, an improved random walk algorithm for a mobile collector to collect data from a sensing field. The proposed scheme exploits Kronecker compressive sensing (KCS) for spatial-temporal correlation of sensory data by allowing the mobile collector to gather temporal compressive measurements from a small subset of randomly selected nodes along a random routing path. More importantly, from the theoretical perspective we prove that the equivalent sensing matrix constructed from the proposed scheme for spatial-temporal compressible signal can satisfy the property of KCS models. The simulation results demonstrate that the proposed scheme can not only significantly reduce communication cost but also improve recovery accuracy for mobile data gathering compared to the other existing schemes. In particular, we also show that the proposed scheme is robust in unreliable wireless environment under various packet losses. All this indicates that the proposed scheme can be an efficient alternative for data gathering application in WSNs. PMID:29117152

Spatial-Temporal Data Collection with Compressive Sensing in Mobile Sensor Networks.

PubMed

Zheng, Haifeng; Li, Jiayin; Feng, Xinxin; Guo, Wenzhong; Chen, Zhonghui; Xiong, Neal

2017-11-08

Compressive sensing (CS) provides an energy-efficient paradigm for data gathering in wireless sensor networks (WSNs). However, the existing work on spatial-temporal data gathering using compressive sensing only considers either multi-hop relaying based or multiple random walks based approaches. In this paper, we exploit the mobility pattern for spatial-temporal data collection and propose a novel mobile data gathering scheme by employing the Metropolis-Hastings algorithm with delayed acceptance, an improved random walk algorithm for a mobile collector to collect data from a sensing field. The proposed scheme exploits Kronecker compressive sensing (KCS) for spatial-temporal correlation of sensory data by allowing the mobile collector to gather temporal compressive measurements from a small subset of randomly selected nodes along a random routing path. More importantly, from the theoretical perspective we prove that the equivalent sensing matrix constructed from the proposed scheme for spatial-temporal compressible signal can satisfy the property of KCS models. The simulation results demonstrate that the proposed scheme can not only significantly reduce communication cost but also improve recovery accuracy for mobile data gathering compared to the other existing schemes. In particular, we also show that the proposed scheme is robust in unreliable wireless environment under various packet losses. All this indicates that the proposed scheme can be an efficient alternative for data gathering application in WSNs .

Evaluation of Heterogeneous Metabolic Profile in an Orthotopic Human Glioblastoma Xenograft Model Using Compressed Sensing Hyperpolarized 3D 13C Magnetic Resonance Spectroscopic Imaging

PubMed Central

Park, Ilwoo; Hu, Simon; Bok, Robert; Ozawa, Tomoko; Ito, Motokazu; Mukherjee, Joydeep; Phillips, Joanna J.; James, C. David; Pieper, Russell O.; Ronen, Sabrina M.; Vigneron, Daniel B.; Nelson, Sarah J.

2013-01-01

High resolution compressed sensing hyperpolarized 13C magnetic resonance spectroscopic imaging was applied in orthotopic human glioblastoma xenografts for quantitative assessment of spatial variations in 13C metabolic profiles and comparison with histopathology. A new compressed sensing sampling design with a factor of 3.72 acceleration was implemented to enable a factor of 4 increase in spatial resolution. Compressed sensing 3D 13C magnetic resonance spectroscopic imaging data were acquired from a phantom and 10 tumor-bearing rats following injection of hyperpolarized [1-13C]-pyruvate using a 3T scanner. The 13C metabolic profiles were compared with hematoxylin and eosin staining and carbonic anhydrase 9 staining. The high-resolution compressed sensing 13C magnetic resonance spectroscopic imaging data enabled the differentiation of distinct 13C metabolite patterns within abnormal tissues with high specificity in similar scan times compared to the fully sampled method. The results from pathology confirmed the different characteristics of 13C metabolic profiles between viable, non-necrotic, nonhypoxic tumor, and necrotic, hypoxic tissue. PMID:22851374

Evaluation of heterogeneous metabolic profile in an orthotopic human glioblastoma xenograft model using compressed sensing hyperpolarized 3D 13C magnetic resonance spectroscopic imaging.

PubMed

Park, Ilwoo; Hu, Simon; Bok, Robert; Ozawa, Tomoko; Ito, Motokazu; Mukherjee, Joydeep; Phillips, Joanna J; James, C David; Pieper, Russell O; Ronen, Sabrina M; Vigneron, Daniel B; Nelson, Sarah J

2013-07-01

High resolution compressed sensing hyperpolarized (13)C magnetic resonance spectroscopic imaging was applied in orthotopic human glioblastoma xenografts for quantitative assessment of spatial variations in (13)C metabolic profiles and comparison with histopathology. A new compressed sensing sampling design with a factor of 3.72 acceleration was implemented to enable a factor of 4 increase in spatial resolution. Compressed sensing 3D (13)C magnetic resonance spectroscopic imaging data were acquired from a phantom and 10 tumor-bearing rats following injection of hyperpolarized [1-(13)C]-pyruvate using a 3T scanner. The (13)C metabolic profiles were compared with hematoxylin and eosin staining and carbonic anhydrase 9 staining. The high-resolution compressed sensing (13)C magnetic resonance spectroscopic imaging data enabled the differentiation of distinct (13)C metabolite patterns within abnormal tissues with high specificity in similar scan times compared to the fully sampled method. The results from pathology confirmed the different characteristics of (13)C metabolic profiles between viable, non-necrotic, nonhypoxic tumor, and necrotic, hypoxic tissue. Copyright © 2012 Wiley Periodicals, Inc.

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.

Compression in wearable sensor nodes: impacts of node topology.

PubMed

Imtiaz, Syed Anas; Casson, Alexander J; Rodriguez-Villegas, Esther

2014-04-01

Wearable sensor nodes monitoring the human body must operate autonomously for very long periods of time. Online and low-power data compression embedded within the sensor node is therefore essential to minimize data storage/transmission overheads. This paper presents a low-power MSP430 compressive sensing implementation for providing such compression, focusing particularly on the impact of the sensor node architecture on the compression performance. Compression power performance is compared for four different sensor nodes incorporating different strategies for wireless transmission/on-sensor-node local storage of data. The results demonstrate that the compressive sensing used must be designed differently depending on the underlying node topology, and that the compression strategy should not be guided only by signal processing considerations. We also provide a practical overview of state-of-the-art sensor node topologies. Wireless transmission of data is often preferred as it offers increased flexibility during use, but in general at the cost of increased power consumption. We demonstrate that wireless sensor nodes can highly benefit from the use of compressive sensing and now can achieve power consumptions comparable to, or better than, the use of local memory.

Robust Methods for Sensing and Reconstructing Sparse Signals

ERIC Educational Resources Information Center

Carrillo, Rafael E.

2012-01-01

Compressed sensing (CS) is an emerging signal acquisition framework that goes against the traditional Nyquist sampling paradigm. CS demonstrates that a sparse, or compressible, signal can be acquired using a low rate acquisition process. Since noise is always present in practical data acquisition systems, sensing and reconstruction methods are…

Experimental scheme and restoration algorithm of block compression sensing

NASA Astrophysics Data System (ADS)

Zhang, Linxia; Zhou, Qun; Ke, Jun

2018-01-01

Compressed Sensing (CS) can use the sparseness of a target to obtain its image with much less data than that defined by the Nyquist sampling theorem. In this paper, we study the hardware implementation of a block compression sensing system and its reconstruction algorithms. Different block sizes are used. Two algorithms, the orthogonal matching algorithm (OMP) and the full variation minimum algorithm (TV) are used to obtain good reconstructions. The influence of block size on reconstruction is also discussed.

A compressed sensing X-ray camera with a multilayer architecture

NASA Astrophysics Data System (ADS)

Wang, Zhehui; Iaroshenko, O.; Li, S.; Liu, T.; Parab, N.; Chen, W. W.; Chu, P.; Kenyon, G. T.; Lipton, R.; Sun, K.-X.

2018-01-01

Recent advances in compressed sensing theory and algorithms offer new possibilities for high-speed X-ray camera design. In many CMOS cameras, each pixel has an independent on-board circuit that includes an amplifier, noise rejection, signal shaper, an analog-to-digital converter (ADC), and optional in-pixel storage. When X-ray images are sparse, i.e., when one of the following cases is true: (a.) The number of pixels with true X-ray hits is much smaller than the total number of pixels; (b.) The X-ray information is redundant; or (c.) Some prior knowledge about the X-ray images exists, sparse sampling may be allowed. Here we first illustrate the feasibility of random on-board pixel sampling (ROPS) using an existing set of X-ray images, followed by a discussion about signal to noise as a function of pixel size. Next, we describe a possible circuit architecture to achieve random pixel access and in-pixel storage. The combination of a multilayer architecture, sparse on-chip sampling, and computational image techniques, is expected to facilitate the development and applications of high-speed X-ray camera technology.

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.

High-Frequency Subband Compressed Sensing MRI Using Quadruplet Sampling

PubMed Central

Sung, Kyunghyun; Hargreaves, Brian A

2013-01-01

Purpose To presents and validates a new method that formalizes a direct link between k-space and wavelet domains to apply separate undersampling and reconstruction for high- and low-spatial-frequency k-space data. Theory and Methods High- and low-spatial-frequency regions are defined in k-space based on the separation of wavelet subbands, and the conventional compressed sensing (CS) problem is transformed into one of localized k-space estimation. To better exploit wavelet-domain sparsity, CS can be used for high-spatial-frequency regions while parallel imaging can be used for low-spatial-frequency regions. Fourier undersampling is also customized to better accommodate each reconstruction method: random undersampling for CS and regular undersampling for parallel imaging. Results Examples using the proposed method demonstrate successful reconstruction of both low-spatial-frequency content and fine structures in high-resolution 3D breast imaging with a net acceleration of 11 to 12. Conclusion The proposed method improves the reconstruction accuracy of high-spatial-frequency signal content and avoids incoherent artifacts in low-spatial-frequency regions. This new formulation also reduces the reconstruction time due to the smaller problem size. PMID:23280540

Motion-adaptive spatio-temporal regularization for accelerated dynamic MRI.

PubMed

Asif, M Salman; Hamilton, Lei; Brummer, Marijn; Romberg, Justin

2013-09-01

Accelerated magnetic resonance imaging techniques reduce signal acquisition time by undersampling k-space. A fundamental problem in accelerated magnetic resonance imaging is the recovery of quality images from undersampled k-space data. Current state-of-the-art recovery algorithms exploit the spatial and temporal structures in underlying images to improve the reconstruction quality. In recent years, compressed sensing theory has helped formulate mathematical principles and conditions that ensure recovery of (structured) sparse signals from undersampled, incoherent measurements. In this article, a new recovery algorithm, motion-adaptive spatio-temporal regularization, is presented that uses spatial and temporal structured sparsity of MR images in the compressed sensing framework to recover dynamic MR images from highly undersampled k-space data. In contrast to existing algorithms, our proposed algorithm models temporal sparsity using motion-adaptive linear transformations between neighboring images. The efficiency of motion-adaptive spatio-temporal regularization is demonstrated with experiments on cardiac magnetic resonance imaging for a range of reduction factors. Results are also compared with k-t FOCUSS with motion estimation and compensation-another recently proposed recovery algorithm for dynamic magnetic resonance imaging. . Copyright © 2012 Wiley Periodicals, Inc.

Distributed Coding of Compressively Sensed Sources

NASA Astrophysics Data System (ADS)

Goukhshtein, Maxim

In this work we propose a new method for compressing multiple correlated sources with a very low-complexity encoder in the presence of side information. Our approach uses ideas from compressed sensing and distributed source coding. At the encoder, syndromes of the quantized compressively sensed sources are generated and transmitted. The decoder uses side information to predict the compressed sources. The predictions are then used to recover the quantized measurements via a two-stage decoding process consisting of bitplane prediction and syndrome decoding. Finally, guided by the structure of the sources and the side information, the sources are reconstructed from the recovered measurements. As a motivating example, we consider the compression of multispectral images acquired on board satellites, where resources, such as computational power and memory, are scarce. Our experimental results exhibit a significant improvement in the rate-distortion trade-off when compared against approaches with similar encoder complexity.

Optical image encryption using chaos-based compressed sensing and phase-shifting interference in fractional wavelet domain

NASA Astrophysics Data System (ADS)

Liu, Qi; Wang, Ying; Wang, Jun; Wang, Qiong-Hua

2018-02-01

In this paper, a novel optical image encryption system combining compressed sensing with phase-shifting interference in fractional wavelet domain is proposed. To improve the encryption efficiency, the volume data of original image are decreased by compressed sensing. Then the compacted image is encoded through double random phase encoding in asymmetric fractional wavelet domain. In the encryption system, three pseudo-random sequences, generated by three-dimensional chaos map, are used as the measurement matrix of compressed sensing and two random-phase masks in the asymmetric fractional wavelet transform. It not only simplifies the keys to storage and transmission, but also enhances our cryptosystem nonlinearity to resist some common attacks. Further, holograms make our cryptosystem be immune to noises and occlusion attacks, which are obtained by two-step-only quadrature phase-shifting interference. And the compression and encryption can be achieved in the final result simultaneously. Numerical experiments have verified the security and validity of the proposed algorithm.

Adaptive compressed sensing of remote-sensing imaging based on the sparsity prediction

NASA Astrophysics Data System (ADS)

Yang, Senlin; Li, Xilong; Chong, Xin

2017-10-01

The conventional compressive sensing works based on the non-adaptive linear projections, and the parameter of its measurement times is usually set empirically. As a result, the quality of image reconstruction is always affected. Firstly, the block-based compressed sensing (BCS) with conventional selection for compressive measurements was given. Then an estimation method for the sparsity of image was proposed based on the two dimensional discrete cosine transform (2D DCT). With an energy threshold given beforehand, the DCT coefficients were processed with both energy normalization and sorting in descending order, and the sparsity of the image can be achieved by the proportion of dominant coefficients. And finally, the simulation result shows that, the method can estimate the sparsity of image effectively, and provides an active basis for the selection of compressive observation times. The result also shows that, since the selection of observation times is based on the sparse degree estimated with the energy threshold provided, the proposed method can ensure the quality of image reconstruction.

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.

The integrated design and archive of space-borne signal processing and compression coding

NASA Astrophysics Data System (ADS)

He, Qiang-min; Su, Hao-hang; Wu, Wen-bo

2017-10-01

With the increasing demand of users for the extraction of remote sensing image information, it is very urgent to significantly enhance the whole system's imaging quality and imaging ability by using the integrated design to achieve its compact structure, light quality and higher attitude maneuver ability. At this present stage, the remote sensing camera's video signal processing unit and image compression and coding unit are distributed in different devices. The volume, weight and consumption of these two units is relatively large, which unable to meet the requirements of the high mobility remote sensing camera. This paper according to the high mobility remote sensing camera's technical requirements, designs a kind of space-borne integrated signal processing and compression circuit by researching a variety of technologies, such as the high speed and high density analog-digital mixed PCB design, the embedded DSP technology and the image compression technology based on the special-purpose chips. This circuit lays a solid foundation for the research of the high mobility remote sensing camera.

Reconstruction of Complex Network based on the Noise via QR Decomposition and Compressed Sensing.

PubMed

Li, Lixiang; Xu, Dafei; Peng, Haipeng; Kurths, Jürgen; Yang, Yixian

2017-11-08

It is generally known that the states of network nodes are stable and have strong correlations in a linear network system. We find that without the control input, the method of compressed sensing can not succeed in reconstructing complex networks in which the states of nodes are generated through the linear network system. However, noise can drive the dynamics between nodes to break the stability of the system state. Therefore, a new method integrating QR decomposition and compressed sensing is proposed to solve the reconstruction problem of complex networks under the assistance of the input noise. The state matrix of the system is decomposed by QR decomposition. We construct the measurement matrix with the aid of Gaussian noise so that the sparse input matrix can be reconstructed by compressed sensing. We also discover that noise can build a bridge between the dynamics and the topological structure. Experiments are presented to show that the proposed method is more accurate and more efficient to reconstruct four model networks and six real networks by the comparisons between the proposed method and only compressed sensing. In addition, the proposed method can reconstruct not only the sparse complex networks, but also the dense complex networks.

Matched Filtering for Heart Rate Estimation on Compressive Sensing ECG Measurements.

PubMed

Da Poian, Giulia; Rozell, Christopher J; Bernardini, Riccardo; Rinaldo, Roberto; Clifford, Gari D

2017-09-14

Compressive Sensing (CS) has recently been applied as a low complexity compression framework for long-term monitoring of electrocardiogram signals using Wireless Body Sensor Networks. Long-term recording of ECG signals can be useful for diagnostic purposes and to monitor the evolution of several widespread diseases. In particular, beat to beat intervals provide important clinical information, and these can be derived from the ECG signal by computing the distance between QRS complexes (R-peaks). Numerous methods for R-peak detection are available for uncompressed ECG. However, in case of compressed sensed data, signal reconstruction can be performed with relatively complex optimisation algorithms, which may require significant energy consumption. This article addresses the problem of hearth rate estimation from compressive sensing electrocardiogram (ECG) recordings, avoiding the reconstruction of the entire signal. We consider a framework where the ECG signals are represented under the form of CS linear measurements. The QRS locations are estimated in the compressed domain by computing the correlation of the compressed ECG and a known QRS template. Experiments on actual ECG signals show that our novel solution is competitive with methods applied to the reconstructed signals. Avoiding the reconstruction procedure, the proposed method proves to be very convenient for real-time, low-power applications.

High-resolution remotely sensed small target detection by imitating fly visual perception mechanism.

PubMed

Huang, Fengchen; Xu, Lizhong; Li, Min; Tang, Min

2012-01-01

The difficulty and limitation of small target detection methods for high-resolution remote sensing data have been a recent research hot spot. Inspired by the information capture and processing theory of fly visual system, this paper endeavors to construct a characterized model of information perception and make use of the advantages of fast and accurate small target detection under complex varied nature environment. The proposed model forms a theoretical basis of small target detection for high-resolution remote sensing data. After the comparison of prevailing simulation mechanism behind fly visual systems, we propose a fly-imitated visual system method of information processing for high-resolution remote sensing data. A small target detector and corresponding detection algorithm are designed by simulating the mechanism of information acquisition, compression, and fusion of fly visual system and the function of pool cell and the character of nonlinear self-adaption. Experiments verify the feasibility and rationality of the proposed small target detection model and fly-imitated visual perception method.

A Viscoplastic Constitutive Theory for Monolithic Ceramic Materials. Series 1

NASA Technical Reports Server (NTRS)

Janosik, Lesley A.; Duffy, Stephen F.

1997-01-01

With increasing use of ceramic materials in high temperature structural applications such as advanced heat engine components, the need arises to accurately predict thermomechanical behavior. This paper, which is the first of two in a series, will focus on inelastic deformation behavior associated with these service conditions by providing an overview of a viscoplastic constitutive model that accounts for time-dependent hereditary material deformation (e.g., creep, stress relaxation, etc.) in monolithic structural ceramics. Early work in the field of metal plasticity indicated that inelastic deformations are essentially unaffected by hydrostatic stress. This is not the case, however, for ceramic-based material systems, unless the ceramic is fully dense. The theory presented here allows for fully dense material behavior as a limiting case. In addition, ceramic materials exhibit different time-dependent behavior in tension and compression. Thus, inelastic deformation models for ceramics must be constructed in a fashion that admits both sensitivity to hydrostatic stress and differing behavior in tension and compression. A number of constitutive theories for materials that exhibit sensitivity to the hydrostatic component of stress have been proposed that characterize deformation using time-independent classical plasticity as a foundation. However, none of these theories allow different behavior in tension and compression. In addition, these theories are somewhat lacking in that they are unable to capture creep, relaxation, and rate-sensitive phenomena exhibited by ceramic materials at high temperature. When subjected to elevated service temperatures, ceramic materials exhibit complex thermomechanical behavior that is inherently time-dependent, and hereditary in the sense that current behavior depends not only on current conditions, but also on thermo-mechanical history. The objective of this work is to present the formulation of a macroscopic continuum theory that captures these time-dependent phenomena. Specifically, the overview contained in this paper focuses on the multiaxial derivation of the constitutive model, and examines the scalar threshold function and its attending geometrical implications.

Constitutive Theory Developed for Monolithic Ceramic Materials

NASA Technical Reports Server (NTRS)

Janosik, Lesley A.

1998-01-01

With the increasing use of advanced ceramic materials in high-temperature structural applications such as advanced heat engine components, the need arises to accurately predict thermomechanical behavior that is inherently time-dependent and that is hereditary in the sense that the current behavior depends not only on current conditions but also on the material's thermomechanical history. Most current analytical life prediction methods for both subcritical crack growth and creep models use elastic stress fields to predict the time-dependent reliability response of components subjected to elevated service temperatures. Inelastic response at high temperatures has been well documented in the materials science literature for these material systems, but this issue has been ignored by the engineering design community. From a design engineer's perspective, it is imperative to emphasize that accurate predictions of time-dependent reliability demand accurate stress field information. Ceramic materials exhibit different time-dependent behavior in tension and compression. Thus, inelastic deformation models for ceramics must be constructed in a fashion that admits both sensitivity to hydrostatic stress and differing behavior in tension and compression. A number of constitutive theories for materials that exhibit sensitivity to the hydrostatic component of stress have been proposed that characterize deformation using time-independent classical plasticity as a foundation. However, none of these theories allow different behavior in tension and compression. In addition, these theories are somewhat lacking in that they are unable to capture the creep, relaxation, and rate-sensitive phenomena exhibited by ceramic materials at high temperatures. The objective of this effort at the NASA Lewis Research Center has been to formulate a macroscopic continuum theory that captures these time-dependent phenomena. Specifically, the effort has focused on inelastic deformation behavior associated with these service conditions by developing a multiaxial viscoplastic constitutive model that accounts for time-dependent hereditary material deformation (such as creep and stress relaxation) in monolithic structural ceramics. Using continuum principles of engineering mechanics, we derived the complete viscoplastic theory from a scalar dissipative potential function.

Experimental investigations on airborne gravimetry based on compressed sensing.

PubMed

Yang, Yapeng; Wu, Meiping; Wang, Jinling; Zhang, Kaidong; Cao, Juliang; Cai, Shaokun

2014-03-18

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.

Experimental Investigations on Airborne Gravimetry Based on Compressed Sensing

PubMed Central

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

Training-free compressed sensing for wireless neural recording using analysis model and group weighted {{\\ell}_{1}} -minimization

NASA Astrophysics Data System (ADS)

Sun, Biao; Zhao, Wenfeng; Zhu, Xinshan

2017-06-01

Objective. Data compression is crucial for resource-constrained wireless neural recording applications with limited data bandwidth, and compressed sensing (CS) theory has successfully demonstrated its potential in neural recording applications. In this paper, an analytical, training-free CS recovery method, termed group weighted analysis {{\\ell}1} -minimization (GWALM), is proposed for wireless neural recording. Approach. The GWALM method consists of three parts: (1) the analysis model is adopted to enforce sparsity of the neural signals, therefore overcoming the drawbacks of conventional synthesis models and enhancing the recovery performance. (2) A multi-fractional-order difference matrix is constructed as the analysis operator, thus avoiding the dictionary learning procedure and reducing the need for previously acquired data and computational complexities. (3) By exploiting the statistical properties of the analysis coefficients, a group weighting approach is developed to enhance the performance of analysis {{\\ell}1} -minimization. Main results. Experimental results on synthetic and real datasets reveal that the proposed approach outperforms state-of-the-art CS-based methods in terms of both spike recovery quality and classification accuracy. Significance. Energy and area efficiency of the GWALM make it an ideal candidate for resource-constrained, large scale wireless neural recording applications. The training-free feature of the GWALM further improves its robustness to spike shape variation, thus making it more practical for long term wireless neural recording.

Training-free compressed sensing for wireless neural recording using analysis model and group weighted [Formula: see text]-minimization.

PubMed

Sun, Biao; Zhao, Wenfeng; Zhu, Xinshan

2017-06-01

Data compression is crucial for resource-constrained wireless neural recording applications with limited data bandwidth, and compressed sensing (CS) theory has successfully demonstrated its potential in neural recording applications. In this paper, an analytical, training-free CS recovery method, termed group weighted analysis [Formula: see text]-minimization (GWALM), is proposed for wireless neural recording. The GWALM method consists of three parts: (1) the analysis model is adopted to enforce sparsity of the neural signals, therefore overcoming the drawbacks of conventional synthesis models and enhancing the recovery performance. (2) A multi-fractional-order difference matrix is constructed as the analysis operator, thus avoiding the dictionary learning procedure and reducing the need for previously acquired data and computational complexities. (3) By exploiting the statistical properties of the analysis coefficients, a group weighting approach is developed to enhance the performance of analysis [Formula: see text]-minimization. Experimental results on synthetic and real datasets reveal that the proposed approach outperforms state-of-the-art CS-based methods in terms of both spike recovery quality and classification accuracy. Energy and area efficiency of the GWALM make it an ideal candidate for resource-constrained, large scale wireless neural recording applications. The training-free feature of the GWALM further improves its robustness to spike shape variation, thus making it more practical for long term wireless neural recording.

Compressed Sensing in On-Grid MIMO Radar.

PubMed

Minner, Michael F

2015-01-01

The accurate detection of targets is a significant problem in multiple-input multiple-output (MIMO) radar. Recent advances of Compressive Sensing offer a means of efficiently accomplishing this task. The sparsity constraints needed to apply the techniques of Compressive Sensing to problems in radar systems have led to discretizations of the target scene in various domains, such as azimuth, time delay, and Doppler. Building upon recent work, we investigate the feasibility of on-grid Compressive Sensing-based MIMO radar via a threefold azimuth-delay-Doppler discretization for target detection and parameter estimation. We utilize a colocated random sensor array and transmit distinct linear chirps to a small scene with few, slowly moving targets. Relying upon standard far-field and narrowband assumptions, we analyze the efficacy of various recovery algorithms in determining the parameters of the scene through numerical simulations, with particular focus on the ℓ 1-squared Nonnegative Regularization method.

A Riemannian geometric mapping technique for identifying incompressible equivalents to subsonic potential flows

NASA Astrophysics Data System (ADS)

German, Brian Joseph

This research develops a technique for the solution of incompressible equivalents to planar steady subsonic potential flows. Riemannian geometric formalism is used to develop a gauge transformation of the length measure followed by a curvilinear coordinate transformation to map the given subsonic flow into a canonical Laplacian flow with the same boundary conditions. The effect of the transformation is to distort both the immersed profile shape and the domain interior nonuniformly as a function of local flow properties. The method represents the full nonlinear generalization of the classical methods of Prandtl-Glauert and Karman-Tsien. Unlike the classical methods which are "corrections," this method gives exact results in the sense that the inverse mapping produces the subsonic full potential solution over the original airfoil, up to numerical accuracy. The motivation for this research was provided by an observed analogy between linear potential flow and the special theory of relativity that emerges from the invariance of the d'Alembert wave equation under Lorentz transformations. This analogy is well known in an operational sense, being leveraged widely in linear unsteady aerodynamics and acoustics, stemming largely from the work of Kussner. Whereas elements of the special theory can be invoked for compressibility effects that are linear and global in nature, the question posed in this work was whether other mathematical techniques from the realm of relativity theory could be used to similar advantage for effects that are nonlinear and local. This line of thought led to a transformation leveraging Riemannian geometric methods common to the general theory of relativity. A gauge transformation is used to geometrize compressibility through the metric tensor of the underlying space to produce an equivalent incompressible flow that lives not on a plane but on a curved surface. In this sense, forces owing to compressibility can be ascribed to the geometry of space in much the same way that general relativity ascribes gravitational forces to the curvature of space-time. Although the analogy with general relativity is fruitful, it is important not to overstate the similarities between compressibility and the physics of gravity, as the interest for this thesis is primarily in the mathematical framework and not physical phenomenology or epistemology. The thesis presents the philosophy and theory for the transformation method followed by a numerical method for practical solutions of equivalent incompressible flows over arbitrary closed profiles. The numerical method employs an iterative approach involving the solution of the equivalent incompressible flow with a panel method, the calculation of the metric tensor for the gauge transformation, and the solution of the curvilinear coordinate mapping to the canonical flow with a finite difference approach for the elliptic boundary value problem. This method is demonstrated for non-circulatory flow over a circular cylinder and both symmetric and lifting flows over a NACA 0012 profile. Results are validated with accepted subcritical full potential test cases available in the literature. For chord-preserving mapping boundary conditions, the results indicate that the equivalent incompressible profiles thicken with Mach number and develop a leading edge droop with increased angle of attack. Two promising areas of potential applicability of the method have been identified. The first is in airfoil inverse design methods leveraging incompressible flow knowledge including heuristics and empirical data for the potential field effects on viscous phenomena such as boundary layer transition and separation. The second is in aerodynamic testing using distorted similarity-scaled models.

Compressive sensing based wireless sensor for structural health monitoring

NASA Astrophysics Data System (ADS)

Bao, Yuequan; Zou, Zilong; Li, Hui

2014-03-01

Data loss is a common problem for monitoring systems based on wireless sensors. Reliable communication protocols, which enhance communication reliability by repetitively transmitting unreceived packets, is one approach to tackle the problem of data loss. An alternative approach allows data loss to some extent and seeks to recover the lost data from an algorithmic point of view. Compressive sensing (CS) provides such a data loss recovery technique. This technique can be embedded into smart wireless sensors and effectively increases wireless communication reliability without retransmitting the data. The basic idea of CS-based approach is that, instead of transmitting the raw signal acquired by the sensor, a transformed signal that is generated by projecting the raw signal onto a random matrix, is transmitted. Some data loss may occur during the transmission of this transformed signal. However, according to the theory of CS, the raw signal can be effectively reconstructed from the received incomplete transformed signal given that the raw signal is compressible in some basis and the data loss ratio is low. This CS-based technique is implemented into the Imote2 smart sensor platform using the foundation of Illinois Structural Health Monitoring Project (ISHMP) Service Tool-suite. To overcome the constraints of limited onboard resources of wireless sensor nodes, a method called random demodulator (RD) is employed to provide memory and power efficient construction of the random sampling matrix. Adaptation of RD sampling matrix is made to accommodate data loss in wireless transmission and meet the objectives of the data recovery. The embedded program is tested in a series of sensing and communication experiments. Examples and parametric study are presented to demonstrate the applicability of the embedded program as well as to show the efficacy of CS-based data loss recovery for real wireless SHM systems.

A Comparison of Compressed Sensing and Sparse Recovery Algorithms Applied to Simulation Data

DOE PAGES

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

A Comparison of Compressed Sensing and Sparse Recovery Algorithms Applied to Simulation Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fan, Ya Ju; Kamath, Chandrika

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

Enhancing the image resolution in a single-pixel sub-THz imaging system based on compressed sensing

NASA Astrophysics Data System (ADS)

Alkus, Umit; Ermeydan, Esra Sengun; Sahin, Asaf Behzat; Cankaya, Ilyas; Altan, Hakan

2018-04-01

Compressed sensing (CS) techniques allow for faster imaging when combined with scan architectures, which typically suffer from speed. This technique when implemented with a subterahertz (sub-THz) single detector scan imaging system provides images whose resolution is only limited by the pixel size of the pattern used to scan the image plane. To overcome this limitation, the image of the target can be oversampled; however, this results in slower imaging rates especially if this is done in two-dimensional across the image plane. We show that by implementing a one-dimensional (1-D) scan of the image plane, a modified approach to CS theory applied with an appropriate reconstruction algorithm allows for successful reconstruction of the reflected oversampled image of a target placed in standoff configuration from the source. The experiments are done in reflection mode configuration where the operating frequency is 93 GHz and the corresponding wavelength is λ = 3.2 mm. To reconstruct the image with fewer samples, CS theory is applied using masks where the pixel size is 5 mm × 5 mm, and each mask covers an image area of 5 cm × 5 cm, meaning that the basic image is resolved as 10 × 10 pixels. To enhance the resolution, the information between two consecutive pixels is used, and oversampling along 1-D coupled with a modification of the masks in CS theory allowed for oversampled images to be reconstructed rapidly in 20 × 20 and 40 × 40 pixel formats. These are then compared using two different reconstruction algorithms, TVAL3 and ℓ1-MAGIC. The performance of these methods is compared for both simulated signals and real signals. It is found that the modified CS theory approach coupled with the TVAL3 reconstruction process, even when scanning along only 1-D, allows for rapid precise reconstruction of the oversampled target.

Ultra-Low Power Dynamic Knob in Adaptive Compressed Sensing Towards Biosignal Dynamics.

PubMed

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.

Fast electron microscopy via compressive sensing

DOEpatents

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.

Complex-Difference Constrained Compressed Sensing Reconstruction for Accelerated PRF Thermometry with Application to MRI Induced RF Heating

PubMed Central

Cao, Zhipeng; Oh, Sukhoon; Otazo, Ricardo; Sica, Christopher T.; Griswold, Mark A.; Collins, Christopher M.

2014-01-01

Purpose Introduce a novel compressed sensing reconstruction method to accelerate proton resonance frequency (PRF) shift temperature imaging for MRI induced radiofrequency (RF) heating evaluation. Methods A compressed sensing approach that exploits sparsity of the complex difference between post-heating and baseline images is proposed to accelerate PRF temperature mapping. The method exploits the intra- and inter-image correlations to promote sparsity and remove shared aliasing artifacts. Validations were performed on simulations and retrospectively undersampled data acquired in ex-vivo and in-vivo studies by comparing performance with previously proposed techniques. Results The proposed complex difference constrained compressed sensing reconstruction method improved the reconstruction of smooth and local PRF temperature change images compared to various available reconstruction methods in a simulation study, a retrospective study with heating of a human forearm in vivo, and a retrospective study with heating of a sample of beef ex vivo . Conclusion Complex difference based compressed sensing with utilization of a fully-sampled baseline image improves the reconstruction accuracy for accelerated PRF thermometry. It can be used to improve the volumetric coverage and temporal resolution in evaluation of RF heating due to MRI, and may help facilitate and validate temperature-based methods for safety assurance. PMID:24753099

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 concerns include the decision of which algorithms to implement as well as the problem of optimal setting of adjustable parameters. It will take imaging vendors several years to work through these challenges and provide solutions for a wide range of applications.

Poromechanics of compressible charged porous media using the theory of mixtures.

PubMed

Huyghe, J M; Molenaar, M M; Baajens, F P T

2007-10-01

Osmotic, electrostatic, and/or hydrational swellings are essential mechanisms in the deformation behavior of porous media, such as biological tissues, synthetic hydrogels, and clay-rich rocks. Present theories are restricted to incompressible constituents. This assumption typically fails for bone, in which electrokinetic effects are closely coupled to deformation. An electrochemomechanical formulation of quasistatic finite deformation of compressible charged porous media is derived from the theory of mixtures. The model consists of a compressible charged porous solid saturated with a compressible ionic solution. Four constituents following different kinematic paths are identified: a charged solid and three streaming constituents carrying either a positive, negative, or no electrical charge, which are the cations, anions, and fluid, respectively. The finite deformation model is reduced to infinitesimal theory. In the limiting case without ionic effects, the presented model is consistent with Blot's theory. Viscous drag compression is computed under closed circuit and open circuit conditions. Viscous drag compression is shown to be independent of the storage modulus. A compressible version of the electrochemomechanical theory is formulated. Using material parameter values for bone, the theory predicts a substantial influence of density changes on a viscous drag compression simulation. In the context of quasistatic deformations, conflicts between poromechanics and mixture theory are only semantic in nature.

Compressive Network Analysis

PubMed Central

Jiang, Xiaoye; Yao, Yuan; Liu, Han; Guibas, Leonidas

2014-01-01

Modern data acquisition routinely produces massive amounts of network data. Though many methods and models have been proposed to analyze such data, the research of network data is largely disconnected with the classical theory of statistical learning and signal processing. In this paper, we present a new framework for modeling network data, which connects two seemingly different areas: network data analysis and compressed sensing. From a nonparametric perspective, we model an observed network using a large dictionary. In particular, we consider the network clique detection problem and show connections between our formulation with a new algebraic tool, namely Randon basis pursuit in homogeneous spaces. Such a connection allows us to identify rigorous recovery conditions for clique detection problems. Though this paper is mainly conceptual, we also develop practical approximation algorithms for solving empirical problems and demonstrate their usefulness on real-world datasets. PMID:25620806

Time-jittered marine seismic data acquisition via compressed sensing and sparsity-promoting wavefield reconstruction

NASA Astrophysics Data System (ADS)

Wason, H.; Herrmann, F. J.; Kumar, R.

2016-12-01

Current efforts towards dense shot (or receiver) sampling and full azimuthal coverage to produce high resolution images have led to the deployment of multiple source vessels (or streamers) across marine survey areas. Densely sampled marine seismic data acquisition, however, is expensive, and hence necessitates the adoption of sampling schemes that save acquisition costs and time. Compressed sensing is a sampling paradigm that aims to reconstruct a signal--that is sparse or compressible in some transform domain--from relatively fewer measurements than required by the Nyquist sampling criteria. Leveraging ideas from the field of compressed sensing, we show how marine seismic acquisition can be setup as a compressed sensing problem. A step ahead from multi-source seismic acquisition is simultaneous source acquisition--an emerging technology that is stimulating both geophysical research and commercial efforts--where multiple source arrays/vessels fire shots simultaneously resulting in better coverage in marine surveys. Following the design principles of compressed sensing, we propose a pragmatic simultaneous time-jittered time-compressed marine acquisition scheme where single or multiple source vessels sail across an ocean-bottom array firing airguns at jittered times and source locations, resulting in better spatial sampling and speedup acquisition. Our acquisition is low cost since our measurements are subsampled. Simultaneous source acquisition generates data with overlapping shot records, which need to be separated for further processing. We can significantly impact the reconstruction quality of conventional seismic data from jittered data and demonstrate successful recovery by sparsity promotion. In contrast to random (sub)sampling, acquisition via jittered (sub)sampling helps in controlling the maximum gap size, which is a practical requirement of wavefield reconstruction with localized sparsifying transforms. We illustrate our results with simulations of simultaneous time-jittered marine acquisition for 2D and 3D ocean-bottom cable survey.

A compressed sensing X-ray camera with a multilayer architecture

DOE PAGES

Wang, Zhehui; Laroshenko, O.; Li, S.; ...

2018-01-25

Recent advances in compressed sensing theory and algorithms offer new possibilities for high-speed X-ray camera design. In many CMOS cameras, each pixel has an independent on-board circuit that includes an amplifier, noise rejection, signal shaper, an analog-to-digital converter (ADC), and optional in-pixel storage. When X-ray images are sparse, i.e., when one of the following cases is true: (a.) The number of pixels with true X-ray hits is much smaller than the total number of pixels; (b.) The X-ray information is redundant; or (c.) Some prior knowledge about the X-ray images exists, sparse sampling may be allowed. In this work, wemore » first illustrate the feasibility of random on-board pixel sampling (ROPS) using an existing set of X-ray images, followed by a discussion about signal to noise as a function of pixel size. Next, we describe a possible circuit architecture to achieve random pixel access and in-pixel storage. The combination of a multilayer architecture, sparse on-chip sampling, and computational image techniques, is expected to facilitate the development and applications of high-speed X-ray camera technology.« less

Comparison and analysis of nonlinear algorithms for compressed sensing in MRI.

PubMed

Yu, Yeyang; Hong, Mingjian; Liu, Feng; Wang, Hua; Crozier, Stuart

2010-01-01

Compressed sensing (CS) theory has been recently applied in Magnetic Resonance Imaging (MRI) to accelerate the overall imaging process. In the CS implementation, various algorithms have been used to solve the nonlinear equation system for better image quality and reconstruction speed. However, there are no explicit criteria for an optimal CS algorithm selection in the practical MRI application. A systematic and comparative study of those commonly used algorithms is therefore essential for the implementation of CS in MRI. In this work, three typical algorithms, namely, the Gradient Projection For Sparse Reconstruction (GPSR) algorithm, Interior-point algorithm (l(1)_ls), and the Stagewise Orthogonal Matching Pursuit (StOMP) algorithm are compared and investigated in three different imaging scenarios, brain, angiogram and phantom imaging. The algorithms' performances are characterized in terms of image quality and reconstruction speed. The theoretical results show that the performance of the CS algorithms is case sensitive; overall, the StOMP algorithm offers the best solution in imaging quality, while the GPSR algorithm is the most efficient one among the three methods. In the next step, the algorithm performances and characteristics will be experimentally explored. It is hoped that this research will further support the applications of CS in MRI.

A compressed sensing X-ray camera with a multilayer architecture

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Zhehui; Laroshenko, O.; Li, S.

Recent advances in compressed sensing theory and algorithms offer new possibilities for high-speed X-ray camera design. In many CMOS cameras, each pixel has an independent on-board circuit that includes an amplifier, noise rejection, signal shaper, an analog-to-digital converter (ADC), and optional in-pixel storage. When X-ray images are sparse, i.e., when one of the following cases is true: (a.) The number of pixels with true X-ray hits is much smaller than the total number of pixels; (b.) The X-ray information is redundant; or (c.) Some prior knowledge about the X-ray images exists, sparse sampling may be allowed. In this work, wemore » first illustrate the feasibility of random on-board pixel sampling (ROPS) using an existing set of X-ray images, followed by a discussion about signal to noise as a function of pixel size. Next, we describe a possible circuit architecture to achieve random pixel access and in-pixel storage. The combination of a multilayer architecture, sparse on-chip sampling, and computational image techniques, is expected to facilitate the development and applications of high-speed X-ray camera technology.« less

A novel 3D Cartesian random sampling strategy for Compressive Sensing Magnetic Resonance Imaging.

PubMed

Valvano, Giuseppe; Martini, Nicola; Santarelli, Maria Filomena; Chiappino, Dante; Landini, Luigi

2015-01-01

In this work we propose a novel acquisition strategy for accelerated 3D Compressive Sensing Magnetic Resonance Imaging (CS-MRI). This strategy is based on a 3D cartesian sampling with random switching of the frequency encoding direction with other K-space directions. Two 3D sampling strategies are presented. In the first strategy, the frequency encoding direction is randomly switched with one of the two phase encoding directions. In the second strategy, the frequency encoding direction is randomly chosen between all the directions of the K-Space. These strategies can lower the coherence of the acquisition, in order to produce reduced aliasing artifacts and to achieve a better image quality after Compressive Sensing (CS) reconstruction. Furthermore, the proposed strategies can reduce the typical smoothing of CS due to the limited sampling of high frequency locations. We demonstrated by means of simulations that the proposed acquisition strategies outperformed the standard Compressive Sensing acquisition. This results in a better quality of the reconstructed images and in a greater achievable acceleration.

Reconstructing high-dimensional two-photon entangled states via compressive sensing

PubMed Central

Tonolini, Francesco; Chan, Susan; Agnew, Megan; Lindsay, Alan; Leach, Jonathan

2014-01-01

Accurately establishing the state of large-scale quantum systems is an important tool in quantum information science; however, the large number of unknown parameters hinders the rapid characterisation of such states, and reconstruction procedures can become prohibitively time-consuming. Compressive sensing, a procedure for solving inverse problems by incorporating prior knowledge about the form of the solution, provides an attractive alternative to the problem of high-dimensional quantum state characterisation. Using a modified version of compressive sensing that incorporates the principles of singular value thresholding, we reconstruct the density matrix of a high-dimensional two-photon entangled system. The dimension of each photon is equal to d = 17, corresponding to a system of 83521 unknown real parameters. Accurate reconstruction is achieved with approximately 2500 measurements, only 3% of the total number of unknown parameters in the state. The algorithm we develop is fast, computationally inexpensive, and applicable to a wide range of quantum states, thus demonstrating compressive sensing as an effective technique for measuring the state of large-scale quantum systems. PMID:25306850

A method of vehicle license plate recognition based on PCANet and compressive sensing

NASA Astrophysics Data System (ADS)

Ye, Xianyi; Min, Feng

2018-03-01

The manual feature extraction of the traditional method for vehicle license plates has no good robustness to change in diversity. And the high feature dimension that is extracted with Principal Component Analysis Network (PCANet) leads to low classification efficiency. For solving these problems, a method of vehicle license plate recognition based on PCANet and compressive sensing is proposed. First, PCANet is used to extract the feature from the images of characters. And then, the sparse measurement matrix which is a very sparse matrix and consistent with Restricted Isometry Property (RIP) condition of the compressed sensing is used to reduce the dimensions of extracted features. Finally, the Support Vector Machine (SVM) is used to train and recognize the features whose dimension has been reduced. Experimental results demonstrate that the proposed method has better performance than Convolutional Neural Network (CNN) in the recognition and time. Compared with no compression sensing, the proposed method has lower feature dimension for the increase of efficiency.

A Two-Stage Reconstruction Processor for Human Detection in Compressive Sensing CMOS Radar.

PubMed

Tsao, Kuei-Chi; Lee, Ling; Chu, Ta-Shun; Huang, Yuan-Hao

2018-04-05

Complementary metal-oxide-semiconductor (CMOS) radar has recently gained much research attraction because small and low-power CMOS devices are very suitable for deploying sensing nodes in a low-power wireless sensing system. This study focuses on the signal processing of a wireless CMOS impulse radar system that can detect humans and objects in the home-care internet-of-things sensing system. The challenges of low-power CMOS radar systems are the weakness of human signals and the high computational complexity of the target detection algorithm. The compressive sensing-based detection algorithm can relax the computational costs by avoiding the utilization of matched filters and reducing the analog-to-digital converter bandwidth requirement. The orthogonal matching pursuit (OMP) is one of the popular signal reconstruction algorithms for compressive sensing radar; however, the complexity is still very high because the high resolution of human respiration leads to high-dimension signal reconstruction. Thus, this paper proposes a two-stage reconstruction algorithm for compressive sensing radar. The proposed algorithm not only has lower complexity than the OMP algorithm by 75% but also achieves better positioning performance than the OMP algorithm especially in noisy environments. This study also designed and implemented the algorithm by using Vertex-7 FPGA chip (Xilinx, San Jose, CA, USA). The proposed reconstruction processor can support the 256 × 13 real-time radar image display with a throughput of 28.2 frames per second.

Side information in coded aperture compressive spectral imaging

NASA Astrophysics Data System (ADS)

Galvis, Laura; Arguello, Henry; Lau, Daniel; Arce, Gonzalo R.

2017-02-01

Coded aperture compressive spectral imagers sense a three-dimensional cube by using two-dimensional projections of the coded and spectrally dispersed source. These imagers systems often rely on FPA detectors, SLMs, micromirror devices (DMDs), and dispersive elements. The use of the DMDs to implement the coded apertures facilitates the capture of multiple projections, each admitting a different coded aperture pattern. The DMD allows not only to collect the sufficient number of measurements for spectrally rich scenes or very detailed spatial scenes but to design the spatial structure of the coded apertures to maximize the information content on the compressive measurements. Although sparsity is the only signal characteristic usually assumed for reconstruction in compressing sensing, other forms of prior information such as side information have been included as a way to improve the quality of the reconstructions. This paper presents the coded aperture design in a compressive spectral imager with side information in the form of RGB images of the scene. The use of RGB images as side information of the compressive sensing architecture has two main advantages: the RGB is not only used to improve the reconstruction quality but to optimally design the coded apertures for the sensing process. The coded aperture design is based on the RGB scene and thus the coded aperture structure exploits key features such as scene edges. Real reconstructions of noisy compressed measurements demonstrate the benefit of the designed coded apertures in addition to the improvement in the reconstruction quality obtained by the use of side information.

Design of a Single Channel Modulated Wideband Converter for Wideband Spectrum Sensing: Theory, Architecture and Hardware Implementation

PubMed Central

Liu, Weisong; Huang, Zhitao; Wang, Xiang; Sun, Weichao

2017-01-01

In a cognitive radio sensor network (CRSN), wideband spectrum sensing devices which aims to effectively exploit temporarily vacant spectrum intervals as soon as possible are of great importance. However, the challenge of increasingly high signal frequency and wide bandwidth requires an extremely high sampling rate which may exceed today’s best analog-to-digital converters (ADCs) front-end bandwidth. Recently, the newly proposed architecture called modulated wideband converter (MWC), is an attractive analog compressed sensing technique that can highly reduce the sampling rate. However, the MWC has high hardware complexity owing to its parallel channel structure especially when the number of signals increases. In this paper, we propose a single channel modulated wideband converter (SCMWC) scheme for spectrum sensing of band-limited wide-sense stationary (WSS) signals. With one antenna or sensor, this scheme can save not only sampling rate but also hardware complexity. We then present a new, SCMWC based, single node CR prototype System, on which the spectrum sensing algorithm was tested. Experiments on our hardware prototype show that the proposed architecture leads to successful spectrum sensing. And the total sampling rate as well as hardware size is only one channel’s consumption of MWC. PMID:28471410

The extraction of motion-onset VEP BCI features based on deep learning and compressed sensing.

PubMed

Ma, Teng; Li, Hui; Yang, Hao; Lv, Xulin; Li, Peiyang; Liu, Tiejun; Yao, Dezhong; Xu, Peng

2017-01-01

Motion-onset visual evoked potentials (mVEP) can provide a softer stimulus with reduced fatigue, and it has potential applications for brain computer interface(BCI)systems. However, the mVEP waveform is seriously masked in the strong background EEG activities, and an effective approach is needed to extract the corresponding mVEP features to perform task recognition for BCI control. In the current study, we combine deep learning with compressed sensing to mine discriminative mVEP information to improve the mVEP BCI performance. The deep learning and compressed sensing approach can generate the multi-modality features which can effectively improve the BCI performance with approximately 3.5% accuracy incensement over all 11 subjects and is more effective for those subjects with relatively poor performance when using the conventional features. Compared with the conventional amplitude-based mVEP feature extraction approach, the deep learning and compressed sensing approach has a higher classification accuracy and is more effective for subjects with relatively poor performance. According to the results, the deep learning and compressed sensing approach is more effective for extracting the mVEP feature to construct the corresponding BCI system, and the proposed feature extraction framework is easy to extend to other types of BCIs, such as motor imagery (MI), steady-state visual evoked potential (SSVEP)and P300. Copyright © 2016 Elsevier B.V. All rights reserved.

Adaptive compressed sensing of multi-view videos based on the sparsity estimation

NASA Astrophysics Data System (ADS)

Yang, Senlin; Li, Xilong; Chong, Xin

2017-11-01

The conventional compressive sensing for videos based on the non-adaptive linear projections, and the measurement times is usually set empirically. As a result, the quality of videos reconstruction is always affected. Firstly, the block-based compressed sensing (BCS) with conventional selection for compressive measurements was described. Then an estimation method for the sparsity of multi-view videos was proposed based on the two dimensional discrete wavelet transform (2D DWT). With an energy threshold given beforehand, the DWT coefficients were processed with both energy normalization and sorting by descending order, and the sparsity of the multi-view video can be achieved by the proportion of dominant coefficients. And finally, the simulation result shows that, the method can estimate the sparsity of video frame effectively, and provides an active basis for the selection of compressive observation times. The result also shows that, since the selection of observation times is based on the sparsity estimated with the energy threshold provided, the proposed method can ensure the reconstruction quality of multi-view videos.

Temporal compressive sensing systems

DOEpatents

Reed, Bryan W.

2017-12-12

Methods and systems for temporal compressive sensing are disclosed, where within each of one or more sensor array data acquisition periods, one or more sensor array measurement datasets comprising distinct linear combinations of time slice data are acquired, and where mathematical reconstruction allows for calculation of accurate representations of the individual time slice datasets.

Compressive sensing method for recognizing cat-eye effect targets.

PubMed

Li, Li; Li, Hui; Dang, Ersheng; Liu, Bo

2013-10-01

This paper proposes a cat-eye effect target recognition method with compressive sensing (CS) and presents a recognition method (sample processing before reconstruction based on compressed sensing, or SPCS) for image processing. In this method, the linear projections of original image sequences are applied to remove dynamic background distractions and extract cat-eye effect targets. Furthermore, the corresponding imaging mechanism for acquiring active and passive image sequences is put forward. This method uses fewer images to recognize cat-eye effect targets, reduces data storage, and translates the traditional target identification, based on original image processing, into measurement vectors processing. The experimental results show that the SPCS method is feasible and superior to the shape-frequency dual criteria method.

A Two-Stage Reconstruction Processor for Human Detection in Compressive Sensing CMOS Radar

PubMed Central

Tsao, Kuei-Chi; Lee, Ling; Chu, Ta-Shun

2018-01-01

Complementary metal-oxide-semiconductor (CMOS) radar has recently gained much research attraction because small and low-power CMOS devices are very suitable for deploying sensing nodes in a low-power wireless sensing system. This study focuses on the signal processing of a wireless CMOS impulse radar system that can detect humans and objects in the home-care internet-of-things sensing system. The challenges of low-power CMOS radar systems are the weakness of human signals and the high computational complexity of the target detection algorithm. The compressive sensing-based detection algorithm can relax the computational costs by avoiding the utilization of matched filters and reducing the analog-to-digital converter bandwidth requirement. The orthogonal matching pursuit (OMP) is one of the popular signal reconstruction algorithms for compressive sensing radar; however, the complexity is still very high because the high resolution of human respiration leads to high-dimension signal reconstruction. Thus, this paper proposes a two-stage reconstruction algorithm for compressive sensing radar. The proposed algorithm not only has lower complexity than the OMP algorithm by 75% but also achieves better positioning performance than the OMP algorithm especially in noisy environments. This study also designed and implemented the algorithm by using Vertex-7 FPGA chip (Xilinx, San Jose, CA, USA). The proposed reconstruction processor can support the 256×13 real-time radar image display with a throughput of 28.2 frames per second. PMID:29621170

A New Approach for Fingerprint Image Compression

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mazieres, Bertrand

1997-12-01

The FBI has been collecting fingerprint cards since 1924 and now has over 200 million of them. Digitized with 8 bits of grayscale resolution at 500 dots per inch, it means 2000 terabytes of information. Also, without any compression, transmitting a 10 Mb card over a 9600 baud connection will need 3 hours. Hence we need a compression and a compression as close to lossless as possible: all fingerprint details must be kept. A lossless compression usually do not give a better compression ratio than 2:1, which is not sufficient. Compressing these images with the JPEG standard leads to artefactsmore » which appear even at low compression rates. Therefore the FBI has chosen in 1993 a scheme of compression based on a wavelet transform, followed by a scalar quantization and an entropy coding : the so-called WSQ. This scheme allows to achieve compression ratios of 20:1 without any perceptible loss of quality. The publication of the FBI specifies a decoder, which means that many parameters can be changed in the encoding process: the type of analysis/reconstruction filters, the way the bit allocation is made, the number of Huffman tables used for the entropy coding. The first encoder used 9/7 filters for the wavelet transform and did the bit allocation using a high-rate bit assumption. Since the transform is made into 64 subbands, quite a lot of bands receive only a few bits even at an archival quality compression rate of 0.75 bit/pixel. Thus, after a brief overview of the standard, we will discuss a new approach for the bit-allocation that seems to make more sense where theory is concerned. Then we will talk about some implementation aspects, particularly for the new entropy coder and the features that allow other applications than fingerprint image compression. Finally, we will compare the performances of the new encoder to those of the first encoder.« less

High-resolution coded-aperture design for compressive X-ray tomography using low resolution detectors

NASA Astrophysics Data System (ADS)

Mojica, Edson; Pertuz, Said; Arguello, Henry

2017-12-01

One of the main challenges in Computed Tomography (CT) is obtaining accurate reconstructions of the imaged object while keeping a low radiation dose in the acquisition process. In order to solve this problem, several researchers have proposed the use of compressed sensing for reducing the amount of measurements required to perform CT. This paper tackles the problem of designing high-resolution coded apertures for compressed sensing computed tomography. In contrast to previous approaches, we aim at designing apertures to be used with low-resolution detectors in order to achieve super-resolution. The proposed method iteratively improves random coded apertures using a gradient descent algorithm subject to constraints in the coherence and homogeneity of the compressive sensing matrix induced by the coded aperture. Experiments with different test sets show consistent results for different transmittances, number of shots and super-resolution factors.

Experimental quantum compressed sensing for a seven-qubit system

PubMed Central

Riofrío, C. A.; Gross, D.; Flammia, S. T.; Monz, T.; Nigg, D.; Blatt, R.; Eisert, J.

2017-01-01

Well-controlled quantum devices with their increasing system size face a new roadblock hindering further development of quantum technologies. The effort of quantum tomography—the reconstruction of states and processes of a quantum device—scales unfavourably: state-of-the-art systems can no longer be characterized. Quantum compressed sensing mitigates this problem by reconstructing states from incomplete data. Here we present an experimental implementation of compressed tomography of a seven-qubit system—a topological colour code prepared in a trapped ion architecture. We are in the highly incomplete—127 Pauli basis measurement settings—and highly noisy—100 repetitions each—regime. Originally, compressed sensing was advocated for states with few non-zero eigenvalues. We argue that low-rank estimates are appropriate in general since statistical noise enables reliable reconstruction of only the leading eigenvectors. The remaining eigenvectors behave consistently with a random-matrix model that carries no information about the true state. PMID:28513587

Wireless Computing Architecture III

DTIC Science & Technology

2013-09-01

MIMO Multiple-Input and Multiple-Output MIMO /CON MIMO with concurrent hannel access and estimation MU- MIMO Multiuser MIMO OFDM Orthogonal...compressive sensing \\; a design for concurrent channel estimation in scalable multiuser MIMO networking; and novel networking protocols based on machine...Network, Antenna Arrays, UAV networking, Angle of Arrival, Localization MIMO , Access Point, Channel State Information, Compressive Sensing 16

Compressed Sensing for Resolution Enhancement of Hyperpolarized 13C Flyback 3D-MRSI

PubMed Central

Hu, Simon; Lustig, Michael; Chen, Albert P.; Crane, Jason; Kerr, Adam; Kelley, Douglas A.C.; Hurd, Ralph; Kurhanewicz, John; Nelson, Sarah J.; Pauly, John M.; Vigneron, Daniel B.

2008-01-01

High polarization of nuclear spins in liquid state through dynamic nuclear polarization has enabled the direct monitoring of 13C metabolites in vivo at very high signal to noise, allowing for rapid assessment of tissue metabolism. The abundant SNR afforded by this hyperpolarization technique makes high resolution 13C 3D-MRSI feasible. However, the number of phase encodes that can be fit into the short acquisition time for hyperpolarized imaging limits spatial coverage and resolution. To take advantage of the high SNR available from hyperpolarization, we have applied compressed sensing to achieve a factor of 2 enhancement in spatial resolution without increasing acquisition time or decreasing coverage. In this paper, the design and testing of compressed sensing suited for a flyback 13C 3D-MRSI sequence are presented. The key to this design was the undersampling of spectral k-space using a novel blipped scheme, thus taking advantage of the considerable sparsity in typical hyperpolarized 13C spectra. Phantom tests validated the accuracy of the compressed sensing approach and initial mouse experiments demonstrated in vivo feasibility. PMID:18367420

An L1-norm phase constraint for half-Fourier compressed sensing in 3D MR imaging.

PubMed

Li, Guobin; Hennig, Jürgen; Raithel, Esther; Büchert, Martin; Paul, Dominik; Korvink, Jan G; Zaitsev, Maxim

2015-10-01

In most half-Fourier imaging methods, explicit phase replacement is used. In combination with parallel imaging, or compressed sensing, half-Fourier reconstruction is usually performed in a separate step. The purpose of this paper is to report that integration of half-Fourier reconstruction into iterative reconstruction minimizes reconstruction errors. The L1-norm phase constraint for half-Fourier imaging proposed in this work is compared with the L2-norm variant of the same algorithm, with several typical half-Fourier reconstruction methods. Half-Fourier imaging with the proposed phase constraint can be seamlessly combined with parallel imaging and compressed sensing to achieve high acceleration factors. In simulations and in in-vivo experiments half-Fourier imaging with the proposed L1-norm phase constraint enables superior performance both reconstruction of image details and with regard to robustness against phase estimation errors. The performance and feasibility of half-Fourier imaging with the proposed L1-norm phase constraint is reported. Its seamless combination with parallel imaging and compressed sensing enables use of greater acceleration in 3D MR imaging.

Aquatic Debris Detection Using Embedded Camera Sensors

PubMed Central

Wang, Yong; Wang, Dianhong; Lu, Qian; Luo, Dapeng; Fang, Wu

2015-01-01

Aquatic debris monitoring is of great importance to human health, aquatic habitats and water transport. In this paper, we first introduce the prototype of an aquatic sensor node equipped with an embedded camera sensor. Based on this sensing platform, we propose a fast and accurate debris detection algorithm. Our method is specifically designed based on compressive sensing theory to give full consideration to the unique challenges in aquatic environments, such as waves, swaying reflections, and tight energy budget. To upload debris images, we use an efficient sparse recovery algorithm in which only a few linear measurements need to be transmitted for image reconstruction. Besides, we implement the host software and test the debris detection algorithm on realistically deployed aquatic sensor nodes. The experimental results demonstrate that our approach is reliable and feasible for debris detection using camera sensors in aquatic environments. PMID:25647741

Compressive self-interference Fresnel digital holography with faithful reconstruction

NASA Astrophysics Data System (ADS)

Wan, Yuhong; Man, Tianlong; Han, Ying; Zhou, Hongqiang; Wang, Dayong

2017-05-01

We developed compressive self-interference digital holographic approach that allows retrieving three-dimensional information of the spatially incoherent objects from single-shot captured hologram. The Fresnel incoherent correlation holography is combined with parallel phase-shifting technique to instantaneously obtain spatial-multiplexed phase-shifting holograms. The recording scheme is regarded as compressive forward sensing model, thus the compressive-sensing-based reconstruction algorithm is implemented to reconstruct the original object from the under sampled demultiplexed sub-holograms. The concept was verified by simulations and experiments with simulating use of the polarizer array. The proposed technique has great potential to be applied in 3D tracking of spatially incoherent samples.

Compressed sensing system considerations for ECG and EMG wireless biosensors.

PubMed

Dixon, Anna M R; Allstot, Emily G; Gangopadhyay, Daibashish; Allstot, David J

2012-04-01

Compressed sensing (CS) is an emerging signal processing paradigm that enables sub-Nyquist processing of sparse signals such as electrocardiogram (ECG) and electromyogram (EMG) biosignals. Consequently, it can be applied to biosignal acquisition systems to reduce the data rate to realize ultra-low-power performance. CS is compared to conventional and adaptive sampling techniques and several system-level design considerations are presented for CS acquisition systems including sparsity and compression limits, thresholding techniques, encoder bit-precision requirements, and signal recovery algorithms. Simulation studies show that compression factors greater than 16X are achievable for ECG and EMG signals with signal-to-quantization noise ratios greater than 60 dB.

Theoretical and experimental examination of near-field acoustic levitation.

PubMed

Nomura, Hideyuki; Kamakura, Tomoo; Matsuda, Kazuhisa

2002-04-01

A planar object can be levitated stably close to a piston sound source by making use of acoustic radiation pressure. This phenomenon is called near-field acoustic levitation [Y. Hashimoto et al., J. Acoust. Soc. Am. 100, 2057-2061 (1996)]. In the present article, the levitation distance is predicted theoretically by numerically solving basic equations in a compressible viscous fluid subject to the appropriate initial and boundary conditions. Additionally, experiments are carried out using a 19.5-kHz piston source with a 40-mm aperture and various aluminum disks of different sizes. The measured levitation distance agrees well with the theory, which is different from a conventional theory, and the levitation distance is not inversely proportional to the square root of the surface density of the levitated disk in a strict sense.

Theoretical and experimental examination of near-field acoustic levitation

NASA Astrophysics Data System (ADS)

Nomura, Hideyuki; Kamakura, Tomoo; Matsuda, Kazuhisa

2002-04-01

A planar object can be levitated stably close to a piston sound source by making use of acoustic radiation pressure. This phenomenon is called near-field acoustic levitation [Y. Hashimoto et al., J. Acoust. Soc. Am. 100, 2057-2061 (1996)]. In the present article, the levitation distance is predicted theoretically by numerically solving basic equations in a compressible viscous fluid subject to the appropriate initial and boundary conditions. Additionally, experiments are carried out using a 19.5-kHz piston source with a 40-mm aperture and various aluminum disks of different sizes. The measured levitation distance agrees well with the theory, which is different from a conventional theory, and the levitation distance is not inversely proportional to the square root of the surface density of the levitated disk in a strict sense.

Compressed sensing for energy-efficient wireless telemonitoring of noninvasive fetal ECG via block sparse Bayesian learning.

PubMed

Zhang, Zhilin; Jung, Tzyy-Ping; Makeig, Scott; Rao, Bhaskar D

2013-02-01

Fetal ECG (FECG) telemonitoring is an important branch in telemedicine. The design of a telemonitoring system via a wireless body area network with low energy consumption for ambulatory use is highly desirable. As an emerging technique, compressed sensing (CS) shows great promise in compressing/reconstructing data with low energy consumption. However, due to some specific characteristics of raw FECG recordings such as nonsparsity and strong noise contamination, current CS algorithms generally fail in this application. This paper proposes to use the block sparse Bayesian learning framework to compress/reconstruct nonsparse raw FECG recordings. Experimental results show that the framework can reconstruct the raw recordings with high quality. Especially, the reconstruction does not destroy the interdependence relation among the multichannel recordings. This ensures that the independent component analysis decomposition of the reconstructed recordings has high fidelity. Furthermore, the framework allows the use of a sparse binary sensing matrix with much fewer nonzero entries to compress recordings. Particularly, each column of the matrix can contain only two nonzero entries. This shows that the framework, compared to other algorithms such as current CS algorithms and wavelet algorithms, can greatly reduce code execution in CPU in the data compression stage.

Compressive Sensing of Roller Bearing Faults via Harmonic Detection from Under-Sampled Vibration Signals

PubMed Central

Tang, Gang; Hou, Wei; Wang, Huaqing; Luo, Ganggang; Ma, Jianwei

2015-01-01

The Shannon sampling principle requires substantial amounts of data to ensure the accuracy of on-line monitoring of roller bearing fault signals. Challenges are often encountered as a result of the cumbersome data monitoring, thus a novel method focused on compressed vibration signals for detecting roller bearing faults is developed in this study. Considering that harmonics often represent the fault characteristic frequencies in vibration signals, a compressive sensing frame of characteristic harmonics is proposed to detect bearing faults. A compressed vibration signal is first acquired from a sensing matrix with information preserved through a well-designed sampling strategy. A reconstruction process of the under-sampled vibration signal is then pursued as attempts are conducted to detect the characteristic harmonics from sparse measurements through a compressive matching pursuit strategy. In the proposed method bearing fault features depend on the existence of characteristic harmonics, as typically detected directly from compressed data far before reconstruction completion. The process of sampling and detection may then be performed simultaneously without complete recovery of the under-sampled signals. The effectiveness of the proposed method is validated by simulations and experiments. PMID:26473858

Stainless steel component with compressed fiber Bragg grating for high temperature sensing applications

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 Use of Compressive Sensing to Reconstruct Radiation Characteristics of Wide-Band Antennas from Sparse Measurements

DTIC Science & Technology

2015-06-01

of uniform- versus nonuniform -pattern reconstruction, of transform function used, and of minimum randomly distributed measurements needed to...the radiation-frequency pattern’s reconstruction using uniform and nonuniform randomly distributed samples even though the pattern error manifests...5 Fig. 3 The nonuniform compressive-sensing reconstruction of the radiation

Compressive Sensing Image Sensors-Hardware Implementation

PubMed Central

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

LOW-VELOCITY COMPRESSIBLE FLOW THEORY

EPA Science Inventory

The widespread application of incompressible flow theory dominates low-velocity fluid dynamics, virtually preventing research into compressible low-velocity flow dynamics. Yet, compressible solutions to simple and well-defined flow problems and a series of contradictions in incom...

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.

A Compressed Sensing Based Method for Reducing the Sampling Time of A High Resolution Pressure Sensor Array System

PubMed Central

Sun, Chenglu; Li, Wei; Chen, Wei

2017-01-01

For extracting the pressure distribution image and respiratory waveform unobtrusively and comfortably, we proposed a smart mat which utilized a flexible pressure sensor array, printed electrodes and novel soft seven-layer structure to monitor those physiological information. However, in order to obtain high-resolution pressure distribution and more accurate respiratory waveform, it needs more time to acquire the pressure signal of all the pressure sensors embedded in the smart mat. In order to reduce the sampling time while keeping the same resolution and accuracy, a novel method based on compressed sensing (CS) theory was proposed. By utilizing the CS based method, 40% of the sampling time can be decreased by means of acquiring nearly one-third of original sampling points. Then several experiments were carried out to validate the performance of the CS based method. While less than one-third of original sampling points were measured, the correlation degree coefficient between reconstructed respiratory waveform and original waveform can achieve 0.9078, and the accuracy of the respiratory rate (RR) extracted from the reconstructed respiratory waveform can reach 95.54%. The experimental results demonstrated that the novel method can fit the high resolution smart mat system and be a viable option for reducing the sampling time of the pressure sensor array. PMID:28796188

Acoustic emission signal processing for rolling bearing running state assessment using compressive sensing

NASA Astrophysics Data System (ADS)

Liu, Chang; Wu, Xing; Mao, Jianlin; Liu, Xiaoqin

2017-07-01

In the signal processing domain, there has been growing interest in using acoustic emission (AE) signals for the fault diagnosis and condition assessment instead of vibration signals, which has been advocated as an effective technique for identifying fracture, crack or damage. The AE signal has high frequencies up to several MHz which can avoid some signals interference, such as the parts of bearing (i.e. rolling elements, ring and so on) and other rotating parts of machine. However, acoustic emission signal necessitates advanced signal sampling capabilities and requests ability to deal with large amounts of sampling data. In this paper, compressive sensing (CS) is introduced as a processing framework, and then a compressive features extraction method is proposed. We use it for extracting the compressive features from compressively-sensed data directly, and also prove the energy preservation properties. First, we study the AE signals under the CS framework. The sparsity of AE signal of the rolling bearing is checked. The observation and reconstruction of signal is also studied. Second, we present a method of extraction AE compressive feature (AECF) from compressively-sensed data directly. We demonstrate the energy preservation properties and the processing of the extracted AECF feature. We assess the running state of the bearing using the AECF trend. The AECF trend of the running state of rolling bearings is consistent with the trend of traditional features. Thus, the method is an effective way to evaluate the running trend of rolling bearings. The results of the experiments have verified that the signal processing and the condition assessment based on AECF is simpler, the amount of data required is smaller, and the amount of computation is greatly reduced.

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.

3D Compressed Sensing for Highly Accelerated Hyperpolarized 13C MRSI With In Vivo Applications to Transgenic Mouse Models of Cancer

PubMed Central

Hu, Simon; Lustig, Michael; Balakrishnan, Asha; Larson, Peder E. Z.; Bok, Robert; Kurhanewicz, John; Nelson, Sarah J.; Goga, Andrei; Pauly, John M.; Vigneron, Daniel B.

2010-01-01

High polarization of nuclear spins in liquid state through hyperpolarized technology utilizing dynamic nuclear polarization has enabled the direct monitoring of 13C metabolites in vivo at a high signal-to-noise ratio. Acquisition time limitations due to T1 decay of the hyperpolarized signal require accelerated imaging methods, such as compressed sensing, for optimal speed and spatial coverage. In this paper, the design and testing of a new echo-planar 13C three-dimensional magnetic resonance spectroscopic imaging (MRSI) compressed sensing sequence is presented. The sequence provides up to a factor of 7.53 in acceleration with minimal reconstruction artifacts. The key to the design is employing x and y gradient blips during a fly-back readout to pseudorandomly undersample kf-kx-ky space. The design was validated in simulations and phantom experiments where the limits of undersampling and the effects of noise on the compressed sensing nonlinear reconstruction were tested. Finally, this new pulse sequence was applied in vivo in preclinical studies involving transgenic prostate cancer and transgenic liver cancer murine models to obtain much higher spatial and temporal resolution than possible with conventional echo-planar spectroscopic imaging methods. PMID:20017160

Sparse signals recovered by non-convex penalty in quasi-linear systems.

PubMed

Cui, Angang; Li, Haiyang; Wen, Meng; Peng, Jigen

2018-01-01

The goal of compressed sensing is to reconstruct a sparse signal under a few linear measurements far less than the dimension of the ambient space of the signal. However, many real-life applications in physics and biomedical sciences carry some strongly nonlinear structures, and the linear model is no longer suitable. Compared with the compressed sensing under the linear circumstance, this nonlinear compressed sensing is much more difficult, in fact also NP-hard, combinatorial problem, because of the discrete and discontinuous nature of the [Formula: see text]-norm and the nonlinearity. In order to get a convenience for sparse signal recovery, we set the nonlinear models have a smooth quasi-linear nature in this paper, and study a non-convex fraction function [Formula: see text] in this quasi-linear compressed sensing. We propose an iterative fraction thresholding algorithm to solve the regularization problem [Formula: see text] for all [Formula: see text]. With the change of parameter [Formula: see text], our algorithm could get a promising result, which is one of the advantages for our algorithm compared with some state-of-art algorithms. Numerical experiments show that our method performs much better than some state-of-the-art methods.

RMP: Reduced-set matching pursuit approach for efficient compressed sensing signal reconstruction.

PubMed

Abdel-Sayed, Michael M; Khattab, Ahmed; Abu-Elyazeed, Mohamed F

2016-11-01

Compressed sensing enables the acquisition of sparse signals at a rate that is much lower than the Nyquist rate. Compressed sensing initially adopted [Formula: see text] minimization for signal reconstruction which is computationally expensive. Several greedy recovery algorithms have been recently proposed for signal reconstruction at a lower computational complexity compared to the optimal [Formula: see text] minimization, while maintaining a good reconstruction accuracy. In this paper, the Reduced-set Matching Pursuit (RMP) greedy recovery algorithm is proposed for compressed sensing. Unlike existing approaches which either select too many or too few values per iteration, RMP aims at selecting the most sufficient number of correlation values per iteration, which improves both the reconstruction time and error. Furthermore, RMP prunes the estimated signal, and hence, excludes the incorrectly selected values. The RMP algorithm achieves a higher reconstruction accuracy at a significantly low computational complexity compared to existing greedy recovery algorithms. It is even superior to [Formula: see text] minimization in terms of the normalized time-error product, a new metric introduced to measure the trade-off between the reconstruction time and error. RMP superior performance is illustrated with both noiseless and noisy samples.

Mismatch and resolution in compressive imaging

NASA Astrophysics Data System (ADS)

Fannjiang, Albert; Liao, Wenjing

2011-09-01

Highly coherent sensing matrices arise in discretization of continuum problems such as radar and medical imaging when the grid spacing is below the Rayleigh threshold as well as in using highly coherent, redundant dictionaries as sparsifying operators. Algorithms (BOMP, BLOOMP) based on techniques of band exclusion and local optimization are proposed to enhance Orthogonal Matching Pursuit (OMP) and deal with such coherent sensing matrices. BOMP and BLOOMP have provably performance guarantee of reconstructing sparse, widely separated objects independent of the redundancy and have a sparsity constraint and computational cost similar to OMP's. Numerical study demonstrates the effectiveness of BLOOMP for compressed sensing with highly coherent, redundant sensing matrices.

A Fourier dimensionality reduction model for big data interferometric imaging

NASA Astrophysics Data System (ADS)

Vijay Kartik, S.; Carrillo, Rafael E.; Thiran, Jean-Philippe; Wiaux, Yves

2017-06-01

Data dimensionality reduction in radio interferometry can provide savings of computational resources for image reconstruction through reduced memory footprints and lighter computations per iteration, which is important for the scalability of imaging methods to the big data setting of the next-generation telescopes. This article sheds new light on dimensionality reduction from the perspective of the compressed sensing theory and studies its interplay with imaging algorithms designed in the context of convex optimization. We propose a post-gridding linear data embedding to the space spanned by the left singular vectors of the measurement operator, providing a dimensionality reduction below image size. This embedding preserves the null space of the measurement operator and hence its sampling properties are also preserved in light of the compressed sensing theory. We show that this can be approximated by first computing the dirty image and then applying a weighted subsampled discrete Fourier transform to obtain the final reduced data vector. This Fourier dimensionality reduction model ensures a fast implementation of the full measurement operator, essential for any iterative image reconstruction method. The proposed reduction also preserves the independent and identically distributed Gaussian properties of the original measurement noise. For convex optimization-based imaging algorithms, this is key to justify the use of the standard ℓ2-norm as the data fidelity term. Our simulations confirm that this dimensionality reduction approach can be leveraged by convex optimization algorithms with no loss in imaging quality relative to reconstructing the image from the complete visibility data set. Reconstruction results in simulation settings with no direction dependent effects or calibration errors show promising performance of the proposed dimensionality reduction. Further tests on real data are planned as an extension of the current work. matlab code implementing the proposed reduction method is available on GitHub.

Efficient Sparse Signal Transmission over a Lossy Link Using Compressive Sensing

PubMed Central

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

An improved flexible telemetry system to autonomously monitor sub-bandage pressure and wound moisture.

PubMed

Mehmood, Nasir; Hariz, Alex; Templeton, Sue; Voelcker, Nicolas H

2014-11-18

This paper presents the development of an improved mobile-based telemetric dual mode sensing system to monitor pressure and moisture levels in compression bandages and dressings used for chronic wound management. The system is fabricated on a 0.2 mm thick flexible printed circuit material, and is capable of sensing pressure and moisture at two locations simultaneously within a compression bandage and wound dressing. The sensors are calibrated to sense both parameters accurately, and the data are then transmitted wirelessly to a receiver connected to a mobile device. An error-correction algorithm is developed to compensate the degradation in measurement quality due to battery power drop over time. An Android application is also implemented to automatically receive, process, and display the sensed wound parameters. The performance of the sensing system is first validated on a mannequin limb using a compression bandage and wound dressings, and then tested on a healthy volunteer to acquire real-time performance parameters. The results obtained here suggest that this dual mode sensor can perform reliably when placed on a human limb.

An Improved Flexible Telemetry System to Autonomously Monitor Sub-Bandage Pressure and Wound Moisture

PubMed Central

Mehmood, Nasir; Hariz, Alex; Templeton, Sue; Voelcker, Nicolas H.

2014-01-01

This paper presents the development of an improved mobile-based telemetric dual mode sensing system to monitor pressure and moisture levels in compression bandages and dressings used for chronic wound management. The system is fabricated on a 0.2 mm thick flexible printed circuit material, and is capable of sensing pressure and moisture at two locations simultaneously within a compression bandage and wound dressing. The sensors are calibrated to sense both parameters accurately, and the data are then transmitted wirelessly to a receiver connected to a mobile device. An error-correction algorithm is developed to compensate the degradation in measurement quality due to battery power drop over time. An Android application is also implemented to automatically receive, process, and display the sensed wound parameters. The performance of the sensing system is first validated on a mannequin limb using a compression bandage and wound dressings, and then tested on a healthy volunteer to acquire real-time performance parameters. The results obtained here suggest that this dual mode sensor can perform reliably when placed on a human limb. PMID:25412216

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.

Symmetric and asymmetric hybrid cryptosystem based on compressive sensing and computer generated holography

NASA Astrophysics Data System (ADS)

Ma, Lihong; Jin, Weimin

2018-01-01

A novel symmetric and asymmetric hybrid optical cryptosystem is proposed based on compressive sensing combined with computer generated holography. In this method there are six encryption keys, among which two decryption phase masks are different from the two random phase masks used in the encryption process. Therefore, the encryption system has the feature of both symmetric and asymmetric cryptography. On the other hand, because computer generated holography can flexibly digitalize the encrypted information and compressive sensing can significantly reduce data volume, what is more, the final encryption image is real function by phase truncation, the method favors the storage and transmission of the encryption data. The experimental results demonstrate that the proposed encryption scheme boosts the security and has high robustness against noise and occlusion attacks.

A knitted glove sensing system with compression strain for finger movements

NASA Astrophysics Data System (ADS)

Ryu, Hochung; Park, Sangki; Park, Jong-Jin; Bae, Jihyun

2018-05-01

Development of a fabric structure strain sensor has received considerable attention due to its broad application in healthcare monitoring and human–machine interfaces. In the knitted textile structure, it is critical to understand the surface structural deformation from a different body motion, inducing the electrical signal characteristics. Here, we report the electromechanical properties of the knitted glove sensing system focusing on the compressive strain behavior. Compared with the electrical response of the tensile strain, the compressive strain shows much higher sensitivity, stability, and linearity via different finger motions. Additionally, the sensor exhibits constant electrical properties after repeated cyclic tests and washing processes. The proposed knitted glove sensing system can be readily extended to a scalable and cost-effective production due to the use of a commercialized manufacturing system.

Lattice Anharmonicity and Thermal Conductivity from Compressive Sensing of First-Principles Calculations

DOE PAGES

Zhou, Fei; Nielson, Weston; Xia, Yi; ...

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 12Sb 4S 13, an earth-abundant thermoelectric with strong phononphonon interactions thatmore » limit the room-temperature K L to values near the amorphous limit.« less

The mathematical theory of signal processing and compression-designs

NASA Astrophysics Data System (ADS)

Feria, Erlan H.

2006-05-01

The mathematical theory of signal processing, named processor coding, will be shown to inherently arise as the computational time dual of Shannon's mathematical theory of communication which is also known as source coding. Source coding is concerned with signal source memory space compression while processor coding deals with signal processor computational time compression. Their combination is named compression-designs and referred as Conde in short. A compelling and pedagogically appealing diagram will be discussed highlighting Conde's remarkable successful application to real-world knowledge-aided (KA) airborne moving target indicator (AMTI) radar.

Supplemental Analysis on Compressed Sensing Based Interior Tomography

PubMed Central

Yu, Hengyong; Yang, Jiansheng; Jiang, Ming; Wang, Ge

2010-01-01

Recently, in the compressed sensing framework we proved that an interior ROI can be exactly reconstructed via the total variation minimization if the ROI is piecewise constant. In the proofs, we implicitly utilized the property that if an artifact image assumes a constant value within the ROI then this constant must be zero. Here we prove this property in the space of square integrable functions. PMID:19717891

Compressed Sensing mm-Wave SAR for Non-Destructive Testing Applications Using Multiple Weighted Side Information.

PubMed

Becquaert, Mathias; Cristofani, Edison; Van Luong, Huynh; Vandewal, Marijke; Stiens, Johan; Deligiannis, Nikos

2018-05-31

This work explores an innovative strategy for increasing the efficiency of compressed sensing applied on mm-wave SAR sensing using multiple weighted side information. The approach is tested on synthetic and on real non-destructive testing measurements performed on a 3D-printed object with defects while taking advantage of multiple previous SAR images of the object with different degrees of similarity. The tested algorithm attributes autonomously weights to the side information at two levels: (1) between the components inside the side information and (2) between the different side information. The reconstruction is thereby almost immune to poor quality side information while exploiting the relevant components hidden inside the added side information. The presented results prove that, in contrast to common compressed sensing, good SAR image reconstruction is achieved at subsampling rates far below the Nyquist rate. Moreover, the algorithm is shown to be much more robust for low quality side information compared to coherent background subtraction.

Phonon and magnetic structure in δ-plutonium from density-functional theory

DOE PAGES

Söderlind, Per; Zhou, F.; Landa, A.; ...

2015-10-30

We present phonon properties of plutonium metal obtained from a combination of density-functional-theory (DFT) electronic structure and the recently developed compressive sensing lattice dynamics (CSLD). The CSLD model is here trained on DFT total energies of several hundreds of quasi-random atomic configurations for best possible accuracy of the phonon properties. The calculated phonon dispersions compare better with experiment than earlier results obtained from dynamical mean-field theory. The density-functional model of the electronic structure consists of disordered magnetic moments with all relativistic effects and explicit orbital-orbital correlations. The magnetic disorder is approximated in two ways: (i) a special quasi-random structure andmore » (ii) the disordered-local-moment (DLM) method within the coherent potential approximation. Magnetism in plutonium has been debated intensely, However, the present magnetic approach for plutonium is validated by the close agreement between the predicted magnetic form factor and that of recent neutron-scattering experiments.« less

Leveraging EAP-Sparsity for Compressed Sensing of MS-HARDI in (k, q)-Space.

PubMed

Sun, Jiaqi; Sakhaee, Elham; Entezari, Alireza; Vemuri, Baba C

2015-01-01

Compressed Sensing (CS) for the acceleration of MR scans has been widely investigated in the past decade. Lately, considerable progress has been made in achieving similar speed ups in acquiring multi-shell high angular resolution diffusion imaging (MS-HARDI) scans. Existing approaches in this context were primarily concerned with sparse reconstruction of the diffusion MR signal S(q) in the q-space. More recently, methods have been developed to apply the compressed sensing framework to the 6-dimensional joint (k, q)-space, thereby exploiting the redundancy in this 6D space. To guarantee accurate reconstruction from partial MS-HARDI data, the key ingredients of compressed sensing that need to be brought together are: (1) the function to be reconstructed needs to have a sparse representation, and (2) the data for reconstruction ought to be acquired in the dual domain (i.e., incoherent sensing) and (3) the reconstruction process involves a (convex) optimization. In this paper, we present a novel approach that uses partial Fourier sensing in the 6D space of (k, q) for the reconstruction of P(x, r). The distinct feature of our approach is a sparsity model that leverages surfacelets in conjunction with total variation for the joint sparse representation of P(x, r). Thus, our method stands to benefit from the practical guarantees for accurate reconstruction from partial (k, q)-space data. Further, we demonstrate significant savings in acquisition time over diffusion spectral imaging (DSI) which is commonly used as the benchmark for comparisons in reported literature. To demonstrate the benefits of this approach,.we present several synthetic and real data examples.

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.

A Distributed Compressive Sensing Scheme for Event Capture in Wireless Visual Sensor Networks

NASA Astrophysics Data System (ADS)

Hou, Meng; Xu, Sen; Wu, Weiling; Lin, Fei

2018-01-01

Image signals which acquired by wireless visual sensor network can be used for specific event capture. This event capture is realized by image processing at the sink node. A distributed compressive sensing scheme is used for the transmission of these image signals from the camera nodes to the sink node. A measurement and joint reconstruction algorithm for these image signals are proposed in this paper. Make advantage of spatial correlation between images within a sensing area, the cluster head node which as the image decoder can accurately co-reconstruct these image signals. The subjective visual quality and the reconstruction error rate are used for the evaluation of reconstructed image quality. Simulation results show that the joint reconstruction algorithm achieves higher image quality at the same image compressive rate than the independent reconstruction algorithm.

Quantum Tomography Protocols with Positivity are Compressed Sensing Protocols (Open Access)

DTIC Science & Technology

2015-12-08

ARTICLE OPEN Quantum tomography protocols with positivity are compressed sensing protocols Amir Kalev1, Robert L Kosut2 and Ivan H Deutsch1...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

Compression induced phase transition of nematic brush: A mean-field theory study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tang, Jiuzhou; Zhang, Xinghua, E-mail: zhangxh@bjtu.edu.cn; Yan, Dadong, E-mail: yandd@bnu.edu.cn

2015-11-28

Responsive behavior of polymer brush to the external compression is one of the most important characters for its application. For the flexible polymer brush, in the case of low grafting density, which is widely studied by the Gaussian chain model based theory, the compression leads to a uniform deformation of the chain. However, in the case of high grafting density, the brush becomes anisotropic and the nematic phase will be formed. The normal compression tends to destroy the nematic order, which leads to a complex responsive behaviors. Under weak compression, chains in the nematic brush are buckled, and the bendingmore » energy and Onsager interaction give rise to the elasticity. Under deep compression, the responsive behaviors of the nematic polymer brush depend on the chain rigidity. For the compressed rigid polymer brush, the chains incline to re-orientate randomly to maximize the orientational entropy and its nematic order is destroyed. For the compressed flexible polymer brush, the chains incline to fold back to keep the nematic order. A buckling-folding transition takes place during the compressing process. For the compressed semiflexible brush, the chains are collectively tilted to a certain direction, which leads to the breaking of the rotational symmetry in the lateral plane. These responsive behaviors of nematic brush relate to the properties of highly frustrated worm-like chain, which is hard to be studied by the traditional self-consistent field theory due to the difficulty to solve the modified diffusion equation. To overcome this difficulty, a single chain in mean-field theory incorporating Monte Carlo simulation and mean-field theory for the worm-like chain model is developed in present work. This method shows high performance for entire region of chain rigidity in the confined condition.« less

Spectrum recovery method based on sparse representation for segmented multi-Gaussian model

NASA Astrophysics Data System (ADS)

Teng, Yidan; Zhang, Ye; Ti, Chunli; Su, Nan

2016-09-01

Hyperspectral images can realize crackajack features discriminability for supplying diagnostic characteristics with high spectral resolution. However, various degradations may generate negative influence on the spectral information, including water absorption, bands-continuous noise. On the other hand, the huge data volume and strong redundancy among spectrums produced intense demand on compressing HSIs in spectral dimension, which also leads to the loss of spectral information. The reconstruction of spectral diagnostic characteristics has irreplaceable significance for the subsequent application of HSIs. This paper introduces a spectrum restoration method for HSIs making use of segmented multi-Gaussian model (SMGM) and sparse representation. A SMGM is established to indicating the unsymmetrical spectral absorption and reflection characteristics, meanwhile, its rationality and sparse property are discussed. With the application of compressed sensing (CS) theory, we implement sparse representation to the SMGM. Then, the degraded and compressed HSIs can be reconstructed utilizing the uninjured or key bands. Finally, we take low rank matrix recovery (LRMR) algorithm for post processing to restore the spatial details. The proposed method was tested on the spectral data captured on the ground with artificial water absorption condition and an AVIRIS-HSI data set. The experimental results in terms of qualitative and quantitative assessments demonstrate that the effectiveness on recovering the spectral information from both degradations and loss compression. The spectral diagnostic characteristics and the spatial geometry feature are well preserved.

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.

Theory of compressive modeling and simulation

NASA Astrophysics Data System (ADS)

Szu, Harold; Cha, Jae; Espinola, Richard L.; Krapels, Keith

2013-05-01

Modeling and Simulation (M&S) has been evolving along two general directions: (i) data-rich approach suffering the curse of dimensionality and (ii) equation-rich approach suffering computing power and turnaround time. We suggest a third approach. We call it (iii) compressive M&S (CM&S); because the basic Minimum Free-Helmholtz Energy (MFE) facilitating CM&S can reproduce and generalize Candes, Romberg, Tao & Donoho (CRT&D) Compressive Sensing (CS) paradigm as a linear Lagrange Constraint Neural network (LCNN) algorithm. CM&S based MFE can generalize LCNN to 2nd order as Nonlinear augmented LCNN. For example, during the sunset, we can avoid a reddish bias of sunlight illumination due to a long-range Rayleigh scattering over the horizon. With CM&S we can take instead of day camera, a night vision camera. We decomposed long wave infrared (LWIR) band with filter into 2 vector components (8~10μm and 10~12μm) and used LCNN to find pixel by pixel the map of Emissive-Equivalent Planck Radiation Sources (EPRS). Then, we up-shifted consistently, according to de-mixed sources map, to the sub-micron RGB color image. Moreover, the night vision imaging can also be down-shifted at Passive Millimeter Wave (PMMW) imaging, suffering less blur owing to dusty smokes scattering and enjoying apparent smoothness of surface reflectivity of man-made objects under the Rayleigh resolution. One loses three orders of magnitudes in the spatial Rayleigh resolution; but gains two orders of magnitude in the reflectivity, and gains another two orders in the propagation without obscuring smog . Since CM&S can generate missing data and hard to get dynamic transients, CM&S can reduce unnecessary measurements and their associated cost and computing in the sense of super-saving CS: measuring one & getting one's neighborhood free .

Compressive sensing for single-shot two-dimensional coherent spectroscopy

NASA Astrophysics Data System (ADS)

Harel, E.; Spencer, A.; Spokoyny, B.

2017-02-01

In this work, we explore the use of compressive sensing for the rapid acquisition of two-dimensional optical spectra that encodes the electronic structure and ultrafast dynamics of condensed-phase molecular species. Specifically, we have developed a means to combine multiplexed single-element detection and single-shot and phase-resolved two-dimensional coherent spectroscopy. The method described, which we call Single Point Array Reconstruction by Spatial Encoding (SPARSE) eliminates the need for costly array detectors while speeding up acquisition by several orders of magnitude compared to scanning methods. Physical implementation of SPARSE is facilitated by combining spatiotemporal encoding of the nonlinear optical response and signal modulation by a high-speed digital micromirror device. We demonstrate the approach by investigating a well-characterized cyanine molecule and a photosynthetic pigment-protein complex. Hadamard and compressive sensing algorithms are demonstrated, with the latter achieving compression factors as high as ten. Both show good agreement with directly detected spectra. We envision a myriad of applications in nonlinear spectroscopy using SPARSE with broadband femtosecond light sources in so-far unexplored regions of the electromagnetic spectrum.

Robust QRS detection for HRV estimation from compressively sensed ECG measurements for remote health-monitoring systems.

PubMed

Pant, Jeevan K; Krishnan, Sridhar

2018-03-15

To present a new compressive sensing (CS)-based method for the acquisition of ECG signals and for robust estimation of heart-rate variability (HRV) parameters from compressively sensed measurements with high compression ratio. CS is used in the biosensor to compress the ECG signal. Estimation of the locations of QRS segments is carried out by applying two algorithms on the compressed measurements. The first algorithm reconstructs the ECG signal by enforcing a block-sparse structure on the first-order difference of the signal, so the transient QRS segments are significantly emphasized on the first-order difference of the signal. Multiple block-divisions of the signals are carried out with various block lengths, and multiple reconstructed signals are combined to enhance the robustness of the localization of the QRS segments. The second algorithm removes errors in the locations of QRS segments by applying low-pass filtering and morphological operations. The proposed CS-based method is found to be effective for the reconstruction of ECG signals by enforcing transient QRS structures on the first-order difference of the signal. It is demonstrated to be robust not only to high compression ratio but also to various artefacts present in ECG signals acquired by using on-body wireless sensors. HRV parameters computed by using the QRS locations estimated from the signals reconstructed with a compression ratio as high as 90% are comparable with that computed by using QRS locations estimated by using the Pan-Tompkins algorithm. The proposed method is useful for the realization of long-term HRV monitoring systems by using CS-based low-power wireless on-body biosensors.

An Ultra-Low Power Turning Angle Based Biomedical Signal Compression Engine with Adaptive Threshold Tuning

PubMed Central

Zhou, Jun; Wang, Chao

2017-01-01

Intelligent sensing is drastically changing our everyday life including healthcare by biomedical signal monitoring, collection, and analytics. However, long-term healthcare monitoring generates tremendous data volume and demands significant wireless transmission power, which imposes a big challenge for wearable healthcare sensors usually powered by batteries. Efficient compression engine design to reduce wireless transmission data rate with ultra-low power consumption is essential for wearable miniaturized healthcare sensor systems. This paper presents an ultra-low power biomedical signal compression engine for healthcare data sensing and analytics in the era of big data and sensor intelligence. It extracts the feature points of the biomedical signal by window-based turning angle detection. The proposed approach has low complexity and thus low power consumption while achieving a large compression ratio (CR) and good quality of reconstructed signal. Near-threshold design technique is adopted to further reduce the power consumption on the circuit level. Besides, the angle threshold for compression can be adaptively tuned according to the error between the original signal and reconstructed signal to address the variation of signal characteristics from person to person or from channel to channel to meet the required signal quality with optimal CR. For demonstration, the proposed biomedical compression engine has been used and evaluated for ECG compression. It achieves an average (CR) of 71.08% and percentage root-mean-square difference (PRD) of 5.87% while consuming only 39 nW. Compared to several state-of-the-art ECG compression engines, the proposed design has significantly lower power consumption while achieving similar CRD and PRD, making it suitable for long-term wearable miniaturized sensor systems to sense and collect healthcare data for remote data analytics. PMID:28783079

An Ultra-Low Power Turning Angle Based Biomedical Signal Compression Engine with Adaptive Threshold Tuning.

PubMed

Zhou, Jun; Wang, Chao

2017-08-06

Intelligent sensing is drastically changing our everyday life including healthcare by biomedical signal monitoring, collection, and analytics. However, long-term healthcare monitoring generates tremendous data volume and demands significant wireless transmission power, which imposes a big challenge for wearable healthcare sensors usually powered by batteries. Efficient compression engine design to reduce wireless transmission data rate with ultra-low power consumption is essential for wearable miniaturized healthcare sensor systems. This paper presents an ultra-low power biomedical signal compression engine for healthcare data sensing and analytics in the era of big data and sensor intelligence. It extracts the feature points of the biomedical signal by window-based turning angle detection. The proposed approach has low complexity and thus low power consumption while achieving a large compression ratio (CR) and good quality of reconstructed signal. Near-threshold design technique is adopted to further reduce the power consumption on the circuit level. Besides, the angle threshold for compression can be adaptively tuned according to the error between the original signal and reconstructed signal to address the variation of signal characteristics from person to person or from channel to channel to meet the required signal quality with optimal CR. For demonstration, the proposed biomedical compression engine has been used and evaluated for ECG compression. It achieves an average (CR) of 71.08% and percentage root-mean-square difference (PRD) of 5.87% while consuming only 39 nW. Compared to several state-of-the-art ECG compression engines, the proposed design has significantly lower power consumption while achieving similar CRD and PRD, making it suitable for long-term wearable miniaturized sensor systems to sense and collect healthcare data for remote data analytics.

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.

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.

Cognitive Radios Exploiting Gray Spaces via Compressed Sensing

NASA Astrophysics Data System (ADS)

Wieruch, Dennis; Jung, Peter; Wirth, Thomas; Dekorsy, Armin; Haustein, Thomas

2016-07-01

We suggest an interweave cognitive radio system with a gray space detector, which is properly identifying a small fraction of unused resources within an active band of a primary user system like 3GPP LTE. Therefore, the gray space detector can cope with frequency fading holes and distinguish them from inactive resources. Different approaches of the gray space detector are investigated, the conventional reduced-rank least squares method as well as the compressed sensing-based orthogonal matching pursuit and basis pursuit denoising algorithm. In addition, the gray space detector is compared with the classical energy detector. Simulation results present the receiver operating characteristic at several SNRs and the detection performance over further aspects like base station system load for practical false alarm rates. The results show, that especially for practical false alarm rates the compressed sensing algorithm are more suitable than the classical energy detector and reduced-rank least squares approach.

An Online Dictionary Learning-Based Compressive Data Gathering Algorithm in Wireless Sensor Networks

PubMed Central

Wang, Donghao; Wan, Jiangwen; Chen, Junying; Zhang, Qiang

2016-01-01

To adapt to sense signals of enormous diversities and dynamics, and to decrease the reconstruction errors caused by ambient noise, a novel online dictionary learning method-based compressive data gathering (ODL-CDG) algorithm is proposed. The proposed dictionary is learned from a two-stage iterative procedure, alternately changing between a sparse coding step and a dictionary update step. The self-coherence of the learned dictionary is introduced as a penalty term during the dictionary update procedure. The dictionary is also constrained with sparse structure. It’s theoretically demonstrated that the sensing matrix satisfies the restricted isometry property (RIP) with high probability. In addition, the lower bound of necessary number of measurements for compressive sensing (CS) reconstruction is given. Simulation results show that the proposed ODL-CDG algorithm can enhance the recovery accuracy in the presence of noise, and reduce the energy consumption in comparison with other dictionary based data gathering methods. PMID:27669250

An Online Dictionary Learning-Based Compressive Data Gathering Algorithm in Wireless Sensor Networks.

PubMed

Wang, Donghao; Wan, Jiangwen; Chen, Junying; Zhang, Qiang

2016-09-22

To adapt to sense signals of enormous diversities and dynamics, and to decrease the reconstruction errors caused by ambient noise, a novel online dictionary learning method-based compressive data gathering (ODL-CDG) algorithm is proposed. The proposed dictionary is learned from a two-stage iterative procedure, alternately changing between a sparse coding step and a dictionary update step. The self-coherence of the learned dictionary is introduced as a penalty term during the dictionary update procedure. The dictionary is also constrained with sparse structure. It's theoretically demonstrated that the sensing matrix satisfies the restricted isometry property (RIP) with high probability. In addition, the lower bound of necessary number of measurements for compressive sensing (CS) reconstruction is given. Simulation results show that the proposed ODL-CDG algorithm can enhance the recovery accuracy in the presence of noise, and reduce the energy consumption in comparison with other dictionary based data gathering methods.

Application of the SP theory of intelligence to the understanding of natural vision and the development of computer vision.

PubMed

Wolff, J Gerard

2014-01-01

The SP theory of intelligence aims to simplify and integrate concepts in computing and cognition, with information compression as a unifying theme. This article is about how the SP theory may, with advantage, be applied to the understanding of natural vision and the development of computer vision. Potential benefits include an overall simplification of concepts in a universal framework for knowledge and seamless integration of vision with other sensory modalities and other aspects of intelligence. Low level perceptual features such as edges or corners may be identified by the extraction of redundancy in uniform areas in the manner of the run-length encoding technique for information compression. The concept of multiple alignment in the SP theory may be applied to the recognition of objects, and to scene analysis, with a hierarchy of parts and sub-parts, at multiple levels of abstraction, and with family-resemblance or polythetic categories. The theory has potential for the unsupervised learning of visual objects and classes of objects, and suggests how coherent concepts may be derived from fragments. As in natural vision, both recognition and learning in the SP system are robust in the face of errors of omission, commission and substitution. The theory suggests how, via vision, we may piece together a knowledge of the three-dimensional structure of objects and of our environment, it provides an account of how we may see things that are not objectively present in an image, how we may recognise something despite variations in the size of its retinal image, and how raster graphics and vector graphics may be unified. And it has things to say about the phenomena of lightness constancy and colour constancy, the role of context in recognition, ambiguities in visual perception, and the integration of vision with other senses and other aspects of intelligence.

Widefield compressive multiphoton microscopy.

PubMed

Alemohammad, Milad; Shin, Jaewook; Tran, Dung N; Stroud, Jasper R; Chin, Sang Peter; Tran, Trac D; Foster, Mark A

2018-06-15

A single-pixel compressively sensed architecture is exploited to simultaneously achieve a 10× reduction in acquired data compared with the Nyquist rate, while alleviating limitations faced by conventional widefield temporal focusing microscopes due to scattering of the fluorescence signal. Additionally, we demonstrate an adaptive sampling scheme that further improves the compression and speed of our approach.

High-performance compression and double cryptography based on compressive ghost imaging with the fast Fourier transform

NASA Astrophysics Data System (ADS)

Leihong, Zhang; Zilan, Pan; Luying, Wu; Xiuhua, Ma

2016-11-01

To solve the problem that large images can hardly be retrieved for stringent hardware restrictions and the security level is low, a method based on compressive ghost imaging (CGI) with Fast Fourier Transform (FFT) is proposed, named FFT-CGI. Initially, the information is encrypted by the sender with FFT, and the FFT-coded image is encrypted by the system of CGI with a secret key. Then the receiver decrypts the image with the aid of compressive sensing (CS) and FFT. Simulation results are given to verify the feasibility, security, and compression of the proposed encryption scheme. The experiment suggests the method can improve the quality of large images compared with conventional ghost imaging and achieve the imaging for large-sized images, further the amount of data transmitted largely reduced because of the combination of compressive sensing and FFT, and improve the security level of ghost images through ciphertext-only attack (COA), chosen-plaintext attack (CPA), and noise attack. This technique can be immediately applied to encryption and data storage with the advantages of high security, fast transmission, and high quality of reconstructed information.

An accelerated proximal augmented Lagrangian method and its application in compressive sensing.

PubMed

Sun, Min; Liu, Jing

2017-01-01

As a first-order method, the augmented Lagrangian method (ALM) is a benchmark solver for linearly constrained convex programming, and in practice some semi-definite proximal terms are often added to its primal variable's subproblem to make it more implementable. In this paper, we propose an accelerated PALM with indefinite proximal regularization (PALM-IPR) for convex programming with linear constraints, which generalizes the proximal terms from semi-definite to indefinite. Under mild assumptions, we establish the worst-case [Formula: see text] convergence rate of PALM-IPR in a non-ergodic sense. Finally, numerical results show that our new method is feasible and efficient for solving compressive sensing.

Electrical and Self-Sensing Properties of Ultra-High-Performance Fiber-Reinforced Concrete with Carbon Nanotubes

PubMed Central

You, Ilhwan; Yoo, Doo-Yeol; Kim, Soonho; Kim, Min-Jae; Zi, Goangseup

2017-01-01

This study examined the electrical and self-sensing capacities of ultra-high-performance fiber-reinforced concrete (UHPFRC) with and without carbon nanotubes (CNTs). For this, the effects of steel fiber content, orientation, and pore water content on the electrical and piezoresistive properties of UHPFRC without CNTs were first evaluated. Then, the effect of CNT content on the self-sensing capacities of UHPFRC under compression and flexure was investigated. Test results indicated that higher steel fiber content, better fiber orientation, and higher amount of pore water led to higher electrical conductivity of UHPFRC. The effects of fiber orientation and drying condition on the electrical conductivity became minor as sufficiently high amount of steel fibers, 3% by volume, was added. Including only steel fibers did not impart UHPFRC with piezoresistive properties. Addition of CNTs substantially improved the electrical conductivity of UHPFRC. Under compression, UHPFRC with a CNT content of 0.3% or greater had a self-sensing ability that was activated by the formation of cracks, and better sensing capacity was achieved by including greater amount of CNTs. Furthermore, the pre-peak flexural behavior of UHPFRC was precisely simulated with a fractional change in resistivity when 0.3% CNTs were incorporated. The pre-cracking self-sensing capacity of UHPFRC with CNTs was more effective under tensile stress state than under compressive stress state. PMID:29109388

Electrical and Self-Sensing Properties of Ultra-High-Performance Fiber-Reinforced Concrete with Carbon Nanotubes.

PubMed

You, Ilhwan; Yoo, Doo-Yeol; Kim, Sooho; Kim, Min-Jae; Zi, Goangseup

2017-10-29

This study examined the electrical and self-sensing capacities of ultra-high-performance fiber-reinforced concrete (UHPFRC) with and without carbon nanotubes (CNTs). For this, the effects of steel fiber content, orientation, and pore water content on the electrical and piezoresistive properties of UHPFRC without CNTs were first evaluated. Then, the effect of CNT content on the self-sensing capacities of UHPFRC under compression and flexure was investigated. Test results indicated that higher steel fiber content, better fiber orientation, and higher amount of pore water led to higher electrical conductivity of UHPFRC. The effects of fiber orientation and drying condition on the electrical conductivity became minor as sufficiently high amount of steel fibers, 3% by volume, was added. Including only steel fibers did not impart UHPFRC with piezoresistive properties. Addition of CNTs substantially improved the electrical conductivity of UHPFRC. Under compression, UHPFRC with a CNT content of 0.3% or greater had a self-sensing ability that was activated by the formation of cracks, and better sensing capacity was achieved by including greater amount of CNTs. Furthermore, the pre-peak flexural behavior of UHPFRC was precisely simulated with a fractional change in resistivity when 0.3% CNTs were incorporated. The pre-cracking self-sensing capacity of UHPFRC with CNTs was more effective under tensile stress state than under compressive stress state.

Energy and Quality Evaluation for Compressive Sensing of Fetal Electrocardiogram Signals

PubMed Central

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

The Theory of a Free Jet of a Compressible Gas

NASA Technical Reports Server (NTRS)

Abramovich, G. N.

1944-01-01

In the present report the theory of free turbulence propagation and the boundary layer theory are developed for a plane-parallel free stream of a compressible fluid. In constructing the theory use was made of the turbulence hypothesis by Taylor (transport of vorticity) which gives best agreement with test results for problems involving heat transfer in free jets.

A modified JPEG-LS lossless compression method for remote sensing images

NASA Astrophysics Data System (ADS)

Deng, Lihua; Huang, Zhenghua

2015-12-01

As many variable length source coders, JPEG-LS is highly vulnerable to channel errors which occur in the transmission of remote sensing images. The error diffusion is one of the important factors which infect its robustness. The common method of improving the error resilience of JPEG-LS is dividing the image into many strips or blocks, and then coding each of them independently, but this method reduces the coding efficiency. In this paper, a block based JPEP-LS lossless compression method with an adaptive parameter is proposed. In the modified scheme, the threshold parameter RESET is adapted to an image and the compression efficiency is close to that of the conventional JPEG-LS.

Application of Compressive Sensing to Gravitational Microlensing Data and Implications for Miniaturized Space Observatories

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.

Enhanced acoustic sensing through wave compression and pressure amplification in anisotropic metamaterials.

PubMed

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.

TH-E-17A-06: Anatomical-Adaptive Compressed Sensing (AACS) Reconstruction for Thoracic 4-Dimensional Cone-Beam CT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shieh, C; Kipritidis, J; OBrien, R

2014-06-15

Purpose: The Feldkamp-Davis-Kress (FDK) algorithm currently used for clinical thoracic 4-dimensional (4D) cone-beam CT (CBCT) reconstruction suffers from noise and streaking artifacts due to projection under-sampling. Compressed sensing theory enables reconstruction of under-sampled datasets via total-variation (TV) minimization, but TV-minimization algorithms such as adaptive-steepest-descent-projection-onto-convex-sets (ASD-POCS) often converge slowly and are prone to over-smoothing anatomical details. These disadvantages can be overcome by incorporating general anatomical knowledge via anatomy segmentation. Based on this concept, we have developed an anatomical-adaptive compressed sensing (AACS) algorithm for thoracic 4D-CBCT reconstruction. Methods: AACS is based on the ASD-POCS framework, where each iteration consists of a TV-minimizationmore » step and a data fidelity constraint step. Prior to every AACS iteration, four major thoracic anatomical structures - soft tissue, lungs, bony anatomy, and pulmonary details - were segmented from the updated solution image. Based on the segmentation, an anatomical-adaptive weighting was applied to the TV-minimization step, so that TV-minimization was enhanced at noisy/streaky regions and suppressed at anatomical structures of interest. The image quality and convergence speed of AACS was compared to conventional ASD-POCS using an XCAT digital phantom and a patient scan. Results: For the XCAT phantom, the AACS image represented the ground truth better than the ASD-POCS image, giving a higher structural similarity index (0.93 vs. 0.84) and lower absolute difference (1.1*10{sup 4} vs. 1.4*10{sup 4}). For the patient case, while both algorithms resulted in much less noise and streaking than FDK, the AACS image showed considerably better contrast and sharpness of the vessels, tumor, and fiducial marker than the ASD-POCS image. In addition, AACS converged over 50% faster than ASD-POCS in both cases. Conclusions: The proposed AACS algorithm was shown to reconstruct thoracic 4D-CBCT images more accurately and with faster convergence compared to ASD-POCS. The superior image quality and rapid convergence makes AACS promising for future clinical use.« less

Autosophy information theory provides lossless data and video compression based on the data content

NASA Astrophysics Data System (ADS)

Holtz, Klaus E.; Holtz, Eric S.; Holtz, Diana

1996-09-01

A new autosophy information theory provides an alternative to the classical Shannon information theory. Using the new theory in communication networks provides both a high degree of lossless compression and virtually unbreakable encryption codes for network security. The bandwidth in a conventional Shannon communication is determined only by the data volume and the hardware parameters, such as image size; resolution; or frame rates in television. The data content, or what is shown on the screen, is irrelevant. In contrast, the bandwidth in autosophy communication is determined only by data content, such as novelty and movement in television images. It is the data volume and hardware parameters that become irrelevant. Basically, the new communication methods use prior 'knowledge' of the data, stored in a library, to encode subsequent transmissions. The more 'knowledge' stored in the libraries, the higher the potential compression ratio. 'Information' is redefined as that which is not already known by the receiver. Everything already known is redundant and need not be re-transmitted. In a perfect communication each transmission code, called a 'tip,' creates a new 'engram' of knowledge in the library in which each tip transmission can represent any amount of data. Autosophy theories provide six separate learning modes, or omni dimensional networks, all of which can be used for data compression. The new information theory reveals the theoretical flaws of other data compression methods, including: the Huffman; Ziv Lempel; LZW codes and commercial compression codes such as V.42bis and MPEG-2.

Statistical Interior Tomography

PubMed Central

Xu, Qiong; Wang, Ge; Sieren, Jered; Hoffman, Eric A.

2011-01-01

This paper presents a statistical interior tomography (SIT) approach making use of compressed sensing (CS) theory. With the projection data modeled by the Poisson distribution, an objective function with a total variation (TV) regularization term is formulated in the maximization of a posteriori (MAP) framework to solve the interior problem. An alternating minimization method is used to optimize the objective function with an initial image from the direct inversion of the truncated Hilbert transform. The proposed SIT approach is extensively evaluated with both numerical and real datasets. The results demonstrate that SIT is robust with respect to data noise and down-sampling, and has better resolution and less bias than its deterministic counterpart in the case of low count data. PMID:21233044

Recent advances in coding theory for near error-free communications

NASA Technical Reports Server (NTRS)

Cheung, K.-M.; Deutsch, L. J.; Dolinar, S. J.; Mceliece, R. J.; Pollara, F.; Shahshahani, M.; Swanson, L.

1991-01-01

Channel and source coding theories are discussed. The following subject areas are covered: large constraint length convolutional codes (the Galileo code); decoder design (the big Viterbi decoder); Voyager's and Galileo's data compression scheme; current research in data compression for images; neural networks for soft decoding; neural networks for source decoding; finite-state codes; and fractals for data compression.

Design of Compressed Sensing Algorithm for Coal Mine IoT Moving Measurement Data Based on a Multi-Hop Network and Total Variation.

PubMed

Wang, Gang; Zhao, Zhikai; Ning, Yongjie

2018-05-28

As the application of a coal mine Internet of Things (IoT), mobile measurement devices, such as intelligent mine lamps, cause moving measurement data to be increased. How to transmit these large amounts of mobile measurement data effectively has become an urgent problem. This paper presents a compressed sensing algorithm for the large amount of coal mine IoT moving measurement data based on a multi-hop network and total variation. By taking gas data in mobile measurement data as an example, two network models for the transmission of gas data flow, namely single-hop and multi-hop transmission modes, are investigated in depth, and a gas data compressed sensing collection model is built based on a multi-hop network. To utilize the sparse characteristics of gas data, the concept of total variation is introduced and a high-efficiency gas data compression and reconstruction method based on Total Variation Sparsity based on Multi-Hop (TVS-MH) is proposed. According to the simulation results, by using the proposed method, the moving measurement data flow from an underground distributed mobile network can be acquired and transmitted efficiently.

Comparison between various patch wise strategies for reconstruction of ultra-spectral cubes captured with a compressive sensing system

NASA Astrophysics Data System (ADS)

Oiknine, Yaniv; August, Isaac Y.; Revah, Liat; Stern, Adrian

2016-05-01

Recently we introduced a Compressive Sensing Miniature Ultra-Spectral Imaging (CS-MUSI) system. The system is based on a single Liquid Crystal (LC) cell and a parallel sensor array where the liquid crystal cell performs spectral encoding. Within the framework of compressive sensing, the CS-MUSI system is able to reconstruct ultra-spectral cubes captured with only an amount of ~10% samples compared to a conventional system. Despite the compression, the technique is extremely complex computationally, because reconstruction of ultra-spectral images requires processing huge data cubes of Gigavoxel size. Fortunately, the computational effort can be alleviated by using separable operation. An additional way to reduce the reconstruction effort is to perform the reconstructions on patches. In this work, we consider processing on various patch shapes. We present an experimental comparison between various patch shapes chosen to process the ultra-spectral data captured with CS-MUSI system. The patches may be one dimensional (1D) for which the reconstruction is carried out spatially pixel-wise, or two dimensional (2D) - working on spatial rows/columns of the ultra-spectral cube, as well as three dimensional (3D).

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.

Real time network traffic monitoring for wireless local area networks based on compressed sensing

NASA Astrophysics Data System (ADS)

Balouchestani, Mohammadreza

2017-05-01

A wireless local area network (WLAN) is an important type of wireless networks which connotes different wireless nodes in a local area network. WLANs suffer from important problems such as network load balancing, large amount of energy, and load of sampling. This paper presents a new networking traffic approach based on Compressed Sensing (CS) for improving the quality of WLANs. The proposed architecture allows reducing Data Delay Probability (DDP) to 15%, which is a good record for WLANs. The proposed architecture is increased Data Throughput (DT) to 22 % and Signal to Noise (S/N) ratio to 17 %, which provide a good background for establishing high qualified local area networks. This architecture enables continuous data acquisition and compression of WLAN's signals that are suitable for a variety of other wireless networking applications. At the transmitter side of each wireless node, an analog-CS framework is applied at the sensing step before analog to digital converter in order to generate the compressed version of the input signal. At the receiver side of wireless node, a reconstruction algorithm is applied in order to reconstruct the original signals from the compressed signals with high probability and enough accuracy. The proposed algorithm out-performs existing algorithms by achieving a good level of Quality of Service (QoS). This ability allows reducing 15 % of Bit Error Rate (BER) at each wireless node.

Formulation and closure of compressible turbulence equations in the light of kinetic theory

NASA Technical Reports Server (NTRS)

Tsuge, S.; Sagara, K.

1976-01-01

Fluid-dynamic moment equations, based on a kinetic hierarchy system, are derived governing the interaction between turbulent and thermal fluctuations. The kinetic theory is shown to reduce the inherent complexity of the conventional formalism of compressible turbulence theory and to minimize arbitrariness in formulating the closure condition.

Bayesian sparse channel estimation

NASA Astrophysics Data System (ADS)

Chen, Chulong; Zoltowski, Michael D.

2012-05-01

In Orthogonal Frequency Division Multiplexing (OFDM) systems, the technique used to estimate and track the time-varying multipath channel is critical to ensure reliable, high data rate communications. It is recognized that wireless channels often exhibit a sparse structure, especially for wideband and ultra-wideband systems. In order to exploit this sparse structure to reduce the number of pilot tones and increase the channel estimation quality, the application of compressed sensing to channel estimation is proposed. In this article, to make the compressed channel estimation more feasible for practical applications, it is investigated from a perspective of Bayesian learning. Under the Bayesian learning framework, the large-scale compressed sensing problem, as well as large time delay for the estimation of the doubly selective channel over multiple consecutive OFDM symbols, can be avoided. Simulation studies show a significant improvement in channel estimation MSE and less computing time compared to the conventional compressed channel estimation techniques.

A Fully Integrated Wireless Compressed Sensing Neural Signal Acquisition System for Chronic Recording and Brain Machine Interface.

PubMed

Liu, Xilin; Zhang, Milin; Xiong, Tao; Richardson, Andrew G; Lucas, Timothy H; Chin, Peter S; Etienne-Cummings, Ralph; Tran, Trac D; Van der Spiegel, Jan

2016-07-18

Reliable, multi-channel neural recording is critical to the neuroscience research and clinical treatment. However, most hardware development of fully integrated, multi-channel wireless neural recorders to-date, is still in the proof-of-concept stage. To be ready for practical use, the trade-offs between performance, power consumption, device size, robustness, and compatibility need to be carefully taken into account. This paper presents an optimized wireless compressed sensing neural signal recording system. The system takes advantages of both custom integrated circuits and universal compatible wireless solutions. The proposed system includes an implantable wireless system-on-chip (SoC) and an external wireless relay. The SoC integrates 16-channel low-noise neural amplifiers, programmable filters and gain stages, a SAR ADC, a real-time compressed sensing module, and a near field wireless power and data transmission link. The external relay integrates a 32 bit low-power microcontroller with Bluetooth 4.0 wireless module, a programming interface, and an inductive charging unit. The SoC achieves high signal recording quality with minimized power consumption, while reducing the risk of infection from through-skin connectors. The external relay maximizes the compatibility and programmability. The proposed compressed sensing module is highly configurable, featuring a SNDR of 9.78 dB with a compression ratio of 8×. The SoC has been fabricated in a 180 nm standard CMOS technology, occupying 2.1 mm × 0.6 mm silicon area. A pre-implantable system has been assembled to demonstrate the proposed paradigm. The developed system has been successfully used for long-term wireless neural recording in freely behaving rhesus monkey.

Adaptive multifocus image fusion using block compressed sensing with smoothed projected Landweber integration in the wavelet domain.

PubMed

V S, Unni; Mishra, Deepak; Subrahmanyam, G R K S

2016-12-01

The need for image fusion in current image processing systems is increasing mainly due to the increased number and variety of image acquisition techniques. Image fusion is the process of combining substantial information from several sensors using mathematical techniques in order to create a single composite image that will be more comprehensive and thus more useful for a human operator or other computer vision tasks. This paper presents a new approach to multifocus image fusion based on sparse signal representation. Block-based compressive sensing integrated with a projection-driven compressive sensing (CS) recovery that encourages sparsity in the wavelet domain is used as a method to get the focused image from a set of out-of-focus images. Compression is achieved during the image acquisition process using a block compressive sensing method. An adaptive thresholding technique within the smoothed projected Landweber recovery process reconstructs high-resolution focused images from low-dimensional CS measurements of out-of-focus images. Discrete wavelet transform and dual-tree complex wavelet transform are used as the sparsifying basis for the proposed fusion. The main finding lies in the fact that sparsification enables a better selection of the fusion coefficients and hence better fusion. A Laplacian mixture model fit is done in the wavelet domain and estimation of the probability density function (pdf) parameters by expectation maximization leads us to the proper selection of the coefficients of the fused image. Using the proposed method compared with the fusion scheme without employing the projected Landweber (PL) scheme and the other existing CS-based fusion approaches, it is observed that with fewer samples itself, the proposed method outperforms other approaches.

Computing Curvature Sensitivity of Biomolecules in Membranes by Simulated Buckling.

PubMed

Elías-Wolff, Federico; Lindén, Martin; Lyubartsev, Alexander P; Brandt, Erik G

2018-03-13

Membrane curvature sensing, where the binding free energies of membrane-associated molecules depend on the local membrane curvature, is a key factor to modulate and maintain the shape and organization of cell membranes. However, the microscopic mechanisms are not well understood, partly due to absence of efficient simulation methods. Here, we describe a method to compute the curvature dependence of the binding free energy of a membrane-associated probe molecule that interacts with a buckled membrane, which has been created by lateral compression of a flat bilayer patch. This buckling approach samples a wide range of curvatures in a single simulation, and anisotropic effects can be extracted from the orientation statistics. We develop an efficient and robust algorithm to extract the motion of the probe along the buckled membrane surface, and evaluate its numerical properties by extensive sampling of three coarse-grained model systems: local lipid density in a curved environment for single-component bilayers, curvature preferences of individual lipids in two-component membranes, and curvature sensing by a homotrimeric transmembrane protein. The method can be used to complement experimental data from curvature partition assays and provides additional insight into mesoscopic theories and molecular mechanisms for curvature sensing.

Application of Compressive Sensing to Digital Holography

DTIC Science & Technology

2015-05-01

WITH ASSIGNED DISTRIBUTION STATEMENT. // Signature// // Signature// DAVID J. RABB BRIAN D. EWERT, Chief Program Manager...Signature// TRACY W. JOHNSTON, Chief Multispectral Sensing and Detection Division Sensors Directorate This report is published in

Compressed sampling and dictionary learning framework for wavelength-division-multiplexing-based distributed fiber sensing.

PubMed

Weiss, Christian; Zoubir, Abdelhak M

2017-05-01

We propose a compressed sampling and dictionary learning framework for fiber-optic sensing using wavelength-tunable lasers. A redundant dictionary is generated from a model for the reflected sensor signal. Imperfect prior knowledge is considered in terms of uncertain local and global parameters. To estimate a sparse representation and the dictionary parameters, we present an alternating minimization algorithm that is equipped with a preprocessing routine to handle dictionary coherence. The support of the obtained sparse signal indicates the reflection delays, which can be used to measure impairments along the sensing fiber. The performance is evaluated by simulations and experimental data for a fiber sensor system with common core architecture.

Compressive Sensing of Foot Gait Signals and Its Application for the Estimation of Clinically Relevant Time Series.

PubMed

Pant, Jeevan K; Krishnan, Sridhar

2016-07-01

A new signal reconstruction algorithm for compressive sensing based on the minimization of a pseudonorm which promotes block-sparse structure on the first-order difference of the signal is proposed. Involved optimization is carried out by using a sequential version of Fletcher-Reeves' conjugate-gradient algorithm, and the line search is based on Banach's fixed-point theorem. The algorithm is suitable for the reconstruction of foot gait signals which admit block-sparse structure on the first-order difference. An additional algorithm for the estimation of stride-interval, swing-interval, and stance-interval time series from the reconstructed foot gait signals is also proposed. This algorithm is based on finding zero crossing indices of the foot gait signal and using the resulting indices for the computation of time series. Extensive simulation results demonstrate that the proposed signal reconstruction algorithm yields improved signal-to-noise ratio and requires significantly reduced computational effort relative to several competing algorithms over a wide range of compression ratio. For a compression ratio in the range from 88% to 94%, the proposed algorithm is found to offer improved accuracy for the estimation of clinically relevant time-series parameters, namely, the mean value, variance, and spectral index of stride-interval, stance-interval, and swing-interval time series, relative to its nearest competitor algorithm. The improvement in performance for compression ratio as high as 94% indicates that the proposed algorithms would be useful for designing compressive sensing-based systems for long-term telemonitoring of human gait signals.

Compressed sensing approach for wrist vein biometrics.

PubMed

Lantsov, Aleksey; Ryabko, Maxim; Shchekin, Aleksey

2018-04-01

The work describes features of the compressed sensing (CS) approach utilized for development of a wearable system for wrist vein recognition with single-pixel detection; we consider this system useful for biometrics authentication purposes. The CS approach implies use of a spatial light modulation (SLM) which, in our case, can be performed differently-with a liquid crystal display or diffusely scattering medium. We show that compressed sensing combined with above-mentioned means of SLM allows us to avoid using an optical system-a limiting factor for wearable devices. The trade-off between the 2 different SLM approaches regarding issues of practical implementation of CS approach for wrist vein recognition purposes is discussed. A possible solution of a misalignment problem-a typical issue for imaging systems based upon 2D arrays of photodiodes-is also proposed. Proposed design of the wearable device for wrist vein recognition is based upon single-pixel detection. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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.

Secure biometric image sensor and authentication scheme based on compressed sensing.

PubMed

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.

Single-pixel imaging based on compressive sensing with spectral-domain optical mixing

NASA Astrophysics Data System (ADS)

Zhu, Zhijing; Chi, Hao; Jin, Tao; Zheng, Shilie; Jin, Xiaofeng; Zhang, Xianmin

2017-11-01

In this letter a single-pixel imaging structure is proposed based on compressive sensing using a spatial light modulator (SLM)-based spectrum shaper. In the approach, an SLM-based spectrum shaper, the pattern of which is a predetermined pseudorandom bit sequence (PRBS), spectrally codes the optical pulse carrying image information. The energy of the spectrally mixed pulse is detected by a single-pixel photodiode and the measurement results are used to reconstruct the image via a sparse recovery algorithm. As the mixing of the image signal and the PRBS is performed in the spectral domain, optical pulse stretching, modulation, compression and synchronization in the time domain are avoided. Experiments are implemented to verify the feasibility of the approach.

Low-rank and Adaptive Sparse Signal (LASSI) Models for Highly Accelerated Dynamic Imaging

PubMed Central

Ravishankar, Saiprasad; Moore, Brian E.; Nadakuditi, Raj Rao; Fessler, Jeffrey A.

2017-01-01

Sparsity-based approaches have been popular in many applications in image processing and imaging. Compressed sensing exploits the sparsity of images in a transform domain or dictionary to improve image recovery from undersampled measurements. In the context of inverse problems in dynamic imaging, recent research has demonstrated the promise of sparsity and low-rank techniques. For example, the patches of the underlying data are modeled as sparse in an adaptive dictionary domain, and the resulting image and dictionary estimation from undersampled measurements is called dictionary-blind compressed sensing, or the dynamic image sequence is modeled as a sum of low-rank and sparse (in some transform domain) components (L+S model) that are estimated from limited measurements. In this work, we investigate a data-adaptive extension of the L+S model, dubbed LASSI, where the temporal image sequence is decomposed into a low-rank component and a component whose spatiotemporal (3D) patches are sparse in some adaptive dictionary domain. We investigate various formulations and efficient methods for jointly estimating the underlying dynamic signal components and the spatiotemporal dictionary from limited measurements. We also obtain efficient sparsity penalized dictionary-blind compressed sensing methods as special cases of our LASSI approaches. Our numerical experiments demonstrate the promising performance of LASSI schemes for dynamic magnetic resonance image reconstruction from limited k-t space data compared to recent methods such as k-t SLR and L+S, and compared to the proposed dictionary-blind compressed sensing method. PMID:28092528

Low-Rank and Adaptive Sparse Signal (LASSI) Models for Highly Accelerated Dynamic Imaging.

PubMed

Ravishankar, Saiprasad; Moore, Brian E; Nadakuditi, Raj Rao; Fessler, Jeffrey A

2017-05-01

Sparsity-based approaches have been popular in many applications in image processing and imaging. Compressed sensing exploits the sparsity of images in a transform domain or dictionary to improve image recovery fromundersampledmeasurements. In the context of inverse problems in dynamic imaging, recent research has demonstrated the promise of sparsity and low-rank techniques. For example, the patches of the underlying data are modeled as sparse in an adaptive dictionary domain, and the resulting image and dictionary estimation from undersampled measurements is called dictionary-blind compressed sensing, or the dynamic image sequence is modeled as a sum of low-rank and sparse (in some transform domain) components (L+S model) that are estimated from limited measurements. In this work, we investigate a data-adaptive extension of the L+S model, dubbed LASSI, where the temporal image sequence is decomposed into a low-rank component and a component whose spatiotemporal (3D) patches are sparse in some adaptive dictionary domain. We investigate various formulations and efficient methods for jointly estimating the underlying dynamic signal components and the spatiotemporal dictionary from limited measurements. We also obtain efficient sparsity penalized dictionary-blind compressed sensing methods as special cases of our LASSI approaches. Our numerical experiments demonstrate the promising performance of LASSI schemes for dynamicmagnetic resonance image reconstruction from limited k-t space data compared to recent methods such as k-t SLR and L+S, and compared to the proposed dictionary-blind compressed sensing method.

Dual-wavelength OR-PAM with compressed sensing for cell tracking in a 3D cell culture system

NASA Astrophysics Data System (ADS)

Huang, Rou-Xuan; Fu, Ying; Liu, Wang; Ma, Yu-Ting; Hsieh, Bao-Yu; Chen, Shu-Ching; Sun, Mingjian; Li, Pai-Chi

2018-02-01

Monitoring dynamic interactions of T cells migrating toward tumor is beneficial to understand how cancer immunotherapy works. Optical-resolution photoacoustic microscope (OR-PAM) can provide not only high spatial resolution but also deeper penetration than conventional optical microscopy. With the aid of exogenous contrast agents, the dual-wavelength OR-PAM can be applied to map the distribution of CD8+ cytotoxic T lymphocytes (CTLs) with gold nanospheres (AuNS) under 523nm laser irradiation and Hepta1-6 tumor spheres with indocyanine green (ICG) under 800nm irradiation. However, at 1K laser PRF, it takes approximately 20 minutes to obtain a full sample volume of 160 × 160 × 150 μm3 . To increase the imaging rate, we propose a random non-uniform sparse sampling mechanism to achieve fast sparse photoacoustic data acquisition. The image recovery process is formulated as a low-rank matrix recovery (LRMR) based on compressed sensing (CS) theory. We show that it could be stably recovered via nuclear-norm minimization optimization problem to maintain image quality from a significantly fewer measurement. In this study, we use the dual-wavelength OR-PAM with CS to visualize T cell trafficking in a 3D culture system with higher temporal resolution. Data acquisition time is reduced by 40% in such sample volume where sampling density is 0.5. The imaging system reveals the potential to understand the dynamic cellular process for preclinical screening of anti-cancer drugs.

Civil Engineering Applications of Ground Penetrating Radar Recent Advances @ the ELEDIA Research Center

NASA Astrophysics Data System (ADS)

Salucci, Marco; Tenuti, Lorenza; Nardin, Cristina; Oliveri, Giacomo; Viani, Federico; Rocca, Paolo; Massa, Andrea

2014-05-01

The application of non-destructive testing and evaluation (NDT/NDE) methodologies in civil engineering has raised a growing interest during the last years because of its potential impact in several different scenarios. As a consequence, Ground Penetrating Radar (GPR) technologies have been widely adopted as an instrument for the inspection of the structural stability of buildings and for the detection of cracks and voids. In this framework, the development and validation of GPR algorithms and methodologies represents one of the most active research areas within the ELEDIA Research Center of the University of Trento. More in detail, great efforts have been devoted towards the development of inversion techniques based on the integration of deterministic and stochastic search algorithms with multi-focusing strategies. These approaches proved to be effective in mitigating the effects of both nonlinearity and ill-posedness of microwave imaging problems, which represent the well-known issues arising in GPR inverse scattering formulations. More in detail, a regularized multi-resolution approach based on the Inexact Newton Method (INM) has been recently applied to subsurface prospecting, showing a remarkable advantage over a single-resolution implementation [1]. Moreover, the use of multi-frequency or frequency-hopping strategies to exploit the information coming from GPR data collected in time domain and transformed into its frequency components has been proposed as well. In this framework, the effectiveness of the multi-resolution multi-frequency techniques has been proven on synthetic data generated with numerical models such as GprMax [2]. The application of inversion algorithms based on Bayesian Compressive Sampling (BCS) [3][4] to GPR is currently under investigation, as well, in order to exploit their capability to provide satisfactory reconstructions in presence of single and multiple sparse scatterers [3][4]. Furthermore, multi-scaling approaches exploiting level-set-based optimization have been developed for the qualitative reconstruction of multiple and disconnected homogeneous scatterers [5]. Finally, the real-time detection and classification of subsurface scatterers has been investigated by means of learning-by-examples (LBE) techniques, such as Support Vector Machines (SVM) [6]. Acknowledgment - This work was partially supported by COST Action TU1208 'Civil Engineering Applications of Ground Penetrating Radar' References [1] M. Salucci, D. Sartori, N. Anselmi, A. Randazzo, G. Oliveri, and A. Massa, 'Imaging Buried Objects within the Second-Order Born Approximation through a Multiresolution Regularized Inexact-Newton Method', 2013 International Symposium on Electromagnetic Theory (EMTS), (Hiroshima, Japan), May 20-24 2013 (invited). [2] A. Giannopoulos, 'Modelling ground penetrating radar by GprMax', Construct. Build. Mater., vol. 19, no. 10, pp.755 -762 2005 [3] L. Poli, G. Oliveri, P. Rocca, and A. Massa, "Bayesian compressive sensing approaches for the reconstruction of two-dimensional sparse scatterers under TE illumination," IEEE Trans. Geosci. Remote Sensing, vol. 51, no. 5, pp. 2920-2936, May. 2013. [4] L. Poli, G. Oliveri, and A. Massa, "Imaging sparse metallic cylinders through a Local Shape Function Bayesian Compressive Sensing approach," Journal of Optical Society of America A, vol. 30, no. 6, pp. 1261-1272, 2013. [5] M. Benedetti, D. Lesselier, M. Lambert, and A. Massa, "Multiple shapes reconstruction by means of multi-region level sets," IEEE Trans. Geosci. Remote Sensing, vol. 48, no. 5, pp. 2330-2342, May 2010. [6] L. Lizzi, F. Viani, P. Rocca, G. Oliveri, M. Benedetti and A. Massa, "Three-dimensional real-time localization of subsurface objects - From theory to experimental validation," 2009 IEEE International Geoscience and Remote Sensing Symposium, vol. 2, pp. II-121-II-124, 12-17 July 2009.

Lossless Compression of Classification-Map Data

NASA Technical Reports Server (NTRS)

Hua, Xie; Klimesh, Matthew

2009-01-01

A lossless image-data-compression algorithm intended specifically for application to classification-map data is based on prediction, context modeling, and entropy coding. The algorithm was formulated, in consideration of the differences between classification maps and ordinary images of natural scenes, so as to be capable of compressing classification- map data more effectively than do general-purpose image-data-compression algorithms. Classification maps are typically generated from remote-sensing images acquired by instruments aboard aircraft (see figure) and spacecraft. A classification map is a synthetic image that summarizes information derived from one or more original remote-sensing image(s) of a scene. The value assigned to each pixel in such a map is the index of a class that represents some type of content deduced from the original image data for example, a type of vegetation, a mineral, or a body of water at the corresponding location in the scene. When classification maps are generated onboard the aircraft or spacecraft, it is desirable to compress the classification-map data in order to reduce the volume of data that must be transmitted to a ground station.

Architecture for one-shot compressive imaging using computer-generated holograms.

PubMed

Macfaden, Alexander J; Kindness, Stephen J; Wilkinson, Timothy D

2016-09-10

We propose a synchronous implementation of compressive imaging. This method is mathematically equivalent to prevailing sequential methods, but uses a static holographic optical element to create a spatially distributed spot array from which the image can be reconstructed with an instantaneous measurement. We present the holographic design requirements and demonstrate experimentally that the linear algebra of compressed imaging can be implemented with this technique. We believe this technique can be integrated with optical metasurfaces, which will allow the development of new compressive sensing methods.

Highly compressible fluorescent particles for pressure sensing in liquids

NASA Astrophysics Data System (ADS)

Cellini, F.; Peterson, S. D.; Porfiri, M.

2017-05-01

Pressure sensing in liquids is important for engineering applications ranging from industrial processing to naval architecture. Here, we propose a pressure sensor based on highly compressible polydimethylsiloxane foam particles embedding fluorescent Nile Red molecules. The particles display pressure sensitivities as low as 0.0018 kPa-1, which are on the same order of magnitude of sensitivities reported in commercial pressure-sensitive paints for air flows. We envision the application of the proposed sensor in particle image velocimetry toward an improved understanding of flow kinetics in liquids.

Multifunctional Cement Composites Strain and Damage Sensors Applied on Reinforced Concrete (RC) Structural Elements

PubMed Central

Baeza, Francisco Javier; Galao, Oscar; Zornoza, Emilio; Garcés, Pedro

2013-01-01

In this research, strain-sensing and damage-sensing functional properties of cement composites have been studied on a conventional reinforced concrete (RC) beam. Carbon nanofiber (CNFCC) and fiber (CFCC) cement composites were used as sensors on a 4 m long RC beam. Different casting conditions (in situ or attached), service location (under tension or compression) and electrical contacts (embedded or superficial) were compared. Both CNFCC and CFCC were suitable as strain sensors in reversible (elastic) sensing condition testing. CNFCC showed higher sensitivities (gage factor up to 191.8), while CFCC only reached gage factors values of 178.9 (tension) or 49.5 (compression). Furthermore, damage-sensing tests were run, increasing the applied load progressively up to the RC beam failure. In these conditions, CNFCC sensors were also strain sensitive, but no damage sensing mechanism was detected for the strain levels achieved during the tests. Hence, these cement composites could act as strain sensors, even for severe damaged structures near to their collapse. PMID:28809343

Multifunctional Cement Composites Strain and Damage Sensors Applied on Reinforced Concrete (RC) Structural Elements.

PubMed

Baeza, Francisco Javier; Galao, Oscar; Zornoza, Emilio; Garcés, Pedro

2013-03-06

In this research, strain-sensing and damage-sensing functional properties of cement composites have been studied on a conventional reinforced concrete (RC) beam. Carbon nanofiber (CNFCC) and fiber (CFCC) cement composites were used as sensors on a 4 m long RC beam. Different casting conditions ( in situ or attached), service location (under tension or compression) and electrical contacts (embedded or superficial) were compared. Both CNFCC and CFCC were suitable as strain sensors in reversible (elastic) sensing condition testing. CNFCC showed higher sensitivities (gage factor up to 191.8), while CFCC only reached gage factors values of 178.9 (tension) or 49.5 (compression). Furthermore, damage-sensing tests were run, increasing the applied load progressively up to the RC beam failure. In these conditions, CNFCC sensors were also strain sensitive, but no damage sensing mechanism was detected for the strain levels achieved during the tests. Hence, these cement composites could act as strain sensors, even for severe damaged structures near to their collapse.

Super resolution reconstruction of infrared images based on classified dictionary learning

NASA Astrophysics Data System (ADS)

Liu, Fei; Han, Pingli; Wang, Yi; Li, Xuan; Bai, Lu; Shao, Xiaopeng

2018-05-01

Infrared images always suffer from low-resolution problems resulting from limitations of imaging devices. An economical approach to combat this problem involves reconstructing high-resolution images by reasonable methods without updating devices. Inspired by compressed sensing theory, this study presents and demonstrates a Classified Dictionary Learning method to reconstruct high-resolution infrared images. It classifies features of the samples into several reasonable clusters and trained a dictionary pair for each cluster. The optimal pair of dictionaries is chosen for each image reconstruction and therefore, more satisfactory results is achieved without the increase in computational complexity and time cost. Experiments and results demonstrated that it is a viable method for infrared images reconstruction since it improves image resolution and recovers detailed information of targets.

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 %.

Compressed sensing for ultrasound computed tomography.

PubMed

van Sloun, Ruud; Pandharipande, Ashish; Mischi, Massimo; Demi, Libertario

2015-06-01

Ultrasound computed tomography (UCT) allows the reconstruction of quantitative tissue characteristics, such as speed of sound, mass density, and attenuation. Lowering its acquisition time would be beneficial; however, this is fundamentally limited by the physical time of flight and the number of transmission events. In this letter, we propose a compressed sensing solution for UCT. The adopted measurement scheme is based on compressed acquisitions, with concurrent randomised transmissions in a circular array configuration. Reconstruction of the image is then obtained by combining the born iterative method and total variation minimization, thereby exploiting variation sparsity in the image domain. Evaluation using simulated UCT scattering measurements shows that the proposed transmission scheme performs better than uniform undersampling, and is able to reduce acquisition time by almost one order of magnitude, while maintaining high spatial resolution.

Compressive passive millimeter wave imager

DOEpatents

Gopalsami, Nachappa; Liao, Shaolin; Elmer, Thomas W; Koehl, Eugene R; Heifetz, Alexander; Raptis, Apostolos C

2015-01-27

A compressive scanning approach for millimeter wave imaging and sensing. A Hadamard mask is positioned to receive millimeter waves from an object to be imaged. A subset of the full set of Hadamard acquisitions is sampled. The subset is used to reconstruct an image representing the object.

Data compressive paradigm for multispectral sensing using tunable DWELL mid-infrared detectors.

PubMed

Jang, Woo-Yong; Hayat, Majeed M; Godoy, Sebastián E; Bender, Steven C; Zarkesh-Ha, Payman; Krishna, Sanjay

2011-09-26

While quantum dots-in-a-well (DWELL) infrared photodetectors have the feature that their spectral responses can be shifted continuously by varying the applied bias, the width of the spectral response at any applied bias is not sufficiently narrow for use in multispectral sensing without the aid of spectral filters. To achieve higher spectral resolutions without using physical spectral filters, algorithms have been developed for post-processing the DWELL's bias-dependent photocurrents resulting from probing an object of interest repeatedly over a wide range of applied biases. At the heart of these algorithms is the ability to approximate an arbitrary spectral filter, which we desire the DWELL-algorithm combination to mimic, by forming a weighted superposition of the DWELL's non-orthogonal spectral responses over a range of applied biases. However, these algorithms assume availability of abundant DWELL data over a large number of applied biases (>30), leading to large overall acquisition times in proportion with the number of biases. This paper reports a new multispectral sensing algorithm to substantially compress the number of necessary bias values subject to a prescribed performance level across multiple sensing applications. The algorithm identifies a minimal set of biases to be used in sensing only the relevant spectral information for remote-sensing applications of interest. Experimental results on target spectrometry and classification demonstrate a reduction in the number of required biases by a factor of 7 (e.g., from 30 to 4). The tradeoff between performance and bias compression is thoroughly investigated. © 2011 Optical Society of America

Compression force sensing regulates integrin αIIbβ3 adhesive function on diabetic platelets.

PubMed

Ju, Lining; McFadyen, James D; Al-Daher, Saheb; Alwis, Imala; Chen, Yunfeng; Tønnesen, Lotte L; Maiocchi, Sophie; Coulter, Brianna; Calkin, Anna C; Felner, Eric I; Cohen, Neale; Yuan, Yuping; Schoenwaelder, Simone M; Cooper, Mark E; Zhu, Cheng; Jackson, Shaun P

2018-03-14

Diabetes is associated with an exaggerated platelet thrombotic response at sites of vascular injury. Biomechanical forces regulate platelet activation, although the impact of diabetes on this process remains ill-defined. Using a biomembrane force probe (BFP), we demonstrate that compressive force activates integrin α IIb β 3 on discoid diabetic platelets, increasing its association rate with immobilized fibrinogen. This compressive force-induced integrin activation is calcium and PI 3-kinase dependent, resulting in enhanced integrin affinity maturation and exaggerated shear-dependent platelet adhesion. Analysis of discoid platelet aggregation in the mesenteric circulation of mice confirmed that diabetes leads to a marked enhancement in the formation and stability of discoid platelet aggregates, via a mechanism that is not inhibited by therapeutic doses of aspirin and clopidogrel, but is eliminated by PI 3-kinase inhibition. These studies demonstrate the existence of a compression force sensing mechanism linked to α IIb β 3 adhesive function that leads to a distinct prothrombotic phenotype in diabetes.

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.

Comparison of Open Source Compression Algorithms on Vhr Remote Sensing Images for Efficient Storage Hierarchy

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.

Output MSE and PSNR prediction in DCT-based lossy compression of remote sensing images

NASA Astrophysics Data System (ADS)

Kozhemiakin, Ruslan A.; Abramov, Sergey K.; Lukin, Vladimir V.; Vozel, Benoit; Chehdi, Kacem

2017-10-01

Amount and size of remote sensing (RS) images acquired by modern systems are so large that data have to be compressed in order to transfer, save and disseminate them. Lossy compression becomes more popular for aforementioned situations. But lossy compression has to be applied carefully with providing acceptable level of introduced distortions not to lose valuable information contained in data. Then introduced losses have to be controlled and predicted and this is problematic for many coders. In this paper, we analyze possibilities of predicting mean square error or, equivalently, PSNR for coders based on discrete cosine transform (DCT) applied either for compressing singlechannel RS images or multichannel data in component-wise manner. The proposed approach is based on direct dependence between distortions introduced due to DCT coefficient quantization and losses in compressed data. One more innovation deals with possibility to employ a limited number (percentage) of blocks for which DCT-coefficients have to be calculated. This accelerates prediction and makes it considerably faster than compression itself. There are two other advantages of the proposed approach. First, it is applicable for both uniform and non-uniform quantization of DCT coefficients. Second, the approach is quite general since it works for several analyzed DCT-based coders. The simulation results are obtained for standard test images and then verified for real-life RS data.

Compressive Sensing with Cross-Validation and Stop-Sampling for Sparse Polynomial Chaos Expansions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huan, Xun; Safta, Cosmin; Sargsyan, Khachik

Compressive sensing is a powerful technique for recovering sparse solutions of underdetermined linear systems, which is often encountered in uncertainty quanti cation analysis of expensive and high-dimensional physical models. We perform numerical investigations employing several com- pressive sensing solvers that target the unconstrained LASSO formulation, with a focus on linear systems that arise in the construction of polynomial chaos expansions. With core solvers of l1 ls, SpaRSA, CGIST, FPC AS, and ADMM, we develop techniques to mitigate over tting through an automated selection of regularization constant based on cross-validation, and a heuristic strategy to guide the stop-sampling decision. Practical recommendationsmore » on parameter settings for these tech- niques are provided and discussed. The overall method is applied to a series of numerical examples of increasing complexity, including large eddy simulations of supersonic turbulent jet-in-cross flow involving a 24-dimensional input. Through empirical phase-transition diagrams and convergence plots, we illustrate sparse recovery performance under structures induced by polynomial chaos, accuracy and computational tradeoffs between polynomial bases of different degrees, and practi- cability of conducting compressive sensing for a realistic, high-dimensional physical application. Across test cases studied in this paper, we find ADMM to have demonstrated empirical advantages through consistent lower errors and faster computational times.« less

An Efficient Image Compressor for Charge Coupled Devices Camera

PubMed Central

Li, Jin; Xing, Fei; You, Zheng

2014-01-01

Recently, the discrete wavelet transforms- (DWT-) based compressor, such as JPEG2000 and CCSDS-IDC, is widely seen as the state of the art compression scheme for charge coupled devices (CCD) camera. However, CCD images project on the DWT basis to produce a large number of large amplitude high-frequency coefficients because these images have a large number of complex texture and contour information, which are disadvantage for the later coding. In this paper, we proposed a low-complexity posttransform coupled with compressing sensing (PT-CS) compression approach for remote sensing image. First, the DWT is applied to the remote sensing image. Then, a pair base posttransform is applied to the DWT coefficients. The pair base are DCT base and Hadamard base, which can be used on the high and low bit-rate, respectively. The best posttransform is selected by the l p-norm-based approach. The posttransform is considered as the sparse representation stage of CS. The posttransform coefficients are resampled by sensing measurement matrix. Experimental results on on-board CCD camera images show that the proposed approach significantly outperforms the CCSDS-IDC-based coder, and its performance is comparable to that of the JPEG2000 at low bit rate and it does not have the high excessive implementation complexity of JPEG2000. PMID:25114977

Greedy algorithms for diffuse optical tomography reconstruction

NASA Astrophysics Data System (ADS)

Dileep, B. P. V.; Das, Tapan; Dutta, Pranab K.

2018-03-01

Diffuse optical tomography (DOT) is a noninvasive imaging modality that reconstructs the optical parameters of a highly scattering medium. However, the inverse problem of DOT is ill-posed and highly nonlinear due to the zig-zag propagation of photons that diffuses through the cross section of tissue. The conventional DOT imaging methods iteratively compute the solution of forward diffusion equation solver which makes the problem computationally expensive. Also, these methods fail when the geometry is complex. Recently, the theory of compressive sensing (CS) has received considerable attention because of its efficient use in biomedical imaging applications. The objective of this paper is to solve a given DOT inverse problem by using compressive sensing framework and various Greedy algorithms such as orthogonal matching pursuit (OMP), compressive sampling matching pursuit (CoSaMP), and stagewise orthogonal matching pursuit (StOMP), regularized orthogonal matching pursuit (ROMP) and simultaneous orthogonal matching pursuit (S-OMP) have been studied to reconstruct the change in the absorption parameter i.e, Δα from the boundary data. Also, the Greedy algorithms have been validated experimentally on a paraffin wax rectangular phantom through a well designed experimental set up. We also have studied the conventional DOT methods like least square method and truncated singular value decomposition (TSVD) for comparison. One of the main features of this work is the usage of less number of source-detector pairs, which can facilitate the use of DOT in routine applications of screening. The performance metrics such as mean square error (MSE), normalized mean square error (NMSE), structural similarity index (SSIM), and peak signal to noise ratio (PSNR) have been used to evaluate the performance of the algorithms mentioned in this paper. Extensive simulation results confirm that CS based DOT reconstruction outperforms the conventional DOT imaging methods in terms of computational efficiency. The main advantage of this study is that the forward diffusion equation solver need not be repeatedly solved.

Regularized spherical polar fourier diffusion MRI with optimal dictionary learning.

PubMed

Cheng, Jian; Jiang, Tianzi; Deriche, Rachid; Shen, Dinggang; Yap, Pew-Thian

2013-01-01

Compressed Sensing (CS) takes advantage of signal sparsity or compressibility and allows superb signal reconstruction from relatively few measurements. Based on CS theory, a suitable dictionary for sparse representation of the signal is required. In diffusion MRI (dMRI), CS methods proposed for reconstruction of diffusion-weighted signal and the Ensemble Average Propagator (EAP) utilize two kinds of Dictionary Learning (DL) methods: 1) Discrete Representation DL (DR-DL), and 2) Continuous Representation DL (CR-DL). DR-DL is susceptible to numerical inaccuracy owing to interpolation and regridding errors in a discretized q-space. In this paper, we propose a novel CR-DL approach, called Dictionary Learning - Spherical Polar Fourier Imaging (DL-SPFI) for effective compressed-sensing reconstruction of the q-space diffusion-weighted signal and the EAP. In DL-SPFI, a dictionary that sparsifies the signal is learned from the space of continuous Gaussian diffusion signals. The learned dictionary is then adaptively applied to different voxels using a weighted LASSO framework for robust signal reconstruction. Compared with the start-of-the-art CR-DL and DR-DL methods proposed by Merlet et al. and Bilgic et al., respectively, our work offers the following advantages. First, the learned dictionary is proved to be optimal for Gaussian diffusion signals. Second, to our knowledge, this is the first work to learn a voxel-adaptive dictionary. The importance of the adaptive dictionary in EAP reconstruction will be demonstrated theoretically and empirically. Third, optimization in DL-SPFI is only performed in a small subspace resided by the SPF coefficients, as opposed to the q-space approach utilized by Merlet et al. We experimentally evaluated DL-SPFI with respect to L1-norm regularized SPFI (L1-SPFI), which uses the original SPF basis, and the DR-DL method proposed by Bilgic et al. The experiment results on synthetic and real data indicate that the learned dictionary produces sparser coefficients than the original SPF basis and results in significantly lower reconstruction error than Bilgic et al.'s method.

Communications and information research: Improved space link performance via concatenated forward error correction coding

NASA Technical Reports Server (NTRS)

Rao, T. R. N.; Seetharaman, G.; Feng, G. L.

1996-01-01

With the development of new advanced instruments for remote sensing applications, sensor data will be generated at a rate that not only requires increased onboard processing and storage capability, but imposes demands on the space to ground communication link and ground data management-communication system. Data compression and error control codes provide viable means to alleviate these demands. Two types of data compression have been studied by many researchers in the area of information theory: a lossless technique that guarantees full reconstruction of the data, and a lossy technique which generally gives higher data compaction ratio but incurs some distortion in the reconstructed data. To satisfy the many science disciplines which NASA supports, lossless data compression becomes a primary focus for the technology development. While transmitting the data obtained by any lossless data compression, it is very important to use some error-control code. For a long time, convolutional codes have been widely used in satellite telecommunications. To more efficiently transform the data obtained by the Rice algorithm, it is required to meet the a posteriori probability (APP) for each decoded bit. A relevant algorithm for this purpose has been proposed which minimizes the bit error probability in the decoding linear block and convolutional codes and meets the APP for each decoded bit. However, recent results on iterative decoding of 'Turbo codes', turn conventional wisdom on its head and suggest fundamentally new techniques. During the past several months of this research, the following approaches have been developed: (1) a new lossless data compression algorithm, which is much better than the extended Rice algorithm for various types of sensor data, (2) a new approach to determine the generalized Hamming weights of the algebraic-geometric codes defined by a large class of curves in high-dimensional spaces, (3) some efficient improved geometric Goppa codes for disk memory systems and high-speed mass memory systems, and (4) a tree based approach for data compression using dynamic programming.

Compressed-Sensing Reconstruction Based on Block Sparse Bayesian Learning in Bearing-Condition Monitoring

PubMed Central

Sun, Jiedi; Yu, Yang; Wen, Jiangtao

2017-01-01

Remote monitoring of bearing conditions, using wireless sensor network (WSN), is a developing trend in the industrial field. In complicated industrial environments, WSN face three main constraints: low energy, less memory, and low operational capability. Conventional data-compression methods, which concentrate on data compression only, cannot overcome these limitations. Aiming at these problems, this paper proposed a compressed data acquisition and reconstruction scheme based on Compressed Sensing (CS) which is a novel signal-processing technique and applied it for bearing conditions monitoring via WSN. The compressed data acquisition is realized by projection transformation and can greatly reduce the data volume, which needs the nodes to process and transmit. The reconstruction of original signals is achieved in the host computer by complicated algorithms. The bearing vibration signals not only exhibit the sparsity property, but also have specific structures. This paper introduced the block sparse Bayesian learning (BSBL) algorithm which works by utilizing the block property and inherent structures of signals to reconstruct CS sparsity coefficients of transform domains and further recover the original signals. By using the BSBL, CS reconstruction can be improved remarkably. Experiments and analyses showed that BSBL method has good performance and is suitable for practical bearing-condition monitoring. PMID:28635623

Digital holographic image fusion for a larger size object using compressive sensing

NASA Astrophysics Data System (ADS)

Tian, Qiuhong; Yan, Liping; Chen, Benyong; Yao, Jiabao; Zhang, Shihua

2017-05-01

Digital holographic imaging fusion for a larger size object using compressive sensing is proposed. In this method, the high frequency component of the digital hologram under discrete wavelet transform is represented sparsely by using compressive sensing so that the data redundancy of digital holographic recording can be resolved validly, the low frequency component is retained totally to ensure the image quality, and multiple reconstructed images with different clear parts corresponding to a laser spot size are fused to realize the high quality reconstructed image of a larger size object. In addition, a filter combing high-pass and low-pass filters is designed to remove the zero-order term from a digital hologram effectively. The digital holographic experimental setup based on off-axis Fresnel digital holography was constructed. The feasible and comparative experiments were carried out. The fused image was evaluated by using the Tamura texture features. The experimental results demonstrated that the proposed method can improve the processing efficiency and visual characteristics of the fused image and enlarge the size of the measured object effectively.

Compressive sensing for sparse time-frequency representation of nonstationary signals in the presence of impulsive noise

NASA Astrophysics Data System (ADS)

Orović, Irena; Stanković, Srdjan; Amin, Moeness

2013-05-01

A modified robust two-dimensional compressive sensing algorithm for reconstruction of sparse time-frequency representation (TFR) is proposed. The ambiguity function domain is assumed to be the domain of observations. The two-dimensional Fourier bases are used to linearly relate the observations to the sparse TFR, in lieu of the Wigner distribution. We assume that a set of available samples in the ambiguity domain is heavily corrupted by an impulsive type of noise. Consequently, the problem of sparse TFR reconstruction cannot be tackled using standard compressive sensing optimization algorithms. We introduce a two-dimensional L-statistics based modification into the transform domain representation. It provides suitable initial conditions that will produce efficient convergence of the reconstruction algorithm. This approach applies sorting and weighting operations to discard an expected amount of samples corrupted by noise. The remaining samples serve as observations used in sparse reconstruction of the time-frequency signal representation. The efficiency of the proposed approach is demonstrated on numerical examples that comprise both cases of monocomponent and multicomponent signals.

Compressive sensing imaging through a drywall barrier at sub-THz and THz frequencies in transmission and reflection modes

NASA Astrophysics Data System (ADS)

Takan, Taylan; Özkan, Vedat A.; Idikut, Fırat; Yildirim, Ihsan Ozan; Şahin, Asaf B.; Altan, Hakan

2014-10-01

In this work sub-terahertz imaging using Compressive Sensing (CS) techniques for targets placed behind a visibly opaque barrier is demonstrated both experimentally and theoretically. Using a multiplied Schottky diode based millimeter wave source working at 118 GHz, metal cutout targets were illuminated in both reflection and transmission configurations with and without barriers which were made out of drywall. In both modes the image is spatially discretized using laser machined, 10 × 10 pixel metal apertures to demonstrate the technique of compressive sensing. The images were collected by modulating the source and measuring the transmitted flux through the apertures using a Golay cell. Experimental results were compared to simulations of the expected transmission through the metal apertures. Image quality decreases as expected when going from the non-obscured transmission case to the obscured transmission case and finally to the obscured reflection case. However, in all instances the image appears below the Nyquist rate which demonstrates that this technique is a viable option for Through the Wall Reflection Imaging (TWRI) applications.

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.

Study and simulation of low rate video coding schemes

NASA Technical Reports Server (NTRS)

Sayood, Khalid; Chen, Yun-Chung; Kipp, G.

1992-01-01

The semiannual report is included. Topics covered include communication, information science, data compression, remote sensing, color mapped images, robust coding scheme for packet video, recursively indexed differential pulse code modulation, image compression technique for use on token ring networks, and joint source/channel coder design.

Sub-bandage sensing system for remote monitoring of chronic wounds in healthcare

NASA Astrophysics Data System (ADS)

Hariz, Alex; Mehmood, Nasir; Voelcker, Nico

2015-12-01

Chronic wounds, such as venous leg ulcers, can be monitored non-invasively by using modern sensing devices and wireless technologies. The development of such wireless diagnostic tools may improve chronic wound management by providing evidence on efficacy of treatments being provided. In this paper we present a low-power portable telemetric system for wound condition sensing and monitoring. The system aims at measuring and transmitting real-time information of wound-site temperature, sub-bandage pressure and moisture level from within the wound dressing. The system comprises commercially available non-invasive temperature, moisture, and pressure sensors, which are interfaced with a telemetry device on a flexible 0.15 mm thick printed circuit material, making up a lightweight biocompatible sensing device. The real-time data obtained is transmitted wirelessly to a portable receiver which displays the measured values. The performance of the whole telemetric sensing system is validated on a mannequin leg using commercial compression bandages and dressings. A number of trials on a healthy human volunteer are performed where treatment conditions were emulated using various compression bandage configurations. A reliable and repeatable performance of the system is achieved under compression bandage and with minimal discomfort to the volunteer. The system is capable of reporting instantaneous changes in bandage pressure, moisture level and local temperature at wound site with average measurement resolutions of 0.5 mmHg, 3.0 %RH, and 0.2 °C respectively. Effective range of data transmission is 4-5 m in an open environment.

Sidelobe apodization in optical pulse compression reflectometry for fiber optic distributed acoustic sensing.

PubMed

Mompó, Juan José; Martín-López, Sonia; González-Herráez, Miguel; Loayssa, Alayn

2018-04-01

We demonstrate a technique to reduce the sidelobes in optical pulse compression reflectometry for distributed acoustic sensing. The technique is based on using a Gaussian probe pulse with linear frequency modulation. This is shown to improve the sidelobe suppression by 13 dB compared to the use of square pulses without any significant penalty in terms of spatial resolution. In addition, a 2.25 dB enhancement in signal-to-noise ratio is calculated compared to the use of receiver-side windowing. The method is tested by measuring 700 Hz vibrations with a 140  nε amplitude at the end of a 50 km fiber sensing link with 34 cm spatial resolution, giving a record 147,058 spatially resolved points.

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.

Enhancing sparsity of Hermite polynomial expansions by iterative rotations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Xiu; Lei, Huan; Baker, Nathan A.

2016-02-01

Compressive sensing has become a powerful addition to uncertainty quantification in recent years. This paper identifies new bases for random variables through linear mappings such that the representation of the quantity of interest is more sparse with new basis functions associated with the new random variables. This sparsity increases both the efficiency and accuracy of the compressive sensing-based uncertainty quantification method. Specifically, we consider rotation- based linear mappings which are determined iteratively for Hermite polynomial expansions. We demonstrate the effectiveness of the new method with applications in solving stochastic partial differential equations and high-dimensional (O(100)) problems.

Parallel phase-shifting self-interference digital holography with faithful reconstruction using compressive sensing

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 Genotyping

PubMed Central

Erlich, Yaniv; Gordon, Assaf; Brand, Michael; Hannon, Gregory J.; Mitra, Partha P.

2011-01-01

Over the past three decades we have steadily increased our knowledge on the genetic basis of many severe disorders. Nevertheless, there are still great challenges in applying this knowledge routinely in the clinic, mainly due to the relatively tedious and expensive process of genotyping. Since the genetic variations that underlie the disorders are relatively rare in the population, they can be thought of as a sparse signal. Using methods and ideas from compressed sensing and group testing, we have developed a cost-effective genotyping protocol to detect carriers for severe genetic disorders. In particular, we have adapted our scheme to a recently developed class of high throughput DNA sequencing technologies. The mathematical framework presented here has some important distinctions from the ’traditional’ compressed sensing and group testing frameworks in order to address biological and technical constraints of our setting. PMID:21451737

Experimental Study of Super-Resolution Using a Compressive Sensing Architecture

DTIC Science & Technology

2015-03-01

Intelligence 24(9), 1167–1183 (2002). [3] Lin, Z. and Shum, H.-Y., “Fundamental limits of reconstruction-based superresolution algorithms under local...IEEE Transactions on 52, 1289–1306 (April 2006). [9] Marcia, R. and Willett, R., “Compressive coded aperture superresolution image reconstruction,” in

Evaluation of Flexible Force Sensors for Pressure Monitoring in Treatment of Chronic Venous Disorders.

PubMed

Parmar, Suresh; Khodasevych, Iryna; Troynikov, Olga

2017-08-21

The recent use of graduated compression therapy for treatment of chronic venous disorders such as leg ulcers and oedema has led to considerable research interest in flexible and low-cost force sensors. Properly applied low pressure during compression therapy can substantially improve the treatment of chronic venous disorders. However, achievement of the recommended low pressure levels and its accurate determination in real-life conditions is still a challenge. Several thin and flexible force sensors, which can also function as pressure sensors, are commercially available, but their real-life sensing performance has not been evaluated. Moreover, no researchers have reported information on sensor performance during static and dynamic loading within the realistic test conditions required for compression therapy. This research investigated the sensing performance of five low-cost commercial pressure sensors on a human-leg-like test apparatus and presents quantitative results on the accuracy and drift behaviour of these sensors in both static and dynamic conditions required for compression therapy. Extensive experimental work on this new human-leg-like test setup demonstrated its utility for evaluating the sensors. Results showed variation in static and dynamic sensing performance, including accuracy and drift characteristics. Only one commercially available pressure sensor was found to reliably deliver accuracy of 95% and above for all three test pressure points of 30, 50 and 70 mmHg.

Evaluation of Flexible Force Sensors for Pressure Monitoring in Treatment of Chronic Venous Disorders

PubMed Central

Parmar, Suresh; Khodasevych, Iryna; Troynikov, Olga

2017-01-01

The recent use of graduated compression therapy for treatment of chronic venous disorders such as leg ulcers and oedema has led to considerable research interest in flexible and low-cost force sensors. Properly applied low pressure during compression therapy can substantially improve the treatment of chronic venous disorders. However, achievement of the recommended low pressure levels and its accurate determination in real-life conditions is still a challenge. Several thin and flexible force sensors, which can also function as pressure sensors, are commercially available, but their real-life sensing performance has not been evaluated. Moreover, no researchers have reported information on sensor performance during static and dynamic loading within the realistic test conditions required for compression therapy. This research investigated the sensing performance of five low-cost commercial pressure sensors on a human-leg-like test apparatus and presents quantitative results on the accuracy and drift behaviour of these sensors in both static and dynamic conditions required for compression therapy. Extensive experimental work on this new human-leg-like test setup demonstrated its utility for evaluating the sensors. Results showed variation in static and dynamic sensing performance, including accuracy and drift characteristics. Only one commercially available pressure sensor was found to reliably deliver accuracy of 95% and above for all three test pressure points of 30, 50 and 70 mmHg. PMID:28825672

Validation of a Pseudo-Sound Theory for the Pressure-Dilatation in DNS of Compressible Turbulence

NASA Technical Reports Server (NTRS)

Ristorcelli, J. R.; Blaisdell, G. A.

1997-01-01

The results of an asymptotic theory for statistical closures for compressible turbulence are explored and validated with the direct numerical simulation of the isotropic decay and the homogeneous shear. An excellent collapse of the data is seen. The slow portion is found to scale, as predicted by the theory, with the quantity M(sub t)(sup 2) and epsilon(sub s). The rapid portion has an unambiguous scaling with alpha(sup 2)M(sub t)(sup s)epsilon(sub s)[P(sub k)/epsilon - l](Sk/epsilon)(sup 2). Implicit in the scaling is a dependence, as has been noted by others, on the gradient Mach number. A new feature of the effects of compressibility, that of the Kolmogorov scaling coefficient, alpha, is discussed. It is suggested that alpha may contain flow specific physics associated with large scales that might provide further insight into the structural effects of compressibility.

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.

Optimal Compressed Sensing and Reconstruction of Unstructured Mesh Datasets

DOE PAGES

Salloum, Maher; Fabian, Nathan D.; Hensinger, David M.; ...

2017-08-09

Exascale computing promises quantities of data too large to efficiently store and transfer across networks in order to be able to analyze and visualize the results. We investigate compressed sensing (CS) as an in situ method to reduce the size of the data as it is being generated during a large-scale simulation. CS works by sampling the data on the computational cluster within an alternative function space such as wavelet bases and then reconstructing back to the original space on visualization platforms. While much work has gone into exploring CS on structured datasets, such as image data, we investigate itsmore » usefulness for point clouds such as unstructured mesh datasets often found in finite element simulations. We sample using a technique that exhibits low coherence with tree wavelets found to be suitable for point clouds. We reconstruct using the stagewise orthogonal matching pursuit algorithm that we improved to facilitate automated use in batch jobs. We analyze the achievable compression ratios and the quality and accuracy of reconstructed results at each compression ratio. In the considered case studies, we are able to achieve compression ratios up to two orders of magnitude with reasonable reconstruction accuracy and minimal visual deterioration in the data. Finally, our results suggest that, compared to other compression techniques, CS is attractive in cases where the compression overhead has to be minimized and where the reconstruction cost is not a significant concern.« less

High-speed imaging using compressed sensing and wavelength-dependent scattering (Conference Presentation)

NASA Astrophysics Data System (ADS)

Shin, Jaewook; Bosworth, Bryan T.; Foster, Mark A.

2017-02-01

The process of multiple scattering has inherent characteristics that are attractive for high-speed imaging with high spatial resolution and a wide field-of-view. A coherent source passing through a multiple-scattering medium naturally generates speckle patterns with diffraction-limited features over an arbitrarily large field-of-view. In addition, the process of multiple scattering is deterministic allowing a given speckle pattern to be reliably reproduced with identical illumination conditions. Here, by exploiting wavelength dependent multiple scattering and compressed sensing, we develop a high-speed 2D time-stretch microscope. Highly chirped pulses from a 90-MHz mode-locked laser are sent through a 2D grating and a ground-glass diffuser to produce 2D speckle patterns that rapidly evolve with the instantaneous frequency of the chirped pulse. To image a scene, we first characterize the high-speed evolution of the generated speckle patterns. Subsequently we project the patterns onto the microscopic region of interest and collect the total light from the scene using a single high-speed photodetector. Thus the wavelength dependent speckle patterns serve as high-speed pseudorandom structured illumination of the scene. An image sequence is then recovered using the time-dependent signal received by the photodetector, the known speckle pattern evolution, and compressed sensing algorithms. Notably, the use of compressed sensing allows for reconstruction of a time-dependent scene using a highly sub-Nyquist number of measurements, which both increases the speed of the imager and reduces the amount of data that must be collected and stored. We will discuss our experimental demonstration of this approach and the theoretical limits on imaging speed.

Joint 6D k-q Space Compressed Sensing for Accelerated High Angular Resolution Diffusion MRI.

PubMed

Cheng, Jian; Shen, Dinggang; Basser, Peter J; Yap, Pew-Thian

2015-01-01

High Angular Resolution Diffusion Imaging (HARDI) avoids the Gaussian. diffusion assumption that is inherent in Diffusion Tensor Imaging (DTI), and is capable of characterizing complex white matter micro-structure with greater precision. However, HARDI methods such as Diffusion Spectrum Imaging (DSI) typically require significantly more signal measurements than DTI, resulting in prohibitively long scanning times. One of the goals in HARDI research is therefore to improve estimation of quantities such as the Ensemble Average Propagator (EAP) and the Orientation Distribution Function (ODF) with a limited number of diffusion-weighted measurements. A popular approach to this problem, Compressed Sensing (CS), affords highly accurate signal reconstruction using significantly fewer (sub-Nyquist) data points than required traditionally. Existing approaches to CS diffusion MRI (CS-dMRI) mainly focus on applying CS in the q-space of diffusion signal measurements and fail to take into consideration information redundancy in the k-space. In this paper, we propose a framework, called 6-Dimensional Compressed Sensing diffusion MRI (6D-CS-dMRI), for reconstruction of the diffusion signal and the EAP from data sub-sampled in both 3D k-space and 3D q-space. To our knowledge, 6D-CS-dMRI is the first work that applies compressed sensing in the full 6D k-q space and reconstructs the diffusion signal in the full continuous q-space and the EAP in continuous displacement space. Experimental results on synthetic and real data demonstrate that, compared with full DSI sampling in k-q space, 6D-CS-dMRI yields excellent diffusion signal and EAP reconstruction with low root-mean-square error (RMSE) using 11 times less samples (3-fold reduction in k-space and 3.7-fold reduction in q-space).

Four-body interaction energy for compressed solid krypton from quantum theory.

PubMed

Tian, Chunling; Wu, Na; Liu, Fusheng; Saxena, Surendra K; Zheng, Xingrong

2012-07-28

The importance of the four-body contribution in compressed solid krypton was first evaluated using the many-body expansion method and the coupled cluster theory with full single and double excitations plus perturbative treatment of triples. All different four-atom clusters existing in the first- and second-nearest neighbor shells of face-centered cubic krypton were considered, and both self-consistent-field Hartree-Fock and correlation parts of the four-body interaction were accurately determined from the ambient conditions up to eightfold volume compression. We find that the four-body interaction energy is negative at compression ratio lower than 2, where the dispersive forces play a dominant role. With increasing the compression, the four-body contribution becomes repulsive and significantly cancels the over-softening effects of the three-body potential. The obtained equation of state (EOS) was compared with the experiments and the density-functional theory calculations. It shows that combination of the four-body effects with two- and three-body interactions leads to an excellent agreement with EOS measurements throughout the whole experimental range 0-130 GPa, and extends the prediction to 300 GPa.

Some Practical Universal Noiseless Coding Techniques

NASA Technical Reports Server (NTRS)

Rice, Robert F.

1994-01-01

Report discusses noiseless data-compression-coding algorithms, performance characteristics and practical consideration in implementation of algorithms in coding modules composed of very-large-scale integrated circuits. Report also has value as tutorial document on data-compression-coding concepts. Coding techniques and concepts in question "universal" in sense that, in principle, applicable to streams of data from variety of sources. However, discussion oriented toward compression of high-rate data generated by spaceborne sensors for lower-rate transmission back to earth.

The Performance of Wavelets for Data Compression in Selected Military Applications

DTIC Science & Technology

1990-02-23

reported. 14. SUBJECT TERMS IS. NUMBER OF PAGES 56 16. PRICE CODE 17. SICURITY CLASSIFICATION I lL SECURITY CLASSIFICATION 19. SECURITY CLASSIF4CATION 20...compression ratio is conservative in the sense that it understates the theoretical compression ratio by taking into account the actual memory...effect of reducing the compresion ratios quoted in the table by the factor 7.8/8.0 = 0.975. AWARE, Inc. 14 registration was then calculated for each

Pressure mapping with textile sensors for compression therapy monitoring.

PubMed

Baldoli, Ilaria; Mazzocchi, Tommaso; Paoletti, Clara; Ricotti, Leonardo; Salvo, Pietro; Dini, Valentina; Laschi, Cecilia; Francesco, Fabio Di; Menciassi, Arianna

2016-08-01

Compression therapy is the cornerstone of treatment in the case of venous leg ulcers. The therapy outcome is strictly dependent on the pressure distribution produced by bandages along the lower limb length. To date, pressure monitoring has been carried out using sensors that present considerable drawbacks, such as single point instead of distributed sensing, no shape conformability, bulkiness and constraints on patient's movements. In this work, matrix textile sensing technologies were explored in terms of their ability to measure the sub-bandage pressure with a suitable temporal and spatial resolution. A multilayered textile matrix based on a piezoresistive sensing principle was developed, calibrated and tested with human subjects, with the aim of assessing real-time distributed pressure sensing at the skin/bandage interface. Experimental tests were carried out on three healthy volunteers, using two different bandage types, from among those most commonly used. Such tests allowed the trends of pressure distribution to be evaluated over time, both at rest and during daily life activities. Results revealed that the proposed device enables the dynamic assessment of compression mapping, with a suitable spatial and temporal resolution (20 mm and 10 Hz, respectively). In addition, the sensor is flexible and conformable, thus well accepted by the patient. Overall, this study demonstrates the adequacy of the proposed piezoresistive textile sensor for the real-time monitoring of bandage-based therapeutic treatments. © IMechE 2016.

An Adaptive Data Gathering Scheme for Multi-Hop Wireless Sensor Networks Based on Compressed Sensing and Network Coding.

PubMed

Yin, Jun; Yang, Yuwang; Wang, Lei

2016-04-01

Joint design of compressed sensing (CS) and network coding (NC) has been demonstrated to provide a new data gathering paradigm for multi-hop wireless sensor networks (WSNs). By exploiting the correlation of the network sensed data, a variety of data gathering schemes based on NC and CS (Compressed Data Gathering--CDG) have been proposed. However, these schemes assume that the sparsity of the network sensed data is constant and the value of the sparsity is known before starting each data gathering epoch, thus they ignore the variation of the data observed by the WSNs which are deployed in practical circumstances. In this paper, we present a complete design of the feedback CDG scheme where the sink node adaptively queries those interested nodes to acquire an appropriate number of measurements. The adaptive measurement-formation procedure and its termination rules are proposed and analyzed in detail. Moreover, in order to minimize the number of overall transmissions in the formation procedure of each measurement, we have developed a NP-complete model (Maximum Leaf Nodes Minimum Steiner Nodes--MLMS) and realized a scalable greedy algorithm to solve the problem. Experimental results show that the proposed measurement-formation method outperforms previous schemes, and experiments on both datasets from ocean temperature and practical network deployment also prove the effectiveness of our proposed feedback CDG scheme.

Energy-efficient ECG compression on wireless biosensors via minimal coherence sensing and weighted ℓ₁ minimization reconstruction.

PubMed

Zhang, Jun; Gu, Zhenghui; Yu, Zhu Liang; Li, Yuanqing

2015-03-01

Low energy consumption is crucial for body area networks (BANs). In BAN-enabled ECG monitoring, the continuous monitoring entails the need of the sensor nodes to transmit a huge data to the sink node, which leads to excessive energy consumption. To reduce airtime over energy-hungry wireless links, this paper presents an energy-efficient compressed sensing (CS)-based approach for on-node ECG compression. At first, an algorithm called minimal mutual coherence pursuit is proposed to construct sparse binary measurement matrices, which can be used to encode the ECG signals with superior performance and extremely low complexity. Second, in order to minimize the data rate required for faithful reconstruction, a weighted ℓ1 minimization model is derived by exploring the multisource prior knowledge in wavelet domain. Experimental results on MIT-BIH arrhythmia database reveals that the proposed approach can obtain higher compression ratio than the state-of-the-art CS-based methods. Together with its low encoding complexity, our approach can achieve significant energy saving in both encoding process and wireless transmission.

Compressed sensing of ECG signal for wireless system with new fast iterative method.

PubMed

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. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing

NASA Astrophysics Data System (ADS)

Shao, Haidong; Jiang, Hongkai; Zhang, Haizhou; Duan, Wenjing; Liang, Tianchen; Wu, Shuaipeng

2018-02-01

The vibration signals collected from rolling bearing are usually complex and non-stationary with heavy background noise. Therefore, it is a great challenge to efficiently learn the representative fault features of the collected vibration signals. In this paper, a novel method called improved convolutional deep belief network (CDBN) with compressed sensing (CS) is developed for feature learning and fault diagnosis of rolling bearing. Firstly, CS is adopted for reducing the vibration data amount to improve analysis efficiency. Secondly, a new CDBN model is constructed with Gaussian visible units to enhance the feature learning ability for the compressed data. Finally, exponential moving average (EMA) technique is employed to improve the generalization performance of the constructed deep model. The developed method is applied to analyze the experimental rolling bearing vibration signals. The results confirm that the developed method is more effective than the traditional methods.

Miniature Compressive Ultra-spectral Imaging System Utilizing a Single Liquid Crystal Phase Retarder

NASA Astrophysics Data System (ADS)

August, Isaac; Oiknine, Yaniv; Abuleil, Marwan; Abdulhalim, Ibrahim; Stern, Adrian

2016-03-01

Spectroscopic imaging has been proved to be an effective tool for many applications in a variety of fields, such as biology, medicine, agriculture, remote sensing and industrial process inspection. However, due to the demand for high spectral and spatial resolution it became extremely challenging to design and implement such systems in a miniaturized and cost effective manner. Using a Compressive Sensing (CS) setup based on a single variable Liquid Crystal (LC) retarder and a sensor array, we present an innovative Miniature Ultra-Spectral Imaging (MUSI) system. The LC retarder acts as a compact wide band spectral modulator. Within the framework of CS, a sequence of spectrally modulated images is used to recover ultra-spectral image cubes. Using the presented compressive MUSI system, we demonstrate the reconstruction of gigapixel spatio-spectral image cubes from spectral scanning shots numbering an order of magnitude less than would be required using conventional systems.

Method and algorithm for efficient calibration of compressive hyperspectral imaging system based on a liquid crystal retarder

NASA Astrophysics Data System (ADS)

Shecter, Liat; Oiknine, Yaniv; August, Isaac; Stern, Adrian

2017-09-01

Recently we presented a Compressive Sensing Miniature Ultra-spectral Imaging System (CS-MUSI)1 . This system consists of a single Liquid Crystal (LC) phase retarder as a spectral modulator and a gray scale sensor array to capture a multiplexed signal of the imaged scene. By designing the LC spectral modulator in compliance with the Compressive Sensing (CS) guidelines and applying appropriate algorithms we demonstrated reconstruction of spectral (hyper/ ultra) datacubes from an order of magnitude fewer samples than taken by conventional sensors. The LC modulator is designed to have an effective width of a few tens of micrometers, therefore it is prone to imperfections and spatial nonuniformity. In this work, we present the study of this nonuniformity and present a mathematical algorithm that allows the inference of the spectral transmission over the entire cell area from only a few calibration measurements.

Data-Driven Sampling Matrix Boolean Optimization for Energy-Efficient Biomedical Signal Acquisition by Compressive Sensing.

PubMed

Wang, Yuhao; Li, Xin; Xu, Kai; Ren, Fengbo; Yu, Hao

2017-04-01

Compressive sensing is widely used in biomedical applications, and the sampling matrix plays a critical role on both quality and power consumption of signal acquisition. It projects a high-dimensional vector of data into a low-dimensional subspace by matrix-vector multiplication. An optimal sampling matrix can ensure accurate data reconstruction and/or high compression ratio. Most existing optimization methods can only produce real-valued embedding matrices that result in large energy consumption during data acquisition. In this paper, we propose an efficient method that finds an optimal Boolean sampling matrix in order to reduce the energy consumption. Compared to random Boolean embedding, our data-driven Boolean sampling matrix can improve the image recovery quality by 9 dB. Moreover, in terms of sampling hardware complexity, it reduces the energy consumption by 4.6× and the silicon area by 1.9× over the data-driven real-valued embedding.

Phase reconstruction using compressive two-step parallel phase-shifting digital holography

NASA Astrophysics Data System (ADS)

Ramachandran, Prakash; Alex, Zachariah C.; Nelleri, Anith

2018-04-01

The linear relationship between the sample complex object wave and its approximated complex Fresnel field obtained using single shot parallel phase-shifting digital holograms (PPSDH) is used in compressive sensing framework and an accurate phase reconstruction is demonstrated. It is shown that the accuracy of phase reconstruction of this method is better than that of compressive sensing adapted single exposure inline holography (SEOL) method. It is derived that the measurement model of PPSDH method retains both the real and imaginary parts of the Fresnel field but with an approximation noise and the measurement model of SEOL retains only the real part exactly equal to the real part of the complex Fresnel field and its imaginary part is completely not available. Numerical simulation is performed for CS adapted PPSDH and CS adapted SEOL and it is demonstrated that the phase reconstruction is accurate for CS adapted PPSDH and can be used for single shot digital holographic reconstruction.

Evaluation of the effect of custom burr holes on a surgeon's sense of screw fixation in revision porous metal cups.

PubMed

Nyland, Mark A; Lanting, Brent A; Nikolov, Hristo N; Somerville, Lyndsay E; Teeter, Matthew G; Howard, James L

2016-12-01

It is common practice to burr custom holes in revision porous metal cups for screw insertion. The objective of this study was to determine how different hole types affect a surgeon's sense of screw fixation. Porous revision cups were prepared with pre-drilled and custom burred holes. Cups were held in place adjacent to synthetic bone material of varying density. Surgeons inserted screws through the different holes and materials. Surgeon subjective rating, compression, and torque was recorded. The torque achieved was greater ( p  = 0.002) for screws through custom holes than pre-fabricated holes in low and medium density material, with no difference for high density. Peak compression was greater ( p  = 0.026) through the pre-fabricated holes only in high density material. Use of burred holes affects the torque generated, and may decrease the amount of cup-acetabulum compression achieved.

Miniature Compressive Ultra-spectral Imaging System Utilizing a Single Liquid Crystal Phase Retarder.

PubMed

August, Isaac; Oiknine, Yaniv; AbuLeil, Marwan; Abdulhalim, Ibrahim; Stern, Adrian

2016-03-23

Spectroscopic imaging has been proved to be an effective tool for many applications in a variety of fields, such as biology, medicine, agriculture, remote sensing and industrial process inspection. However, due to the demand for high spectral and spatial resolution it became extremely challenging to design and implement such systems in a miniaturized and cost effective manner. Using a Compressive Sensing (CS) setup based on a single variable Liquid Crystal (LC) retarder and a sensor array, we present an innovative Miniature Ultra-Spectral Imaging (MUSI) system. The LC retarder acts as a compact wide band spectral modulator. Within the framework of CS, a sequence of spectrally modulated images is used to recover ultra-spectral image cubes. Using the presented compressive MUSI system, we demonstrate the reconstruction of gigapixel spatio-spectral image cubes from spectral scanning shots numbering an order of magnitude less than would be required using conventional systems.

Deterministic Compressed Sensing

DTIC Science & Technology

2011-11-01

of the algorithm can be derived by using the Bregman divergence based on the Kullback - Leibler function, and an additive update...regularized goodness - of - fit objective function. In contrast to many CS approaches, however, we measure the fit of an esti- mate to the data using the...sensing is information theoretically possible using any (2k, )-RIP sensing matrix . The following celebrated results of Candès, Romberg and Tao

Photon-limited Sensing and Surveillance

DTIC Science & Technology

2015-01-29

considerable time delay). More specifically, there were four main outcomes from this work: • Improved understanding of the fundmental limitations of...that we design novel cameras for photon-limited settings based on the principles of CS. Most prior theoretical results in compressed sensing and related...inverse problems apply to idealized settings where the noise is i.i.d., and do not account for signal-dependent noise and physical sensing

DMD-based implementation of patterned optical filter arrays for compressive spectral imaging.

PubMed

Rueda, Hoover; Arguello, Henry; Arce, Gonzalo R

2015-01-01

Compressive spectral imaging (CSI) captures multispectral imagery using fewer measurements than those required by traditional Shannon-Nyquist theory-based sensing procedures. CSI systems acquire coded and dispersed random projections of the scene rather than direct measurements of the voxels. To date, the coding procedure in CSI has been realized through the use of block-unblock coded apertures (CAs), commonly implemented as chrome-on-quartz photomasks. These apertures block or permit us to pass the entire spectrum from the scene at given spatial locations, thus modulating the spatial characteristics of the scene. This paper extends the framework of CSI by replacing the traditional block-unblock photomasks by patterned optical filter arrays, referred to as colored coded apertures (CCAs). These, in turn, allow the source to be modulated not only spatially but spectrally as well, entailing more powerful coding strategies. The proposed CCAs are synthesized through linear combinations of low-pass, high-pass, and bandpass filters, paired with binary pattern ensembles realized by a digital micromirror device. The optical forward model of the proposed CSI architecture is presented along with a proof-of-concept implementation, which achieves noticeable improvements in the quality of the reconstruction.

DLA based compressed sensing for high resolution MR microscopy of neuronal tissue.

PubMed

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. Copyright © 2015 Elsevier Inc. All rights reserved.

Single image super-resolution based on compressive sensing and improved TV minimization sparse recovery

NASA Astrophysics Data System (ADS)

Vishnukumar, S.; Wilscy, M.

2017-12-01

In this paper, we propose a single image Super-Resolution (SR) method based on Compressive Sensing (CS) and Improved Total Variation (TV) Minimization Sparse Recovery. In the CS framework, low-resolution (LR) image is treated as the compressed version of high-resolution (HR) image. Dictionary Training and Sparse Recovery are the two phases of the method. K-Singular Value Decomposition (K-SVD) method is used for dictionary training and the dictionary represents HR image patches in a sparse manner. Here, only the interpolated version of the LR image is used for training purpose and thereby the structural self similarity inherent in the LR image is exploited. In the sparse recovery phase the sparse representation coefficients with respect to the trained dictionary for LR image patches are derived using Improved TV Minimization method. HR image can be reconstructed by the linear combination of the dictionary and the sparse coefficients. The experimental results show that the proposed method gives better results quantitatively as well as qualitatively on both natural and remote sensing images. The reconstructed images have better visual quality since edges and other sharp details are preserved.

Electrical percolation threshold of cementitious composites possessing self-sensing functionality incorporating different carbon-based materials

NASA Astrophysics Data System (ADS)

Al-Dahawi, Ali; Haroon Sarwary, Mohammad; Öztürk, Oğuzhan; Yıldırım, Gürkan; Akın, Arife; Şahmaran, Mustafa; Lachemi, Mohamed

2016-10-01

An experimental study was carried out to understand the electrical percolation thresholds of different carbon-based nano- and micro-scale materials in cementitious composites. Multi-walled carbon nanotubes (CNTs), graphene nanoplatelets (GNPs) and carbon black (CB) were selected as the nano-scale materials, while 6 and 12 mm long carbon fibers (CF6 and CF12) were used as the micro-scale carbon-based materials. After determining the percolation thresholds of different electrical conductive materials, mechanical properties and piezoresistive properties of specimens produced with the abovementioned conductive materials at percolation threshold were investigated under uniaxial compressive loading. Results demonstrate that regardless of initial curing age, the percolation thresholds of CNT, GNP, CB and CFs in ECC mortar specimens were around 0.55%, 2.00%, 2.00% and 1.00%, respectively. Including different carbon-based conductive materials did not harm compressive strength results; on the contrary, it improved overall values. All cementitious composites produced with carbon-based materials, with the exception of the control mixtures, exhibited piezoresistive behavior under compression, which is crucial for sensing capability. It is believed that incorporating the sensing attribute into cementitious composites will enhance benefits for sustainable civil infrastructures.

A new optimization method using a compressed sensing inspired solver for real-time LDR-brachytherapy treatment planning

NASA Astrophysics Data System (ADS)

Guthier, C.; Aschenbrenner, K. P.; Buergy, D.; Ehmann, M.; Wenz, F.; Hesser, J. W.

2015-03-01

This work discusses a novel strategy for inverse planning in low dose rate brachytherapy. It applies the idea of compressed sensing to the problem of inverse treatment planning and a new solver for this formulation is developed. An inverse planning algorithm was developed incorporating brachytherapy dose calculation methods as recommended by AAPM TG-43. For optimization of the functional a new variant of a matching pursuit type solver is presented. The results are compared with current state-of-the-art inverse treatment planning algorithms by means of real prostate cancer patient data. The novel strategy outperforms the best state-of-the-art methods in speed, while achieving comparable quality. It is able to find solutions with comparable values for the objective function and it achieves these results within a few microseconds, being up to 542 times faster than competing state-of-the-art strategies, allowing real-time treatment planning. The sparse solution of inverse brachytherapy planning achieved with methods from compressed sensing is a new paradigm for optimization in medical physics. Through the sparsity of required needles and seeds identified by this method, the cost of intervention may be reduced.

A new optimization method using a compressed sensing inspired solver for real-time LDR-brachytherapy treatment planning.

PubMed

Guthier, C; Aschenbrenner, K P; Buergy, D; Ehmann, M; Wenz, F; Hesser, J W

2015-03-21

This work discusses a novel strategy for inverse planning in low dose rate brachytherapy. It applies the idea of compressed sensing to the problem of inverse treatment planning and a new solver for this formulation is developed. An inverse planning algorithm was developed incorporating brachytherapy dose calculation methods as recommended by AAPM TG-43. For optimization of the functional a new variant of a matching pursuit type solver is presented. The results are compared with current state-of-the-art inverse treatment planning algorithms by means of real prostate cancer patient data. The novel strategy outperforms the best state-of-the-art methods in speed, while achieving comparable quality. It is able to find solutions with comparable values for the objective function and it achieves these results within a few microseconds, being up to 542 times faster than competing state-of-the-art strategies, allowing real-time treatment planning. The sparse solution of inverse brachytherapy planning achieved with methods from compressed sensing is a new paradigm for optimization in medical physics. Through the sparsity of required needles and seeds identified by this method, the cost of intervention may be reduced.

Infrared super-resolution imaging based on compressed sensing

NASA Astrophysics Data System (ADS)

Sui, Xiubao; Chen, Qian; Gu, Guohua; Shen, Xuewei

2014-03-01

The theoretical basis of traditional infrared super-resolution imaging method is Nyquist sampling theorem. The reconstruction premise is that the relative positions of the infrared objects in the low-resolution image sequences should keep fixed and the image restoration means is the inverse operation of ill-posed issues without fixed rules. The super-resolution reconstruction ability of the infrared image, algorithm's application area and stability of reconstruction algorithm are limited. To this end, we proposed super-resolution reconstruction method based on compressed sensing in this paper. In the method, we selected Toeplitz matrix as the measurement matrix and realized it by phase mask method. We researched complementary matching pursuit algorithm and selected it as the recovery algorithm. In order to adapt to the moving target and decrease imaging time, we take use of area infrared focal plane array to acquire multiple measurements at one time. Theoretically, the method breaks though Nyquist sampling theorem and can greatly improve the spatial resolution of the infrared image. The last image contrast and experiment data indicate that our method is effective in improving resolution of infrared images and is superior than some traditional super-resolution imaging method. The compressed sensing super-resolution method is expected to have a wide application prospect.

Compressive sampling by artificial neural networks for video

NASA Astrophysics Data System (ADS)

Szu, Harold; Hsu, Charles; Jenkins, Jeffrey; Reinhardt, Kitt

2011-06-01

We describe a smart surveillance strategy for handling novelty changes. Current sensors seem to keep all, redundant or not. The Human Visual System's Hubel-Wiesel (wavelet) edge detection mechanism pays attention to changes in movement, which naturally produce organized sparseness because a stagnant edge is not reported to the brain's visual cortex by retinal neurons. Sparseness is defined as an ordered set of ones (movement or not) relative to zeros that could be pseudo-orthogonal among themselves; then suited for fault tolerant storage and retrieval by means of Associative Memory (AM). The firing is sparse at the change locations. Unlike purely random sparse masks adopted in medical Compressive Sensing, these organized ones have an additional benefit of using the image changes to make retrievable graphical indexes. We coined this organized sparseness as Compressive Sampling; sensing but skipping over redundancy without altering the original image. Thus, we turn illustrate with video the survival tactics which animals that roam the Earth use daily. They acquire nothing but the space-time changes that are important to satisfy specific prey-predator relationships. We have noticed a similarity between the mathematical Compressive Sensing and this biological mechanism used for survival. We have designed a hardware implementation of the Human Visual System's Compressive Sampling scheme. To speed up further, our mixedsignal circuit design of frame differencing is built in on-chip processing hardware. A CMOS trans-conductance amplifier is designed here to generate a linear current output using a pair of differential input voltages from 2 photon detectors for change detection---one for the previous value and the other the subsequent value, ("write" synaptic weight by Hebbian outer products; "read" by inner product & pt. NL threshold) to localize and track the threat targets.

Adaptive temporal compressive sensing for video with motion estimation

NASA Astrophysics Data System (ADS)

Wang, Yeru; Tang, Chaoying; Chen, Yueting; Feng, Huajun; Xu, Zhihai; Li, Qi

2018-04-01

In this paper, we present an adaptive reconstruction method for temporal compressive imaging with pixel-wise exposure. The motion of objects is first estimated from interpolated images with a designed coding mask. With the help of motion estimation, image blocks are classified according to the degree of motion and reconstructed with the corresponding dictionary, which was trained beforehand. Both the simulation and experiment results show that the proposed method can obtain accurate motion information before reconstruction and efficiently reconstruct compressive video.

Perfect gas effects in compressible rapid distortion theory

NASA Technical Reports Server (NTRS)

Kerschen, E. J.; Myers, M. R.

1987-01-01

The governing equations presented for small amplitude unsteady disturbances imposed on steady, compressible mean flows that are two-dimensional and nearly uniform have their basis in the perfect gas equations of state, and therefore generalize previous results based on tangent gas theory. While these equations are more complex, this complexity is required for adequate treatment of high frequency disturbances, especially when the base flow Mach number is large; under such circumstances, the simplifying assumptions of tangent gas theory are not applicable.

Three-wave interaction solitons in optical parametric amplification.

PubMed

Ibragimov, E; Struthers, A A; Kaup, D J; Khaydarov, J D; Singer, K D

1999-05-01

This paper applies three-wave interaction (TWI)-soliton theory to optical parametric amplification when the signal, idler, and pump wave can all contain TWI solitons. We use an analogy between two different velocity regimes to compare the theory with output from an experimental synchronously pumped optical parametric amplifier. The theory explains the observed inability to compress the intermediate group-velocity wave and 20-fold pulse compression in this experiment. The theory and supporting numerics show that one can effectively control the shape and energy of the optical pulses by shifting the TWI solitons in the pulses.

Bilinear Inverse Problems: Theory, Algorithms, and Applications

NASA Astrophysics Data System (ADS)

Ling, Shuyang

We will discuss how several important real-world signal processing problems, such as self-calibration and blind deconvolution, can be modeled as bilinear inverse problems and solved by convex and nonconvex optimization approaches. In Chapter 2, we bring together three seemingly unrelated concepts, self-calibration, compressive sensing and biconvex optimization. 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 y = DAx, where the diagonal matrix D (which models the calibration error) is unknown and x is an unknown sparse signal. By "lifting" this biconvex inverse problem and exploiting sparsity in this model, we derive explicit theoretical guarantees under which both x and D can be recovered exactly, robustly, and numerically efficiently. In Chapter 3, we study the question of the joint blind deconvolution and blind demixing, i.e., extracting a sequence of functions [special characters omitted] from observing only the sum of their convolutions [special characters omitted]. In particular, for the special case s = 1, it becomes the well-known blind deconvolution problem. We present a non-convex algorithm which guarantees exact recovery under conditions that are competitive with convex optimization methods, with the additional advantage of being computationally much more efficient. We discuss several applications of the proposed framework in image processing and wireless communications in connection with the Internet-of-Things. In Chapter 4, we consider three different self-calibration models of practical relevance. We show how their corresponding bilinear inverse problems can be solved by both the simple linear least squares approach and the SVD-based approach. As a consequence, the proposed algorithms are numerically extremely efficient, thus allowing for real-time deployment. Explicit theoretical guarantees and stability theory are derived and the number of sampling complexity is nearly optimal (up to a poly-log factor). Applications in imaging sciences and signal processing are discussed and numerical simulations are presented to demonstrate the effectiveness and efficiency of our approach.

The application of compressed sensing to long-term acoustic emission-based structural health monitoring

NASA Astrophysics Data System (ADS)

Cattaneo, Alessandro; Park, Gyuhae; Farrar, Charles; Mascareñas, David

2012-04-01

The acoustic emission (AE) phenomena generated by a rapid release in the internal stress of a material represent a promising technique for structural health monitoring (SHM) applications. AE events typically result in a discrete number of short-time, transient signals. The challenge associated with capturing these events using classical techniques is that very high sampling rates must be used over extended periods of time. The result is that a very large amount of data is collected to capture a phenomenon that rarely occurs. Furthermore, the high energy consumption associated with the required high sampling rates makes the implementation of high-endurance, low-power, embedded AE sensor nodes difficult to achieve. The relatively rare occurrence of AE events over long time scales implies that these measurements are inherently sparse in the spike domain. The sparse nature of AE measurements makes them an attractive candidate for the application of compressed sampling techniques. Collecting compressed measurements of sparse AE signals will relax the requirements on the sampling rate and memory demands. The focus of this work is to investigate the suitability of compressed sensing techniques for AE-based SHM. The work explores estimating AE signal statistics in the compressed domain for low-power classification applications. In the event compressed classification finds an event of interest, ι1 norm minimization will be used to reconstruct the measurement for further analysis. The impact of structured noise on compressive measurements is specifically addressed. The suitability of a particular algorithm, called Justice Pursuit, to increase robustness to a small amount of arbitrary measurement corruption is investigated.

The development of machine technology processing for earth resource survey

NASA Technical Reports Server (NTRS)

Landgrebe, D. A.

1970-01-01

The following technologies are considered for automatic processing of earth resources data: (1) registration of multispectral and multitemporal images, (2) digital image display systems, (3) data system parameter effects on satellite remote sensing systems, and (4) data compression techniques based on spectral redundancy. The importance of proper spectral band and compression algorithm selections is pointed out.

Compressed NMR: Combining compressive sampling and pure shift NMR techniques.

PubMed

Aguilar, Juan A; Kenwright, Alan M

2017-12-26

Historically, the resolution of multidimensional nuclear magnetic resonance (NMR) has been orders of magnitude lower than the intrinsic resolution that NMR spectrometers are capable of producing. The slowness of Nyquist sampling as well as the existence of signals as multiplets instead of singlets have been two of the main reasons for this underperformance. Fortunately, two compressive techniques have appeared that can overcome these limitations. Compressive sensing, also known as compressed sampling (CS), avoids the first limitation by exploiting the compressibility of typical NMR spectra, thus allowing sampling at sub-Nyquist rates, and pure shift techniques eliminate the second issue "compressing" multiplets into singlets. This paper explores the possibilities and challenges presented by this combination (compressed NMR). First, a description of the CS framework is given, followed by a description of the importance of combining it with the right pure shift experiment. Second, examples of compressed NMR spectra and how they can be combined with covariance methods will be shown. Copyright © 2017 John Wiley & Sons, Ltd.

Sense of coherence and mental health rehabilitation.

PubMed

Griffiths, Christopher Alan

2009-01-01

To provide an understanding of Antonovsky's sense of coherence theory in relation to rehabilitation and to explain how applying his theory in rehabilitation practice can be beneficial. The focus of this paper will be on the rehabilitation and recovery of those with mental health issues. Sense of coherence theory helps explain the process of recovery for those with mental health issues. There is substantial evidence that sense of coherence plays a central role in coping with stressors in the rehabilitation/recovery process and that it contributes to mental health and psychosocial functioning. If rehabilitation services adopt a salutogenic approach and seek to enhance a client's sense of coherence then this can be beneficial in terms of the client's rehabilitation and recovery. Rehabilitation services should ensure that they have rehabilitation goals that strengthen individuals' sense of coherence. Further investigation is required into the development of rehabilitation programmes with sense of coherence theory as part of their foundation.

Electrodes for solid state gas sensor

DOEpatents

Mukundan, Rangachary [Santa Fe, NM; Brosha, Eric L [Los Alamos, NM; Garzon, Fernando [Santa Fe, NM

2007-05-08

A mixed potential electrochemical sensor for the detection of gases has a ceria-based electrolyte with a surface for exposing to the gases to be detected, and with a reference wire electrode and a sensing wire electrode extending through the surface and fixed within the electrolyte as the electrolyte is compressed and sintered. The electrochemical sensor is formed by placing a wire reference electrode and a wire sensing electrode in a die, where each electrode has a first compressed planar section and a second section depending from the first section with the second section of each electrode extending axially within the die. The die is filled with an oxide-electrolyte powder and the powder is pressed within the die with the wire electrodes. The wire-electrodes and the pressed oxide-electrolyte powder are sintered to form a ceramic electrolyte base with a reference wire electrode and a sensing wire electrode depending therefrom.

Electrodes for solid state gas sensor

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mukundan, Rangachary; Brosha, Eric L; Garzon, Fernando

2007-05-08

A mixed potential electrochemical sensor for the detection of gases has a ceria-based electrolyte with a surface for exposing to the gases to be detected, and with a reference wire electrode and a sensing wire electrode extending through the surface and fixed within the electrolyte as the electrolyte is compressed and sintered. The electrochemical sensor is formed by placing a wire reference electrode and a wire sensing electrode in a die, where each electrode has a first compressed planar section and a second section depending from the first section with the second section of each electrode extending axially within themore » die. The die is filled with an oxide-electrolyte powder and the powder is pressed within the die with the wire electrodes. The wire-electrodes and the pressed oxide-electrolyte powder are sintered to form a ceramic electrolyte base with a reference wire electrode and a sensing wire electrode depending therefrom.« less

Electrodes for solid state gas sensor

DOEpatents

Mukundan, Rangachary; Brosha, Eric L.; Garzon, Fernando

2003-08-12

A mixed potential electrochemical sensor for the detection of gases has a ceria-based electrolyte with a surface for exposing to the gases to be detected, and with a reference wire electrode and a sensing wire electrode extending through the surface and fixed within the electrolyte as the electrolyte is compressed and sintered. The electrochemical sensor is formed by placing a wire reference electrode and a wire sensing electrode in a die, where each electrode has a first compressed planar section and a second section depending from the first section with the second section of each electrode extending axially within the die. The die is filled with an oxide-electrolyte powder and the powder is pressed within the die with the wire electrodes. The wire-electrodes and the pressed oxide-electrolyte powder are sintered to form a ceramic electrolyte base with a reference wire electrode and a sensing wire electrode depending therefrom.

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.

Review: Regional land subsidence accompanying groundwater extraction

USGS Publications Warehouse

Galloway, Devin L.; Burbey, Thomas J.

2011-01-01

The extraction of groundwater can generate land subsidence by causing the compaction of susceptible aquifer systems, typically unconsolidated alluvial or basin-fill aquifer systems comprising aquifers and aquitards. Various ground-based and remotely sensed methods are used to measure and map subsidence. Many areas of subsidence caused by groundwater pumping have been identified and monitored, and corrective measures to slow or halt subsidence have been devised. Two principal means are used to mitigate subsidence caused by groundwater withdrawal—reduction of groundwater withdrawal, and artificial recharge. Analysis and simulation of aquifer-system compaction follow from the basic relations between head, stress, compressibility, and groundwater flow and are addressed primarily using two approaches—one based on conventional groundwater flow theory and one based on linear poroelasticity theory. Research and development to improve the assessment and analysis of aquifer-system compaction, the accompanying subsidence and potential ground ruptures are needed in the topic areas of the hydromechanical behavior of aquitards, the role of horizontal deformation, the application of differential synthetic aperture radar interferometry, and the regional-scale simulation of coupled groundwater flow and aquifer-system deformation to support resource management and hazard mitigation measures.

Adaptive Integration of the Compressed Algorithm of CS and NPC for the ECG Signal Compressed Algorithm in VLSI Implementation

PubMed Central

Tseng, Yun-Hua; Lu, Chih-Wen

2017-01-01

Compressed sensing (CS) is a promising approach to the compression and reconstruction of electrocardiogram (ECG) signals. It has been shown that following reconstruction, most of the changes between the original and reconstructed signals are distributed in the Q, R, and S waves (QRS) region. Furthermore, any increase in the compression ratio tends to increase the magnitude of the change. This paper presents a novel approach integrating the near-precise compressed (NPC) and CS algorithms. The simulation results presented notable improvements in signal-to-noise ratio (SNR) and compression ratio (CR). The efficacy of this approach was verified by fabricating a highly efficient low-cost chip using the Taiwan Semiconductor Manufacturing Company’s (TSMC) 0.18-μm Complementary Metal-Oxide-Semiconductor (CMOS) technology. The proposed core has an operating frequency of 60 MHz and gate counts of 2.69 K. PMID:28991216

Generalized massive optimal data compression

NASA Astrophysics Data System (ADS)

Alsing, Justin; Wandelt, Benjamin

2018-05-01

In this paper, we provide a general procedure for optimally compressing N data down to n summary statistics, where n is equal to the number of parameters of interest. We show that compression to the score function - the gradient of the log-likelihood with respect to the parameters - yields n compressed statistics that are optimal in the sense that they preserve the Fisher information content of the data. Our method generalizes earlier work on linear Karhunen-Loéve compression for Gaussian data whilst recovering both lossless linear compression and quadratic estimation as special cases when they are optimal. We give a unified treatment that also includes the general non-Gaussian case as long as mild regularity conditions are satisfied, producing optimal non-linear summary statistics when appropriate. As a worked example, we derive explicitly the n optimal compressed statistics for Gaussian data in the general case where both the mean and covariance depend on the parameters.

Pulse-compression ghost imaging lidar via coherent detection.

PubMed

Deng, Chenjin; Gong, Wenlin; Han, Shensheng

2016-11-14

Ghost imaging (GI) lidar, as a novel remote sensing technique, has been receiving increasing interest in recent years. By combining pulse-compression technique and coherent detection with GI, we propose a new lidar system called pulse-compression GI lidar. Our analytical results, which are backed up by numerical simulations, demonstrate that pulse-compression GI lidar can obtain the target's spatial intensity distribution, range and moving velocity. Compared with conventional pulsed GI lidar system, pulse-compression GI lidar, without decreasing the range resolution, is easy to obtain high single pulse energy with the use of a long pulse, and the mechanism of coherent detection can eliminate the influence of the stray light, which is helpful to improve the detection sensitivity and detection range.

Faster and less phototoxic 3D fluorescence microscopy using a versatile compressed sensing scheme

PubMed Central

Woringer, Maxime; Darzacq, Xavier; Zimmer, Christophe

2017-01-01

Three-dimensional fluorescence microscopy based on Nyquist sampling of focal planes faces harsh trade-offs between acquisition time, light exposure, and signal-to-noise. We propose a 3D compressed sensing approach that uses temporal modulation of the excitation intensity during axial stage sweeping and can be adapted to fluorescence microscopes without hardware modification. We describe implementations on a lattice light sheet microscope and an epifluorescence microscope, and show that images of beads and biological samples can be reconstructed with a 5-10 fold reduction of light exposure and acquisition time. Our scheme opens a new door towards faster and less damaging 3D fluorescence microscopy. PMID:28788909

A compressive sensing-based computational method for the inversion of wide-band ground penetrating radar data

NASA Astrophysics Data System (ADS)

Gelmini, A.; Gottardi, G.; Moriyama, T.

2017-10-01

This work presents an innovative computational approach for the inversion of wideband ground penetrating radar (GPR) data. The retrieval of the dielectric characteristics of sparse scatterers buried in a lossy soil is performed by combining a multi-task Bayesian compressive sensing (MT-BCS) solver and a frequency hopping (FH) strategy. The developed methodology is able to benefit from the regularization capabilities of the MT-BCS as well as to exploit the multi-chromatic informative content of GPR measurements. A set of numerical results is reported in order to assess the effectiveness of the proposed GPR inverse scattering technique, as well as to compare it to a simpler single-task implementation.

A novel secret sharing with two users based on joint transform correlator and compressive sensing

NASA Astrophysics Data System (ADS)

Zhao, Tieyu; Chi, Yingying

2018-05-01

Recently, joint transform correlator (JTC) has been widely applied to image encryption and authentication. This paper presents a novel secret sharing scheme with two users based on JTC. Two users must be present during the decryption that the system has high security and reliability. In the scheme, two users use their fingerprints to encrypt plaintext, and they can decrypt only if both of them provide the fingerprints which are successfully authenticated. The linear relationship between the plaintext and ciphertext is broken using the compressive sensing, which can resist existing attacks on JTC. The results of the theoretical analysis and numerical simulation confirm the validity of the system.

Mache: No-Loss Trace Compaction

DTIC Science & Technology

1988-09-15

Data Compression . IEEE Computer 176 (June 1984), 8-19. 10. ZIV , J. AND LEMPEL , A. A Universal Algorithm for Sequential Data Com- pression. IEEE... compression scheme which takes ad- vantage of repeating patterns in the sequence of bytes. I have used the Lempel - Ziv compression algorithm [9,10,11...Transactions on Information Theory 23 (1976), 75-81. 11. ZIV , J. AND LEMPEL , A. Compression of Individual Sequences via Variable-

Low-dose CT reconstruction via L1 dictionary learning regularization using iteratively reweighted least-squares.

PubMed

Zhang, Cheng; Zhang, Tao; Li, Ming; Peng, Chengtao; Liu, Zhaobang; Zheng, Jian

2016-06-18

In order to reduce the radiation dose of CT (computed tomography), compressed sensing theory has been a hot topic since it provides the possibility of a high quality recovery from the sparse sampling data. Recently, the algorithm based on DL (dictionary learning) was developed to deal with the sparse CT reconstruction problem. However, the existing DL algorithm focuses on the minimization problem with the L2-norm regularization term, which leads to reconstruction quality deteriorating while the sampling rate declines further. Therefore, it is essential to improve the DL method to meet the demand of more dose reduction. In this paper, we replaced the L2-norm regularization term with the L1-norm one. It is expected that the proposed L1-DL method could alleviate the over-smoothing effect of the L2-minimization and reserve more image details. The proposed algorithm solves the L1-minimization problem by a weighting strategy, solving the new weighted L2-minimization problem based on IRLS (iteratively reweighted least squares). Through the numerical simulation, the proposed algorithm is compared with the existing DL method (adaptive dictionary based statistical iterative reconstruction, ADSIR) and other two typical compressed sensing algorithms. It is revealed that the proposed algorithm is more accurate than the other algorithms especially when further reducing the sampling rate or increasing the noise. The proposed L1-DL algorithm can utilize more prior information of image sparsity than ADSIR. By transforming the L2-norm regularization term of ADSIR with the L1-norm one and solving the L1-minimization problem by IRLS strategy, L1-DL could reconstruct the image more exactly.

A comparison of spectral decorrelation techniques and performance evaluation metrics for a wavelet-based, multispectral data compression algorithm

NASA Technical Reports Server (NTRS)

Matic, Roy M.; Mosley, Judith I.

1994-01-01

Future space-based, remote sensing systems will have data transmission requirements that exceed available downlinks necessitating the use of lossy compression techniques for multispectral data. In this paper, we describe several algorithms for lossy compression of multispectral data which combine spectral decorrelation techniques with an adaptive, wavelet-based, image compression algorithm to exploit both spectral and spatial correlation. We compare the performance of several different spectral decorrelation techniques including wavelet transformation in the spectral dimension. The performance of each technique is evaluated at compression ratios ranging from 4:1 to 16:1. Performance measures used are visual examination, conventional distortion measures, and multispectral classification results. We also introduce a family of distortion metrics that are designed to quantify and predict the effect of compression artifacts on multi spectral classification of the reconstructed data.

Rapid distortion analysis and direct simulation of compressible homogeneous turbulence at finite Mach number

NASA Technical Reports Server (NTRS)

Cambon, C.; Coleman, G. N.; Mansour, N. N.

1992-01-01

The effect of rapid mean compression on compressible turbulence at a range of turbulent Mach numbers is investigated. Rapid distortion theory (RDT) and direct numerical simulation results for the case of axial (one-dimensional) compression are used to illustrate the existence of two distinct rapid compression regimes. These regimes are set by the relationships between the timescales of the mean distortion, the turbulence, and the speed of sound. A general RDT formulation is developed and is proposed as a means of improving turbulence models for compressible flows.

Tensor-based Dictionary Learning for Dynamic Tomographic Reconstruction

PubMed Central

Tan, Shengqi; Zhang, Yanbo; Wang, Ge; Mou, Xuanqin; Cao, Guohua; Wu, Zhifang; Yu, Hengyong

2015-01-01

In dynamic computed tomography (CT) reconstruction, the data acquisition speed limits the spatio-temporal resolution. Recently, compressed sensing theory has been instrumental in improving CT reconstruction from far few-view projections. In this paper, we present an adaptive method to train a tensor-based spatio-temporal dictionary for sparse representation of an image sequence during the reconstruction process. The correlations among atoms and across phases are considered to capture the characteristics of an object. The reconstruction problem is solved by the alternating direction method of multipliers. To recover fine or sharp structures such as edges, the nonlocal total variation is incorporated into the algorithmic framework. Preclinical examples including a sheep lung perfusion study and a dynamic mouse cardiac imaging demonstrate that the proposed approach outperforms the vectorized dictionary-based CT reconstruction in the case of few-view reconstruction. PMID:25779991

Carbon Nanofiber Cement Sensors to Detect Strain and Damage of Concrete Specimens Under Compression

PubMed Central

Baeza, F. Javier; Garcés, Pedro

2017-01-01

Cement composites with nano-additions have been vastly studied for their functional applications, such as strain and damage sensing. The capacity of a carbon nanofiber (CNF) cement paste has already been tested. However, this study is focused on the use of CNF cement composites as sensors in regular concrete samples. Different measuring techniques and humidity conditions of CNF samples were tested to optimize the strain and damage sensing of this material. In the strain sensing tests (for compressive stresses up to 10 MPa), the response depends on the maximum stress applied. The material was more sensitive at higher loads. Furthermore, the actual load time history did not influence the electrical response, and similar curves were obtained for different test configurations. On the other hand, damage sensing tests proved the capability of CNF cement composites to measure the strain level of concrete samples, even for loads close to the material’s strength. Some problems were detected in the strain transmission between sensor and concrete specimens, which will require specific calibration of each sensor one attached to the structure. PMID:29186797

An approach to improve the spatial resolution of a force mapping sensing system

NASA Astrophysics Data System (ADS)

Negri, Lucas Hermann; Manfron Schiefer, Elberth; Sade Paterno, Aleksander; Muller, Marcia; Luís Fabris, José

2016-02-01

This paper proposes a smart sensor system capable of detecting sparse forces applied to different positions of a metal plate. The sensing is performed with strain transducers based on fiber Bragg gratings (FBG) distributed under the plate. Forces actuating in nine squared regions of the plate, resulting from up to three different loads applied simultaneously to the plate, were monitored with seven transducers. The system determines the magnitude of the force/pressure applied on each specific area, even in the absence of a dedicated transducer for that area. The set of strain transducers with coupled responses and a compressive sensing algorithm are employed to solve the underdetermined inverse problem which emerges from mapping the force. In this configuration, experimental results have shown that the system is capable of recovering the value of the load distributed on the plate with a signal-to-noise ratio better than 12 dB, when the plate is submitted to three simultaneous test loads. The proposed method is a practical illustration of compressive sensing algorithms for the reduction of the number of FBG-based transducers used in a quasi-distributed configuration.

Carbon Nanofiber Cement Sensors to Detect Strain and Damage of Concrete Specimens Under Compression.

PubMed

Galao, Oscar; Baeza, F Javier; Zornoza, Emilio; Garcés, Pedro

2017-11-24

Cement composites with nano-additions have been vastly studied for their functional applications, such as strain and damage sensing. The capacity of a carbon nanofiber (CNF) cement paste has already been tested. However, this study is focused on the use of CNF cement composites as sensors in regular concrete samples. Different measuring techniques and humidity conditions of CNF samples were tested to optimize the strain and damage sensing of this material. In the strain sensing tests (for compressive stresses up to 10 MPa), the response depends on the maximum stress applied. The material was more sensitive at higher loads. Furthermore, the actual load time history did not influence the electrical response, and similar curves were obtained for different test configurations. On the other hand, damage sensing tests proved the capability of CNF cement composites to measure the strain level of concrete samples, even for loads close to the material's strength. Some problems were detected in the strain transmission between sensor and concrete specimens, which will require specific calibration of each sensor one attached to the structure.

Energy Analysis of Decoders for Rakeness-Based Compressed Sensing of ECG Signals.

PubMed

Pareschi, Fabio; Mangia, Mauro; Bortolotti, Daniele; Bartolini, Andrea; Benini, Luca; Rovatti, Riccardo; Setti, Gianluca

2017-12-01

In recent years, compressed sensing (CS) has proved to be effective in lowering the power consumption of sensing nodes in biomedical signal processing devices. This is due to the fact the CS is capable of reducing the amount of data to be transmitted to ensure correct reconstruction of the acquired waveforms. Rakeness-based CS has been introduced to further reduce the amount of transmitted data by exploiting the uneven distribution to the sensed signal energy. Yet, so far no thorough analysis exists on the impact of its adoption on CS decoder performance. The latter point is of great importance, since body-area sensor network architectures may include intermediate gateway nodes that receive and reconstruct signals to provide local services before relaying data to a remote server. In this paper, we fill this gap by showing that rakeness-based design also improves reconstruction performance. We quantify these findings in the case of ECG signals and when a variety of reconstruction algorithms are used either in a low-power microcontroller or a heterogeneous mobile computing platform.

Second-order subsonic airfoil theory including edge effects

NASA Technical Reports Server (NTRS)

Van Dyke, Milton D

1956-01-01

Several recent advances in plane subsonic flow theory are combined into a unified second-order theory for airfoil sections of arbitrary shape. The solution is reached in three steps: the incompressible result is found by integration, it is converted into the corresponding subsonic compressible result by means of the second-order compressibility rule, and it is rendered uniformly valid near stagnation points by further rules. Solutions for a number of airfoils are given and are compared with the results of other theories and of experiment. A straight-forward computing scheme is outlined for calculating the surface velocities and pressures on any airfoil at any angle of attack

Testing of motor unit synchronization model for localized muscle fatigue.

PubMed

Naik, Ganesh R; Kumar, Dinesh K; Yadav, Vivek; Wheeler, Katherine; Arjunan, Sridhar

2009-01-01

Spectral compression of surface electromyogram (sEMG) is associated with onset of localized muscle fatigue. The spectral compression has been explained based on motor unit synchronization theory. According to this theory, motor units are pseudo randomly excited during muscle contraction, and with the onset of muscle fatigue the recruitment pattern changes such that motor unit firings become more synchronized. While this is widely accepted, there is little experimental proof of this phenomenon. This paper has used source dependence measures developed in research related to independent component analysis (ICA) to test this theory.

A Digital Compressed Sensing-Based Energy-Efficient Single-Spot Bluetooth ECG Node

PubMed Central

Cai, Zhipeng; Zou, Fumin; Zhang, Xiangyu

2018-01-01

Energy efficiency is still the obstacle for long-term real-time wireless ECG monitoring. In this paper, a digital compressed sensing- (CS-) based single-spot Bluetooth ECG node is proposed to deal with the challenge in wireless ECG application. A periodic sleep/wake-up scheme and a CS-based compression algorithm are implemented in a node, which consists of ultra-low-power analog front-end, microcontroller, Bluetooth 4.0 communication module, and so forth. The efficiency improvement and the node's specifics are evidenced by the experiments using the ECG signals sampled by the proposed node under daily activities of lay, sit, stand, walk, and run. Under using sparse binary matrix (SBM), block sparse Bayesian learning (BSBL) method, and discrete cosine transform (DCT) basis, all ECG signals were essentially undistorted recovered with root-mean-square differences (PRDs) which are less than 6%. The proposed sleep/wake-up scheme and data compression can reduce the airtime over energy-hungry wireless links, the energy consumption of proposed node is 6.53 mJ, and the energy consumption of radio decreases 77.37%. Moreover, the energy consumption increase caused by CS code execution is negligible, which is 1.3% of the total energy consumption. PMID:29599945

A Digital Compressed Sensing-Based Energy-Efficient Single-Spot Bluetooth ECG Node.

PubMed

Luo, Kan; Cai, Zhipeng; Du, Keqin; Zou, Fumin; Zhang, Xiangyu; Li, Jianqing

2018-01-01

Energy efficiency is still the obstacle for long-term real-time wireless ECG monitoring. In this paper, a digital compressed sensing- (CS-) based single-spot Bluetooth ECG node is proposed to deal with the challenge in wireless ECG application. A periodic sleep/wake-up scheme and a CS-based compression algorithm are implemented in a node, which consists of ultra-low-power analog front-end, microcontroller, Bluetooth 4.0 communication module, and so forth. The efficiency improvement and the node's specifics are evidenced by the experiments using the ECG signals sampled by the proposed node under daily activities of lay, sit, stand, walk, and run. Under using sparse binary matrix (SBM), block sparse Bayesian learning (BSBL) method, and discrete cosine transform (DCT) basis, all ECG signals were essentially undistorted recovered with root-mean-square differences (PRDs) which are less than 6%. The proposed sleep/wake-up scheme and data compression can reduce the airtime over energy-hungry wireless links, the energy consumption of proposed node is 6.53 mJ, and the energy consumption of radio decreases 77.37%. Moreover, the energy consumption increase caused by CS code execution is negligible, which is 1.3% of the total energy consumption.

COGNITRON THEORY,

DTIC Science & Technology

ARTIFICIAL INTELLIGENCE , THEORY), NERVE CELLS, SIMULATION, SENSE ORGANS, SENSES(PHYSIOLOGY), CONDITIONED RESPONSE, MATRICES(MATHEMATICS), MAPPING (TRANSFORMATIONS), MATHEMATICAL MODELS, FEEDBACK, BIONICS

Comparative study of lossy and lossless data compression in distributed optical fiber sensing systems

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.

Effects of transverse shear deformation on buckling of laminated cylinders as a function of thickness and ply orientation

NASA Technical Reports Server (NTRS)

Jegley, Dawn C.

1987-01-01

Buckling loads of thick-walled, orthotropic, simply-supported right circular cylinders are predicted using a new higher-order transverse shear deformation theory. The higher-order theory shows that, by more accurately accounting for transverse shear deformation effects, the predicted buckling load may be reduced by as much as 80 percent compared to predictions based on conventional transverse shear deformation theory. A parametric study of the effect of ply orientation on the buckling load of axially compressed cylinders indicates that laminates containing 0 deg plies are the most sensitive to transverse shear deformation effects. Interaction curves for buckling of cylinders with axial compressive and external pressure loadings indicate that buckling loads due to external pressure loadings are much less sensitive to transverse shear deformation effects than those due to axial compressive loadings.

Securing image information using double random phase encoding and parallel compressive sensing with updated sampling processes

NASA Astrophysics Data System (ADS)

Hu, Guiqiang; Xiao, Di; Wang, Yong; Xiang, Tao; Zhou, Qing

2017-11-01

Recently, a new kind of image encryption approach using compressive sensing (CS) and double random phase encoding has received much attention due to the advantages such as compressibility and robustness. However, this approach is found to be vulnerable to chosen plaintext attack (CPA) if the CS measurement matrix is re-used. Therefore, designing an efficient measurement matrix updating mechanism that ensures resistance to CPA is of practical significance. In this paper, we provide a novel solution to update the CS measurement matrix by altering the secret sparse basis with the help of counter mode operation. Particularly, the secret sparse basis is implemented by a reality-preserving fractional cosine transform matrix. Compared with the conventional CS-based cryptosystem that totally generates all the random entries of measurement matrix, our scheme owns efficiency superiority while guaranteeing resistance to CPA. Experimental and analysis results show that the proposed scheme has a good security performance and has robustness against noise and occlusion.

Evaluation of computational endomicroscopy architectures for minimally-invasive optical biopsy

NASA Astrophysics Data System (ADS)

Dumas, John P.; Lodhi, Muhammad A.; Bajwa, Waheed U.; Pierce, Mark C.

2017-02-01

We are investigating compressive sensing architectures for applications in endomicroscopy, where the narrow diameter probes required for tissue access can limit the achievable spatial resolution. We hypothesize that the compressive sensing framework can be used to overcome the fundamental pixel number limitation in fiber-bundle based endomicroscopy by reconstructing images with more resolvable points than fibers in the bundle. An experimental test platform was assembled to evaluate and compare two candidate architectures, based on introducing a coded amplitude mask at either a conjugate image or Fourier plane within the optical system. The benchtop platform consists of a common illumination and object path followed by separate imaging arms for each compressive architecture. The imaging arms contain a digital micromirror device (DMD) as a reprogrammable mask, with a CCD camera for image acquisition. One arm has the DMD positioned at a conjugate image plane ("IP arm"), while the other arm has the DMD positioned at a Fourier plane ("FP arm"). Lenses were selected and positioned within each arm to achieve an element-to-pixel ratio of 16 (230,400 mask elements mapped onto 14,400 camera pixels). We discuss our mathematical model for each system arm and outline the importance of accounting for system non-idealities. Reconstruction of a 1951 USAF resolution target using optimization-based compressive sensing algorithms produced images with higher spatial resolution than bicubic interpolation for both system arms when system non-idealities are included in the model. Furthermore, images generated with image plane coding appear to exhibit higher spatial resolution, but more noise, than images acquired through Fourier plane coding.

An infrared-visible image fusion scheme based on NSCT and compressed sensing

NASA Astrophysics Data System (ADS)

Zhang, Qiong; Maldague, Xavier

2015-05-01

Image fusion, as a research hot point nowadays in the field of infrared computer vision, has been developed utilizing different varieties of methods. Traditional image fusion algorithms are inclined to bring problems, such as data storage shortage and computational complexity increase, etc. Compressed sensing (CS) uses sparse sampling without knowing the priori knowledge and greatly reconstructs the image, which reduces the cost and complexity of image processing. In this paper, an advanced compressed sensing image fusion algorithm based on non-subsampled contourlet transform (NSCT) is proposed. NSCT provides better sparsity than the wavelet transform in image representation. Throughout the NSCT decomposition, the low-frequency and high-frequency coefficients can be obtained respectively. For the fusion processing of low-frequency coefficients of infrared and visible images , the adaptive regional energy weighting rule is utilized. Thus only the high-frequency coefficients are specially measured. Here we use sparse representation and random projection to obtain the required values of high-frequency coefficients, afterwards, the coefficients of each image block can be fused via the absolute maximum selection rule and/or the regional standard deviation rule. In the reconstruction of the compressive sampling results, a gradient-based iterative algorithm and the total variation (TV) method are employed to recover the high-frequency coefficients. Eventually, the fused image is recovered by inverse NSCT. Both the visual effects and the numerical computation results after experiments indicate that the presented approach achieves much higher quality of image fusion, accelerates the calculations, enhances various targets and extracts more useful information.

A stretchable strain sensor based on a metal nanoparticle thin film for human motion detection

NASA Astrophysics Data System (ADS)

Lee, Jaehwan; Kim, Sanghyeok; Lee, Jinjae; Yang, Daejong; Park, Byong Chon; Ryu, Seunghwa; Park, Inkyu

2014-09-01

Wearable strain sensors for human motion detection are being highlighted in various fields such as medical, entertainment and sports industry. In this paper, we propose a new type of stretchable strain sensor that can detect both tensile and compressive strains and can be fabricated by a very simple process. A silver nanoparticle (Ag NP) thin film patterned on the polydimethylsiloxane (PDMS) stamp by a single-step direct transfer process is used as the strain sensing material. The working principle is the change in the electrical resistance caused by the opening/closure of micro-cracks under mechanical deformation. The fabricated stretchable strain sensor shows highly sensitive and durable sensing performances in various tensile/compressive strains, long-term cyclic loading and relaxation tests. We demonstrate the applications of our stretchable strain sensors such as flexible pressure sensors and wearable human motion detection devices with high sensitivity, response speed and mechanical robustness.Wearable strain sensors for human motion detection are being highlighted in various fields such as medical, entertainment and sports industry. In this paper, we propose a new type of stretchable strain sensor that can detect both tensile and compressive strains and can be fabricated by a very simple process. A silver nanoparticle (Ag NP) thin film patterned on the polydimethylsiloxane (PDMS) stamp by a single-step direct transfer process is used as the strain sensing material. The working principle is the change in the electrical resistance caused by the opening/closure of micro-cracks under mechanical deformation. The fabricated stretchable strain sensor shows highly sensitive and durable sensing performances in various tensile/compressive strains, long-term cyclic loading and relaxation tests. We demonstrate the applications of our stretchable strain sensors such as flexible pressure sensors and wearable human motion detection devices with high sensitivity, response speed and mechanical robustness. Electronic supplementary information (ESI) available. See DOI: 10.1039/c4nr03295k

A closed-loop compressive-sensing-based neural recording system.

PubMed

Zhang, Jie; Mitra, Srinjoy; Suo, Yuanming; Cheng, Andrew; Xiong, Tao; Michon, Frederic; Welkenhuysen, Marleen; Kloosterman, Fabian; Chin, Peter S; Hsiao, Steven; Tran, Trac D; Yazicioglu, Firat; Etienne-Cummings, Ralph

2015-06-01

This paper describes a low power closed-loop compressive sensing (CS) based neural recording system. This system provides an efficient method to reduce data transmission bandwidth for implantable neural recording devices. By doing so, this technique reduces a majority of system power consumption which is dissipated at data readout interface. The design of the system is scalable and is a viable option for large scale integration of electrodes or recording sites onto a single device. The entire system consists of an application-specific integrated circuit (ASIC) with 4 recording readout channels with CS circuits, a real time off-chip CS recovery block and a recovery quality evaluation block that provides a closed feedback to adaptively adjust compression rate. Since CS performance is strongly signal dependent, the ASIC has been tested in vivo and with standard public neural databases. Implemented using efficient digital circuit, this system is able to achieve >10 times data compression on the entire neural spike band (500-6KHz) while consuming only 0.83uW (0.53 V voltage supply) additional digital power per electrode. When only the spikes are desired, the system is able to further compress the detected spikes by around 16 times. Unlike other similar systems, the characteristic spikes and inter-spike data can both be recovered which guarantes a >95% spike classification success rate. The compression circuit occupied 0.11mm(2)/electrode in a 180nm CMOS process. The complete signal processing circuit consumes <16uW/electrode. Power and area efficiency demonstrated by the system make it an ideal candidate for integration into large recording arrays containing thousands of electrode. Closed-loop recording and reconstruction performance evaluation further improves the robustness of the compression method, thus making the system more practical for long term recording.

Sense of Community, a Theory Not a Value: A Response to Nowell and Boyd

ERIC Educational Resources Information Center

McMillan, David W.

2011-01-01

This is a response to the Nowell and Boyd (2010) article printed in this journal titled: Viewing Community as Responsibility as Well as Resource: Deconstructing the Theoretical Roots of Psychological Sense of Community. In that article, they argued that the McMillan theory of Sense of Community is a simplistic, needs-based theory that excludes…

Compressible, unsteady lifting-surface theory for a helicopter rotor in forward flight

NASA Technical Reports Server (NTRS)

Runyan, H. L.; Tai, H.

1985-01-01

A lifting-surface theory has been developed for a helicopter rotor in forward flight for compressible and incompressible flow. The method utilizes the concept of the linearized acceleration potential and makes use of the doublet lattice procedure. Calculations demonstrating the application of the method are given in terms of the lift distribution on a one-bladed rotor, a two-bladed rotor, and a rotor with swept-forward and swept-back tips. Also, the lift on a rotor vibrating in a pitching mode at 4 per revolution is given. Compressibility effects and interference effects for a two-bladed rotor are discussed.

Is the compressibility positive or negative in a strongly-coupled dusty plasma?

NASA Astrophysics Data System (ADS)

Goree, John; Ruhunusiri, W. D. Suranga

2014-10-01

In dusty plasmas, dust particles are often strongly coupled with a large Coulomb coupling parameter Γ, while the electrons and ions that share the same volume are weakly coupled. In most substances, compressibility β must be positive; otherwise there would be an explosive instability. In a multicomponent plasma, however, one could entertain the idea that β for a single strongly coupled component could be negative, provided that the restoring force from charge separation overwhelms the destabilizing effect. Indeed, the compressibility for a strongly-coupled dust component is assumed to be negative in three theories we identified in the literature for dust acoustic waves. These theories use a multi-fluid model, with an OCP (one component plasma) or Yukawa-OCP approach for the dust fluid. We performed dusty plasma experiments designed to determine the value of the inverse compressibility β-1, and in particular its sign. We fit an experimentally measured dispersion relation to theory, with β-1 as a free parameter, taking into account the systematic errors in the experiment and model. We find that β-1 is either positive, or it has a negligibly small negative value, which is not in agreement with the assumptions of the OCP-based theories. Supported by NSF and NASA.

Phase transitions during compression and decompression of clots from platelet-poor plasma, platelet-rich plasma and whole blood.

PubMed

Liang, Xiaojun; Chernysh, Irina; Purohit, Prashant K; Weisel, John W

2017-09-15

Blood clots are required to stem bleeding and are subject to a variety of stresses, but they can also block blood vessels and cause heart attacks and ischemic strokes. We measured the compressive response of human platelet-poor plasma (PPP) clots, platelet-rich plasma (PRP) clots and whole blood clots and correlated these measurements with confocal and scanning electron microscopy to track changes in clot structure. Stress-strain curves revealed four characteristic regions, for compression-decompression: (1) linear elastic region; (2) upper plateau or softening region; (3) non-linear elastic region or re-stretching of the network; (4) lower plateau in which dissociation of some newly made connections occurs. Our experiments revealed that compression proceeds by the passage of a phase boundary through the clot separating rarefied and densified phases. This observation motivates a model of fibrin mechanics based on the continuum theory of phase transitions, which accounts for the pre-stress caused by platelets, the adhesion of fibrin fibers in the densified phase, the compression of red blood cells (RBCs), and the pumping of liquids through the clot during compression/decompression. Our experiments and theory provide insights into the mechanical behavior of blood clots that could have implications clinically and in the design of fibrin-based biomaterials. The objective of this paper is to measure and mathematically model the compression behavior of various human blood clots. We show by a combination of confocal and scanning electron microscopy that compression proceeds by the passage of a front through the sample that separates a densified region of the clot from a rarefied region, and that the compression/decompression response is reversible with hysteresis. These observations form the basis of a model for the compression response of clots based on the continuum theory of phase transitions. Our studies may reveal how clot rheology under large compression in vivo due to muscle contraction, platelet retraction and hydrodynamic flow varies under various pathophysiological conditions and could inform the design of fibrin based biomaterials. Copyright © 2017 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Compressed Sensing SEMAC: 8-fold Accelerated High Resolution Metal Artifact Reduction MRI of Cobalt-Chromium Knee Arthroplasty Implants.

PubMed

Fritz, Jan; Ahlawat, Shivani; Demehri, Shadpour; Thawait, Gaurav K; Raithel, Esther; Gilson, Wesley D; Nittka, Mathias

2016-10-01

The aim of this study was to prospectively test the hypothesis that a compressed sensing-based slice encoding for metal artifact correction (SEMAC) turbo spin echo (TSE) pulse sequence prototype facilitates high-resolution metal artifact reduction magnetic resonance imaging (MRI) of cobalt-chromium knee arthroplasty implants within acquisition times of less than 5 minutes, thereby yielding better image quality than high-bandwidth (BW) TSE of similar length and similar image quality than lengthier SEMAC standard of reference pulse sequences. This prospective study was approved by our institutional review board. Twenty asymptomatic subjects (12 men, 8 women; mean age, 56 years; age range, 44-82 years) with total knee arthroplasty implants underwent MRI of the knee using a commercially available, clinical 1.5 T MRI system. Two compressed sensing-accelerated SEMAC prototype pulse sequences with 8-fold undersampling and acquisition times of approximately 5 minutes each were compared with commercially available high-BW and SEMAC pulse sequences with acquisition times of approximately 5 minutes and 11 minutes, respectively. For each pulse sequence type, sagittal intermediate-weighted (TR, 3750-4120 milliseconds; TE, 26-28 milliseconds; voxel size, 0.5 × 0.5 × 3 mm) and short tau inversion recovery (TR, 4010 milliseconds; TE, 5.2-7.5 milliseconds; voxel size, 0.8 × 0.8 × 4 mm) were acquired. Outcome variables included image quality, display of the bone-implant interfaces and pertinent knee structures, artifact size, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). Statistical analysis included Friedman, repeated measures analysis of variances, and Cohen weighted k tests. Bonferroni-corrected P values of 0.005 and less were considered statistically significant. Image quality, bone-implant interfaces, anatomic structures, artifact size, SNR, and CNR parameters were statistically similar between the compressed sensing-accelerated SEMAC prototype and SEMAC commercial pulse sequences. There was mild blur on images of both SEMAC sequences when compared with high-BW images (P < 0.001), which however did not impair the assessment of knee structures. Metal artifact reduction and visibility of central knee structures and bone-implant interfaces were good to very good and significantly better on both types of SEMAC than on high-BW images (P < 0.004). All 3 pulse sequences showed peripheral structures similarly well. The implant artifact size was 46% to 51% larger on high-BW images when compared with both types of SEMAC images (P < 0.0001). Signal-to-noise ratios and CNRs of fat tissue, tendon tissue, muscle tissue, and fluid were statistically similar on intermediate-weighted MR images of all 3 pulse sequence types. On short tau inversion recovery images, the SNRs of tendon tissue and the CNRs of fat and fluid, fluid and muscle, as well as fluid and tendon were significantly higher on SEMAC and compressed sensing SEMAC images (P < 0.005, respectively). We accept the hypothesis that prospective compressed sensing acceleration of SEMAC is feasible for high-quality metal artifact reduction MRI of cobalt-chromium knee arthroplasty implants in less than 5 minutes and yields better quality than high-BW TSE and similarly high quality than lengthier SEMAC pulse sequences.

Some practical aspects of lossless and nearly-lossless compression of AVHRR imagery

NASA Technical Reports Server (NTRS)

Hogan, David B.; Miller, Chris X.; Christensen, Than Lee; Moorti, Raj

1994-01-01

Compression of Advanced Very high Resolution Radiometers (AVHRR) imagery operating in a lossless or nearly-lossless mode is evaluated. Several practical issues are analyzed including: variability of compression over time and among channels, rate-smoothing buffer size, multi-spectral preprocessing of data, day/night handling, and impact on key operational data applications. This analysis is based on a DPCM algorithm employing the Universal Noiseless Coder, which is a candidate for inclusion in many future remote sensing systems. It is shown that compression rates of about 2:1 (daytime) can be achieved with modest buffer sizes (less than or equal to 2.5 Mbytes) and a relatively simple multi-spectral preprocessing step.

Impact of multilayered compression bandages on sub-bandage interface pressure: a model.

PubMed

Al Khaburi, J; Nelson, E A; Hutchinson, J; Dehghani-Sanij, A A

2011-03-01

Multi-component medical compression bandages are widely used to treat venous leg ulcers. The sub-bandage interface pressures induced by individual components of the multi-component compression bandage systems are not always simply additive. Current models to explain compression bandage performance do not take account of the increase in leg circumference when each bandage is applied, and this may account for the difference between predicted and actual pressures. To calculate the interface pressure when a multi-component compression bandage system is applied to a leg. Use thick wall cylinder theory to estimate the sub-bandage pressure over the leg when a multi-component compression bandage is applied to a leg. A mathematical model was developed based on thick cylinder theory to include bandage thickness in the calculation of the interface pressure in multi-component compression systems. In multi-component compression systems, the interface pressure corresponds to the sum of the pressures applied by individual bandage layers. However, the change in the limb diameter caused by additional bandage layers should be considered in the calculation. Adding the interface pressure produced by single components without considering the bandage thickness will result in an overestimate of the overall interface pressure produced by the multi-component compression systems. At the ankle (circumference 25 cm) this error can be 19.2% or even more in the case of four components bandaging systems. Bandage thickness should be considered when calculating the pressure applied using multi-component compression systems.

Free-beam soliton self-compression in air

NASA Astrophysics Data System (ADS)

Voronin, A. A.; Mitrofanov, A. V.; Sidorov-Biryukov, D. A.; Fedotov, A. B.; Pugžlys, A.; Panchenko, V. Ya; Shumakova, V.; Ališauskas, S.; Baltuška, A.; Zheltikov, A. M.

2018-02-01

We identify a physical scenario whereby soliton transients generated in freely propagating laser beams within the regions of anomalous dispersion in air can be compressed as a part of their free-beam spatiotemporal evolution to yield few-cycle mid- and long-wavelength-infrared field waveforms, whose peak power is substantially higher than the peak power of the input pulses. We show that this free-beam soliton self-compression scenario does not require ionization or laser-induced filamentation, enabling high-throughput self-compression of mid- and long-wavelength-infrared laser pulses within a broad range of peak powers from tens of gigawatts up to the terawatt level. We also demonstrate that this method of pulse compression can be extended to long-range propagation, providing self-compression of high-peak-power laser pulses in atmospheric air within propagation ranges as long as hundreds of meters, suggesting new ways towards longer-range standoff detection and remote sensing.

Improved Target Detection in Urban Structures Using Distributed Sensing and Fast Data Acquisition Techniques

DTIC Science & Technology

2013-04-01

Trans. Signal Process., vol. 57, no. 6, pp. 2275-2284, 2009. [83] A. Gurbuz, J. IVIcClellan, and W. Scott, "Compressive sensing for subsurface ... imaging using ground penetrating radar," Signal Pracess., vol. 89, no. 10, pp. 1959 -1972, 2009. [84] A. Gurbuz, J. McClellan, and W. Scott, "A

Lightweight, compressible and electrically conductive polyurethane sponges coated with synergistic multiwalled carbon nanotubes and graphene for piezoresistive sensors.

PubMed

Ma, Zhonglei; Wei, Ajing; Ma, Jianzhong; Shao, Liang; Jiang, Huie; Dong, Diandian; Ji, Zhanyou; Wang, Qian; Kang, Songlei

2018-04-19

Lightweight, compressible and highly sensitive pressure/strain sensing materials are highly desirable for the development of health monitoring, wearable devices and artificial intelligence. Herein, a very simple, low-cost and solution-based approach is presented to fabricate versatile piezoresistive sensors based on conductive polyurethane (PU) sponges coated with synergistic multiwalled carbon nanotubes (MWCNTs) and graphene. These sensor materials are fabricated by convenient dip-coating layer-by-layer (LBL) electrostatic assembly followed by in situ reduction without using any complicated microfabrication processes. The resultant conductive MWCNT/RGO@PU sponges exhibit very low densities (0.027-0.064 g cm-3), outstanding compressibility (up to 75%) and high electrical conductivity benefiting from the porous PU sponges and synergistic conductive MWCNT/RGO structures. In addition, the MWCNT/RGO@PU sponges present larger relative resistance changes and superior sensing performances under external applied pressures (0-5.6 kPa) and a wide range of strains (0-75%) compared with the RGO@PU and MWCNT@PU sponges, due to the synergistic effect of multiple mechanisms: "disconnect-connect" transition of nanogaps, microcracks and fractured skeletons at low compression strain and compressive contact of the conductive skeletons at high compression strain. The electrical and piezoresistive properties of MWCNT/RGO@PU sponges are strongly associated with the dip-coating cycle, suspension concentration, and the applied pressure and strain. Fully functional applications of MWCNT/RGO@PU sponge-based piezoresistive sensors in lighting LED lamps and detecting human body movements are demonstrated, indicating their excellent potential for emerging applications such as health monitoring, wearable devices and artificial intelligence.

Bundle block adjustment of large-scale remote sensing data with Block-based Sparse Matrix Compression combined with Preconditioned Conjugate Gradient

NASA Astrophysics Data System (ADS)

Zheng, Maoteng; Zhang, Yongjun; Zhou, Shunping; Zhu, Junfeng; Xiong, Xiaodong

2016-07-01

In recent years, new platforms and sensors in photogrammetry, remote sensing and computer vision areas have become available, such as Unmanned Aircraft Vehicles (UAV), oblique camera systems, common digital cameras and even mobile phone cameras. Images collected by all these kinds of sensors could be used as remote sensing data sources. These sensors can obtain large-scale remote sensing data which consist of a great number of images. Bundle block adjustment of large-scale data with conventional algorithm is very time and space (memory) consuming due to the super large normal matrix arising from large-scale data. In this paper, an efficient Block-based Sparse Matrix Compression (BSMC) method combined with the Preconditioned Conjugate Gradient (PCG) algorithm is chosen to develop a stable and efficient bundle block adjustment system in order to deal with the large-scale remote sensing data. The main contribution of this work is the BSMC-based PCG algorithm which is more efficient in time and memory than the traditional algorithm without compromising the accuracy. Totally 8 datasets of real data are used to test our proposed method. Preliminary results have shown that the BSMC method can efficiently decrease the time and memory requirement of large-scale data.

Channel Estimation and Pilot Design for Massive MIMO Systems with Block-Structured Compressive Sensing

NASA Astrophysics Data System (ADS)

Lv, ZhuoKai; Yang, Tiejun; Zhu, Chunhua

2018-03-01

Through utilizing the technology of compressive sensing (CS), the channel estimation methods can achieve the purpose of reducing pilots and improving spectrum efficiency. The channel estimation and pilot design scheme are explored during the correspondence under the help of block-structured CS in massive MIMO systems. The block coherence property of the aggregate system matrix can be minimized so that the pilot design scheme based on stochastic search is proposed. Moreover, the block sparsity adaptive matching pursuit (BSAMP) algorithm under the common sparsity model is proposed so that the channel estimation can be caught precisely. Simulation results are to be proved the proposed design algorithm with superimposed pilots design and the BSAMP algorithm can provide better channel estimation than existing methods.

Kinetics of the B1-B2 phase transition in KCl under rapid compression

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lin, Chuanlong; Smith, Jesse S.; Sinogeikin, Stanislav V.

2016-01-28

Kinetics of the B1-B2 phase transition in KCl has been investigated under various compression rates (0.03–13.5 GPa/s) in a dynamic diamond anvil cell using time-resolved x-ray diffraction and fast imaging. Our experimental data show that the volume fraction across the transition generally gives sigmoidal curves as a function of pressure during rapid compression. Based upon classical nucleation and growth theories (Johnson-Mehl-Avrami-Kolmogorov theories), we propose a model that is applicable for studying kinetics for the compression rates studied. The fit of the experimental volume fraction as a function of pressure provides information on effective activation energy and average activation volume at amore » given compression rate. The resulting parameters are successfully used for interpreting several experimental observables that are compression-rate dependent, such as the transition time, grain size, and over-pressurization. The effective activation energy (Q{sub eff}) is found to decrease linearly with the logarithm of compression rate. When Q{sub eff} is applied to the Arrhenius equation, this relationship can be used to interpret the experimentally observed linear relationship between the logarithm of the transition time and logarithm of the compression rates. The decrease of Q{sub eff} with increasing compression rate results in the decrease of the nucleation rate, which is qualitatively in agreement with the observed change of the grain size with compression rate. The observed over-pressurization is also well explained by the model when an exponential relationship between the average activation volume and the compression rate is assumed.« less

Fast, accurate 2D-MR relaxation exchange spectroscopy (REXSY): Beyond compressed sensing

PubMed Central

Bai, Ruiliang; Benjamini, Dan; Cheng, Jian; Basser, Peter J.

2016-01-01

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. PMID:27782473

Simultaneous compression and encryption for secure real-time secure transmission of sensitive video transmission

NASA Astrophysics Data System (ADS)

Al-Hayani, Nazar; Al-Jawad, Naseer; Jassim, Sabah A.

2014-05-01

Video compression and encryption became very essential in a secured real time video transmission. Applying both techniques simultaneously is one of the challenges where the size and the quality are important in multimedia transmission. In this paper we proposed a new technique for video compression and encryption. Both encryption and compression are based on edges extracted from the high frequency sub-bands of wavelet decomposition. The compression algorithm based on hybrid of: discrete wavelet transforms, discrete cosine transform, vector quantization, wavelet based edge detection, and phase sensing. The compression encoding algorithm treats the video reference and non-reference frames in two different ways. The encryption algorithm utilized A5 cipher combined with chaotic logistic map to encrypt the significant parameters and wavelet coefficients. Both algorithms can be applied simultaneously after applying the discrete wavelet transform on each individual frame. Experimental results show that the proposed algorithms have the following features: high compression, acceptable quality, and resistance to the statistical and bruteforce attack with low computational processing.

Compression evaluation of surgery video recordings retaining diagnostic credibility (compression evaluation of surgery video)

NASA Astrophysics Data System (ADS)

Duplaga, M.; Leszczuk, M. I.; Papir, Z.; Przelaskowski, A.

2008-12-01

Wider dissemination of medical digital video libraries is affected by two correlated factors, resource effective content compression that directly influences its diagnostic credibility. It has been proved that it is possible to meet these contradictory requirements halfway for long-lasting and low motion surgery recordings at compression ratios close to 100 (bronchoscopic procedures were a case study investigated). As the main supporting assumption, it has been accepted that the content can be compressed as far as clinicians are not able to sense a loss of video diagnostic fidelity (a visually lossless compression). Different market codecs were inspected by means of the combined subjective and objective tests toward their usability in medical video libraries. Subjective tests involved a panel of clinicians who had to classify compressed bronchoscopic video content according to its quality under the bubble sort algorithm. For objective tests, two metrics (hybrid vector measure and hosaka Plots) were calculated frame by frame and averaged over a whole sequence.

Real-time contaminant sensing and control in civil infrastructure systems

NASA Astrophysics Data System (ADS)

Rimer, Sara; Katopodes, Nikolaos

2014-11-01

A laboratory-scale prototype has been designed and implemented to test the feasibility of real-time contaminant sensing and control in civil infrastructure systems. A blower wind tunnel is the basis of the prototype design, with propylene glycol smoke as the ``contaminant.'' A camera sensor and compressed-air vacuum nozzle system is set up at the test section portion of the prototype to visually sense and then control the contaminant; a real-time controller is programmed to read in data from the camera sensor and administer pressure to regulators controlling the compressed air operating the vacuum nozzles. A computational fluid dynamics model is being integrated in with this prototype to inform the correct pressure to supply to the regulators in order to optimally control the contaminant's removal from the prototype. The performance of the prototype has been evaluated against the computational fluid dynamics model and is discussed in this presentation. Furthermore, the initial performance of the sensor-control system implemented in the test section of the prototype is discussed. NSF-CMMI 0856438.

COSAL: A black-box compressible stability analysis code for transition prediction in three-dimensional boundary layers

NASA Technical Reports Server (NTRS)

Malik, M. R.

1982-01-01

A fast computer code COSAL for transition prediction in three dimensional boundary layers using compressible stability analysis is described. The compressible stability eigenvalue problem is solved using a finite difference method, and the code is a black box in the sense that no guess of the eigenvalue is required from the user. Several optimization procedures were incorporated into COSAL to calculate integrated growth rates (N factor) for transition correlation for swept and tapered laminar flow control wings using the well known e to the Nth power method. A user's guide to the program is provided.

Making Better Use of Bandwidth: Data Compression and Network Management Technologies

DTIC Science & Technology

2005-01-01

data , the compression would not be a success. A key feature of the Lempel - Ziv family of algorithms is that the...citeseer.nj.nec.com/yu02motion.html. Ziv , J., and A. Lempel , “A Universal Algorithm for Sequential Data Compression ,” IEEE Transac- tions on Information Theory, Vol. 23, 1977, pp. 337–342. ...probability models – Lempel - Ziv – Prediction by partial matching The central component of a lossless compression algorithm

A sparsity-based simplification method for segmentation of spectral-domain optical coherence tomography images

NASA Astrophysics Data System (ADS)

Meiniel, William; Gan, Yu; Olivo-Marin, Jean-Christophe; Angelini, Elsa

2017-08-01

Optical coherence tomography (OCT) has emerged as a promising image modality to characterize biological tissues. With axio-lateral resolutions at the micron-level, OCT images provide detailed morphological information and enable applications such as optical biopsy and virtual histology for clinical needs. Image enhancement is typically required for morphological segmentation, to improve boundary localization, rather than enrich detailed tissue information. We propose to formulate image enhancement as an image simplification task such that tissue layers are smoothed while contours are enhanced. For this purpose, we exploit a Total Variation sparsity-based image reconstruction, inspired by the Compressed Sensing (CS) theory, but specialized for images with structures arranged in layers. We demonstrate the potential of our approach on OCT human heart and retinal images for layers segmentation. We also compare our image enhancement capabilities to the state-of-the-art denoising techniques.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gilbert, Andrew J.; Miller, Brian W.; Robinson, Sean M.

Imaging technology is generally considered too invasive for arms control inspections due to the concern that it cannot properly secure sensitive features of the inspected item. But, this same sensitive information, which could include direct information on the form and function of the items under inspection, could be used for robust arms control inspections. The single-pixel X-ray imager (SPXI) is introduced as a method to make such inspections, capturing the salient spatial information of an object in a secure manner while never forming an actual image. We built this method on the theory of compressive sensing and the single pixelmore » optical camera. The performance of the system is quantified using simulated inspections of simple objects. Measures of the robustness and security of the method are introduced and used to determine how robust and secure such an inspection would be. Particularly, it is found that an inspection with low noise (<1%) and high undersampling (>256×) exhibits high robustness and security.« less

Iterative feature refinement for accurate undersampled MR image reconstruction

NASA Astrophysics Data System (ADS)

Wang, Shanshan; Liu, Jianbo; Liu, Qiegen; Ying, Leslie; Liu, Xin; Zheng, Hairong; Liang, Dong

2016-05-01

Accelerating MR scan is of great significance for clinical, research and advanced applications, and one main effort to achieve this is the utilization of compressed sensing (CS) theory. Nevertheless, the existing CSMRI approaches still have limitations such as fine structure loss or high computational complexity. This paper proposes a novel iterative feature refinement (IFR) module for accurate MR image reconstruction from undersampled K-space data. Integrating IFR with CSMRI which is equipped with fixed transforms, we develop an IFR-CS method to restore meaningful structures and details that are originally discarded without introducing too much additional complexity. Specifically, the proposed IFR-CS is realized with three iterative steps, namely sparsity-promoting denoising, feature refinement and Tikhonov regularization. Experimental results on both simulated and in vivo MR datasets have shown that the proposed module has a strong capability to capture image details, and that IFR-CS is comparable and even superior to other state-of-the-art reconstruction approaches.

Distributed Compressive Sensing

DTIC Science & Technology

2009-01-01

example, smooth signals are sparse in the Fourier basis, and piecewise smooth signals are sparse in a wavelet basis [8]; the commercial coding standards MP3...including wavelets [8], Gabor bases [8], curvelets [35], etc., are widely used for representation and compression of natural signals, images, and...spikes and the sine waves of a Fourier basis, or the Fourier basis and wavelets . Signals that are sparsely represented in frames or unions of bases can

Real-Time Data Filtering and Compression in Wide Area Simulation Networks

DTIC Science & Technology

1992-10-02

Area Simulation Networks Achieving the real-time linkage among multiple , geographically-distant, local area networks that support distributed...November 1989, pp. 52-61. [IEEE85] IEEE/ANSI Standard 8802/3 "Carrier sense multiple access with collision detection (CSMA/CD) access method and...decoding/encoding of multiple bits. The hardware is programmable, easily adaptable and yields a high compression rate. A prototype 2-micron VLSI chip

Near real-time estimation of the seismic source parameters in a compressed domain

NASA Astrophysics Data System (ADS)

Rodriguez, Ismael A. Vera

Seismic events can be characterized by its origin time, location and moment tensor. Fast estimations of these source parameters are important in areas of geophysics like earthquake seismology, and the monitoring of seismic activity produced by volcanoes, mining operations and hydraulic injections in geothermal and oil and gas reservoirs. Most available monitoring systems estimate the source parameters in a sequential procedure: first determining origin time and location (e.g., epicentre, hypocentre or centroid of the stress glut density), and then using this information to initialize the evaluation of the moment tensor. A more efficient estimation of the source parameters requires a concurrent evaluation of the three variables. The main objective of the present thesis is to address the simultaneous estimation of origin time, location and moment tensor of seismic events. The proposed method displays the benefits of being: 1) automatic, 2) continuous and, depending on the scale of application, 3) of providing results in real-time or near real-time. The inversion algorithm is based on theoretical results from sparse representation theory and compressive sensing. The feasibility of implementation is determined through the analysis of synthetic and real data examples. The numerical experiments focus on the microseismic monitoring of hydraulic fractures in oil and gas wells, however, an example using real earthquake data is also presented for validation. The thesis is complemented with a resolvability analysis of the moment tensor. The analysis targets common monitoring geometries employed in hydraulic fracturing in oil wells. Additionally, it is presented an application of sparse representation theory for the denoising of one-component and three-component microseismicity records, and an algorithm for improved automatic time-picking using non-linear inversion constraints.

Polymer planar waveguide Bragg gratings: fabrication, characterization, and sensing applications

NASA Astrophysics Data System (ADS)

Rosenberger, M.; Hessler, S.; Pauer, H.; Girschikofsky, M.; Roth, G. L.; Adelmann, B.; Woern, H.; Schmauss, B.; Hellmann, R.

2017-02-01

In this contribution, we give a comprehensive overview of the fabrication, characterization, and application of integrated planar waveguide Bragg gratings (PPBGs) in cyclo-olefin copolymers (COC). Starting with the measurement of the refractive index depth profile of integrated UV-written structures in COC by phase shifting Mach-Zehnder- Interferometry, we analyze the light propagation using numerical simulations. Furthermore, we show the rapid fabrication of humidity insensitive polymer waveguide Bragg gratings in cyclo-olefin copolymers and discuss the influence of the UV-dosage onto the spectral characteristics and the transmission behavior of the waveguide. Based on these measurements we exemplify that our Bragg gratings exhibit a reflectivity of over 99 % and are highly suitable for sensing applications. With regard to a negligible affinity to absorb water and in conjunction with high temperature stability these polymer devices are ideal for mechanical deformation sensing. Since planar structures are not limited to tensile but can also be applied for measuring compressive strain, we manufacture different functional devices and corroborate their applicability as optical sensors. Exemplarily, we highlight a temperature referenced PPBG sensor written into a femtosecond-laser cut tensile test geometry for tensile and compressive strain sensing. Furthermore, a flexible polymer planar shape sensor is presented.

A Robust Feedforward Model of the Olfactory System

NASA Astrophysics Data System (ADS)

Zhang, Yilun; Sharpee, Tatyana

Most natural odors have sparse molecular composition. This makes the principles of compressing sensing potentially relevant to the structure of the olfactory code. Yet, the largely feedforward organization of the olfactory system precludes reconstruction using standard compressed sensing algorithms. To resolve this problem, recent theoretical work has proposed that signal reconstruction could take place as a result of a low dimensional dynamical system converging to one of its attractor states. The dynamical aspects of optimization, however, would slow down odor recognition and were also found to be susceptible to noise. Here we describe a feedforward model of the olfactory system that achieves both strong compression and fast reconstruction that is also robust to noise. A key feature of the proposed model is a specific relationship between how odors are represented at the glomeruli stage, which corresponds to a compression, and the connections from glomeruli to Kenyon cells, which in the model corresponds to reconstruction. We show that provided this specific relationship holds true, the reconstruction will be both fast and robust to noise, and in particular to failure of glomeruli. The predicted connectivity rate from glomeruli to the Kenyon cells can be tested experimentally. This research was supported by James S. McDonnell Foundation, NSF CAREER award IIS-1254123, NSF Ideas Lab Collaborative Research IOS 1556388.

Block sparsity-based joint compressed sensing recovery of multi-channel ECG signals.

PubMed

Singh, Anurag; Dandapat, Samarendra

2017-04-01

In recent years, compressed sensing (CS) has emerged as an effective alternative to conventional wavelet based data compression techniques. This is due to its simple and energy-efficient data reduction procedure, which makes it suitable for resource-constrained wireless body area network (WBAN)-enabled electrocardiogram (ECG) telemonitoring applications. Both spatial and temporal correlations exist simultaneously in multi-channel ECG (MECG) signals. Exploitation of both types of correlations is very important in CS-based ECG telemonitoring systems for better performance. However, most of the existing CS-based works exploit either of the correlations, which results in a suboptimal performance. In this work, within a CS framework, the authors propose to exploit both types of correlations simultaneously using a sparse Bayesian learning-based approach. A spatiotemporal sparse model is employed for joint compression/reconstruction of MECG signals. Discrete wavelets transform domain block sparsity of MECG signals is exploited for simultaneous reconstruction of all the channels. Performance evaluations using Physikalisch-Technische Bundesanstalt MECG diagnostic database show a significant gain in the diagnostic reconstruction quality of the MECG signals compared with the state-of-the art techniques at reduced number of measurements. Low measurement requirement may lead to significant savings in the energy-cost of the existing CS-based WBAN systems.

The use of compressive sensing and peak detection in the reconstruction of microtubules length time series in the process of dynamic instability.

PubMed

Mahrooghy, Majid; Yarahmadian, Shantia; Menon, Vineetha; Rezania, Vahid; Tuszynski, Jack A

2015-10-01

Microtubules (MTs) are intra-cellular cylindrical protein filaments. They exhibit a unique phenomenon of stochastic growth and shrinkage, called dynamic instability. In this paper, we introduce a theoretical framework for applying Compressive Sensing (CS) to the sampled data of the microtubule length in the process of dynamic instability. To reduce data density and reconstruct the original signal with relatively low sampling rates, we have applied CS to experimental MT lament length time series modeled as a Dichotomous Markov Noise (DMN). The results show that using CS along with the wavelet transform significantly reduces the recovery errors comparing in the absence of wavelet transform, especially in the low and the medium sampling rates. In a sampling rate ranging from 0.2 to 0.5, the Root-Mean-Squared Error (RMSE) decreases by approximately 3 times and between 0.5 and 1, RMSE is small. We also apply a peak detection technique to the wavelet coefficients to detect and closely approximate the growth and shrinkage of MTs for computing the essential dynamic instability parameters, i.e., transition frequencies and specially growth and shrinkage rates. The results show that using compressed sensing along with the peak detection technique and wavelet transform in sampling rates reduces the recovery errors for the parameters. Copyright © 2015 Elsevier Ltd. All rights reserved.

Accelerated self-gated UTE MRI of the murine heart

NASA Astrophysics Data System (ADS)

Motaal, Abdallah G.; Noorman, Nils; De Graaf, Wolter L.; Florack, Luc J.; Nicolay, Klaas; Strijkers, Gustav J.

2014-03-01

We introduce a new protocol to obtain radial Ultra-Short TE (UTE) MRI Cine of the beating mouse heart within reasonable measurement time. The method is based on a self-gated UTE with golden angle radial acquisition and compressed sensing reconstruction. The stochastic nature of the retrospective triggering acquisition scheme produces an under-sampled and random kt-space filling that allows for compressed sensing reconstruction, hence reducing scan time. As a standard, an intragate multislice FLASH sequence with an acquisition time of 4.5 min per slice was used to produce standard Cine movies of 4 mice hearts with 15 frames per cardiac cycle. The proposed self-gated sequence is used to produce Cine movies with short echo time. The total scan time was 11 min per slice. 6 slices were planned to cover the heart from the base to the apex. 2X, 4X and 6X under-sampled k-spaces cine movies were produced from 2, 1 and 0.7 min data acquisitions for each slice. The accelerated cine movies of the mouse hearts were successfully reconstructed with a compressed sensing algorithm. Compared to the FLASH cine images, the UTE images showed much less flow artifacts due to the short echo time. Besides, the accelerated movies had high image quality and the undersampling artifacts were effectively removed. Left ventricular functional parameters derived from the standard and the accelerated cine movies were nearly identical.

XD-GRASP: Golden-angle radial MRI with reconstruction of extra motion-state dimensions using compressed sensing.

PubMed

Feng, Li; Axel, Leon; Chandarana, Hersh; Block, Kai Tobias; Sodickson, Daniel K; Otazo, Ricardo

2016-02-01

To develop a novel framework for free-breathing MRI called XD-GRASP, which sorts dynamic data into extra motion-state dimensions using the self-navigation properties of radial imaging and reconstructs the multidimensional dataset using compressed sensing. Radial k-space data are continuously acquired using the golden-angle sampling scheme and sorted into multiple motion-states based on respiratory and/or cardiac motion signals derived directly from the data. The resulting undersampled multidimensional dataset is reconstructed using a compressed sensing approach that exploits sparsity along the new dynamic dimensions. The performance of XD-GRASP is demonstrated for free-breathing three-dimensional (3D) abdominal imaging, two-dimensional (2D) cardiac cine imaging and 3D dynamic contrast-enhanced (DCE) MRI of the liver, comparing against reconstructions without motion sorting in both healthy volunteers and patients. XD-GRASP separates respiratory motion from cardiac motion in cardiac imaging, and respiratory motion from contrast enhancement in liver DCE-MRI, which improves image quality and reduces motion-blurring artifacts. XD-GRASP represents a new use of sparsity for motion compensation and a novel way to handle motions in the context of a continuous acquisition paradigm. Instead of removing or correcting motion, extra motion-state dimensions are reconstructed, which improves image quality and also offers new physiological information of potential clinical value. © 2015 Wiley Periodicals, Inc.

XD-GRASP: Golden-Angle Radial MRI with Reconstruction of Extra Motion-State Dimensions Using Compressed Sensing

PubMed Central

Feng, Li; Axel, Leon; Chandarana, Hersh; Block, Kai Tobias; Sodickson, Daniel K.; Otazo, Ricardo

2015-01-01

Purpose To develop a novel framework for free-breathing MRI called XD-GRASP, which sorts dynamic data into extra motion-state dimensions using the self-navigation properties of radial imaging and reconstructs the multidimensional dataset using compressed sensing. Methods Radial k-space data are continuously acquired using the golden-angle sampling scheme and sorted into multiple motion-states based on respiratory and/or cardiac motion signals derived directly from the data. The resulting under-sampled multidimensional dataset is reconstructed using a compressed sensing approach that exploits sparsity along the new dynamic dimensions. The performance of XD-GRASP is demonstrated for free-breathing three-dimensional (3D) abdominal imaging, two-dimensional (2D) cardiac cine imaging and 3D dynamic contrast-enhanced (DCE) MRI of the liver, comparing against reconstructions without motion sorting in both healthy volunteers and patients. Results XD-GRASP separates respiratory motion from cardiac motion in cardiac imaging, and respiratory motion from contrast enhancement in liver DCE-MRI, which improves image quality and reduces motion-blurring artifacts. Conclusion XD-GRASP represents a new use of sparsity for motion compensation and a novel way to handle motions in the context of a continuous acquisition paradigm. Instead of removing or correcting motion, extra motion-state dimensions are reconstructed, which improves image quality and also offers new physiological information of potential clinical value. PMID:25809847

Secure and Energy-Efficient Data Transmission System Based on Chaotic Compressive Sensing in Body-to-Body Networks.

PubMed

Peng, Haipeng; Tian, Ye; Kurths, Jurgen; Li, Lixiang; Yang, Yixian; Wang, Daoshun

2017-06-01

Applications of wireless body area networks (WBANs) are extended from remote health care to military, sports, disaster relief, etc. With the network scale expanding, nodes increasing, and links complicated, a WBAN evolves to a body-to-body network. Along with the development, energy saving and data security problems are highlighted. In this paper, chaotic compressive sensing (CCS) is proposed to solve these two crucial problems, simultaneously. Compared with the traditional compressive sensing, CCS can save vast storage space by only storing the matrix generation parameters. Additionally, the sensitivity of chaos can improve the security of data transmission. Aimed at image transmission, modified CCS is proposed, which uses two encryption mechanisms, confusion and mask, and performs a much better encryption quality. Simulation is conducted to verify the feasibility and effectiveness of the proposed methods. The results show that the energy efficiency and security are strongly improved, while the storage space is saved. And the secret key is extremely sensitive, [Formula: see text] perturbation of the secret key could lead to a total different decoding, the relative error is larger than 100%. Particularly for image encryption, the performance of the modified method is excellent. The adjacent pixel correlation is smaller than 0.04 in different directions including horizontal, vertical, and diagonal; the entropy of the cipher image with a 256-level gray value is larger than 7.98.

Variable density randomized stack of spirals (VDR-SoS) for compressive sensing MRI.

PubMed

Valvano, Giuseppe; Martini, Nicola; Landini, Luigi; Santarelli, Maria Filomena

2016-07-01

To develop a 3D sampling strategy based on a stack of variable density spirals for compressive sensing MRI. A random sampling pattern was obtained by rotating each spiral by a random angle and by delaying for few time steps the gradient waveforms of the different interleaves. A three-dimensional (3D) variable sampling density was obtained by designing different variable density spirals for each slice encoding. The proposed approach was tested with phantom simulations up to a five-fold undersampling factor. Fully sampled 3D dataset of a human knee, and of a human brain, were obtained from a healthy volunteer. The proposed approach was tested with off-line reconstructions of the knee dataset up to a four-fold acceleration and compared with other noncoherent trajectories. The proposed approach outperformed the standard stack of spirals for various undersampling factors. The level of coherence and the reconstruction quality of the proposed approach were similar to those of other trajectories that, however, require 3D gridding for the reconstruction. The variable density randomized stack of spirals (VDR-SoS) is an easily implementable trajectory that could represent a valid sampling strategy for 3D compressive sensing MRI. It guarantees low levels of coherence without requiring 3D gridding. Magn Reson Med 76:59-69, 2016. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Three-dimensional dictionary-learning reconstruction of (23)Na MRI data.

PubMed

Behl, Nicolas G R; Gnahm, Christine; Bachert, Peter; Ladd, Mark E; Nagel, Armin M

2016-04-01

To reduce noise and artifacts in (23)Na MRI with a Compressed Sensing reconstruction and a learned dictionary as sparsifying transform. A three-dimensional dictionary-learning compressed sensing reconstruction algorithm (3D-DLCS) for the reconstruction of undersampled 3D radial (23)Na data is presented. The dictionary used as the sparsifying transform is learned with a K-singular-value-decomposition (K-SVD) algorithm. The reconstruction parameters are optimized on simulated data, and the quality of the reconstructions is assessed with peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The performance of the algorithm is evaluated in phantom and in vivo (23)Na MRI data of seven volunteers and compared with nonuniform fast Fourier transform (NUFFT) and other Compressed Sensing reconstructions. The reconstructions of simulated data have maximal PSNR and SSIM for an undersampling factor (USF) of 10 with numbers of averages equal to the USF. For 10-fold undersampling, the PSNR is increased by 5.1 dB compared with the NUFFT reconstruction, and the SSIM by 24%. These results are confirmed by phantom and in vivo (23)Na measurements in the volunteers that show markedly reduced noise and undersampling artifacts in the case of 3D-DLCS reconstructions. The 3D-DLCS algorithm enables precise reconstruction of undersampled (23)Na MRI data with markedly reduced noise and artifact levels compared with NUFFT reconstruction. Small structures are well preserved. © 2015 Wiley Periodicals, Inc.

Compressed multi-block local binary pattern for object tracking

NASA Astrophysics Data System (ADS)

Li, Tianwen; Gao, Yun; Zhao, Lei; Zhou, Hao

2018-04-01

Both robustness and real-time are very important for the application of object tracking under a real environment. The focused trackers based on deep learning are difficult to satisfy with the real-time of tracking. Compressive sensing provided a technical support for real-time tracking. In this paper, an object can be tracked via a multi-block local binary pattern feature. The feature vector was extracted based on the multi-block local binary pattern feature, which was compressed via a sparse random Gaussian matrix as the measurement matrix. The experiments showed that the proposed tracker ran in real-time and outperformed the existed compressive trackers based on Haar-like feature on many challenging video sequences in terms of accuracy and robustness.

Compressive Classification for TEM-EELS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hao, Weituo; Stevens, Andrew; Yang, Hao

Electron energy loss spectroscopy (EELS) is typically conducted in STEM mode with a spectrometer, or in TEM mode with energy selction. These methods produce a 3D data set (x, y, energy). Some compressive sensing [1,2] and inpainting [3,4,5] approaches have been proposed for recovering a full set of spectra from compressed measurements. In many cases the final form of the spectral data is an elemental map (an image with channels corresponding to elements). This means that most of the collected data is unused or summarized. We propose a method to directly recover the elemental map with reduced dose and acquisitionmore » time. We have designed a new computational TEM sensor for compressive classification [6,7] of energy loss spectra called TEM-EELS.« less

Reconstruction of atmospheric pollutant concentrations from remote sensing data - An application of distributed parameter observer theory

NASA Technical Reports Server (NTRS)

Koda, M.; Seinfeld, J. H.

1982-01-01

The reconstruction of a concentration distribution from spatially averaged and noise-corrupted data is a central problem in processing atmospheric remote sensing data. Distributed parameter observer theory is used to develop reconstructibility conditions for distributed parameter systems having measurements typical of those in remote sensing. The relation of the reconstructibility condition to the stability of the distributed parameter observer is demonstrated. The theory is applied to a variety of remote sensing situations, and it is found that those in which concentrations are measured as a function of altitude satisfy the conditions of distributed state reconstructibility.

Designing manufacturable filters for a 16-band plenoptic camera using differential evolution

NASA Astrophysics Data System (ADS)

Doster, Timothy; Olson, Colin C.; Fleet, Erin; Yetzbacher, Michael; Kanaev, Andrey; Lebow, Paul; Leathers, Robert

2017-05-01

A 16-band plenoptic camera allows for the rapid exchange of filter sets via a 4x4 filter array on the lens's front aperture. This ability to change out filters allows for an operator to quickly adapt to different locales or threat intelligence. Typically, such a system incorporates a default set of 16 equally spaced at-topped filters. Knowing the operating theater or the likely targets of interest it becomes advantageous to tune the filters. We propose using a modified beta distribution to parameterize the different possible filters and differential evolution (DE) to search over the space of possible filter designs. The modified beta distribution allows us to jointly optimize the width, taper and wavelength center of each single- or multi-pass filter in the set over a number of evolutionary steps. Further, by constraining the function parameters we can develop solutions which are not just theoretical but manufacturable. We examine two independent tasks: general spectral sensing and target detection. In the general spectral sensing task we utilize the theory of compressive sensing (CS) and find filters that generate codings which minimize the CS reconstruction error based on a fixed spectral dictionary of endmembers. For the target detection task and a set of known targets, we train the filters to optimize the separation of the background and target signature. We compare our results to the default 16 at-topped non-overlapping filter set which comes with the plenoptic camera and full hyperspectral resolution data which was previously acquired.

C-FSCV: Compressive Fast-Scan Cyclic Voltammetry for Brain Dopamine Recording.

PubMed

Zamani, Hossein; Bahrami, Hamid Reza; Chalwadi, Preeti; Garris, Paul A; Mohseni, Pedram

2018-01-01

This paper presents a novel compressive sensing framework for recording brain dopamine levels with fast-scan cyclic voltammetry (FSCV) at a carbon-fiber microelectrode. Termed compressive FSCV (C-FSCV), this approach compressively samples the measured total current in each FSCV scan and performs basic FSCV processing steps, e.g., background current averaging and subtraction, directly with compressed measurements. The resulting background-subtracted faradaic currents, which are shown to have a block-sparse representation in the discrete cosine transform domain, are next reconstructed from their compressively sampled counterparts with the block sparse Bayesian learning algorithm. Using a previously recorded dopamine dataset, consisting of electrically evoked signals recorded in the dorsal striatum of an anesthetized rat, the C-FSCV framework is shown to be efficacious in compressing and reconstructing brain dopamine dynamics and associated voltammograms with high fidelity (correlation coefficient, ), while achieving compression ratio, CR, values as high as ~ 5. Moreover, using another set of dopamine data recorded 5 minutes after administration of amphetamine (AMPH) to an ambulatory rat, C-FSCV once again compresses (CR = 5) and reconstructs the temporal pattern of dopamine release with high fidelity ( ), leading to a true-positive rate of 96.4% in detecting AMPH-induced dopamine transients.

Dynamic magnetic resonance imaging method based on golden-ratio cartesian sampling and compressed sensing.

PubMed

Li, Shuo; Zhu, Yanchun; Xie, Yaoqin; Gao, Song

2018-01-01

Dynamic magnetic resonance imaging (DMRI) is used to noninvasively trace the movements of organs and the process of drug delivery. The results can provide quantitative or semiquantitative pathology-related parameters, thus giving DMRI great potential for clinical applications. However, conventional DMRI techniques suffer from low temporal resolution and long scan time owing to the limitations of the k-space sampling scheme and image reconstruction algorithm. In this paper, we propose a novel DMRI sampling scheme based on a golden-ratio Cartesian trajectory in combination with a compressed sensing reconstruction algorithm. The results of two simulation experiments, designed according to the two major DMRI techniques, showed that the proposed method can improve the temporal resolution and shorten the scan time and provide high-quality reconstructed images.

A Compressed Sensing-based Image Reconstruction Algorithm for Solar Flare X-Ray Observations

NASA Astrophysics Data System (ADS)

Felix, Simon; Bolzern, Roman; Battaglia, Marina

2017-11-01

One way of imaging X-ray emission from solar flares is to measure Fourier components of the spatial X-ray source distribution. We present a new compressed sensing-based algorithm named VIS_CS, which reconstructs the spatial distribution from such Fourier components. We demonstrate the application of the algorithm on synthetic and observed solar flare X-ray data from the Reuven Ramaty High Energy Solar Spectroscopic Imager satellite and compare its performance with existing algorithms. VIS_CS produces competitive results with accurate photometry and morphology, without requiring any algorithm- and X-ray-source-specific parameter tuning. Its robustness and performance make this algorithm ideally suited for the generation of quicklook images or large image cubes without user intervention, such as for imaging spectroscopy analysis.

A Compressed Sensing-based Image Reconstruction Algorithm for Solar Flare X-Ray Observations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Felix, Simon; Bolzern, Roman; Battaglia, Marina, E-mail: simon.felix@fhnw.ch, E-mail: roman.bolzern@fhnw.ch, E-mail: marina.battaglia@fhnw.ch

One way of imaging X-ray emission from solar flares is to measure Fourier components of the spatial X-ray source distribution. We present a new compressed sensing-based algorithm named VIS-CS, which reconstructs the spatial distribution from such Fourier components. We demonstrate the application of the algorithm on synthetic and observed solar flare X-ray data from the Reuven Ramaty High Energy Solar Spectroscopic Imager satellite and compare its performance with existing algorithms. VIS-CS produces competitive results with accurate photometry and morphology, without requiring any algorithm- and X-ray-source-specific parameter tuning. Its robustness and performance make this algorithm ideally suited for the generation ofmore » quicklook images or large image cubes without user intervention, such as for imaging spectroscopy analysis.« less

Compression and R-wave detection of ECG/VCG data

NASA Technical Reports Server (NTRS)

Hayden, W. L.; Conover, M. F.; Bennett, W. P.

1972-01-01

Application of information theory to eliminate redundant part of electrocardiogram or vectorcardiogram is described. Operation of medical equipment to obtain three dimensional study of patient is discussed. Use of fast Fourier transform to accomplish data compression is explained.

Enhanced gas adsorption on graphitic substrates via defects and local curvature: A density functional theory study

DOE PAGES

Dutta, Debosruti; Wood, Brandon C.; Bhide, Shreyas Y.; ...

2014-03-24

Using van-der-Waals-corrected density functional theory calculations, we explore the possibility of engineering the local structure and morphology of high-surface-area graphene-derived materials to improve the uptake of methane and carbon dioxide for gas storage and sensing. We test the sensitivity of the gas adsorption energy to the introduction of native point defects, curvature, and the application of strain. The binding energy at topological point defect sites is inversely correlated with the number of missing carbon atoms, causing Stone–Wales defects to show the largest enhancement with respect to pristine graphene (~20%). Improvements of similar magnitude are observed at concavely curved surfaces inmore » buckled graphene sheets under compressive strain, whereas tensile strain tends to weaken gas binding. Trends for CO 2 and CH 4 are similar, although CO 2 binding is generally stronger by ~4 to 5 kJ mol –1. Furthermore, the differential between the adsorption of CO 2 and CH 4 is much higher on folded graphene sheets and at concave curvatures; this could possibly be leveraged for CH 4/CO 2 flow separation and gas-selective sensors.« less

Multiscale and Multitemporal Urban Remote Sensing

NASA Astrophysics Data System (ADS)

Mesev, V.

2012-07-01

The remote sensing of urban areas has received much attention from scientists conducting studies on measuring sprawl, congestion, pollution, poverty, and environmental encroachment. Yet much of the research is case and data-specific where results are greatly influenced by prevailing local conditions. There seems to be a lack of epistemological links between remote sensing and conventional theoretical urban geography; in other words, an oversight for the appreciation of how urban theory fuels urban change and how urban change is measured by remotely sensed data. This paper explores basic urban theories such as centrality, mobility, materiality, nature, public space, consumption, segregation and exclusion, and how they can be measured by remote sensing sources. In particular, the link between structure (tangible objects) and function (intangible or immaterial behavior) is addressed as the theory that supports the wellknow contrast between land cover and land use classification from remotely sensed data. The paper then couches these urban theories and contributions from urban remote sensing within two analytical fields. The first is the search for an "appropriate" spatial scale of analysis, which is conveniently divided between micro and macro urban remote sensing for measuring urban structure, understanding urban processes, and perhaps contributions to urban theory at a variety of scales of analysis. The second is on the existence of a temporal lag between materiality of urban objects and the planning process that approved their construction, specifically how time-dependence in urban structural-functional models produce temporal lags that alter the causal links between societal and political functional demands and structural ramifications.

A comparative study of SAR data compression schemes

NASA Technical Reports Server (NTRS)

Lambert-Nebout, C.; Besson, O.; Massonnet, D.; Rogron, B.

1994-01-01

The amount of data collected from spaceborne remote sensing has substantially increased in the last years. During same time period, the ability to store or transmit data has not increased as quickly. At this time, there is a growing interest in developing compression schemes that could provide both higher compression ratios and lower encoding/decoding errors. In the case of the spaceborne Synthetic Aperture Radar (SAR) earth observation system developed by the French Space Agency (CNES), the volume of data to be processed will exceed both the on-board storage capacities and the telecommunication link. The objective of this paper is twofold: to present various compression schemes adapted to SAR data; and to define a set of evaluation criteria and compare the algorithms on SAR data. In this paper, we review two classical methods of SAR data compression and propose novel approaches based on Fourier Transforms and spectrum coding.

Freeing Space for NASA: Incorporating a Lossless Compression Algorithm into NASA's FOSS System

NASA Technical Reports Server (NTRS)

Fiechtner, Kaitlyn; Parker, Allen

2011-01-01

NASA's Fiber Optic Strain Sensing (FOSS) system can gather and store up to 1,536,000 bytes (1.46 megabytes) per second. Since the FOSS system typically acquires hours - or even days - of data, the system can gather hundreds of gigabytes of data for a given test event. To store such large quantities of data more effectively, NASA is modifying a Lempel-Ziv-Oberhumer (LZO) lossless data compression program to compress data as it is being acquired in real time. After proving that the algorithm is capable of compressing the data from the FOSS system, the LZO program will be modified and incorporated into the FOSS system. Implementing an LZO compression algorithm will instantly free up memory space without compromising any data obtained. With the availability of memory space, the FOSS system can be used more efficiently on test specimens, such as Unmanned Aerial Vehicles (UAVs) that can be in flight for days. By integrating the compression algorithm, the FOSS system can continue gathering data, even on longer flights.

Plastic buckling. [post-bifurcation and imperfection sensitivity

NASA Technical Reports Server (NTRS)

Hutchinson, J. W.

1974-01-01

The present article is concerned mainly with the post-bifurcation and imperfection-sensitivity aspects of plastic buckling. A simple two-degree-of-freedom model is used to introduce post-bifurcation behavior and a second model illustrates features of the behavior of continuous solids and structures. Hill's bifurcation criterion for a class of three-dimensional solids is applied to the Donnell-Mushtari-Vlasov (DMV) theory of plates and shells. A general treatment of the initial post-bifurcation behavior of plates and shells is given within the context of the DMV theory. This is illustrated by problems involving columns and circular plates under radial compression. Numerical results are given for a column under axial compression, a circular plate under radial compression, and spherical and cylindrical shells.

Data based identification and prediction of nonlinear and complex dynamical systems

NASA Astrophysics Data System (ADS)

Wang, Wen-Xu; Lai, Ying-Cheng; Grebogi, Celso

2016-07-01

The problem of reconstructing nonlinear and complex dynamical systems from measured data or time series is central to many scientific disciplines including physical, biological, computer, and social sciences, as well as engineering and economics. The classic approach to phase-space reconstruction through the methodology of delay-coordinate embedding has been practiced for more than three decades, but the paradigm is effective mostly for low-dimensional dynamical systems. Often, the methodology yields only a topological correspondence of the original system. There are situations in various fields of science and engineering where the systems of interest are complex and high dimensional with many interacting components. A complex system typically exhibits a rich variety of collective dynamics, and it is of great interest to be able to detect, classify, understand, predict, and control the dynamics using data that are becoming increasingly accessible due to the advances of modern information technology. To accomplish these goals, especially prediction and control, an accurate reconstruction of the original system is required. Nonlinear and complex systems identification aims at inferring, from data, the mathematical equations that govern the dynamical evolution and the complex interaction patterns, or topology, among the various components of the system. With successful reconstruction of the system equations and the connecting topology, it may be possible to address challenging and significant problems such as identification of causal relations among the interacting components and detection of hidden nodes. The "inverse" problem thus presents a grand challenge, requiring new paradigms beyond the traditional delay-coordinate embedding methodology. The past fifteen years have witnessed rapid development of contemporary complex graph theory with broad applications in interdisciplinary science and engineering. The combination of graph, information, and nonlinear dynamical systems theories with tools from statistical physics, optimization, engineering control, applied mathematics, and scientific computing enables the development of a number of paradigms to address the problem of nonlinear and complex systems reconstruction. In this Review, we describe the recent advances in this forefront and rapidly evolving field, with a focus on compressive sensing based methods. In particular, compressive sensing is a paradigm developed in recent years in applied mathematics, electrical engineering, and nonlinear physics to reconstruct sparse signals using only limited data. It has broad applications ranging from image compression/reconstruction to the analysis of large-scale sensor networks, and it has become a powerful technique to obtain high-fidelity signals for applications where sufficient observations are not available. We will describe in detail how compressive sensing can be exploited to address a diverse array of problems in data based reconstruction of nonlinear and complex networked systems. The problems include identification of chaotic systems and prediction of catastrophic bifurcations, forecasting future attractors of time-varying nonlinear systems, reconstruction of complex networks with oscillatory and evolutionary game dynamics, detection of hidden nodes, identification of chaotic elements in neuronal networks, reconstruction of complex geospatial networks and nodal positioning, and reconstruction of complex spreading networks with binary data.. A number of alternative methods, such as those based on system response to external driving, synchronization, and noise-induced dynamical correlation, will also be discussed. Due to the high relevance of network reconstruction to biological sciences, a special section is devoted to a brief survey of the current methods to infer biological networks. Finally, a number of open problems including control and controllability of complex nonlinear dynamical networks are discussed. The methods outlined in this Review are principled on various concepts in complexity science and engineering such as phase transitions, bifurcations, stabilities, and robustness. The methodologies have the potential to significantly improve our ability to understand a variety of complex dynamical systems ranging from gene regulatory systems to social networks toward the ultimate goal of controlling such systems.

An analytical study of the effects of transverse shear deformation and anisotropy on buckling loads of laminated cylinders. M.S. Thesis - George Washington Univ.

NASA Technical Reports Server (NTRS)

Jegley, Dawn C.

1987-01-01

Buckling loads of thick-walled orthotropic and anisotropic simply supported circular cylinders are predicted using a higher-order transverse-shear deformation theory. A comparison of buckling loads predicted by the conventional first-order transverse-shear deformation theory and the higher-order theory show that the additional allowance for transverse shear deformation has a negligible effect on the predicted buckling loads of medium-thick metallic isotropic cylinders. However, the higher-order theory predicts buckling loads which are significantly lower than those predicted by the first-order transverse-shear deformation theory for certain short, thick-walled cylinders which have low through-the-thickness shear moduli. A parametric study of the effects of ply orientation on the buckling load of axially compressed cylinders indicates that laminates containing 45 degree plies are most sensitive to transverse-shear deformation effects. Interaction curves for buckling loads of cylinders subjected to axial compressive and external pressure loadings indicate that buckling loads due to external pressure loadings are as sensitive to transverse-shear deformation effects as buckling loads due to axial compressive loadings. The effects of anisotropy are important over a much wider range of cylinder geometries than the effects of transverse shear deformation.

Multispectral Image Compression Based on DSC Combined with CCSDS-IDC

PubMed Central

Li, Jin; Xing, Fei; Sun, Ting; You, Zheng

2014-01-01

Remote sensing multispectral image compression encoder requires low complexity, high robust, and high performance because it usually works on the satellite where the resources, such as power, memory, and processing capacity, are limited. For multispectral images, the compression algorithms based on 3D transform (like 3D DWT, 3D DCT) are too complex to be implemented in space mission. In this paper, we proposed a compression algorithm based on distributed source coding (DSC) combined with image data compression (IDC) approach recommended by CCSDS for multispectral images, which has low complexity, high robust, and high performance. First, each band is sparsely represented by DWT to obtain wavelet coefficients. Then, the wavelet coefficients are encoded by bit plane encoder (BPE). Finally, the BPE is merged to the DSC strategy of Slepian-Wolf (SW) based on QC-LDPC by deep coupling way to remove the residual redundancy between the adjacent bands. A series of multispectral images is used to test our algorithm. Experimental results show that the proposed DSC combined with the CCSDS-IDC (DSC-CCSDS)-based algorithm has better compression performance than the traditional compression approaches. PMID:25110741

Multispectral image compression based on DSC combined with CCSDS-IDC.

PubMed

Li, Jin; Xing, Fei; Sun, Ting; You, Zheng

2014-01-01

Remote sensing multispectral image compression encoder requires low complexity, high robust, and high performance because it usually works on the satellite where the resources, such as power, memory, and processing capacity, are limited. For multispectral images, the compression algorithms based on 3D transform (like 3D DWT, 3D DCT) are too complex to be implemented in space mission. In this paper, we proposed a compression algorithm based on distributed source coding (DSC) combined with image data compression (IDC) approach recommended by CCSDS for multispectral images, which has low complexity, high robust, and high performance. First, each band is sparsely represented by DWT to obtain wavelet coefficients. Then, the wavelet coefficients are encoded by bit plane encoder (BPE). Finally, the BPE is merged to the DSC strategy of Slepian-Wolf (SW) based on QC-LDPC by deep coupling way to remove the residual redundancy between the adjacent bands. A series of multispectral images is used to test our algorithm. Experimental results show that the proposed DSC combined with the CCSDS-IDC (DSC-CCSDS)-based algorithm has better compression performance than the traditional compression approaches.

Clinical Feasibility of Free-Breathing Dynamic T1-Weighted Imaging With Gadoxetic Acid-Enhanced Liver Magnetic Resonance Imaging Using a Combination of Variable Density Sampling and Compressed Sensing.

PubMed

Yoon, Jeong Hee; Yu, Mi Hye; Chang, Won; Park, Jin-Young; Nickel, Marcel Dominik; Son, Yohan; Kiefer, Berthold; Lee, Jeong Min

2017-10-01

The purpose of the study was to investigate the clinical feasibility of free-breathing dynamic T1-weighted imaging (T1WI) using Cartesian sampling, compressed sensing, and iterative reconstruction in gadoxetic acid-enhanced liver magnetic resonance imaging (MRI). This retrospective study was approved by our institutional review board, and the requirement for informed consent was waived. A total of 51 patients at high risk of breath-holding failure underwent dynamic T1WI in a free-breathing manner using volumetric interpolated breath-hold (BH) examination with compressed sensing reconstruction (CS-VIBE) and hard gating. Timing, motion artifacts, and image quality were evaluated by 4 radiologists on a 4-point scale. For patients with low image quality scores (<3) on the late arterial phase, respiratory motion-resolved (extradimension [XD]) reconstruction was additionally performed and reviewed in the same manner. In addition, in 68.6% (35/51) patients who had previously undergone liver MRI, image quality and motion artifacts on dynamic phases using CS-VIBE were compared with previous BH-T1WIs. In all patients, adequate arterial-phase timing was obtained at least once. Overall image quality of free-breathing T1WI was 3.30 ± 0.59 on precontrast and 2.68 ± 0.70, 2.93 ± 0.65, and 3.30 ± 0.49 on early arterial, late arterial, and portal venous phases, respectively. In 13 patients with lower than average image quality (<3) on the late arterial phase, motion-resolved reconstructed T1WI (XD-reconstructed CS-VIBE) significantly reduced motion artifacts (P < 0.002-0.021) and improved image quality (P < 0.0001-0.002). In comparison with previous BH-T1WI, CS-VIBE with hard gating or XD reconstruction showed less motion artifacts and better image quality on precontrast, arterial, and portal venous phases (P < 0.0001-0.013). Volumetric interpolated breath-hold examination with compressed sensing has the potential to provide consistent, motion-corrected free-breathing dynamic T1WI for liver MRI in patients at high risk of breath-holding failure.

Accelerated echo-planar J-resolved spectroscopic imaging in the human brain using compressed sensing: a pilot validation in obstructive sleep apnea.

PubMed

Sarma, M K; Nagarajan, R; Macey, P M; Kumar, R; Villablanca, J P; Furuyama, J; Thomas, M A

2014-06-01

Echo-planar J-resolved spectroscopic imaging is a fast spectroscopic technique to record the biochemical information in multiple regions of the brain, but for clinical applications, time is still a constraint. Investigations of neural injury in obstructive sleep apnea have revealed structural changes in the brain, but determining the neurochemical changes requires more detailed measurements across multiple brain regions, demonstrating a need for faster echo-planar J-resolved spectroscopic imaging. Hence, we have extended the compressed sensing reconstruction of prospectively undersampled 4D echo-planar J-resolved spectroscopic imaging to investigate metabolic changes in multiple brain locations of patients with obstructive sleep apnea and healthy controls. Nonuniform undersampling was imposed along 1 spatial and 1 spectral dimension of 4D echo-planar J-resolved spectroscopic imaging, and test-retest reliability of the compressed sensing reconstruction of the nonuniform undersampling data was tested by using a brain phantom. In addition, 9 patients with obstructive sleep apnea and 11 healthy controls were investigated by using a 3T MR imaging/MR spectroscopy scanner. Significantly reduced metabolite differences were observed between patients with obstructive sleep apnea and healthy controls in multiple brain regions: NAA/Cr in the left hippocampus; total Cho/Cr and Glx/Cr in the right hippocampus; total NAA/Cr, taurine/Cr, scyllo-Inositol/Cr, phosphocholine/Cr, and total Cho/Cr in the occipital gray matter; total NAA/Cr and NAA/Cr in the medial frontal white matter; and taurine/Cr and total Cho/Cr in the left frontal white matter regions. The 4D echo-planar J-resolved spectroscopic imaging technique using the nonuniform undersampling-based acquisition and compressed sensing reconstruction in patients with obstructive sleep apnea and healthy brain is feasible in a clinically suitable time. In addition to brain metabolite changes previously reported by 1D MR spectroscopy, our results show changes of additional metabolites in patients with obstructive sleep apnea compared with healthy controls. © 2014 by American Journal of Neuroradiology.

Wavelet-based scalable L-infinity-oriented compression.

PubMed

Alecu, Alin; Munteanu, Adrian; Cornelis, Jan P H; Schelkens, Peter

2006-09-01

Among the different classes of coding techniques proposed in literature, predictive schemes have proven their outstanding performance in near-lossless compression. However, these schemes are incapable of providing embedded L(infinity)-oriented compression, or, at most, provide a very limited number of potential L(infinity) bit-stream truncation points. We propose a new multidimensional wavelet-based L(infinity)-constrained scalable coding framework that generates a fully embedded L(infinity)-oriented bit stream and that retains the coding performance and all the scalability options of state-of-the-art L2-oriented wavelet codecs. Moreover, our codec instantiation of the proposed framework clearly outperforms JPEG2000 in L(infinity) coding sense.

A general mixture theory. I. Mixtures of spherical molecules

NASA Astrophysics Data System (ADS)

Hamad, Esam Z.

1996-08-01

We present a new general theory for obtaining mixture properties from the pure species equations of state. The theory addresses the composition and the unlike interactions dependence of mixture equation of state. The density expansion of the mixture equation gives the exact composition dependence of all virial coefficients. The theory introduces multiple-index parameters that can be calculated from binary unlike interaction parameters. In this first part of the work, details are presented for the first and second levels of approximations for spherical molecules. The second order model is simple and very accurate. It predicts the compressibility factor of additive hard spheres within simulation uncertainty (equimolar with size ratio of three). For nonadditive hard spheres, comparison with compressibility factor simulation data over a wide range of density, composition, and nonadditivity parameter, gave an average error of 2%. For mixtures of Lennard-Jones molecules, the model predictions are better than the Weeks-Chandler-Anderson perturbation theory.

Negative emotional outcomes attenuate sense of agency over voluntary actions.

PubMed

Yoshie, Michiko; Haggard, Patrick

2013-10-21

Sense of agency (SoA) refers to the feeling that one's voluntary actions produce external sensory events [1, 2]. Several psychological theories hypothesized links between SoA and affective evaluation [3-6]. For example, people tend to attribute positive outcomes to their own actions, perhaps reflecting high-level narrative processes that enhance self-esteem [3]. Here we provide the first evidence that such emotional modulations also involve changes in the low-level sensorimotor basis of agency. The intentional binding paradigm [1] was used to quantify the subjective temporal compression between a voluntary action and its sensory consequences, providing an implicit measure of SoA. Emotional valence of action outcomes was manipulated by following participants' key-press actions with negative or positive emotional vocalizations [7], or neutral sounds. We found that intentional binding was reduced for negative compared to positive or neutral outcomes. Discriminant analyses identified a change in time perception of both actions and their negative outcomes, demonstrating that the experience of action itself is subject to affective modulation. A small binding benefit was also found for positive action outcomes. Emotional modulation of SoA may contribute to regulating social behavior. Correctly tracking the valenced effects of one's voluntary actions on other people could underlie successful social interactions. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

Nonlinear anelastic modal theory for solar convection

NASA Technical Reports Server (NTRS)

Latour, J.; Toomre, J.; Zahn, J.-P.

1983-01-01

Solar envelope models are developed using single-mode anelastic equations as a description of turbulent convection which provide estimates for the variation with depth of the largest convective cellular flows, with horizontal sizes comparable to the total depth of the convection zone. These models can be used to describe compressible motions occurring over many density scale heights. Single-mode anelastic solutions are obtained for a solar envelope whose mean stratification is nearly adiabatic over most of its vertical extent because of the enthalpy flux explicitly carried by the big cell, while a subgrid scale representation of turbulent heat transport is incorporated into the treatment near the surface. It is shown that the single-mode equations allow two solutions for the same horizontal wavelength which are distinguished by the sense of the vertical velocity at the center of the three-dimensional cell. It is found that the upward directed flow experiences large pressure effects which can modify the density fluctuations so that the sense of the buoyancy force is changed, with buoyancy braking actually achieved near the top of the convection zone. It is suggested that such dynamical processes may explain why the amplitudes of flows related to the largest scales of convection are so weak in the solar atmosphere.

Extracting neuronal functional network dynamics via adaptive Granger causality analysis.

PubMed

Sheikhattar, Alireza; Miran, Sina; Liu, Ji; Fritz, Jonathan B; Shamma, Shihab A; Kanold, Patrick O; Babadi, Behtash

2018-04-24

Quantifying the functional relations between the nodes in a network based on local observations is a key challenge in studying complex systems. Most existing time series analysis techniques for this purpose provide static estimates of the network properties, pertain to stationary Gaussian data, or do not take into account the ubiquitous sparsity in the underlying functional networks. When applied to spike recordings from neuronal ensembles undergoing rapid task-dependent dynamics, they thus hinder a precise statistical characterization of the dynamic neuronal functional networks underlying adaptive behavior. We develop a dynamic estimation and inference paradigm for extracting functional neuronal network dynamics in the sense of Granger, by integrating techniques from adaptive filtering, compressed sensing, point process theory, and high-dimensional statistics. We demonstrate the utility of our proposed paradigm through theoretical analysis, algorithm development, and application to synthetic and real data. Application of our techniques to two-photon Ca 2+ imaging experiments from the mouse auditory cortex reveals unique features of the functional neuronal network structures underlying spontaneous activity at unprecedented spatiotemporal resolution. Our analysis of simultaneous recordings from the ferret auditory and prefrontal cortical areas suggests evidence for the role of rapid top-down and bottom-up functional dynamics across these areas involved in robust attentive behavior.

A philosophy of science perspective on the quantitative analysis of behavior.

PubMed

Smith, Terry L

2015-05-01

B.F. Skinner argued that the science of behavior would progress more rapidly without appealing to theories of learning. He also suggested that theories in a quite different sense were possible, but that the science of behavior as of 1950 was not ready for them. The following analysis distinguishes between Skinner's two concepts of theory. It argues that theory in the second sense has arisen in the quantitative analysis of behavior. The attempt to give a dynamic account of the static regularities of this theory, however, has produced a theory in the first sense. Within its limited domain, this theory offers a rigorous alternative to cognitive accounts of behavior. Rather than distracting attention from actual behavior, it has now led to novel predictions about it. This article is part of a Special Issue entitled 'SQAB 2014'. Copyright © 2015 Elsevier B.V. All rights reserved.

Cardiac pacemaker dysfunction in children after thoracic drainage catheter manipulation.

PubMed

Lobdell, K W; Walters, H L; Hudson, C; Hakimi, M

1997-05-01

Two children underwent placement of permanent, epicardial-lead, dual-chamber, unipolar pacemaker systems for complete heart block. Postoperatively, both patients demonstrated subcutaneous emphysema-in the area of their pulse generators-temporally related to thoracic catheter manipulation. Acutely, each situation was managed with manual compression of the pulse generator, ascertaining appropriate pacemaker sensing and pacing. Maintenance of compression with pressure dressings, vigilant observation/monitoring, and education of the care givers resulted in satisfactory pacemaker function without invasive intervention.

Designing for Compressive Sensing: Compressive Art, Camouflage, Fonts, and Quick Response Codes

DTIC Science & Technology

2018-01-01

an example where the signal is non-sparse in the standard basis, but sparse in the discrete cosine basis . The top plot shows the signal from the...previous example, now used as sparse discrete cosine transform (DCT) coefficients . The next plot shows the non-sparse signal in the standard...Romberg JK, Tao T. Stable signal recovery from incomplete and inaccurate measurements. Commun Pure Appl Math . 2006;59(8):1207–1223. 3. Donoho DL

Nonuniform dependence on initial data for compressible gas dynamics: The periodic Cauchy problem

NASA Astrophysics Data System (ADS)

Keyfitz, B. L.; Tığlay, F.

2017-11-01

We start with the classic result that the Cauchy problem for ideal compressible gas dynamics is locally well posed in time in the sense of Hadamard; there is a unique solution that depends continuously on initial data in Sobolev space Hs for s > d / 2 + 1 where d is the space dimension. We prove that the data to solution map for periodic data in two dimensions although continuous is not uniformly continuous.

Efficient cooperative compressive spectrum sensing by identifying multi-candidate and exploiting deterministic matrix

NASA Astrophysics Data System (ADS)

Li, Jia; Wang, Qiang; Yan, Wenjie; Shen, Yi

2015-12-01

Cooperative spectrum sensing exploits the spatial diversity to improve the detection of occupied channels in cognitive radio networks (CRNs). Cooperative compressive spectrum sensing (CCSS) utilizing the sparsity of channel occupancy further improves the efficiency by reducing the number of reports without degrading detection performance. In this paper, we firstly and mainly propose the referred multi-candidate orthogonal matrix matching pursuit (MOMMP) algorithms to efficiently and effectively detect occupied channels at fusion center (FC), where multi-candidate identification and orthogonal projection are utilized to respectively reduce the number of required iterations and improve the probability of exact identification. Secondly, two common but different approaches based on threshold and Gaussian distribution are introduced to realize the multi-candidate identification. Moreover, to improve the detection accuracy and energy efficiency, we propose the matrix construction based on shrinkage and gradient descent (MCSGD) algorithm to provide a deterministic filter coefficient matrix of low t-average coherence. Finally, several numerical simulations validate that our proposals provide satisfactory performance with higher probability of detection, lower probability of false alarm and less detection time.

Deterministic matrices matching the compressed sensing phase transitions of Gaussian random matrices

PubMed Central

Monajemi, Hatef; Jafarpour, Sina; Gavish, Matan; Donoho, David L.; Ambikasaran, Sivaram; Bacallado, Sergio; Bharadia, Dinesh; Chen, Yuxin; Choi, Young; Chowdhury, Mainak; Chowdhury, Soham; Damle, Anil; Fithian, Will; Goetz, Georges; Grosenick, Logan; Gross, Sam; Hills, Gage; Hornstein, Michael; Lakkam, Milinda; Lee, Jason; Li, Jian; Liu, Linxi; Sing-Long, Carlos; Marx, Mike; Mittal, Akshay; Monajemi, Hatef; No, Albert; Omrani, Reza; Pekelis, Leonid; Qin, Junjie; Raines, Kevin; Ryu, Ernest; Saxe, Andrew; Shi, Dai; Siilats, Keith; Strauss, David; Tang, Gary; Wang, Chaojun; Zhou, Zoey; Zhu, Zhen

2013-01-01

In compressed sensing, one takes samples of an N-dimensional vector using an matrix A, obtaining undersampled measurements . For random matrices with independent standard Gaussian entries, it is known that, when is k-sparse, there is a precisely determined phase transition: for a certain region in the (,)-phase diagram, convex optimization typically finds the sparsest solution, whereas outside that region, it typically fails. It has been shown empirically that the same property—with the same phase transition location—holds for a wide range of non-Gaussian random matrix ensembles. We report extensive experiments showing that the Gaussian phase transition also describes numerous deterministic matrices, including Spikes and Sines, Spikes and Noiselets, Paley Frames, Delsarte-Goethals Frames, Chirp Sensing Matrices, and Grassmannian Frames. Namely, for each of these deterministic matrices in turn, for a typical k-sparse object, we observe that convex optimization is successful over a region of the phase diagram that coincides with the region known for Gaussian random matrices. Our experiments considered coefficients constrained to for four different sets , and the results establish our finding for each of the four associated phase transitions. PMID:23277588

Determining building interior structures using compressive sensing

NASA Astrophysics Data System (ADS)

Lagunas, Eva; Amin, Moeness G.; Ahmad, Fauzia; Nájar, Montse

2013-04-01

We consider imaging of the building interior structures using compressive sensing (CS) with applications to through-the-wall imaging and urban sensing. We consider a monostatic synthetic aperture radar imaging system employing stepped frequency waveform. The proposed approach exploits prior information of building construction practices to form an appropriate sparse representation of the building interior layout. We devise a dictionary of possible wall locations, which is consistent with the fact that interior walls are typically parallel or perpendicular to the front wall. The dictionary accounts for the dominant normal angle reflections from exterior and interior walls for the monostatic imaging system. CS is applied to a reduced set of observations to recover the true positions of the walls. Additional information about interior walls can be obtained using a dictionary of possible corner reflectors, which is the response of the junction of two walls. Supporting results based on simulation and laboratory experiments are provided. It is shown that the proposed sparsifying basis outperforms the conventional through-the-wall CS model, the wavelet sparsifying basis, and the block sparse model for building interior layout detection.

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.

Collaborative Wideband Compressed Signal Detection in Interplanetary Internet

NASA Astrophysics Data System (ADS)

Wang, Yulin; Zhang, Gengxin; Bian, Dongming; Gou, Liang; Zhang, Wei

2014-07-01

As the development of autonomous radio in deep space network, it is possible to actualize communication between explorers, aircrafts, rovers and satellites, e.g. from different countries, adopting different signal modes. The first mission to enforce the autonomous radio is to detect signals of the explorer autonomously without disturbing the original communication. This paper develops a collaborative wideband compressed signal detection approach for InterPlaNetary (IPN) Internet where there exist sparse active signals in the deep space environment. Compressed sensing (CS) can be utilized by exploiting the sparsity of IPN Internet communication signal, whose useful frequency support occupies only a small portion of an entirely wide spectrum. An estimate of the signal spectrum can be obtained by using reconstruction algorithms. Against deep space shadowing and channel fading, multiple satellites collaboratively sense and make a final decision according to certain fusion rule to gain spatial diversity. A couple of novel discrete cosine transform (DCT) and walsh-hadamard transform (WHT) based compressed spectrum detection methods are proposed which significantly improve the performance of spectrum recovery and signal detection. Finally, extensive simulation results are presented to show the effectiveness of our proposed collaborative scheme for signal detection in IPN Internet. Compared with the conventional discrete fourier transform (DFT) based method, our DCT and WHT based methods reduce computational complexity, decrease processing time, save energy and enhance probability of detection.

Elastic MCF Rubber with Photovoltaics and Sensing for Use as Artificial or Hybrid Skin (H-Skin): 1st Report on Dry-Type Solar Cell Rubber with Piezoelectricity for Compressive Sensing.

PubMed

Shimada, Kunio

2018-06-05

Ordinary solar cells are very difficult to bend, squash by compression, or extend by tensile strength. However, if they were to possess elastic, flexible, and extensible properties, in addition to piezo-electricity and resistivity, they could be put to effective use as artificial skin installed over human-like robots or humanoids. Further, it could serve as a husk that generates electric power from solar energy and perceives any force or temperature changes. Therefore, we propose a new type of artificial skin, called hybrid skin (H-Skin), for a humanoid robot having hybrid functions. In this study, a novel elastic solar cell is developed from natural rubber that is electrolytically polymerized with a configuration of magnetic clusters of metal particles incorporated into the rubber, by applying a magnetic field. The material thus produced is named magnetic compound fluid rubber (MCF rubber) that is elastic, flexible, and extensible. The present report deals with a dry-type MCF rubber solar cell that uses photosensitized dye molecules. First, the photovoltaic mechanism in the material is investigated. Next, the changes in the photovoltaic properties of its molecules due to irradiation by visible light are measured under compression. The effect of the compression on its piezoelectric properties is investigated.

Anisotropic Constitutive Relationships in Energetic Materials: Nitromethane and Rdx

NASA Astrophysics Data System (ADS)

Oleynik, I. I.; Conroy, M.; White, C. T.

2007-12-01

The anisotropic constitutive relationships in solid nitromethane (NM) and α-RDX were studied using first-principles density functional theory (DFT). In addition to hydrostatic compressions, we performed uniaxial compressions in the [100], [010], [001], [110], [101], [011], and [111] directions up to the compression ratio V/V0 = 0.70. Equilibrium properties, including lattice parameters and elastic constants, as well as hydrostatic EOS, are in good agreement with available experimental data. The shear stresses of uniaxially compressed NM and α-RDX were used to predict the relative shock sensitivity between different crystallographic directions.

Accessing the dynamics of end-grafted flexible polymer chains by atomic force-electrochemical microscopy. Theoretical modeling of the approach curves by the elastic bounded diffusion model and Monte Carlo simulations. Evidence for compression-induced lateral chain escape.

PubMed

Abbou, Jeremy; Anne, Agnès; Demaille, Christophe

2006-11-16

The dynamics of a molecular layer of linear poly(ethylene glycol) (PEG) chains of molecular weight 3400, bearing at one end a ferrocene (Fc) label and thiol end-grafted at a low surface coverage onto a gold substrate, is probed using combined atomic force-electrochemical microscopy (AFM-SECM), at the scale of approximately 100 molecules. Force and current approach curves are simultaneously recorded as a force-sensing microelectrode (tip) is inserted within the approximately 10 nm thick, redox labeled, PEG chain layer. Whereas the force approach curve gives access to the structure of the compressed PEG layer, the tip-current, resulting from tip-to-substrate redox cycling of the Fc head of the chain, is controlled by chain dynamics. The elastic bounded diffusion model, which considers the motion of the Fc head as diffusion in a conformational field, complemented by Monte Carlo (MC) simulations, from which the chain conformation can be derived for any degree of confinement, allows the theoretical tip-current approach curve to be calculated. The experimental current approach curve can then be very satisfyingly reproduced by theory, down to a tip-substrate separation of approximately 2 nm, using only one adjustable parameter characterizing the chain dynamics: the effective diffusion coefficient of the chain head. At closer tip-substrate separations, an unpredicted peak is observed in the experimental current approach curve, which is shown to find its origin in a compression-induced escape of the chain from within the narrowing tip-substrate gap. MC simulations provide quantitative support for lateral chain elongation as the escape mechanism.

Enhanced compressed sensing for visual target tracking in wireless visual sensor networks

NASA Astrophysics Data System (ADS)

Qiang, Guo

2017-11-01

Moving object tracking in wireless sensor networks (WSNs) has been widely applied in various fields. Designing low-power WSNs for the limited resources of the sensor, such as energy limitation, energy restriction, and bandwidth constraints, is of high priority. However, most existing works focus on only single conflicting optimization criteria. An efficient compressive sensing technique based on a customized memory gradient pursuit algorithm with early termination in WSNs is presented, which strikes compelling trade-offs among energy dissipation for wireless transmission, certain types of bandwidth, and minimum storage. Then, the proposed approach adopts an unscented particle filter to predict the location of the target. The experimental results with a theoretical analysis demonstrate the substantially superior effectiveness of the proposed model and framework in regard to the energy and speed under the resource limitation of a visual sensor node.

Sparse-View Ultrasound Diffraction Tomography Using Compressed Sensing with Nonuniform FFT

PubMed Central

2014-01-01

Accurate reconstruction of the object from sparse-view sampling data is an appealing issue for ultrasound diffraction tomography (UDT). In this paper, we present a reconstruction method based on compressed sensing framework for sparse-view UDT. Due to the piecewise uniform characteristics of anatomy structures, the total variation is introduced into the cost function to find a more faithful sparse representation of the object. The inverse problem of UDT is iteratively resolved by conjugate gradient with nonuniform fast Fourier transform. Simulation results show the effectiveness of the proposed method that the main characteristics of the object can be properly presented with only 16 views. Compared to interpolation and multiband method, the proposed method can provide higher resolution and lower artifacts with the same view number. The robustness to noise and the computation complexity are also discussed. PMID:24868241

Multicontrast reconstruction using compressed sensing with low rank and spatially varying edge-preserving constraints for high-resolution MR characterization of myocardial infarction.

PubMed

Zhang, Li; Athavale, Prashant; Pop, Mihaela; Wright, Graham A

2017-08-01

To enable robust reconstruction for highly accelerated three-dimensional multicontrast late enhancement imaging to provide improved MR characterization of myocardial infarction with isotropic high spatial resolution. A new method using compressed sensing with low rank and spatially varying edge-preserving constraints (CS-LASER) is proposed to improve the reconstruction of fine image details from highly undersampled data. CS-LASER leverages the low rank feature of the multicontrast volume series in MR relaxation and integrates spatially varying edge preservation into the explicit low rank constrained compressed sensing framework using weighted total variation. With an orthogonal temporal basis pre-estimated, a multiscale iterative reconstruction framework is proposed to enable the practice of CS-LASER with spatially varying weights of appropriate accuracy. In in vivo pig studies with both retrospective and prospective undersamplings, CS-LASER preserved fine image details better and presented tissue characteristics with a higher degree of consistency with histopathology, particularly in the peri-infarct region, than an alternative technique for different acceleration rates. An isotropic resolution of 1.5 mm was achieved in vivo within a single breath-hold using the proposed techniques. Accelerated three-dimensional multicontrast late enhancement with CS-LASER can achieve improved MR characterization of myocardial infarction with high spatial resolution. Magn Reson Med 78:598-610, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

On the security of compressed encryption with partial unitary sensing matrices embedding a secret keystream

NASA Astrophysics Data System (ADS)

Yu, Nam Yul

2017-12-01

The principle of compressed sensing (CS) can be applied in a cryptosystem by providing the notion of security. In this paper, we study the computational security of a CS-based cryptosystem that encrypts a plaintext with a partial unitary sensing matrix embedding a secret keystream. The keystream is obtained by a keystream generator of stream ciphers, where the initial seed becomes the secret key of the CS-based cryptosystem. For security analysis, the total variation distance, bounded by the relative entropy and the Hellinger distance, is examined as a security measure for the indistinguishability. By developing upper bounds on the distance measures, we show that the CS-based cryptosystem can be computationally secure in terms of the indistinguishability, as long as the keystream length for each encryption is sufficiently large with low compression and sparsity ratios. In addition, we consider a potential chosen plaintext attack (CPA) from an adversary, which attempts to recover the key of the CS-based cryptosystem. Associated with the key recovery attack, we show that the computational security of our CS-based cryptosystem is brought by the mathematical intractability of a constrained integer least-squares (ILS) problem. For a sub-optimal, but feasible key recovery attack, we consider a successive approximate maximum-likelihood detection (SAMD) and investigate the performance by developing an upper bound on the success probability. Through theoretical and numerical analyses, we demonstrate that our CS-based cryptosystem can be secure against the key recovery attack through the SAMD.

SparseCT: interrupted-beam acquisition and sparse reconstruction for radiation dose reduction

NASA Astrophysics Data System (ADS)

Koesters, Thomas; Knoll, Florian; Sodickson, Aaron; Sodickson, Daniel K.; Otazo, Ricardo

2017-03-01

State-of-the-art low-dose CT methods reduce the x-ray tube current and use iterative reconstruction methods to denoise the resulting images. However, due to compromises between denoising and image quality, only moderate dose reductions up to 30-40% are accepted in clinical practice. An alternative approach is to reduce the number of x-ray projections and use compressed sensing to reconstruct the full-tube-current undersampled data. This idea was recognized in the early days of compressed sensing and proposals for CT dose reduction appeared soon afterwards. However, no practical means of undersampling has yet been demonstrated in the challenging environment of a rapidly rotating CT gantry. In this work, we propose a moving multislit collimator as a practical incoherent undersampling scheme for compressed sensing CT and evaluate its application for radiation dose reduction. The proposed collimator is composed of narrow slits and moves linearly along the slice dimension (z), to interrupt the incident beam in different slices for each x-ray tube angle (θ). The reduced projection dataset is then reconstructed using a sparse approach, where 3D image gradients are employed to enforce sparsity. The effects of the collimator slits on the beam profile were measured and represented as a continuous slice profile. SparseCT was tested using retrospective undersampling and compared against commercial current-reduction techniques on phantoms and in vivo studies. Initial results suggest that SparseCT may enable higher performance than current-reduction, particularly for high dose reduction factors.

Compressive hyperspectral sensor for LWIR gas detection

NASA Astrophysics Data System (ADS)

Russell, Thomas A.; McMackin, Lenore; Bridge, Bob; Baraniuk, Richard

2012-06-01

Focal plane arrays with associated electronics and cooling are a substantial portion of the cost, complexity, size, weight, and power requirements of Long-Wave IR (LWIR) imagers. Hyperspectral LWIR imagers add significant data volume burden as they collect a high-resolution spectrum at each pixel. We report here on a LWIR Hyperspectral Sensor that applies Compressive Sensing (CS) in order to achieve benefits in these areas. The sensor applies single-pixel detection technology demonstrated by Rice University. The single-pixel approach uses a Digital Micro-mirror Device (DMD) to reflect and multiplex the light from a random assortment of pixels onto the detector. This is repeated for a number of measurements much less than the total number of scene pixels. We have extended this architecture to hyperspectral LWIR sensing by inserting a Fabry-Perot spectrometer in the optical path. This compressive hyperspectral imager collects all three dimensions on a single detection element, greatly reducing the size, weight and power requirements of the system relative to traditional approaches, while also reducing data volume. The CS architecture also supports innovative adaptive approaches to sensing, as the DMD device allows control over the selection of spatial scene pixels to be multiplexed on the detector. We are applying this advantage to the detection of plume gases, by adaptively locating and concentrating target energy. A key challenge in this system is the diffraction loss produce by the DMD in the LWIR. We report the results of testing DMD operation in the LWIR, as well as system spatial and spectral performance.

Postbuckling response of long thick plates loaded in compression including higher order transverse shearing effects

NASA Technical Reports Server (NTRS)

Stein, Manuel; Sydow, P. Daniel; Librescu, Liviu

1990-01-01

Buckling and postbuckling results are presented for compression-loaded simply-supported aluminum plates and composite plates with a symmetric lay-up of thin + or - 45 deg plies composed of many layers. Buckling results for aluminum plates of finite length are given for various length-to-width ratios. Asymptotes to the curves based on buckling results give N(sub xcr) for plates of infinite length. Postbuckling results for plates with transverse shearing flexibility are compared to results from classical theory for various width-to-thickness ratios. Characteristic curves indicating the average longitudinal direct stress resultant as a function of the applied displacements are calculated based on four different theories: Classical von Karman theory using the Kirchoff assumptions, first-order shear deformation theory, higher-order shear deformation theory, and 3-D flexibility theory. Present results indicate that the 3-D flexibility theory gives the lowest buckling loads. The higher-order shear deformation theory has fewer unknowns than the 3-D flexibility theory but does not take into account through-the-thickness effects. The figures presented show that small differences occur in the average longitudinal direct stress resultants from the four theories that are functions of applied end-shortening displacement.

Information theory in econophysics: stock market and retirement funds

NASA Astrophysics Data System (ADS)

Vogel, Eugenio; Saravia, G.; Astete, J.; Díaz, J.; Erribarren, R.; Riadi, F.

2013-03-01

Information theory can help to recognize magnetic phase transitions, what can be seen as a way to recognize different regimes. This is achieved by means of zippers specifically designed to compact data in a meaningful way at is the case for compressor wlzip. In the present contribution we first apply wlzip to the Chilean stock market interpreting the compression rates for the files storing the minute variation of the IPSA indicator. Agitated days yield poor compression rates while calm days yield high compressibility. We then correlate this behavior to the value of the five retirement funds related to the Chilean economy. It is found that the covariance between the profitability of the retirement funds and the compressibility of the IPSA values of previous day is high for those funds investing in risky stocks. Surprisingly, there seems to be no great difference among the three riskier funds contrary to what could be expected from the limitations on the portfolio composition established by the laws that regulate this market.

Method of transmission of dynamic multibit digital images from micro-unmanned aerial vehicles

NASA Astrophysics Data System (ADS)

Petrov, E. P.; Kharina, N. L.

2018-01-01

In connection with successful usage of nanotechnologies in remote sensing great attention is paid to the systems in micro-unmanned aerial vehicles (MUAVs) capable to provide high spatial resolution of dynamic multibit digital images (MDI). Limited energy resources on board the MUAV do not allow transferring a large amount of video information in the shortest possible time. It keeps back the broad development of MUAV. The search for methods to shorten the transmission time of dynamic MDIs from MUAV over the radio channel leads to the methods of MDI compression without computational operations onboard the MUAV. The known compression codecs of video information can not be applied because of the limited energy resources. In this paper we propose a method for reducing the transmission time of dynamic MDIs without computational operations and distortions onboard the MUAV. To develop the method a mathematical apparatus of the theory of conditional Markov processes with discrete arguments was used. On its basis a mathematical model for the transformation of the MDI represented by binary images (BI) in the MDI, consisting of groups of neighboring BIs (GBI) transmitted by multiphase (MP) signals, is constructed. The algorithm for multidimensional nonlinear filtering of MP signals is synthesized, realizing the statistical redundancy of the MDI to compensate for the noise stability losses caused by the use of MP signals.

Buckling behavior of composite cylinders subjected to compressive loading

NASA Technical Reports Server (NTRS)

Carri, R. L.

1973-01-01

Room temperature compressive buckling strengths of eight cylinders, four boron-epoxy and four boron-epoxy reinforced-titanium, with diameter to thickness ratios ranging between 40 and 67 are determined experimentally and compared with analytical predictions. Numerical buckling strengths are presented for Donnell's, Flugge's and Sanders' shell theories for anisotropic and orthotropic material cases. Comparison of analytical predictions with experimental results indicates good agreement and the recommended correlation factor suggested in the literature is applicable for design. For the cylinders tested, the correlation between experiment and theory ranged from 0.73 to 0.97.

Laser-pulse compression in a collisional plasma under weak-relativistic ponderomotive nonlinearity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Singh, Mamta; Gupta, D. N., E-mail: dngupta@physics.du.ac.in

We present theory and numerical analysis which demonstrate laser-pulse compression in a collisional plasma under the weak-relativistic ponderomotive nonlinearity. Plasma equilibrium density is modified due to the ohmic heating of electrons, the collisions, and the weak relativistic-ponderomotive force during the interaction of a laser pulse with plasmas. First, within one-dimensional analysis, the longitudinal self-compression mechanism is discussed. Three-dimensional analysis (spatiotemporal) of laser pulse propagation is also investigated by coupling the self-compression with the self-focusing. In the regime in which the laser becomes self-focused due to the weak relativistic-ponderomotive nonlinearity, we provide results for enhanced pulse compression. The results show thatmore » the matched interplay between self-focusing and self-compression can improve significantly the temporal profile of the compressed pulse. Enhanced pulse compression can be achieved by optimizing and selecting the parameters such as collision frequency, ion-temperature, and laser intensity.« less

[Effects of real-time audiovisual feedback on secondary-school students' performance of chest compressions].

PubMed

Abelairas-Gómez, Cristian; Rodríguez-Núñez, Antonio; Vilas-Pintos, Elisardo; Prieto Saborit, José Antonio; Barcala-Furelos, Roberto

2015-06-01

To describe the quality of chest compressions performed by secondary-school students trained with a realtime audiovisual feedback system. The learners were 167 students aged 12 to 15 years who had no prior experience with cardiopulmonary resuscitation (CPR). They received an hour of instruction in CPR theory and practice and then took a 2-minute test, performing hands-only CPR on a child mannequin (Prestan Professional Child Manikin). Lights built into the mannequin gave learners feedback about how many compressions they had achieved and clicking sounds told them when compressions were deep enough. All the learners were able to maintain a steady enough rhythm of compressions and reached at least 80% of the targeted compression depth. Fewer correct compressions were done in the second minute than in the first (P=.016). Real-time audiovisual feedback helps schoolchildren aged 12 to 15 years to achieve quality chest compressions on a mannequin.

Radar Range Sidelobe Reduction Using Adaptive Pulse Compression Technique

NASA Technical Reports Server (NTRS)

Li, Lihua; Coon, Michael; McLinden, Matthew

2013-01-01

Pulse compression has been widely used in radars so that low-power, long RF pulses can be transmitted, rather than a highpower short pulse. Pulse compression radars offer a number of advantages over high-power short pulsed radars, such as no need of high-power RF circuitry, no need of high-voltage electronics, compact size and light weight, better range resolution, and better reliability. However, range sidelobe associated with pulse compression has prevented the use of this technique on spaceborne radars since surface returns detected by range sidelobes may mask the returns from a nearby weak cloud or precipitation particles. Research on adaptive pulse compression was carried out utilizing a field-programmable gate array (FPGA) waveform generation board and a radar transceiver simulator. The results have shown significant improvements in pulse compression sidelobe performance. Microwave and millimeter-wave radars present many technological challenges for Earth and planetary science applications. The traditional tube-based radars use high-voltage power supply/modulators and high-power RF transmitters; therefore, these radars usually have large size, heavy weight, and reliability issues for space and airborne platforms. Pulse compression technology has provided a path toward meeting many of these radar challenges. Recent advances in digital waveform generation, digital receivers, and solid-state power amplifiers have opened a new era for applying pulse compression to the development of compact and high-performance airborne and spaceborne remote sensing radars. The primary objective of this innovative effort is to develop and test a new pulse compression technique to achieve ultrarange sidelobes so that this technique can be applied to spaceborne, airborne, and ground-based remote sensing radars to meet future science requirements. By using digital waveform generation, digital receiver, and solid-state power amplifier technologies, this improved pulse compression technique could bring significant impact on future radar development. The novel feature of this innovation is the non-linear FM (NLFM) waveform design. The traditional linear FM has the limit (-20 log BT -3 dB) for achieving ultra-low-range sidelobe in pulse compression. For this study, a different combination of 20- or 40-microsecond chirp pulse width and 2- or 4-MHz chirp bandwidth was used. These are typical operational parameters for airborne or spaceborne weather radars. The NLFM waveform design was then implemented on a FPGA board to generate a real chirp signal, which was then sent to the radar transceiver simulator. The final results have shown significant improvement on sidelobe performance compared to that obtained using a traditional linear FM chirp.

Holographic techniques for cellular fluorescence microscopy

NASA Astrophysics Data System (ADS)

Kim, Myung K.

2017-04-01

We have constructed a prototype instrument for holographic fluorescence microscopy (HFM) based on self-interference incoherent digital holography (SIDH) and demonstrate novel imaging capabilities such as differential 3D fluorescence microscopy and optical sectioning by compressive sensing.

GPU-accelerated algorithms for compressed signals recovery with application to astronomical imagery deblurring

NASA Astrophysics Data System (ADS)

Fiandrotti, Attilio; Fosson, Sophie M.; Ravazzi, Chiara; Magli, Enrico

2018-04-01

Compressive sensing promises to enable bandwidth-efficient on-board compression of astronomical data by lifting the encoding complexity from the source to the receiver. The signal is recovered off-line, exploiting GPUs parallel computation capabilities to speedup the reconstruction process. However, inherent GPU hardware constraints limit the size of the recoverable signal and the speedup practically achievable. In this work, we design parallel algorithms that exploit the properties of circulant matrices for efficient GPU-accelerated sparse signals recovery. Our approach reduces the memory requirements, allowing us to recover very large signals with limited memory. In addition, it achieves a tenfold signal recovery speedup thanks to ad-hoc parallelization of matrix-vector multiplications and matrix inversions. Finally, we practically demonstrate our algorithms in a typical application of circulant matrices: deblurring a sparse astronomical image in the compressed domain.

Quantum Tomography via Compressed Sensing: Error Bounds, Sample Complexity and Efficient Estimators (Open Access, Publisher’s Version)

DTIC Science & Technology

2012-09-27

we require no entangling gates or ancillary systems for the procedure. In contrast with [19], our method is not restricted to processes that are...states, such as those recently developed for use with permutation-invariant states [60], matrix product states [61] or multi-scale entangled states [62...by adjoining an ancilla, preparing the maximally entangled state |ψ0〉, and applying E); then do compressed quantum state tomography on ρE ; see

Optimization of compressive 4D-spatio-spectral snapshot imaging

NASA Astrophysics Data System (ADS)

Zhao, Xia; Feng, Weiyi; Lin, Lihua; Su, Wu; Xu, Guoqing

2017-10-01

In this paper, a modified 3D computational reconstruction method in the compressive 4D-spectro-volumetric snapshot imaging system is proposed for better sensing spectral information of 3D objects. In the design of the imaging system, a microlens array (MLA) is used to obtain a set of multi-view elemental images (EIs) of the 3D scenes. Then, these elemental images with one dimensional spectral information and different perspectives are captured by the coded aperture snapshot spectral imager (CASSI) which can sense the spectral data cube onto a compressive 2D measurement image. Finally, the depth images of 3D objects at arbitrary depths, like a focal stack, are computed by inversely mapping the elemental images according to geometrical optics. With the spectral estimation algorithm, the spectral information of 3D objects is also reconstructed. Using a shifted translation matrix, the contrast of the reconstruction result is further enhanced. Numerical simulation results verify the performance of the proposed method. The system can obtain both 3D spatial information and spectral data on 3D objects using only one single snapshot, which is valuable in the agricultural harvesting robots and other 3D dynamic scenes.

D-DSC: Decoding Delay-based Distributed Source Coding for Internet of Sensing Things

PubMed Central

Akan, Ozgur B.

2018-01-01

Spatial correlation between densely deployed sensor nodes in a wireless sensor network (WSN) can be exploited to reduce the power consumption through a proper source coding mechanism such as distributed source coding (DSC). In this paper, we propose the Decoding Delay-based Distributed Source Coding (D-DSC) to improve the energy efficiency of the classical DSC by employing the decoding delay concept which enables the use of the maximum correlated portion of sensor samples during the event estimation. In D-DSC, network is partitioned into clusters, where the clusterheads communicate their uncompressed samples carrying the side information, and the cluster members send their compressed samples. Sink performs joint decoding of the compressed and uncompressed samples and then reconstructs the event signal using the decoded sensor readings. Based on the observed degree of the correlation among sensor samples, the sink dynamically updates and broadcasts the varying compression rates back to the sensor nodes. Simulation results for the performance evaluation reveal that D-DSC can achieve reliable and energy-efficient event communication and estimation for practical signal detection/estimation applications having massive number of sensors towards the realization of Internet of Sensing Things (IoST). PMID:29538405

Evaluation of Digital Compressed Sensing for Real-Time Wireless ECG System with Bluetooth low Energy.

PubMed

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.

Wireless Sensor Array Network DoA Estimation from Compressed Array Data via Joint Sparse Representation.

PubMed

Yu, Kai; Yin, Ming; Luo, Ji-An; Wang, Yingguan; Bao, Ming; Hu, Yu-Hen; Wang, Zhi

2016-05-23

A compressive sensing joint sparse representation direction of arrival estimation (CSJSR-DoA) approach is proposed for wireless sensor array networks (WSAN). By exploiting the joint spatial and spectral correlations of acoustic sensor array data, the CSJSR-DoA approach provides reliable DoA estimation using randomly-sampled acoustic sensor data. Since random sampling is performed at remote sensor arrays, less data need to be transmitted over lossy wireless channels to the fusion center (FC), and the expensive source coding operation at sensor nodes can be avoided. To investigate the spatial sparsity, an upper bound of the coherence of incoming sensor signals is derived assuming a linear sensor array configuration. This bound provides a theoretical constraint on the angular separation of acoustic sources to ensure the spatial sparsity of the received acoustic sensor array signals. The Cram e ´ r-Rao bound of the CSJSR-DoA estimator that quantifies the theoretical DoA estimation performance is also derived. The potential performance of the CSJSR-DoA approach is validated using both simulations and field experiments on a prototype WSAN platform. Compared to existing compressive sensing-based DoA estimation methods, the CSJSR-DoA approach shows significant performance improvement.

Securing While Sampling in Wireless Body Area Networks With Application to Electrocardiography.

PubMed

Dautov, Ruslan; Tsouri, Gill R

2016-01-01

Stringent resource constraints and broadcast transmission in wireless body area network raise serious security concerns when employed in biomedical applications. Protecting data transmission where any minor alteration is potentially harmful is of significant importance in healthcare. Traditional security methods based on public or private key infrastructure require considerable memory and computational resources, and present an implementation obstacle in compact sensor nodes. This paper proposes a lightweight encryption framework augmenting compressed sensing with wireless physical layer security. Augmenting compressed sensing to secure information is based on the use of the measurement matrix as an encryption key, and allows for incorporating security in addition to compression at the time of sampling an analog signal. The proposed approach eliminates the need for a separate encryption algorithm, as well as the predeployment of a key thereby conserving sensor node's limited resources. The proposed framework is evaluated using analysis, simulation, and experimentation applied to a wireless electrocardiogram setup consisting of a sensor node, an access point, and an eavesdropper performing a proximity attack. Results show that legitimate communication is reliable and secure given that the eavesdropper is located at a reasonable distance from the sensor node and the access point.

Multiple-image encryption based on double random phase encoding and compressive sensing by using a measurement array preprocessed with orthogonal-basis matrices

NASA Astrophysics Data System (ADS)

Zhang, Luozhi; Zhou, Yuanyuan; Huo, Dongming; Li, Jinxi; Zhou, Xin

2018-09-01

A method is presented for multiple-image encryption by using the combination of orthogonal encoding and compressive sensing based on double random phase encoding. As an original thought in optical encryption, it is demonstrated theoretically and carried out by using the orthogonal-basis matrices to build a modified measurement array, being projected onto the images. In this method, all the images can be compressed in parallel into a stochastic signal and be diffused to be a stationary white noise. Meanwhile, each single-image can be separately reestablished by adopting a proper decryption key combination through the block-reconstruction rather than the entire-rebuilt, for its costs of data and decryption time are greatly decreased, which may be promising both in multi-user multiplexing and huge-image encryption/decryption. Besides, the security of this method is characterized by using the bit-length of key, and the parallelism is investigated as well. The simulations and discussions are also made on the effects of decryption as well as the correlation coefficient by using a series of sampling rates, occlusion attacks, keys with various error rates, etc.

D-DSC: Decoding Delay-based Distributed Source Coding for Internet of Sensing Things.

PubMed

Aktas, Metin; Kuscu, Murat; Dinc, Ergin; Akan, Ozgur B

2018-01-01

Spatial correlation between densely deployed sensor nodes in a wireless sensor network (WSN) can be exploited to reduce the power consumption through a proper source coding mechanism such as distributed source coding (DSC). In this paper, we propose the Decoding Delay-based Distributed Source Coding (D-DSC) to improve the energy efficiency of the classical DSC by employing the decoding delay concept which enables the use of the maximum correlated portion of sensor samples during the event estimation. In D-DSC, network is partitioned into clusters, where the clusterheads communicate their uncompressed samples carrying the side information, and the cluster members send their compressed samples. Sink performs joint decoding of the compressed and uncompressed samples and then reconstructs the event signal using the decoded sensor readings. Based on the observed degree of the correlation among sensor samples, the sink dynamically updates and broadcasts the varying compression rates back to the sensor nodes. Simulation results for the performance evaluation reveal that D-DSC can achieve reliable and energy-efficient event communication and estimation for practical signal detection/estimation applications having massive number of sensors towards the realization of Internet of Sensing Things (IoST).

Single-scan patient-specific scatter correction in computed tomography using peripheral detection of scatter and compressed sensing scatter retrieval

PubMed Central

Meng, Bowen; Lee, Ho; Xing, Lei; Fahimian, Benjamin P.

2013-01-01

Purpose: X-ray scatter results in a significant degradation of image quality in computed tomography (CT), representing a major limitation in cone-beam CT (CBCT) and large field-of-view diagnostic scanners. In this work, a novel scatter estimation and correction technique is proposed that utilizes peripheral detection of scatter during the patient scan to simultaneously acquire image and patient-specific scatter information in a single scan, and in conjunction with a proposed compressed sensing scatter recovery technique to reconstruct and correct for the patient-specific scatter in the projection space. Methods: The method consists of the detection of patient scatter at the edges of the field of view (FOV) followed by measurement based compressed sensing recovery of the scatter through-out the projection space. In the prototype implementation, the kV x-ray source of the Varian TrueBeam OBI system was blocked at the edges of the projection FOV, and the image detector in the corresponding blocked region was used for scatter detection. The design enables image data acquisition of the projection data on the unblocked central region of and scatter data at the blocked boundary regions. For the initial scatter estimation on the central FOV, a prior consisting of a hybrid scatter model that combines the scatter interpolation method and scatter convolution model is estimated using the acquired scatter distribution on boundary region. With the hybrid scatter estimation model, compressed sensing optimization is performed to generate the scatter map by penalizing the L1 norm of the discrete cosine transform of scatter signal. The estimated scatter is subtracted from the projection data by soft-tuning, and the scatter-corrected CBCT volume is obtained by the conventional Feldkamp-Davis-Kress algorithm. Experimental studies using image quality and anthropomorphic phantoms on a Varian TrueBeam system were carried out to evaluate the performance of the proposed scheme. Results: The scatter shading artifacts were markedly suppressed in the reconstructed images using the proposed method. On the Catphan©504 phantom, the proposed method reduced the error of CT number to 13 Hounsfield units, 10% of that without scatter correction, and increased the image contrast by a factor of 2 in high-contrast regions. On the anthropomorphic phantom, the spatial nonuniformity decreased from 10.8% to 6.8% after correction. Conclusions: A novel scatter correction method, enabling unobstructed acquisition of the high frequency image data and concurrent detection of the patient-specific low frequency scatter data at the edges of the FOV, is proposed and validated in this work. Relative to blocker based techniques, rather than obstructing the central portion of the FOV which degrades and limits the image reconstruction, compressed sensing is used to solve for the scatter from detection of scatter at the periphery of the FOV, enabling for the highest quality reconstruction in the central region and robust patient-specific scatter correction. PMID:23298098

Estimation and Compression over Large Alphabets

ERIC Educational Resources Information Center

Acharya, Jayadev

2014-01-01

Compression, estimation, and prediction are basic problems in Information theory, statistics and machine learning. These problems have been extensively studied in all these fields, though the primary focus in a large portion of the work has been on understanding and solving the problems in the asymptotic regime, "i.e." the alphabet size…

A theory of post-stall transients in axial compression systems. I - Development of equations

NASA Technical Reports Server (NTRS)

Moore, F. K.; Greitzer, E. M.

1985-01-01

An approximate theory is presented for post-stall transients in multistage axial compression systems. The theory leads to a set of three simultaneous nonlinear third-order partial differential equations for pressure rise, and average and disturbed values of flow coefficient, as functions of time and angle around the compressor. By a Galerkin procedure, angular dependence is averaged, and the equations become first order in time. These final equations are capable of describing the growth and possible decay of a rotating-stall cell during a compressor mass-flow transient. It is shown how rotating-stall-like and surgelike motions are coupled through these equations, and also how the instantaneous compressor pumping characteristic changes during the transient stall process.

Compressed sparse tensor based quadrature for vibrational quantum mechanics integrals

DOE PAGES

Rai, Prashant; Sargsyan, Khachik; Najm, Habib N.

2018-03-20

A new method for fast evaluation of high dimensional integrals arising in quantum mechanics is proposed. Here, the method is based on sparse approximation of a high dimensional function followed by a low-rank compression. In the first step, we interpret the high dimensional integrand as a tensor in a suitable tensor product space and determine its entries by a compressed sensing based algorithm using only a few function evaluations. Secondly, we implement a rank reduction strategy to compress this tensor in a suitable low-rank tensor format using standard tensor compression tools. This allows representing a high dimensional integrand function asmore » a small sum of products of low dimensional functions. Finally, a low dimensional Gauss–Hermite quadrature rule is used to integrate this low-rank representation, thus alleviating the curse of dimensionality. Finally, numerical tests on synthetic functions, as well as on energy correction integrals for water and formaldehyde molecules demonstrate the efficiency of this method using very few function evaluations as compared to other integration strategies.« less

Compressed sparse tensor based quadrature for vibrational quantum mechanics integrals

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rai, Prashant; Sargsyan, Khachik; Najm, Habib N.

A new method for fast evaluation of high dimensional integrals arising in quantum mechanics is proposed. Here, the method is based on sparse approximation of a high dimensional function followed by a low-rank compression. In the first step, we interpret the high dimensional integrand as a tensor in a suitable tensor product space and determine its entries by a compressed sensing based algorithm using only a few function evaluations. Secondly, we implement a rank reduction strategy to compress this tensor in a suitable low-rank tensor format using standard tensor compression tools. This allows representing a high dimensional integrand function asmore » a small sum of products of low dimensional functions. Finally, a low dimensional Gauss–Hermite quadrature rule is used to integrate this low-rank representation, thus alleviating the curse of dimensionality. Finally, numerical tests on synthetic functions, as well as on energy correction integrals for water and formaldehyde molecules demonstrate the efficiency of this method using very few function evaluations as compared to other integration strategies.« less

Entropy-aware projected Landweber reconstruction for quantized block compressive sensing of aerial imagery

NASA Astrophysics Data System (ADS)

Liu, Hao; Li, Kangda; Wang, Bing; Tang, Hainie; Gong, Xiaohui

2017-01-01

A quantized block compressive sensing (QBCS) framework, which incorporates the universal measurement, quantization/inverse quantization, entropy coder/decoder, and iterative projected Landweber reconstruction, is summarized. Under the QBCS framework, this paper presents an improved reconstruction algorithm for aerial imagery, QBCS, with entropy-aware projected Landweber (QBCS-EPL), which leverages the full-image sparse transform without Wiener filter and an entropy-aware thresholding model for wavelet-domain image denoising. Through analyzing the functional relation between the soft-thresholding factors and entropy-based bitrates for different quantization methods, the proposed model can effectively remove wavelet-domain noise of bivariate shrinkage and achieve better image reconstruction quality. For the overall performance of QBCS reconstruction, experimental results demonstrate that the proposed QBCS-EPL algorithm significantly outperforms several existing algorithms. With the experiment-driven methodology, the QBCS-EPL algorithm can obtain better reconstruction quality at a relatively moderate computational cost, which makes it more desirable for aerial imagery applications.

Near-source noise suppression of AMT by compressive sensing and mathematical morphology filtering

NASA Astrophysics Data System (ADS)

Li, Guang; Xiao, Xiao; Tang, Jing-Tian; Li, Jin; Zhu, Hui-Jie; Zhou, Cong; Yan, Fa-Bao

2017-12-01

In deep mineral exploration, the acquisition of audio magnetotelluric (AMT) data is severely affected by ambient noise near the observation sites; This near-field noise restricts investigation depths. Mathematical morphological filtering (MMF) proved effective in suppressing large-scale strong and variably shaped noise, typically low-frequency noise, but can not deal with pulse noise of AMT data. We combine compressive sensing and MMF. First, we use MMF to suppress the large-scale strong ambient noise; second, we use the improved orthogonal match pursuit (IOMP) algorithm to remove the residual pulse noise. To remove the noise and protect the useful AMT signal, a redundant dictionary that matches with spikes and is insensitive to the useful signal is designed. Synthetic and field data from the Luzong field suggest that the proposed method suppresses the near-source noise and preserves the signal well; thus, better results are obtained that improve the output of either MMF or IOMP.

Single image non-uniformity correction using compressive sensing

NASA Astrophysics Data System (ADS)

Jian, Xian-zhong; Lu, Rui-zhi; Guo, Qiang; Wang, Gui-pu

2016-05-01

A non-uniformity correction (NUC) method for an infrared focal plane array imaging system was proposed. The algorithm, based on compressive sensing (CS) of single image, overcame the disadvantages of "ghost artifacts" and bulk calculating costs in traditional NUC algorithms. A point-sampling matrix was designed to validate the measurements of CS on the time domain. The measurements were corrected using the midway infrared equalization algorithm, and the missing pixels were solved with the regularized orthogonal matching pursuit algorithm. Experimental results showed that the proposed method can reconstruct the entire image with only 25% pixels. A small difference was found between the correction results using 100% pixels and the reconstruction results using 40% pixels. Evaluation of the proposed method on the basis of the root-mean-square error, peak signal-to-noise ratio, and roughness index (ρ) proved the method to be robust and highly applicable.

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.

Compressive sensing based machine learning strategy for characterizing the flow around a cylinder with limited pressure measurements

NASA Astrophysics Data System (ADS)

Bright, Ido; Lin, Guang; Kutz, J. Nathan

2013-12-01

Compressive sensing is used to determine the flow characteristics around a cylinder (Reynolds number and pressure/flow field) from a sparse number of pressure measurements on the cylinder. Using a supervised machine learning strategy, library elements encoding the dimensionally reduced dynamics are computed for various Reynolds numbers. Convex L1 optimization is then used with a limited number of pressure measurements on the cylinder to reconstruct, or decode, the full pressure field and the resulting flow field around the cylinder. Aside from the highly turbulent regime (large Reynolds number) where only the Reynolds number can be identified, accurate reconstruction of the pressure field and Reynolds number is achieved. The proposed data-driven strategy thus achieves encoding of the fluid dynamics using the L2 norm, and robust decoding (flow field reconstruction) using the sparsity promoting L1 norm.

Demonstration of temperature imaging by H₂O absorption spectroscopy using compressed sensing tomography.

PubMed

An, Xinliang; Brittelle, Mack S; Lauzier, Pascal T; Gord, James R; Roy, Sukesh; Chen, Guang-Hong; Sanders, Scott T

2015-11-01

This paper introduces temperature imaging by total-variation-based compressed sensing (CS) tomography of H2O vapor absorption spectroscopy. A controlled laboratory setup is used to generate a constant two-dimensional temperature distribution in air (a roughly Gaussian temperature profile with a central temperature of 677 K). A wavelength-tunable laser beam is directed through the known distribution; the beam is translated and rotated using motorized stages to acquire complete absorption spectra in the 1330-1365 nm range at each of 64 beam locations and 60 view angles. Temperature reconstructions are compared to independent thermocouple measurements. Although the distribution studied is approximately axisymmetric, axisymmetry is not assumed and simulations show similar performance for arbitrary temperature distributions. We study the measurement error as a function of number of beams and view angles used in reconstruction to gauge the potential for application of CS in practical test articles where optical access is limited.

Compressed-sensing wavenumber-scanning interferometry

NASA Astrophysics Data System (ADS)

Bai, Yulei; Zhou, Yanzhou; He, Zhaoshui; Ye, Shuangli; Dong, Bo; Xie, Shengli

2018-01-01

The Fourier transform (FT), the nonlinear least-squares algorithm (NLSA), and eigenvalue decomposition algorithm (EDA) are used to evaluate the phase field in depth-resolved wavenumber-scanning interferometry (DRWSI). However, because the wavenumber series of the laser's output is usually accompanied by nonlinearity and mode-hop, FT, NLSA, and EDA, which are only suitable for equidistant interference data, often lead to non-negligible phase errors. In this work, a compressed-sensing method for DRWSI (CS-DRWSI) is proposed to resolve this problem. By using the randomly spaced inverse Fourier matrix and solving the underdetermined equation in the wavenumber domain, CS-DRWSI determines the nonuniform sampling and spectral leakage of the interference spectrum. Furthermore, it can evaluate interference data without prior knowledge of the object. The experimental results show that CS-DRWSI improves the depth resolution and suppresses sidelobes. It can replace the FT as a standard algorithm for DRWSI.

Quantitative Inspection of Remanence of Broken Wire Rope Based on Compressed Sensing.

PubMed

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.

Compressive spherical beamforming for localization of incipient tip vortex cavitation.

PubMed

Choo, Youngmin; Seong, Woojae

2016-12-01

Noises by incipient propeller tip vortex cavitation (TVC) are generally generated at regions near the propeller tip. Localization of these sparse noises is performed using compressive sensing (CS) with measurement data from cavitation tunnel experiments. Since initial TVC sound radiates in all directions as a monopole source, a sensing matrix for CS is formulated by adopting spherical beamforming. CS localization is examined with known source acoustic measurements, where the CS estimated source position coincides with the known source position. Afterwards, CS is applied to initial cavitation noise cases. The result of cavitation localization was detected near the upper downstream area of the propeller and showed less ambiguity compared to Bartlett spherical beamforming. Standard constraint in CS was modified by exploiting the physical features of cavitation to suppress remaining ambiguity. CS localization of TVC using the modified constraint is shown according to cavitation numbers and compared to high-speed camera images.

High-Performance 3D Compressive Sensing MRI Reconstruction Using Many-Core Architectures.

PubMed

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.

Quantitative Inspection of Remanence of Broken Wire Rope Based on Compressed Sensing

PubMed Central

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

Optimized Projection Matrix for Compressive Sensing

NASA Astrophysics Data System (ADS)

Xu, Jianping; Pi, Yiming; Cao, Zongjie

2010-12-01

Compressive sensing (CS) is mainly concerned with low-coherence pairs, since the number of samples needed to recover the signal is proportional to the mutual coherence between projection matrix and sparsifying matrix. Until now, papers on CS always assume the projection matrix to be a random matrix. In this paper, aiming at minimizing the mutual coherence, a method is proposed to optimize the projection matrix. This method is based on equiangular tight frame (ETF) design because an ETF has minimum coherence. It is impossible to solve the problem exactly because of the complexity. Therefore, an alternating minimization type method is used to find a feasible solution. The optimally designed projection matrix can further reduce the necessary number of samples for recovery or improve the recovery accuracy. The proposed method demonstrates better performance than conventional optimization methods, which brings benefits to both basis pursuit and orthogonal matching pursuit.

LCAMP: Location Constrained Approximate Message Passing for Compressed Sensing MRI

PubMed Central

Sung, Kyunghyun; Daniel, Bruce L; Hargreaves, Brian A

2016-01-01

Iterative thresholding methods have been extensively studied as faster alternatives to convex optimization methods for solving large-sized problems in compressed sensing. A novel iterative thresholding method called LCAMP (Location Constrained Approximate Message Passing) is presented for reducing computational complexity and improving reconstruction accuracy when a nonzero location (or sparse support) constraint can be obtained from view shared images. LCAMP modifies the existing approximate message passing algorithm by replacing the thresholding stage with a location constraint, which avoids adjusting regularization parameters or thresholding levels. This work is first compared with other conventional reconstruction methods using random 1D signals and then applied to dynamic contrast-enhanced breast MRI to demonstrate the excellent reconstruction accuracy (less than 2% absolute difference) and low computation time (5 - 10 seconds using Matlab) with highly undersampled 3D data (244 × 128 × 48; overall reduction factor = 10). PMID:23042658

An adaptive image sparse reconstruction method combined with nonlocal similarity and cosparsity for mixed Gaussian-Poisson noise removal

NASA Astrophysics Data System (ADS)

Chen, Yong-fei; Gao, Hong-xia; Wu, Zi-ling; Kang, Hui

2018-01-01

Compressed sensing (CS) has achieved great success in single noise removal. However, it cannot restore the images contaminated with mixed noise efficiently. This paper introduces nonlocal similarity and cosparsity inspired by compressed sensing to overcome the difficulties in mixed noise removal, in which nonlocal similarity explores the signal sparsity from similar patches, and cosparsity assumes that the signal is sparse after a possibly redundant transform. Meanwhile, an adaptive scheme is designed to keep the balance between mixed noise removal and detail preservation based on local variance. Finally, IRLSM and RACoSaMP are adopted to solve the objective function. Experimental results demonstrate that the proposed method is superior to conventional CS methods, like K-SVD and state-of-art method nonlocally centralized sparse representation (NCSR), in terms of both visual results and quantitative measures.

Adaptive Data Aggregation and Compression to Improve Energy Utilization in Solar-Powered Wireless Sensor Networks

PubMed Central

Yoon, Ikjune; Kim, Hyeok; Noh, Dong Kun

2017-01-01

A node in a solar-powered wireless sensor network (WSN) collects energy when the sun shines and stores it in a battery or capacitor for use when no solar power is available, in particular at night. In our scheme, each tiny node in a WSN periodically determines its energy budget, which takes into account its residual energy, and its likely acquisition and consumption. If it expects to acquire more energy than it can store, the data which has it has sensed is aggregated with data from other nodes, compressed, and transmitted. Otherwise, the node continues to sense data, but turns off its wireless communication to reduce energy consumption. We compared several schemes by simulation. Our scheme reduced the number of nodes forced to black out due to lack of energy so that more data arrives at the sink node. PMID:28555010

Adaptive Data Aggregation and Compression to Improve Energy Utilization in Solar-Powered Wireless Sensor Networks.

PubMed

Yoon, Ikjune; Kim, Hyeok; Noh, Dong Kun

2017-05-27

A node in a solar-powered wireless sensor network (WSN) collects energy when the sun shines and stores it in a battery or capacitor for use when no solar power is available, in particular at night. In our scheme, each tiny node in a WSN periodically determines its energy budget, which takes into account its residual energy, and its likely acquisition and consumption. If it expects to acquire more energy than it can store, the data which has it has sensed is aggregated with data from other nodes, compressed, and transmitted. Otherwise, the node continues to sense data, but turns off its wireless communication to reduce energy consumption. We compared several schemes by simulation. Our scheme reduced the number of nodes forced to black out due to lack of energy so that more data arrives at the sink node.

Under-sampling trajectory design for compressed sensing based DCE-MRI.

PubMed

Liu, Duan-duan; Liang, Dong; Zhang, Na; Liu, Xin; Zhang, Yuan-ting

2013-01-01

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) needs high temporal and spatial resolution to accurately estimate quantitative parameters and characterize tumor vasculature. Compressed Sensing (CS) has the potential to accomplish this mutual importance. However, the randomness in CS under-sampling trajectory designed using the traditional variable density (VD) scheme may translate to uncertainty in kinetic parameter estimation when high reduction factors are used. Therefore, accurate parameter estimation using VD scheme usually needs multiple adjustments on parameters of Probability Density Function (PDF), and multiple reconstructions even with fixed PDF, which is inapplicable for DCE-MRI. In this paper, an under-sampling trajectory design which is robust to the change on PDF parameters and randomness with fixed PDF is studied. The strategy is to adaptively segment k-space into low-and high frequency domain, and only apply VD scheme in high-frequency domain. Simulation results demonstrate high accuracy and robustness comparing to VD design.

A guided wave dispersion compensation method based on compressed sensing

NASA Astrophysics Data System (ADS)

Xu, Cai-bin; Yang, Zhi-bo; Chen, Xue-feng; Tian, Shao-hua; Xie, Yong

2018-03-01

The ultrasonic guided wave has emerged as a promising tool for structural health monitoring (SHM) and nondestructive testing (NDT) due to their capability to propagate over long distances with minimal loss and sensitivity to both surface and subsurface defects. The dispersion effect degrades the temporal and spatial resolution of guided waves. A novel ultrasonic guided wave processing method for both single mode and multi-mode guided waves dispersion compensation is proposed in this work based on compressed sensing, in which a dispersion signal dictionary is built by utilizing the dispersion curves of the guided wave modes in order to sparsely decompose the recorded dispersive guided waves. Dispersion-compensated guided waves are obtained by utilizing a non-dispersion signal dictionary and the results of sparse decomposition. Numerical simulations and experiments are implemented to verify the effectiveness of the developed method for both single mode and multi-mode guided waves.

A sparse equivalent source method for near-field acoustic holography.

PubMed

Fernandez-Grande, Efren; Xenaki, Angeliki; Gerstoft, Peter

2017-01-01

This study examines a near-field acoustic holography method consisting of a sparse formulation of the equivalent source method, based on the compressive sensing (CS) framework. The method, denoted Compressive-Equivalent Source Method (C-ESM), encourages spatially sparse solutions (based on the superposition of few waves) that are accurate when the acoustic sources are spatially localized. The importance of obtaining a non-redundant representation, i.e., a sensing matrix with low column coherence, and the inherent ill-conditioning of near-field reconstruction problems is addressed. Numerical and experimental results on a classical guitar and on a highly reactive dipole-like source are presented. C-ESM is valid beyond the conventional sampling limits, making wide-band reconstruction possible. Spatially extended sources can also be addressed with C-ESM, although in this case the obtained solution does not recover the spatial extent of the source.

High efficient optical remote sensing images acquisition for nano-satellite-framework

NASA Astrophysics Data System (ADS)

Li, Feng; Xin, Lei; Liu, Yang; Fu, Jie; Liu, Yuhong; Guo, Yi

2017-09-01

It is more difficult and challenging to implement Nano-satellite (NanoSat) based optical Earth observation missions than conventional satellites because of the limitation of volume, weight and power consumption. In general, an image compression unit is a necessary onboard module to save data transmission bandwidth and disk space. The image compression unit can get rid of redundant information of those captured images. In this paper, a new image acquisition framework is proposed for NanoSat based optical Earth observation applications. The entire process of image acquisition and compression unit can be integrated in the photo detector array chip, that is, the output data of the chip is already compressed. That is to say, extra image compression unit is no longer needed; therefore, the power, volume, and weight of the common onboard image compression units consumed can be largely saved. The advantages of the proposed framework are: the image acquisition and image compression are combined into a single step; it can be easily built in CMOS architecture; quick view can be provided without reconstruction in the framework; Given a certain compression ratio, the reconstructed image quality is much better than those CS based methods. The framework holds promise to be widely used in the future.

Compression Freezing Kinetics of Water to Ice VII

DOE PAGES

Gleason, A. E.; Bolme, C. A.; Galtier, E.; ...

2017-07-11

Time-resolved x-ray diffraction (XRD) of compressed liquid water shows transformation to ice VII in 6 nsec, revealing crystallization rather than amorphous solidification during compression freezing. Application of classical nucleation theory indicates heterogeneous nucleation and one-dimensional (e.g., needlelike) growth. In conclusion, these first XRD data demonstrate rapid growth kinetics of ice VII with implications for fundamental physics of diffusion-mediated crystallization and thermodynamic modeling of collision or impact events on ice-rich planetary bodies.

Compression Freezing Kinetics of Water to Ice VII

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gleason, A. E.; Bolme, C. A.; Galtier, E.

Time-resolved x-ray diffraction (XRD) of compressed liquid water shows transformation to ice VII in 6 nsec, revealing crystallization rather than amorphous solidification during compression freezing. Application of classical nucleation theory indicates heterogeneous nucleation and one-dimensional (e.g., needlelike) growth. In conclusion, these first XRD data demonstrate rapid growth kinetics of ice VII with implications for fundamental physics of diffusion-mediated crystallization and thermodynamic modeling of collision or impact events on ice-rich planetary bodies.

Mechanical Metamaterials with Negative Compressibility Transitions

NASA Astrophysics Data System (ADS)

Motter, Adilson

2015-03-01

When tensioned, ordinary materials expand along the direction of the applied force. In this presentation, I will explore network concepts to design metamaterials exhibiting negative compressibility transitions, during which the material undergoes contraction when tensioned (or expansion when pressured). Such transitions, which are forbidden in thermodynamic equilibrium, are possible during the decay of metastable, super-strained states. I will introduce a statistical physics theory for negative compressibility transitions, derive a first-principles model to predict these transitions, and present a validation of the model using molecular dynamics simulations. Aside from its immediate mechanical implications, our theory points to a wealth of analogous inverted responses, such as inverted susceptibility or heat-capacity transitions, allowed when considering realistic scales. This research was done in collaboration with Zachary Nicolaou, and was supported by the National Science Foundation and the Alfred P. Sloan Foundation.

Compression failure of angle-ply laminates

NASA Technical Reports Server (NTRS)

Peel, L. D.; Hyer, M. W.; Shuart, M. J.

1992-01-01

Test results from the compression loading of (+ or - Theta/ - or + Theta)(sub 6s) angle-ply IM7-8551-7a specimens, 0 less than or = Theta less than or = 90 degs, are presented. The observed failure strengths and modes are discussed, and typical stress-strain relations shown. Using classical lamination theory and the maximum stress criterion, an attempt is made to predict failure stress as a function of Theta. This attempt results in poor correlation with test results and thus a more advanced model is used. The model, which is based on a geometrically nonlinear theory, and which was taken from previous work, includes the influence of observed layer waviness. The waviness is described by the wave length and the wave amplitude. The theory is briefly described and results from the theory are correlated with test results. It is shown that by using levels of waviness observed in the specimens, the correlation between predictions and observations is good.

ROI-Based On-Board Compression for Hyperspectral Remote Sensing Images on GPU.

PubMed

Giordano, Rossella; Guccione, Pietro

2017-05-19

In recent years, hyperspectral sensors for Earth remote sensing have become very popular. Such systems are able to provide the user with images having both spectral and spatial information. The current hyperspectral spaceborne sensors are able to capture large areas with increased spatial and spectral resolution. For this reason, the volume of acquired data needs to be reduced on board in order to avoid a low orbital duty cycle due to limited storage space. Recently, literature has focused the attention on efficient ways for on-board data compression. This topic is a challenging task due to the difficult environment (outer space) and due to the limited time, power and computing resources. Often, the hardware properties of Graphic Processing Units (GPU) have been adopted to reduce the processing time using parallel computing. The current work proposes a framework for on-board operation on a GPU, using NVIDIA's CUDA (Compute Unified Device Architecture) architecture. The algorithm aims at performing on-board compression using the target's related strategy. In detail, the main operations are: the automatic recognition of land cover types or detection of events in near real time in regions of interest (this is a user related choice) with an unsupervised classifier; the compression of specific regions with space-variant different bit rates including Principal Component Analysis (PCA), wavelet and arithmetic coding; and data volume management to the Ground Station. Experiments are provided using a real dataset taken from an AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) airborne sensor in a harbor area.

A Compressed Sensing-Based Wearable Sensor Network for Quantitative Assessment of Stroke Patients

PubMed Central

Yu, Lei; Xiong, Daxi; Guo, Liquan; Wang, Jiping

2016-01-01

Clinical rehabilitation assessment is an important part of the therapy process because it is the premise for prescribing suitable rehabilitation interventions. However, the commonly used assessment scales have the following two drawbacks: (1) they are susceptible to subjective factors; (2) they only have several rating levels and are influenced by a ceiling effect, making it impossible to exactly detect any further improvement in the movement. Meanwhile, energy constraints are a primary design consideration in wearable sensor network systems since they are often battery-operated. Traditionally, for wearable sensor network systems that follow the Shannon/Nyquist sampling theorem, there are many data that need to be sampled and transmitted. This paper proposes a novel wearable sensor network system to monitor and quantitatively assess the upper limb motion function, based on compressed sensing technology. With the sparse representation model, less data is transmitted to the computer than with traditional systems. The experimental results show that the accelerometer signals of Bobath handshake and shoulder touch exercises can be compressed, and the length of the compressed signal is less than 1/3 of the raw signal length. More importantly, the reconstruction errors have no influence on the predictive accuracy of the Brunnstrom stage classification model. It also indicated that the proposed system can not only reduce the amount of data during the sampling and transmission processes, but also, the reconstructed accelerometer signals can be used for quantitative assessment without any loss of useful information. PMID:26861337

A Compressed Sensing-Based Wearable Sensor Network for Quantitative Assessment of Stroke Patients.

PubMed

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.

Remote sensing of Earth terrain

NASA Technical Reports Server (NTRS)

Kong, J. A.

1992-01-01

Research findings are summarized for projects dealing with the following: application of theoretical models to active and passive remote sensing of saline ice; radiative transfer theory for polarimetric remote sensing of pine forest; scattering of electromagnetic waves from a dense medium consisting of correlated Mie scatterers with size distribution and applications to dry snow; variance of phase fluctuations of waves propagating through a random medium; theoretical modeling for passive microwave remote sensing of earth terrain; polarimetric signatures of a canopy of dielectric cylinders based on first and second order vector radiative transfer theory; branching model for vegetation; polarimetric passive remote sensing of periodic surfaces; composite volume and surface scattering model; and radar image classification.

Distinguishing one from many using super-resolution compressive sensing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Anthony, Stephen Michael; Mulcahy-Stanislawczyk, Johnathan; Shields, Eric A.

We present that distinguishing whether a signal corresponds to a single source or a limited number of highly overlapping point spread functions (PSFs) is a ubiquitous problem across all imaging scales, whether detecting receptor-ligand interactions in cells or detecting binary stars. Super-resolution imaging based upon compressed sensing exploits the relative sparseness of the point sources to successfully resolve sources which may be separated by much less than the Rayleigh criterion. However, as a solution to an underdetermined system of linear equations, compressive sensing requires the imposition of constraints which may not always be valid. One typical constraint is that themore » PSF is known. However, the PSF of the actual optical system may reflect aberrations not present in the theoretical ideal optical system. Even when the optics are well characterized, the actual PSF may reflect factors such as non-uniform emission of the point source (e.g. fluorophore dipole emission). As such, the actual PSF may differ from the PSF used as a constraint. Similarly, multiple different regularization constraints have been suggested including the l 1-norm, l 0-norm, and generalized Gaussian Markov random fields (GGMRFs), each of which imposes a different constraint. Other important factors include the signal-to-noise ratio of the point sources and whether the point sources vary in intensity. In this work, we explore how these factors influence super-resolution image recovery robustness, determining the sensitivity and specificity. In conclusion, we determine an approach that is more robust to the types of PSF errors present in actual optical systems.« less

Distinguishing one from many using super-resolution compressive sensing

DOE PAGES

Anthony, Stephen Michael; Mulcahy-Stanislawczyk, Johnathan; Shields, Eric A.; ...

2018-05-14

We present that distinguishing whether a signal corresponds to a single source or a limited number of highly overlapping point spread functions (PSFs) is a ubiquitous problem across all imaging scales, whether detecting receptor-ligand interactions in cells or detecting binary stars. Super-resolution imaging based upon compressed sensing exploits the relative sparseness of the point sources to successfully resolve sources which may be separated by much less than the Rayleigh criterion. However, as a solution to an underdetermined system of linear equations, compressive sensing requires the imposition of constraints which may not always be valid. One typical constraint is that themore » PSF is known. However, the PSF of the actual optical system may reflect aberrations not present in the theoretical ideal optical system. Even when the optics are well characterized, the actual PSF may reflect factors such as non-uniform emission of the point source (e.g. fluorophore dipole emission). As such, the actual PSF may differ from the PSF used as a constraint. Similarly, multiple different regularization constraints have been suggested including the l 1-norm, l 0-norm, and generalized Gaussian Markov random fields (GGMRFs), each of which imposes a different constraint. Other important factors include the signal-to-noise ratio of the point sources and whether the point sources vary in intensity. In this work, we explore how these factors influence super-resolution image recovery robustness, determining the sensitivity and specificity. In conclusion, we determine an approach that is more robust to the types of PSF errors present in actual optical systems.« less

Fast Dynamic 3D MRSI with Compressed Sensing and Multiband Excitation Pulses for Hyperpolarized 13C Studies

PubMed Central

Larson, Peder E. Z.; Hu, Simon; Lustig, Michael; Kerr, Adam B.; Nelson, Sarah J.; Kurhanewicz, John; Pauly, John M.; Vigneron, Daniel B.

2010-01-01

Hyperpolarized 13C MRSI can detect not only the uptake of the pre-polarized molecule but also its metabolic products in vivo, thus providing a powerful new method to study cellular metabolism. Imaging the dynamic perfusion and conversion of these metabolites provides additional tissue information but requires methods for efficient hyperpolarization usage and rapid acquisitions. In this work, we have developed a time-resolved 3D MRSI method for acquiring hyperpolarized 13C data by combining compressed sensing methods for acceleration and multiband excitation pulses to efficiently use the magnetization. This method achieved a 2 sec temporal resolution with full volumetric coverage of a mouse, and metabolites were observed for up to 60 sec following injection of hyperpolarized [1-13C]-pyruvate. The compressed sensing acquisition used random phase encode gradient blips to create a novel random undersampling pattern tailored to dynamic MRSI with sampling incoherency in four (time, frequency and two spatial) dimensions. The reconstruction was also tailored to dynamic MRSI by applying a temporal wavelet sparsifying transform in order to exploit the inherent temporal sparsity. Customized multiband excitation pulses were designed with a lower flip angle for the [1-13C]-pyruvate substrate given its higher concentration than its metabolic products ([1-13C]-lactate and [1-13C]-alanine), thus using less hyperpolarization per excitation. This approach has enabled the monitoring of perfusion and uptake of the pyruvate, and the conversion dynamics to lactate and alanine throughout a volume with high spatial and temporal resolution. PMID:20939089

Multichannel Compressive Sensing MRI Using Noiselet Encoding

PubMed Central

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

A single-pixel X-ray imager concept and its application to secure radiographic inspections

DOE PAGES

Gilbert, Andrew J.; Miller, Brian W.; Robinson, Sean M.; ...

2017-07-01

Imaging technology is generally considered too invasive for arms control inspections due to the concern that it cannot properly secure sensitive features of the inspected item. But, this same sensitive information, which could include direct information on the form and function of the items under inspection, could be used for robust arms control inspections. The single-pixel X-ray imager (SPXI) is introduced as a method to make such inspections, capturing the salient spatial information of an object in a secure manner while never forming an actual image. We built this method on the theory of compressive sensing and the single pixelmore » optical camera. The performance of the system is quantified using simulated inspections of simple objects. Measures of the robustness and security of the method are introduced and used to determine how robust and secure such an inspection would be. Particularly, it is found that an inspection with low noise (<1%) and high undersampling (>256×) exhibits high robustness and security.« less

A Unified Multi-Functional Dynamic Spectrum Access Framework: Tutorial, Theory and Multi-GHz Wideband Testbed

PubMed Central

Qiu, Robert; Guo, Nan; Li, Husheng; Wu, Zhiqiang; Chakravarthy, Vasu; Song, Yu; Hu, Zhen; Zhang, Peng; Chen, Zhe

2009-01-01

Dynamic spectrum access is a must-have ingredient for future sensors that are ideally cognitive. The goal of this paper is a tutorial treatment of wideband cognitive radio and radar—a convergence of (1) algorithms survey, (2) hardware platforms survey, (3) challenges for multi-function (radar/communications) multi-GHz front end, (4) compressed sensing for multi-GHz waveforms—revolutionary A/D, (5) machine learning for cognitive radio/radar, (6) quickest detection, and (7) overlay/underlay cognitive radio waveforms. One focus of this paper is to address the multi-GHz front end, which is the challenge for the next-generation cognitive sensors. The unifying theme of this paper is to spell out the convergence for cognitive radio, radar, and anti-jamming. Moore’s law drives the system functions into digital parts. From a system viewpoint, this paper gives the first comprehensive treatment for the functions and the challenges of this multi-function (wideband) system. This paper brings together the inter-disciplinary knowledge. PMID:22454598

A Model of Regularization Parameter Determination in Low-Dose X-Ray CT Reconstruction Based on Dictionary Learning.

PubMed

Zhang, Cheng; Zhang, Tao; Zheng, Jian; Li, Ming; Lu, Yanfei; You, Jiali; Guan, Yihui

2015-01-01

In recent years, X-ray computed tomography (CT) is becoming widely used to reveal patient's anatomical information. However, the side effect of radiation, relating to genetic or cancerous diseases, has caused great public concern. The problem is how to minimize radiation dose significantly while maintaining image quality. As a practical application of compressed sensing theory, one category of methods takes total variation (TV) minimization as the sparse constraint, which makes it possible and effective to get a reconstruction image of high quality in the undersampling situation. On the other hand, a preliminary attempt of low-dose CT reconstruction based on dictionary learning seems to be another effective choice. But some critical parameters, such as the regularization parameter, cannot be determined by detecting datasets. In this paper, we propose a reweighted objective function that contributes to a numerical calculation model of the regularization parameter. A number of experiments demonstrate that this strategy performs well with better reconstruction images and saving of a large amount of time.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gilbert, Andrew J.; Miller, Brian W.; Robinson, Sean M.

Imaging technology is generally considered too invasive for arms control inspections due to the concern that it cannot properly secure sensitive features of the inspected item. However, this same sensitive information, which could include direct information on the form and function of the items under inspection, could be used for robust arms control inspections. The single-pixel X-ray imager (SPXI) is introduced as a method to make such inspections, capturing the salient spatial information of an object in a secure manner while never forming an actual image. The method is built on the theory of compressive sensing and the single pixelmore » optical camera. The performance of the system is quantified here using simulated inspections of simple objects. Measures of the robustness and security of the method are introduced and used to determine how such an inspection would be made which can maintain high robustness and security. In particular, it is found that an inspection with low noise (<1%) and high undersampling (>256×) exhibits high robustness and security.« less

A Fast and Accurate Sparse Continuous Signal Reconstruction by Homotopy DCD with Non-Convex Regularization

PubMed Central

Wang, Tianyun; Lu, Xinfei; Yu, Xiaofei; Xi, Zhendong; Chen, Weidong

2014-01-01

In recent years, various applications regarding sparse continuous signal recovery such as source localization, radar imaging, communication channel estimation, etc., have been addressed from the perspective of compressive sensing (CS) theory. However, there are two major defects that need to be tackled when considering any practical utilization. The first issue is off-grid problem caused by the basis mismatch between arbitrary located unknowns and the pre-specified dictionary, which would make conventional CS reconstruction methods degrade considerably. The second important issue is the urgent demand for low-complexity algorithms, especially when faced with the requirement of real-time implementation. In this paper, to deal with these two problems, we have presented three fast and accurate sparse reconstruction algorithms, termed as HR-DCD, Hlog-DCD and Hlp-DCD, which are based on homotopy, dichotomous coordinate descent (DCD) iterations and non-convex regularizations, by combining with the grid refinement technique. Experimental results are provided to demonstrate the effectiveness of the proposed algorithms and related analysis. PMID:24675758

A single-pixel X-ray imager concept and its application to secure radiographic inspections

NASA Astrophysics Data System (ADS)

Gilbert, Andrew J.; Miller, Brian W.; Robinson, Sean M.; White, Timothy A.; Pitts, William Karl; Jarman, Kenneth D.; Seifert, Allen

2017-07-01

Imaging technology is generally considered too invasive for arms control inspections due to the concern that it cannot properly secure sensitive features of the inspected item. However, this same sensitive information, which could include direct information on the form and function of the items under inspection, could be used for robust arms control inspections. The single-pixel X-ray imager (SPXI) is introduced as a method to make such inspections, capturing the salient spatial information of an object in a secure manner while never forming an actual image. The method is built on the theory of compressive sensing and the single pixel optical camera. The performance of the system is quantified using simulated inspections of simple objects. Measures of the robustness and security of the method are introduced and used to determine how robust and secure such an inspection would be. In particular, it is found that an inspection with low noise ( < 1 %) and high undersampling ( > 256 ×) exhibits high robustness and security.

Model Classes, Approximation, and Metrics for Dynamic Processing of Urban Terrain Data

DTIC Science & Technology

2013-01-01

Sensing,” DARPA IPTO Retreat, Annapolis, 2008. R. Baraniuk, “Compressive Sensing, Wavelets, and Sparsity,” SPIE Defense + Security (acceptance speech ... Speech and Signal Processing (ICASSP). 2011/05/22 00:00:00, Prague, Czech Republic. : , 08/31/2011 33.00 Sang-Mook Lee, Jeong Joon Im, Bo-Hee Lee... KNN ) points to define a local intrinsic coordinate system using PCA and to construct the manifold and function locally using least squares. Local

NIR hyperspectral compressive imager based on a modified Fabry–Perot resonator

NASA Astrophysics Data System (ADS)

Oiknine, Yaniv; August, Isaac; Blumberg, Dan G.; Stern, Adrian

2018-04-01

The acquisition of hyperspectral (HS) image datacubes with available 2D sensor arrays involves a time consuming scanning process. In the last decade, several compressive sensing (CS) techniques were proposed to reduce the HS acquisition time. In this paper, we present a method for near-infrared (NIR) HS imaging which relies on our rapid CS resonator spectroscopy technique. Within the framework of CS, and by using a modified Fabry–Perot resonator, a sequence of spectrally modulated images is used to recover NIR HS datacubes. Owing to the innovative CS design, we demonstrate the ability to reconstruct NIR HS images with hundreds of spectral bands from an order of magnitude fewer measurements, i.e. with a compression ratio of about 10:1. This high compression ratio, together with the high optical throughput of the system, facilitates fast acquisition of large HS datacubes.

Signal Recovery and System Calibration from Multiple Compressive Poisson Measurements

DOE PAGES

Wang, Liming; Huang, Jiaji; Yuan, Xin; ...

2015-09-17

The measurement matrix employed in compressive sensing typically cannot be known precisely a priori and must be estimated via calibration. One may take multiple compressive measurements, from which the measurement matrix and underlying signals may be estimated jointly. This is of interest as well when the measurement matrix may change as a function of the details of what is measured. This problem has been considered recently for Gaussian measurement noise, and here we develop this idea with application to Poisson systems. A collaborative maximum likelihood algorithm and alternating proximal gradient algorithm are proposed, and associated theoretical performance guarantees are establishedmore » based on newly derived concentration-of-measure results. A Bayesian model is then introduced, to improve flexibility and generality. Connections between the maximum likelihood methods and the Bayesian model are developed, and example results are presented for a real compressive X-ray imaging system.« less

Blind compressive sensing dynamic MRI

PubMed Central

Lingala, Sajan Goud; Jacob, Mathews

2013-01-01

We propose a novel blind compressive sensing (BCS) frame work to recover dynamic magnetic resonance images from undersampled measurements. This scheme models the dynamic signal as a sparse linear combination of temporal basis functions, chosen from a large dictionary. In contrast to classical compressed sensing, the BCS scheme simultaneously estimates the dictionary and the sparse coefficients from the undersampled measurements. Apart from the sparsity of the coefficients, the key difference of the BCS scheme with current low rank methods is the non-orthogonal nature of the dictionary basis functions. Since the number of degrees of freedom of the BCS model is smaller than that of the low-rank methods, it provides improved reconstructions at high acceleration rates. We formulate the reconstruction as a constrained optimization problem; the objective function is the linear combination of a data consistency term and sparsity promoting ℓ1 prior of the coefficients. The Frobenius norm dictionary constraint is used to avoid scale ambiguity. We introduce a simple and efficient majorize-minimize algorithm, which decouples the original criterion into three simpler sub problems. An alternating minimization strategy is used, where we cycle through the minimization of three simpler problems. This algorithm is seen to be considerably faster than approaches that alternates between sparse coding and dictionary estimation, as well as the extension of K-SVD dictionary learning scheme. The use of the ℓ1 penalty and Frobenius norm dictionary constraint enables the attenuation of insignificant basis functions compared to the ℓ0 norm and column norm constraint assumed in most dictionary learning algorithms; this is especially important since the number of basis functions that can be reliably estimated is restricted by the available measurements. We also observe that the proposed scheme is more robust to local minima compared to K-SVD method, which relies on greedy sparse coding. Our phase transition experiments demonstrate that the BCS scheme provides much better recovery rates than classical Fourier-based CS schemes, while being only marginally worse than the dictionary aware setting. Since the overhead in additionally estimating the dictionary is low, this method can be very useful in dynamic MRI applications, where the signal is not sparse in known dictionaries. We demonstrate the utility of the BCS scheme in accelerating contrast enhanced dynamic data. We observe superior reconstruction performance with the BCS scheme in comparison to existing low rank and compressed sensing schemes. PMID:23542951

Minimum-domain impulse theory for unsteady aerodynamic force

NASA Astrophysics Data System (ADS)

Kang, L. L.; Liu, L. Q.; Su, W. D.; Wu, J. Z.

2018-01-01

We extend the impulse theory for unsteady aerodynamics from its classic global form to finite-domain formulation then to minimum-domain form and from incompressible to compressible flows. For incompressible flow, the minimum-domain impulse theory raises the finding of Li and Lu ["Force and power of flapping plates in a fluid," J. Fluid Mech. 712, 598-613 (2012)] to a theorem: The entire force with discrete wake is completely determined by only the time rate of impulse of those vortical structures still connecting to the body, along with the Lamb-vector integral thereof that captures the contribution of all the rest disconnected vortical structures. For compressible flows, we find that the global form in terms of the curl of momentum ∇ × (ρu), obtained by Huang [Unsteady Vortical Aerodynamics (Shanghai Jiaotong University Press, 1994)], can be generalized to having an arbitrary finite domain, but the formula is cumbersome and in general ∇ × (ρu) no longer has discrete structures and hence no minimum-domain theory exists. Nevertheless, as the measure of transverse process only, the unsteady field of vorticity ω or ρω may still have a discrete wake. This leads to a minimum-domain compressible vorticity-moment theory in terms of ρω (but it is beyond the classic concept of impulse). These new findings and applications have been confirmed by our numerical experiments. The results not only open an avenue to combine the theory with computation-experiment in wide applications but also reveal a physical truth that it is no longer necessary to account for all wake vortical structures in computing the force and moment.

Entropy and Certainty in Lossless Data Compression

ERIC Educational Resources Information Center

Jacobs, James Jay

2009-01-01

Data compression is the art of using encoding techniques to represent data symbols using less storage space compared to the original data representation. The encoding process builds a relationship between the entropy of the data and the certainty of the system. The theoretical limits of this relationship are defined by the theory of entropy in…

Stochastic Acceleration of Galactic Cosmic Rays by Compressible Plasma Fluctuations in Supernova Shells

NASA Astrophysics Data System (ADS)

Zhang, Ming

2015-10-01

A theory of 2-stage acceleration of Galactic cosmic rays in supernova remnants is proposed. The first stage is accomplished by the supernova shock front, where a power-law spectrum is established up to a certain cutoff energy. It is followed by stochastic acceleration with compressible waves/turbulence in the downstream medium. With a broad \\propto {k}-2 spectrum for the compressible plasma fluctuations, the rate of stochastic acceleration is constant over a wide range of particle momentum. In this case, the stochastic acceleration process extends the power-law spectrum cutoff energy of Galactic cosmic rays to the knee without changing the spectral slope. This situation happens as long as the rate of stochastic acceleration is faster than 1/5 of the adiabatic cooling rate. A steeper spectrum of compressible plasma fluctuations that concentrate their power in long wavelengths will accelerate cosmic rays to the knee with a small bump before its cutoff in the comic-ray energy spectrum. This theory does not require a strong amplification of the magnetic field in the upstream interstellar medium in order to accelerate cosmic rays to the knee energy.

Nonlinear fractional waves at elastic interfaces

NASA Astrophysics Data System (ADS)

Kappler, Julian; Shrivastava, Shamit; Schneider, Matthias F.; Netz, Roland R.

2017-11-01

We derive the nonlinear fractional surface wave equation that governs compression waves at an elastic interface that is coupled to a viscous bulk medium. The fractional character of the differential equation comes from the fact that the effective thickness of the bulk layer that is coupled to the interface is frequency dependent. The nonlinearity arises from the nonlinear dependence of the interface compressibility on the local compression, which is obtained from experimental measurements and reflects a phase transition at the interface. Numerical solutions of our nonlinear fractional theory reproduce several experimental key features of surface waves in phospholipid monolayers at the air-water interface without freely adjustable fitting parameters. In particular, the propagation distance of the surface wave abruptly increases at a threshold excitation amplitude. The wave velocity is found to be of the order of 40 cm/s in both experiments and theory and slightly increases as a function of the excitation amplitude. Nonlinear acoustic switching effects in membranes are thus shown to arise purely based on intrinsic membrane properties, namely, the presence of compressibility nonlinearities that accompany phase transitions at the interface.

Design optimization of natural laminar flow bodies in compressible flow

NASA Technical Reports Server (NTRS)

Dodbele, Simha S.

1992-01-01

An optimization method has been developed to design axisymmetric body shapes such as fuselages, nacelles, and external fuel tanks with increased transition Reynolds numbers in subsonic compressible flow. The new design method involves a constraint minimization procedure coupled with analysis of the inviscid and viscous flow regions and linear stability analysis of the compressible boundary-layer. In order to reduce the computer time, Granville's transition criterion is used to predict boundary-layer transition and to calculate the gradients of the objective function, and linear stability theory coupled with the e(exp n)-method is used to calculate the objective function at the end of each design iteration. Use of a method to design an axisymmetric body with extensive natural laminar flow is illustrated through the design of a tiptank of a business jet. For the original tiptank, boundary layer transition is predicted to occur at a transition Reynolds number of 6.04 x 10(exp 6). For the designed body shape, a transition Reynolds number of 7.22 x 10(exp 6) is predicted using compressible linear stability theory coupled with the e(exp n)-method.

Piezoelectric film load cell robot collision detector

DOEpatents

Lembke, J.R.

1988-03-15

A piezoelectric load cell which can be utilized for detecting collisions and obstruction of a robot arm end effector includes a force sensing element of metallized polyvinylidene fluoride (PVDF) film. The piezoelectric film sensing element and a resilient support pad are clamped in compression between upper and lower plates. The lower plate has a central recess in its upper face for supporting the support pad and sensing element, while the upper plate has a corresponding central projection formed on its lower face for bearing on the sensing element and support pad. The upper and lower plates are dowelled together for concentric alignment and screwed together. The upper and lower plates are also adapted for mounting between the robot arm wrist and end effector. 3 figs.

Piezoelectric film load cell robot collision detector

DOEpatents

Lembke, John R.

1989-04-18

A piezoelectric load cell which can be utilized for detecting collisions and obstruction of a robot arm end effector includes a force sensing element of metallized polyvinylidene fluoride (PVDF) film. The piezoelectric film sensing element and a resilient support pad are clamped in compression between upper and lower plates. The lower plate has a central recess in its upper face for supporting the support pad and sensing element, while the upper plate has a corresponding central projection formed on its lower face for bearing on the sensing element and support pad. The upper and lower plates are dowelled together for concentric alignment and screwed together. The upper and lower plates are also adapted for mounting between the robot arm wrist and end effector.

Piezoelectric film load cell robot collision detector

DOEpatents

Lembke, J.R.

1989-04-18

A piezoelectric load cell which can be utilized for detecting collisions and obstruction of a robot arm end effector includes a force sensing element of metallized polyvinylidene fluoride (PVDF) film. The piezoelectric film sensing element and a resilient support pad are clamped in compression between upper and lower plates. The lower plate has a central recess in its upper face for supporting the support pad and sensing element, while the upper plate has a corresponding central projection formed on its lower face for bearing on the sensing element and support pad. The upper and lower plates are doweled together for concentric alignment and screwed together. The upper and lower plates are also adapted for mounting between the robot arm wrist and end effector. 3 figs.

Compressed Sensing Techniques Applied to Ultrasonic Imaging of Cargo Containers.

PubMed

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.

Mechanisms of Local Stress Sensing in Multifunctional Polymer Films Using Fluorescent Tetrapod Nanocrystals

DOE PAGES

Raja, Shilpa N.; Zherebetskyy, Danylo; Wu, Siva; ...

2016-07-13

Nanoscale stress-sensing can be used across fields ranging from detection of incipient cracks in structural mechanics to monitoring forces in biological tissues. We demonstrate how tetrapod quantum dots (tQDs) embedded in block copolymers act as sensors of tensile/compressive stress. Remarkably, tQDs can detect their own composite dispersion and mechanical properties with a switch in optomechanical response when tQDs are in direct contact. Using experimental characterizations, atomistic simulations and finite-element analyses, we show that under tensile stress, densely packed tQDs exhibit a photoluminescence peak shifted to higher energies ("blue-shift") due to volumetric compressive stress in their core; loosely packed tQDs exhibitmore » a peak shifted to lower energies ("red-shift") from tensile stress in the core. The stress shifts result from the tQD's unique branched morphology in which the CdS arms act as antennas that amplify the stress in the CdSe core. Our nanocomposites exhibit excellent cyclability and scalability with no degraded properties of the host polymer. Colloidal tQDs allow sensing in many materials to potentially enable autoresponsive, smart structural nanocomposites that self-predict upcoming fracture.« less

Compressed Sensing Techniques Applied to Ultrasonic Imaging of Cargo Containers

PubMed Central

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

Hyperspectral imagery super-resolution by compressive sensing inspired dictionary learning and spatial-spectral regularization.

PubMed

Huang, Wei; Xiao, Liang; Liu, Hongyi; Wei, Zhihui

2015-01-19

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.

Whole left ventricular functional assessment from two minutes free breathing multi-slice CINE acquisition

NASA Astrophysics Data System (ADS)

Usman, M.; Atkinson, D.; Heathfield, E.; Greil, G.; Schaeffter, T.; Prieto, C.

2015-04-01

Two major challenges in cardiovascular MRI are long scan times due to slow MR acquisition and motion artefacts due to respiratory motion. Recently, a Motion Corrected-Compressed Sensing (MC-CS) technique has been proposed for free breathing 2D dynamic cardiac MRI that addresses these challenges by simultaneously accelerating MR acquisition and correcting for any arbitrary motion in a compressed sensing reconstruction. In this work, the MC-CS framework is combined with parallel imaging for further acceleration, and is termed Motion Corrected Sparse SENSE (MC-SS). Validation of the MC-SS framework is demonstrated in eight volunteers and three patients for left ventricular functional assessment and results are compared with the breath-hold acquisitions as reference. A non-significant difference (P > 0.05) was observed in the volumetric functional measurements (end diastolic volume, end systolic volume, ejection fraction) and myocardial border sharpness values obtained with the proposed and gold standard methods. The proposed method achieves whole heart multi-slice coverage in 2 min under free breathing acquisition eliminating the time needed between breath-holds for instructions and recovery. This results in two-fold speed up of the total acquisition time in comparison to the breath-hold acquisition.

Viscosity and compressibility of diacylglycerol under high pressure

NASA Astrophysics Data System (ADS)

Malanowski, Aleksander; Rostocki, A. J.; Kiełczyński, P.; Szalewski, M.; Balcerzak, A.; Kościesza, R.; Tarakowski, R.; Ptasznik, S.; Siegoczyński, R. M.

2013-03-01

The influence of high pressure on viscosity and compressibility of diacylglycerol (DAG) oil has been presented in this paper. The investigated DAG oil was composed of 82% of DAGs and 18% TAGs (triacylglycerols). The dynamic viscosity of DAG was investigated as a function of the pressure up to 400 MPa. The viscosity was measured by means of the surface acoustic wave method, where the acoustic waveguides were used as sensing elements. As the pressure was rising, the larger ultrasonic wave attenuation was observed, whereas amplitude decreased with the liquid viscosity augmentation. Measured changes of physical properties were most significant in the pressure range near the phase transition. Deeper understanding of DAG viscosity and compressibility changes versus pressure could shed more light on thermodynamic properties of edible oils.

Proceedings of the Scientific Data Compression Workshop

NASA Technical Reports Server (NTRS)

Ramapriyan, H. K. (Editor)

1989-01-01

Continuing advances in space and Earth science requires increasing amounts of data to be gathered from spaceborne sensors. NASA expects to launch sensors during the next two decades which will be capable of producing an aggregate of 1500 Megabits per second if operated simultaneously. Such high data rates cause stresses in all aspects of end-to-end data systems. Technologies and techniques are needed to relieve such stresses. Potential solutions to the massive data rate problems are: data editing, greater transmission bandwidths, higher density and faster media, and data compression. Through four subpanels on Science Payload Operations, Multispectral Imaging, Microwave Remote Sensing and Science Data Management, recommendations were made for research in data compression and scientific data applications to space platforms.

Nonparametric Bayesian Dictionary Learning for Analysis of Noisy and Incomplete Images

PubMed Central

Zhou, Mingyuan; Chen, Haojun; Paisley, John; Ren, Lu; Li, Lingbo; Xing, Zhengming; Dunson, David; Sapiro, Guillermo; Carin, Lawrence

2013-01-01

Nonparametric Bayesian methods are considered for recovery of imagery based upon compressive, incomplete, and/or noisy measurements. A truncated beta-Bernoulli process is employed to infer an appropriate dictionary for the data under test and also for image recovery. In the context of compressive sensing, significant improvements in image recovery are manifested using learned dictionaries, relative to using standard orthonormal image expansions. The compressive-measurement projections are also optimized for the learned dictionary. Additionally, we consider simpler (incomplete) measurements, defined by measuring a subset of image pixels, uniformly selected at random. Spatial interrelationships within imagery are exploited through use of the Dirichlet and probit stick-breaking processes. Several example results are presented, with comparisons to other methods in the literature. PMID:21693421

Methods of training the graduate level and professional geologist in remote sensing technology

NASA Technical Reports Server (NTRS)

Kolm, K. E.

1981-01-01

Requirements for a basic course in remote sensing to accommodate the needs of the graduate level and professional geologist are described. The course should stress the general topics of basic remote sensing theory, the theory and data types relating to different remote sensing systems, an introduction to the basic concepts of computer image processing and analysis, the characteristics of different data types, the development of methods for geological interpretations, the integration of all scales and data types of remote sensing in a given study, the integration of other data bases (geophysical and geochemical) into a remote sensing study, and geological remote sensing applications. The laboratories should stress hands on experience to reinforce the concepts and procedures presented in the lecture. The geologist should then be encouraged to pursue a second course in computer image processing and analysis of remotely sensed data.

Remote sensing of Earth terrain

NASA Technical Reports Server (NTRS)

Kong, J. A.

1993-01-01

Progress report on remote sensing of Earth terrain covering the period from Jan. to June 1993 is presented. Areas of research include: radiative transfer model for active and passive remote sensing of vegetation canopy; polarimetric thermal emission from rough ocean surfaces; polarimetric passive remote sensing of ocean wind vectors; polarimetric thermal emission from periodic water surfaces; layer model with tandom spheriodal scatterers for remote sensing of vegetation canopy; application of theoretical models to active and passive remote sensing of saline ice; radiative transfer theory for polarimetric remote sensing of pine forest; scattering of electromagnetic waves from a dense medium consisting of correlated mie scatterers with size distributions and applications to dry snow; variance of phase fluctuations of waves propagating through a random medium; polarimetric signatures of a canopy of dielectric cylinders based on first and second order vector radiative transfer theory; branching model for vegetation; polarimetric passive remote sensing of periodic surfaces; composite volume and surface scattering model; and radar image classification.

The effect of an external mechanical compression on in vivo optical properties of human skin

NASA Astrophysics Data System (ADS)

Nakhaeva, I. A.; Mohammed, M. R.; Zyuryukina, O. A.; Sinichkin, Yu. P.

2014-09-01

We have studied the influence of an external mechanical compression on diffuse reflection spectra of skin tissue under in vivo conditions. An analysis of these spectra based on the diffusion approximation of the radiation transfer theory has allowed us to find that the application of the external compression weakens absorbing and scattering properties of skin tissue. After the removal of the compression, the recovery time of the skin tissue (on the order of 1 h) considerably exceeds the stabilization time of its parameters after application of external mechanical compression (several minutes). In this case, at the initial moment of time after the removal of the compression, the fullness of blood vessels and the degree of oxygenation of blood hemoglobin in the skin tissue increase considerably compared to normal skin.

Ko Displacement Theory for Structural Shape Predictions

NASA Technical Reports Server (NTRS)

Ko, William L.

2010-01-01

The development of the Ko displacement theory for predictions of structure deformed shapes was motivated in 2003 by the Helios flying wing, which had a 247-ft (75-m) wing span with wingtip deflections reaching 40 ft (12 m). The Helios flying wing failed in midair in June 2003, creating the need to develop new technology to predict in-flight deformed shapes of unmanned aircraft wings for visual display before the ground-based pilots. Any types of strain sensors installed on a structure can only sense the surface strains, but are incapable to sense the overall deformed shapes of structures. After the invention of the Ko displacement theory, predictions of structure deformed shapes could be achieved by feeding the measured surface strains into the Ko displacement transfer functions for the calculations of out-of-plane deflections and cross sectional rotations at multiple locations for mapping out overall deformed shapes of the structures. The new Ko displacement theory combined with a strain-sensing system thus created a revolutionary new structure- shape-sensing technology.

A Nonvolume Preserving Plasticity Theory with Applications to Powder Metallurgy

NASA Technical Reports Server (NTRS)

Cassenti, B. N.

1983-01-01

A plasticity theory has been developed to predict the mechanical response of powder metals during hot isostatic pressing. The theory parameters were obtained through an experimental program consisting of hydrostatic pressure tests, uniaxial compression and uniaxial tension tests. A nonlinear finite element code was modified to include the theory and the results of themodified code compared favorably to the results from a verification experiment.

Utilizing a Sense of Community Theory in Order to Optimize Interagency Response to Complex Contingencies

DTIC Science & Technology

2010-06-01

of Not at all Somewhat Mostly Completely membership such as clothes , signs, art, architecture, logos , landmarks, and flags that people can...on a ?whole of nation? approach to solving complex problems. Psychological sense of community (PSOC) theory provides the link that explains how an...States during complex contingency operations depends on a “whole of nation” approach to solving complex problems. Psychological sense of community

Acquisition of STEM Images by Adaptive Compressive Sensing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xie, Weiyi; Feng, Qianli; Srinivasan, Ramprakash

Compressive Sensing (CS) allows a signal to be sparsely measured first and accurately recovered later in software [1]. In scanning transmission electron microscopy (STEM), it is possible to compress an image spatially by reducing the number of measured pixels, which decreases electron dose and increases sensing speed [2,3,4]. The two requirements for CS to work are: (1) sparsity of basis coefficients and (2) incoherence of the sensing system and the representation system. However, when pixels are missing from the image, it is difficult to have an incoherent sensing matrix. Nevertheless, dictionary learning techniques such as Beta-Process Factor Analysis (BPFA) [5]more » are able to simultaneously discover a basis and the sparse coefficients in the case of missing pixels. On top of CS, we would like to apply active learning [6,7] to further reduce the proportion of pixels being measured, while maintaining image reconstruction quality. Suppose we initially sample 10% of random pixels. We wish to select the next 1% of pixels that are most useful in recovering the image. Now, we have 11% of pixels, and we want to decide the next 1% of “most informative” pixels. Active learning methods are online and sequential in nature. Our goal is to adaptively discover the best sensing mask during acquisition using feedback about the structures in the image. In the end, we hope to recover a high quality reconstruction with a dose reduction relative to the non-adaptive (random) sensing scheme. In doing this, we try three metrics applied to the partial reconstructions for selecting the new set of pixels: (1) variance, (2) Kullback-Leibler (KL) divergence using a Radial Basis Function (RBF) kernel, and (3) entropy. Figs. 1 and 2 display the comparison of Peak Signal-to-Noise (PSNR) using these three different active learning methods at different percentages of sampled pixels. At 20% level, all the three active learning methods underperform the original CS without active learning. However, they all beat the original CS as more of the “most informative” pixels are sampled. One can also argue that CS equipped with active learning requires less sampled pixels to achieve the same value of PSNR than CS with pixels randomly sampled, since all the three PSNR curves with active learning grow at a faster pace than that without active learning. For this particular STEM image, by observing the reconstructed images and the sensing masks, we find that while the method based on RBF kernel acquires samples more uniformly, the one on entropy samples more areas of significant change, thus less uniformly. The KL-divergence method performs the best in terms of reconstruction error (PSNR) for this example [8].« less

Nonlinear theory of magnetohydrodynamic flows of a compressible fluid in the shallow water approximation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Klimachkov, D. A., E-mail: klimchakovdmitry@gmail.com; Petrosyan, A. S., E-mail: apetrosy@iki.rssi.ru

2016-09-15

Shallow water magnetohydrodynamic (MHD) theory describing incompressible flows of plasma is generalized to the case of compressible flows. A system of MHD equations is obtained that describes the flow of a thin layer of compressible rotating plasma in a gravitational field in the shallow water approximation. The system of quasilinear hyperbolic equations obtained admits a complete simple wave analysis and a solution to the initial discontinuity decay problem in the simplest version of nonrotating flows. In the new equations, sound waves are filtered out, and the dependence of density on pressure on large scales is taken into account that describesmore » static compressibility phenomena. In the equations obtained, the mass conservation law is formulated for a variable that nontrivially depends on the shape of the lower boundary, the characteristic vertical scale of the flow, and the scale of heights at which the variation of density becomes significant. A simple wave theory is developed for the system of equations obtained. All self-similar discontinuous solutions and all continuous centered self-similar solutions of the system are obtained. The initial discontinuity decay problem is solved explicitly for compressible MHD equations in the shallow water approximation. It is shown that there exist five different configurations that provide a solution to the initial discontinuity decay problem. For each configuration, conditions are found that are necessary and sufficient for its implementation. Differences between incompressible and compressible cases are analyzed. In spite of the formal similarity between the solutions in the classical case of MHD flows of an incompressible and compressible fluids, the nonlinear dynamics described by the solutions are essentially different due to the difference in the expressions for the squared propagation velocity of weak perturbations. In addition, the solutions obtained describe new physical phenomena related to the dependence of the height of the free boundary on the density of the fluid. Self-similar continuous and discontinuous solutions are obtained for a system on a slope, and a solution is found to the initial discontinuity decay problem in this case.« less

Use of a wave reverberation technique to infer the density compression of shocked liquid deuterium to 75 GPa.

PubMed

Knudson, M D; Hanson, D L; Bailey, J E; Hall, C A; Asay, J R

2003-01-24

A novel approach was developed to probe density compression of liquid deuterium (L-D2) along the principal Hugoniot. Relative transit times of shock waves reverberating within the sample are shown to be sensitive to the compression due to the first shock. This technique has proven to be more sensitive than the conventional method of inferring density from the shock and mass velocity, at least in this high-pressure regime. Results in the range of 22-75 GPa indicate an approximately fourfold density compression, and provide data to differentiate between proposed theories for hydrogen and its isotopes.

Analysis and testing of axial compression in imperfect slender truss struts

NASA Technical Reports Server (NTRS)

Lake, Mark S.; Georgiadis, Nicholas

1990-01-01

The axial compression of imperfect slender struts for large space structures is addressed. The load-shortening behavior of struts with initially imperfect shapes and eccentric compressive end loading is analyzed using linear beam-column theory and results are compared with geometrically nonlinear solutions to determine the applicability of linear analysis. A set of developmental aluminum clad graphite/epoxy struts sized for application to the Space Station Freedom truss are measured to determine their initial imperfection magnitude, load eccentricity, and cross sectional area and moment of inertia. Load-shortening curves are determined from axial compression tests of these specimens and are correlated with theoretical curves generated using linear analysis.