Implementation of a Cross-Layer Sensing Medium-Access Control Scheme.

PubMed

Su, Yishan; Fu, Xiaomei; Han, Guangyao; Xu, Naishen; Jin, Zhigang

2017-04-10

In this paper, compressed sensing (CS) theory is utilized in a medium-access control (MAC) scheme for wireless sensor networks (WSNs). We propose a new, cross-layer compressed sensing medium-access control (CL CS-MAC) scheme, combining the physical layer and data link layer, where the wireless transmission in physical layer is considered as a compress process of requested packets in a data link layer according to compressed sensing (CS) theory. We first introduced using compressive complex requests to identify the exact active sensor nodes, which makes the scheme more efficient. Moreover, because the reconstruction process is executed in a complex field of a physical layer, where no bit and frame synchronizations are needed, the asynchronous and random requests scheme can be implemented without synchronization payload. We set up a testbed based on software-defined radio (SDR) to implement the proposed CL CS-MAC scheme practically and to demonstrate the validation. For large-scale WSNs, the simulation results show that the proposed CL CS-MAC scheme provides higher throughput and robustness than the carrier sense multiple access (CSMA) and compressed sensing medium-access control (CS-MAC) schemes.

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.

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

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-

Distributed Compressive Sensing vs. Dynamic Compressive Sensing: Improving the Compressive Line Sensing Imaging System through Their Integration

DTIC Science & Technology

2015-01-01

streak tube imaging Lidar [15]. Nevertheless, instead of one- dimensional (1D) fan beam, a laser source modulates the digital micromirror device DMD and...Trans. Inform. Theory, vol. 52, pp. 1289-1306, 2006. [10] D. Dudley, W. Duncan and J. Slaughter, "Emerging Digital Micromirror Device (DMD) Applications

New Algorithms and Lower Bounds for Sequential-Access Data Compression

NASA Astrophysics Data System (ADS)

Gagie, Travis

2009-02-01

This thesis concerns sequential-access data compression, i.e., by algorithms that read the input one or more times from beginning to end. In one chapter we consider adaptive prefix coding, for which we must read the input character by character, outputting each character's self-delimiting codeword before reading the next one. We show how to encode and decode each character in constant worst-case time while producing an encoding whose length is worst-case optimal. In another chapter we consider one-pass compression with memory bounded in terms of the alphabet size and context length, and prove a nearly tight tradeoff between the amount of memory we can use and the quality of the compression we can achieve. In a third chapter we consider compression in the read/write streams model, which allows us passes and memory both polylogarithmic in the size of the input. We first show how to achieve universal compression using only one pass over one stream. We then show that one stream is not sufficient for achieving good grammar-based compression. Finally, we show that two streams are necessary and sufficient for achieving entropy-only bounds.

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.

Membrane augmented distillation to separate solvents from water

DOEpatents

Huang, Yu; Baker, Richard W.; Daniels, Rami; Aldajani, Tiem; Ly, Jennifer H.; Alvarez, Franklin R.; Vane, Leland M.

2012-09-11

Processes for removing water from organic solvents, such as ethanol. The processes include distillation to form a rectified overhead vapor, compression of the rectified vapor, and treatment of the compressed vapor by two sequential membrane separation steps.

The efficacy of the new SCD response compression system in the prevention of venous stasis.

PubMed

Kakkos, S K; Szendro, G; Griffin, M; Daskalopoulou, S S; Nicolaides, A N

2000-11-01

The current commercially available sequential intermittent pneumatic compression device used for the prevention of deep venous thrombosis has a constant cycle of 11 seconds' compression and 60 seconds' deflation. This deflation period ensures that the veins are filled before the subsequent cycle begins. It has been suggested that in some positions (eg, semirecumbent or sitting) and with different patients (eg, those with venous reflux), refilling of the veins may occur much earlier than 60 seconds, and thus a more frequent cycle may be more effective in expelling blood proximally. The aim of the study was to test the effectiveness of a new sequential compression system (the SCD Response Compression System), which has the ability to detect the change in the venous volume and to respond by initiating the subsequent cycle when the veins are substantially full. In an open controlled trial at an academic vascular laboratory, the SCD Response Compression System was tested against the existing SCD Sequel Compression System in 12 healthy volunteers who were in supine, semirecumbent, and sitting positions. The refilling time sensed by the device was compared with that determined from recordings of femoral vein flow velocity by the use of duplex ultrasound scan. The total volume of blood expelled per hour during compression was compared with that produced by the existing SCD system in the same volunteers and positions. The refilling time determined automatically by the SCD Response Compression System varied from 24 to 60 seconds in the subjects tested, demonstrating individual patient variation. The refilling time (mean +/- SD) in the sitting position was 40.6 +/- 10. 0 seconds, which was significantly longer (P <.001) than that measured in the supine and semirecumbent positions, 33.8 +/- 4.1 and 35.6 +/- 4.9 seconds, respectively. There was a linear relationship between the duplex scan-derived refill time (mean of 6 readings per leg) and the SCD Response device-derived refill time (r = 0.85, P <. 001). The total volume of blood (mean +/- SD) expelled per hour by the existing SCD Sequel device in the supine, semirecumbent, and sitting positions was 2.23 +/- 0.90 L/h, 2.47 +/- 0.86 L/h, and 3.28 +/- 1.24 L/h, respectively. The SCD Response device increased the volume expelled to 3.92 +/- 1.60 L/h or a 76% increase (P =.001) in the supine position, to 3.93 +/- 1.55 L/h or a 59% increase (P =. 001) in the semirecumbent position, and to 3.97 +/- 1.42 L/h or a 21% increase (P =.026) in the sitting position. By achieving more appropriately timed compression cycles over time, the new SCD Response System is effective in preventing venous stasis by means of a new method that improves on the clinically documented effectiveness of the existing SCD system. Further studies testing its potential for improved efficacy in preventing deep venous thrombosis are justified.

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.

Effect of Compression Devices on Preventing Deep Vein Thrombosis Among Adult Trauma Patients: A Systematic Review.

PubMed

Ibrahim, Mona; Ahmed, Azza; Mohamed, Warda Yousef; El-Sayed Abu Abduo, Somaya

2015-01-01

Trauma is the leading cause of death in Americans up to 44 years old each year. Deep vein thrombosis (DVT) is a significant condition occurring in trauma, and prophylaxis is essential to the appropriate management of trauma patients. The incidence of DVT varies in trauma patients, depending on patients' risk factors, modality of prophylaxis, and methods of detection. However, compression devices and arteriovenous (A-V) foot pumps prophylaxis are recommended in trauma patients, but the efficacy and optimal use of it is not well documented in the literature. The aim of this study was to review the literature on the effect of compression devices in preventing DVT among adult trauma patients. We searched through PubMed, CINAHL, and Cochrane Central Register of Controlled Trials for eligible studies published from 1990 until June 2014. Reviewers identified all randomized controlled trials that satisfied the study criteria, and the quality of included studies was assessed by Cochrane risk of bias tool. Five randomized controlled trials were included with a total of 1072 patients. Sequential compression devices significantly reduced the incidence of DVT in trauma patients. Also, foot pumps were more effective in reducing incidence of DVT compared with sequential compression devices. Sequential compression devices and foot pumps reduced the incidence of DVT in trauma patients. However, the evidence is limited to a small sample size and did not take into account other confounding variables that may affect the incidence of DVT in trauma patients. Future randomized controlled trials with larger probability samples to investigate the optimal use of mechanical prophylaxis in trauma patients are needed.

Sequential neural text compression.

PubMed

Schmidhuber, J; Heil, S

1996-01-01

The purpose of this paper is to show that neural networks may be promising tools for data compression without loss of information. We combine predictive neural nets and statistical coding techniques to compress text files. We apply our methods to certain short newspaper articles and obtain compression ratios exceeding those of the widely used Lempel-Ziv algorithms (which build the basis of the UNIX functions "compress" and "gzip"). The main disadvantage of our methods is that they are about three orders of magnitude slower than standard methods.

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.

CaSPIAN: A Causal Compressive Sensing Algorithm for Discovering Directed Interactions in Gene Networks

PubMed Central

Emad, Amin; Milenkovic, Olgica

2014-01-01

We introduce a novel algorithm for inference of causal gene interactions, termed CaSPIAN (Causal Subspace Pursuit for Inference and Analysis of Networks), which is based on coupling compressive sensing and Granger causality techniques. The core of the approach is to discover sparse linear dependencies between shifted time series of gene expressions using a sequential list-version of the subspace pursuit reconstruction algorithm and to estimate the direction of gene interactions via Granger-type elimination. The method is conceptually simple and computationally efficient, and it allows for dealing with noisy measurements. Its performance as a stand-alone platform without biological side-information was tested on simulated networks, on the synthetic IRMA network in Saccharomyces cerevisiae, and on data pertaining to the human HeLa cell network and the SOS network in E. coli. The results produced by CaSPIAN are compared to the results of several related algorithms, demonstrating significant improvements in inference accuracy of documented interactions. These findings highlight the importance of Granger causality techniques for reducing the number of false-positives, as well as the influence of noise and sampling period on the accuracy of the estimates. In addition, the performance of the method was tested in conjunction with biological side information of the form of sparse “scaffold networks”, to which new edges were added using available RNA-seq or microarray data. These biological priors aid in increasing the sensitivity and precision of the algorithm in the small sample regime. PMID:24622336

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.

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

Design of Restoration Method Based on Compressed Sensing and TwIST Algorithm

NASA Astrophysics Data System (ADS)

Zhang, Fei; Piao, Yan

2018-04-01

In order to improve the subjective and objective quality of degraded images at low sampling rates effectively,save storage space and reduce computational complexity at the same time, this paper proposes a joint restoration algorithm of compressed sensing and two step iterative threshold shrinkage (TwIST). The algorithm applies the TwIST algorithm which used in image restoration to the compressed sensing theory. Then, a small amount of sparse high-frequency information is obtained in frequency domain. The TwIST algorithm based on compressed sensing theory is used to accurately reconstruct the high frequency image. The experimental results show that the proposed algorithm achieves better subjective visual effects and objective quality of degraded images while accurately restoring degraded images.

Performance of target detection algorithm in compressive sensing miniature ultraspectral imaging compressed sensing system

NASA Astrophysics Data System (ADS)

Gedalin, Daniel; Oiknine, Yaniv; August, Isaac; Blumberg, Dan G.; Rotman, Stanley R.; Stern, Adrian

2017-04-01

Compressive sensing theory was proposed to deal with the high quantity of measurements demanded by traditional hyperspectral systems. Recently, a compressive spectral imaging technique dubbed compressive sensing miniature ultraspectral imaging (CS-MUSI) was presented. This system uses a voltage controlled liquid crystal device to create multiplexed hyperspectral cubes. We evaluate the utility of the data captured using the CS-MUSI system for the task of target detection. Specifically, we compare the performance of the matched filter target detection algorithm in traditional hyperspectral systems and in CS-MUSI multiplexed hyperspectral cubes. We found that the target detection algorithm performs similarly in both cases, despite the fact that the CS-MUSI data is up to an order of magnitude less than that in conventional hyperspectral cubes. Moreover, the target detection is approximately an order of magnitude faster in CS-MUSI data.

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.

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.

Compressed Sensing for Chemistry

NASA Astrophysics Data System (ADS)

Sanders, Jacob Nathan

Many chemical applications, from spectroscopy to quantum chemistry, involve measuring or computing a large amount of data, and then compressing this data to retain the most chemically-relevant information. In contrast, compressed sensing is an emergent technique that makes it possible to measure or compute an amount of data that is roughly proportional to its information content. In particular, compressed sensing enables the recovery of a sparse quantity of information from significantly undersampled data by solving an ℓ 1-optimization problem. This thesis represents the application of compressed sensing to problems in chemistry. The first half of this thesis is about spectroscopy. Compressed sensing is used to accelerate the computation of vibrational and electronic spectra from real-time time-dependent density functional theory simulations. Using compressed sensing as a drop-in replacement for the discrete Fourier transform, well-resolved frequency spectra are obtained at one-fifth the typical simulation time and computational cost. The technique is generalized to multiple dimensions and applied to two-dimensional absorption spectroscopy using experimental data collected on atomic rubidium vapor. Finally, a related technique known as super-resolution is applied to open quantum systems to obtain realistic models of a protein environment, in the form of atomistic spectral densities, at lower computational cost. The second half of this thesis deals with matrices in quantum chemistry. It presents a new use of compressed sensing for more efficient matrix recovery whenever the calculation of individual matrix elements is the computational bottleneck. The technique is applied to the computation of the second-derivative Hessian matrices in electronic structure calculations to obtain the vibrational modes and frequencies of molecules. When applied to anthracene, this technique results in a threefold speed-up, with greater speed-ups possible for larger molecules. The implementation of the method in the Q-Chem commercial software package is described. Moreover, the method provides a general framework for bootstrapping cheap low-accuracy calculations in order to reduce the required number of expensive high-accuracy calculations.

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…

Real time on-chip sequential adaptive principal component analysis for data feature extraction and image compression

NASA Technical Reports Server (NTRS)

Duong, T. A.

2004-01-01

In this paper, we present a new, simple, and optimized hardware architecture sequential learning technique for adaptive Principle Component Analysis (PCA) which will help optimize the hardware implementation in VLSI and to overcome the difficulties of the traditional gradient descent in learning convergence and hardware implementation.

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.

GEOMETRIC CROSS SECTIONS OF DUST AGGREGATES AND A COMPRESSION MODEL FOR AGGREGATE COLLISIONS

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

Suyama, Toru; Wada, Koji; Tanaka, Hidekazu

2012-07-10

Geometric cross sections of dust aggregates determine their coupling with disk gas, which governs their motions in protoplanetary disks. Collisional outcomes also depend on geometric cross sections of initial aggregates. In a previous paper, we performed three-dimensional N-body simulations of sequential collisions of aggregates composed of a number of sub-micron-sized icy particles and examined radii of gyration (and bulk densities) of the obtained aggregates. We showed that collisional compression of aggregates is not efficient and that aggregates remain fluffy. In the present study, we examine geometric cross sections of the aggregates. Their cross sections decrease due to compression as wellmore » as to their gyration radii. It is found that a relation between the cross section and the gyration radius proposed by Okuzumi et al. is valid for the compressed aggregates. We also refine the compression model proposed in our previous paper. The refined model enables us to calculate the evolution of both gyration radii and cross sections of growing aggregates and reproduces well our numerical results of sequential aggregate collisions. The refined model can describe non-equal-mass collisions as well as equal-mass cases. Although we do not take into account oblique collisions in the present study, oblique collisions would further hinder compression of aggregates.« less

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.

Research on assessment and improvement method of remote sensing image reconstruction

NASA Astrophysics Data System (ADS)

Sun, Li; Hua, Nian; Yu, Yanbo; Zhao, Zhanping

2018-01-01

Remote sensing image quality assessment and improvement is an important part of image processing. Generally, the use of compressive sampling theory in remote sensing imaging system can compress images while sampling which can improve efficiency. A method of two-dimensional principal component analysis (2DPCA) is proposed to reconstruct the remote sensing image to improve the quality of the compressed image in this paper, which contain the useful information of image and can restrain the noise. Then, remote sensing image quality influence factors are analyzed, and the evaluation parameters for quantitative evaluation are introduced. On this basis, the quality of the reconstructed images is evaluated and the different factors influence on the reconstruction is analyzed, providing meaningful referential data for enhancing the quality of remote sensing images. The experiment results show that evaluation results fit human visual feature, and the method proposed have good application value in the field of remote sensing image processing.

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.

A higher-speed compressive sensing camera through multi-diode design

NASA Astrophysics Data System (ADS)

Herman, Matthew A.; Tidman, James; Hewitt, Donna; Weston, Tyler; McMackin, Lenore

2013-05-01

Obtaining high frame rates is a challenge with compressive sensing (CS) systems that gather measurements in a sequential manner, such as the single-pixel CS camera. One strategy for increasing the frame rate is to divide the FOV into smaller areas that are sampled and reconstructed in parallel. Following this strategy, InView has developed a multi-aperture CS camera using an 8×4 array of photodiodes that essentially act as 32 individual simultaneously operating single-pixel cameras. Images reconstructed from each of the photodiode measurements are stitched together to form the full FOV. To account for crosstalk between the sub-apertures, novel modulation patterns have been developed to allow neighboring sub-apertures to share energy. Regions of overlap not only account for crosstalk energy that would otherwise be reconstructed as noise, but they also allow for tolerance in the alignment of the DMD to the lenslet array. Currently, the multi-aperture camera is built into a computational imaging workstation configuration useful for research and development purposes. In this configuration, modulation patterns are generated in a CPU and sent to the DMD via PCI express, which allows the operator to develop and change the patterns used in the data acquisition step. The sensor data is collected and then streamed to the workstation via an Ethernet or USB connection for the reconstruction step. Depending on the amount of data taken and the amount of overlap between sub-apertures, frame rates of 2-5 frames per second can be achieved. In a stand-alone camera platform, currently in development, pattern generation and reconstruction will be implemented on-board.

Adaptive foveated single-pixel imaging with dynamic supersampling

PubMed Central

Phillips, David B.; Sun, Ming-Jie; Taylor, Jonathan M.; Edgar, Matthew P.; Barnett, Stephen M.; Gibson, Graham M.; Padgett, Miles J.

2017-01-01

In contrast to conventional multipixel cameras, single-pixel cameras capture images using a single detector that measures the correlations between the scene and a set of patterns. However, these systems typically exhibit low frame rates, because to fully sample a scene in this way requires at least the same number of correlation measurements as the number of pixels in the reconstructed image. To mitigate this, a range of compressive sensing techniques have been developed which use a priori knowledge to reconstruct images from an undersampled measurement set. Here, we take a different approach and adopt a strategy inspired by the foveated vision found in the animal kingdom—a framework that exploits the spatiotemporal redundancy of many dynamic scenes. In our system, a high-resolution foveal region tracks motion within the scene, yet unlike a simple zoom, every frame delivers new spatial information from across the entire field of view. This strategy rapidly records the detail of quickly changing features in the scene while simultaneously accumulating detail of more slowly evolving regions over several consecutive frames. This architecture provides video streams in which both the resolution and exposure time spatially vary and adapt dynamically in response to the evolution of the scene. The degree of local frame rate enhancement is scene-dependent, but here, we demonstrate a factor of 4, thereby helping to mitigate one of the main drawbacks of single-pixel imaging techniques. The methods described here complement existing compressive sensing approaches and may be applied to enhance computational imagers that rely on sequential correlation measurements. PMID:28439538

Digital micromirror devices in Raman trace detection of explosives

NASA Astrophysics Data System (ADS)

Glimtoft, Martin; Svanqvist, Mattias; Ågren, Matilda; Nordberg, Markus; Östmark, Henric

2016-05-01

Imaging Raman spectroscopy based on tunable filters is an established technique for detecting single explosives particles at stand-off distances. However, large light losses are inherent in the design due to sequential imaging at different wavelengths, leading to effective transmission often well below 1 %. The use of digital micromirror devices (DMD) and compressive sensing (CS) in imaging Raman explosives trace detection can improve light throughput and add significant flexibility compared to existing systems. DMDs are based on mature microelectronics technology, and are compact, scalable, and can be customized for specific tasks, including new functions not available with current technologies. This paper has been focusing on investigating how a DMD can be used when applying CS-based imaging Raman spectroscopy on stand-off explosives trace detection, and evaluating the performance in terms of light throughput, image reconstruction ability and potential detection limits. This type of setup also gives the possibility to combine imaging Raman with non-spatially resolved fluorescence suppression techniques, such as Kerr gating. The system used consists of a 2nd harmonics Nd:YAG laser for sample excitation, collection optics, DMD, CMOScamera and a spectrometer with ICCD camera for signal gating and detection. Initial results for compressive sensing imaging Raman shows a stable reconstruction procedure even at low signals and in presence of interfering background signal. It is also shown to give increased effective light transmission without sacrificing molecular specificity or area coverage compared to filter based imaging Raman. At the same time it adds flexibility so the setup can be customized for new functionality.

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.

Compressive sensing scalp EEG signals: implementations and practical performance.

PubMed

Abdulghani, Amir M; Casson, Alexander J; Rodriguez-Villegas, Esther

2012-11-01

Highly miniaturised, wearable computing and communication systems allow unobtrusive, convenient and long term monitoring of a range of physiological parameters. For long term operation from the physically smallest batteries, the average power consumption of a wearable device must be very low. It is well known that the overall power consumption of these devices can be reduced by the inclusion of low power consumption, real-time compression of the raw physiological data in the wearable device itself. Compressive sensing is a new paradigm for providing data compression: it has shown significant promise in fields such as MRI; and is potentially suitable for use in wearable computing systems as the compression process required in the wearable device has a low computational complexity. However, the practical performance very much depends on the characteristics of the signal being sensed. As such the utility of the technique cannot be extrapolated from one application to another. Long term electroencephalography (EEG) is a fundamental tool for the investigation of neurological disorders and is increasingly used in many non-medical applications, such as brain-computer interfaces. This article investigates in detail the practical performance of different implementations of the compressive sensing theory when applied to scalp EEG signals.

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 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

Recent advances in lossless coding techniques

NASA Astrophysics Data System (ADS)

Yovanof, Gregory S.

Current lossless techniques are reviewed with reference to both sequential data files and still images. Two major groups of sequential algorithms, dictionary and statistical techniques, are discussed. In particular, attention is given to Lempel-Ziv coding, Huffman coding, and arithmewtic coding. The subject of lossless compression of imagery is briefly discussed. Finally, examples of practical implementations of lossless algorithms and some simulation results are given.

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.

Compressed normalized block difference for object tracking

NASA Astrophysics Data System (ADS)

Gao, Yun; Zhang, Dengzhuo; Cai, Donglan; Zhou, Hao; Lan, Ge

2018-04-01

Feature extraction is very important for robust and real-time tracking. Compressive sensing provided a technical support for real-time feature extraction. However, all existing compressive tracking were based on compressed Haar-like feature, and how to compress many more excellent high-dimensional features is worth researching. In this paper, a novel compressed normalized block difference feature (CNBD) was proposed. For resisting noise effectively in a highdimensional normalized pixel difference feature (NPD), a normalized block difference feature extends two pixels in the original formula of NPD to two blocks. A CNBD feature can be obtained by compressing a normalized block difference feature based on compressive sensing theory, with the sparse random Gaussian matrix as the measurement matrix. The comparative experiments of 7 trackers on 20 challenging sequences showed that the tracker based on CNBD feature can perform better than other trackers, especially than FCT tracker based on compressed Haar-like feature, in terms of AUC, SR and Precision.

Design of a digital compression technique for shuttle television

NASA Technical Reports Server (NTRS)

Habibi, A.; Fultz, G.

1976-01-01

The determination of the performance and hardware complexity of data compression algorithms applicable to color television signals, were studied to assess the feasibility of digital compression techniques for shuttle communications applications. For return link communications, it is shown that a nonadaptive two dimensional DPCM technique compresses the bandwidth of field-sequential color TV to about 13 MBPS and requires less than 60 watts of secondary power. For forward link communications, a facsimile coding technique is recommended which provides high resolution slow scan television on a 144 KBPS channel. The onboard decoder requires about 19 watts of secondary power.

A sequential compression mechanical pump to prevent hypotension during elective cesarean section under spinal anesthesia.

PubMed

Sujata, N; Arora, D; Panigrahi, B P; Hanjoora, V M

2012-04-01

Spinal anesthesia is a standard technique for cesarean section but can cause hypotension which may be related to venous pooling secondary to progesterone-induced decreases in vascular tone. This study investigated the use of a sequential compression mechanical pump with thigh-high sleeves with compression cycles timed to venous refilling. We hypothesized that this would recruit pooled venous blood from the lower limbs, maintain the central blood volume and thus decrease the incidence of hypotension. One hundred parturients scheduled for elective cesarean section under spinal anesthesia were recruited and randomly assigned to use of either a mechanical pump (Group M) or control (Group C). A standardized protocol for co-hydration and anesthesia was followed. Hypotension, defined as a decrease in systolic blood pressure by >20% from baseline, was treated with 6-mg boluses of intravenous ephedrine. The incidence of hypotension was defined as the primary outcome. Median ephedrine requirement was taken as a measure of the severity of hypotension. Hypotension occurred in 12 of 47 (25.5%) patients in Group M compared to 27 of 45 (60%) in Group C (P=0.001). The median [range] ephedrine dose was greater in Group C (12 [0-24]mg) compared to Group M (0 [0-12]mg) (P<0.001). There was no difference between groups in the time to onset of hypotension. The use of a sequential compression mechanical pump that detects venous refilling and cycles accordingly, reduced the incidence and severity of hypotension after spinal anesthesia for cesarean section. Copyright © 2012 Elsevier Ltd. All rights reserved.

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 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.

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.

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.

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.

The Maximum Cross-Correlation approach to detecting translational motions from sequential remote-sensing images

NASA Astrophysics Data System (ADS)

Gao, J.; Lythe, M. B.

1996-06-01

This paper presents the principle of the Maximum Cross-Correlation (MCC) approach in detecting translational motions within dynamic fields from time-sequential remotely sensed images. A C program implementing the approach is presented and illustrated in a flowchart. The program is tested with a pair of sea-surface temperature images derived from Advanced Very High Resolution Radiometer (AVHRR) images near East Cape, New Zealand. Results show that the mean currents in the region have been detected satisfactorily with the approach.

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.

New image compression scheme for digital angiocardiography application

NASA Astrophysics Data System (ADS)

Anastassopoulos, George C.; Lymberopoulos, Dimitris C.; Kotsopoulos, Stavros A.; Kokkinakis, George C.

1993-06-01

The present paper deals with the development and evaluation of a new compression scheme, for angiocardiography images. This scheme provides considerable compression of the medical data file, through two different stages. The first stage obliterates the redundancy inside a single frame domain since the second stage obliterates the redundancy among the sequential frames. Within these stages the employed data compression ratio can be easily adjusted according to the needs of the angiocardiography applications, where still or moving (in slow or full motion) images are hauled. The developed scheme has been tailored on the real needs of the diagnosis oriented conferencing-teleworking processes, where Unified Image Viewing facilities are required.

The Production of Porous Hydroxyapatite Scaffolds with Graded Porosity by Sequential Freeze-Casting.

PubMed

Lee, Hyun; Jang, Tae-Sik; Song, Juha; Kim, Hyoun-Ee; Jung, Hyun-Do

2017-03-31

Porous hydroxyapatite (HA) scaffolds with porosity-graded structures were fabricated by sequential freeze-casting. The pore structures, compressive strengths, and biocompatibilities of the fabricated porous HA scaffolds were evaluated. The porosities of the inner and outer layers of the graded HA scaffolds were controlled by adjusting the initial HA contents of the casting slurries. The interface between the dense and porous parts was compact and tightly adherent. The porosity and compressive strengths of the scaffold were controlled by the relative thicknesses of the dense/porous parts. In addition, the porous HA scaffolds showed good biocompatibility in terms of preosteoblast cell attachment and proliferation. The results suggest that porous HA scaffolds with load-bearing parts have potential as bone grafts in hard-tissue engineering.

The Production of Porous Hydroxyapatite Scaffolds with Graded Porosity by Sequential Freeze-Casting

PubMed Central

Lee, Hyun; Jang, Tae-Sik; Song, Juha; Kim, Hyoun-Ee; Jung, Hyun-Do

2017-01-01

Porous hydroxyapatite (HA) scaffolds with porosity-graded structures were fabricated by sequential freeze-casting. The pore structures, compressive strengths, and biocompatibilities of the fabricated porous HA scaffolds were evaluated. The porosities of the inner and outer layers of the graded HA scaffolds were controlled by adjusting the initial HA contents of the casting slurries. The interface between the dense and porous parts was compact and tightly adherent. The porosity and compressive strengths of the scaffold were controlled by the relative thicknesses of the dense/porous parts. In addition, the porous HA scaffolds showed good biocompatibility in terms of preosteoblast cell attachment and proliferation. The results suggest that porous HA scaffolds with load-bearing parts have potential as bone grafts in hard-tissue engineering. PMID:28772735

Observation Uncertainty in Gaussian Sensor Networks

DTIC Science & Technology

2006-01-23

Ziv , J., and Lempel , A. A universal algorithm for sequential data compression . IEEE Transactions on Information Theory 23, 3 (1977), 337–343. 73 ...using the Lempel - Ziv algorithm [42], context-tree weighting [41], or the Burrows-Wheeler Trans- form [4], [15], for example. These source codes will...and Computation (Monticello, IL, September 2004). [4] Burrows, M., and Wheeler, D. A block sorting lossless data compression algorithm . Tech.

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

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.

A theoretical framework for the study of compression sensing in ionic polymer metal composites

NASA Astrophysics Data System (ADS)

Volpini, Valentina; Bardella, Lorenzo; Rodella, Andrea; Cha, Youngsu; Porfiri, Maurizio

2017-04-01

Ionic Polymer Metal Composites (IPMCs) are electro-responsive materials for sensing and actuation, consisting of an ion-exchange polymeric membrane with ionized units, plated within noble metal electrodes. In this work, we investigate the sensing response of IPMCs that are subject to a through-the-thickness compression, by specializing the continuum model introduced by Cha and Porfiri,1 to this one-dimensional problem. This model modifies the classical Poisson-Nernst-Plank system governing the electrochemistry in the absence of mechanical effects, by accounting for finite deformations underlying the actuation and sensing processes. With the aim of accurately describing the IPMC dynamic compressive behavior, we introduce a spatial asymmetry in the properties of the membrane, which must be accounted for to trigger a sensing response. Then, we determine an analytical solution by applying the singular perturbation theory, and in particular the method of matched asymptotic expansions. This solution shows a good agreement with experimental findings reported in literature.

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.

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

Tolerant compressed sensing with partially coherent sensing matrices

NASA Astrophysics Data System (ADS)

Birnbaum, Tobias; Eldar, Yonina C.; Needell, Deanna

2017-08-01

Most of compressed sensing (CS) theory to date is focused on incoherent sensing, that is, columns from the sensing matrix are highly uncorrelated. However, sensing systems with naturally occurring correlations arise in many applications, such as signal detection, motion detection and radar. Moreover, in these applications it is often not necessary to know the support of the signal exactly, but instead small errors in the support and signal are tolerable. Despite the abundance of work utilizing incoherent sensing matrices, for this type of tolerant recovery we suggest that coherence is actually beneficial . We promote the use of coherent sampling when tolerant support recovery is acceptable, and demonstrate its advantages empirically. In addition, we provide a first step towards theoretical analysis by considering a specific reconstruction method for selected signal classes.

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

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.

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.

Smartphone home monitoring of ECG

NASA Astrophysics Data System (ADS)

Szu, Harold; Hsu, Charles; Moon, Gyu; Landa, Joseph; Nakajima, Hiroshi; Hata, Yutaka

2012-06-01

A system of ambulatory, halter, electrocardiography (ECG) monitoring system has already been commercially available for recording and transmitting heartbeats data by the Internet. However, it enjoys the confidence with a reservation and thus a limited market penetration, our system was targeting at aging global villagers having an increasingly biomedical wellness (BMW) homecare needs, not hospital related BMI (biomedical illness). It was designed within SWaP-C (Size, Weight, and Power, Cost) using 3 innovative modules: (i) Smart Electrode (lowpower mixed signal embedded with modern compressive sensing and nanotechnology to improve the electrodes' contact impedance); (ii) Learnable Database (in terms of adaptive wavelets transform QRST feature extraction, Sequential Query Relational database allowing home care monitoring retrievable Aided Target Recognition); (iii) Smartphone (touch screen interface, powerful computation capability, caretaker reporting with GPI, ID, and patient panic button for programmable emergence procedure). It can provide a supplementary home screening system for the post or the pre-diagnosis care at home with a build-in database searchable with the time, the place, and the degree of urgency happened, using in-situ screening.

A multi-signal fluorescent probe for simultaneously distinguishing and sequentially sensing cysteine/homocysteine, glutathione, and hydrogen sulfide in living cells† †Electronic supplementary information (ESI) available: Experimental details for chemical synthesis of all compounds, chemical structure characterization, supplementary spectra of probe, and fluorescence imaging methods and data. See DOI: 10.1039/c7sc00423k Click here for additional data file.

PubMed Central

He, Longwei; Yang, Xueling; Xu, Kaixin; Kong, Xiuqi

2017-01-01

Biothiols, which have a close network of generation and metabolic pathways among them, are essential reactive sulfur species (RSS) in the cells and play vital roles in human physiology. However, biothiols possess highly similar chemical structures and properties, resulting in it being an enormous challenge to simultaneously discriminate them from each other. Herein, we develop a unique fluorescent probe (HMN) for not only simultaneously distinguishing Cys/Hcy, GSH, and H2S from each other, but also sequentially sensing Cys/Hcy/GSH and H2S using a multi-channel fluorescence mode for the first time. When responding to the respective biothiols, the robust probe exhibits multiple sets of fluorescence signals at three distinct emission bands (blue-green-red). The new probe can also sense H2S at different concentration levels with changes of fluorescence at the blue and red emission bands. In addition, the novel probe HMN is able to discriminate and sequentially sense biothiols in biological environments via three-color fluorescence imaging. We expect that the development of the robust probe HMN will provide a powerful strategy to design fluorescent probes for the discrimination and sequential detection of biothiols, and offer a promising tool for exploring the interrelated roles of biothiols in various physiological and pathological conditions. PMID:28989659

Reducing acquisition time in clinical MRI by data undersampling and compressed sensing reconstruction

NASA Astrophysics Data System (ADS)

Hollingsworth, Kieren Grant

2015-11-01

MRI is often the most sensitive or appropriate technique for important measurements in clinical diagnosis and research, but lengthy acquisition times limit its use due to cost and considerations of patient comfort and compliance. Once an image field of view and resolution is chosen, the minimum scan acquisition time is normally fixed by the amount of raw data that must be acquired to meet the Nyquist criteria. Recently, there has been research interest in using the theory of compressed sensing (CS) in MR imaging to reduce scan acquisition times. The theory argues that if our target MR image is sparse, having signal information in only a small proportion of pixels (like an angiogram), or if the image can be mathematically transformed to be sparse then it is possible to use that sparsity to recover a high definition image from substantially less acquired data. This review starts by considering methods of k-space undersampling which have already been incorporated into routine clinical imaging (partial Fourier imaging and parallel imaging), and then explains the basis of using compressed sensing in MRI. The practical considerations of applying CS to MRI acquisitions are discussed, such as designing k-space undersampling schemes, optimizing adjustable parameters in reconstructions and exploiting the power of combined compressed sensing and parallel imaging (CS-PI). A selection of clinical applications that have used CS and CS-PI prospectively are considered. The review concludes by signposting other imaging acceleration techniques under present development before concluding with a consideration of the potential impact and obstacles to bringing compressed sensing into routine use in clinical MRI.

A Novel Ship-Tracking Method for GF-4 Satellite Sequential Images.

PubMed

Yao, Libo; Liu, Yong; He, You

2018-06-22

The geostationary remote sensing satellite has the capability of wide scanning, persistent observation and operational response, and has tremendous potential for maritime target surveillance. The GF-4 satellite is the first geostationary orbit (GEO) optical remote sensing satellite with medium resolution in China. In this paper, a novel ship-tracking method in GF-4 satellite sequential imagery is proposed. The algorithm has three stages. First, a local visual saliency map based on local peak signal-to-noise ratio (PSNR) is used to detect ships in a single frame of GF-4 satellite sequential images. Second, the accuracy positioning of each potential target is realized by a dynamic correction using the rational polynomial coefficients (RPCs) and automatic identification system (AIS) data of ships. Finally, an improved multiple hypotheses tracking (MHT) algorithm with amplitude information is used to track ships by further removing the false targets, and to estimate ships’ motion parameters. The algorithm has been tested using GF-4 sequential images and AIS data. The results of the experiment demonstrate that the algorithm achieves good tracking performance in GF-4 satellite sequential images and estimates the motion information of ships accurately.

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.

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

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

Adaptive compressive ghost imaging based on wavelet trees and sparse representation.

PubMed

Yu, Wen-Kai; Li, Ming-Fei; Yao, Xu-Ri; Liu, Xue-Feng; Wu, Ling-An; Zhai, Guang-Jie

2014-03-24

Compressed sensing is a theory which can reconstruct an image almost perfectly with only a few measurements by finding its sparsest representation. However, the computation time consumed for large images may be a few hours or more. In this work, we both theoretically and experimentally demonstrate a method that combines the advantages of both adaptive computational ghost imaging and compressed sensing, which we call adaptive compressive ghost imaging, whereby both the reconstruction time and measurements required for any image size can be significantly reduced. The technique can be used to improve the performance of all computational ghost imaging protocols, especially when measuring ultra-weak or noisy signals, and can be extended to imaging applications at any wavelength.

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.

Data compression: The end-to-end information systems perspective for NASA space science missions

NASA Technical Reports Server (NTRS)

Tai, Wallace

1991-01-01

The unique characteristics of compressed data have important implications to the design of space science data systems, science applications, and data compression techniques. The sequential nature or data dependence between each of the sample values within a block of compressed data introduces an error multiplication or propagation factor which compounds the effects of communication errors. The data communication characteristics of the onboard data acquisition, storage, and telecommunication channels may influence the size of the compressed blocks and the frequency of included re-initialization points. The organization of the compressed data are continually changing depending on the entropy of the input data. This also results in a variable output rate from the instrument which may require buffering to interface with the spacecraft data system. On the ground, there exist key tradeoff issues associated with the distribution and management of the science data products when data compression techniques are applied in order to alleviate the constraints imposed by ground communication bandwidth and data storage capacity.

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.

Two-level image authentication by two-step phase-shifting interferometry and compressive sensing

NASA Astrophysics Data System (ADS)

Zhang, Xue; Meng, Xiangfeng; Yin, Yongkai; Yang, Xiulun; Wang, Yurong; Li, Xianye; Peng, Xiang; He, Wenqi; Dong, Guoyan; Chen, Hongyi

2018-01-01

A two-level image authentication method is proposed; the method is based on two-step phase-shifting interferometry, double random phase encoding, and compressive sensing (CS) theory, by which the certification image can be encoded into two interferograms. Through discrete wavelet transform (DWT), sparseness processing, Arnold transform, and data compression, two compressed signals can be generated and delivered to two different participants of the authentication system. Only the participant who possesses the first compressed signal attempts to pass the low-level authentication. The application of Orthogonal Match Pursuit CS algorithm reconstruction, inverse Arnold transform, inverse DWT, two-step phase-shifting wavefront reconstruction, and inverse Fresnel transform can result in the output of a remarkable peak in the central location of the nonlinear correlation coefficient distributions of the recovered image and the standard certification image. Then, the other participant, who possesses the second compressed signal, is authorized to carry out the high-level authentication. Therefore, both compressed signals are collected to reconstruct the original meaningful certification image with a high correlation coefficient. Theoretical analysis and numerical simulations verify the feasibility of the proposed method.

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

Fundamental Interactions in Gasoline Compression Ignition Engines with Fuel Stratification

NASA Astrophysics Data System (ADS)

Wolk, Benjamin Matthew

Transportation accounted for 28% of the total U.S. energy demand in 2011, with 93% of U.S. transportation energy coming from petroleum. The large impact of the transportation sector on global climate change necessitates more-efficient, cleaner-burning internal combustion engine operating strategies. One such strategy that has received substantial research attention in the last decade is Homogeneous Charge Compression Ignition (HCCI). Although the efficiency and emissions benefits of HCCI are well established, practical limits on the operating range of HCCI engines have inhibited their application in consumer vehicles. One such limit is at high load, where the pressure rise rate in the combustion chamber becomes excessively large. Fuel stratification is a potential strategy for reducing the maximum pressure rise rate in HCCI engines. The aim is to introduce reactivity gradients through fuel stratification to promote sequential auto-ignition rather than a bulk-ignition, as in the homogeneous case. A gasoline-fueled compression ignition engine with fuel stratification is termed a Gasoline Compression Ignition (GCI) engine. Although a reasonable amount of experimental research has been performed for fuel stratification in GCI engines, a clear understanding of how the fundamental in-cylinder processes of fuel spray evaporation, mixing, and heat release contribute to the observed phenomena is lacking. Of particular interest is gasoline's pressure sensitive low-temperature chemistry and how it impacts the sequential auto-ignition of the stratified charge. In order to computationally study GCI with fuel stratification using three-dimensional computational fluid dynamics (CFD) and chemical kinetics, two reduced mechanisms have been developed. The reduced mechanisms were developed from a large, detailed mechanism with about 1400 species for a 4-component gasoline surrogate. The two versions of the reduced mechanism developed in this work are: (1) a 96-species version and (2) a 98-species version including nitric oxide formation reactions. Development of reduced mechanisms is necessary because the detailed mechanism is computationally prohibitive in three-dimensional CFD and chemical kinetics simulations. Simulations of Partial Fuel Stratification (PFS), a GCI strategy, have been performed using CONVERGE with the 96-species reduced mechanism developed in this work for a 4-component gasoline surrogate. Comparison is made to experimental data from the Sandia HCCI/GCI engine at a compression ratio 14:1 at intake pressures of 1 bar and 2 bar. Analysis of the heat release and temperature in the different equivalence ratio regions reveals that sequential auto-ignition of the stratified charge occurs in order of increasing equivalence ratio for 1 bar intake pressure and in order of decreasing equivalence ratio for 2 bar intake pressure. Increased low- and intermediate-temperature heat release with increasing equivalence ratio at 2 bar intake pressure compensates for decreased temperatures in higher-equivalence ratio regions due to evaporative cooling from the liquid fuel spray and decreased compression heating from lower values of the ratio of specific heats. The presence of low- and intermediate-temperature heat release at 2 bar intake pressure alters the temperature distribution of the mixture stratification before hot-ignition, promoting the desired sequential auto-ignition. At 1 bar intake pressure, the sequential auto-ignition occurs in the reverse order compared to 2 bar intake pressure and too fast for useful reduction of the maximum pressure rise rate compared to HCCI. Additionally, the premixed portion of the charge auto-ignites before the highest-equivalence ratio regions. Conversely, at 2 bar intake pressure, the premixed portion of the charge auto-ignites last, after the higher-equivalence ratio regions. More importantly, the sequential auto-ignition occurs over a longer time period for 2 bar intake pressure than at 1 bar intake pressure such that a sizable reduction in the maximum pressure rise rate compared to HCCI can be achieved.

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.

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.

High efficient optical remote sensing images acquisition for nano-satellite: reconstruction algorithms

NASA Astrophysics Data System (ADS)

Liu, Yang; Li, Feng; Xin, Lei; Fu, Jie; Huang, Puming

2017-10-01

Large amount of data is one of the most obvious features in satellite based remote sensing systems, which is also a burden for data processing and transmission. The theory of compressive sensing(CS) has been proposed for almost a decade, and massive experiments show that CS has favorable performance in data compression and recovery, so we apply CS theory to remote sensing images acquisition. In CS, the construction of classical sensing matrix for all sparse signals has to satisfy the Restricted Isometry Property (RIP) strictly, which limits applying CS in practical in image compression. While for remote sensing images, we know some inherent characteristics such as non-negative, smoothness and etc.. Therefore, the goal of this paper is to present a novel measurement matrix that breaks RIP. The new sensing matrix consists of two parts: the standard Nyquist sampling matrix for thumbnails and the conventional CS sampling matrix. Since most of sun-synchronous based satellites fly around the earth 90 minutes and the revisit cycle is also short, lots of previously captured remote sensing images of the same place are available in advance. This drives us to reconstruct remote sensing images through a deep learning approach with those measurements from the new framework. Therefore, we propose a novel deep convolutional neural network (CNN) architecture which takes in undersampsing measurements as input and outputs an intermediate reconstruction image. It is well known that the training procedure to the network costs long time, luckily, the training step can be done only once, which makes the approach attractive for a host of sparse recovery problems.

Allosteric substrate switching in a voltage-sensing lipid phosphatase.

PubMed

Grimm, Sasha S; Isacoff, Ehud Y

2016-04-01

Allostery provides a critical control over enzyme activity, biasing the catalytic site between inactive and active states. We found that the Ciona intestinalis voltage-sensing phosphatase (Ci-VSP), which modifies phosphoinositide signaling lipids (PIPs), has not one but two sequential active states with distinct substrate specificities, whose occupancy is allosterically controlled by sequential conformations of the voltage-sensing domain (VSD). Using fast fluorescence resonance energy transfer (FRET) reporters of PIPs to monitor enzyme activity and voltage-clamp fluorometry to monitor conformational changes in the VSD, we found that Ci-VSP switches from inactive to a PIP3-preferring active state when the VSD undergoes an initial voltage-sensing motion and then into a second PIP2-preferring active state when the VSD activates fully. This two-step allosteric control over a dual-specificity enzyme enables voltage to shape PIP concentrations in time, and provides a mechanism for the complex modulation of PIP-regulated ion channels, transporters, cell motility, endocytosis and exocytosis.

Allosteric substrate switching in a voltage sensing lipid phosphatase

PubMed Central

Grimm, Sasha S.; Isacoff, Ehud Y.

2016-01-01

Allostery provides a critical control over enzyme activity, biasing the catalytic site between inactive and active states. We find the Ciona intestinalis voltage-sensing phosphatase (Ci-VSP), which modifies phosphoinositide signaling lipids (PIPs), to have not one but two sequential active states with distinct substrate specificities, whose occupancy is allosterically controlled by sequential conformations of the voltage sensing domain (VSD). Using fast FRET reporters of PIPs to monitor enzyme activity and voltage clamp fluorometry to monitor conformational changes in the VSD, we find that Ci-VSP switches from inactive to a PIP3-preferring active state when the VSD undergoes an initial voltage sensing motion and then into a second PIP2-preferring active state when the VSD activates fully. This novel 2-step allosteric control over a dual specificity enzyme enables voltage to shape PIP concentrations in time, and provides a mechanism for the complex modulation of PIP-regulated ion channels, transporters, cell motility and endo/exocytosis. PMID:26878552

First year medical students' learning style preferences and their correlation with performance in different subjects within the medical course.

PubMed

Hernández-Torrano, Daniel; Ali, Syed; Chan, Chee-Kai

2017-08-08

Students commencing their medical training arrive with different educational backgrounds and a diverse range of learning experiences. Consequently, students would have developed preferred approaches to acquiring and processing information or learning style preferences. Understanding first-year students' learning style preferences is important to success in learning. However, little is understood about how learning styles impact learning and performance across different subjects within the medical curriculum. Greater understanding of the relationship between students' learning style preferences and academic performance in specific medical subjects would be valuable. This cross-sectional study examined the learning style preferences of first-year medical students and how they differ across gender. This research also analyzed the effect of learning styles on academic performance across different subjects within a medical education program in a Central Asian university. A total of 52 students (57.7% females) from two batches of first-year medical school completed the Index of Learning Styles Questionnaire, which measures four dimensions of learning styles: sensing-intuitive; visual-verbal; active-reflective; sequential-global. First-year medical students reported preferences for visual (80.8%) and sequential (60.5%) learning styles, suggesting that these students preferred to learn through demonstrations and diagrams and in a linear and sequential way. Our results indicate that male medical students have higher preference for visual learning style over verbal, while females seemed to have a higher preference for sequential learning style over global. Significant associations were found between sensing-intuitive learning styles and performance in Genetics [β = -0.46, B = -0.44, p < 0.01] and Anatomy [β = -0.41, B = -0.61, p < 0.05] and between sequential-global styles and performance in Genetics [β = 0.36, B = 0.43, p < 0.05]. More specifically, sensing learners were more likely to perform better than intuitive learners in the two subjects and global learners were more likely to perform better than sequential learners in Genetics. This knowledge will be helpful to individual students to improve their performance in these subjects by adopting new sensing learning techniques. Instructors can also benefit by modifying and adapting more appropriate teaching approaches in these subjects. Future studies to validate this observation will be valuable.

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.

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.

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.

Approximate equiangular tight frames for compressed sensing and CDMA applications

NASA Astrophysics Data System (ADS)

Tsiligianni, Evaggelia; Kondi, Lisimachos P.; Katsaggelos, Aggelos K.

2017-12-01

Performance guarantees for recovery algorithms employed in sparse representations, and compressed sensing highlights the importance of incoherence. Optimal bounds of incoherence are attained by equiangular unit norm tight frames (ETFs). Although ETFs are important in many applications, they do not exist for all dimensions, while their construction has been proven extremely difficult. In this paper, we construct frames that are close to ETFs. According to results from frame and graph theory, the existence of an ETF depends on the existence of its signature matrix, that is, a symmetric matrix with certain structure and spectrum consisting of two distinct eigenvalues. We view the construction of a signature matrix as an inverse eigenvalue problem and propose a method that produces frames of any dimensions that are close to ETFs. Due to the achieved equiangularity property, the so obtained frames can be employed as spreading sequences in synchronous code-division multiple access (s-CDMA) systems, besides compressed sensing.

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.

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.

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.

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

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.

Segmentation of remotely sensed data using parallel region growing

NASA Technical Reports Server (NTRS)

Tilton, J. C.; Cox, S. C.

1983-01-01

The improved spatial resolution of the new earth resources satellites will increase the need for effective utilization of spatial information in machine processing of remotely sensed data. One promising technique is scene segmentation by region growing. Region growing can use spatial information in two ways: only spatially adjacent regions merge together, and merging criteria can be based on region-wide spatial features. A simple region growing approach is described in which the similarity criterion is based on region mean and variance (a simple spatial feature). An effective way to implement region growing for remote sensing is as an iterative parallel process on a large parallel processor. A straightforward parallel pixel-based implementation of the algorithm is explored and its efficiency is compared with sequential pixel-based, sequential region-based, and parallel region-based implementations. Experimental results from on aircraft scanner data set are presented, as is a discussioon of proposed improvements to the segmentation algorithm.

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.

Low-pressure sequential compression of lower limbs enhances forearm skin blood flow.

PubMed

Amah, Guy; Voicu, Sebastian; Bonnin, Philippe; Kubis, Nathalie

2016-12-01

We investigated whether forearm skin blood flow could be improved when a multilayer pulsatile inflatable suit was applied at a low pressure to the lower limbs and abdomen. We hypothesized that a non-invasive purely mechanical stimulation of the lower limbs could induce remote forearm blood flow modifications. The pulsatile suit induced a sequential compartmentalized low compression (65 mmHg), which was synchronized with each diastole of the cardiac cycle with each phase evolving centripetally (lower limbs to abdomen). Modifications of the forearm skin blood flow were continuously recorded by laser Doppler flowmetry (LDF) at baseline and during the pulsatile suit application. Endothelium-dependent and endothelium-independent vasodilations of the forearm skin microcirculation were measured by LDF in response to a local transdermal iontophoretic application of acetylcholine (ACh-test) and to hyperthermia (hyperT- test). Twenty-four healthy volunteers, 12 men and 12 women (43±14 years) were included in the study. LDF responses increased 1) under pulsatile suit (97±106%, p.

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.

A class of temporal boundaries derived by quantifying the sense of separation.

PubMed

Paine, Llewyn Elise; Gilden, David L

2013-12-01

The perception of moment-to-moment environmental flux as being composed of meaningful events requires that memory processes coordinate with cues that signify beginnings and endings. We have constructed a technique that allows this coordination to be monitored indirectly. This technique works by embedding a sequential priming task into the event under study. Memory and perception must be coordinated to resolve temporal flux into scenes. The implicit memory processes inherent in sequential priming are able to effectively shadow then mirror scene-forming processes. Certain temporal boundaries are found to weaken the strength of irrelevant feature priming, a signal which can then be used in more ambiguous cases to infer how people segment time. Over the course of 13 independent studies, we were able to calibrate the technique and then use it to measure the strength of event segmentation in several instructive contexts that involved both visual and auditory modalities. The signal generated by sequential priming may permit the sense of separation between events to be measured as an extensive psychophysical quantity.

Sequentially-crosslinked biomimetic bioactive glass/gelatin methacryloyl composites hydrogels for bone regeneration.

PubMed

Zheng, Jiafu; Zhao, Fujian; Zhang, Wen; Mo, Yunfei; Zeng, Lei; Li, Xian; Chen, Xiaofeng

2018-08-01

In recent years, gelatin-based composites hydrogels have been intensively investigated because of their inherent bioactivity, biocompatibility and biodegradability. Herein, we fabricated photocrosslinkable biomimetic composites hydrogels from bioactive glass (BG) and gelatin methacryloyl (GelMA) by a sequential physical and chemical crosslinking (gelation + UV) approach. The results showed that the compressive modulus of composites hydrogels increased significantly through the sequential crosslinking approach. The addition of BG resulted in a significant increase in physiological stability and apatite-forming ability. In vitro data indicated that BG/GelMA composites hydrogels promoted cell attachment, proliferation and differentiation. Overall, the BG/GelMA composites hydrogels combined the advantages of good biocompatibility and bioactivity, and had potential applications in bone regeneration. Copyright © 2018. Published by Elsevier B.V.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

Sequential buckling of an elastic wall

NASA Astrophysics Data System (ADS)

Bico, Jose; Bense, Hadrien; Keiser, Ludovic; Roman, Benoit; Melo, Francisco; Abkarian, Manouk

A beam under quasistatic compression classically buckles beyond a critical threshold. In the case of a free beam, the lowest buckling mode is selected. We investigate the case of a long ``wall'' grounded of a compliant base and compressed in the axial compression. In the case of a wall of slender rectangular cross section, the selected buckling mode adopts a nearly fixed wavelength proportional to the height of the wall. Higher compressive loads only increase the amplitude of the buckle. However if the cross section has a sharp shape (such as an Eiffel tower profile), we observe successive buckling modes of increasing wavelength. We interpret this unusual evolution in terms of scaling arguments. At small scales, this variable periodicity might be used to develop tunable optical devices. We thank ECOS C12E07, CNRS-CONICYT, and Fondecyt Grant No. N1130922 for partially funding this work.

The Impact Induced Demagnetization Mechanism in NdFeB Permanent Magnets

NASA Astrophysics Data System (ADS)

Li, Yan-Feng; Zhu, Ming-Gang; Li, Wei; Zhou, Dong; Lu, Feng; Chen, Lang; Wu, Jun-Ying; Qi, Yan; Du, An

2013-09-01

Compression of unmagnetized Nd2Fe14B permanent magnets is executed by using shock waves with different pressures in a one-stage light gas gun system. The microstructure, crystal structure, and magnetic properties of the magnets are examined with scanning electronic microscopy, x-ray diffraction, hysteresis loop instruments, and a vibrating sample magnetometer, respectively. The NdFeB magnets display a demagnetization phenomenon after shock wave compression. The coercivity dropped from about 21.4 kOe to 3.2 kOe. The critical pressure of irreversible demagnetization of NdFeB magnets should be less than 4.92 GPa. The coercivity of the NdFeB magnets compressed by shock waves could be recovered after annealing at 900°C and 520°C for 2 h, sequentially. The chaotic orientation of Nd2Fe14B grains in the compressed magnets is the source of demagnetization.

Unsupervised classification of remote multispectral sensing data

NASA Technical Reports Server (NTRS)

Su, M. Y.

1972-01-01

The new unsupervised classification technique for classifying multispectral remote sensing data which can be either from the multispectral scanner or digitized color-separation aerial photographs consists of two parts: (a) a sequential statistical clustering which is a one-pass sequential variance analysis and (b) a generalized K-means clustering. In this composite clustering technique, the output of (a) is a set of initial clusters which are input to (b) for further improvement by an iterative scheme. Applications of the technique using an IBM-7094 computer on multispectral data sets over Purdue's Flight Line C-1 and the Yellowstone National Park test site have been accomplished. Comparisons between the classification maps by the unsupervised technique and the supervised maximum liklihood technique indicate that the classification accuracies are in agreement.

Sequential deconvolution from wave-front sensing using bivariate simplex splines

NASA Astrophysics Data System (ADS)

Guo, Shiping; Zhang, Rongzhi; Li, Jisheng; Zou, Jianhua; Xu, Rong; Liu, Changhai

2015-05-01

Deconvolution from wave-front sensing (DWFS) is an imaging compensation technique for turbulence degraded images based on simultaneous recording of short exposure images and wave-front sensor data. This paper employs the multivariate splines method for the sequential DWFS: a bivariate simplex splines based average slopes measurement model is built firstly for Shack-Hartmann wave-front sensor; next, a well-conditioned least squares estimator for the spline coefficients is constructed using multiple Shack-Hartmann measurements; then, the distorted wave-front is uniquely determined by the estimated spline coefficients; the object image is finally obtained by non-blind deconvolution processing. Simulated experiments in different turbulence strength show that our method performs superior image restoration results and noise rejection capability especially when extracting the multidirectional phase derivatives.

Remote sensing of the Fram Strait marginal ice zone

USGS Publications Warehouse

Shuchman, R.A.; Burns, B.A.; Johannessen, O.M.; Josberger, E.G.; Campbell, W.J.; Manley, T.O.; Lannelongue, N.

1987-01-01

Sequential remote sensing images of the Fram Strait marginal ice zone played a key role in elucidating the complex interactions of the atmosphere, ocean, and sea ice. Analysis of a subset of these images covering a 1-week period provided quantitative data on the mesoscale ice morphology, including ice edge positions, ice concentrations, floe size distribution, and ice kinematics. The analysis showed that, under light to moderate wind conditions, the morphology of the marginal ice zone reflects the underlying ocean circulation. High-resolution radar observations showed the location and size of ocean eddies near the ice edge. Ice kinematics from sequential radar images revealed an ocean eddy beneath the interior pack ice that was verified by in situ oceanographic measurements.

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.

Enantioselective Construction of 3-Hydroxypiperidine Scaffolds by Sequential Action of Light and Rhodium upon N-Allylglyoxylamides.

PubMed

Ishida, Naoki; Nečas, David; Masuda, Yusuke; Murakami, Masahiro

2015-06-15

3-Hydroxypiperidine scaffolds were enantioselectively constructed in an atom-economical way by sequential action of light and rhodium upon N-allylglyoxylamides. In a formal sense, the allylic C-H bond was selectively cleaved and enantioselectively added across the ketonic carbonyl group with migration of the double bond (carbonyl-ene-type reaction). © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Iterative dictionary construction for compression of large DNA data sets.

PubMed

Kuruppu, Shanika; Beresford-Smith, Bryan; Conway, Thomas; Zobel, Justin

2012-01-01

Genomic repositories increasingly include individual as well as reference sequences, which tend to share long identical and near-identical strings of nucleotides. However, the sequential processing used by most compression algorithms, and the volumes of data involved, mean that these long-range repetitions are not detected. An order-insensitive, disk-based dictionary construction method can detect this repeated content and use it to compress collections of sequences. We explore a dictionary construction method that improves repeat identification in large DNA data sets. Our adaptation, COMRAD, of an existing disk-based method identifies exact repeated content in collections of sequences with similarities within and across the set of input sequences. COMRAD compresses the data over multiple passes, which is an expensive process, but allows COMRAD to compress large data sets within reasonable time and space. COMRAD allows for random access to individual sequences and subsequences without decompressing the whole data set. COMRAD has no competitor in terms of the size of data sets that it can compress (extending to many hundreds of gigabytes) and, even for smaller data sets, the results are competitive compared to alternatives; as an example, 39 S. cerevisiae genomes compressed to 0.25 bits per base.

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.

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 %.

An investigation of several numerical procedures for time-asymptotic compressible Navier-Stokes solutions

NASA Technical Reports Server (NTRS)

Rudy, D. H.; Morris, D. J.; Blanchard, D. K.; Cooke, C. H.; Rubin, S. G.

1975-01-01

The status of an investigation of four numerical techniques for the time-dependent compressible Navier-Stokes equations is presented. Results for free shear layer calculations in the Reynolds number range from 1000 to 81000 indicate that a sequential alternating-direction implicit (ADI) finite-difference procedure requires longer computing times to reach steady state than a low-storage hopscotch finite-difference procedure. A finite-element method with cubic approximating functions was found to require excessive computer storage and computation times. A fourth method, an alternating-direction cubic spline technique which is still being tested, is also described.

Fuel mixture stratification as a method for improving homogeneous charge compression ignition engine operation

DOEpatents

Dec, John E [Livermore, CA; Sjoberg, Carl-Magnus G [Livermore, CA

2006-10-31

A method for slowing the heat-release rate in homogeneous charge compression ignition ("HCCI") engines that allows operation without excessive knock at higher engine loads than are possible with conventional HCCI. This method comprises injecting a fuel charge in a manner that creates a stratified fuel charge in the engine cylinder to provide a range of fuel concentrations in the in-cylinder gases (typically with enough oxygen for complete combustion) using a fuel with two-stage ignition fuel having appropriate cool-flame chemistry so that regions of different fuel concentrations autoignite sequentially.

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.

Sequential Classifier Training for Rice Mapping with Multitemporal Remote Sensing Imagery

NASA Astrophysics Data System (ADS)

Guo, Y.; Jia, X.; Paull, D.

2017-10-01

Most traditional methods for rice mapping with remote sensing data are effective when they are applied to the initial growing stage of rice, as the practice of flooding during this period makes the spectral characteristics of rice fields more distinguishable. In this study, we propose a sequential classifier training approach for rice mapping that can be used over the whole growing period of rice for monitoring various growth stages. Rice fields are firstly identified during the initial flooding period. The identified rice fields are used as training data to train a classifier that separates rice and non-rice pixels. The classifier is then used as a priori knowledge to assist the training of classifiers for later rice growing stages. This approach can be applied progressively to sequential image data, with only a small amount of training samples being required from each image. In order to demonstrate the effectiveness of the proposed approach, experiments were conducted at one of the major rice-growing areas in Australia. The proposed approach was applied to a set of multitemporal remote sensing images acquired by the Sentinel-2A satellite. Experimental results show that, compared with traditional spectral-indexbased algorithms, the proposed method is able to achieve more stable and consistent rice mapping accuracies and it reaches higher than 80% during the whole rice growing period.

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

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 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

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

Longitudinal Linkages among Parent-Child Acculturation Discrepancy, Parenting, Parent-Child Sense of Alienation, and Adolescent Adjustment in Chinese Immigrant Families

ERIC Educational Resources Information Center

Kim, Su Yeong; Chen, Qi; Wang, Yijie; Shen, Yishan; Orozco-Lapray, Diana

2013-01-01

Parent-child acculturation discrepancy is a risk factor in the development of children in immigrant families. Using a longitudinal sample of Chinese immigrant families, the authors of the current study examined how unsupportive parenting and parent-child sense of alienation sequentially mediate the relationship between parent-child acculturation…

High-speed optical 3D sensing and its applications

NASA Astrophysics Data System (ADS)

Watanabe, Yoshihiro

2016-12-01

This paper reviews high-speed optical 3D sensing technologies for obtaining the 3D shape of a target using a camera. The focusing speed is from 100 to 1000 fps, exceeding normal camera frame rates, which are typically 30 fps. In particular, contactless, active, and real-time systems are introduced. Also, three example applications of this type of sensing technology are introduced, including surface reconstruction from time-sequential depth images, high-speed 3D user interaction, and high-speed digital archiving.

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.

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.

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.

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.

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

Experimental investigations of the time and flow-direction responses of shear-stress-sensitive liquid crystal coatings

NASA Technical Reports Server (NTRS)

Reda, Daniel C.; Muratore, Joseph J., Jr.; Heineck, James T.

1993-01-01

Time and flow-direction responses of shearstress-sensitive liquid crystal coatings were explored experimentally. For the time-response experiments, coatings were exposed to transient, compressible flows created during the startup and off-design operation of an injector-driven supersonic wind tunnel. Flow transients were visualized with a focusing Schlieren system and recorded with a 1000 frame/sec color video camera. Liquid crystal responses to these changing-shear environments were then recorded with the same video system, documenting color-play response times equal to, or faster than, the time interval between sequential frames (i.e., 1 millisecond). For the flow-direction experiments, a planar test surface was exposed to equal-magnitude and known-direction surface shear stresses generated by both normal and tangential subsonic jet-impingement flows. Under shear, the sense of the angular displacement of the liquid crystal dispersed (reflected) spectrum was found to be a function of the instantaneous direction of the applied shear. This technique thus renders dynamic flow reversals or flow divergences visible over entire test surfaces at image recording rates up to 1 KHz. Extensions of the technique to visualize relatively small changes in surface shear stress direction appear feasible.

Complementary aspects of spatial resolution and signal-to-noise ratio in computational imaging

NASA Astrophysics Data System (ADS)

Gureyev, T. E.; Paganin, D. M.; Kozlov, A.; Nesterets, Ya. I.; Quiney, H. M.

2018-05-01

A generic computational imaging setup is considered which assumes sequential illumination of a semitransparent object by an arbitrary set of structured coherent illumination patterns. For each incident illumination pattern, all transmitted light is collected by a photon-counting bucket (single-pixel) detector. The transmission coefficients measured in this way are then used to reconstruct the spatial distribution of the object's projected transmission. It is demonstrated that the square of the spatial resolution of such a setup is usually equal to the ratio of the image area to the number of linearly independent illumination patterns. If the noise in the measured transmission coefficients is dominated by photon shot noise, then the ratio of the square of the mean signal to the noise variance is proportional to the ratio of the mean number of registered photons to the number of illumination patterns. The signal-to-noise ratio in a reconstructed transmission distribution is always lower if the illumination patterns are nonorthogonal, because of spatial correlations in the measured data. Examples of imaging methods relevant to the presented analysis include conventional imaging with a pixelated detector, computational ghost imaging, compressive sensing, super-resolution imaging, and computed tomography.

Compressive sensing of electrocardiogram signals by promoting sparsity on the second-order difference and by using dictionary learning.

PubMed

Pant, Jeevan K; Krishnan, Sridhar

2014-04-01

A new algorithm for the reconstruction of electrocardiogram (ECG) signals and a dictionary learning algorithm for the enhancement of its reconstruction performance for a class of signals are proposed. The signal reconstruction algorithm is based on minimizing the lp pseudo-norm of the second-order difference, called as the lp(2d) pseudo-norm, of the signal. The optimization involved is carried out using a sequential conjugate-gradient algorithm. The dictionary learning algorithm uses an iterative procedure wherein a signal reconstruction and a dictionary update steps are repeated until a convergence criterion is satisfied. The signal reconstruction step is implemented by using the proposed signal reconstruction algorithm and the dictionary update step is implemented by using the linear least-squares method. Extensive simulation results demonstrate that the proposed algorithm yields improved reconstruction performance for temporally correlated ECG signals relative to the state-of-the-art lp(1d)-regularized least-squares and Bayesian learning based algorithms. Also for a known class of signals, the reconstruction performance of the proposed algorithm can be improved by applying it in conjunction with a dictionary obtained using the proposed dictionary learning algorithm.

Bandwidth compression of color video signals. Ph.D. Thesis Final Report, 1 Oct. 1979 - 30 Sep. 1980

NASA Technical Reports Server (NTRS)

Schilling, D. L.

1980-01-01

The different encoder/decoder strategies to digitally encode video using an adaptive delta modulation are described. The techniques employed are: (1) separately encoding the R, G, and B components; (2) separately encoding the I, Y, and Q components; and (3) encoding the picture in a line sequential manner.

Strain driven sequential magnetic transitions in strained GdTiO3 on compressive substrates: a first-principles study.

PubMed

Yang, Li-Juan; Weng, Ya-Kui; Zhang, Hui-Min; Dong, Shuai

2014-11-26

The compressive strain effect on the magnetic ground state and electronic structure of strained GdTiO3 has been studied using the first-principles method. Unlike the cases of congeneric YTiO3 and LaTiO3, both of which become the A-type antiferromagnetism on the (0 0 1) LaAlO3 substrate despite their contrastive magnetism, the ground state of strained GdTiO3 on the LaAlO3 substrate changes from the original ferromagnetism to a G-type antiferromagnetim, instead of the A-type one although Gd(3+) is between Y(3+) and La(3+). It is only when the in-plane compressive strain is large enough, e.g. on the (0 0 1) YAlO3 substrate, that the ground state finally becomes the A-type. The band structure calculation shows that the compressive strained GdTiO3 remains insulating, although the band gap changes a little in the strained GdTiO3.

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.

Non-linear properties of metallic cellular materials with a negative Poisson's ratio

NASA Technical Reports Server (NTRS)

Choi, J. B.; Lakes, R. S.

1992-01-01

Negative Poisson's ratio copper foam was prepared and characterized experimentally. The transformation into re-entrant foam was accomplished by applying sequential permanent compressions above the yield point to achieve a triaxial compression. The Poisson's ratio of the re-entrant foam depended on strain and attained a relative minimum at strains near zero. Poisson's ratio as small as -0.8 was achieved. The strain dependence of properties occurred over a narrower range of strain than in the polymer foams studied earlier. Annealing of the foam resulted in a slightly greater magnitude of negative Poisson's ratio and greater toughness at the expense of a decrease in the Young's modulus.

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).

Preloaded latching device

NASA Technical Reports Server (NTRS)

Wesselski, Clarence J. (Inventor); Nagy, Kornel (Inventor)

1992-01-01

A latching device is disclosed which is lever operated sequentially to actuate a set of collet fingers to provide a radial expansion and to actuate a force mechanism to provide a compressive gripping force for attaching first and second devices to one another. The latching device includes a body member having elongated collet fingers which, in a deactuated condition, is insertable through bores on the first and second devices so that gripping terminal portions on the collet fingers are proximate to the end of the bore of the first device while a spring assembly on the body member is located proximate to the outer surface of a second device. A lever is rotatable through 90 deg to move a latching rod to sequentially actuate and expand collet fingers and to actuate the spring assembly by compressing it. During the first 30 deg of movement of the lever, the collet fingers are actuated by the latching rod to provide a radial expansion and during the last 60 deg of movement of the lever, the spring assembly acts as a force mechanism and is actuated to develop a compressive latching force on the devices. The latching rod and lever are connected by a camming mechanism. The amount of spring force in the spring assembly can be adjusted; the body member can be permanently attached by a telescoping assembly to one of the devices; and the structure can be used as a pulling device for removing annular bearings or the like from blind bores.

Method of Real-Time Principal-Component Analysis

NASA Technical Reports Server (NTRS)

Duong, Tuan; Duong, Vu

2005-01-01

Dominant-element-based gradient descent and dynamic initial learning rate (DOGEDYN) is a method of sequential principal-component analysis (PCA) that is well suited for such applications as data compression and extraction of features from sets of data. In comparison with a prior method of gradient-descent-based sequential PCA, this method offers a greater rate of learning convergence. Like the prior method, DOGEDYN can be implemented in software. However, the main advantage of DOGEDYN over the prior method lies in the facts that it requires less computation and can be implemented in simpler hardware. It should be possible to implement DOGEDYN in compact, low-power, very-large-scale integrated (VLSI) circuitry that could process data in real time.

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.

Sequential estimation and satellite data assimilation in meteorology and oceanography

NASA Technical Reports Server (NTRS)

Ghil, M.

1986-01-01

The role of dynamics in estimating the state of the atmosphere and ocean from incomplete and noisy data is discussed and the classical applications of four-dimensional data assimilation to large-scale atmospheric dynamics are presented. It is concluded that sequential updating of a forecast model with continuously incoming conventional and remote-sensing data is the most natural way of extracting the maximum amount of information from the imperfectly known dynamics, on the one hand, and the inaccurate and incomplete observations, on the other.

Scanner. [photography from a spin stabilized synchronous satellite

NASA Technical Reports Server (NTRS)

Hummer, R. F.; Upton, D. T. (Inventor)

1981-01-01

An aerial vehicle rotating in gyroscopic fashion about one of its axes has an optical system which scans an area below the vehicle in determined relation to vehicle rotation. A sensing device is provided to sense the physical condition of the area of scan and optical means are associated to direct the physical intelligence received from the scan area to the sensing means. Means are provided to incrementally move the optical means through a series of steps to effect sequential line scan of the area being viewed keyed to the rotational rate of the vehicle.

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.

System design of an optical interferometer based on compressive sensing

NASA Astrophysics Data System (ADS)

Liu, Gang; Wen, De-Sheng; Song, Zong-Xi

2018-07-01

In this paper, we develop a new optical interferometric telescope architecture based on compressive sensing (CS) theory. Traditional optical telescopes with large apertures must be large in size, heavy and have high-power consumption, which limits the development of space-based telescopes. A turning point has occurred in the advent of imaging technology that utilizes Fourier-domain interferometry. This technology can reduce the system size, weight and power consumption by an order of magnitude compared to traditional optical telescopes at the same resolution. CS theory demonstrates that incomplete and noisy Fourier measurements may suffice for the exact reconstruction of sparse or compressible signals. Our proposed architecture combines advantages from the two frameworks, and the performance is evaluated through simulations. The results indicate the ability to efficiently sample spatial frequencies, while being lightweight and compact in size. Another attractive property of our architecture is the strong denoising ability for Gaussian noise.

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

The relationship between internalized stigma and quality of life among people with mental illness: are self-esteem and sense of coherence sequential mediators?

PubMed

Świtaj, Piotr; Grygiel, Paweł; Chrostek, Anna; Nowak, Izabela; Wciórka, Jacek; Anczewska, Marta

2017-09-01

To elucidate the mechanism through which internalized stigma reduces the quality of life (QoL) of people with mental illness by exploring the mediating roles of self-esteem and sense of coherence (SOC). A cross-sectional analysis of 229 patients diagnosed with schizophrenia or affective disorders was undertaken to test a sequential mediation model assuming that more severe internalized stigma is related to lower self-esteem, which is associated with weaker SOC, which in turn relates to worse QoL. The proposed model was supported by the data. A sequential indirect effect from internalized stigma to QoL via self-esteem and SOC turned out to be significant [beta = -0.06, SE = 0.02; 95% CI (-0.11, -0.03)]. Support was also found for simple mediation models with either self-esteem or SOC as single mediators between internalized stigma and QoL. Self-esteem and SOC are personal resources that should be considered as potential targets of interventions aiming to prevent the harmful consequences of internalized stigma for the QoL of people receiving psychiatric treatment.

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.

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

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.

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

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.

Agriculture and forestry: Identification, vigor, and disease

NASA Technical Reports Server (NTRS)

Jenkins, D. W.

1972-01-01

The agricultural and forestry areas which comprise the watershed of the Chesapeake Bay are described. Major problems of watershed creation and management with emphasis on the erosion problem are discussed. Remote sensing as it relates to the identification of plant species and vigor, pollution, disease, and insect infestation are examined. The application of infrared photography, multispectral sensing, and sequential survey is recommended to identify ecological changes and improve resources management.

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.

Security Criteria for Distributed Systems: Functional Requirements.

DTIC Science & Technology

1995-09-01

Open Company Limited. Ziv , J. and A. Lempel . 1977. A Universal Algorithm for Sequential Data Compression . IEEE Transactions on Information Theory Vol...3, SCF-5 DCF-7. Configurable Cryptographic Algorithms (a) It shall be possible to configure the system such that the data confidentiality functions...use different cryptographic algorithms for different protocols (e.g., mail or interprocess communication data ). (b) The modes of encryption

Sequential Geoacoustic Filtering and Geoacoustic Inversion

DTIC Science & Technology

2015-09-30

and online algorithms. We show here that CS obtains higher resolution than MVDR, even in scenarios, which favor classical high-resolution methods...windows actually performs better than conventional beamforming and MVDR/ MUSIC (see Figs. 1-2). Compressive geoacoustic inversion Geoacoustic...histograms based on 100 Monte Carlo simulations, and c)(CS, exhaustive-search, CBF, MVDR, and MUSIC performance versus SNR. The true source positions

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.

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.

Mind the Gap: The Effects of Temporal and Spatial Separation in Localization of Dual Touches on the Hand.

PubMed

Sadibolova, Renata; Tamè, Luigi; Walsh, Eamonn; Longo, Matthew R

2018-01-01

In this study, we aimed to relate the findings from two predominantly separate streams of literature, one reporting on the localization of single touches on the skin, and the other on the distance perception of dual touches. Participants were touched with two points, delivered either simultaneously or separated by a short delay to various locations on their left hand dorsum. They then indicated on a size-matched hand silhouette the perceived locations of tactile stimuli. We quantified the deviations between the actual stimulus grid and the corresponding perceptual map which was constructed from the perceived tactile locations, and we calculated the precision of tactile localization (i.e., the variability across localization attempts). The evidence showed that the dual touches, akin to single touch stimulations, were mislocalized distally and that their variable localization error was reduced near joints, particularly near knuckles. However, contrary to single-touch localization literature, we observed for the dual touches to be mislocalized towards the ulnar side of the hand, particularly when they were presented sequentially. Further, the touches presented in a sequential order were slightly "repelled" from each other and their perceived distance increased, while the simultaneous tactile pairs were localized closer to each other and their distance was compressed. Whereas the sequential touches may have been localized with reference to the body, the compression of tactile perceptual space for simultaneous touches was related in the previous literature to signal summation and inhibition and the low-level factors, including the innervation density and properties of receptive fields (RFs) of somatosensory neurons.

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.

Sequential voluntary cough and aspiration or aspiration risk in Parkinson's disease.

PubMed

Hegland, Karen Wheeler; Okun, Michael S; Troche, Michelle S

2014-08-01

Disordered swallowing, or dysphagia, is almost always present to some degree in people with Parkinson's disease (PD), either causing aspiration or greatly increasing the risk for aspiration during swallowing. This likely contributes to aspiration pneumonia, a leading cause of death in this patient population. Effective airway protection is dependent upon multiple behaviors, including cough and swallowing. Single voluntary cough function is disordered in people with PD and dysphagia. However, the appropriate response to aspirate material is more than one cough, or sequential cough. The goal of this study was to examine voluntary sequential coughing in people with PD, with and without dysphagia. Forty adults diagnosed with idiopathic PD produced two trials of sequential voluntary cough. The cough airflows were obtained using pneumotachograph and facemask and subsequently digitized and recorded. All participants received a modified barium swallow study as part of their clinical care, and the worst penetration-aspiration score observed was used to determine whether the patient had dysphagia. There were significant differences in the compression phase duration, peak expiratory flow rates, and amount of air expired of the sequential cough produced by participants with and without dysphagia. The presence of dysphagia in people with PD is associated with disordered cough function. Sequential cough, which is important in removing aspirate material from large- and smaller-diameter airways, is also impaired in people with PD and dysphagia compared with those without dysphagia. There may be common neuroanatomical substrates for cough and swallowing impairment in PD leading to the co-occurrence of these dysfunctions.

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.

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.

Parallel design of JPEG-LS encoder on graphics processing units

NASA Astrophysics Data System (ADS)

Duan, Hao; Fang, Yong; Huang, Bormin

2012-01-01

With recent technical advances in graphic processing units (GPUs), GPUs have outperformed CPUs in terms of compute capability and memory bandwidth. Many successful GPU applications to high performance computing have been reported. JPEG-LS is an ISO/IEC standard for lossless image compression which utilizes adaptive context modeling and run-length coding to improve compression ratio. However, adaptive context modeling causes data dependency among adjacent pixels and the run-length coding has to be performed in a sequential way. Hence, using JPEG-LS to compress large-volume hyperspectral image data is quite time-consuming. We implement an efficient parallel JPEG-LS encoder for lossless hyperspectral compression on a NVIDIA GPU using the computer unified device architecture (CUDA) programming technology. We use the block parallel strategy, as well as such CUDA techniques as coalesced global memory access, parallel prefix sum, and asynchronous data transfer. We also show the relation between GPU speedup and AVIRIS block size, as well as the relation between compression ratio and AVIRIS block size. When AVIRIS images are divided into blocks, each with 64×64 pixels, we gain the best GPU performance with 26.3x speedup over its original CPU code.

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.

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.

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.

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.

Effective Sequential Classifier Training for SVM-Based Multitemporal Remote Sensing Image Classification

NASA Astrophysics Data System (ADS)

Guo, Yiqing; Jia, Xiuping; Paull, David

2018-06-01

The explosive availability of remote sensing images has challenged supervised classification algorithms such as Support Vector Machines (SVM), as training samples tend to be highly limited due to the expensive and laborious task of ground truthing. The temporal correlation and spectral similarity between multitemporal images have opened up an opportunity to alleviate this problem. In this study, a SVM-based Sequential Classifier Training (SCT-SVM) approach is proposed for multitemporal remote sensing image classification. The approach leverages the classifiers of previous images to reduce the required number of training samples for the classifier training of an incoming image. For each incoming image, a rough classifier is firstly predicted based on the temporal trend of a set of previous classifiers. The predicted classifier is then fine-tuned into a more accurate position with current training samples. This approach can be applied progressively to sequential image data, with only a small number of training samples being required from each image. Experiments were conducted with Sentinel-2A multitemporal data over an agricultural area in Australia. Results showed that the proposed SCT-SVM achieved better classification accuracies compared with two state-of-the-art model transfer algorithms. When training data are insufficient, the overall classification accuracy of the incoming image was improved from 76.18% to 94.02% with the proposed SCT-SVM, compared with those obtained without the assistance from previous images. These results demonstrate that the leverage of a priori information from previous images can provide advantageous assistance for later images in multitemporal image classification.

An unsupervised classification technique for multispectral remote sensing data.

NASA Technical Reports Server (NTRS)

Su, M. Y.; Cummings, R. E.

1973-01-01

Description of a two-part clustering technique consisting of (a) a sequential statistical clustering, which is essentially a sequential variance analysis, and (b) a generalized K-means clustering. In this composite clustering technique, the output of (a) is a set of initial clusters which are input to (b) for further improvement by an iterative scheme. This unsupervised composite technique was employed for automatic classification of two sets of remote multispectral earth resource observations. The classification accuracy by the unsupervised technique is found to be comparable to that by traditional supervised maximum-likelihood classification techniques.

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.

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.

Prospective Study of Burn Wound Excision of the Hands

DTIC Science & Technology

1983-06-01

Houston, Texas. sion (10, 11). This method allows the sequential removal Presented at the Forty-second Annual Session of The American of nonviable tissue...ultrasonography. Es- days, after which all dressings were removed and a more charotomies of upper extremities were carried out if vigorous physical therapy...sponges, followed by mild compression wrapping and interphalangeal joints. The thumb was abducted and elevation. Electrocoagulation of bleeding points

Strain driven sequential magnetic transitions in strained GdTiO3 on compressive substrates: a first-principles study

NASA Astrophysics Data System (ADS)

Yang, Li-Juan; Weng, Ya-Kui; Zhang, Hui-Min; Dong, Shuai

2014-11-01

The compressive strain effect on the magnetic ground state and electronic structure of strained GdTiO3 has been studied using the first-principles method. Unlike the cases of congeneric YTiO3 and LaTiO3, both of which become the A-type antiferromagnetism on the (0 0 1) LaAlO3 substrate despite their contrastive magnetism, the ground state of strained GdTiO3 on the LaAlO3 substrate changes from the original ferromagnetism to a G-type antiferromagnetim, instead of the A-type one although Gd3+ is between Y3+ and La3+. It is only when the in-plane compressive strain is large enough, e.g. on the (0 0 1) YAlO3 substrate, that the ground state finally becomes the A-type. The band structure calculation shows that the compressive strained GdTiO3 remains insulating, although the band gap changes a little in the strained GdTiO3.

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.

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.

Real-Time Mobile Device-Assisted Chest Compression During Cardiopulmonary Resuscitation.

PubMed

Sarma, Satyam; Bucuti, Hakiza; Chitnis, Anurag; Klacman, Alex; Dantu, Ram

2017-07-15

Prompt administration of high-quality cardiopulmonary resuscitation (CPR) is a key determinant of survival from cardiac arrest. Strategies to improve CPR quality at point of care could improve resuscitation outcomes. We tested whether a low cost and scalable mobile phone- or smart watch-based solution could provide accurate measures of compression depth and rate during simulated CPR. Fifty health care providers (58% intensive care unit nurses) performed simulated CPR on a calibrated training manikin (Resusci Anne, Laerdal) while wearing both devices. Subjects received real-time audiovisual feedback from each device sequentially. Primary outcome was accuracy of compression depth and rate compared with the calibrated training manikin. Secondary outcome was improvement in CPR quality as defined by meeting both guideline-recommend compression depth (5 to 6 cm) and rate (100 to 120/minute). Compared with the training manikin, typical error for compression depth was <5 mm (smart phone 4.6 mm; 95% CI 4.1 to 5.3 mm; smart watch 4.3 mm; 95% CI 3.8 to 5.0 mm). Compression rates were similarly accurate (smart phone Pearson's R = 0.93; smart watch R = 0.97). There was no difference in improved CPR quality defined as the number of sessions meeting both guideline-recommended compression depth (50 to 60 mm) and rate (100 to 120 compressions/minute) with mobile device feedback (60% vs 50%; p = 0.3). Sessions that did not meet guideline recommendations failed primarily because of inadequate compression depth (46 ± 2 mm). In conclusion, a mobile device application-guided CPR can accurately track compression depth and rate during simulation in a practice environment in accordance with resuscitation guidelines. Copyright © 2017 Elsevier Inc. All rights reserved.

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.

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.

3D fold growth rates in transpressional tectonic settings

NASA Astrophysics Data System (ADS)

Frehner, Marcel

2015-04-01

Geological folds are inherently three-dimensional (3D) structures; hence, they also grow in 3D. In this study, fold growth in all three dimensions is quantified numerically using a finite-element algorithm for simulating deformation of Newtonian media in 3D. The presented study is an extension and generalization of the work presented in Frehner (2014), which only considered unidirectional layer-parallel compression. In contrast, the full range from strike slip settings (i.e., simple shear) to unidirectional layer-parallel compression is considered here by varying the convergence angle of the boundary conditions; hence the results are applicable to general transpressional tectonic settings. Only upright symmetrical single-layer fold structures are considered. The horizontal higher-viscous layer exhibits an initial point-like perturbation. Due to the mixed pure- and simple shear boundary conditions a mechanical buckling instability grows from this perturbation in all three dimensions, described by: Fold amplification (vertical growth): Fold amplification describes the growth from a fold shape with low limb-dip angle to a shape with higher limb-dip angle. Fold elongation (growth parallel to fold axis): Fold elongation describes the growth from a dome-shaped (3D) structure to a more cylindrical fold (2D). Sequential fold growth (growth perpendicular to fold axial plane): Sequential fold growth describes the growth of secondary (and further) folds adjacent to the initial isolated fold. The term 'lateral fold growth' is used as an umbrella term for both fold elongation and sequential fold growth. In addition, the orientation of the fold axis is tracked as a function of the convergence angle. Even though the absolute values of all three growth rates are markedly reduced with increasing simple-shear component at the boundaries, the general pattern of the quantified fold growth under the studied general-shear boundary conditions is surprisingly similar to the end-member case of unidirectional layer-parallel compression (Frehner, 2014). Fold growth rates in the two lateral directions are almost identical resulting in bulk fold structures with aspect ratios in map view close to 1. Fold elongation is continuous with increasing bulk deformation, while sequential fold growth exhibits jumps whenever a new sequential fold appears. Compared with the two lateral growth directions, fold amplification exhibits a slightly higher growth rate. The orientation of the fold axis has an angle equal to 1 2 of 90° minus the convergence angle; and this orientation is stable with increasing bulk deformation, i.e. the fold axis does not rotate with increasing general-shear deformation. For example, for simple-shear boundary conditions (convergence angle 0°) the fold axis is stable at an angle of 45° to the boundaries; for a convergence angle of 45° the fold axis is stable at an angle of 22.5° to the boundaries. REFERENCE: Frehner M., 2014: 3D fold growth rates, Terra Nova 26, 417-424, doi:10.1111/ter.12116.

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.

Index of learning styles in a u.s. School of pharmacy.

PubMed

Teevan, Colleen J; Li, Michael; Schlesselman, Lauren S

2011-04-01

The goal of this study was to assess for a predominance of learning styles among pharmacy students at an accredited U.S. school of pharmacy. Following approval by the Institutional Review Board, the Index of Learning Styles© was administered to 210 pharmacy students. The survey provides results within 4 domains: perception, input, processing, and understanding. Analyses were conducted to determine trends in student learning styles. Within the four domains, 84% of students showed a preference toward sensory perception, 66% toward visual input, and 74% toward sequential understanding. Students showed no significant preference for active or reflective processing. Preferences were of moderate strength for the sensing, visual, and sequential learning styles. Students showed preferences for sensing, visual, and sequential learning styles with gender playing a role in learning style preferences. Faculty should be aware, despite some preferences, a mix of learning styles exists. To focus on the preferences found, instructors should focus teaching in a logical progression while adding visual aids. To account for other types of learning styles found, the instructors can offer other approaches and provide supplemental activities for those who would benefit from them. Further research is necessary to compare these learning styles to the teaching styles of pharmacy preceptors and faculty at schools of pharmacy.

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.

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.

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.

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.

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.

Sibling cycle piston and valving method

NASA Technical Reports Server (NTRS)

Mitchell, Matthew P. (Inventor); Bauwens, Luc (Inventor)

1990-01-01

A double-acting, rotating piston reciprocating in a cylinder with the motion of the piston providing the valving action of the Sibling Cycle through the medium of passages between the piston and cylinder wall. The rotating piston contains regenerators ported to the walls of the piston. The piston fits closely in the cylinder at each end of the cylinder except in areas where the wall of the cylinder is relieved to provide passages between the cylinder wall and the piston leading to the expansion and compression spaces, respectively. The piston reciprocates as it rotates. The cylinder and piston together comprise an integral valve that seqentially opens and closes the ports at the ends of the regenerators alternately allowing them to communicate with the expansion space and compression space and blocking that communication. The relieved passages in the cylinder and the ports in the piston are so arranged that each regenerator is sequentially (1) charged with compressed working gas from the compression space; (2) isolated from both expansion and compression spaces; (3) discharged of working gas into the expansion space; and (4) simultaneously charged with working gas from the expansion space while being discharged of working gas into the compression space, in the manner of the Sibling Cycle. In an alterate embodiment, heat exchangers are external to the cylinder and ports in the cylinder wall are alternately closed by the wall of the piston and opened to the expansion and compression spaces through relieved passages in the wall of the reciprocating, rotating piston.

A comparison of intermittent pneumatic compression of the calf and whole leg in preventing deep venous thrombosis in urological surgery.

PubMed

Soderdahl, D W; Henderson, S R; Hansberry, K L

1997-05-01

Intermittent pneumatic compression of the calf and/or thigh effectively decreases the incidence of deep venous thrombosis and other thrombotic sequelae but clinical data comparing these modalities are currently lacking. A total of 90 patients undergoing major urological surgery was randomly assigned to receive calf length or thigh length pneumatic compression for antithrombotic prophylaxis. Duplex ultrasound of the lower extremities was performed preoperatively and twice postoperatively to evaluate for deep venous thrombosis. Health care providers in the operating room, recovery room and ward were asked to compare the compression systems, and a cost analysis was performed. A total of 47 patients wore the thigh length sequential pneumatic sleeves and 43 wore calf length uniform compression systems. A pulmonary embolus without evidence of deep venous thrombosis was detected in 1 patient (2%) using the thigh length system. A thrombus was detected in the common femoral vein by duplex ultrasonography in 1 patient (2%) with the calf length system. Nursing personnel found the calf length sleeves easier to apply and more comfortable by patient account but they were satisfied with both systems. There was a significant cost savings with the calf length pneumatic compression system. Calf and thigh length pneumatic compression systems similarly decrease the risk of deep venous thrombosis in patients undergoing urological surgery. The calf length system has the added advantage of being less expensive and easier to use.

4D imaging of polymer electrolyte membrane fuel cell catalyst layers by soft X-ray spectro-tomography

NASA Astrophysics Data System (ADS)

Wu, Juan; Melo, Lis G. A.; Zhu, Xiaohui; West, Marcia M.; Berejnov, Viatcheslav; Susac, Darija; Stumper, Juergen; Hitchcock, Adam P.

2018-03-01

4D imaging - the three-dimensional distributions of chemical species determined using multi-energy X-ray tomography - of cathode catalyst layers of polymer electrolyte membrane fuel cells (PEM-FC) has been measured by scanning transmission x-ray microscopy (STXM) spectro-tomography at the C 1s and F 1s edges. In order to monitor the effects of radiation damage on the composition and 3D structure of the perfluorosulfonic acid (PFSA) ionomer, the same volume was measured 3 times sequentially, with spectral characterization of that same volume at several time points during the measurements. The changes in the average F 1s spectrum of the ionomer in the cathode as the measurements progressed gave insights into the degree of chemical modification, fluorine mass loss, and changes in the 3D distributions of ionomer that accompanied the spectro-tomographic measurement. The PFSA ionomer-in-cathode is modified both chemically and physically by radiation damage. The 3D volume decreases anisotropically. By reducing the incident flux, partial defocusing (50 nm spot size), limiting the number of tilt angles to 14, and using compressed sensing reconstruction, we show it is possible to reproducibly measure the 3D structure of ionomer in PEM-FC cathodes at ambient temperature while causing minimal radiation damage.

Sequential Voluntary Cough and Aspiration or Aspiration Risk in Parkinson’s Disease

PubMed Central

Hegland, Karen Wheeler; Okun, Michael S.; Troche, Michelle S.

2015-01-01

Background Disordered swallowing, or dysphagia, is almost always present to some degree in people with Parkinson’s disease (PD), either causing aspiration or greatly increasing the risk for aspiration during swallowing. This likely contributes to aspiration pneumonia, a leading cause of death in this patient population. Effective airway protection is dependent upon multiple behaviors, including cough and swallowing. Single voluntary cough function is disordered in people with PD and dysphagia. However, the appropriate response to aspirate material is more than one cough, or sequential cough. The goal of this study was to examine voluntary sequential coughing in people with PD, with and without dysphagia. Methods Forty adults diagnosed with idiopathic PD produced two trials of sequential voluntary cough. The cough airflows were obtained using pneumotachograph and facemask and subsequently digitized and recorded. All participants received a modified barium swallow study as part of their clinical care, and the worst penetration–aspiration score observed was used to determine whether the patient had dysphagia. Results There were significant differences in the compression phase duration, peak expiratory flow rates, and amount of air expired of the sequential cough produced by participants with and without dysphagia. Conclusions The presence of dysphagia in people with PD is associated with disordered cough function. Sequential cough, which is important in removing aspirate material from large- and smaller-diameter airways, is also impaired in people with PD and dysphagia compared with those without dysphagia. There may be common neuroanatomical substrates for cough and swallowing impairment in PD leading to the co-occurrence of these dysfunctions. PMID:24792231

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.

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.

Recovery from distal ulnar motor conduction block injury: serial EMG studies.

PubMed

Montoya, Liliana; Felice, Kevin J

2002-07-01

Acute conduction block injuries often result from nerve compression or trauma. The temporal pattern of clinical, electrophysiologic, and histopathologic changes following these injuries has been extensively studied in experimental animal models but not in humans. Our recent evaluation of a young man with an injury to the deep motor branch of the ulnar nerve following nerve compression from weightlifting exercises provided the opportunity to follow the course and recovery of a severe conduction block injury with sequential nerve conduction studies. The conduction block slowly and completely resolved, as did the clinical deficit, over a 14-week period. The reduction in conduction block occurred at a linear rate of -6.1% per week. Copyright 2002 Wiley Periodicals, Inc.

Method for removing solid particulate material from within liquid fuel injector assemblies

DOEpatents

Simandl, R.F.; Brown, J.D.; Andriulli, J.B.; Strain, P.D.

1998-09-08

A method is described for removing residual solid particulate material from the interior of liquid fuel injectors and other fluid flow control mechanisms having or being operatively associated with a flow-regulating fixed or variable orifice. The method comprises the sequential and alternate introduction of columns of a non-compressible liquid phase and columns of a compressed gas phase into the body of a fuel injector whereby the expansion of each column of the gas phase across the orifice accelerates the liquid phase in each trailing column of the liquid phase and thereby generates turbulence in each liquid phase for lifting and entraining the solid particulates for the subsequent removal thereof from the body of the fuel injector. 1 fig.

Method for removing solid particulate material from within liquid fuel injector assemblies

DOEpatents

Simandl, Ronald F.; Brown, John D.; Andriulli, John B.; Strain, Paul D.

1998-01-01

A method for removing residual solid particulate material from the interior of liquid fuel injectors and other fluid flow control mechanisms having or being operatively associated with a flow-regulating fixed or variable orifice. The method comprises the sequential and alternate introduction of columns of a non-compressible liquid phase and columns of a compressed gas phase into the body of a fuel injector whereby the expansion of each column of the gas phase across the orifice accelerates the liquid phase in each trailing column of the liquid phase and thereby generates turbulence in each liquid phase for lifting and entraining the solid particulates for the subsequent removal thereof from the body of the fuel injector.

Sequential compression device with thigh-high sleeves supports mean arterial pressure during Caesarean section under spinal anaesthesia.

PubMed

Adsumelli, R S N; Steinberg, E S; Schabel, J E; Saunders, T A; Poppers, P J

2003-11-01

This study investigated the use of a Sequential Compression Device (SCD) with thigh-high sleeves and a preset pressure of 50 mm Hg that recruits blood from the lower limbs intermittently, as a method to prevent spinal hypotension during elective Caesarean section. Possible association of arterial pressure changes with maternal, fetal, haemodynamic, and anaesthetic factors were studied. Fifty healthy parturients undergoing elective Caesarean section under spinal anaesthesia were randomly assigned to either SCD (n=25) or control (n=25) groups. A standardized protocol for pre-hydration and anaesthetic technique was followed. Hypotension was defined as a decrease in any mean arterial pressure (MAP) measurement by more than 20% of the baseline MAP. Systolic (SAP), MAP and diastolic (DAP) arterial pressure, pulse pressure (PP), and heart rate (HR) were noted at baseline and every minute after the spinal block until delivery. A greater than 20% decrease in MAP occurred in 52% of patients in the SCD group vs 92% in the control group (P=0.004, odds ratio 0.094, 95% CI 0.018-0.488). There were no significant differences in SAP, DAP, HR, and PP between the groups. SCD use in conjunction with vasopressor significantly reduced the incidence of a 20% reduction of MAP.

A prospective study of nurse and patient education on compliance with sequential compression devices.

PubMed

Stewart, David; Zalamea, Nia; Waxman, Ken; Schuster, Rob; Bozuk, Michael

2006-10-01

Sequential compression devices (SCD) have become the most common form of prophylaxis against the formation of deep venous thrombosis (DVT) among surgical patients. However, compliance with SCD has traditionally been poor. The aim of this study was to assess the affect of patient and nurse education by surgeons on SCD compliance. This was a prospective study involving a single teaching hospital. Compliance was checked twice daily. The main outcomes were compliance rates with SCD use before and after nurse and patient education. Nurses were not aware of the study. Surgical floors had a history of resident and attending interactions regarding SCD, whereas nonsurgical floors did not. A handout that emphasized SCD importance was also given to patients on surgical units. Before education, surgical units had a compliance rate of 61.5 per cent, whereas nonsurgical units had a 48 per cent compliance rate. This difference was significant (P = 0.014). After nursing and patient education on the busiest surgical floor, compliance rates on the surgical ward increased to 65 per cent, a difference that was not of statistical significance (P = 0.515). A nursing unit's daily experience is the most important factor in their compliance rates with SCD use. Focused nursing lectures and patient education may have incremental value.

Method and apparatus for telemetry adaptive bandwidth compression

NASA Technical Reports Server (NTRS)

Graham, Olin L.

1987-01-01

Methods and apparatus are provided for automatic and/or manual adaptive bandwidth compression of telemetry. An adaptive sampler samples a video signal from a scanning sensor and generates a sequence of sampled fields. Each field and range rate information from the sensor are hence sequentially transmitted to and stored in a multiple and adaptive field storage means. The field storage means then, in response to an automatic or manual control signal, transfers the stored sampled field signals to a video monitor in a form for sequential or simultaneous display of a desired number of stored signal fields. The sampling ratio of the adaptive sample, the relative proportion of available communication bandwidth allocated respectively to transmitted data and video information, and the number of fields simultaneously displayed are manually or automatically selectively adjustable in functional relationship to each other and detected range rate. In one embodiment, when relatively little or no scene motion is detected, the control signal maximizes sampling ratio and causes simultaneous display of all stored fields, thus maximizing resolution and bandwidth available for data transmission. When increased scene motion is detected, the control signal is adjusted accordingly to cause display of fewer fields. If greater resolution is desired, the control signal is adjusted to increase the sampling ratio.

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.

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.

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.

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

Global geomorphology: Report of Working Group Number 1

NASA Technical Reports Server (NTRS)

Douglas, I.

1985-01-01

Remote sensing was considered invaluable for seeing landforms in their regional context and in relationship to each other. Sequential images, such as those available from LANDSAT orbits provide a means of detecting landform change and the operation of large scale processes, such as major floods in semiarid regions. The use of remote sensing falls into two broad stages: (1) the characterization or accurate description of the features of the Earth's surface; and (2) the study of landform evolution. Recommendations for future research are made.

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.

Facile Determination of Sodium Ion and Osmolarity in Artificial Tears by Sequential DNAzymes.

PubMed

Kim, Eun Hye; Lee, Eun-Song; Lee, Dong Yun; Kim, Young-Pil

2017-12-07

Despite high relevance of tear osmolarity and eye abnormality, numerous methods for detecting tear osmolarity rely upon expensive osmometers. We report a reliable method for simply determining sodium ion-based osmolarity in artificial tears using sequential DNAzymes. When sodium ion-specific DNAzyme and peroxidase-like DNAzyme were used as a sensing and detecting probe, respectively, the concentration of Na⁺ in artificial tears could be measured by absorbance or fluorescence intensity, which was highly correlated with osmolarity over the diagnostic range ( R ² > 0.98). Our approach is useful for studying eye diseases in relation to osmolarity.

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.

OpenCL-based vicinity computation for 3D multiresolution mesh compression

NASA Astrophysics Data System (ADS)

Hachicha, Soumaya; Elkefi, Akram; Ben Amar, Chokri

2017-03-01

3D multiresolution mesh compression systems are still widely addressed in many domains. These systems are more and more requiring volumetric data to be processed in real-time. Therefore, the performance is becoming constrained by material resources usage and an overall reduction in the computational time. In this paper, our contribution entirely lies on computing, in real-time, triangles neighborhood of 3D progressive meshes for a robust compression algorithm based on the scan-based wavelet transform(WT) technique. The originality of this latter algorithm is to compute the WT with minimum memory usage by processing data as they are acquired. However, with large data, this technique is considered poor in term of computational complexity. For that, this work exploits the GPU to accelerate the computation using OpenCL as a heterogeneous programming language. Experiments demonstrate that, aside from the portability across various platforms and the flexibility guaranteed by the OpenCL-based implementation, this method can improve performance gain in speedup factor of 5 compared to the sequential CPU implementation.

Flexible theta sequence compression mediated via phase precessing interneurons

PubMed Central

Chadwick, Angus; van Rossum, Mark CW; Nolan, Matthew F

2016-01-01

Encoding of behavioral episodes as spike sequences during hippocampal theta oscillations provides a neural substrate for computations on events extended across time and space. However, the mechanisms underlying the numerous and diverse experimentally observed properties of theta sequences remain poorly understood. Here we account for theta sequences using a novel model constrained by the septo-hippocampal circuitry. We show that when spontaneously active interneurons integrate spatial signals and theta frequency pacemaker inputs, they generate phase precessing action potentials that can coordinate theta sequences in place cell populations. We reveal novel constraints on sequence generation, predict cellular properties and neural dynamics that characterize sequence compression, identify circuit organization principles for high capacity sequential representation, and show that theta sequences can be used as substrates for association of conditioned stimuli with recent and upcoming events. Our results suggest mechanisms for flexible sequence compression that are suited to associative learning across an animal’s lifespan. DOI: http://dx.doi.org/10.7554/eLife.20349.001 PMID:27929374

Biphasic Finite Element Modeling Reconciles Mechanical Properties of Tissue-Engineered Cartilage Constructs Across Testing Platforms.

PubMed

Meloni, Gregory R; Fisher, Matthew B; Stoeckl, Brendan D; Dodge, George R; Mauck, Robert L

2017-07-01

Cartilage tissue engineering is emerging as a promising treatment for osteoarthritis, and the field has progressed toward utilizing large animal models for proof of concept and preclinical studies. Mechanical testing of the regenerative tissue is an essential outcome for functional evaluation. However, testing modalities and constitutive frameworks used to evaluate in vitro grown samples differ substantially from those used to evaluate in vivo derived samples. To address this, we developed finite element (FE) models (using FEBio) of unconfined compression and indentation testing, modalities commonly used for such samples. We determined the model sensitivity to tissue radius and subchondral bone modulus, as well as its ability to estimate material parameters using the built-in parameter optimization tool in FEBio. We then sequentially tested agarose gels of 4%, 6%, 8%, and 10% weight/weight using a custom indentation platform, followed by unconfined compression. Similarly, we evaluated the ability of the model to generate material parameters for living constructs by evaluating engineered cartilage. Juvenile bovine mesenchymal stem cells were seeded (2 × 10 7 cells/mL) in 1% weight/volume hyaluronic acid hydrogels and cultured in a chondrogenic medium for 3, 6, and 9 weeks. Samples were planed and tested sequentially in indentation and unconfined compression. The model successfully completed parameter optimization routines for each testing modality for both acellular and cell-based constructs. Traditional outcome measures and the FE-derived outcomes showed significant changes in material properties during the maturation of engineered cartilage tissue, capturing dynamic changes in functional tissue mechanics. These outcomes were significantly correlated with one another, establishing this FE modeling approach as a singular method for the evaluation of functional engineered and native tissue regeneration, both in vitro and in vivo.

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

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.

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.

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

Space-time adaptive ADER-DG schemes for dissipative flows: Compressible Navier-Stokes and resistive MHD equations

NASA Astrophysics Data System (ADS)

Fambri, Francesco; Dumbser, Michael; Zanotti, Olindo

2017-11-01

This paper presents an arbitrary high-order accurate ADER Discontinuous Galerkin (DG) method on space-time adaptive meshes (AMR) for the solution of two important families of non-linear time dependent partial differential equations for compressible dissipative flows : the compressible Navier-Stokes equations and the equations of viscous and resistive magnetohydrodynamics in two and three space-dimensions. The work continues a recent series of papers concerning the development and application of a proper a posteriori subcell finite volume limiting procedure suitable for discontinuous Galerkin methods (Dumbser et al., 2014, Zanotti et al., 2015 [40,41]). It is a well known fact that a major weakness of high order DG methods lies in the difficulty of limiting discontinuous solutions, which generate spurious oscillations, namely the so-called 'Gibbs phenomenon'. In the present work, a nonlinear stabilization of the scheme is sequentially and locally introduced only for troubled cells on the basis of a novel a posteriori detection criterion, i.e. the MOOD approach. The main benefits of the MOOD paradigm, i.e. the computational robustness even in the presence of strong shocks, are preserved and the numerical diffusion is considerably reduced also for the limited cells by resorting to a proper sub-grid. In practice the method first produces a so-called candidate solution by using a high order accurate unlimited DG scheme. Then, a set of numerical and physical detection criteria is applied to the candidate solution, namely: positivity of pressure and density, absence of floating point errors and satisfaction of a discrete maximum principle in the sense of polynomials. Furthermore, in those cells where at least one of these criteria is violated the computed candidate solution is detected as troubled and is locally rejected. Subsequently, a more reliable numerical solution is recomputed a posteriori by employing a more robust but still very accurate ADER-WENO finite volume scheme on the subgrid averages within that troubled cell. Finally, a high order DG polynomial is reconstructed back from the evolved subcell averages. We apply the whole approach for the first time to the equations of compressible gas dynamics and magnetohydrodynamics in the presence of viscosity, thermal conductivity and magnetic resistivity, therefore extending our family of adaptive ADER-DG schemes to cases for which the numerical fluxes also depend on the gradient of the state vector. The distinguished high-resolution properties of the presented numerical scheme standout against a wide number of non-trivial test cases both for the compressible Navier-Stokes and the viscous and resistive magnetohydrodynamics equations. The present results show clearly that the shock-capturing capability of the news schemes is significantly enhanced within a cell-by-cell Adaptive Mesh Refinement (AMR) implementation together with time accurate local time stepping (LTS).

Numerical simulation of electromagnetic surface treatment

NASA Astrophysics Data System (ADS)

Sonde, Emmanuel; Chaise, Thibaut; Nelias, Daniel; Robin, Vincent

2018-01-01

Surface treatment methods, such as shot peening or laser shock peening, are generally used to introduce superficial compressive residual stresses in mechanical parts. These processes are carried out during the manufacturing steps or for the purpose of repairing. The compressive residual stresses prevent the initiation and growth of cracks and thus improve the fatigue life of mechanical components. Electromagnetic pulse peening (EMP) is an innovative process that could be used to introduce compressive residual stresses in conductive materials. It acts by generating a high transient electromagnetic field near the working surface. In this paper, the EMP process is presented and a sequentially coupled electromagnetic and mechanical model is developed for its simulation. This 2D axisymmetric model is set up with the commercial finite element software SYSWELD. After description and validation, the numerical model is used to simulate a case of introducing residual stresses of compression in a nickel-based alloy 690 thick sample, by the means of electromagnetic pulses. The results are presented in terms of effective plastic strain and residual mean stress. The influence of the process parameters, such as current intensity and frequency, on the results is analyzed. Finally, the predictability of the process is shown by several correlation studies.

A new complexity measure for time series analysis and classification

NASA Astrophysics Data System (ADS)

Nagaraj, Nithin; Balasubramanian, Karthi; Dey, Sutirth

2013-07-01

Complexity measures are used in a number of applications including extraction of information from data such as ecological time series, detection of non-random structure in biomedical signals, testing of random number generators, language recognition and authorship attribution etc. Different complexity measures proposed in the literature like Shannon entropy, Relative entropy, Lempel-Ziv, Kolmogrov and Algorithmic complexity are mostly ineffective in analyzing short sequences that are further corrupted with noise. To address this problem, we propose a new complexity measure ETC and define it as the "Effort To Compress" the input sequence by a lossless compression algorithm. Here, we employ the lossless compression algorithm known as Non-Sequential Recursive Pair Substitution (NSRPS) and define ETC as the number of iterations needed for NSRPS to transform the input sequence to a constant sequence. We demonstrate the utility of ETC in two applications. ETC is shown to have better correlation with Lyapunov exponent than Shannon entropy even with relatively short and noisy time series. The measure also has a greater rate of success in automatic identification and classification of short noisy sequences, compared to entropy and a popular measure based on Lempel-Ziv compression (implemented by Gzip).

Real-time sensing of lint quality

USDA-ARS?s Scientific Manuscript database

Modem cotton gins have the purpose of extracting lint (the cotton) from trash and seeds- usually the sticks, leaves and burrs that are entrained with the cotton. These modem gins include many individual machine components that are operated sequentially to form the gin processing line. Recent on-line...

Gestalt Imagery: A Critical Factor in Language Comprehension.

ERIC Educational Resources Information Center

Bell, Nanci

1991-01-01

Lack of gestalt imagery (the ability to create imaged wholes) can contribute to language comprehension disorder characterized by weak reading comprehension, weak oral language comprehension, weak oral language expression, weak written language expression, difficulty following directions, and a weak sense of humor. Sequential stimulation using an…

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

Relevance of postmortem radiology to the diagnosis of fatal cerebral gas embolism from compressed air diving.

PubMed

Cole, A J; Griffiths, D; Lavender, S; Summers, P; Rich, K

2006-05-01

To test the hypothesis that artefact caused by postmortem off-gassing is at least partly responsible for the presence of gas within the vascular system and tissues of the cadaver following death associated with compressed air diving. Controlled experiment sacrificing sheep after a period of simulated diving in a hyperbaric chamber and carrying out sequential postmortem computed tomography (CT) on the cadavers. All the subject sheep developed significant quantities of gas in the vascular system within 24 hours, as demonstrated by CT and necropsy, while the control animals did not. The presence of gas in the vascular system of human cadavers following diving associated fatalities is to be expected, and is not necessarily connected with gas embolism following pulmonary barotrauma, as has previously been claimed.

Potential digitization/compression techniques for Shuttle video

NASA Technical Reports Server (NTRS)

Habibi, A.; Batson, B. H.

1978-01-01

The Space Shuttle initially will be using a field-sequential color television system but it is possible that an NTSC color TV system may be used for future missions. In addition to downlink color TV transmission via analog FM links, the Shuttle will use a high resolution slow-scan monochrome system for uplink transmission of text and graphics information. This paper discusses the characteristics of the Shuttle video systems, and evaluates digitization and/or bandwidth compression techniques for the various links. The more attractive techniques for the downlink video are based on a two-dimensional DPCM encoder that utilizes temporal and spectral as well as the spatial correlation of the color TV imagery. An appropriate technique for distortion-free coding of the uplink system utilizes two-dimensional HCK codes.

Making sense of quantum operators, eigenstates and quantum measurements

NASA Astrophysics Data System (ADS)

Gire, Elizabeth; Manogue, Corinne

2012-02-01

Operators play a central role in the formalism of quantum mechanics. In particular, operators corresponding to observables encode important information about the results of quantum measurements. We interviewed upper-level undergraduate physics majors about their understanding of the role of operators in quantum measurements. Previous studies have shown that many students think of measurements on quantum systems as being deterministic and that measurements mathematically correspond to operators acting on the initial quantum state. This study is consistent with and expands on those results. We report on how two students make sense of a quantum measurement problem involving sequential measurements and the role that the eigenvalue equation plays in this sense-making.

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.

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.

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.

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.

Applications of remote sensing to estuarine management

NASA Technical Reports Server (NTRS)

Munday, J. C., Jr.; Gordon, H. H.; Hennigar, H. F.

1977-01-01

Remote sensing was used in the resolution of estuarine problems facing federal and Virginia governmental agencies. A prototype Elizabeth River Surface Circulation Atlas was produced from photogrammetry to aid in oil spill cleanup and source identification. Aerial photo analysis twice led to selection of alternative plans for dredging and spoil disposal which minimized marsh damage. Marsh loss due to a mud wave from a highway dyke was measured on sequential aerial photographs. An historical aerial photographic sequence gave basis to a potential Commonwealth of Virginia legal claim to accreting and migrating coastal islands.

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.

The architecture of visual narrative comprehension: the interaction of narrative structure and page layout in understanding comics.

PubMed

Cohn, Neil

2014-01-01

How do people make sense of the sequential images in visual narratives like comics? A growing literature of recent research has suggested that this comprehension involves the interaction of multiple systems: The creation of meaning across sequential images relies on a "narrative grammar" that packages conceptual information into categorical roles organized in hierarchic constituents. These images are encapsulated into panels arranged in the layout of a physical page. Finally, how panels frame information can impact both the narrative structure and page layout. Altogether, these systems operate in parallel to construct the Gestalt whole of comprehension of this visual language found in comics.

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.

Hemodynamic effect and safety of intermittent sequential pneumatic compression leg sleeves in patients with congestive heart failure.

PubMed

Bickel, Amitai; Shturman, Alexander; Sergeiev, Michael; Ivry, Shimon; Eitan, Arieh; Atar, Shaul

2014-10-01

Pneumatic leg sleeves are widely used after prolonged operations for prevention of venous stasis. In healthy volunteers they increase cardiac function. We evaluated the hemodynamic effects and safety of intermittent sequential pneumatic compression (ISPC) leg sleeves in patients with chronic congestive heart failure (CHF). We studied 19 patients with systolic left ventricular dysfunction and CHF. ISPC leg sleeves, each with 10 air cells, were operated by a computerized compressor, exerting 2 cycles/min. Hemodynamic and echocardiographic parameters were measured before, during, and after ISPC activation. The baseline mean left ventricular ejection fraction was 29 ± 9.2%, median 32%, range 10%-40%. Cardiac output (from 4.26 to 4.83 L/min; P = .008) and stroke volume (from 56.1 to 63.5 mL; P = .029) increased significantly after ISPC activation, without a reciprocal increase in heart rate, and declined after sleeve deactivation. Systemic vascular resistance (SVR) decreased significantly (from 1,520 to 1,216 dyne-s/cm5; P = .0005), and remained lower than the baseline level throughout the study. There was no detrimental effect on diastolic function and no adverse clinical events, despite increased pulmonary venous return. ISPC leg sleeves in patients with chronic CHF do not exacerbate symptoms and transiently improve cardiac output through an increase in stroke volume and a reduction in SVR. Copyright © 2014 Elsevier Inc. 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.

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".

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.

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.

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.

myo-Inositol 1,4,5-trisphosphate and Ca(2+)/calmodulin-dependent factors mediate transduction of compression-induced signals in bovine articular chondrocytes.

PubMed Central

Valhmu, Wilmot B; Raia, Frank J

2002-01-01

Although the effects of mechanical loading on chondrocyte metabolic activities have been extensively characterized, the sequence of events through which extracellular mechanical signals are transduced into chondrocytes and ultimately modulate cell activities is not well understood. Here, studies were performed to map out the sequential intracellular signalling pathways through which compression-induced signals modulate aggrecan mRNA levels in bovine articular chondrocytes. Bovine articular cartilage explants were subjected to a compressive stress of 0.1 MPa for 1 h in the presence or absence of inhibitors or antagonists of the phosphoinositol and Ca(2+)/calmodulin signalling pathways in order to determine the roles of second messengers and effector molecules of these pathways in transducing the compression-induced signals. In the absence of the inhibitors, aggrecan mRNA levels were stimulated by compression 2-4-fold relative to levels in tare-loaded (see below) explants. Treatment of the explants with graded levels of the protein kinase C inhibitor chelerythrine or bisindolylmaleimide I, followed by 1 h compressive loading, did not significantly alter the load-induced elevation of aggrecan mRNA levels. In contrast, thapsigargin, which depletes the Ins(1,4,5)P3-sensitive intracellular Ca(2+) stores, completely blocked the load response without significantly altering aggrecan mRNA levels in tare-loaded explants. Similarly, antagonists of the Ca(2+)/calmodulin signalling pathway dose-dependently or completely blocked the load-response. The results obtained demonstrate that transduction of the compression-induced aggrecan mRNA-regulating signals requires Ins(1,4,5)P3- and Ca(2+)/calmodulin-dependent signalling processes in bovine articular chondrocytes. PMID:11802800

Numerically stable algorithm for combining census and sample estimates with the multivariate composite estimator

Treesearch

R. L. Czaplewski

2009-01-01

The minimum variance multivariate composite estimator is a relatively simple sequential estimator for complex sampling designs (Czaplewski 2009). Such designs combine a probability sample of expensive field data with multiple censuses and/or samples of relatively inexpensive multi-sensor, multi-resolution remotely sensed data. Unfortunately, the multivariate composite...

Apparatus for installing condition-sensing means in subterranean earth formations

DOEpatents

Shuck, Lowell Z.

1981-01-01

The present invention is directed to an apparatus for installing strain gages or other sensors-transducers in wellbores penetrating subterranean earth formations. The subject apparatus comprises an assembly which is lowered into the wellbore, secured in place, and then actuated to sequentially clean the wellbore or casing surface at a selected location with suitable solvents, etchants and neutralizers, grind the surface to a relatively smooth finish, apply an adhesive to the surface, and attach the strain gages or the like to the adhesive-bearing surface. After installing the condition-sensing gages to the casing or earth formation the assembly is withdrawn from the wellbore leaving the sensing gages securely attached to the casing or the subterranean earth formation.

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.

Biomechanical Comparison of External Fixation and Compression Screws for Transverse Tarsal Joint Arthrodesis.

PubMed

Latt, L Daniel; Glisson, Richard R; Adams, Samuel B; Schuh, Reinhard; Narron, John A; Easley, Mark E

2015-10-01

Transverse tarsal joint arthrodesis is commonly performed in the operative treatment of hindfoot arthritis and acquired flatfoot deformity. While fixation is typically achieved using screws, failure to obtain and maintain joint compression sometimes occurs, potentially leading to nonunion. External fixation is an alternate method of achieving arthrodesis site compression and has the advantage of allowing postoperative compression adjustment when necessary. However, its performance relative to standard screw fixation has not been quantified in this application. We hypothesized that external fixation could provide transverse tarsal joint compression exceeding that possible with screw fixation. Transverse tarsal joint fixation was performed sequentially, first with a circular external fixator and then with compression screws, on 9 fresh-frozen cadaveric legs. The external fixator was attached in abutting rings fixed to the tibia and the hindfoot and a third anterior ring parallel to the hindfoot ring using transverse wires and half-pins in the tibial diaphysis, calcaneus, and metatarsals. Screw fixation comprised two 4.3 mm headless compression screws traversing the talonavicular joint and 1 across the calcaneocuboid joint. Compressive forces generated during incremental fixator foot ring displacement to 20 mm and incremental screw tightening were measured using a custom-fabricated instrumented miniature external fixator spanning the transverse tarsal joint. The maximum compressive force generated by the external fixator averaged 186% of that produced by the screws (range, 104%-391%). Fixator compression surpassed that obtainable with screws at 12 mm of ring displacement and decreased when the tibial ring was detached. No correlation was found between bone density and the compressive force achievable by either fusion method. The compression across the transverse tarsal joint that can be obtained with a circular external fixator including a tibial ring exceeds that which can be obtained with 3 headless compression screws. Screw and external fixator performance did not correlate with bone mineral density. This study supports the use of external fixation as an alternative method of generating compression to help stimulate fusion across the transverse tarsal joints. The findings provide biomechanical evidence to support the use of external fixation as a viable option in transverse tarsal joint fusion cases in which screw fixation has failed or is anticipated to be inadequate due to suboptimal bone quality. © The Author(s) 2015.

Periodic buckling of constrained cylindrical elastic shells

NASA Astrophysics Data System (ADS)

Marthelot, Joel; Brun, Pierre-Thomas; Lopez Jimenez, Francisco; Reis, Pedro M.

We revisit the classic problem of buckling of a thin cylindrical elastic shell loaded either by pneumatic depressurization or axial compression. The control of the resulting dimpled pattern is achieved by using a concentric inner rigid mandrel that constrains and stabilizes the post-buckling response. Under axial compression, a regular lattice of diamond-like dimples appears sequentially on the surface of the shell to form a robust spatially extended periodic pattern. Under pressure loading, a periodic array of ridges facets the surface of the elastic cylindrical shell. The sharpness of these ridges can be readily varied and controlled through a single scalar parameter, the applied pressure. A combination of experiments, simulations and scaling analyses is used to rationalize the combined role of geometry and mechanics in the nucleation and evolution of the diamond-like dimples and ridges networks.

Relevance of postmortem radiology to the diagnosis of fatal cerebral gas embolism from compressed air diving

PubMed Central

Cole, A J; Griffiths, D; Lavender, S; Summers, P; Rich, K

2006-01-01

Aims To test the hypothesis that artefact caused by postmortem off‐gassing is at least partly responsible for the presence of gas within the vascular system and tissues of the cadaver following death associated with compressed air diving. Methods Controlled experiment sacrificing sheep after a period of simulated diving in a hyperbaric chamber and carrying out sequential postmortem computed tomography (CT) on the cadavers. Results All the subject sheep developed significant quantities of gas in the vascular system within 24 hours, as demonstrated by CT and necropsy, while the control animals did not. Conclusions The presence of gas in the vascular system of human cadavers following diving associated fatalities is to be expected, and is not necessarily connected with gas embolism following pulmonary barotrauma, as has previously been claimed. PMID:16489175

Remote sensing data with the conditional latin hypercube sampling and geostatistical approach to delineate landscape changes induced by large chronological physical disturbances.

PubMed

Lin, Yu-Pin; Chu, Hone-Jay; Wang, Cheng-Long; Yu, Hsiao-Hsuan; Wang, Yung-Chieh

2009-01-01

This study applies variogram analyses of normalized difference vegetation index (NDVI) images derived from SPOT HRV images obtained before and after the ChiChi earthquake in the Chenyulan watershed, Taiwan, as well as images after four large typhoons, to delineate the spatial patterns, spatial structures and spatial variability of landscapes caused by these large disturbances. The conditional Latin hypercube sampling approach was applied to select samples from multiple NDVI images. Kriging and sequential Gaussian simulation with sufficient samples were then used to generate maps of NDVI images. The variography of NDVI image results demonstrate that spatial patterns of disturbed landscapes were successfully delineated by variogram analysis in study areas. The high-magnitude Chi-Chi earthquake created spatial landscape variations in the study area. After the earthquake, the cumulative impacts of typhoons on landscape patterns depended on the magnitudes and paths of typhoons, but were not always evident in the spatiotemporal variability of landscapes in the study area. The statistics and spatial structures of multiple NDVI images were captured by 3,000 samples from 62,500 grids in the NDVI images. Kriging and sequential Gaussian simulation with the 3,000 samples effectively reproduced spatial patterns of NDVI images. However, the proposed approach, which integrates the conditional Latin hypercube sampling approach, variogram, kriging and sequential Gaussian simulation in remotely sensed images, efficiently monitors, samples and maps the effects of large chronological disturbances on spatial characteristics of landscape changes including spatial variability and heterogeneity.

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.

SMA texture and reorientation: simulations and neutron diffraction studies

NASA Astrophysics Data System (ADS)

Gao, Xiujie; Brown, Donald W.; Brinson, L. Catherine

2005-05-01

With increased usage of shape memory alloys (SMA) for applications in various fields, it is important to understand how the material behavior is affected by factors such as texture, stress state and loading history, especially for complex multiaxial loading states. Using the in-situ neutron diffraction loading facility (SMARTS diffractometer) and ex situ inverse pole figure measurement facility (HIPPO diffractometer) at the Los Alamos Neutron Science Center (LANCE), the macroscopic mechanical behavior and texture evolution of Nickel-Titanium (Nitinol) SMAs under sequential compression in alternating directions were studied. The simplified multivariant model developed at Northwestern University was then used to simulate the macroscopic behavior and the microstructural change of Nitinol under this sequential loading. Pole figures were obtained via post-processing of the multivariant results for volume fraction evolution and compared quantitatively well to the experimental results. The experimental results can also be used to test or verify other SMA constitutive models.

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).

The architecture of visual narrative comprehension: the interaction of narrative structure and page layout in understanding comics

PubMed Central

Cohn, Neil

2014-01-01

How do people make sense of the sequential images in visual narratives like comics? A growing literature of recent research has suggested that this comprehension involves the interaction of multiple systems: The creation of meaning across sequential images relies on a “narrative grammar” that packages conceptual information into categorical roles organized in hierarchic constituents. These images are encapsulated into panels arranged in the layout of a physical page. Finally, how panels frame information can impact both the narrative structure and page layout. Altogether, these systems operate in parallel to construct the Gestalt whole of comprehension of this visual language found in comics. PMID:25071651

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

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.

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.

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.

[Basic life support in pediatrics].

PubMed

Calvo Macías, A; Manrique Martínez, I; Rodríguez Núñez, A; López-Herce Cid, J

2006-09-01

Basic life support (BLS) is the combination of maneuvers that identifies the child in cardiopulmonary arrest and initiates the substitution of respiratory and circulatory function, without the use of technical adjuncts, until the child can receive more advanced treatment. BLS includes a sequence of steps or maneuvers that should be performed sequentially: ensuring the safety of rescuer and child, assessing unconsciousness, calling for help, positioning the victim, opening the airway, assessing breathing, ventilating, assessing signs of circulation and/or central arterial pulse, performing chest compressions, activating the emergency medical service system, and checking the results of resuscitation. The most important changes in the new guidelines are the compression: ventilation ratio and the algorithm for relieving foreign body airway obstruction. A compression/ ventilation ratio of 30:2 will be recommended for lay rescuers of infants, children and adults. Health professionals will use a compression: ventilation ratio of 15:2 for infants and children. If the health professional is alone, he/she may also use a ratio of 30:2 to avoid fatigue. In the algorithm for relieving foreign body airway obstruction, when the child becomes unconscious, the maneuvers will be similar to the BLS sequence with chest compressions (functioning as a deobstruction procedure) and ventilation, with reassessment of the mouth every 2 min to check for a foreign body, and evaluation of breathing and the presence of vital signs. BLS maneuvers are easy to learn and can be performed by anyone with adequate training. Therefore, BLS should be taught to all citizens.

Memory and decision making: Effects of sequential presentation of probabilities and outcomes in risky prospects.

PubMed

Millroth, Philip; Guath, Mona; Juslin, Peter

2018-06-07

The rationality of decision making under risk is of central concern in psychology and other behavioral sciences. In real-life, the information relevant to a decision often arrives sequentially or changes over time, implying nontrivial demands on memory. Yet, little is known about how this affects the ability to make rational decisions and a default assumption is rather that information about outcomes and probabilities are simultaneously available at the time of the decision. In 4 experiments, we show that participants receiving probability- and outcome information sequentially report substantially (29 to 83%) higher certainty equivalents than participants with simultaneous presentation. This holds also for monetary-incentivized participants with perfect recall of the information. Participants in the sequential conditions often violate stochastic dominance in the sense that they pay more for a lottery with low probability of an outcome than participants in the simultaneous condition pay for a high probability of the same outcome. Computational modeling demonstrates that Cumulative Prospect Theory (Tversky & Kahneman, 1992) fails to account for the effects of sequential presentation, but a model assuming anchoring-and adjustment constrained by memory can account for the data. By implication, established assumptions of rationality may need to be reconsidered to account for the effects of memory in many real-life tasks. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Making Sense of Phenomena from Sequential Images versus Illustrated Text

ERIC Educational Resources Information Center

Scalco, Karina C.; Talanquer, Vicente; Kiill, Keila B.; Cordeiro, Marcia R.

2018-01-01

We present the results of a qualitative research study designed to explore differences in the types of reasoning triggered by information presented to chemistry students in two different formats. One group of students was asked to analyze a sequence of images designed to represent critical elements in the explanation of a target phenomenon.…

The Geoscience Laser Altimeter System (GLAS) Laser Transmitter

NASA Technical Reports Server (NTRS)

Afzal, Robert S.; Yu, Anthony W.; Dallas, Joseph L.; Melak, Anthony; Lukemir, Alan; Ramos-Izqueirdo, L.; Mamakos, William

2007-01-01

The Geoscience Laser Altimeter System (GLAS), launched in January 2003, is a laser altimeter and lidar for the Earth Observing System's (EOS) ICESat mission. GLAS accommodates three, sequentially operated, diode-pumped, solid-state, Nd:YAG laser transmitters. The laser transmitter requirements, design and qualification test results for this space-based remote sensing instrument is summarized and presented

Exploring the Relationship between Fidelity of Implementation and Academic Achievement in a Third-Grade Gifted Curriculum: A Mixed-Methods Study

ERIC Educational Resources Information Center

Azano, Amy; Missett, Tracy C.; Callahan, Carolyn M.; Oh, Sarah; Brunner, Marguerite; Foster, Lisa H.; Moon, Tonya R.

2011-01-01

This study used sequential mixed-methods analyses to investigate the effectiveness of a research-based language arts curriculum for gifted third graders. Using analytic induction, researchers found that teachers' beliefs and expectations (time, sense of autonomy, expectations for students, professional expertise) influenced the degree to which…

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.

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

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.

Sequential compression pump effect on hypotension due to spinal anesthesia for cesarean section: A double blind clinical trial.

PubMed

Zadeh, Fatemeh Javaherforoosh; Alqozat, Mostafa; Zadeh, Reza Akhond

2017-05-01

Spinal anesthesia (SA) is a standard technique for cesarean section. Hypotension presents an incident of 80-85% after SA in pregnant women. To determine the effect of intermittent pneumatic compression of lower limbs on declining spinal anesthesia induced hypotension during cesarean section. This double-blind clinical prospective study was conducted on 76 non-laboring parturient patients, aged 18-45 years, with the American Society of Anesthesiologist physical status I or II who were scheduled for elective cesarean section at Razi Hospital, Ahvaz, Iran from December 21, 2015 to January 20, 2016. Patients were divided into treatment mechanical pump (Group M) or control group (Group C) with simple random sampling. Fetal presentation, birth weight, Apgar at 1 and 5 min, time taken for pre-hydration (min), pre-hydration to the administration of spinal anesthesia (min), initiation of spinal to the delivery (min) and total volume of intravenous fluids, total dose of ephedrine and metoclopramide were recorded. Data were analyzed by SPSS version 19, using repeated measures of ANOVA and Chi square test. Heart rate, MPA, DAP and SAP changes were significantly higher in off-pump group in the baseline and 1st-minute (p<0.05), and in the other times, this change was significantly different with control groups. This research showed the suitability of the use of Sequential Compression Device (SCD) in reducing hypotension after spinal anesthesia for cesarean section, also this method can cause reducing vasopressor dosage for increased blood pressure, but the approval of its effectiveness requires repetition of the study with a larger sample size. The trial was registered at the Iranian Registry of Clinical Trials (http://www.irct.ir) with the IRCT ID: IRCT2015011217742N3. The authors received no financial support for the research, authorship, and/or publication of this article.

Sequential compression pump effect on hypotension due to spinal anesthesia for cesarean section: A double blind clinical trial

PubMed Central

Zadeh, Fatemeh Javaherforoosh; Alqozat, Mostafa; Zadeh, Reza Akhond

2017-01-01

Background Spinal anesthesia (SA) is a standard technique for cesarean section. Hypotension presents an incident of 80–85% after SA in pregnant women. Objective To determine the effect of intermittent pneumatic compression of lower limbs on declining spinal anesthesia induced hypotension during cesarean section. Methods This double-blind clinical prospective study was conducted on 76 non-laboring parturient patients, aged 18–45 years, with the American Society of Anesthesiologist physical status I or II who were scheduled for elective cesarean section at Razi Hospital, Ahvaz, Iran from December 21, 2015 to January 20, 2016. Patients were divided into treatment mechanical pump (Group M) or control group (Group C) with simple random sampling. Fetal presentation, birth weight, Apgar at 1 and 5 min, time taken for pre-hydration (min), pre-hydration to the administration of spinal anesthesia (min), initiation of spinal to the delivery (min) and total volume of intravenous fluids, total dose of ephedrine and metoclopramide were recorded. Data were analyzed by SPSS version 19, using repeated measures of ANOVA and Chi square test. Results Heart rate, MPA, DAP and SAP changes were significantly higher in off-pump group in the baseline and 1st-minute (p<0.05), and in the other times, this change was significantly different with control groups. Conclusion This research showed the suitability of the use of Sequential Compression Device (SCD) in reducing hypotension after spinal anesthesia for cesarean section, also this method can cause reducing vasopressor dosage for increased blood pressure, but the approval of its effectiveness requires repetition of the study with a larger sample size. Trial registration The trial was registered at the Iranian Registry of Clinical Trials (http://www.irct.ir) with the IRCT ID: IRCT2015011217742N3. Funding The authors received no financial support for the research, authorship, and/or publication of this article. PMID:28713516

Deep venous thrombosis prophylaxis in trauma: improved compliance with a novel miniaturized pneumatic compression device.

PubMed

Murakami, Maki; McDill, Tandace L; Cindrick-Pounds, Lori; Loran, David B; Woodside, Kenneth J; Mileski, William J; Hunter, Glenn C; Killewich, Lois A

2003-11-01

Intermittent pneumatic compression (IPC) devices prevent lower-extremity deep venous thrombosis (LEDVT) when used properly, but compliance remains an issue. Devices are frequently discontinued when patients are out of bed, and they are rarely used in emergency departments. Trauma patients are at high risk for LEDVT; however, IPCs are underused in this population because of compliance limitations. The hypothesis of this study was that a new miniaturized, portable, battery-powered pneumatic compression device improves compliance in trauma patients over that provided by a standard device. This was a prospective trial in which trauma patients (mean age, 46 years; revised trauma score, 11.7) were randomized to DVT prophylaxis with a standard calf-length sequential IPC device (SCD group) or a miniaturized sequential device (continuous enhanced-circulation therapy [CECT] group). The CECT device can be battery-operated for up to 6 hours and worn during ambulation. Timers attached to the devices, which recorded the time each device was applied to the legs and functioning, were used to quantify compliance. For each subject in each location during hospitalization, compliance rates were determined by dividing the number of minutes the device was functioning by the total minutes in that location. Compliance rates for all subjects were averaged in each location: emergency department, operating room, intensive care unit, and nursing ward. Total compliance rate in the CECT group was significantly higher than in the SCD group (77.7% vs. 58.9%, P =.004). Compliance in the emergency department and nursing ward were also significantly greater with the CECT device (P =.002 and P =.008 respectively). Previous studies have demonstrated that reduced compliance with IPC devices results in a higher incidence of LEDVT. Given its ability to improve compliance, the CECT may provide superior DVT prevention compared with that provided by standard devices.

Efficient Controls for Finitely Convergent Sequential Algorithms

PubMed Central

Chen, Wei; Herman, Gabor T.

2010-01-01

Finding a feasible point that satisfies a set of constraints is a common task in scientific computing: examples are the linear feasibility problem and the convex feasibility problem. Finitely convergent sequential algorithms can be used for solving such problems; an example of such an algorithm is ART3, which is defined in such a way that its control is cyclic in the sense that during its execution it repeatedly cycles through the given constraints. Previously we found a variant of ART3 whose control is no longer cyclic, but which is still finitely convergent and in practice it usually converges faster than ART3 does. In this paper we propose a general methodology for automatic transformation of finitely convergent sequential algorithms in such a way that (i) finite convergence is retained and (ii) the speed of convergence is improved. The first of these two properties is proven by mathematical theorems, the second is illustrated by applying the algorithms to a practical problem. PMID:20953327

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

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.

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.

A Truthful Incentive Mechanism for Online Recruitment in Mobile Crowd Sensing System.

PubMed

Chen, Xiao; Liu, Min; Zhou, Yaqin; Li, Zhongcheng; Chen, Shuang; He, Xiangnan

2017-01-01

We investigate emerging mobile crowd sensing (MCS) systems, in which new cloud-based platforms sequentially allocate homogenous sensing jobs to dynamically-arriving users with uncertain service qualities. Given that human beings are selfish in nature, it is crucial yet challenging to design an efficient and truthful incentive mechanism to encourage users to participate. To address the challenge, we propose a novel truthful online auction mechanism that can efficiently learn to make irreversible online decisions on winner selections for new MCS systems without requiring previous knowledge of users. Moreover, we theoretically prove that our incentive possesses truthfulness, individual rationality and computational efficiency. Extensive simulation results under both real and synthetic traces demonstrate that our incentive mechanism can reduce the payment of the platform, increase the utility of the platform and social welfare.

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.

Mesoscale modeling of vacancy-mediated Si segregation near an edge dislocation in Ni under irradiation

NASA Astrophysics Data System (ADS)

Li, Zebo; Trinkle, Dallas R.

2017-04-01

We use a continuum method informed by transport coefficients computed using self-consistent mean field theory to model vacancy-mediated diffusion of substitutional Si solutes in FCC Ni near an a/2 [1 1 ¯0 ] (111 ) edge dislocation. We perform two sequential simulations: first under equilibrium boundary conditions and then under irradiation. The strain field around the dislocation induces heterogeneity and anisotropy in the defect transport properties and determines the steady-state vacancy and Si distributions. At equilibrium both vacancies and Si solutes diffuse to form Cottrell atmospheres with vacancies accumulating in the compressive region above the dislocation core while Si segregates to the tensile region below the core. Irradiation raises the bulk vacancy concentration, driving vacancies to flow into the dislocation core. The out-of-equilibrium vacancy fluxes drag Si atoms towards the core, causing segregation to the compressive region, despite Si being an oversized solute in Ni.

WIND: Computer program for calculation of three dimensional potential compressible flow about wind turbine rotor blades

NASA Technical Reports Server (NTRS)

Dulikravich, D. S.

1980-01-01

A computer program is presented which numerically solves an exact, full potential equation (FPE) for three dimensional, steady, inviscid flow through an isolated wind turbine rotor. The program automatically generates a three dimensional, boundary conforming grid and iteratively solves the FPE while fully accounting for both the rotating cascade and Coriolis effects. The numerical techniques incorporated involve rotated, type dependent finite differencing, a finite volume method, artificial viscosity in conservative form, and a successive line overrelaxation combined with the sequential grid refinement procedure to accelerate the iterative convergence rate. Consequently, the WIND program is capable of accurately analyzing incompressible and compressible flows, including those that are locally transonic and terminated by weak shocks. The program can also be used to analyze the flow around isolated aircraft propellers and helicopter rotors in hover as long as the total relative Mach number of the oncoming flow is subsonic.

Apparatus for the liquefaction of a gas and methods relating to same

DOEpatents

Turner, Terry D [Idaho Falls, ID; Wilding, Bruce M [Idaho Falls, ID; McKellar, Michael G [Idaho Falls, ID

2009-12-29

Apparatuses and methods are provided for producing liquefied gas, such as liquefied natural gas. In one embodiment, a liquefaction plant may be coupled to a source of unpurified natural gas, such as a natural gas pipeline at a pressure letdown station. A portion of the gas is drawn off and split into a process stream and a cooling stream. The cooling stream may be sequentially pass through a compressor and an expander. The process stream may also pass through a compressor. The compressed process stream is cooled, such as by the expanded cooling stream. The cooled, compressed process stream is expanded to liquefy the natural gas. A gas-liquid separator separates the vapor from the liquid natural gas. A portion of the liquid gas may be used for additional cooling. Gas produced within the system may be recompressed for reintroduction into a receiving line.

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

Algorithm for Compressing Time-Series Data

NASA Technical Reports Server (NTRS)

Hawkins, S. Edward, III; Darlington, Edward Hugo

2012-01-01

An algorithm based on Chebyshev polynomials effects lossy compression of time-series data or other one-dimensional data streams (e.g., spectral data) that are arranged in blocks for sequential transmission. The algorithm was developed for use in transmitting data from spacecraft scientific instruments to Earth stations. In spite of its lossy nature, the algorithm preserves the information needed for scientific analysis. The algorithm is computationally simple, yet compresses data streams by factors much greater than two. The algorithm is not restricted to spacecraft or scientific uses: it is applicable to time-series data in general. The algorithm can also be applied to general multidimensional data that have been converted to time-series data, a typical example being image data acquired by raster scanning. However, unlike most prior image-data-compression algorithms, this algorithm neither depends on nor exploits the two-dimensional spatial correlations that are generally present in images. In order to understand the essence of this compression algorithm, it is necessary to understand that the net effect of this algorithm and the associated decompression algorithm is to approximate the original stream of data as a sequence of finite series of Chebyshev polynomials. For the purpose of this algorithm, a block of data or interval of time for which a Chebyshev polynomial series is fitted to the original data is denoted a fitting interval. Chebyshev approximation has two properties that make it particularly effective for compressing serial data streams with minimal loss of scientific information: The errors associated with a Chebyshev approximation are nearly uniformly distributed over the fitting interval (this is known in the art as the "equal error property"); and the maximum deviations of the fitted Chebyshev polynomial from the original data have the smallest possible values (this is known in the art as the "min-max property").

Comprehensive Space-Object Characterization using Spectrally Compressive Polarimetric Sensing

DTIC Science & Technology

2015-04-08

90o, 45o, and 135o polarization channels for lin- ear polarization state estimation. This linear polarimetry would satisfy several applications without...persive element. This technique eliminates mechanical movements that hinder conventional polarimetry . The experimental results show clear spatial

Boundary conditions estimation on a road network using compressed sensing.

DOT National Transportation Integrated Search

2016-02-01

This report presents a new boundary condition estimation framework for transportation networks in which : the state is modeled by a first order scalar conservation law. Using an equivalent formulation based on a : Hamilton-Jacobi equation, we pose th...

Performance evaluation of an asynchronous multisensor track fusion filter

NASA Astrophysics Data System (ADS)

Alouani, Ali T.; Gray, John E.; McCabe, D. H.

2003-08-01

Recently the authors developed a new filter that uses data generated by asynchronous sensors to produce a state estimate that is optimal in the minimum mean square sense. The solution accounts for communications delay between sensors platform and fusion center. It also deals with out of sequence data as well as latent data by processing the information in a batch-like manner. This paper compares, using simulated targets and Monte Carlo simulations, the performance of the filter to the optimal sequential processing approach. It was found that the new asynchronous Multisensor track fusion filter (AMSTFF) performance is identical to that of the extended sequential Kalman filter (SEKF), while the new filter updates its track at a much lower rate than the SEKF.

A reversible fluorescence "off-on-off" sensor for sequential detection of aluminum and acetate/fluoride ions.

PubMed

Gupta, Vinod Kumar; Mergu, Naveen; Kumawat, Lokesh Kumar; Singh, Ashok Kumar

2015-11-01

A new rhodamine functionalized fluorogenic Schiff base CS was synthesized and its colorimetric and fluorescence responses toward various metal ions were explored. The sensor exhibited highly selective and sensitive colorimetric and "off-on" fluorescence response towards Al(3+) in the presence of other competing metal ions. These spectral changes are large enough in the visible region of the spectrum and thus enable naked-eye detection. Studies proved that the formation of CS-Al(3+) complex is fully reversible and can sense to AcO(-)/F(-) via dissociation. The results revealed that the sensor provides fluorescence "off-on-off" strategy for the sequential detection of Al(3+) and AcO(-)/F(-). Copyright © 2015 Elsevier B.V. All rights reserved.

Protein kinase C and calcineurin cooperatively mediate cell survival under compressive mechanical stress.

PubMed

Mishra, Ranjan; van Drogen, Frank; Dechant, Reinhard; Oh, Soojung; Jeon, Noo Li; Lee, Sung Sik; Peter, Matthias

2017-12-19

Cells experience compressive stress while growing in limited space or migrating through narrow constrictions. To survive such stress, cells reprogram their intracellular organization to acquire appropriate mechanical properties. However, the mechanosensors and downstream signaling networks mediating these changes remain largely unknown. Here, we have established a microfluidic platform to specifically trigger compressive stress, and to quantitatively monitor single-cell responses of budding yeast in situ. We found that yeast senses compressive stress via the cell surface protein Mid2 and the calcium channel proteins Mid1 and Cch1, which then activate the Pkc1/Mpk1 MAP kinase pathway and calcium signaling, respectively. Genetic analysis revealed that these pathways work in parallel to mediate cell survival. Mid2 contains a short intracellular tail and a serine-threonine-rich extracellular domain with spring-like properties, and both domains are required for mechanosignaling. Mid2-dependent spatial activation of the Pkc1/Mpk1 pathway depolarizes the actin cytoskeleton in budding or shmooing cells, thereby antagonizing polarized growth to protect cells under compressive stress conditions. Together, these results identify a conserved signaling network responding to compressive mechanical stress, which, in higher eukaryotes, may ensure cell survival in confined environments.

Introductory comments on the USGS geographic applications program

NASA Technical Reports Server (NTRS)

Gerlach, A. C.

1970-01-01

The third phase of remote sensing technologies and potentials applied to the operations of the U.S. Geological Survey is introduced. Remote sensing data with multidisciplinary spatial data from traditional sources is combined with geographic theory and techniques of environmental modeling. These combined imputs are subject to four sequential activities that involve: (1) thermatic mapping of land use and environmental factors; (2) the dynamics of change detection; (3) environmental surveillance to identify sudden changes and general trends; and (4) preparation of statistical model and analytical reports. Geography program functions, products, clients, and goals are presented in graphical form, along with aircraft photo missions, geography test sites, and FY-70.

Mechanic stress generated by a time-varying electromagnetic field on bone surface.

PubMed

Ye, Hui

2018-03-19

Bone cells sense mechanical load, which is essential for bone growth and remodeling. In a fracture, this mechanism is compromised. Electromagnetic stimulation has been widely used to assist in bone healing, but the underlying mechanisms are largely unknown. A recent hypothesis suggests that electromagnetic stimulation could influence tissue biomechanics; however, a detailed quantitative understanding of EM-induced biomechanical changes in the bone is unavailable. This paper used a muscle/bone model to study the biomechanics of the bone under EM exposure. Due to the dielectric properties of the muscle/bone interface, a time-varying magnetic field can generate both compressing and shear stresses on the bone surface, where many mechanical sensing cells are available for cellular mechanotransduction. I calculated these stresses and found that the shear stress is significantly greater than the compressing stress. Detailed parametric analysis suggests that both the compressing and shear stresses are dependent on the geometrical and electrical properties of the muscle and the bone. These stresses are also functions of the orientation of the coil and the frequency of the magnetic field. It is speculated that the EM field could apply biomechanical influence to fractured bone, through the fine-tuning of the controllable field parameters. Graphical abstract Mechanic stress on bone surface in a time-varying magnetic field.

An optical color image watermarking scheme by using compressive sensing with human visual characteristics in gyrator domain

NASA Astrophysics Data System (ADS)

Liansheng, Sui; Bei, Zhou; Zhanmin, Wang; Ailing, Tian

2017-05-01

A novel optical color image watermarking scheme considering human visual characteristics is presented in gyrator transform domain. Initially, an appropriate reference image is constructed of significant blocks chosen from the grayscale host image by evaluating visual characteristics such as visual entropy and edge entropy. Three components of the color watermark image are compressed based on compressive sensing, and the corresponding results are combined to form the grayscale watermark. Then, the frequency coefficients of the watermark image are fused into the frequency data of the gyrator-transformed reference image. The fused result is inversely transformed and partitioned, and eventually the watermarked image is obtained by mapping the resultant blocks into their original positions. The scheme can reconstruct the watermark with high perceptual quality and has the enhanced security due to high sensitivity of the secret keys. Importantly, the scheme can be implemented easily under the framework of double random phase encoding with the 4f optical system. To the best of our knowledge, it is the first report on embedding the color watermark into the grayscale host image which will be out of attacker's expectation. Simulation results are given to verify the feasibility and its superior performance in terms of noise and occlusion robustness.

Compressed sensing with cyclic-S Hadamard matrix for terahertz imaging applications

NASA Astrophysics Data System (ADS)

Ermeydan, Esra Şengün; ćankaya, Ilyas

2018-01-01

Compressed Sensing (CS) with Cyclic-S Hadamard matrix is proposed for single pixel imaging applications in this study. In single pixel imaging scheme, N = r . c samples should be taken for r×c pixel image where . denotes multiplication. CS is a popular technique claiming that the sparse signals can be reconstructed with samples under Nyquist rate. Therefore to solve the slow data acquisition problem in Terahertz (THz) single pixel imaging, CS is a good candidate. However, changing mask for each measurement is a challenging problem since there is no commercial Spatial Light Modulators (SLM) for THz band yet, therefore circular masks are suggested so that for each measurement one or two column shifting will be enough to change the mask. The CS masks are designed using cyclic-S matrices based on Hadamard transform for 9 × 7 and 15 × 17 pixel images within the framework of this study. The %50 compressed images are reconstructed using total variation based TVAL3 algorithm. Matlab simulations demonstrates that cyclic-S matrices can be used for single pixel imaging based on CS. The circular masks have the advantage to reduce the mechanical SLMs to a single sliding strip, whereas the CS helps to reduce acquisition time and energy since it allows to reconstruct the image from fewer samples.

Electron beam accelerator with magnetic pulse compression and accelerator switching

DOEpatents

Birx, Daniel L.; Reginato, Louis L.

1988-01-01

An electron beam accelerator comprising an electron beam generator-injector to produce a focused beam of .gtoreq.0.1 MeV energy electrons; a plurality of substantially identical, aligned accelerator modules to sequentially receive and increase the kinetic energies of the beam electrons by about 0.1-1 MeV per module. Each accelerator module includes a pulse-forming network that delivers a voltage pulse to the module of substantially .gtoreq.0.1-1 MeV maximum energy over a time duration of .ltoreq.1 .mu.sec.

Electron beam accelerator with magnetic pulse compression and accelerator switching

DOEpatents

Birx, Daniel L.; Reginato, Louis L.

1987-01-01

An electron beam accelerator comprising an electron beam generator-injector to produce a focused beam of .gtoreq.0.1 MeV energy electrons; a plurality of substantially identical, aligned accelerator modules to sequentially receive and increase the kinetic energies of the beam electrons by about 0.1-1 MeV per module. Each accelerator module includes a pulse-forming network that delivers a voltage pulse to the module of substantially 0.1-1 MeV maximum energy over a time duration of .ltoreq.1 .mu.sec.

Electron beam accelerator with magnetic pulse compression and accelerator switching

DOEpatents

Birx, D.L.; Reginato, L.L.

1984-03-22

An electron beam accelerator is described comprising an electron beam generator-injector to produce a focused beam of greater than or equal to .1 MeV energy electrons; a plurality of substantially identical, aligned accelerator modules to sequentially receive and increase the kinetic energies of the beam electron by about .1-1 MeV per module. Each accelerator module includes a pulse-forming network that delivers a voltage pulse to the module of substantially .1-1 MeV maximum energy over a time duration of less than or equal to 1 ..mu..sec.

Cholinergic modulation of the CAN current may adjust neural dynamics for active memory maintenance, spatial navigation and time-compressed replay

PubMed Central

Yoshida, Motoharu; Knauer, Beate; Jochems, Arthur

2012-01-01

Suppression of cholinergic receptors and inactivation of the septum impair short-term memory, and disrupt place cell and grid cell activity in the medial temporal lobe (MTL). Location-dependent hippocampal place cell firing during active waking, when the acetylcholine level is high, switches to time-compressed replay activity during quiet waking and slow-wave-sleep (SWS), when the acetylcholine level is low. However, it remains largely unknown how acetylcholine supports short-term memory, spatial navigation, and the functional switch to replay mode in the MTL. In this paper, we focus on the role of the calcium-activated non-specific cationic (CAN) current which is activated by acetylcholine. The CAN current is known to underlie persistent firing, which could serve as a memory trace in many neurons in the MTL. Here, we review the CAN current and discuss possible roles of the CAN current in short-term memory and spatial navigation. We further propose a novel theoretical model where the CAN current switches the hippocampal place cell activity between real-time and time-compressed sequential activity during encoding and consolidation, respectively. PMID:22435051

Predictor variable resolution governs modeled soil types

USDA-ARS?s Scientific Manuscript database

Soil mapping identifies different soil types by compressing a unique suite of spatial patterns and processes across multiple spatial scales. It can be quite difficult to quantify spatial patterns of soil properties with remotely sensed predictor variables. More specifically, matching the right scale...