A Compressed Sensing Framework for Magnetic Resonance Fingerprinting
Davies, Mike; Puy, Gilles; Vandergheynst, Pierre; Wiaux, Yves
2013-01-01
Inspired by the recently proposed Magnetic Resonance Fingerprinting (MRF) technique, we develop a principled compressed sensing framework for quantitative MRI. The three key components are: a random pulse excitation sequence following the MRF technique; a random EPI subsampling strategy and an iterative projection algorithm that imposes consistency with the Bloch equations. We show that theoretically, as long as the excitation sequence possesses an appropriate form of persistent excitation, w...
A compressive sensing based secure watermark detection and privacy preserving storage framework.
Qia Wang; Wenjun Zeng; Jun Tian
2014-03-01
Privacy is a critical issue when the data owners outsource data storage or processing to a third party computing service, such as the cloud. In this paper, we identify a cloud computing application scenario that requires simultaneously performing secure watermark detection and privacy preserving multimedia data storage. We then propose a compressive sensing (CS)-based framework using secure multiparty computation (MPC) protocols to address such a requirement. In our framework, the multimedia data and secret watermark pattern are presented to the cloud for secure watermark detection in a CS domain to protect the privacy. During CS transformation, the privacy of the CS matrix and the watermark pattern is protected by the MPC protocols under the semi-honest security model. We derive the expected watermark detection performance in the CS domain, given the target image, watermark pattern, and the size of the CS matrix (but without the CS matrix itself). The correctness of the derived performance has been validated by our experiments. Our theoretical analysis and experimental results show that secure watermark detection in the CS domain is feasible. Our framework can also be extended to other collaborative secure signal processing and data-mining applications in the cloud.
Compressed sensing & sparse filtering
Carmi, Avishy Y; Godsill, Simon J
2013-01-01
This book is aimed at presenting concepts, methods and algorithms ableto cope with undersampled and limited data. One such trend that recently gained popularity and to some extent revolutionised signal processing is compressed sensing. Compressed sensing builds upon the observation that many signals in nature are nearly sparse (or compressible, as they are normally referred to) in some domain, and consequently they can be reconstructed to within high accuracy from far fewer observations than traditionally held to be necessary.Â Apart from compressed sensing this book contains other related app
International Nuclear Information System (INIS)
Mille, Matthew; Su, Lin; Yazici, Birsen; Xu, X. George
2011-01-01
Compressive sensing is a 5-year old theory that has already resulted in an extremely large number of publications in the literature and that has the potential to impact every field of engineering and applied science that has to do with data acquisition and processing. This paper introduces the mathematics, presents a simple demonstration of radiation dose reduction in x-ray CT imaging, and discusses potential application in nuclear science and engineering. (author)
Temporal compressive sensing systems
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.
Compressed sensing electron tomography
International Nuclear Information System (INIS)
Leary, Rowan; Saghi, Zineb; Midgley, Paul A.; Holland, Daniel J.
2013-01-01
The recent mathematical concept of compressed sensing (CS) asserts that a small number of well-chosen measurements can suffice to reconstruct signals that are amenable to sparse or compressible representation. In addition to powerful theoretical results, the principles of CS are being exploited increasingly across a range of experiments to yield substantial performance gains relative to conventional approaches. In this work we describe the application of CS to electron tomography (ET) reconstruction and demonstrate the efficacy of CS–ET with several example studies. Artefacts present in conventional ET reconstructions such as streaking, blurring of object boundaries and elongation are markedly reduced, and robust reconstruction is shown to be possible from far fewer projections than are normally used. The CS–ET approach enables more reliable quantitative analysis of the reconstructions as well as novel 3D studies from extremely limited data. - Highlights: • Compressed sensing (CS) theory and its application to electron tomography (ET) is described. • The practical implementation of CS–ET is outlined and its efficacy demonstrated with examples. • High fidelity tomographic reconstruction is possible from a small number of images. • The CS–ET reconstructions can be more reliably segmented and analysed quantitatively. • CS–ET is applicable to different image content by choice of an appropriate sparsifying transform
Compressive sensing in medical imaging.
Graff, Christian G; Sidky, Emil Y
2015-03-10
The promise of compressive sensing, exploitation of compressibility to achieve high quality image reconstructions with less data, has attracted a great deal of attention in the medical imaging community. At the Compressed Sensing Incubator meeting held in April 2014 at OSA Headquarters in Washington, DC, presentations were given summarizing some of the research efforts ongoing in compressive sensing for x-ray computed tomography and magnetic resonance imaging systems. This article provides an expanded version of these presentations. Sparsity-exploiting reconstruction algorithms that have gained popularity in the medical imaging community are studied, and examples of clinical applications that could benefit from compressive sensing ideas are provided. The current and potential future impact of compressive sensing on the medical imaging field is discussed.
Directory of Open Access Journals (Sweden)
Xiangwei Li
2014-12-01
Full Text Available Compressive Sensing Imaging (CSI is a new framework for image acquisition, which enables the simultaneous acquisition and compression of a scene. Since the characteristics of Compressive Sensing (CS acquisition are very different from traditional image acquisition, the general image compression solution may not work well. In this paper, we propose an efficient lossy compression solution for CS acquisition of images by considering the distinctive features of the CSI. First, we design an adaptive compressive sensing acquisition method for images according to the sampling rate, which could achieve better CS reconstruction quality for the acquired image. Second, we develop a universal quantization for the obtained CS measurements from CS acquisition without knowing any a priori information about the captured image. Finally, we apply these two methods in the CSI system for efficient lossy compression of CS acquisition. Simulation results demonstrate that the proposed solution improves the rate-distortion performance by 0.4~2 dB comparing with current state-of-the-art, while maintaining a low computational complexity.
Compressive sensing for urban radar
Amin, Moeness
2014-01-01
With the emergence of compressive sensing and sparse signal reconstruction, approaches to urban radar have shifted toward relaxed constraints on signal sampling schemes in time and space, and to effectively address logistic difficulties in data acquisition. Traditionally, these challenges have hindered high resolution imaging by restricting both bandwidth and aperture, and by imposing uniformity and bounds on sampling rates.Compressive Sensing for Urban Radar is the first book to focus on a hybrid of two key areas: compressive sensing and urban sensing. It explains how reliable imaging, tracki
Compressive Sensing in Communication Systems
DEFF Research Database (Denmark)
Fyhn, Karsten
2013-01-01
. The need for cheaper, smarter and more energy efficient wireless devices is greater now than ever. This thesis addresses this problem and concerns the application of the recently developed sampling theory of compressive sensing in communication systems. Compressive sensing is the merging of signal...... acquisition and compression. It allows for sampling a signal with a rate below the bound dictated by the celebrated Shannon-Nyquist sampling theorem. In some communication systems this necessary minimum sample rate, dictated by the Shannon-Nyquist sampling theorem, is so high it is at the limit of what...... with using compressive sensing in communication systems. The main contribution of this thesis is two-fold: 1) a new compressive sensing hardware structure for spread spectrum signals, which is simpler than the current state-of-the-art, and 2) a range of algorithms for parameter estimation for the class...
Compressed sensing for distributed systems
Coluccia, Giulio; Magli, Enrico
2015-01-01
This book presents a survey of the state-of-the art in the exciting and timely topic of compressed sensing for distributed systems. It has to be noted that, while compressed sensing has been studied for some time now, its distributed applications are relatively new. Remarkably, such applications are ideally suited to exploit all the benefits that compressed sensing can provide. The objective of this book is to provide the reader with a comprehensive survey of this topic, from the basic concepts to different classes of centralized and distributed reconstruction algorithms, as well as a comparison of these techniques. This book collects different contributions on these aspects. It presents the underlying theory in a complete and unified way for the first time, presenting various signal models and their use cases. It contains a theoretical part collecting latest results in rate-distortion analysis of distributed compressed sensing, as well as practical implementations of algorithms obtaining performance close to...
Deterministic Compressed Sensing
2011-11-01
39 4.3 Digital Communications . . . . . . . . . . . . . . . . . . . . . . . . . 40 4.4 Group Testing ...deterministic de - sign matrices. All bounds ignore the O() constants. . . . . . . . . . . 131 xvi List of Algorithms 1 Iterative Hard Thresholding Algorithm...sensing is information theoretically possible using any (2k, )-RIP sensing matrix . The following celebrated results of Candès, Romberg and Tao [54
Discrete Wigner Function Reconstruction and Compressed Sensing
Zhang, Jia-Ning; Fang, Lei; Ge, Mo-Lin
2011-01-01
A new reconstruction method for Wigner function is reported for quantum tomography based on compressed sensing. By analogy with computed tomography, Wigner functions for some quantum states can be reconstructed with less measurements utilizing this compressed sensing based method.
Biomedical sensor design using analog compressed sensing
Balouchestani, Mohammadreza; Krishnan, Sridhar
2015-05-01
The main drawback of current healthcare systems is the location-specific nature of the system due to the use of fixed/wired biomedical sensors. Since biomedical sensors are usually driven by a battery, power consumption is the most important factor determining the life of a biomedical sensor. They are also restricted by size, cost, and transmission capacity. Therefore, it is important to reduce the load of sampling by merging the sampling and compression steps to reduce the storage usage, transmission times, and power consumption in order to expand the current healthcare systems to Wireless Healthcare Systems (WHSs). In this work, we present an implementation of a low-power biomedical sensor using analog Compressed Sensing (CS) framework for sparse biomedical signals that addresses both the energy and telemetry bandwidth constraints of wearable and wireless Body-Area Networks (BANs). This architecture enables continuous data acquisition and compression of biomedical signals that are suitable for a variety of diagnostic and treatment purposes. At the transmitter side, an analog-CS framework is applied at the sensing step before Analog to Digital Converter (ADC) in order to generate the compressed version of the input analog bio-signal. At the receiver side, a reconstruction algorithm based on Restricted Isometry Property (RIP) condition is applied in order to reconstruct the original bio-signals form the compressed bio-signals with high probability and enough accuracy. We examine the proposed algorithm with healthy and neuropathy surface Electromyography (sEMG) signals. The proposed algorithm achieves a good level for Average Recognition Rate (ARR) at 93% and reconstruction accuracy at 98.9%. In addition, The proposed architecture reduces total computation time from 32 to 11.5 seconds at sampling-rate=29 % of Nyquist rate, Percentage Residual Difference (PRD)=26 %, Root Mean Squared Error (RMSE)=3 %.
Compressive sensing based ptychography image encryption
Rawat, Nitin
2015-09-01
A compressive sensing (CS) based ptychography combined with an optical image encryption is proposed. The diffraction pattern is recorded through ptychography technique further compressed by non-uniform sampling via CS framework. The system requires much less encrypted data and provides high security. The diffraction pattern as well as the lesser measurements of the encrypted samples serves as a secret key which make the intruder attacks more difficult. Furthermore, CS shows that the linearly projected few random samples have adequate information for decryption with a dramatic volume reduction. Experimental results validate the feasibility and effectiveness of our proposed technique compared with the existing techniques. The retrieved images do not reveal any information with the original information. In addition, the proposed system can be robust even with partial encryption and under brute-force attacks.
Blind compressive sensing dynamic MRI
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
Je, U. K.; Cho, H. M.; Cho, H. S.; Park, Y. O.; Park, C. K.; Lim, H. W.; Kim, K. S.; Kim, G. A.; Park, S. Y.; Woo, T. H.; Choi, S. I.
2016-02-01
In this paper, we propose a new/next-generation type of CT examinations, the so-called Interior Computed Tomography (ICT), which may presumably lead to dose reduction to the patient outside the target region-of-interest (ROI), in dental x-ray imaging. Here an x-ray beam from each projection position covers only a relatively small ROI containing a target of diagnosis from the examined structure, leading to imaging benefits such as decreasing scatters and system cost as well as reducing imaging dose. We considered the compressed-sensing (CS) framework, rather than common filtered-backprojection (FBP)-based algorithms, for more accurate ICT reconstruction. We implemented a CS-based ICT algorithm and performed a systematic simulation to investigate the imaging characteristics. Simulation conditions of two ROI ratios of 0.28 and 0.14 between the target and the whole phantom sizes and four projection numbers of 360, 180, 90, and 45 were tested. We successfully reconstructed ICT images of substantially high image quality by using the CS framework even with few-view projection data, still preserving sharp edges in the images.
A Compressed Sensing Perspective of Hippocampal Function
Directory of Open Access Journals (Sweden)
Panagiotis ePetrantonakis
2014-08-01
Full Text Available Hippocampus is one of the most important information processing units in the brain. Input from the cortex passes through convergent axon pathways to the downstream hippocampal subregions and, after being appropriately processed, is fanned out back to the cortex. Here, we review evidence of the hypothesis that information flow and processing in the hippocampus complies with the principles of Compressed Sensing (CS. The CS theory comprises a mathematical framework that describes how and under which conditions, restricted sampling of information (data set can lead to condensed, yet concise, forms of the initial, subsampled information entity (i.e. of the original data set. In this work, hippocampus related regions and their respective circuitry are presented as a CS-based system whose different components collaborate to realize efficient memory encoding and decoding processes. This proposition introduces a unifying mathematical framework for hippocampal function and opens new avenues for exploring coding and decoding strategies in the brain.
Compressed Sensing with Rank Deficient Dictionaries
DEFF Research Database (Denmark)
Hansen, Thomas Lundgaard; Johansen, Daniel Højrup; Jørgensen, Peter Bjørn
2012-01-01
In compressed sensing it is generally assumed that the dictionary matrix constitutes a (possibly overcomplete) basis of the signal space. In this paper we consider dictionaries that do not span the signal space, i.e. rank deficient dictionaries. We show that in this case the signal-to-noise ratio...... (SNR) in the compressed samples can be increased by selecting the rows of the measurement matrix from the column space of the dictionary. As an example application of compressed sensing with a rank deficient dictionary, we present a case study of compressed sensing applied to the Coarse Acquisition (C...
WSNs Microseismic Signal Subsection Compression Algorithm Based on Compressed Sensing
Directory of Open Access Journals (Sweden)
Zhouzhou Liu
2015-01-01
Full Text Available For wireless network microseismic monitoring and the problems of low compression ratio and high energy consumption of communication, this paper proposes a segmentation compression algorithm according to the characteristics of the microseismic signals and the compression perception theory (CS used in the transmission process. The algorithm will be collected as a number of nonzero elements of data segmented basis, by reducing the number of combinations of nonzero elements within the segment to improve the accuracy of signal reconstruction, while taking advantage of the characteristics of compressive sensing theory to achieve a high compression ratio of the signal. Experimental results show that, in the quantum chaos immune clone refactoring (Q-CSDR algorithm for reconstruction algorithm, under the condition of signal sparse degree higher than 40, to be more than 0.4 of the compression ratio to compress the signal, the mean square error is less than 0.01, prolonging the network life by 2 times.
Multichannel compressive sensing MRI using noiselet encoding.
Directory of Open Access Journals (Sweden)
Kamlesh Pawar
Full Text Available The incoherence between measurement and sparsifying transform matrices and the restricted isometry property (RIP of measurement matrix are two of the key factors in determining the performance of compressive sensing (CS. In CS-MRI, the randomly under-sampled Fourier matrix is used as the measurement matrix and the wavelet transform is usually used as sparsifying transform matrix. However, the incoherence between the randomly under-sampled Fourier matrix and the wavelet matrix is not optimal, which can deteriorate the performance of CS-MRI. Using the mathematical result that noiselets are maximally incoherent with wavelets, this paper introduces the noiselet unitary bases as the measurement matrix to improve the incoherence and RIP in CS-MRI. Based on an empirical RIP analysis that compares the multichannel noiselet and multichannel Fourier measurement matrices in CS-MRI, we propose a multichannel compressive sensing (MCS framework to take the advantage of multichannel data acquisition used in MRI scanners. Simulations are presented in the MCS framework to compare the performance of noiselet encoding reconstructions and Fourier encoding reconstructions at different acceleration factors. The comparisons indicate that multichannel noiselet measurement matrix has better RIP than that of its Fourier counterpart, and that noiselet encoded MCS-MRI outperforms Fourier encoded MCS-MRI in preserving image resolution and can achieve higher acceleration factors. To demonstrate the feasibility of the proposed noiselet encoding scheme, a pulse sequences with tailored spatially selective RF excitation pulses was designed and implemented on a 3T scanner to acquire the data in the noiselet domain from a phantom and a human brain. The results indicate that noislet encoding preserves image resolution better than Fouirer encoding.
Compressive sensing based algorithms for electronic defence
Mishra, Amit Kumar
2017-01-01
This book details some of the major developments in the implementation of compressive sensing in radio applications for electronic defense and warfare communication use. It provides a comprehensive background to the subject and at the same time describes some novel algorithms. It also investigates application value and performance-related parameters of compressive sensing in scenarios such as direction finding, spectrum monitoring, detection, and classification.
Spectral Compressive Sensing with Polar Interpolation
DEFF Research Database (Denmark)
Fyhn, Karsten; Dadkhahi, Hamid; F. Duarte, Marco
2013-01-01
. In this paper, we introduce a greedy recovery algorithm that leverages a band-exclusion function and a polar interpolation function to address these two issues in spectral compressive sensing. Our algorithm is geared towards line spectral estimation from compressive measurements and outperforms most existing...
The possibilities of compressed sensing based migration
Aldawood, Ali; Hoteit, Ibrahim; Alkhalifah, Tariq Ali
2013-01-01
Linearized waveform inversion or Least-square migration helps reduce migration artifacts caused by limited acquisition aperture, coarse sampling of sources and receivers, and low subsurface illumination. However, leastsquare migration, based on L2-norm minimization of the misfit function, tends to produce a smeared (smoothed) depiction of the true subsurface reflectivity. Assuming that the subsurface reflectivity distribution is a sparse signal, we use a compressed-sensing (Basis Pursuit) algorithm to retrieve this sparse distribution from a small number of linear measurements. We applied a compressed-sensing algorithm to image a synthetic fault model using dense and sparse acquisition geometries. Tests on synthetic data demonstrate the ability of compressed-sensing to produce highly resolved migrated images. We, also, studied the robustness of the Basis Pursuit algorithm in the presence of Gaussian random noise.
The possibilities of compressed sensing based migration
Aldawood, Ali
2013-09-22
Linearized waveform inversion or Least-square migration helps reduce migration artifacts caused by limited acquisition aperture, coarse sampling of sources and receivers, and low subsurface illumination. However, leastsquare migration, based on L2-norm minimization of the misfit function, tends to produce a smeared (smoothed) depiction of the true subsurface reflectivity. Assuming that the subsurface reflectivity distribution is a sparse signal, we use a compressed-sensing (Basis Pursuit) algorithm to retrieve this sparse distribution from a small number of linear measurements. We applied a compressed-sensing algorithm to image a synthetic fault model using dense and sparse acquisition geometries. Tests on synthetic data demonstrate the ability of compressed-sensing to produce highly resolved migrated images. We, also, studied the robustness of the Basis Pursuit algorithm in the presence of Gaussian random noise.
Fast electron microscopy via compressive sensing
Larson, Kurt W; Anderson, Hyrum S; Wheeler, Jason W
2014-12-09
Various technologies described herein pertain to compressive sensing electron microscopy. A compressive sensing electron microscope includes a multi-beam generator and a detector. The multi-beam generator emits a sequence of electron patterns over time. Each of the electron patterns can include a plurality of electron beams, where the plurality of electron beams is configured to impart a spatially varying electron density on a sample. Further, the spatially varying electron density varies between each of the electron patterns in the sequence. Moreover, the detector collects signals respectively corresponding to interactions between the sample and each of the electron patterns in the sequence.
Compressive sensing with a microwave photonic filter
DEFF Research Database (Denmark)
Chen, Ying; Yu, Xianbin; Chi, Hao
2015-01-01
In this letter, we present a novel approach to realizing photonics-assisted compressive sensing (CS) with the technique of microwave photonic fi ltering. In the proposed system, an input spectrally sparse signal to be captured and a random sequence are modulated on an optical carrier via two Mach...
Object specific reconstruction using compressively sensed data
International Nuclear Information System (INIS)
Mahalanobis, Abhijit
2008-01-01
Compressed sensing holds the promise for radically novel sensors that can perfectly reconstruct images using considerably less samples of data than required by the otherwise general Shannon sampling theorem. In surveillance systems however, it is also desirable to cue regions of the image where objects of interest may exist. Thus in this paper, we are interested in imaging interesting objects in a scene, without necessarily seeking perfect reconstruction of the whole image. We show that our goals are achieved by minimizing a modified L2-norm criterion with good results when the reconstruction of only specific objects is of interest. The method yields a simple closed form analytical solution that does not require iterative processing. Objects can be meaningfully sensed in considerable detail while heavily compressing the scene elsewhere. Essentially, this embeds the object detection and clutter discrimination function in the sensing and imaging process.
Coding Strategies and Implementations of Compressive Sensing
Tsai, Tsung-Han
This dissertation studies the coding strategies of computational imaging to overcome the limitation of conventional sensing techniques. The information capacity of conventional sensing is limited by the physical properties of optics, such as aperture size, detector pixels, quantum efficiency, and sampling rate. These parameters determine the spatial, depth, spectral, temporal, and polarization sensitivity of each imager. To increase sensitivity in any dimension can significantly compromise the others. This research implements various coding strategies subject to optical multidimensional imaging and acoustic sensing in order to extend their sensing abilities. The proposed coding strategies combine hardware modification and signal processing to exploiting bandwidth and sensitivity from conventional sensors. We discuss the hardware architecture, compression strategies, sensing process modeling, and reconstruction algorithm of each sensing system. Optical multidimensional imaging measures three or more dimensional information of the optical signal. Traditional multidimensional imagers acquire extra dimensional information at the cost of degrading temporal or spatial resolution. Compressive multidimensional imaging multiplexes the transverse spatial, spectral, temporal, and polarization information on a two-dimensional (2D) detector. The corresponding spectral, temporal and polarization coding strategies adapt optics, electronic devices, and designed modulation techniques for multiplex measurement. This computational imaging technique provides multispectral, temporal super-resolution, and polarization imaging abilities with minimal loss in spatial resolution and noise level while maintaining or gaining higher temporal resolution. The experimental results prove that the appropriate coding strategies may improve hundreds times more sensing capacity. Human auditory system has the astonishing ability in localizing, tracking, and filtering the selected sound sources or
Dynamical Functional Theory for Compressed Sensing
DEFF Research Database (Denmark)
Cakmak, Burak; Opper, Manfred; Winther, Ole
2017-01-01
the Thouless Anderson-Palmer (TAP) equations corresponding to the ensemble. Using a dynamical functional approach we are able to derive an effective stochastic process for the marginal statistics of a single component of the dynamics. This allows us to design memory terms in the algorithm in such a way...... that the resulting fields become Gaussian random variables allowing for an explicit analysis. The asymptotic statistics of these fields are consistent with the replica ansatz of the compressed sensing problem....
Compressed Sensing-Based Direct Conversion Receiver
DEFF Research Database (Denmark)
Pierzchlewski, Jacek; Arildsen, Thomas; Larsen, Torben
2012-01-01
Due to the continuously increasing computational power of modern data receivers it is possible to move more and more processing from the analog to the digital domain. This paper presents a compressed sensing approach to relaxing the analog filtering requirements prior to the ADCs in a direct......-converted radio signals. As shown in an experiment presented in the article, when the proposed method is used, it is possible to relax the requirements for the quadrature down-converter filters. A random sampling device and an additional digital signal processing module is the price to pay for these relaxed...
Compressive Sensing for Spread Spectrum Receivers
DEFF Research Database (Denmark)
Fyhn, Karsten; Jensen, Tobias Lindstrøm; Larsen, Torben
2013-01-01
With the advent of ubiquitous computing there are two design parameters of wireless communication devices that become very important: power efficiency and production cost. Compressive sensing enables the receiver in such devices to sample below the Shannon-Nyquist sampling rate, which may lead...... the bit error rate performance is degraded by the subsampling in the CS-enabled receivers, this may be remedied by including quantization in the receiver model.We also study the computational complexity of the proposed receiver design under different sparsity and measurement ratios. Our work shows...
Harmonic analysis in integrated energy system based on compressed sensing
International Nuclear Information System (INIS)
Yang, Ting; Pen, Haibo; Wang, Dan; Wang, Zhaoxia
2016-01-01
Highlights: • We propose a harmonic/inter-harmonic analysis scheme with compressed sensing theory. • Property of sparseness of harmonic signal in electrical power system is proved. • The ratio formula of fundamental and harmonic components sparsity is presented. • Spectral Projected Gradient-Fundamental Filter reconstruction algorithm is proposed. • SPG-FF enhances the precision of harmonic detection and signal reconstruction. - Abstract: The advent of Integrated Energy Systems enabled various distributed energy to access the system through different power electronic devices. The development of this has made the harmonic environment more complex. It needs low complexity and high precision of harmonic detection and analysis methods to improve power quality. To solve the shortages of large data storage capacities and high complexity of compression in sampling under the Nyquist sampling framework, this research paper presents a harmonic analysis scheme based on compressed sensing theory. The proposed scheme enables the performance of the functions of compressive sampling, signal reconstruction and harmonic detection simultaneously. In the proposed scheme, the sparsity of the harmonic signals in the base of the Discrete Fourier Transform (DFT) is numerically calculated first. This is followed by providing a proof of the matching satisfaction of the necessary conditions for compressed sensing. The binary sparse measurement is then leveraged to reduce the storage space in the sampling unit in the proposed scheme. In the recovery process, the scheme proposed a novel reconstruction algorithm called the Spectral Projected Gradient with Fundamental Filter (SPG-FF) algorithm to enhance the reconstruction precision. One of the actual microgrid systems is used as simulation example. The results of the experiment shows that the proposed scheme effectively enhances the precision of harmonic and inter-harmonic detection with low computing complexity, and has good
Mehranian, Abolfazl; Rad, Hamidreza Saligheh; Rahmim, Arman; Ay, Mohammad Reza; Zaidi, Habib
2013-01-01
Purpose: Compressed sensing (CS) provides a promising framework for MR image reconstruction from highly undersampled data, thus reducing data acquisition time. In this context, sparsity-promoting regularization techniques exploit the prior knowledge that MR images are sparse or compressible in a
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.
Compressed sensing approach for wrist vein biometrics.
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.
On-Chip Neural Data Compression Based On Compressed Sensing With Sparse Sensing Matrices.
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.
Optimized Projection Matrix for Compressive Sensing
Directory of Open Access Journals (Sweden)
Jianping Xu
2010-01-01
Full Text Available 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.
Compressed-sensing wavenumber-scanning interferometry
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.
Correspondence normalized ghost imaging on compressive sensing
International Nuclear Information System (INIS)
Zhao Sheng-Mei; Zhuang Peng
2014-01-01
Ghost imaging (GI) offers great potential with respect to conventional imaging techniques. It is an open problem in GI systems that a long acquisition time is be required for reconstructing images with good visibility and signal-to-noise ratios (SNRs). In this paper, we propose a new scheme to get good performance with a shorter construction time. We call it correspondence normalized ghost imaging based on compressive sensing (CCNGI). In the scheme, we enhance the signal-to-noise performance by normalizing the reference beam intensity to eliminate the noise caused by laser power fluctuations, and reduce the reconstruction time by using both compressive sensing (CS) and time-correspondence imaging (CI) techniques. It is shown that the qualities of the images have been improved and the reconstruction time has been reduced using CCNGI scheme. For the two-grayscale ''double-slit'' image, the mean square error (MSE) by GI and the normalized GI (NGI) schemes with the measurement number of 5000 are 0.237 and 0.164, respectively, and that is 0.021 by CCNGI scheme with 2500 measurements. For the eight-grayscale ''lena'' object, the peak signal-to-noise rates (PSNRs) are 10.506 and 13.098, respectively using GI and NGI schemes while the value turns to 16.198 using CCNGI scheme. The results also show that a high-fidelity GI reconstruction has been achieved using only 44% of the number of measurements corresponding to the Nyquist limit for the two-grayscale “double-slit'' object. The qualities of the reconstructed images using CCNGI are almost the same as those from GI via sparsity constraints (GISC) with a shorter reconstruction time. (electromagnetism, optics, acoustics, heat transfer, classical mechanics, and fluid dynamics)
Determining building interior structures using compressive sensing
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.
Blind compressed sensing image reconstruction based on alternating direction method
Liu, Qinan; Guo, Shuxu
2018-04-01
In order to solve the problem of how to reconstruct the original image under the condition of unknown sparse basis, this paper proposes an image reconstruction method based on blind compressed sensing model. In this model, the image signal is regarded as the product of a sparse coefficient matrix and a dictionary matrix. Based on the existing blind compressed sensing theory, the optimal solution is solved by the alternative minimization method. The proposed method solves the problem that the sparse basis in compressed sensing is difficult to represent, which restrains the noise and improves the quality of reconstructed image. This method ensures that the blind compressed sensing theory has a unique solution and can recover the reconstructed original image signal from a complex environment with a stronger self-adaptability. The experimental results show that the image reconstruction algorithm based on blind compressed sensing proposed in this paper can recover high quality image signals under the condition of under-sampling.
Application of Compressive Sensing to Gravitational Microlensing Experiments
Korde-Patel, Asmita; Barry, Richard K.; Mohsenin, Tinoosh
2016-01-01
Compressive Sensing is an emerging technology for data compression and simultaneous data acquisition. This is an enabling technique for significant reduction in data bandwidth, and transmission power and hence, can greatly benefit spaceflight instruments. We apply this process to detect exoplanets via gravitational microlensing. We experiment with various impact parameters that describe microlensing curves to determine the effectiveness and uncertainty caused by Compressive Sensing. Finally, we describe implications for spaceflight missions.
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.
Compressed Sensing and Low-Rank Matrix Decomposition in Multisource Images Fusion
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Kan Ren
2014-01-01
Full Text Available We propose a novel super-resolution multisource images fusion scheme via compressive sensing and dictionary learning theory. Under the sparsity prior of images patches and the framework of the compressive sensing theory, the multisource images fusion is reduced to a signal recovery problem from the compressive measurements. Then, a set of multiscale dictionaries are learned from several groups of high-resolution sample image’s patches via a nonlinear optimization algorithm. Moreover, a new linear weights fusion rule is proposed to obtain the high-resolution image. Some experiments are taken to investigate the performance of our proposed method, and the results prove its superiority to its counterparts.
Compressive sensing using optimized sensing matrix for face verification
Oey, Endra; Jeffry; Wongso, Kelvin; Tommy
2017-12-01
Biometric appears as one of the solutions which is capable in solving problems that occurred in the usage of password in terms of data access, for example there is possibility in forgetting password and hard to recall various different passwords. With biometrics, physical characteristics of a person can be captured and used in the identification process. In this research, facial biometric is used in the verification process to determine whether the user has the authority to access the data or not. Facial biometric is chosen as its low cost implementation and generate quite accurate result for user identification. Face verification system which is adopted in this research is Compressive Sensing (CS) technique, in which aims to reduce dimension size as well as encrypt data in form of facial test image where the image is represented in sparse signals. Encrypted data can be reconstructed using Sparse Coding algorithm. Two types of Sparse Coding namely Orthogonal Matching Pursuit (OMP) and Iteratively Reweighted Least Squares -ℓp (IRLS-ℓp) will be used for comparison face verification system research. Reconstruction results of sparse signals are then used to find Euclidean norm with the sparse signal of user that has been previously saved in system to determine the validity of the facial test image. Results of system accuracy obtained in this research are 99% in IRLS with time response of face verification for 4.917 seconds and 96.33% in OMP with time response of face verification for 0.4046 seconds with non-optimized sensing matrix, while 99% in IRLS with time response of face verification for 13.4791 seconds and 98.33% for OMP with time response of face verification for 3.1571 seconds with optimized sensing matrix.
Energy Preserved Sampling for Compressed Sensing MRI
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Yudong Zhang
2014-01-01
Full Text Available The sampling patterns, cost functions, and reconstruction algorithms play important roles in optimizing compressed sensing magnetic resonance imaging (CS-MRI. Simple random sampling patterns did not take into account the energy distribution in k-space and resulted in suboptimal reconstruction of MR images. Therefore, a variety of variable density (VD based samplings patterns had been developed. To further improve it, we propose a novel energy preserving sampling (ePRESS method. Besides, we improve the cost function by introducing phase correction and region of support matrix, and we propose iterative thresholding algorithm (ITA to solve the improved cost function. We evaluate the proposed ePRESS sampling method, improved cost function, and ITA reconstruction algorithm by 2D digital phantom and 2D in vivo MR brains of healthy volunteers. These assessments demonstrate that the proposed ePRESS method performs better than VD, POWER, and BKO; the improved cost function can achieve better reconstruction quality than conventional cost function; and the ITA is faster than SISTA and is competitive with FISTA in terms of computation time.
Accelerated Compressed Sensing Based CT Image Reconstruction.
Hashemi, SayedMasoud; Beheshti, Soosan; Gill, Patrick R; Paul, Narinder S; Cobbold, Richard S C
2015-01-01
In X-ray computed tomography (CT) an important objective is to reduce the radiation dose without significantly degrading the image quality. Compressed sensing (CS) enables the radiation dose to be reduced by producing diagnostic images from a limited number of projections. However, conventional CS-based algorithms are computationally intensive and time-consuming. We propose a new algorithm that accelerates the CS-based reconstruction by using a fast pseudopolar Fourier based Radon transform and rebinning the diverging fan beams to parallel beams. The reconstruction process is analyzed using a maximum-a-posterior approach, which is transformed into a weighted CS problem. The weights involved in the proposed model are calculated based on the statistical characteristics of the reconstruction process, which is formulated in terms of the measurement noise and rebinning interpolation error. Therefore, the proposed method not only accelerates the reconstruction, but also removes the rebinning and interpolation errors. Simulation results are shown for phantoms and a patient. For example, a 512 × 512 Shepp-Logan phantom when reconstructed from 128 rebinned projections using a conventional CS method had 10% error, whereas with the proposed method the reconstruction error was less than 1%. Moreover, computation times of less than 30 sec were obtained using a standard desktop computer without numerical optimization.
Accelerated Compressed Sensing Based CT Image Reconstruction
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SayedMasoud Hashemi
2015-01-01
Full Text Available In X-ray computed tomography (CT an important objective is to reduce the radiation dose without significantly degrading the image quality. Compressed sensing (CS enables the radiation dose to be reduced by producing diagnostic images from a limited number of projections. However, conventional CS-based algorithms are computationally intensive and time-consuming. We propose a new algorithm that accelerates the CS-based reconstruction by using a fast pseudopolar Fourier based Radon transform and rebinning the diverging fan beams to parallel beams. The reconstruction process is analyzed using a maximum-a-posterior approach, which is transformed into a weighted CS problem. The weights involved in the proposed model are calculated based on the statistical characteristics of the reconstruction process, which is formulated in terms of the measurement noise and rebinning interpolation error. Therefore, the proposed method not only accelerates the reconstruction, but also removes the rebinning and interpolation errors. Simulation results are shown for phantoms and a patient. For example, a 512 × 512 Shepp-Logan phantom when reconstructed from 128 rebinned projections using a conventional CS method had 10% error, whereas with the proposed method the reconstruction error was less than 1%. Moreover, computation times of less than 30 sec were obtained using a standard desktop computer without numerical optimization.
Multimode waveguide speckle patterns for compressive sensing.
Valley, George C; Sefler, George A; Justin Shaw, T
2016-06-01
Compressive sensing (CS) of sparse gigahertz-band RF signals using microwave photonics may achieve better performances with smaller size, weight, and power than electronic CS or conventional Nyquist rate sampling. The critical element in a CS system is the device that produces the CS measurement matrix (MM). We show that passive speckle patterns in multimode waveguides potentially provide excellent MMs for CS. We measure and calculate the MM for a multimode fiber and perform simulations using this MM in a CS system. We show that the speckle MM exhibits the sharp phase transition and coherence properties needed for CS and that these properties are similar to those of a sub-Gaussian MM with the same mean and standard deviation. We calculate the MM for a multimode planar waveguide and find dimensions of the planar guide that give a speckle MM with a performance similar to that of the multimode fiber. The CS simulations show that all measured and calculated speckle MMs exhibit a robust performance with equal amplitude signals that are sparse in time, in frequency, and in wavelets (Haar wavelet transform). The planar waveguide results indicate a path to a microwave photonic integrated circuit for measuring sparse gigahertz-band RF signals using CS.
Compressed sensing in imaging mass spectrometry
International Nuclear Information System (INIS)
Bartels, Andreas; Dülk, Patrick; Trede, Dennis; Alexandrov, Theodore; Maaß, Peter
2013-01-01
Imaging mass spectrometry (IMS) is a technique of analytical chemistry for spatially resolved, label-free and multipurpose analysis of biological samples that is able to detect the spatial distribution of hundreds of molecules in one experiment. The hyperspectral IMS data is typically generated by a mass spectrometer analyzing the surface of the sample. In this paper, we propose a compressed sensing approach to IMS which potentially allows for faster data acquisition by collecting only a part of the pixels in the hyperspectral image and reconstructing the full image from this data. We present an integrative approach to perform both peak-picking spectra and denoising m/z-images simultaneously, whereas the state of the art data analysis methods solve these problems separately. We provide a proof of the robustness of the recovery of both the spectra and individual channels of the hyperspectral image and propose an algorithm to solve our optimization problem which is based on proximal mappings. The paper concludes with the numerical reconstruction results for an IMS dataset of a rat brain coronal section. (paper)
Compressed Sensing, Pseudodictionary-Based, Superresolution Reconstruction
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Chun-mei Li
2016-01-01
Full Text Available The spatial resolution of digital images is the critical factor that affects photogrammetry precision. Single-frame, superresolution, image reconstruction is a typical underdetermined, inverse problem. To solve this type of problem, a compressive, sensing, pseudodictionary-based, superresolution reconstruction method is proposed in this study. The proposed method achieves pseudodictionary learning with an available low-resolution image and uses the K-SVD algorithm, which is based on the sparse characteristics of the digital image. Then, the sparse representation coefficient of the low-resolution image is obtained by solving the norm of l0 minimization problem, and the sparse coefficient and high-resolution pseudodictionary are used to reconstruct image tiles with high resolution. Finally, single-frame-image superresolution reconstruction is achieved. The proposed method is applied to photogrammetric images, and the experimental results indicate that the proposed method effectively increase image resolution, increase image information content, and achieve superresolution reconstruction. The reconstructed results are better than those obtained from traditional interpolation methods in aspect of visual effects and quantitative indicators.
Bayesian signal reconstruction for 1-bit compressed sensing
International Nuclear Information System (INIS)
Xu, Yingying; Kabashima, Yoshiyuki; Zdeborová, Lenka
2014-01-01
The 1-bit compressed sensing framework enables the recovery of a sparse vector x from the sign information of each entry of its linear transformation. Discarding the amplitude information can significantly reduce the amount of data, which is highly beneficial in practical applications. In this paper, we present a Bayesian approach to signal reconstruction for 1-bit compressed sensing and analyze its typical performance using statistical mechanics. As a basic setup, we consider the case that the measuring matrix Φ has i.i.d entries and the measurements y are noiseless. Utilizing the replica method, we show that the Bayesian approach enables better reconstruction than the l 1 -norm minimization approach, asymptotically saturating the performance obtained when the non-zero entry positions of the signal are known, for signals whose non-zero entries follow zero mean Gaussian distributions. We also test a message passing algorithm for signal reconstruction on the basis of belief propagation. The results of numerical experiments are consistent with those of the theoretical analysis. (paper)
Statistical mechanics approach to 1-bit compressed sensing
International Nuclear Information System (INIS)
Xu, Yingying; Kabashima, Yoshiyuki
2013-01-01
Compressed sensing is a framework that makes it possible to recover an N-dimensional sparse vector x∈R N from its linear transformation y∈R M of lower dimensionality M 1 -norm-based signal recovery scheme for 1-bit compressed sensing using statistical mechanics methods. We show that the signal recovery performance predicted by the replica method under the replica symmetric ansatz, which turns out to be locally unstable for modes breaking the replica symmetry, is in good consistency with experimental results of an approximate recovery algorithm developed earlier. This suggests that the l 1 -based recovery problem typically has many local optima of a similar recovery accuracy, which can be achieved by the approximate algorithm. We also develop another approximate recovery algorithm inspired by the cavity method. Numerical experiments show that when the density of nonzero entries in the original signal is relatively large the new algorithm offers better performance than the abovementioned scheme and does so with a lower computational cost. (paper)
Energy-efficient sensing in wireless sensor networks using compressed sensing.
Razzaque, Mohammad Abdur; Dobson, Simon
2014-02-12
Sensing of the application environment is the main purpose of a wireless sensor network. Most existing energy management strategies and compression techniques assume that the sensing operation consumes significantly less energy than radio transmission and reception. This assumption does not hold in a number of practical applications. Sensing energy consumption in these applications may be comparable to, or even greater than, that of the radio. In this work, we support this claim by a quantitative analysis of the main operational energy costs of popular sensors, radios and sensor motes. In light of the importance of sensing level energy costs, especially for power hungry sensors, we consider compressed sensing and distributed compressed sensing as potential approaches to provide energy efficient sensing in wireless sensor networks. Numerical experiments investigating the effectiveness of compressed sensing and distributed compressed sensing using real datasets show their potential for efficient utilization of sensing and overall energy costs in wireless sensor networks. It is shown that, for some applications, compressed sensing and distributed compressed sensing can provide greater energy efficiency than transform coding and model-based adaptive sensing in wireless sensor networks.
Wireless Sensor Networks Data Processing Summary Based on Compressive Sensing
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Caiyun Huang
2014-07-01
Full Text Available As a newly proposed theory, compressive sensing (CS is commonly used in signal processing area. This paper investigates the applications of compressed sensing (CS in wireless sensor networks (WSNs. First, the development and research status of compressed sensing technology and wireless sensor networks are described, then a detailed investigation of WSNs research based on CS are conducted from aspects of data fusion, signal acquisition, signal routing transmission, and signal reconstruction. At the end of the paper, we conclude our survey and point out the possible future research directions.
Learning-based compressed sensing for infrared image super resolution
Zhao, Yao; Sui, Xiubao; Chen, Qian; Wu, Shaochi
2016-05-01
This paper presents an infrared image super-resolution method based on compressed sensing (CS). First, the reconstruction model under the CS framework is established and a Toeplitz matrix is selected as the sensing matrix. Compared with traditional learning-based methods, the proposed method uses a set of sub-dictionaries instead of two coupled dictionaries to recover high resolution (HR) images. And Toeplitz sensing matrix allows the proposed method time-efficient. Second, all training samples are divided into several feature spaces by using the proposed adaptive k-means classification method, which is more accurate than the standard k-means method. On the basis of this approach, a complex nonlinear mapping from the HR space to low resolution (LR) space can be converted into several compact linear mappings. Finally, the relationships between HR and LR image patches can be obtained by multi-sub-dictionaries and HR infrared images are reconstructed by the input LR images and multi-sub-dictionaries. The experimental results show that the proposed method is quantitatively and qualitatively more effective than other state-of-the-art methods.
Sparse representations and compressive sensing for imaging and vision
Patel, Vishal M
2013-01-01
Compressed sensing or compressive sensing is a new concept in signal processing where one measures a small number of non-adaptive linear combinations of the signal. These measurements are usually much smaller than the number of samples that define the signal. From these small numbers of measurements, the signal is then reconstructed by non-linear procedure. Compressed sensing has recently emerged as a powerful tool for efficiently processing data in non-traditional ways. In this book, we highlight some of the key mathematical insights underlying sparse representation and compressed sensing and illustrate the role of these theories in classical vision, imaging and biometrics problems.
Efficient two-dimensional compressive sensing in MIMO radar
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.
Experimental scheme and restoration algorithm of block compression sensing
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.
Phase Imaging: A Compressive Sensing Approach
Energy Technology Data Exchange (ETDEWEB)
Schneider, Sebastian; Stevens, Andrew; Browning, Nigel D.; Pohl, Darius; Nielsch, Kornelius; Rellinghaus, Bernd
2017-07-01
Since Wolfgang Pauli posed the question in 1933, whether the probability densities |Ψ(r)|² (real-space image) and |Ψ(q)|² (reciprocal space image) uniquely determine the wave function Ψ(r) [1], the so called Pauli Problem sparked numerous methods in all fields of microscopy [2, 3]. Reconstructing the complete wave function Ψ(r) = a(r)e-iφ(r) with the amplitude a(r) and the phase φ(r) from the recorded intensity enables the possibility to directly study the electric and magnetic properties of the sample through the phase. In transmission electron microscopy (TEM), electron holography is by far the most established method for phase reconstruction [4]. Requiring a high stability of the microscope, next to the installation of a biprism in the TEM, holography cannot be applied to any microscope straightforwardly. Recently, a phase retrieval approach was proposed using conventional TEM electron diffractive imaging (EDI). Using the SAD aperture as reciprocal-space constraint, a localized sample structure can be reconstructed from its diffraction pattern and a real-space image using the hybrid input-output algorithm [5]. We present an alternative approach using compressive phase-retrieval [6]. Our approach does not require a real-space image. Instead, random complimentary pairs of checkerboard masks are cut into a 200 nm Pt foil covering a conventional TEM aperture (cf. Figure 1). Used as SAD aperture, subsequently diffraction patterns are recorded from the same sample area. Hereby every mask blocks different parts of gold particles on a carbon support (cf. Figure 2). The compressive sensing problem has the following formulation. First, we note that the complex-valued reciprocal-space wave-function is the Fourier transform of the (also complex-valued) real-space wave-function, Ψ(q) = F[Ψ(r)], and subsequently the diffraction pattern image is given by |Ψ(q)|2 = |F[Ψ(r)]|2. We want to find Ψ(r) given a few differently coded diffraction pattern measurements yn
Development of a Neutron Spectroscopic System Utilizing Compressed Sensing Measurements
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Vargas Danilo
2016-01-01
Full Text Available A new approach to neutron detection capable of gathering spectroscopic information has been demonstrated. The approach relies on an asymmetrical arrangement of materials, geometry, and an ability to change the orientation of the detector with respect to the neutron field. Measurements are used to unfold the energy characteristics of the neutron field using a new theoretical framework of compressed sensing. Recent theoretical results show that the number of multiplexed samples can be lower than the full number of traditional samples while providing the ability to have some super-resolution. Furthermore, the solution approach does not require a priori information or inclusion of physics models. Utilizing the MCNP code, a number of candidate detector geometries and materials were modeled. Simulations were carried out for a number of neutron energies and distributions with preselected orientations for the detector. The resulting matrix (A consists of n rows associated with orientation and m columns associated with energy and distribution where n < m. The library of known responses is used for new measurements Y (n × 1 and the solver is able to determine the system, Y = Ax where x is a sparse vector. Therefore, energy spectrum measurements are a combination of the energy distribution information of the identified elements of A. This approach allows for determination of neutron spectroscopic information using a single detector system with analog multiplexing. The analog multiplexing allows the use of a compressed sensing solution similar to approaches used in other areas of imaging. A single detector assembly provides improved flexibility and is expected to reduce uncertainty associated with current neutron spectroscopy measurement.
Information theoretic bounds for compressed sensing in SAR imaging
International Nuclear Information System (INIS)
Jingxiong, Zhang; Ke, Yang; Jianzhong, Guo
2014-01-01
Compressed sensing (CS) is a new framework for sampling and reconstructing sparse signals from measurements significantly fewer than those prescribed by Nyquist rate in the Shannon sampling theorem. This new strategy, applied in various application areas including synthetic aperture radar (SAR), relies on two principles: sparsity, which is related to the signals of interest, and incoherence, which refers to the sensing modality. An important question in CS-based SAR system design concerns sampling rate necessary and sufficient for exact or approximate recovery of sparse signals. In the literature, bounds of measurements (or sampling rate) in CS have been proposed from the perspective of information theory. However, these information-theoretic bounds need to be reviewed and, if necessary, validated for CS-based SAR imaging, as there are various assumptions made in the derivations of lower and upper bounds on sub-Nyquist sampling rates, which may not hold true in CS-based SAR imaging. In this paper, information-theoretic bounds of sampling rate will be analyzed. For this, the SAR measurement system is modeled as an information channel, with channel capacity and rate-distortion characteristics evaluated to enable the determination of sampling rates required for recovery of sparse scenes. Experiments based on simulated data will be undertaken to test the theoretic bounds against empirical results about sampling rates required to achieve certain detection error probabilities
PRESS: A Novel Framework of Trajectory Compression in Road Networks
Song, Renchu; Sun, Weiwei; Zheng, Baihua; Zheng, Yu
2014-01-01
Location data becomes more and more important. In this paper, we focus on the trajectory data, and propose a new framework, namely PRESS (Paralleled Road-Network-Based Trajectory Compression), to effectively compress trajectory data under road network constraints. Different from existing work, PRESS proposes a novel representation for trajectories to separate the spatial representation of a trajectory from the temporal representation, and proposes a Hybrid Spatial Compression (HSC) algorithm ...
Block Compressed Sensing of Images Using Adaptive Granular Reconstruction
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Ran Li
2016-01-01
Full Text Available In the framework of block Compressed Sensing (CS, the reconstruction algorithm based on the Smoothed Projected Landweber (SPL iteration can achieve the better rate-distortion performance with a low computational complexity, especially for using the Principle Components Analysis (PCA to perform the adaptive hard-thresholding shrinkage. However, during learning the PCA matrix, it affects the reconstruction performance of Landweber iteration to neglect the stationary local structural characteristic of image. To solve the above problem, this paper firstly uses the Granular Computing (GrC to decompose an image into several granules depending on the structural features of patches. Then, we perform the PCA to learn the sparse representation basis corresponding to each granule. Finally, the hard-thresholding shrinkage is employed to remove the noises in patches. The patches in granule have the stationary local structural characteristic, so that our method can effectively improve the performance of hard-thresholding shrinkage. Experimental results indicate that the reconstructed image by the proposed algorithm has better objective quality when compared with several traditional ones. The edge and texture details in the reconstructed image are better preserved, which guarantees the better visual quality. Besides, our method has still a low computational complexity of reconstruction.
Compressive sensing scalp EEG signals: implementations and practical performance.
Abdulghani, Amir M; Casson, Alexander J; Rodriguez-Villegas, Esther
2012-11-01
Highly miniaturised, wearable computing and communication systems allow unobtrusive, convenient and long term monitoring of a range of physiological parameters. For long term operation from the physically smallest batteries, the average power consumption of a wearable device must be very low. It is well known that the overall power consumption of these devices can be reduced by the inclusion of low power consumption, real-time compression of the raw physiological data in the wearable device itself. Compressive sensing is a new paradigm for providing data compression: it has shown significant promise in fields such as MRI; and is potentially suitable for use in wearable computing systems as the compression process required in the wearable device has a low computational complexity. However, the practical performance very much depends on the characteristics of the signal being sensed. As such the utility of the technique cannot be extrapolated from one application to another. Long term electroencephalography (EEG) is a fundamental tool for the investigation of neurological disorders and is increasingly used in many non-medical applications, such as brain-computer interfaces. This article investigates in detail the practical performance of different implementations of the compressive sensing theory when applied to scalp EEG signals.
Polarimetric and Indoor Imaging Fusion Based on Compressive Sensing
2013-04-01
34 in Proc. IEEE Radar Conf, Rome, Italy , May 2008. [17] M. G. Amin, F. Ahmad, W. Zhang, "A compressive sensing approach to moving target... Ferrara , J. Jackson, and M. Stuff, "Three-dimensional sparse-aperture moving-target imaging," in Proc. SPIE, vol. 6970, 2008. [43] M. Skolnik (Ed
Design and analysis of compressed sensing radar detectors
Anitori, L.; Maleki, A.; Otten, M.P.G.; Baraniuk, R.G.; Hoogeboom, P.
2013-01-01
We consider the problem of target detection from a set of Compressed Sensing (CS) radar measurements corrupted by additive white Gaussian noise. We propose two novel architectures and compare their performance by means of Receiver Operating Characteristic (ROC) curves. Using asymptotic arguments and
Compressive sensing for high resolution profiles with enhanced Doppler performance
Anitori, L.; Hoogeboom, P.; Chevalier, F. Le; Otten, M.P.G.
2012-01-01
In this paper we demonstrate how Compressive Sensing (CS) can be used in pulse-Doppler radars to improve the Doppler performance while preserving range resolution. We investigate here two types of stepped frequency waveforms, the coherent frequency bursts and successive frequency ramps, which can be
Microfluidic pressure sensing using trapped air compression.
Srivastava, Nimisha; Burns, Mark A
2007-05-01
We have developed a microfluidic method for measuring the fluid pressure head experienced at any location inside a microchannel. The principal component is a microfabricated sealed chamber with a single inlet and no exit; the entrance to the single inlet is positioned at the location where pressure is to be measured. The pressure measurement is then based on monitoring the movement of a liquid-air interface as it compresses air trapped inside the microfabricated sealed chamber and calculating the pressure using the ideal gas law. The method has been used to measure the pressure of the air stream and continuous liquid flow inside microfluidic channels (d approximately 50 microm). Further, a pressure drop has also been measured using multiple microfabricated sealed chambers. For air pressure, a resolution of 700 Pa within a full-scale range of 700-100 kPa was obtained. For liquids, pressure drops as low as 70 Pa were obtained in an operating range from 70 Pa to 10 kPa. Since the method primarily uses a microfluidic sealed chamber, it does not require additional fabrication steps and may easily be incorporated in several lab-on-a-chip fluidic applications for laminar as well as turbulent flow conditions.
Sparse BLIP: BLind Iterative Parallel imaging reconstruction using compressed sensing.
She, Huajun; Chen, Rong-Rong; Liang, Dong; DiBella, Edward V R; Ying, Leslie
2014-02-01
To develop a sensitivity-based parallel imaging reconstruction method to reconstruct iteratively both the coil sensitivities and MR image simultaneously based on their prior information. Parallel magnetic resonance imaging reconstruction problem can be formulated as a multichannel sampling problem where solutions are sought analytically. However, the channel functions given by the coil sensitivities in parallel imaging are not known exactly and the estimation error usually leads to artifacts. In this study, we propose a new reconstruction algorithm, termed Sparse BLind Iterative Parallel, for blind iterative parallel imaging reconstruction using compressed sensing. The proposed algorithm reconstructs both the sensitivity functions and the image simultaneously from undersampled data. It enforces the sparseness constraint in the image as done in compressed sensing, but is different from compressed sensing in that the sensing matrix is unknown and additional constraint is enforced on the sensitivities as well. Both phantom and in vivo imaging experiments were carried out with retrospective undersampling to evaluate the performance of the proposed method. Experiments show improvement in Sparse BLind Iterative Parallel reconstruction when compared with Sparse SENSE, JSENSE, IRGN-TV, and L1-SPIRiT reconstructions with the same number of measurements. The proposed Sparse BLind Iterative Parallel algorithm reduces the reconstruction errors when compared to the state-of-the-art parallel imaging methods. Copyright © 2013 Wiley Periodicals, Inc.
Accelerated whole-brain multi-parameter mapping using blind compressed sensing.
Bhave, Sampada; Lingala, Sajan Goud; Johnson, Casey P; Magnotta, Vincent A; Jacob, Mathews
2016-03-01
To introduce a blind compressed sensing (BCS) framework to accelerate multi-parameter MR mapping, and demonstrate its feasibility in high-resolution, whole-brain T1ρ and T2 mapping. BCS models the evolution of magnetization at every pixel as a sparse linear combination of bases in a dictionary. Unlike compressed sensing, the dictionary and the sparse coefficients are jointly estimated from undersampled data. Large number of non-orthogonal bases in BCS accounts for more complex signals than low rank representations. The low degree of freedom of BCS, attributed to sparse coefficients, translates to fewer artifacts at high acceleration factors (R). From 2D retrospective undersampling experiments, the mean square errors in T1ρ and T2 maps were observed to be within 0.1% up to R = 10. BCS was observed to be more robust to patient-specific motion as compared to other compressed sensing schemes and resulted in minimal degradation of parameter maps in the presence of motion. Our results suggested that BCS can provide an acceleration factor of 8 in prospective 3D imaging with reasonable reconstructions. BCS considerably reduces scan time for multiparameter mapping of the whole brain with minimal artifacts, and is more robust to motion-induced signal changes compared to current compressed sensing and principal component analysis-based techniques. © 2015 Wiley Periodicals, Inc.
The extraction of motion-onset VEP BCI features based on deep learning and compressed sensing.
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 Â© 2016 Elsevier B.V. All rights reserved.
Identification of Coupled Map Lattice Based on Compressed Sensing
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Dong Xie
2016-01-01
Full Text Available A novel approach for the parameter identification of coupled map lattice (CML based on compressed sensing is presented in this paper. We establish a meaningful connection between these two seemingly unrelated study topics and identify the weighted parameters using the relevant recovery algorithms in compressed sensing. Specifically, we first transform the parameter identification problem of CML into the sparse recovery problem of underdetermined linear system. In fact, compressed sensing provides a feasible method to solve underdetermined linear system if the sensing matrix satisfies some suitable conditions, such as restricted isometry property (RIP and mutual coherence. Then we give a low bound on the mutual coherence of the coefficient matrix generated by the observed values of CML and also prove that it satisfies the RIP from a theoretical point of view. If the weighted vector of each element is sparse in the CML system, our proposed approach can recover all the weighted parameters using only about M samplings, which is far less than the number of the lattice elements N. Another important and significant advantage is that if the observed data are contaminated with some types of noises, our approach is still effective. In the simulations, we mainly show the effects of coupling parameter and noise on the recovery rate.
Compressive sensing in a photonic link with optical integration
DEFF Research Database (Denmark)
Chen, Ying; Yu, Xianbin; Chi, Hao
2014-01-01
In this Letter, we present a novel structure to realize photonics-assisted compressive sensing (CS) with optical integration. In the system, a spectrally sparse signal modulates a multiwavelength continuous-wave light and then is mixed with a random sequence in optical domain. The optical signal......, which is equivalent to the function of integration required in CS. A proof-of-concept experiment with four wavelengths, corresponding to a compression factor of 4, is demonstrated. More simulation results are also given to show the potential of the technique....
Image compression-encryption scheme based on hyper-chaotic system and 2D compressive sensing
Zhou, Nanrun; Pan, Shumin; Cheng, Shan; Zhou, Zhihong
2016-08-01
Most image encryption algorithms based on low-dimensional chaos systems bear security risks and suffer encryption data expansion when adopting nonlinear transformation directly. To overcome these weaknesses and reduce the possible transmission burden, an efficient image compression-encryption scheme based on hyper-chaotic system and 2D compressive sensing is proposed. The original image is measured by the measurement matrices in two directions to achieve compression and encryption simultaneously, and then the resulting image is re-encrypted by the cycle shift operation controlled by a hyper-chaotic system. Cycle shift operation can change the values of the pixels efficiently. The proposed cryptosystem decreases the volume of data to be transmitted and simplifies the keys distribution simultaneously as a nonlinear encryption system. Simulation results verify the validity and the reliability of the proposed algorithm with acceptable compression and security performance.
Curvelet-based compressive sensing for InSAR raw data
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
Approximate equiangular tight frames for compressed sensing and CDMA applications
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.
Compressed Sensing with Linear Correlation Between Signal and Measurement Noise
DEFF Research Database (Denmark)
Arildsen, Thomas; Larsen, Torben
2014-01-01
reconstruction algorithms, but is not known in existing literature. The proposed technique reduces reconstruction error considerably in the case of linearly correlated measurements and noise. Numerical experiments confirm the efficacy of the technique. The technique is demonstrated with application to low......Existing convex relaxation-based approaches to reconstruction in compressed sensing assume that noise in the measurements is independent of the signal of interest. We consider the case of noise being linearly correlated with the signal and introduce a simple technique for improving compressed...... sensing reconstruction from such measurements. The technique is based on a linear model of the correlation of additive noise with the signal. The modification of the reconstruction algorithm based on this model is very simple and has negligible additional computational cost compared to standard...
Sampling theory, a renaissance compressive sensing and other developments
2015-01-01
Reconstructing or approximating objects from seemingly incomplete information is a frequent challenge in mathematics, science, and engineering. A multitude of tools designed to recover hidden information are based on Shannon’s classical sampling theorem, a central pillar of Sampling Theory. The growing need to efficiently obtain precise and tailored digital representations of complex objects and phenomena requires the maturation of available tools in Sampling Theory as well as the development of complementary, novel mathematical theories. Today, research themes such as Compressed Sensing and Frame Theory re-energize the broad area of Sampling Theory. This volume illustrates the renaissance that the area of Sampling Theory is currently experiencing. It touches upon trendsetting areas such as Compressed Sensing, Finite Frames, Parametric Partial Differential Equations, Quantization, Finite Rate of Innovation, System Theory, as well as sampling in Geometry and Algebraic Topology.
A framework for developing remote sensing applications
International Nuclear Information System (INIS)
Ahmad, T.; Hayat, M.F.; Afzal, M.; Asif, H.M.S.; Asif, K.H.
2014-01-01
Remote Sensing Application (RSA) is important as one of the critical enabler of e-systems such as e- governments, e-commerce, and e-sciences. In this study, we argued that owning to the specialized needs of RSA such as volatility and interactive nature, a customized Software Engineering (SE) approach should be adapted for their development. Based on this argument we have also identified the shortcomings of the conventional SE approaches and the classical waterfall software development life cycle model. In this study, we have proposed a modification to the classical waterfall software development life cycle model for proposing a customized software development Framework for RSAs. We have identified four (4) different types of changes that can occur to an already developed RS application. The proposed framework was capable to incorporate all four types of changes. Remote Sensing, software engineering, functional requirements, types of changes. (author)
Compressive Sensing: Analysis of Signals in Radio Astronomy
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Gaigals G.
2013-12-01
Full Text Available The compressive sensing (CS theory says that for some kind of signals there is no need to keep or transfer all the data acquired accordingly to the Nyquist criterion. In this work we investigate if the CS approach is applicable for recording and analysis of radio astronomy (RA signals. Since CS methods are applicable for the signals with sparse (and compressible representations, the compressibility of RA signals is verified. As a result, we identify which RA signals can be processed using CS, find the parameters which can improve or degrade CS application to RA results, describe the optimum way how to perform signal filtering in CS applications. Also, a range of virtual LabVIEW instruments are created for the signal analysis with the CS theory.
Compressed Sensing in Vibration Monitoring Wireless Sensor Network
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Osvaldo Casares-Quirós
2014-12-01
After an experimental test using Waspmotes the fixed-variable variant has a 56.58% reduction of power consumption by introducing a maximum error ± 0.00195g and compress in 52.44% the amount of samples. This algorithm increased the network energy autonomy from 17 hours to 26.5 hours. Through mathematical analysis, the variable-fixed technique reduces in 74.81% the power consumption in sensing nodes transmissions and decrease in 90% the number of samples.
Large scale 2D spectral compressed sensing in continuous domain
Cai, Jian-Feng
2017-06-20
We consider the problem of spectral compressed sensing in continuous domain, which aims to recover a 2-dimensional spectrally sparse signal from partially observed time samples. The signal is assumed to be a superposition of s complex sinusoids. We propose a semidefinite program for the 2D signal recovery problem. Our model is able to handle large scale 2D signals of size 500 × 500, whereas traditional approaches only handle signals of size around 20 × 20.
Large scale 2D spectral compressed sensing in continuous domain
Cai, Jian-Feng; Xu, Weiyu; Yang, Yang
2017-01-01
We consider the problem of spectral compressed sensing in continuous domain, which aims to recover a 2-dimensional spectrally sparse signal from partially observed time samples. The signal is assumed to be a superposition of s complex sinusoids. We propose a semidefinite program for the 2D signal recovery problem. Our model is able to handle large scale 2D signals of size 500 × 500, whereas traditional approaches only handle signals of size around 20 × 20.
Accurate reconstruction of hyperspectral images from compressive sensing measurements
Greer, John B.; Flake, J. C.
2013-05-01
The emerging field of Compressive Sensing (CS) provides a new way to capture data by shifting the heaviest burden of data collection from the sensor to the computer on the user-end. This new means of sensing requires fewer measurements for a given amount of information than traditional sensors. We investigate the efficacy of CS for capturing HyperSpectral Imagery (HSI) remotely. We also introduce a new family of algorithms for constructing HSI from CS measurements with Split Bregman Iteration [Goldstein and Osher,2009]. These algorithms combine spatial Total Variation (TV) with smoothing in the spectral dimension. We examine models for three different CS sensors: the Coded Aperture Snapshot Spectral Imager-Single Disperser (CASSI-SD) [Wagadarikar et al.,2008] and Dual Disperser (CASSI-DD) [Gehm et al.,2007] cameras, and a hypothetical random sensing model closer to CS theory, but not necessarily implementable with existing technology. We simulate the capture of remotely sensed images by applying the sensor forward models to well-known HSI scenes - an AVIRIS image of Cuprite, Nevada and the HYMAP Urban image. To measure accuracy of the CS models, we compare the scenes constructed with our new algorithm to the original AVIRIS and HYMAP cubes. The results demonstrate the possibility of accurately sensing HSI remotely with significantly fewer measurements than standard hyperspectral cameras.
Quasi Gradient Projection Algorithm for Sparse Reconstruction in Compressed Sensing
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Xin Meng
2014-02-01
Full Text Available Compressed sensing is a novel signal sampling theory under the condition that the signal is sparse or compressible. The existing recovery algorithms based on the gradient projection can either need prior knowledge or recovery the signal poorly. In this paper, a new algorithm based on gradient projection is proposed, which is referred as Quasi Gradient Projection. The algorithm presented quasi gradient direction and two step sizes schemes along this direction. The algorithm doesn’t need any prior knowledge of the original signal. Simulation results demonstrate that the presented algorithm cans recovery the signal more correctly than GPSR which also don’t need prior knowledge. Meanwhile, the algorithm has a lower computation complexity.
Real time network traffic monitoring for wireless local area networks based on compressed sensing
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.
Acquisition of STEM Images by Adaptive Compressive Sensing
Energy Technology Data Exchange (ETDEWEB)
Xie, Weiyi; Feng, Qianli; Srinivasan, Ramprakash; Stevens, Andrew; Browning, Nigel D.
2017-07-01
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] 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
Compressed-sensing application - Pre-stack kirchhoff migration
Aldawood, Ali; Hoteit, Ibrahim; Alkhalifah, Tariq Ali
2013-01-01
Least-squares migration is a linearized form of waveform inversion that aims to enhance the spatial resolution of the subsurface reflectivity distribution and reduce the migration artifacts due to limited recording aperture, coarse sampling of sources and receivers, and low subsurface illumination. Least-squares migration, however, due to the nature of its minimization process, tends to produce smoothed and dispersed versions of the reflectivity of the subsurface. Assuming that the subsurface reflectivity distribution is sparse, we propose the addition of a non-quadratic L1-norm penalty term on the model space in the objective function. This aims to preserve the sparse nature of the subsurface reflectivity series and enhance resolution. We further use a compressed-sensing algorithm to solve the linear system, which utilizes the sparsity assumption to produce highly resolved migrated images. Thus, the Kirchhoff migration implementation is formulated as a Basis Pursuit denoise (BPDN) problem to obtain the sparse reflectivity model. Applications on synthetic data show that reflectivity models obtained using this compressed-sensing algorithm are highly accurate with optimal resolution.
On the Feedback Reduction of Relay Multiuser Networks using Compressive Sensing
Elkhalil, Khalil; Eltayeb, Mohammed; Kammoun, Abla; Al-Naffouri, Tareq Y.; Bahrami, Hamid Reza
2016-01-01
This paper presents a comprehensive performance analysis of full-duplex multiuser relay networks employing opportunistic scheduling with noisy and compressive feedback. Specifically, two feedback techniques based on compressive sensing (CS) theory
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.
Poster Abstract: Towards a Categorization Framework for Occupancy Sensing Systems
DEFF Research Database (Denmark)
Kjærgaard, Mikkel Baun; Lazarova-Molnar, Sanja; Jradi, Muhyiddine
2015-01-01
on occupancy sensing systems goes beyond basic methods, there is an increasing need for better comparison of proposed occupancy sensing systems. Developers of occupancy sensing systems are also lacking good frameworks for understanding different options when building occupancy sensing systems. This poster...
Leveraging EAP-Sparsity for Compressed Sensing of MS-HARDI in (k, q)-Space.
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.
Compressed Sensing Methods in Radio Receivers Exposed to Noise and Interference
DEFF Research Database (Denmark)
Pierzchlewski, Jacek
, there is a problem of interference, which makes digitization of radio receivers even more dicult. High-order low-pass lters are needed to remove interfering signals and secure a high-quality reception. In the mid-2000s a new method of signal acquisition, called compressed sensing, emerged. Compressed sensing...... the downconverted baseband signal and interference, may be replaced by low-order lters. Additional digital signal processing is a price to pay for this feature. Hence, the signal processing is moved from the analog to the digital domain. Filtering compressed sensing, which is a new application of compressed sensing...
Directory of Open Access Journals (Sweden)
Vibha Tiwari
2015-12-01
Full Text Available Compressive sensing theory enables faithful reconstruction of signals, sparse in domain $ \\Psi $, at sampling rate lesser than Nyquist criterion, while using sampling or sensing matrix $ \\Phi $ which satisfies restricted isometric property. The role played by sensing matrix $ \\Phi $ and sparsity matrix $ \\Psi $ is vital in faithful reconstruction. If the sensing matrix is dense then it takes large storage space and leads to high computational cost. In this paper, effort is made to design sparse sensing matrix with least incurred computational cost while maintaining quality of reconstructed image. The design approach followed is based on sparse block circulant matrix (SBCM with few modifications. The other used sparse sensing matrix consists of 15 ones in each column. The medical images used are acquired from US, MRI and CT modalities. The image quality measurement parameters are used to compare the performance of reconstructed medical images using various sensing matrices. It is observed that, since Gram matrix of dictionary matrix ($ \\Phi \\Psi \\mathrm{} $ is closed to identity matrix in case of proposed modified SBCM, therefore, it helps to reconstruct the medical images of very good quality.
Underwater Acoustic Matched Field Imaging Based on Compressed Sensing
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Huichen Yan
2015-10-01
Full Text Available Matched field processing (MFP is an effective method for underwater target imaging and localizing, but its performance is not guaranteed due to the nonuniqueness and instability problems caused by the underdetermined essence of MFP. By exploiting the sparsity of the targets in an imaging area, this paper proposes a compressive sensing MFP (CS-MFP model from wave propagation theory by using randomly deployed sensors. In addition, the model’s recovery performance is investigated by exploring the lower bounds of the coherence parameter of the CS dictionary. Furthermore, this paper analyzes the robustness of CS-MFP with respect to the displacement of the sensors. Subsequently, a coherence-excluding coherence optimized orthogonal matching pursuit (CCOOMP algorithm is proposed to overcome the high coherent dictionary problem in special cases. Finally, some numerical experiments are provided to demonstrate the effectiveness of the proposed CS-MFP method.
Compressive Sensing for Feedback Reduction in Wireless Multiuser Networks
Elkhalil, Khalil
2015-05-01
User/relay selection is a simple technique that achieves spatial diversity in multiuser networks. However, for user/relay selection algorithms to make a selection decision, channel state information (CSI) from all cooperating users/relays is usually required at a central node. This requirement poses two important challenges. Firstly, CSI acquisition generates a great deal of feedback overhead (air-time) that could result in significant transmission delays. Secondly, the fed-back channel information is usually corrupted by additive noise. This could lead to transmission outages if the central node selects the set of cooperating relays based on inaccurate feedback information. Motivated by the aforementioned challenges, we propose a limited feedback user/relay selection scheme that is based on the theory of compressed sensing. Firstly, we introduce a limited feedback relay selection algorithm for a multicast relay network. The proposed algorithm exploits the theory of compressive sensing to first obtain the identity of the “strong” relays with limited feedback air-time. Following that, the CSI of the selected relays is estimated using minimum mean square error estimation without any additional feedback. To minimize the effect of noise on the fed-back CSI, we introduce a back-off strategy that optimally backs-off on the noisy received CSI. In the second part of the thesis, we propose a feedback reduction scheme for full-duplex relay-aided multiuser networks. The proposed scheme permits the base station (BS) to obtain channel state information (CSI) from a subset of strong users under substantially reduced feedback overhead. More specifically, we cast the problem of user identification and CSI estimation as a block sparse signal recovery problem in compressive sensing (CS). Using existing CS block recovery algorithms, we first obtain the identity of the strong users and then estimate their CSI using the best linear unbiased estimator (BLUE). Moreover, we derive the
A Robust Parallel Algorithm for Combinatorial Compressed Sensing
Mendoza-Smith, Rodrigo; Tanner, Jared W.; Wechsung, Florian
2018-04-01
In previous work two of the authors have shown that a vector $x \\in \\mathbb{R}^n$ with at most $k Parallel-$\\ell_0$ decoding algorithm, where $\\mathrm{nnz}(A)$ denotes the number of nonzero entries in $A \\in \\mathbb{R}^{m \\times n}$. In this paper we present the Robust-$\\ell_0$ decoding algorithm, which robustifies Parallel-$\\ell_0$ when the sketch $Ax$ is corrupted by additive noise. This robustness is achieved by approximating the asymptotic posterior distribution of values in the sketch given its corrupted measurements. We provide analytic expressions that approximate these posteriors under the assumptions that the nonzero entries in the signal and the noise are drawn from continuous distributions. Numerical experiments presented show that Robust-$\\ell_0$ is superior to existing greedy and combinatorial compressed sensing algorithms in the presence of small to moderate signal-to-noise ratios in the setting of Gaussian signals and Gaussian additive noise.
Towards 4D intervention guidance using compressed sensing
Energy Technology Data Exchange (ETDEWEB)
Kuntz, Jan; Bartling, Soenke [Deutsches Krebsforschungszentrum DKFZ, Heidelberg (Germany); Brehm, Marcus; Kachelriess, Marc [Erlangen-Nuernberg Univ., Erlangen (Germany). Inst. of Medical Physics (IMP)
2011-07-01
Interventional radiology is nowadays usually guided with projection radiography using mono- or biplane systems. Due to the projective nature of this guidance imaging certain intraprocedural situations remain unclear. Although helpful, the use of 3D CT is limited due to radiation dose. Using advanced reconstruction techniques incorporating prior knowledge, one could overcome these limitations without exceeding dose limitations. Intervention guidance is especially appealing to those algorithms, because certain constrains apply to useful images in intervention guidance that vary relevantly from other CT applications. These are: key relevance of high contrast structures, sparse temporal updates and little relevance of absolute CT values. In this paper the principal usability of reconstruction algorithms for intervention guidance is tested. Compressed sensing algorithms PICCS and ASD-POCS are compared to the McKinnon-Bates and Feldkamp-Davis-Kress algorithm. Animal experiments as well as simulations are performed. An outlook towards 4D intervention guidance is provided. (orig.)
Sparse Vector Distributions and Recovery from Compressed Sensing
DEFF Research Database (Denmark)
Sturm, Bob L.
It is well known that the performance of sparse vector recovery algorithms from compressive measurements can depend on the distribution underlying the non-zero elements of a sparse vector. However, the extent of these effects has yet to be explored, and formally presented. In this paper, I...... empirically investigate this dependence for seven distributions and fifteen recovery algorithms. The two morals of this work are: 1) any judgement of the recovery performance of one algorithm over that of another must be prefaced by the conditions for which this is observed to be true, including sparse vector...... distributions, and the criterion for exact recovery; and 2) a recovery algorithm must be selected carefully based on what distribution one expects to underlie the sensed sparse signal....
Compressive sensing for feedback reduction in MIMO broadcast channels
Eltayeb, Mohammed E.
2014-09-01
In multi-antenna broadcast networks, the base stations (BSs) rely on the channel state information (CSI) of the users to perform user scheduling and downlink transmission. However, in networks with large number of users, obtaining CSI from all users is arduous, if not impossible, in practice. This paper proposes channel feedback reduction techniques based on the theory of compressive sensing (CS), which permits the BS to obtain CSI with acceptable recovery guarantees under substantially reduced feedback overhead. Additionally, assuming noisy CS measurements at the BS, inexpensive ways for improving post-CS detection are explored. The proposed techniques are shown to reduce the feedback overhead, improve CS detection at the BS, and achieve a sum-rate close to that obtained by noiseless dedicated feedback channels.
Compressive sensing based wireless sensor for structural health monitoring
Bao, Yuequan; Zou, Zilong; Li, Hui
2014-03-01
Data loss is a common problem for monitoring systems based on wireless sensors. Reliable communication protocols, which enhance communication reliability by repetitively transmitting unreceived packets, is one approach to tackle the problem of data loss. An alternative approach allows data loss to some extent and seeks to recover the lost data from an algorithmic point of view. Compressive sensing (CS) provides such a data loss recovery technique. This technique can be embedded into smart wireless sensors and effectively increases wireless communication reliability without retransmitting the data. The basic idea of CS-based approach is that, instead of transmitting the raw signal acquired by the sensor, a transformed signal that is generated by projecting the raw signal onto a random matrix, is transmitted. Some data loss may occur during the transmission of this transformed signal. However, according to the theory of CS, the raw signal can be effectively reconstructed from the received incomplete transformed signal given that the raw signal is compressible in some basis and the data loss ratio is low. This CS-based technique is implemented into the Imote2 smart sensor platform using the foundation of Illinois Structural Health Monitoring Project (ISHMP) Service Tool-suite. To overcome the constraints of limited onboard resources of wireless sensor nodes, a method called random demodulator (RD) is employed to provide memory and power efficient construction of the random sampling matrix. Adaptation of RD sampling matrix is made to accommodate data loss in wireless transmission and meet the objectives of the data recovery. The embedded program is tested in a series of sensing and communication experiments. Examples and parametric study are presented to demonstrate the applicability of the embedded program as well as to show the efficacy of CS-based data loss recovery for real wireless SHM systems.
Vibration-based monitoring and diagnostics using compressive sensing
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.
Compressed Sensing Techniques Applied to Ultrasonic Imaging of Cargo Containers
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Yuri Álvarez López
2017-01-01
Full Text Available 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.
Determination of nonlinear genetic architecture using compressed sensing.
Ho, Chiu Man; Hsu, Stephen D H
2015-01-01
One of the fundamental problems of modern genomics is to extract the genetic architecture of a complex trait from a data set of individual genotypes and trait values. Establishing this important connection between genotype and phenotype is complicated by the large number of candidate genes, the potentially large number of causal loci, and the likely presence of some nonlinear interactions between different genes. Compressed Sensing methods obtain solutions to under-constrained systems of linear equations. These methods can be applied to the problem of determining the best model relating genotype to phenotype, and generally deliver better performance than simply regressing the phenotype against each genetic variant, one at a time. We introduce a Compressed Sensing method that can reconstruct nonlinear genetic models (i.e., including epistasis, or gene-gene interactions) from phenotype-genotype (GWAS) data. Our method uses L1-penalized regression applied to nonlinear functions of the sensing matrix. The computational and data resource requirements for our method are similar to those necessary for reconstruction of linear genetic models (or identification of gene-trait associations), assuming a condition of generalized sparsity, which limits the total number of gene-gene interactions. An example of a sparse nonlinear model is one in which a typical locus interacts with several or even many others, but only a small subset of all possible interactions exist. It seems plausible that most genetic architectures fall in this category. We give theoretical arguments suggesting that the method is nearly optimal in performance, and demonstrate its effectiveness on broad classes of nonlinear genetic models using simulated human genomes and the small amount of currently available real data. A phase transition (i.e., dramatic and qualitative change) in the behavior of the algorithm indicates when sufficient data is available for its successful application. Our results indicate
Two-Dimensional DOA Estimation in Compressed Sensing with Compressive-Reduced Dimension-lp-MUSIC
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Weijian Si
2015-01-01
Full Text Available This paper presents a novel two-dimensional (2D direction of arrival (DOA estimation method in compressed sensing (CS to remove the estimation failure problem and achieve superior performance. The proposed method separates the steering vector into two parts to construct two corresponding noise subspaces by introducing electric angles. Then, electric angles are estimated based on the constructed noise subspaces. In order to estimate the azimuth and elevation angles in terms of estimates of electric angles, arc-tangent operations are exploited. The arc-tangent is a one-to-one function and allows the value of the argument to be larger than unity so that the proposed method never fails. The proposed method can avoid pair matching to reduce the computational complexity and extend the number of snapshots to improve performance. Simulation results show that the proposed method can avoid estimation failure occurrence and has superior performance as compared to existing methods.
Compressive System Identification in the Linear Time-Invariant framework
Toth, Roland
2011-12-01
Selection of an efficient model parametrization (model order, delay, etc.) has crucial importance in parametric system identification. It navigates a trade-off between representation capabilities of the model (structural bias) and effects of over-parametrization (variance increase of the estimates). There exists many approaches to this widely studied problem in terms of statistical regularization methods and information criteria. In this paper, an alternative ℓ 1 regularization scheme is proposed for estimation of sparse linear-regression models based on recent results in compressive sensing. It is shown that the proposed scheme provides consistent estimation of sparse models in terms of the so-called oracle property, it is computationally attractive for large-scale over-parameterized models and it is applicable in case of small data sets, i.e., underdetermined estimation problems. The performance of the approach w.r.t. other regularization schemes is demonstrated in an extensive Monte Carlo study. © 2011 IEEE.
Online sparse representation for remote sensing compressed-sensed video sampling
Wang, Jie; Liu, Kun; Li, Sheng-liang; Zhang, Li
2014-11-01
Most recently, an emerging Compressed Sensing (CS) theory has brought a major breakthrough for data acquisition and recovery. It asserts that a signal, which is highly compressible in a known basis, can be reconstructed with high probability through sampling frequency which is well below Nyquist Sampling Frequency. When applying CS to Remote Sensing (RS) Video imaging, it can directly and efficiently acquire compressed image data by randomly projecting original data to obtain linear and non-adaptive measurements. In this paper, with the help of distributed video coding scheme which is a low-complexity technique for resource limited sensors, the frames of a RS video sequence are divided into Key frames (K frames) and Non-Key frames (CS frames). In other words, the input video sequence consists of many groups of pictures (GOPs) and each GOP consists of one K frame followed by several CS frames. Both of them are measured based on block, but at different sampling rates. In this way, the major encoding computation burden will be shifted to the decoder. At the decoder, the Side Information (SI) is generated for the CS frames using traditional Motion-Compensated Interpolation (MCI) technique according to the reconstructed key frames. The over-complete dictionary is trained by dictionary learning methods based on SI. These learning methods include ICA-like, PCA, K-SVD, MOD, etc. Using these dictionaries, the CS frames could be reconstructed according to sparse-land model. In the numerical experiments, the reconstruction performance of ICA algorithm, which is often evaluated by Peak Signal-to-Noise Ratio (PSNR), has been made compared with other online sparse representation algorithms. The simulation results show its advantages in reducing reconstruction time and robustness in reconstruction performance when applying ICA algorithm to remote sensing video reconstruction.
Accelerated radial Fourier-velocity encoding using compressed sensing
International Nuclear Information System (INIS)
Hilbert, Fabian; Han, Dietbert
2014-01-01
Purpose:Phase Contrast Magnetic Resonance Imaging (MRI) is a tool for non-invasive determination of flow velocities inside blood vessels. Because Phase Contrast MRI only measures a single mean velocity per voxel, it is only applicable to vessels significantly larger than the voxel size. In contrast, Fourier Velocity Encoding measures the entire velocity distribution inside a voxel, but requires a much longer acquisition time. For accurate diagnosis of stenosis in vessels on the scale of spatial resolution, it is important to know the velocity distribution of a voxel. Our aim was to determine velocity distributions with accelerated Fourier Velocity Encoding in an acquisition time required for a conventional Phase Contrast image. Materials and Methods:We imaged the femoral artery of healthy volunteers with ECG - triggered, radial CINE acquisition. Data acquisition was accelerated by undersampling, while missing data were reconstructed by Compressed Sensing. Velocity spectra of the vessel were evaluated by high resolution Phase Contrast images and compared to spectra from fully sampled and undersampled Fourier Velocity Encoding. By means of undersampling, it was possible to reduce the scan time for Fourier Velocity Encoding to the duration required for a conventional Phase Contrast image. Results:Acquisition time for a fully sampled data set with 12 different Velocity Encodings was 40 min. By applying a 12.6 - fold retrospective undersampling, a data set was generated equal to 3:10 min acquisition time, which is similar to a conventional Phase Contrast measurement. Velocity spectra from fully sampled and undersampled Fourier Velocity Encoded images are in good agreement and show the same maximum velocities as compared to velocity maps from Phase Contrast measurements. Conclusion: Compressed Sensing proved to reliably reconstruct Fourier Velocity Encoded data. Our results indicate that Fourier Velocity Encoding allows an accurate determination of the velocity
Accelerated radial Fourier-velocity encoding using compressed sensing
Energy Technology Data Exchange (ETDEWEB)
Hilbert, Fabian; Han, Dietbert [Wuerzburg Univ. (Germany). Inst. of Radiology; Wech, Tobias; Koestler, Herbert [Wuerzburg Univ. (Germany). Inst. of Radiology; Wuerzburg Univ. (Germany). Comprehensive Heart Failure Center (CHFC)
2014-10-01
Purpose:Phase Contrast Magnetic Resonance Imaging (MRI) is a tool for non-invasive determination of flow velocities inside blood vessels. Because Phase Contrast MRI only measures a single mean velocity per voxel, it is only applicable to vessels significantly larger than the voxel size. In contrast, Fourier Velocity Encoding measures the entire velocity distribution inside a voxel, but requires a much longer acquisition time. For accurate diagnosis of stenosis in vessels on the scale of spatial resolution, it is important to know the velocity distribution of a voxel. Our aim was to determine velocity distributions with accelerated Fourier Velocity Encoding in an acquisition time required for a conventional Phase Contrast image. Materials and Methods:We imaged the femoral artery of healthy volunteers with ECG - triggered, radial CINE acquisition. Data acquisition was accelerated by undersampling, while missing data were reconstructed by Compressed Sensing. Velocity spectra of the vessel were evaluated by high resolution Phase Contrast images and compared to spectra from fully sampled and undersampled Fourier Velocity Encoding. By means of undersampling, it was possible to reduce the scan time for Fourier Velocity Encoding to the duration required for a conventional Phase Contrast image. Results:Acquisition time for a fully sampled data set with 12 different Velocity Encodings was 40 min. By applying a 12.6 - fold retrospective undersampling, a data set was generated equal to 3:10 min acquisition time, which is similar to a conventional Phase Contrast measurement. Velocity spectra from fully sampled and undersampled Fourier Velocity Encoded images are in good agreement and show the same maximum velocities as compared to velocity maps from Phase Contrast measurements. Conclusion: Compressed Sensing proved to reliably reconstruct Fourier Velocity Encoded data. Our results indicate that Fourier Velocity Encoding allows an accurate determination of the velocity
A Computational model for compressed sensing RNAi cellular screening
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Tan Hua
2012-12-01
Full Text Available Abstract Background RNA interference (RNAi becomes an increasingly important and effective genetic tool to study the function of target genes by suppressing specific genes of interest. This system approach helps identify signaling pathways and cellular phase types by tracking intensity and/or morphological changes of cells. The traditional RNAi screening scheme, in which one siRNA is designed to knockdown one specific mRNA target, needs a large library of siRNAs and turns out to be time-consuming and expensive. Results In this paper, we propose a conceptual model, called compressed sensing RNAi (csRNAi, which employs a unique combination of group of small interfering RNAs (siRNAs to knockdown a much larger size of genes. This strategy is based on the fact that one gene can be partially bound with several small interfering RNAs (siRNAs and conversely, one siRNA can bind to a few genes with distinct binding affinity. This model constructs a multi-to-multi correspondence between siRNAs and their targets, with siRNAs much fewer than mRNA targets, compared with the conventional scheme. Mathematically this problem involves an underdetermined system of equations (linear or nonlinear, which is ill-posed in general. However, the recently developed compressed sensing (CS theory can solve this problem. We present a mathematical model to describe the csRNAi system based on both CS theory and biological concerns. To build this model, we first search nucleotide motifs in a target gene set. Then we propose a machine learning based method to find the effective siRNAs with novel features, such as image features and speech features to describe an siRNA sequence. Numerical simulations show that we can reduce the siRNA library to one third of that in the conventional scheme. In addition, the features to describe siRNAs outperform the existing ones substantially. Conclusions This csRNAi system is very promising in saving both time and cost for large-scale RNAi
Accelerated radial Fourier-velocity encoding using compressed sensing.
Hilbert, Fabian; Wech, Tobias; Hahn, Dietbert; Köstler, Herbert
2014-09-01
Phase Contrast Magnetic Resonance Imaging (MRI) is a tool for non-invasive determination of flow velocities inside blood vessels. Because Phase Contrast MRI only measures a single mean velocity per voxel, it is only applicable to vessels significantly larger than the voxel size. In contrast, Fourier Velocity Encoding measures the entire velocity distribution inside a voxel, but requires a much longer acquisition time. For accurate diagnosis of stenosis in vessels on the scale of spatial resolution, it is important to know the velocity distribution of a voxel. Our aim was to determine velocity distributions with accelerated Fourier Velocity Encoding in an acquisition time required for a conventional Phase Contrast image. We imaged the femoral artery of healthy volunteers with ECG-triggered, radial CINE acquisition. Data acquisition was accelerated by undersampling, while missing data were reconstructed by Compressed Sensing. Velocity spectra of the vessel were evaluated by high resolution Phase Contrast images and compared to spectra from fully sampled and undersampled Fourier Velocity Encoding. By means of undersampling, it was possible to reduce the scan time for Fourier Velocity Encoding to the duration required for a conventional Phase Contrast image. Acquisition time for a fully sampled data set with 12 different Velocity Encodings was 40 min. By applying a 12.6-fold retrospective undersampling, a data set was generated equal to 3:10 min acquisition time, which is similar to a conventional Phase Contrast measurement. Velocity spectra from fully sampled and undersampled Fourier Velocity Encoded images are in good agreement and show the same maximum velocities as compared to velocity maps from Phase Contrast measurements. Compressed Sensing proved to reliably reconstruct Fourier Velocity Encoded data. Our results indicate that Fourier Velocity Encoding allows an accurate determination of the velocity distribution in vessels in the order of the voxel size. Thus
COxSwAIN: Compressive Sensing for Advanced Imaging and Navigation
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.
DEFF Research Database (Denmark)
2014-01-01
MATLAB simulation software used for the PhD thesis "Acquisition of Multi-Band Signals via Compressed Sensing......MATLAB simulation software used for the PhD thesis "Acquisition of Multi-Band Signals via Compressed Sensing...
Single-snapshot DOA estimation by using Compressed Sensing
Fortunati, Stefano; Grasso, Raffaele; Gini, Fulvio; Greco, Maria S.; LePage, Kevin
2014-12-01
This paper deals with the problem of estimating the directions of arrival (DOA) of multiple source signals from a single observation vector of an array data. In particular, four estimation algorithms based on the theory of compressed sensing (CS), i.e., the classical ℓ 1 minimization (or Least Absolute Shrinkage and Selection Operator, LASSO), the fast smooth ℓ 0 minimization, and the Sparse Iterative Covariance-Based Estimator, SPICE and the Iterative Adaptive Approach for Amplitude and Phase Estimation, IAA-APES algorithms, are analyzed, and their statistical properties are investigated and compared with the classical Fourier beamformer (FB) in different simulated scenarios. We show that unlike the classical FB, a CS-based beamformer (CSB) has some desirable properties typical of the adaptive algorithms (e.g., Capon and MUSIC) even in the single snapshot case. Particular attention is devoted to the super-resolution property. Theoretical arguments and simulation analysis provide evidence that a CS-based beamformer can achieve resolution beyond the classical Rayleigh limit. Finally, the theoretical findings are validated by processing a real sonar dataset.
Identifying Chaotic FitzHugh–Nagumo Neurons Using Compressive Sensing
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Ri-Qi Su
2014-07-01
Full Text Available We develop a completely data-driven approach to reconstructing coupled neuronal networks that contain a small subset of chaotic neurons. Such chaotic elements can be the result of parameter shift in their individual dynamical systems and may lead to abnormal functions of the network. To accurately identify the chaotic neurons may thus be necessary and important, for example, applying appropriate controls to bring the network to a normal state. However, due to couplings among the nodes, the measured time series, even from non-chaotic neurons, would appear random, rendering inapplicable traditional nonlinear time-series analysis, such as the delay-coordinate embedding method, which yields information about the global dynamics of the entire network. Our method is based on compressive sensing. In particular, we demonstrate that identifying chaotic elements can be formulated as a general problem of reconstructing the nodal dynamical systems, network connections and all coupling functions, as well as their weights. The working and efficiency of the method are illustrated by using networks of non-identical FitzHugh–Nagumo neurons with randomly-distributed coupling weights.
The possibilities of compressed-sensing-based Kirchhoff prestack migration
Aldawood, Ali; Hoteit, Ibrahim; Alkhalifah, Tariq Ali
2014-01-01
An approximate subsurface reflectivity distribution of the earth is usually obtained through the migration process. However, conventional migration algorithms, including those based on the least-squares approach, yield structure descriptions that are slightly smeared and of low resolution caused by the common migration artifacts due to limited aperture, coarse sampling, band-limited source, and low subsurface illumination. To alleviate this problem, we use the fact that minimizing the L1-norm of a signal promotes its sparsity. Thus, we formulated the Kirchhoff migration problem as a compressed-sensing (CS) basis pursuit denoise problem to solve for highly focused migrated images compared with those obtained by standard and least-squares migration algorithms. The results of various subsurface reflectivity models revealed that solutions computed using the CS based migration provide a more accurate subsurface reflectivity location and amplitude. We applied the CS algorithm to image synthetic data from a fault model using dense and sparse acquisition geometries. Our results suggest that the proposed approach may still provide highly resolved images with a relatively small number of measurements. We also evaluated the robustness of the basis pursuit denoise algorithm in the presence of Gaussian random observational noise and in the case of imaging the recorded data with inaccurate migration velocities.
Peeling Decoding of LDPC Codes with Applications in Compressed Sensing
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Weijun Zeng
2016-01-01
Full Text Available We present a new approach for the analysis of iterative peeling decoding recovery algorithms in the context of Low-Density Parity-Check (LDPC codes and compressed sensing. The iterative recovery algorithm is particularly interesting for its low measurement cost and low computational complexity. The asymptotic analysis can track the evolution of the fraction of unrecovered signal elements in each iteration, which is similar to the well-known density evolution analysis in the context of LDPC decoding algorithm. Our analysis shows that there exists a threshold on the density factor; if under this threshold, the recovery algorithm is successful; otherwise it will fail. Simulation results are also provided for verifying the agreement between the proposed asymptotic analysis and recovery algorithm. Compared with existing works of peeling decoding algorithm, focusing on the failure probability of the recovery algorithm, our proposed approach gives accurate evolution of performance with different parameters of measurement matrices and is easy to implement. We also show that the peeling decoding algorithm performs better than other schemes based on LDPC codes.
Opportunistic Relay Selection in Multicast Relay Networks using Compressive Sensing
Elkhalil, Khalil
2014-12-01
Relay selection is a simple technique that achieves spatial diversity in cooperative relay networks. However, for relay selection algorithms to make a selection decision, channel state information (CSI) from all cooperating relays is usually required at a central node. This requirement poses two important challenges. Firstly, CSI acquisition generates a great deal of feedback overhead (air-time) that could result in significant transmission delays. Secondly, the fed back channel information is usually corrupted by additive noise. This could lead to transmission outages if the central node selects the set of cooperating relays based on inaccurate feedback information. In this paper, we introduce a limited feedback relay selection algorithm for a multicast relay network. The proposed algorithm exploits the theory of compressive sensing to first obtain the identity of the “strong” relays with limited feedback. Following that, the CSI of the selected relays is estimated using linear minimum mean square error estimation. To minimize the effect of noise on the fed back CSI, we introduce a back-off strategy that optimally backs-off on the noisy estimated CSI. For a fixed group size, we provide closed form expressions for the scaling law of the maximum equivalent SNR for both Decode and Forward (DF) and Amplify and Forward (AF) cases. Numerical results show that the proposed algorithm drastically reduces the feedback air-time and achieves a rate close to that obtained by selection algorithms with dedicated error-free feedback channels.
The possibilities of compressed-sensing-based Kirchhoff prestack migration
Aldawood, Ali
2014-05-08
An approximate subsurface reflectivity distribution of the earth is usually obtained through the migration process. However, conventional migration algorithms, including those based on the least-squares approach, yield structure descriptions that are slightly smeared and of low resolution caused by the common migration artifacts due to limited aperture, coarse sampling, band-limited source, and low subsurface illumination. To alleviate this problem, we use the fact that minimizing the L1-norm of a signal promotes its sparsity. Thus, we formulated the Kirchhoff migration problem as a compressed-sensing (CS) basis pursuit denoise problem to solve for highly focused migrated images compared with those obtained by standard and least-squares migration algorithms. The results of various subsurface reflectivity models revealed that solutions computed using the CS based migration provide a more accurate subsurface reflectivity location and amplitude. We applied the CS algorithm to image synthetic data from a fault model using dense and sparse acquisition geometries. Our results suggest that the proposed approach may still provide highly resolved images with a relatively small number of measurements. We also evaluated the robustness of the basis pursuit denoise algorithm in the presence of Gaussian random observational noise and in the case of imaging the recorded data with inaccurate migration velocities.
Compressive sensing of high betweenness centrality nodes in networks
Mahyar, Hamidreza; Hasheminezhad, Rouzbeh; Ghalebi K., Elahe; Nazemian, Ali; Grosu, Radu; Movaghar, Ali; Rabiee, Hamid R.
2018-05-01
Betweenness centrality is a prominent centrality measure expressing importance of a node within a network, in terms of the fraction of shortest paths passing through that node. Nodes with high betweenness centrality have significant impacts on the spread of influence and idea in social networks, the user activity in mobile phone networks, the contagion process in biological networks, and the bottlenecks in communication networks. Thus, identifying k-highest betweenness centrality nodes in networks will be of great interest in many applications. In this paper, we introduce CS-HiBet, a new method to efficiently detect top- k betweenness centrality nodes in networks, using compressive sensing. CS-HiBet can perform as a distributed algorithm by using only the local information at each node. Hence, it is applicable to large real-world and unknown networks in which the global approaches are usually unrealizable. The performance of the proposed method is evaluated by extensive simulations on several synthetic and real-world networks. The experimental results demonstrate that CS-HiBet outperforms the best existing methods with notable improvements.
Accelerated Air-coupled Ultrasound Imaging of Wood Using Compressed Sensing
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Yiming Fang
2015-12-01
Full Text Available Air-coupled ultrasound has shown excellent sensitivity and specificity for the nondestructive imaging of wood-based material. However, it is time-consuming, due to the high scanning density limited by the Nyquist law. This study investigated the feasibility of applying compressed sensing techniques to air-coupled ultrasound imaging, aiming to reduce the number of scanning lines and then accelerate the imaging. Firstly, an undersampled scanning strategy specified by a random binary matrix was proposed to address the limitation of the compressed sensing framework. The undersampled scanning can be easily implemented, while only minor modification was required for the existing imaging system. Then, discrete cosine transform was selected experimentally as the representation basis. Finally, orthogonal matching pursuit algorithm was utilized to reconstruct the wood images. Experiments on three real air-coupled ultrasound images indicated the potential of the present method to accelerate air-coupled ultrasound imaging of wood. The same quality of ACU images can be obtained with scanning time cut in half.
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.
Optical scanning holography based on compressive sensing using a digital micro-mirror device
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.
A Novel Object Tracking Algorithm Based on Compressed Sensing and Entropy of Information
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Ding Ma
2015-01-01
Full Text Available Object tracking has always been a hot research topic in the field of computer vision; its purpose is to track objects with specific characteristics or representation and estimate the information of objects such as their locations, sizes, and rotation angles in the current frame. Object tracking in complex scenes will usually encounter various sorts of challenges, such as location change, dimension change, illumination change, perception change, and occlusion. This paper proposed a novel object tracking algorithm based on compressed sensing and information entropy to address these challenges. First, objects are characterized by the Haar (Haar-like and ORB features. Second, the dimensions of computation space of the Haar and ORB features are effectively reduced through compressed sensing. Then the above-mentioned features are fused based on information entropy. Finally, in the particle filter framework, an object location was obtained by selecting candidate object locations in the current frame from the local context neighboring the optimal locations in the last frame. Our extensive experimental results demonstrated that this method was able to effectively address the challenges of perception change, illumination change, and large area occlusion, which made it achieve better performance than existing approaches such as MIL and CT.
Bayesian nonparametric dictionary learning for compressed sensing MRI.
Huang, Yue; Paisley, John; Lin, Qin; Ding, Xinghao; Fu, Xueyang; Zhang, Xiao-Ping
2014-12-01
We develop a Bayesian nonparametric model for reconstructing magnetic resonance images (MRIs) from highly undersampled k -space data. We perform dictionary learning as part of the image reconstruction process. To this end, we use the beta process as a nonparametric dictionary learning prior for representing an image patch as a sparse combination of dictionary elements. The size of the dictionary and patch-specific sparsity pattern are inferred from the data, in addition to other dictionary learning variables. Dictionary learning is performed directly on the compressed image, and so is tailored to the MRI being considered. In addition, we investigate a total variation penalty term in combination with the dictionary learning model, and show how the denoising property of dictionary learning removes dependence on regularization parameters in the noisy setting. We derive a stochastic optimization algorithm based on Markov chain Monte Carlo for the Bayesian model, and use the alternating direction method of multipliers for efficiently performing total variation minimization. We present empirical results on several MRI, which show that the proposed regularization framework can improve reconstruction accuracy over other methods.
Enhanced compressed sensing for visual target tracking in wireless visual sensor networks
Qiang, Guo
2017-11-01
Moving object tracking in wireless sensor networks (WSNs) has been widely applied in various fields. Designing low-power WSNs for the limited resources of the sensor, such as energy limitation, energy restriction, and bandwidth constraints, is of high priority. However, most existing works focus on only single conflicting optimization criteria. An efficient compressive sensing technique based on a customized memory gradient pursuit algorithm with early termination in WSNs is presented, which strikes compelling trade-offs among energy dissipation for wireless transmission, certain types of bandwidth, and minimum storage. Then, the proposed approach adopts an unscented particle filter to predict the location of the target. The experimental results with a theoretical analysis demonstrate the substantially superior effectiveness of the proposed model and framework in regard to the energy and speed under the resource limitation of a visual sensor node.
Sparse-View Ultrasound Diffraction Tomography Using Compressed Sensing with Nonuniform FFT
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Shaoyan Hua
2014-01-01
Full Text Available 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.
Accelerated high-resolution photoacoustic tomography via compressed sensing
Arridge, Simon; Beard, Paul; Betcke, Marta; Cox, Ben; Huynh, Nam; Lucka, Felix; Ogunlade, Olumide; Zhang, Edward
2016-12-01
Current 3D photoacoustic tomography (PAT) systems offer either high image quality or high frame rates but are not able to deliver high spatial and temporal resolution simultaneously, which limits their ability to image dynamic processes in living tissue (4D PAT). A particular example is the planar Fabry-Pérot (FP) photoacoustic scanner, which yields high-resolution 3D images but takes several minutes to sequentially map the incident photoacoustic field on the 2D sensor plane, point-by-point. However, as the spatio-temporal complexity of many absorbing tissue structures is rather low, the data recorded in such a conventional, regularly sampled fashion is often highly redundant. We demonstrate that combining model-based, variational image reconstruction methods using spatial sparsity constraints with the development of novel PAT acquisition systems capable of sub-sampling the acoustic wave field can dramatically increase the acquisition speed while maintaining a good spatial resolution: first, we describe and model two general spatial sub-sampling schemes. Then, we discuss how to implement them using the FP interferometer and demonstrate the potential of these novel compressed sensing PAT devices through simulated data from a realistic numerical phantom and through measured data from a dynamic experimental phantom as well as from in vivo experiments. Our results show that images with good spatial resolution and contrast can be obtained from highly sub-sampled PAT data if variational image reconstruction techniques that describe the tissues structures with suitable sparsity-constraints are used. In particular, we examine the use of total variation (TV) regularization enhanced by Bregman iterations. These novel reconstruction strategies offer new opportunities to dramatically increase the acquisition speed of photoacoustic scanners that employ point-by-point sequential scanning as well as reducing the channel count of parallelized schemes that use detector arrays.
Quantitative mapping of chemical compositions with MRI using compressed sensing.
von Harbou, Erik; Fabich, Hilary T; Benning, Martin; Tayler, Alexander B; Sederman, Andrew J; Gladden, Lynn F; Holland, Daniel J
2015-12-01
In this work, a magnetic resonance (MR) imaging method for accelerating the acquisition time of two dimensional concentration maps of different chemical species in mixtures by the use of compressed sensing (CS) is presented. Whilst 2D-concentration maps with a high spatial resolution are prohibitively time-consuming to acquire using full k-space sampling techniques, CS enables the reconstruction of quantitative concentration maps from sub-sampled k-space data. First, the method was tested by reconstructing simulated data. Then, the CS algorithm was used to reconstruct concentration maps of binary mixtures of 1,4-dioxane and cyclooctane in different samples with a field-of-view of 22mm and a spatial resolution of 344μm×344μm. Spiral based trajectories were used as sampling schemes. For the data acquisition, eight scans with slightly different trajectories were applied resulting in a total acquisition time of about 8min. In contrast, a conventional chemical shift imaging experiment at the same resolution would require about 17h. To get quantitative results, a careful weighting of the regularisation parameter (via the L-curve approach) or contrast-enhancing Bregman iterations are applied for the reconstruction of the concentration maps. Both approaches yield relative errors of the concentration map of less than 2mol-% without any calibration prior to the measurement. The accuracy of the reconstructed concentration maps deteriorates when the reconstruction model is biased by systematic errors such as large inhomogeneities in the static magnetic field. The presented method is a powerful tool for the fast acquisition of concentration maps that can provide valuable information for the investigation of many phenomena in chemical engineering applications. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.
Deconvolution of serum cortisol levels by using compressed sensing.
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Rose T Faghih
Full Text Available The pulsatile release of cortisol from the adrenal glands is controlled by a hierarchical system that involves corticotropin releasing hormone (CRH from the hypothalamus, adrenocorticotropin hormone (ACTH from the pituitary, and cortisol from the adrenal glands. Determining the number, timing, and amplitude of the cortisol secretory events and recovering the infusion and clearance rates from serial measurements of serum cortisol levels is a challenging problem. Despite many years of work on this problem, a complete satisfactory solution has been elusive. We formulate this question as a non-convex optimization problem, and solve it using a coordinate descent algorithm that has a principled combination of (i compressed sensing for recovering the amplitude and timing of the secretory events, and (ii generalized cross validation for choosing the regularization parameter. Using only the observed serum cortisol levels, we model cortisol secretion from the adrenal glands using a second-order linear differential equation with pulsatile inputs that represent cortisol pulses released in response to pulses of ACTH. Using our algorithm and the assumption that the number of pulses is between 15 to 22 pulses over 24 hours, we successfully deconvolve both simulated datasets and actual 24-hr serum cortisol datasets sampled every 10 minutes from 10 healthy women. Assuming a one-minute resolution for the secretory events, we obtain physiologically plausible timings and amplitudes of each cortisol secretory event with R (2 above 0.92. Identification of the amplitude and timing of pulsatile hormone release allows (i quantifying of normal and abnormal secretion patterns towards the goal of understanding pathological neuroendocrine states, and (ii potentially designing optimal approaches for treating hormonal disorders.
Undersampling strategies for compressed sensing accelerated MR spectroscopic imaging
Vidya Shankar, Rohini; Hu, Houchun Harry; Bikkamane Jayadev, Nutandev; Chang, John C.; Kodibagkar, Vikram D.
2017-03-01
Compressed sensing (CS) can accelerate magnetic resonance spectroscopic imaging (MRSI), facilitating its widespread clinical integration. The objective of this study was to assess the effect of different undersampling strategy on CS-MRSI reconstruction quality. Phantom data were acquired on a Philips 3 T Ingenia scanner. Four types of undersampling masks, corresponding to each strategy, namely, low resolution, variable density, iterative design, and a priori were simulated in Matlab and retrospectively applied to the test 1X MRSI data to generate undersampled datasets corresponding to the 2X - 5X, and 7X accelerations for each type of mask. Reconstruction parameters were kept the same in each case(all masks and accelerations) to ensure that any resulting differences can be attributed to the type of mask being employed. The reconstructed datasets from each mask were statistically compared with the reference 1X, and assessed using metrics like the root mean square error and metabolite ratios. Simulation results indicate that both the a priori and variable density undersampling masks maintain high fidelity with the 1X up to five-fold acceleration. The low resolution mask based reconstructions showed statistically significant differences from the 1X with the reconstruction failing at 3X, while the iterative design reconstructions maintained fidelity with the 1X till 4X acceleration. In summary, a pilot study was conducted to identify an optimal sampling mask in CS-MRSI. Simulation results demonstrate that the a priori and variable density masks can provide statistically similar results to the fully sampled reference. Future work would involve implementing these two masks prospectively on a clinical scanner.
A joint image encryption and watermarking algorithm based on compressive sensing and chaotic map
International Nuclear Information System (INIS)
Xiao Di; Cai Hong-Kun; Zheng Hong-Ying
2015-01-01
In this paper, a compressive sensing (CS) and chaotic map-based joint image encryption and watermarking algorithm is proposed. The transform domain coefficients of the original image are scrambled by Arnold map firstly. Then the watermark is adhered to the scrambled data. By compressive sensing, a set of watermarked measurements is obtained as the watermarked cipher image. In this algorithm, watermark embedding and data compression can be performed without knowing the original image; similarly, watermark extraction will not interfere with decryption. Due to the characteristics of CS, this algorithm features compressible cipher image size, flexible watermark capacity, and lossless watermark extraction from the compressed cipher image as well as robustness against packet loss. Simulation results and analyses show that the algorithm achieves good performance in the sense of security, watermark capacity, extraction accuracy, reconstruction, robustness, etc. (paper)
2nd International MATHEON Conference on Compressed Sensing and its Applications
Caire, Giuseppe; Calderbank, Robert; März, Maximilian; Kutyniok, Gitta; Mathar, Rudolf
2017-01-01
This contributed volume contains articles written by the plenary and invited speakers from the second international MATHEON Workshop 2015 that focus on applications of compressed sensing. Article authors address their techniques for solving the problems of compressed sensing, as well as connections to related areas like detecting community-like structures in graphs, curbatures on Grassmanians, and randomized tensor train singular value decompositions. Some of the novel applications covered include dimensionality reduction, information theory, random matrices, sparse approximation, and sparse recovery. This book is aimed at both graduate students and researchers in the areas of applied mathematics, computer science, and engineering, as well as other applied scientists exploring the potential applications for the novel methodology of compressed sensing. An introduction to the subject of compressed sensing is also provided for researchers interested in the field who are not as familiar with it. .
Blind Compressed Sensing Parameter Estimation of Non-cooperative Frequency Hopping Signal
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Chen Ying
2016-10-01
Full Text Available To overcome the disadvantages of a non-cooperative frequency hopping communication system, such as a high sampling rate and inadequate prior information, parameter estimation based on Blind Compressed Sensing (BCS is proposed. The signal is precisely reconstructed by the alternating iteration of sparse coding and basis updating, and the hopping frequencies are directly estimated based on the results. Compared with conventional compressive sensing, blind compressed sensing does not require prior information of the frequency hopping signals; hence, it offers an effective solution to the inadequate prior information problem. In the proposed method, the signal is first modeled and then reconstructed by Orthonormal Block Diagonal Blind Compressed Sensing (OBD-BCS, and the hopping frequencies and hop period are finally estimated. The simulation results suggest that the proposed method can reconstruct and estimate the parameters of noncooperative frequency hopping signals with a low signal-to-noise ratio.
Compressive Sensing Based Bio-Inspired Shape Feature Detection CMOS Imager
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.
A motion sensing-based framework for robotic manipulation.
Deng, Hao; Xia, Zeyang; Weng, Shaokui; Gan, Yangzhou; Fang, Peng; Xiong, Jing
2016-01-01
To data, outside of the controlled environments, robots normally perform manipulation tasks operating with human. This pattern requires the robot operators with high technical skills training for varied teach-pendant operating system. Motion sensing technology, which enables human-machine interaction in a novel and natural interface using gestures, has crucially inspired us to adopt this user-friendly and straightforward operation mode on robotic manipulation. Thus, in this paper, we presented a motion sensing-based framework for robotic manipulation, which recognizes gesture commands captured from motion sensing input device and drives the action of robots. For compatibility, a general hardware interface layer was also developed in the framework. Simulation and physical experiments have been conducted for preliminary validation. The results have shown that the proposed framework is an effective approach for general robotic manipulation with motion sensing control.
Nano-Enriched and Autonomous Sensing Framework for Dissolved Oxygen
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Nader Shehata
2015-08-01
Full Text Available This paper investigates a nano-enhanced wireless sensing framework for dissolved oxygen (DO. The system integrates a nanosensor that employs cerium oxide (ceria nanoparticles to monitor the concentration of DO in aqueous media via optical fluorescence quenching. We propose a comprehensive sensing framework with the nanosensor equipped with a digital interface where the sensor output is digitized and dispatched wirelessly to a trustworthy data collection and analysis framework for consolidation and information extraction. The proposed system collects and processes the sensor readings to provide clear indications about the current or the anticipated dissolved oxygen levels in the aqueous media.
Nano-Enriched and Autonomous Sensing Framework for Dissolved Oxygen.
Shehata, Nader; Azab, Mohammed; Kandas, Ishac; Meehan, Kathleen
2015-08-14
This paper investigates a nano-enhanced wireless sensing framework for dissolved oxygen (DO). The system integrates a nanosensor that employs cerium oxide (ceria) nanoparticles to monitor the concentration of DO in aqueous media via optical fluorescence quenching. We propose a comprehensive sensing framework with the nanosensor equipped with a digital interface where the sensor output is digitized and dispatched wirelessly to a trustworthy data collection and analysis framework for consolidation and information extraction. The proposed system collects and processes the sensor readings to provide clear indications about the current or the anticipated dissolved oxygen levels in the aqueous media.
Compressed sensing along physically plausible sampling trajectories in MRI
International Nuclear Information System (INIS)
Chauffert, Nicolas
2015-01-01
Magnetic Resonance Imaging (MRI) is a non-invasive and non-ionizing imaging technique that provides images of body tissues, using the contrast sensitivity coming from the magnetic parameters (T_1, T_2 and proton density). Data are acquired in the κ-space, corresponding to spatial Fourier frequencies. Because of physical constraints, the displacement in the κ-space is subject to kinematic constraints. Indeed, magnetic field gradients and their temporal derivative are upper bounded. Hence, the scanning time increases with the image resolution. Decreasing scanning time is crucial to improve patient comfort, decrease exam costs, limit the image distortions (eg, created by the patient movement), or decrease temporal resolution in functional MRI. Reducing scanning time can be addressed by Compressed Sensing (CS) theory. The latter is a technique that guarantees the perfect recovery of an image from under sampled data in κ-space, by assuming that the image is sparse in a wavelet basis. Unfortunately, CS theory cannot be directly cast to the MRI setting. The reasons are: i) acquisition (Fourier) and representation (wavelets) bases are coherent and ii) sampling schemes obtained using CS theorems are composed of isolated measurements and cannot be realistically implemented by magnetic field gradients: the sampling is usually performed along continuous or more regular curves. However, heuristic application of CS in MRI has provided promising results. In this thesis, we aim to develop theoretical tools to apply CS to MRI and other modalities. On the one hand, we propose a variable density sampling theory to answer the first impediment. The more the sample contains information, the more it is likely to be drawn. On the other hand, we propose sampling schemes and design sampling trajectories that fulfill acquisition constraints, while traversing the κ-space with the sampling density advocated by the theory. The second point is complex and is thus addressed step by step
The MUSIC algorithm for sparse objects: a compressed sensing analysis
International Nuclear Information System (INIS)
Fannjiang, Albert C
2011-01-01
The multiple signal classification (MUSIC) algorithm, and its extension for imaging sparse extended objects, with noisy data is analyzed by compressed sensing (CS) techniques. A thresholding rule is developed to augment the standard MUSIC algorithm. The notion of restricted isometry property (RIP) and an upper bound on the restricted isometry constant (RIC) are employed to establish sufficient conditions for the exact localization by MUSIC with or without noise. In the noiseless case, the sufficient condition gives an upper bound on the numbers of random sampling and incident directions necessary for exact localization. In the noisy case, the sufficient condition assumes additionally an upper bound for the noise-to-object ratio in terms of the RIC and the dynamic range of objects. This bound points to the super-resolution capability of the MUSIC algorithm. Rigorous comparison of performance between MUSIC and the CS minimization principle, basis pursuit denoising (BPDN), is given. In general, the MUSIC algorithm guarantees to recover, with high probability, s scatterers with n=O(s 2 ) random sampling and incident directions and sufficiently high frequency. For the favorable imaging geometry where the scatterers are distributed on a transverse plane MUSIC guarantees to recover, with high probability, s scatterers with a median frequency and n=O(s) random sampling/incident directions. Moreover, for the problems of spectral estimation and source localizations both BPDN and MUSIC guarantee, with high probability, to identify exactly the frequencies of random signals with the number n=O(s) of sampling times. However, in the absence of abundant realizations of signals, BPDN is the preferred method for spectral estimation. Indeed, BPDN can identify the frequencies approximately with just one realization of signals with the recovery error at worst linearly proportional to the noise level. Numerical results confirm that BPDN outperforms MUSIC in the well-resolved case while
Statistical Prior Aided Separate Compressed Image Sensing for Green Internet of Multimedia Things
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Shaohua Wu
2017-01-01
Full Text Available In this paper, we aim to propose an image compression and reconstruction strategy under the compressed sensing (CS framework to enable the green computation and communication for the Internet of Multimedia Things (IoMT. The core idea is to explore the statistics of image representations in the wavelet domain to aid the reconstruction method design. Specifically, the energy distribution of natural images in the wavelet domain is well characterized by an exponential decay model and then used in the two-step separate image reconstruction method, by which the row-wise (or column-wise intermediates and column-wise (or row-wise final results are reconstructed sequentially. Both the intermediates and the final results are constrained to conform with the statistical prior by using a weight matrix. Two recovery strategies with different levels of complexity, namely, the direct recovery with fixed weight matrix (DR-FM and the iterative recovery with refined weight matrix (IR-RM, are designed to obtain different quality of recovery. Extensive simulations show that both DR-FM and IR-RM can achieve much better image reconstruction quality with much faster recovery speed than traditional methods.
Compressed sensing with cyclic-S Hadamard matrix for terahertz imaging applications
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.
Signal Recovery in Compressive Sensing via Multiple Sparsifying Bases
DEFF Research Database (Denmark)
Wijewardhana, U. L.; Belyaev, Evgeny; Codreanu, M.
2017-01-01
is sparse is the key assumption utilized by such algorithms. However, the basis in which the signal is the sparsest is unknown for many natural signals of interest. Instead there may exist multiple bases which lead to a compressible representation of the signal: e.g., an image is compressible in different...... wavelet transforms. We show that a significant performance improvement can be achieved by utilizing multiple estimates of the signal using sparsifying bases in the context of signal reconstruction from compressive samples. Further, we derive a customized interior-point method to jointly obtain multiple...... estimates of a 2-D signal (image) from compressive measurements utilizing multiple sparsifying bases as well as the fact that the images usually have a sparse gradient....
Cai, Weizhao; Katrusiak, Andrzej
2014-07-04
Materials with negative linear compressibility are sought for various technological applications. Such effects were reported mainly in framework materials. When heated, they typically contract in the same direction of negative linear compression. Here we show that this common inverse relationship rule does not apply to a three-dimensional metal-organic framework crystal, [Ag(ethylenediamine)]NO3. In this material, the direction of the largest intrinsic negative linear compression yet observed in metal-organic frameworks coincides with the strongest positive thermal expansion. In the perpendicular direction, the large linear negative thermal expansion and the strongest crystal compressibility are collinear. This seemingly irrational positive relationship of temperature and pressure effects is explained and the mechanism of coupling of compressibility with expansivity is presented. The positive coupling between compression and thermal expansion in this material enhances its piezo-mechanical response in adiabatic process, which may be used for designing new artificial composites and ultrasensitive measuring devices.
Optically compressed sensing by under sampling the polar Fourier plane
International Nuclear Information System (INIS)
Stern, A; Levi, O; Rivenson, Y
2010-01-01
In a previous work we presented a compressed imaging approach that uses a row of rotating sensors to capture indirectly polar strips of the Fourier transform of the image. Here we present further developments of this technique and present new results. The advantages of our technique, compared to other optically compressed imaging techniques, is that its optical implementation is relatively easy, it does not require complicate calibrations and that it can be implemented in near-real time.
Image quality enhancement in low-light-level ghost imaging using modified compressive sensing method
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.
International Nuclear Information System (INIS)
Tang Jie; Nett, Brian E; Chen Guanghong
2009-01-01
Of all available reconstruction methods, statistical iterative reconstruction algorithms appear particularly promising since they enable accurate physical noise modeling. The newly developed compressive sampling/compressed sensing (CS) algorithm has shown the potential to accurately reconstruct images from highly undersampled data. The CS algorithm can be implemented in the statistical reconstruction framework as well. In this study, we compared the performance of two standard statistical reconstruction algorithms (penalized weighted least squares and q-GGMRF) to the CS algorithm. In assessing the image quality using these iterative reconstructions, it is critical to utilize realistic background anatomy as the reconstruction results are object dependent. A cadaver head was scanned on a Varian Trilogy system at different dose levels. Several figures of merit including the relative root mean square error and a quality factor which accounts for the noise performance and the spatial resolution were introduced to objectively evaluate reconstruction performance. A comparison is presented between the three algorithms for a constant undersampling factor comparing different algorithms at several dose levels. To facilitate this comparison, the original CS method was formulated in the framework of the statistical image reconstruction algorithms. Important conclusions of the measurements from our studies are that (1) for realistic neuro-anatomy, over 100 projections are required to avoid streak artifacts in the reconstructed images even with CS reconstruction, (2) regardless of the algorithm employed, it is beneficial to distribute the total dose to more views as long as each view remains quantum noise limited and (3) the total variation-based CS method is not appropriate for very low dose levels because while it can mitigate streaking artifacts, the images exhibit patchy behavior, which is potentially harmful for medical diagnosis.
The application of sparse linear prediction dictionary to compressive sensing in speech signals
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YOU Hanxu
2016-04-01
Full Text Available Appling compressive sensing (CS,which theoretically guarantees that signal sampling and signal compression can be achieved simultaneously,into audio and speech signal processing is one of the most popular research topics in recent years.In this paper,K-SVD algorithm was employed to learn a sparse linear prediction dictionary regarding as the sparse basis of underlying speech signals.Compressed signals was obtained by applying random Gaussian matrix to sample original speech frames.Orthogonal matching pursuit (OMP and compressive sampling matching pursuit (CoSaMP were adopted to recovery original signals from compressed one.Numbers of experiments were carried out to investigate the impact of speech frames length,compression ratios,sparse basis and reconstruction algorithms on CS performance.Results show that sparse linear prediction dictionary can advance the performance of speech signals reconstruction compared with discrete cosine transform (DCT matrix.
Spatial-Temporal Data Collection with Compressive Sensing in Mobile Sensor Networks.
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 .
Photonic compressive sensing enabled data efficient time stretch optical coherence tomography
Mididoddi, Chaitanya K.; Wang, Chao
2018-03-01
Photonic time stretch (PTS) has enabled real time spectral domain optical coherence tomography (OCT). However, this method generates a torrent of massive data at GHz stream rate, which requires capturing as per Nyquist principle. If the OCT interferogram signal is sparse in Fourier domain, which is always true for samples with limited number of layers, it can be captured at lower (sub-Nyquist) acquisition rate as per compressive sensing method. In this work we report a data compressed PTS-OCT system based on photonic compressive sensing with 66% compression with low acquisition rate of 50MHz and measurement speed of 1.51MHz per depth profile. A new method has also been proposed to improve the system with all-optical random pattern generation, which completely avoids electronic bottleneck in traditional binary pseudorandom binary sequence (PRBS) generators.
A method of vehicle license plate recognition based on PCANet and compressive sensing
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.
International Nuclear Information System (INIS)
Vaegler, Sven; Sauer, Otto; Stsepankou, Dzmitry; Hesser, Juergen
2015-01-01
The reduction of dose in cone beam computer tomography (CBCT) arises from the decrease of the tube current for each projection as well as from the reduction of the number of projections. In order to maintain good image quality, sophisticated image reconstruction techniques are required. The Prior Image Constrained Compressed Sensing (PICCS) incorporates prior images into the reconstruction algorithm and outperforms the widespread used Feldkamp-Davis-Kress-algorithm (FDK) when the number of projections is reduced. However, prior images that contain major variations are not appropriately considered so far in PICCS. We therefore propose the partial-PICCS (pPICCS) algorithm. This framework is a problem-specific extension of PICCS and enables the incorporation of the reliability of the prior images additionally. We assumed that the prior images are composed of areas with large and small deviations. Accordingly, a weighting matrix considered the assigned areas in the objective function. We applied our algorithm to the problem of image reconstruction from few views by simulations with a computer phantom as well as on clinical CBCT projections from a head-and-neck case. All prior images contained large local variations. The reconstructed images were compared to the reconstruction results by the FDK-algorithm, by Compressed Sensing (CS) and by PICCS. To show the gain of image quality we compared image details with the reference image and used quantitative metrics (root-mean-square error (RMSE), contrast-to-noise-ratio (CNR)). The pPICCS reconstruction framework yield images with substantially improved quality even when the number of projections was very small. The images contained less streaking, blurring and inaccurately reconstructed structures compared to the images reconstructed by FDK, CS and conventional PICCS. The increased image quality is also reflected in large RMSE differences. We proposed a modification of the original PICCS algorithm. The pPICCS algorithm
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.
Li, Jun; Song, Minghui; Peng, Yuanxi
2018-03-01
Current infrared and visible image fusion methods do not achieve adequate information extraction, i.e., they cannot extract the target information from infrared images while retaining the background information from visible images. Moreover, most of them have high complexity and are time-consuming. This paper proposes an efficient image fusion framework for infrared and visible images on the basis of robust principal component analysis (RPCA) and compressed sensing (CS). The novel framework consists of three phases. First, RPCA decomposition is applied to the infrared and visible images to obtain their sparse and low-rank components, which represent the salient features and background information of the images, respectively. Second, the sparse and low-rank coefficients are fused by different strategies. On the one hand, the measurements of the sparse coefficients are obtained by the random Gaussian matrix, and they are then fused by the standard deviation (SD) based fusion rule. Next, the fused sparse component is obtained by reconstructing the result of the fused measurement using the fast continuous linearized augmented Lagrangian algorithm (FCLALM). On the other hand, the low-rank coefficients are fused using the max-absolute rule. Subsequently, the fused image is superposed by the fused sparse and low-rank components. For comparison, several popular fusion algorithms are tested experimentally. By comparing the fused results subjectively and objectively, we find that the proposed framework can extract the infrared targets while retaining the background information in the visible images. Thus, it exhibits state-of-the-art performance in terms of both fusion effects and timeliness.
Pilotless recovery of clipped OFDM signals by compressive sensing over reliable data carriers
Al-Safadi, Ebrahim B.
2012-06-01
In this paper we propose a novel method of clipping mitigation in OFDM using compressive sensing that completely avoids using reserved tones or channel-estimation pilots. The method builds on selecting the most reliable perturbations from the constellation lattice upon decoding at the receiver (in the frequency domain), and performs compressive sensing over these observations in order to completely recover the sparse nonlinear distortion in the time domain. As such, the method provides a practical solution to the problem of initial erroneous decoding decisions in iterative ML methods, and the ability to recover the distorted signal in one shot. © 2012 IEEE.
Pilotless recovery of clipped OFDM signals by compressive sensing over reliable data carriers
Al-Safadi, Ebrahim B.; Al-Naffouri, Tareq Y.
2012-01-01
In this paper we propose a novel method of clipping mitigation in OFDM using compressive sensing that completely avoids using reserved tones or channel-estimation pilots. The method builds on selecting the most reliable perturbations from the constellation lattice upon decoding at the receiver (in the frequency domain), and performs compressive sensing over these observations in order to completely recover the sparse nonlinear distortion in the time domain. As such, the method provides a practical solution to the problem of initial erroneous decoding decisions in iterative ML methods, and the ability to recover the distorted signal in one shot. © 2012 IEEE.
Support for Implications of Compressive Sensing Concepts to Imaging Systems
2015-08-02
Justin Romberg Georgia Tech jrom@ece.gatech.edu Emil Sidky University of Chicago sidky@uchicago.edu Michael Stenner MITRE mstenner@mitre.org Lei Tian...assessment of image quality. Michael Stenner Michael has broad interests in optical imaging, sensing, and communications, and is published in such
A compressive sensing approach to the calculation of the inverse data space
Khan, Babar Hasan
2012-01-01
Seismic processing in the Inverse Data Space (IDS) has its advantages like the task of removing the multiples simply becomes muting the zero offset and zero time data in the inverse domain. Calculation of the Inverse Data Space by sparse inversion techniques has seen mitigation of some artifacts. We reformulate the problem by taking advantage of some of the developments from the field of Compressive Sensing. The seismic data is compressed at the sensor level by recording projections of the traces. We then process this compressed data directly to estimate the inverse data space. Due to the smaller number of data set we also gain in terms of computational complexity.
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).
Effective Low-Power Wearable Wireless Surface EMG Sensor Design Based on Analog-Compressed Sensing
Directory of Open Access Journals (Sweden)
Mohammadreza Balouchestani
2014-12-01
Full Text Available Surface Electromyography (sEMG is a non-invasive measurement process that does not involve tools and instruments to break the skin or physically enter the body to investigate and evaluate the muscular activities produced by skeletal muscles. The main drawbacks of existing sEMG systems are: (1 they are not able to provide real-time monitoring; (2 they suffer from long processing time and low speed; (3 they are not effective for wireless healthcare systems because they consume huge power. In this work, we present an analog-based Compressed Sensing (CS architecture, which consists of three novel algorithms for design and implementation of wearable wireless sEMG bio-sensor. At the transmitter side, two new algorithms are presented in order to apply the analog-CS theory before Analog to Digital Converter (ADC. At the receiver side, a robust reconstruction algorithm based on a combination of ℓ1-ℓ1-optimization and Block Sparse Bayesian Learning (BSBL framework is presented to reconstruct the original bio-signals from the compressed bio-signals. The proposed architecture allows reducing the sampling rate to 25% of Nyquist Rate (NR. In addition, the proposed architecture reduces the power consumption to 40%, Percentage Residual Difference (PRD to 24%, Root Mean Squared Error (RMSE to 2%, and the computation time from 22 s to 9.01 s, which provide good background for establishing wearable wireless healthcare systems. The proposed architecture achieves robust performance in low Signal-to-Noise Ratio (SNR for the reconstruction process.
A Two-Stage Reconstruction Processor for Human Detection in Compressive Sensing CMOS Radar.
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.
A Two-Stage Reconstruction Processor for Human Detection in Compressive Sensing CMOS Radar
Directory of Open Access Journals (Sweden)
Kuei-Chi Tsao
2018-04-01
Full Text Available 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.
Ali, Hussain; Ahmed, Sajid; Al-Naffouri, Tareq Y.; Sharawi, Mohammad S.; Alouini, Mohamed-Slim
2017-01-01
Conventional algorithms used for parameter estimation in colocated multiple-input-multiple-output (MIMO) radars require the inversion of the covariance matrix of the received spatial samples. In these algorithms, the number of received snapshots should be at least equal to the size of the covariance matrix. For large size MIMO antenna arrays, the inversion of the covariance matrix becomes computationally very expensive. Compressive sensing (CS) algorithms which do not require the inversion of the complete covariance matrix can be used for parameter estimation with fewer number of received snapshots. In this work, it is shown that the spatial formulation is best suitable for large MIMO arrays when CS algorithms are used. A temporal formulation is proposed which fits the CS algorithms framework, especially for small size MIMO arrays. A recently proposed low-complexity CS algorithm named support agnostic Bayesian matching pursuit (SABMP) is used to estimate target parameters for both spatial and temporal formulations for the unknown number of targets. The simulation results show the advantage of SABMP algorithm utilizing low number of snapshots and better parameter estimation for both small and large number of antenna elements. Moreover, it is shown by simulations that SABMP is more effective than other existing algorithms at high signal-to-noise ratio.
Ali, Hussain
2017-01-09
Conventional algorithms used for parameter estimation in colocated multiple-input-multiple-output (MIMO) radars require the inversion of the covariance matrix of the received spatial samples. In these algorithms, the number of received snapshots should be at least equal to the size of the covariance matrix. For large size MIMO antenna arrays, the inversion of the covariance matrix becomes computationally very expensive. Compressive sensing (CS) algorithms which do not require the inversion of the complete covariance matrix can be used for parameter estimation with fewer number of received snapshots. In this work, it is shown that the spatial formulation is best suitable for large MIMO arrays when CS algorithms are used. A temporal formulation is proposed which fits the CS algorithms framework, especially for small size MIMO arrays. A recently proposed low-complexity CS algorithm named support agnostic Bayesian matching pursuit (SABMP) is used to estimate target parameters for both spatial and temporal formulations for the unknown number of targets. The simulation results show the advantage of SABMP algorithm utilizing low number of snapshots and better parameter estimation for both small and large number of antenna elements. Moreover, it is shown by simulations that SABMP is more effective than other existing algorithms at high signal-to-noise ratio.
Joint Sparsity and Frequency Estimation for Spectral Compressive Sensing
DEFF Research Database (Denmark)
Nielsen, Jesper Kjær; Christensen, Mads Græsbøll; Jensen, Søren Holdt
2014-01-01
various interpolation techniques to estimate the continuous frequency parameters. In this paper, we show that solving the problem in a probabilistic framework instead produces an asymptotically efficient estimator which outperforms existing methods in terms of estimation accuracy while still having a low...
Owodunni, Damilola S.; Ali, Anum Z.; Quadeer, Ahmed Abdul; Al-Safadi, Ebrahim B.; Hammi, Oualid; Al-Naffouri, Tareq Y.
2014-01-01
-domain, and three compressed sensing based algorithms are presented to estimate and compensate for these distortions at the receiver using a few and, at times, even no frequency-domain free carriers (i.e. pilot carriers). The first technique is a conventional
Compressive sensing of full wave field data for structural health monitoring applications
DEFF Research Database (Denmark)
di Ianni, Tommaso; De Marchi, Luca; Perelli, Alessandro
2015-01-01
; however, the acquisition process is generally time-consuming, posing a limit in the applicability of such approaches. To reduce the acquisition time, we use a random sampling scheme based on compressive sensing (CS) to minimize the number of points at which the field is measured. The CS reconstruction...
The Physics of Compressive Sensing and the Gradient-Based Recovery Algorithms
Dai, Qi; Sha, Wei
2009-01-01
The physics of compressive sensing (CS) and the gradient-based recovery algorithms are presented. First, the different forms for CS are summarized. Second, the physical meanings of coherence and measurement are given. Third, the gradient-based recovery algorithms and their geometry explanations are provided. Finally, we conclude the report and give some suggestion for future work.
Owodunni, Damilola S.
2014-04-01
In this paper, compressed sensing techniques are proposed to linearize commercial power amplifiers driven by orthogonal frequency division multiplexing signals. The nonlinear distortion is considered as a sparse phenomenon in the time-domain, and three compressed sensing based algorithms are presented to estimate and compensate for these distortions at the receiver using a few and, at times, even no frequency-domain free carriers (i.e. pilot carriers). The first technique is a conventional compressed sensing approach, while the second incorporates a priori information about the distortions to enhance the estimation. Finally, the third technique involves an iterative data-aided algorithm that does not require any pilot carriers and hence allows the system to work at maximum bandwidth efficiency. The performances of all the proposed techniques are evaluated on a commercial power amplifier and compared. The error vector magnitude and symbol error rate results show the ability of compressed sensing to compensate for the amplifier\\'s nonlinear distortions. © 2013 Elsevier B.V.
Foot and ankle compression improves joint position sense but not bipedal stance in older people
Hijmans, J.M.; Zijlstra, W.; Geertzen, J.H.; Hof, A.L.; Postema, K.
This study investigates the effects of foot and ankle compression on joint position sense (JPS) and balance in older people and young adults. 12 independently living healthy older persons (77-93 years) were recruited from a senior accommodation facility. 15 young adults (19-24 years) also
Three-Dimensional Inverse Transport Solver Based on Compressive Sensing Technique
Cheng, Yuxiong; Wu, Hongchun; Cao, Liangzhi; Zheng, Youqi
2013-09-01
According to the direct exposure measurements from flash radiographic image, a compressive sensing-based method for three-dimensional inverse transport problem is presented. The linear absorption coefficients and interface locations of objects are reconstructed directly at the same time. It is always very expensive to obtain enough measurements. With limited measurements, compressive sensing sparse reconstruction technique orthogonal matching pursuit is applied to obtain the sparse coefficients by solving an optimization problem. A three-dimensional inverse transport solver is developed based on a compressive sensing-based technique. There are three features in this solver: (1) AutoCAD is employed as a geometry preprocessor due to its powerful capacity in graphic. (2) The forward projection matrix rather than Gauss matrix is constructed by the visualization tool generator. (3) Fourier transform and Daubechies wavelet transform are adopted to convert an underdetermined system to a well-posed system in the algorithm. Simulations are performed and numerical results in pseudo-sine absorption problem, two-cube problem and two-cylinder problem when using compressive sensing-based solver agree well with the reference value.
Photonic compressive sensing with a micro-ring-resonator-based microwave photonic filter
DEFF Research Database (Denmark)
Chen, Ying; Ding, Yunhong; Zhu, Zhijing
2015-01-01
A novel approach to realize photonic compressive sensing (CS) with a multi-tap microwave photonic filter is proposed and demonstrated. The system takes both advantages of CS and photonics to capture wideband sparse signals with sub-Nyquist sampling rate. The low-pass filtering function required...
Large Negative Linear Compressibility in InH(BDC)₂ from Framework Hinging.
Zeng, Qingxin; Wang, Kai; Zou, Bo
2017-11-08
Materials with negative linear compressibility (NLC) counterintuitively expand along one specific direction coupled to the volume reduction when compressed uniformly. NLC with a large value is desired for compression and materials science. However, NLC is generally smaller than -20 TPa -1 . High-pressure X-ray diffraction experiments reveal that the β-quartz-like InH(BDC) 2 generates an extreme NLC (-62.4 TPa -1 ) by framework hinging. InH(BDC) 2 is much safer and lower-cost than Au + /Ag + and CN - -containing materials that dominated the fields of large NLC. This work reconfirms that a negative thermal expansion flexible framework could likely exhibit large NLC. Moreover, a large NLC could be anticipated to arise from β-quartz-like or related frameworks composed of rigid linear ligands and flexible framework angles.
SenSafe: A Smartphone-Based Traffic Safety Framework by Sensing Vehicle and Pedestrian Behaviors
Directory of Open Access Journals (Sweden)
Zhenyu Liu
2016-01-01
Full Text Available Traffic accident involving vehicles is one of the most serious problems in the transportation system nowadays. How to detect dangerous steering and then alarm drivers in real time is a problem. What is more, walking while using smartphones makes pedestrian more susceptible to various risks. Although dedicated short range communication (DSRC provides the way for safety communications, most of vehicles have not been deployed with DSRC components. Even worse, DSRC is not supported by the smartphones for vehicle-to-pedestrian (V2P communication. In this paper, a smartphone-based framework named SenSafe is developed to improve the traffic safety. SenSafe is a framework which only utilizes the smartphone to sense the surrounding events and provides alerts to drivers. Smartphone-based driving behaviors detection mechanism is developed inside the framework to discover various steering behaviors. Besides, the Wi-Fi association and authentication overhead is reduced to broadcast the compressed sensing data using the Wi-Fi beacon to inform the drivers of the surroundings. Furthermore, a collision estimation algorithm is designed to issue appropriate warnings. Finally, an Android-based implementation of SenSafe framework has been achieved to demonstrate the application reliability in real environments.
Mobile Autonomous Sensing Unit (MASU: A Framework That Supports Distributed Pervasive Data Sensing
Directory of Open Access Journals (Sweden)
Esunly Medina
2016-07-01
Full Text Available Pervasive data sensing is a major issue that transverses various research areas and application domains. It allows identifying people’s behaviour and patterns without overwhelming the monitored persons. Although there are many pervasive data sensing applications, they are typically focused on addressing specific problems in a single application domain, making them difficult to generalize or reuse. On the other hand, the platforms for supporting pervasive data sensing impose restrictions to the devices and operational environments that make them unsuitable for monitoring loosely-coupled or fully distributed work. In order to help address this challenge this paper present a framework that supports distributed pervasive data sensing in a generic way. Developers can use this framework to facilitate the implementations of their applications, thus reducing complexity and effort in such an activity. The framework was evaluated using simulations and also through an empirical test, and the obtained results indicate that it is useful to support such a sensing activity in loosely-coupled or fully distributed work scenarios.
Block-Based Compressed Sensing for Neutron Radiation Image Using WDFB
Directory of Open Access Journals (Sweden)
Wei Jin
2015-01-01
Full Text Available An ideal compression method for neutron radiation image should have high compression ratio while keeping more details of the original image. Compressed sensing (CS, which can break through the restrictions of sampling theorem, is likely to offer an efficient compression scheme for the neutron radiation image. Combining wavelet transform with directional filter banks, a novel nonredundant multiscale geometry analysis transform named Wavelet Directional Filter Banks (WDFB is constructed and applied to represent neutron radiation image sparsely. Then, the block-based CS technique is introduced and a high performance CS scheme for neutron radiation image is proposed. By performing two-step iterative shrinkage algorithm the problem of L1 norm minimization is solved to reconstruct neutron radiation image from random measurements. The experiment results demonstrate that the scheme not only improves the quality of reconstructed image obviously but also retains more details of original image.
A Comparison of Compressed Sensing and Sparse Recovery Algorithms Applied to Simulation Data
Directory of Open Access Journals (Sweden)
Ya Ju Fan
2016-08-01
Full Text Available The move toward exascale computing for scientific simulations is placing new demands on compression techniques. It is expected that the I/O system will not be able to support the volume of data that is expected to be written out. To enable quantitative analysis and scientific discovery, we are interested in techniques that compress high-dimensional simulation data and can provide perfect or near-perfect reconstruction. In this paper, we explore the use of compressed sensing (CS techniques to reduce the size of the data before they are written out. Using large-scale simulation data, we investigate how the sufficient sparsity condition and the contrast in the data affect the quality of reconstruction and the degree of compression. We provide suggestions for the practical implementation of CS techniques and compare them with other sparse recovery methods. 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.
Tang, Gang; Hou, Wei; Wang, Huaqing; Luo, Ganggang; Ma, Jianwei
2015-10-09
The Shannon sampling principle requires substantial amounts of data to ensure the accuracy of on-line monitoring of roller bearing fault signals. Challenges are often encountered as a result of the cumbersome data monitoring, thus a novel method focused on compressed vibration signals for detecting roller bearing faults is developed in this study. Considering that harmonics often represent the fault characteristic frequencies in vibration signals, a compressive sensing frame of characteristic harmonics is proposed to detect bearing faults. A compressed vibration signal is first acquired from a sensing matrix with information preserved through a well-designed sampling strategy. A reconstruction process of the under-sampled vibration signal is then pursued as attempts are conducted to detect the characteristic harmonics from sparse measurements through a compressive matching pursuit strategy. In the proposed method bearing fault features depend on the existence of characteristic harmonics, as typically detected directly from compressed data far before reconstruction completion. The process of sampling and detection may then be performed simultaneously without complete recovery of the under-sampled signals. The effectiveness of the proposed method is validated by simulations and experiments.
Research on compressive sensing reconstruction algorithm based on total variation model
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.
Quantum tomography via compressed sensing: error bounds, sample complexity and efficient estimators
International Nuclear Information System (INIS)
Flammia, Steven T; Gross, David; Liu, Yi-Kai; Eisert, Jens
2012-01-01
Intuitively, if a density operator has small rank, then it should be easier to estimate from experimental data, since in this case only a few eigenvectors need to be learned. We prove two complementary results that confirm this intuition. Firstly, we show that a low-rank density matrix can be estimated using fewer copies of the state, i.e. the sample complexity of tomography decreases with the rank. Secondly, we show that unknown low-rank states can be reconstructed from an incomplete set of measurements, using techniques from compressed sensing and matrix completion. These techniques use simple Pauli measurements, and their output can be certified without making any assumptions about the unknown state. In this paper, we present a new theoretical analysis of compressed tomography, based on the restricted isometry property for low-rank matrices. Using these tools, we obtain near-optimal error bounds for the realistic situation where the data contain noise due to finite statistics, and the density matrix is full-rank with decaying eigenvalues. We also obtain upper bounds on the sample complexity of compressed tomography, and almost-matching lower bounds on the sample complexity of any procedure using adaptive sequences of Pauli measurements. Using numerical simulations, we compare the performance of two compressed sensing estimators—the matrix Dantzig selector and the matrix Lasso—with standard maximum-likelihood estimation (MLE). We find that, given comparable experimental resources, the compressed sensing estimators consistently produce higher fidelity state reconstructions than MLE. In addition, the use of an incomplete set of measurements leads to faster classical processing with no loss of accuracy. Finally, we show how to certify the accuracy of a low-rank estimate using direct fidelity estimation, and describe a method for compressed quantum process tomography that works for processes with small Kraus rank and requires only Pauli eigenstate preparations
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.
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.
Two-level image authentication by two-step phase-shifting interferometry and compressive sensing
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.
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.
An Uncertainty Quantification Framework for Remote Sensing Retrievals
Braverman, A. J.; Hobbs, J.
2017-12-01
Remote sensing data sets produced by NASA and other space agencies are the result of complex algorithms that infer geophysical state from observed radiances using retrieval algorithms. The processing must keep up with the downlinked data flow, and this necessitates computational compromises that affect the accuracies of retrieved estimates. The algorithms are also limited by imperfect knowledge of physics and of ancillary inputs that are required. All of this contributes to uncertainties that are generally not rigorously quantified by stepping outside the assumptions that underlie the retrieval methodology. In this talk we discuss a practical framework for uncertainty quantification that can be applied to a variety of remote sensing retrieval algorithms. Ours is a statistical approach that uses Monte Carlo simulation to approximate the sampling distribution of the retrieved estimates. We will discuss the strengths and weaknesses of this approach, and provide a case-study example from the Orbiting Carbon Observatory 2 mission.
Tang, Xin; Chen, Zhongsheng; Li, Yue; Yang, Yongmin
2018-05-01
When faults happen at gas path components of gas turbines, some sparsely-distributed and charged debris will be generated and released into the exhaust gas. The debris is called abnormal debris. Electrostatic sensors can detect the debris online and further indicate the faults. It is generally considered that, under a specific working condition, a more serious fault generates more and larger debris, and a piece of larger debris carries more charge. Therefore, the amount and charge of the abnormal debris are important indicators of the fault severity. However, because an electrostatic sensor can only detect the superposed effect on the electrostatic field of all the debris, it can hardly identify the amount and position of the debris. Moreover, because signals of electrostatic sensors depend on not only charge but also position of debris, and the position information is difficult to acquire, measuring debris charge accurately using the electrostatic detecting method is still a technical difficulty. To solve these problems, a hemisphere-shaped electrostatic sensors' circular array (HSESCA) is used, and an array signal processing method based on compressive sensing (CS) is proposed in this paper. To research in a theoretical framework of CS, the measurement model of the HSESCA is discretized into a sparse representation form by meshing. In this way, the amount and charge of the abnormal debris are described as a sparse vector. It is further reconstructed by constraining l1-norm when solving an underdetermined equation. In addition, a pre-processing method based on singular value decomposition and a result calibration method based on weighted-centroid algorithm are applied to ensure the accuracy of the reconstruction. The proposed method is validated by both numerical simulations and experiments. Reconstruction errors, characteristics of the results and some related factors are discussed.
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.
Cabrelli, Carlos; Jaffard, Stephane; Molter, Ursula
2016-01-01
This volume is a selection of written notes corresponding to courses taught at the CIMPA School: "New Trends in Applied Harmonic Analysis: Sparse Representations, Compressed Sensing and Multifractal Analysis". New interactions between harmonic analysis and signal and image processing have seen striking development in the last 10 years, and several technological deadlocks have been solved through the resolution of deep theoretical problems in harmonic analysis. New Trends in Applied Harmonic Analysis focuses on two particularly active areas that are representative of such advances: multifractal analysis, and sparse representation and compressed sensing. The contributions are written by leaders in these areas, and covers both theoretical aspects and applications. This work should prove useful not only to PhD students and postdocs in mathematics and signal and image processing, but also to researchers working in related topics.
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 compressed sensing based method with support refinement for impulse noise cancelation in DSL
Quadeer, Ahmed Abdul
2013-06-01
This paper presents a compressed sensing based method to suppress impulse noise in digital subscriber line (DSL). The proposed algorithm exploits the sparse nature of the impulse noise and utilizes the carriers, already available in all practical DSL systems, for its estimation and cancelation. Specifically, compressed sensing is used for a coarse estimate of the impulse position, an a priori information based maximum aposteriori probability (MAP) metric for its refinement, followed by least squares (LS) or minimum mean square error (MMSE) estimation for estimating the impulse amplitudes. Simulation results show that the proposed scheme achieves higher rate as compared to other known sparse estimation algorithms in literature. The paper also demonstrates the superior performance of the proposed scheme compared to the ITU-T G992.3 standard that utilizes RS-coding for impulse noise refinement in DSL signals. © 2013 IEEE.
A Distributed Compressive Sensing Scheme for Event Capture in Wireless Visual Sensor Networks
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.
A knitted glove sensing system with compression strain for finger movements
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.
Ali, Anum Z.
2013-12-01
Linearization of user equipment power amplifiers driven by orthogonal frequency division multiplexing signals is addressed in this paper. Particular attention is paid to the power efficient operation of an orthogonal frequency division multiple access cognitive radio system and realization of such a system using compressed sensing. Specifically, precompensated overdriven amplifiers are employed at the mobile terminal. Over-driven amplifiers result in in-band distortions and out of band interference. Out of band interference mostly occupies the spectrum of inactive users, whereas the in-band distortions are mitigated using compressed sensing at the receiver. It is also shown that the performance of the proposed scheme can be further enhanced using multiple measurements of the distortion signal in single-input multi-output systems. Numerical results verify the ability of the proposed setup to improve error vector magnitude, bit error rate, outage capacity and mean squared error. © 2011 IEEE.
On Compressed Sensing and the Estimation of Continuous Parameters From Noisy Observations
DEFF Research Database (Denmark)
Nielsen, Jesper Kjær; Christensen, Mads Græsbøll; Jensen, Søren Holdt
2012-01-01
Compressed sensing (CS) has in recent years become a very popular way of sampling sparse signals. This sparsity is measured with respect to some known dictionary consisting of a finite number of atoms. Most models for real world signals, however, are parametrised by continuous parameters correspo......Compressed sensing (CS) has in recent years become a very popular way of sampling sparse signals. This sparsity is measured with respect to some known dictionary consisting of a finite number of atoms. Most models for real world signals, however, are parametrised by continuous parameters...... corresponding to a dictionary with an infinite number of atoms. Examples of such parameters are the temporal and spatial frequency. In this paper, we analyse how CS affects the estimation performance of any unbiased estimator when we assume such infinite dictionaries. We base our analysis on the Cramer...
Ali, Anum Z.; Hammi, Oualid; Al-Naffouri, Tareq Y.
2013-01-01
Linearization of user equipment power amplifiers driven by orthogonal frequency division multiplexing signals is addressed in this paper. Particular attention is paid to the power efficient operation of an orthogonal frequency division multiple access cognitive radio system and realization of such a system using compressed sensing. Specifically, precompensated overdriven amplifiers are employed at the mobile terminal. Over-driven amplifiers result in in-band distortions and out of band interference. Out of band interference mostly occupies the spectrum of inactive users, whereas the in-band distortions are mitigated using compressed sensing at the receiver. It is also shown that the performance of the proposed scheme can be further enhanced using multiple measurements of the distortion signal in single-input multi-output systems. Numerical results verify the ability of the proposed setup to improve error vector magnitude, bit error rate, outage capacity and mean squared error. © 2011 IEEE.
Efficient High-Dimensional Entanglement Imaging with a Compressive-Sensing Double-Pixel Camera
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Gregory A. Howland
2013-02-01
Full Text Available We implement a double-pixel compressive-sensing camera to efficiently characterize, at high resolution, the spatially entangled fields that are produced by spontaneous parametric down-conversion. This technique leverages sparsity in spatial correlations between entangled photons to improve acquisition times over raster scanning by a scaling factor up to n^{2}/log(n for n-dimensional images. We image at resolutions up to 1024 dimensions per detector and demonstrate a channel capacity of 8.4 bits per photon. By comparing the entangled photons’ classical mutual information in conjugate bases, we violate an entropic Einstein-Podolsky-Rosen separability criterion for all measured resolutions. More broadly, our result indicates that compressive sensing can be especially effective for higher-order measurements on correlated systems.
Monitoring and diagnosis of Alzheimer's disease using noninvasive compressive sensing EEG
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.
Toward a theoretical framework for trustworthy cyber sensing
Xu, Shouhuai
2010-04-01
Cyberspace is an indispensable part of the economy and society, but has been "polluted" with many compromised computers that can be abused to launch further attacks against the others. Since it is likely that there always are compromised computers, it is important to be aware of the (dynamic) cyber security-related situation, which is however challenging because cyberspace is an extremely large-scale complex system. Our project aims to investigate a theoretical framework for trustworthy cyber sensing. With the perspective of treating cyberspace as a large-scale complex system, the core question we aim to address is: What would be a competent theoretical (mathematical and algorithmic) framework for designing, analyzing, deploying, managing, and adapting cyber sensor systems so as to provide trustworthy information or input to the higher layer of cyber situation-awareness management, even in the presence of sophisticated malicious attacks against the cyber sensor systems?
Deterministic matrices matching the compressed sensing phase transitions of Gaussian random matrices
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
2012-01-01
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 ...
Al-Salihi, Hayder Qahtan Kshash; Nakhai, Mohammad Reza
2017-01-01
Efficient and highly accurate channel state information (CSI) at the base station (BS) is essential to achieve the potential benefits of massive multiple input multiple output (MIMO) systems. However, the achievable accuracy that is attainable is limited in practice due to the problem of pilot contamination. It has recently been shown that compressed sensing (CS) techniques can address the pilot contamination problem. However, CS-based channel estimation requires prior knowledge of channel sp...
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.
An Evaluation Framework for Lossy Compression of Genome Sequencing Quality Values
Alberti, Claudio; Daniels, Noah; Hernaez, Mikel; Voges, Jan; Goldfeder, Rachel L.; Hernandez-Lopez, Ana A.; Mattavelli, Marco; Berger, Bonnie
2016-01-01
This paper provides the specification and an initial validation of an evaluation framework for the comparison of lossy compressors of genome sequencing quality values. The goal is to define reference data, test sets, tools and metrics that shall be used to evaluate the impact of lossy compression of quality values on human genome variant calling. The functionality of the framework is validated referring to two state-of-the-art genomic compressors. This work has been spurred by the current act...
Accelerated high-frame-rate mouse heart cine-MRI using compressed sensing reconstruction.
Motaal, Abdallah G; Coolen, Bram F; Abdurrachim, Desiree; Castro, Rui M; Prompers, Jeanine J; Florack, Luc M J; Nicolay, Klaas; Strijkers, Gustav J
2013-04-01
We introduce a new protocol to obtain very high-frame-rate cinematographic (Cine) MRI movies of the beating mouse heart within a reasonable measurement time. The method is based on a self-gated accelerated fast low-angle shot (FLASH) acquisition and compressed sensing reconstruction. Key to our approach is that we exploit the stochastic nature of the retrospective triggering acquisition scheme to produce an undersampled and random k-t space filling that allows for compressed sensing reconstruction and acceleration. As a standard, a self-gated FLASH sequence with a total acquisition time of 10 min was used to produce single-slice Cine movies of seven mouse hearts with 90 frames per cardiac cycle. Two times (2×) and three times (3×) k-t space undersampled Cine movies were produced from 2.5- and 1.5-min data acquisitions, respectively. The accelerated 90-frame Cine movies of mouse hearts were successfully reconstructed with a compressed sensing algorithm. The movies had high image quality and the undersampling artifacts were effectively removed. Left ventricular functional parameters, i.e. end-systolic and end-diastolic lumen surface areas and early-to-late filling rate ratio as a parameter to evaluate diastolic function, derived from the standard and accelerated Cine movies, were nearly identical. Copyright © 2012 John Wiley & Sons, Ltd.
An Adaptive Joint Sparsity Recovery for Compressive Sensing Based EEG System
Directory of Open Access Journals (Sweden)
Hamza Djelouat
2017-01-01
Full Text Available The last decade has witnessed tremendous efforts to shape the Internet of things (IoT platforms to be well suited for healthcare applications. These platforms are comprised of a network of wireless sensors to monitor several physical and physiological quantities. For instance, long-term monitoring of brain activities using wearable electroencephalogram (EEG sensors is widely exploited in the clinical diagnosis of epileptic seizures and sleeping disorders. However, the deployment of such platforms is challenged by the high power consumption and system complexity. Energy efficiency can be achieved by exploring efficient compression techniques such as compressive sensing (CS. CS is an emerging theory that enables a compressed acquisition using well-designed sensing matrices. Moreover, system complexity can be optimized by using hardware friendly structured sensing matrices. This paper quantifies the performance of a CS-based multichannel EEG monitoring. In addition, the paper exploits the joint sparsity of multichannel EEG using subspace pursuit (SP algorithm as well as a designed sparsifying basis in order to improve the reconstruction quality. Furthermore, the paper proposes a modification to the SP algorithm based on an adaptive selection approach to further improve the performance in terms of reconstruction quality, execution time, and the robustness of the recovery process.
RMP: Reduced-set matching pursuit approach for efficient compressed sensing signal reconstruction
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Michael M. Abdel-Sayed
2016-11-01
Full Text Available Compressed sensing enables the acquisition of sparse signals at a rate that is much lower than the Nyquist rate. Compressed sensing initially adopted ℓ1 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 ℓ1 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 ℓ1 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.
RMP: Reduced-set matching pursuit approach for efficient compressed sensing signal reconstruction.
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.
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.
Efficient Sparse Signal Transmission over a Lossy Link Using Compressive Sensing
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Liantao Wu
2015-08-01
Full Text Available Reliable data transmission over lossy communication link is expensive due to overheads for error protection. For signals that have inherent sparse structures, compressive sensing (CS is applied to facilitate efficient sparse signal transmissions over lossy communication links without data compression or error protection. The natural packet loss in the lossy link is modeled as a random sampling process of the transmitted data, and the original signal will be reconstructed from the lossy transmission results using the CS-based reconstruction method at the receiving end. The impacts of packet lengths on transmission efficiency under different channel conditions have been discussed, and interleaving is incorporated to mitigate the impact of burst data loss. Extensive simulations and experiments have been conducted and compared to the traditional automatic repeat request (ARQ interpolation technique, and very favorable results have been observed in terms of both accuracy of the reconstructed signals and the transmission energy consumption. Furthermore, the packet length effect provides useful insights for using compressed sensing for efficient sparse signal transmission via lossy links.
Compressed RSS Measurement for Communication and Sensing in the Internet of Things
Directory of Open Access Journals (Sweden)
Yanchao Zhao
2017-01-01
Full Text Available The receiving signal strength (RSS is crucial for the Internet of Things (IoT, as it is the key foundation for communication resource allocation, localization, interference management, sensing, and so on. Aside from its significance, the measurement process could be tedious, time consuming, inaccurate, and involving human operations. The state-of-the-art works usually applied the fashion of “measure a few, predict many,” which use measurement calibrated models to generate the RSS for the whole networks. However, this kind of methods still cannot provide accurate results in a short duration with low measurement cost. In addition, they also require careful scheduling of the measurement which is vulnerable to measurement conflict. In this paper, we propose a compressive sensing- (CS- based RSS measurement solution, which is conflict-tolerant, time-efficient, and accuracy-guaranteed without any model-calibrate operation. The CS-based solution takes advantage of compressive sensing theory to enable simultaneous measurement in the same channel, which reduces the time cost to the level of O(logN (where N is the network size and works well for sparse networks. Extensive experiments based on real data trace are conducted to show the efficiency of the proposed solutions.
Compressive Sensing with Cross-Validation and Stop-Sampling for Sparse Polynomial Chaos Expansions
Energy Technology Data Exchange (ETDEWEB)
Huan, Xun; Safta, Cosmin; Sargsyan, Khachik; Vane, Zachary Phillips; Lacaze, Guilhem; Oefelein, Joseph C.; Najm, Habib N.
2017-07-01
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 recommendations 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.
Data compressive paradigm for multispectral sensing using tunable DWELL mid-infrared detectors.
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
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.
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.
International Nuclear Information System (INIS)
Lauzier, Pascal Thériault; Chen Guanghong
2013-01-01
Purpose: The ionizing radiation imparted to patients during computed tomography exams is raising concerns. This paper studies the performance of a scheme called dose reduction using prior image constrained compressed sensing (DR-PICCS). The purpose of this study is to characterize the effects of a statistical model of x-ray detection in the DR-PICCS framework and its impact on spatial resolution. Methods: Both numerical simulations with known ground truth and in vivo animal dataset were used in this study. In numerical simulations, a phantom was simulated with Poisson noise and with varying levels of eccentricity. Both the conventional filtered backprojection (FBP) and the PICCS algorithms were used to reconstruct images. In PICCS reconstructions, the prior image was generated using two different denoising methods: a simple Gaussian blur and a more advanced diffusion filter. Due to the lack of shift-invariance in nonlinear image reconstruction such as the one studied in this paper, the concept of local spatial resolution was used to study the sharpness of a reconstructed image. Specifically, a directional metric of image sharpness, the so-called pseudopoint spread function (pseudo-PSF), was employed to investigate local spatial resolution. Results: In the numerical studies, the pseudo-PSF was reduced from twice the voxel width in the prior image down to less than 1.1 times the voxel width in DR-PICCS reconstructions when the statistical model was not included. At the same noise level, when statistical weighting was used, the pseudo-PSF width in DR-PICCS reconstructed images varied between 1.5 and 0.75 times the voxel width depending on the direction along which it was measured. However, this anisotropy was largely eliminated when the prior image was generated using diffusion filtering; the pseudo-PSF width was reduced to below one voxel width in that case. In the in vivo study, a fourfold improvement in CNR was achieved while qualitatively maintaining sharpness
Energy Technology Data Exchange (ETDEWEB)
AlAfeef, Ala, E-mail: a.al-afeef.1@research.gla.ac.uk [SUPA School of Physics and Astronomy, University of Glasgow, Glasgow G12 8QQ (United Kingdom); School of Computing Science, University of Glasgow, Glasgow G12 8QQ (United Kingdom); Bobynko, Joanna [SUPA School of Physics and Astronomy, University of Glasgow, Glasgow G12 8QQ (United Kingdom); Cockshott, W. Paul. [School of Computing Science, University of Glasgow, Glasgow G12 8QQ (United Kingdom); Craven, Alan J. [SUPA School of Physics and Astronomy, University of Glasgow, Glasgow G12 8QQ (United Kingdom); Zuazo, Ian; Barges, Patrick [ArcelorMittal Maizières Research, Maizières-lès-Metz 57283 (France); MacLaren, Ian, E-mail: ian.maclaren@glasgow.ac.uk [SUPA School of Physics and Astronomy, University of Glasgow, Glasgow G12 8QQ (United Kingdom)
2016-11-15
We have investigated the use of DualEELS in elementally sensitive tilt series tomography in the scanning transmission electron microscope. A procedure is implemented using deconvolution to remove the effects of multiple scattering, followed by normalisation by the zero loss peak intensity. This is performed to produce a signal that is linearly dependent on the projected density of the element in each pixel. This method is compared with one that does not include deconvolution (although normalisation by the zero loss peak intensity is still performed). Additionally, we compare the 3D reconstruction using a new compressed sensing algorithm, DLET, with the well-established SIRT algorithm. VC precipitates, which are extracted from a steel on a carbon replica, are used in this study. It is found that the use of this linear signal results in a very even density throughout the precipitates. However, when deconvolution is omitted, a slight density reduction is observed in the cores of the precipitates (a so-called cupping artefact). Additionally, it is clearly demonstrated that the 3D morphology is much better reproduced using the DLET algorithm, with very little elongation in the missing wedge direction. It is therefore concluded that reliable elementally sensitive tilt tomography using EELS requires the appropriate use of DualEELS together with a suitable reconstruction algorithm, such as the compressed sensing based reconstruction algorithm used here, to make the best use of the limited data volume and signal to noise inherent in core-loss EELS. - Highlights: • DualEELS is essential for chemically sensitive electron tomography using EELS. • A new compressed sensing based algorithm (DLET) gives high fidelity reconstruction. • This combination of DualEELS and DLET will give reliable results from few projections.
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.
Directory of Open Access Journals (Sweden)
Ling Yongfa
2016-01-01
Full Text Available The paper proposes a mobile control sink node data collection method in the wireless sensor network based on compressive sensing. This method, with regular track, selects the optimal data collection points in the monitoring area via the disc method, calcu-lates the shortest path by using the quantum genetic algorithm, and hence determines the data collection route. Simulation results show that this method has higher network throughput and better energy efficiency, capable of collecting a huge amount of data with balanced energy consumption in the network.
Reconstruction algorithm in compressed sensing based on maximum a posteriori estimation
International Nuclear Information System (INIS)
Takeda, Koujin; Kabashima, Yoshiyuki
2013-01-01
We propose a systematic method for constructing a sparse data reconstruction algorithm in compressed sensing at a relatively low computational cost for general observation matrix. It is known that the cost of ℓ 1 -norm minimization using a standard linear programming algorithm is O(N 3 ). We show that this cost can be reduced to O(N 2 ) by applying the approach of posterior maximization. Furthermore, in principle, the algorithm from our approach is expected to achieve the widest successful reconstruction region, which is evaluated from theoretical argument. We also discuss the relation between the belief propagation-based reconstruction algorithm introduced in preceding works and our approach
An Evaluation Framework for Lossy Compression of Genome Sequencing Quality Values.
Alberti, Claudio; Daniels, Noah; Hernaez, Mikel; Voges, Jan; Goldfeder, Rachel L; Hernandez-Lopez, Ana A; Mattavelli, Marco; Berger, Bonnie
2016-01-01
This paper provides the specification and an initial validation of an evaluation framework for the comparison of lossy compressors of genome sequencing quality values. The goal is to define reference data, test sets, tools and metrics that shall be used to evaluate the impact of lossy compression of quality values on human genome variant calling. The functionality of the framework is validated referring to two state-of-the-art genomic compressors. This work has been spurred by the current activity within the ISO/IEC SC29/WG11 technical committee (a.k.a. MPEG), which is investigating the possibility of starting a standardization activity for genomic information representation.
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.
Micro-Doppler Ambiguity Resolution Based on Short-Time Compressed Sensing
Directory of Open Access Journals (Sweden)
Jing-bo Zhuang
2015-01-01
Full Text Available When using a long range radar (LRR to track a target with micromotion, the micro-Doppler embodied in the radar echoes may suffer from ambiguity problem. In this paper, we propose a novel method based on compressed sensing (CS to solve micro-Doppler ambiguity. According to the RIP requirement, a sparse probing pulse train with its transmitting time random is designed. After matched filtering, the slow-time echo signals of the micromotion target can be viewed as randomly sparse sampling of Doppler spectrum. Select several successive pulses to form a short-time window and the CS sensing matrix can be built according to the time stamps of these pulses. Then performing Orthogonal Matching Pursuit (OMP, the unambiguous micro-Doppler spectrum can be obtained. The proposed algorithm is verified using the echo signals generated according to the theoretical model and the signals with micro-Doppler signature produced using the commercial electromagnetic simulation software FEKO.
Directory of Open Access Journals (Sweden)
Sitarenios Panagiotis
2016-01-01
Full Text Available The Modified Cam Clay model is extended to account for the behaviour of unsaturated soils using Bishop’s stress. To describe the Loading – Collapse behaviour, the model incorporates a compressibility framework with suction and degree of saturation dependent compression lines. For simplicity, the present paper describes the model in the triaxial stress space with characteristic simulations of constant suction compression and triaxial tests, as well as wetting tests. The model reproduces an evolving post yield compressibility under constant suction compression, and thus, can adequately describe a maximum of collapse.
A new DWT/MC/DPCM video compression framework based on EBCOT
Mei, L. M.; Wu, H. R.; Tan, D. M.
2005-07-01
A novel Discrete Wavelet Transform (DWT)/Motion Compensation (MC)/Differential Pulse Code Modulation (DPCM) video compression framework is proposed in this paper. Although the Discrete Cosine Transform (DCT)/MC/DPCM is the mainstream framework for video coders in industry and international standards, the idea of DWT/MC/DPCM has existed for more than one decade in the literature and the investigation is still undergoing. The contribution of this work is twofold. Firstly, the Embedded Block Coding with Optimal Truncation (EBCOT) is used here as the compression engine for both intra- and inter-frame coding, which provides good compression ratio and embedded rate-distortion (R-D) optimization mechanism. This is an extension of the EBCOT application from still images to videos. Secondly, this framework offers a good interface for the Perceptual Distortion Measure (PDM) based on the Human Visual System (HVS) where the Mean Squared Error (MSE) can be easily replaced with the PDM in the R-D optimization. Some of the preliminary results are reported here. They are also compared with benchmarks such as MPEG-2 and MPEG-4 version 2. The results demonstrate that under specified condition the proposed coder outperforms the benchmarks in terms of rate vs. distortion.
Jang, Hwanchol; Yoon, Changhyeong; Choi, Wonshik; Eom, Tae Joong; Lee, Heung-No
2016-03-01
We provide an approach to improve the quality of image reconstruction in wide-field imaging through turbid media (WITM). In WITM, a calibration stage which measures the transmission matrix (TM), the set of responses of turbid medium to a set of plane waves with different incident angles, is preceded to the image recovery. Then, the TM is used for estimation of object image in image recovery stage. In this work, we aim to estimate highly resolved angular spectrum and use it for high quality image reconstruction. To this end, we propose to perform a dense sampling for TM measurement in calibration stage with finer incident angle spacing. In conventional approaches, incident angle spacing is made to be large enough so that the columns in TM are out of memory effect of turbid media. Otherwise, the columns in TM are correlated and the inversion becomes difficult. We employ compressed sensing (CS) for a successful high resolution angular spectrum recovery with dense sampled TM. CS is a relatively new information acquisition and reconstruction framework and has shown to provide superb performance in ill-conditioned inverse problems. We observe that the image quality metrics such as contrast-to-noise ratio and mean squared error are improved and the perceptual image quality is improved with reduced speckle noise in the reconstructed image. This results shows that the WITM performance can be improved only by executing dense sampling in the calibration stage and with an efficient signal reconstruction framework without elaborating the overall optical imaging systems.
Directory of Open Access Journals (Sweden)
Aihua Liu
2017-01-01
Full Text Available A method of direction-of-arrival (DOA estimation using array interpolation is proposed in this paper to increase the number of resolvable sources and improve the DOA estimation performance for coprime array configuration with holes in its virtual array. The virtual symmetric nonuniform linear array (VSNLA of coprime array signal model is introduced, with the conventional MUSIC with spatial smoothing algorithm (SS-MUSIC applied on the continuous lags in the VSNLA; the degrees of freedom (DoFs for DOA estimation are obviously not fully exploited. To effectively utilize the extent of DoFs offered by the coarray configuration, a compressing sensing based array interpolation algorithm is proposed. The compressing sensing technique is used to obtain the coarse initial DOA estimation, and a modified iterative initial DOA estimation based interpolation algorithm (IMCA-AI is then utilized to obtain the final DOA estimation, which maps the sample covariance matrix of the VSNLA to the covariance matrix of a filled virtual symmetric uniform linear array (VSULA with the same aperture size. The proposed DOA estimation method can efficiently improve the DOA estimation performance. The numerical simulations are provided to demonstrate the effectiveness of the proposed method.
Xu, Li; Shan, Lin; Adachi, Fumiyuki
2014-01-01
In orthogonal frequency division modulation (OFDM) communication systems, channel state information (CSI) is required at receiver due to the fact that frequency-selective fading channel leads to disgusting intersymbol interference (ISI) over data transmission. Broadband channel model is often described by very few dominant channel taps and they can be probed by compressive sensing based sparse channel estimation (SCE) methods, for example, orthogonal matching pursuit algorithm, which can take the advantage of sparse structure effectively in the channel as for prior information. However, these developed methods are vulnerable to both noise interference and column coherence of training signal matrix. In other words, the primary objective of these conventional methods is to catch the dominant channel taps without a report of posterior channel uncertainty. To improve the estimation performance, we proposed a compressive sensing based Bayesian sparse channel estimation (BSCE) method which cannot only exploit the channel sparsity but also mitigate the unexpected channel uncertainty without scarifying any computational complexity. The proposed method can reveal potential ambiguity among multiple channel estimators that are ambiguous due to observation noise or correlation interference among columns in the training matrix. Computer simulations show that proposed method can improve the estimation performance when comparing with conventional SCE methods. PMID:24983012
Institute of Scientific and Technical Information of China (English)
Song Hui; Wang Zhongmin
2017-01-01
The diversity in the phone placements of different mobile users' dailylife increases the difficulty of recognizing human activities by using mobile phone accelerometer data.To solve this problem,a compressed sensing method to recognize human activities that is based on compressed sensing theory and utilizes both raw mobile phone accelerometer data and phone placement information is proposed.First,an over-complete dictionary matrix is constructed using sufficient raw tri-axis acceleration data labeled with phone placement information.Then,the sparse coefficient is evaluated for the samples that need to be tested by resolving L1 minimization.Finally,residual values are calculated and the minimum value is selected as the indicator to obtain the recognition results.Experimental results show that this method can achieve a recognition accuracy reaching 89.86％,which is higher than that of a recognition method that does not adopt the phone placement information for the recognition process.The recognition accuracy of the proposed method is effective and satisfactory.
Secure biometric image sensor and authentication scheme based on compressed sensing.
Suzuki, Hiroyuki; Suzuki, Masamichi; Urabe, Takuya; Obi, Takashi; Yamaguchi, Masahiro; Ohyama, Nagaaki
2013-11-20
It is important to ensure the security of biometric authentication information, because its leakage causes serious risks, such as replay attacks using the stolen biometric data, and also because it is almost impossible to replace raw biometric information. In this paper, we propose a secure biometric authentication scheme that protects such information by employing an optical data ciphering technique based on compressed sensing. The proposed scheme is based on two-factor authentication, the biometric information being supplemented by secret information that is used as a random seed for a cipher key. In this scheme, a biometric image is optically encrypted at the time of image capture, and a pair of restored biometric images for enrollment and verification are verified in the authentication server. If any of the biometric information is exposed to risk, it can be reenrolled by changing the secret information. Through numerical experiments, we confirm that finger vein images can be restored from the compressed sensing measurement data. We also present results that verify the accuracy of the scheme.
Balanced sparse model for tight frames in compressed sensing magnetic resonance imaging.
Directory of Open Access Journals (Sweden)
Yunsong Liu
Full Text Available Compressed sensing has shown to be promising to accelerate magnetic resonance imaging. In this new technology, magnetic resonance images are usually reconstructed by enforcing its sparsity in sparse image reconstruction models, including both synthesis and analysis models. The synthesis model assumes that an image is a sparse combination of atom signals while the analysis model assumes that an image is sparse after the application of an analysis operator. Balanced model is a new sparse model that bridges analysis and synthesis models by introducing a penalty term on the distance of frame coefficients to the range of the analysis operator. In this paper, we study the performance of the balanced model in tight frame based compressed sensing magnetic resonance imaging and propose a new efficient numerical algorithm to solve the optimization problem. By tuning the balancing parameter, the new model achieves solutions of three models. It is found that the balanced model has a comparable performance with the analysis model. Besides, both of them achieve better results than the synthesis model no matter what value the balancing parameter is. Experiment shows that our proposed numerical algorithm constrained split augmented Lagrangian shrinkage algorithm for balanced model (C-SALSA-B converges faster than previously proposed algorithms accelerated proximal algorithm (APG and alternating directional method of multipliers for balanced model (ADMM-B.
A Digital Compressed Sensing-Based Energy-Efficient Single-Spot Bluetooth ECG Node
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Kan Luo
2018-01-01
Full Text Available 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.
A Compressed Sensing-Based Wearable Sensor Network for Quantitative Assessment of Stroke Patients
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Lei Yu
2016-02-01
Full Text Available Clinical rehabilitation assessment is an important part of the therapy process because it is the premise for prescribing suitable rehabilitation interventions. However, the commonly used assessment scales have the following two drawbacks: (1 they are susceptible to subjective factors; (2 they only have several rating levels and are influenced by a ceiling effect, making it impossible to exactly detect any further improvement in the movement. Meanwhile, energy constraints are a primary design consideration in wearable sensor network systems since they are often battery-operated. Traditionally, for wearable sensor network systems that follow the Shannon/Nyquist sampling theorem, there are many data that need to be sampled and transmitted. This paper proposes a novel wearable sensor network system to monitor and quantitatively assess the upper limb motion function, based on compressed sensing technology. With the sparse representation model, less data is transmitted to the computer than with traditional systems. The experimental results show that the accelerometer signals of Bobath handshake and shoulder touch exercises can be compressed, and the length of the compressed signal is less than 1/3 of the raw signal length. More importantly, the reconstruction errors have no influence on the predictive accuracy of the Brunnstrom stage classification model. It also indicated that the proposed system can not only reduce the amount of data during the sampling and transmission processes, but also, the reconstructed accelerometer signals can be used for quantitative assessment without any loss of useful information.
A Compressed Sensing-Based Wearable Sensor Network for Quantitative Assessment of Stroke Patients
Yu, Lei; Xiong, Daxi; Guo, Liquan; Wang, Jiping
2016-01-01
Clinical rehabilitation assessment is an important part of the therapy process because it is the premise for prescribing suitable rehabilitation interventions. However, the commonly used assessment scales have the following two drawbacks: (1) they are susceptible to subjective factors; (2) they only have several rating levels and are influenced by a ceiling effect, making it impossible to exactly detect any further improvement in the movement. Meanwhile, energy constraints are a primary design consideration in wearable sensor network systems since they are often battery-operated. Traditionally, for wearable sensor network systems that follow the Shannon/Nyquist sampling theorem, there are many data that need to be sampled and transmitted. This paper proposes a novel wearable sensor network system to monitor and quantitatively assess the upper limb motion function, based on compressed sensing technology. With the sparse representation model, less data is transmitted to the computer than with traditional systems. The experimental results show that the accelerometer signals of Bobath handshake and shoulder touch exercises can be compressed, and the length of the compressed signal is less than 1/3 of the raw signal length. More importantly, the reconstruction errors have no influence on the predictive accuracy of the Brunnstrom stage classification model. It also indicated that the proposed system can not only reduce the amount of data during the sampling and transmission processes, but also, the reconstructed accelerometer signals can be used for quantitative assessment without any loss of useful information. PMID:26861337
Compressed sensing of ECG signal for wireless system with new fast iterative method.
Tawfic, Israa; Kayhan, Sema
2015-12-01
Recent experiments in wireless body area network (WBAN) show that compressive sensing (CS) is a promising tool to compress the Electrocardiogram signal ECG signal. The performance of CS is based on algorithms use to reconstruct exactly or approximately the original signal. In this paper, we present two methods work with absence and presence of noise, these methods are Least Support Orthogonal Matching Pursuit (LS-OMP) and Least Support Denoising-Orthogonal Matching Pursuit (LSD-OMP). The algorithms achieve correct support recovery without requiring sparsity knowledge. We derive an improved restricted isometry property (RIP) based conditions over the best known results. The basic procedures are done by observational and analytical of a different Electrocardiogram signal downloaded them from PhysioBankATM. Experimental results show that significant performance in term of reconstruction quality and compression rate can be obtained by these two new proposed algorithms, and help the specialist gathering the necessary information from the patient in less time if we use Magnetic Resonance Imaging (MRI) application, or reconstructed the patient data after sending it through the network. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
A Digital Compressed Sensing-Based Energy-Efficient Single-Spot Bluetooth ECG Node.
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.
A Fast, Open EEG Classification Framework Based on Feature Compression and Channel Ranking
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Jiuqi Han
2018-04-01
Full Text Available Superior feature extraction, channel selection and classification methods are essential for designing electroencephalography (EEG classification frameworks. However, the performance of most frameworks is limited by their improper channel selection methods and too specifical design, leading to high computational complexity, non-convergent procedure and narrow expansibility. In this paper, to remedy these drawbacks, we propose a fast, open EEG classification framework centralized by EEG feature compression, low-dimensional representation, and convergent iterative channel ranking. First, to reduce the complexity, we use data clustering to compress the EEG features channel-wise, packing the high-dimensional EEG signal, and endowing them with numerical signatures. Second, to provide easy access to alternative superior methods, we structurally represent each EEG trial in a feature vector with its corresponding numerical signature. Thus, the recorded signals of many trials shrink to a low-dimensional structural matrix compatible with most pattern recognition methods. Third, a series of effective iterative feature selection approaches with theoretical convergence is introduced to rank the EEG channels and remove redundant ones, further accelerating the EEG classification process and ensuring its stability. Finally, a classical linear discriminant analysis (LDA model is employed to classify a single EEG trial with selected channels. Experimental results on two real world brain-computer interface (BCI competition datasets demonstrate the promising performance of the proposed framework over state-of-the-art methods.
Sitarenios Panagiotis; Kavvadas Michael
2016-01-01
The Modified Cam Clay model is extended to account for the behaviour of unsaturated soils using Bishop’s stress. To describe the Loading – Collapse behaviour, the model incorporates a compressibility framework with suction and degree of saturation dependent compression lines. For simplicity, the present paper describes the model in the triaxial stress space with characteristic simulations of constant suction compression and triaxial tests, as well as wetting tests. The model reproduces an evo...
Energy Analysis of Decoders for Rakeness-Based Compressed Sensing of ECG Signals.
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.
Chen, Xiao; Yang, Yang; Cai, Xiaoying; Auger, Daniel A; Meyer, Craig H; Salerno, Michael; Epstein, Frederick H
2016-06-14
Cine Displacement Encoding with Stimulated Echoes (DENSE) provides accurate quantitative imaging of cardiac mechanics with rapid displacement and strain analysis; however, image acquisition times are relatively long. Compressed sensing (CS) with parallel imaging (PI) can generally provide high-quality images recovered from data sampled below the Nyquist rate. The purposes of the present study were to develop CS-PI-accelerated acquisition and reconstruction methods for cine DENSE, to assess their accuracy for cardiac imaging using retrospective undersampling, and to demonstrate their feasibility for prospectively-accelerated 2D cine DENSE imaging in a single breathhold. An accelerated cine DENSE sequence with variable-density spiral k-space sampling and golden angle rotations through time was implemented. A CS method, Block LOw-rank Sparsity with Motion-guidance (BLOSM), was combined with sensitivity encoding (SENSE) for the reconstruction of under-sampled multi-coil spiral data. Seven healthy volunteers and 7 patients underwent 2D cine DENSE imaging with fully-sampled acquisitions (14-26 heartbeats in duration) and with prospectively rate-2 and rate-4 accelerated acquisitions (14 and 8 heartbeats in duration). Retrospectively- and prospectively-accelerated data were reconstructed using BLOSM-SENSE and SENSE. Image quality of retrospectively-undersampled data was quantified using the relative root mean square error (rRMSE). Myocardial displacement and circumferential strain were computed for functional assessment, and linear correlation and Bland-Altman analyses were used to compare accelerated acquisitions to fully-sampled reference datasets. For retrospectively-undersampled data, BLOSM-SENSE provided similar or lower rRMSE at rate-2 and lower rRMSE at rate-4 acceleration compared to SENSE (p cine DENSE provided good image quality and expected values of displacement and strain. BLOSM-SENSE-accelerated spiral cine DENSE imaging with 2D displacement encoding can be
Quantitative Inspection of Remanence of Broken Wire Rope Based on Compressed Sensing.
Zhang, Juwei; Tan, Xiaojiang
2016-08-25
Most traditional strong magnetic inspection equipment has disadvantages such as big excitation devices, high weight, low detection precision, and inconvenient operation. This paper presents the design of a giant magneto-resistance (GMR) sensor array collection system. The remanence signal is collected to acquire two-dimensional magnetic flux leakage (MFL) data on the surface of wire ropes. Through the use of compressed sensing wavelet filtering (CSWF), the image expression of wire ropes MFL on the surface was obtained. Then this was taken as the input of the designed back propagation (BP) neural network to extract three kinds of MFL image geometry features and seven invariant moments of defect images. Good results were obtained. The experimental results show that nondestructive inspection through the use of remanence has higher accuracy and reliability compared with traditional inspection devices, along with smaller volume, lighter weight and higher precision.
Quantitative Inspection of Remanence of Broken Wire Rope Based on Compressed Sensing
Zhang, Juwei; Tan, Xiaojiang
2016-01-01
Most traditional strong magnetic inspection equipment has disadvantages such as big excitation devices, high weight, low detection precision, and inconvenient operation. This paper presents the design of a giant magneto-resistance (GMR) sensor array collection system. The remanence signal is collected to acquire two-dimensional magnetic flux leakage (MFL) data on the surface of wire ropes. Through the use of compressed sensing wavelet filtering (CSWF), the image expression of wire ropes MFL on the surface was obtained. Then this was taken as the input of the designed back propagation (BP) neural network to extract three kinds of MFL image geometry features and seven invariant moments of defect images. Good results were obtained. The experimental results show that nondestructive inspection through the use of remanence has higher accuracy and reliability compared with traditional inspection devices, along with smaller volume, lighter weight and higher precision. PMID:27571077
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Ahmed M. Khedr
2015-10-01
Full Text Available In designing wireless sensor networks (WSNs, it is important to reduce energy dissipation and prolong network lifetime. Clustering of nodes is one of the most effective approaches for conserving energy in WSNs. Cluster formation protocols generally consider the heterogeneity of sensor nodes in terms of energy difference of nodes but ignore the different transmission ranges of them. In this paper, we propose an effective data acquisition clustered protocol using compressive sensing (EDACP-CS for heterogeneous WSNs that aims to conserve the energy of sensor nodes in the presence of energy and transmission range heterogeneity. In EDACP-CS, cluster heads are selected based on the distance from the base station and sensor residual energy. Simulation results show that our protocol offers a much better performance than the existing protocols in terms of energy consumption, stability, network lifetime, and throughput.
Backtracking-Based Iterative Regularization Method for Image Compressive Sensing Recovery
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Lingjun Liu
2017-01-01
Full Text Available This paper presents a variant of the iterative shrinkage-thresholding (IST algorithm, called backtracking-based adaptive IST (BAIST, for image compressive sensing (CS reconstruction. For increasing iterations, IST usually yields a smoothing of the solution and runs into prematurity. To add back more details, the BAIST method backtracks to the previous noisy image using L2 norm minimization, i.e., minimizing the Euclidean distance between the current solution and the previous ones. Through this modification, the BAIST method achieves superior performance while maintaining the low complexity of IST-type methods. Also, BAIST takes a nonlocal regularization with an adaptive regularizor to automatically detect the sparsity level of an image. Experimental results show that our algorithm outperforms the original IST method and several excellent CS techniques.
A Compressed Sensing-based Image Reconstruction Algorithm for Solar Flare X-Ray Observations
Energy Technology Data Exchange (ETDEWEB)
Felix, Simon; Bolzern, Roman; Battaglia, Marina, E-mail: simon.felix@fhnw.ch, E-mail: roman.bolzern@fhnw.ch, E-mail: marina.battaglia@fhnw.ch [University of Applied Sciences and Arts Northwestern Switzerland FHNW, 5210 Windisch (Switzerland)
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.
Ouyang, Bing; Hou, Weilin; Gong, Cuiling; Caimi, Frank M.; Dalgleish, Fraser R.; Vuorenkoski, Anni K.
2016-05-01
The Compressive Line Sensing (CLS) active imaging system has been demonstrated to be effective in scattering mediums, such as turbid coastal water through simulations and test tank experiments. Since turbulence is encountered in many atmospheric and underwater surveillance applications, a new CLS imaging prototype was developed to investigate the effectiveness of the CLS concept in a turbulence environment. Compared with earlier optical bench top prototype, the new system is significantly more robust and compact. A series of experiments were conducted at the Naval Research Lab's optical turbulence test facility with the imaging path subjected to various turbulence intensities. In addition to validating the system design, we obtained some unexpected exciting results - in the strong turbulence environment, the time-averaged measurements using the new CLS imaging prototype improved both SNR and resolution of the reconstructed images. We will discuss the implications of the new findings, the challenges of acquiring data through strong turbulence environment, and future enhancements.
Near-source noise suppression of AMT by compressive sensing and mathematical morphology filtering
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.
A computationally efficient OMP-based compressed sensing reconstruction for dynamic MRI
International Nuclear Information System (INIS)
Usman, M; Prieto, C; Schaeffter, T; Batchelor, P G; Odille, F; Atkinson, D
2011-01-01
Compressed sensing (CS) methods in MRI are computationally intensive. Thus, designing novel CS algorithms that can perform faster reconstructions is crucial for everyday applications. We propose a computationally efficient orthogonal matching pursuit (OMP)-based reconstruction, specifically suited to cardiac MR data. According to the energy distribution of a y-f space obtained from a sliding window reconstruction, we label the y-f space as static or dynamic. For static y-f space images, a computationally efficient masked OMP reconstruction is performed, whereas for dynamic y-f space images, standard OMP reconstruction is used. The proposed method was tested on a dynamic numerical phantom and two cardiac MR datasets. Depending on the field of view composition of the imaging data, compared to the standard OMP method, reconstruction speedup factors ranging from 1.5 to 2.5 are achieved. (note)
A Compressed Sensing-based Image Reconstruction Algorithm for Solar Flare X-Ray Observations
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.
Quantitative Inspection of Remanence of Broken Wire Rope Based on Compressed Sensing
Directory of Open Access Journals (Sweden)
Juwei Zhang
2016-08-01
Full Text Available 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.
Chang, Chen-Ming; Grant, Alexander M.; Lee, Brian J.; Kim, Ealgoo; Hong, KeyJo; Levin, Craig S.
2015-08-01
In the field of information theory, compressed sensing (CS) had been developed to recover signals at a lower sampling rate than suggested by the Nyquist-Shannon theorem, provided the signals have a sparse representation with respect to some base. CS has recently emerged as a method to multiplex PET detector readouts thanks to the sparse nature of 511 keV photon interactions in a typical PET study. We have shown in our previous numerical studies that, at the same multiplexing ratio, CS achieves higher signal-to-noise ratio (SNR) compared to Anger and cross-strip multiplexing. In addition, unlike Anger logic, multiplexing by CS preserves the capability to resolve multi-hit events, in which multiple pixels are triggered within the resolving time of the detector. In this work, we characterized the time, energy and intrinsic spatial resolution of two CS detectors and a data acquisition system we have developed for a PET insert system for simultaneous PET/MRI. The CS detector comprises a 2× 4 mosaic of 4× 4 arrays of 3.2× 3.2× 20 mm3 lutetium-yttrium orthosilicate crystals coupled one-to-one to eight 4× 4 silicon photomultiplier arrays. The total number of 128 pixels is multiplexed down to 16 readout channels by CS. The energy, coincidence time and intrinsic spatial resolution achieved by two CS detectors were 15.4+/- 0.1 % FWHM at 511 keV, 4.5 ns FWHM and 2.3 mm FWHM, respectively. A series of experiments were conducted to measure the sources of time jitter that limit the time resolution of the current system, which provides guidance for potential system design improvements. These findings demonstrate the feasibility of compressed sensing as a promising multiplexing method for PET detectors.
Feasibility of high temporal resolution breast DCE-MRI using compressed sensing theory.
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.
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Valenzise G
2009-01-01
Full Text Available In the past few years, a large amount of techniques have been proposed to identify whether a multimedia content has been illegally tampered or not. Nevertheless, very few efforts have been devoted to identifying which kind of attack has been carried out, especially due to the large data required for this task. We propose a novel hashing scheme which exploits the paradigms of compressive sensing and distributed source coding to generate a compact hash signature, and we apply it to the case of audio content protection. The audio content provider produces a small hash signature by computing a limited number of random projections of a perceptual, time-frequency representation of the original audio stream; the audio hash is given by the syndrome bits of an LDPC code applied to the projections. At the content user side, the hash is decoded using distributed source coding tools. If the tampering is sparsifiable or compressible in some orthonormal basis or redundant dictionary, it is possible to identify the time-frequency position of the attack, with a hash size as small as 200 bits/second; the bit saving obtained by introducing distributed source coding ranges between 20% to 70%.
On the Feedback Reduction of Relay Multiuser Networks using Compressive Sensing
Elkhalil, Khalil
2016-01-29
This paper presents a comprehensive performance analysis of full-duplex multiuser relay networks employing opportunistic scheduling with noisy and compressive feedback. Specifically, two feedback techniques based on compressive sensing (CS) theory are introduced and their effect on the system performance is analyzed. The problem of joint user identity and signal-tonoise ratio (SNR) estimation at the base-station is casted as a block sparse signal recovery problem in CS. Using existing CS block recovery algorithms, the identity of the strong users is obtained and their corresponding SNRs are estimated using the best linear unbiased estimator (BLUE). To minimize the effect of feedback noise on the estimated SNRs, a back-off strategy that optimally backs-off on the noisy estimated SNRs is introduced, and the error covariance matrix of the noise after CS recovery is derived. Finally, closed-form expressions for the end-to-end SNRs of the system are derived. Numerical results show that the proposed techniques drastically reduce the feedback air-time and achieve a rate close to that obtained by scheduling techniques that require dedicated error-free feedback from all network users. Key findings of this paper suggest that the choice of half-duplex or full-duplex SNR feedback is dependent on the channel coherence interval, and on low coherence intervals, full-duplex feedback is superior to the interference-free half-duplex feedback.
Shrot, Yoav; Frydman, Lucio
2011-04-01
A topic of active investigation in 2D NMR relates to the minimum number of scans required for acquiring this kind of spectra, particularly when these are dictated by sampling rather than by sensitivity considerations. Reductions in this minimum number of scans have been achieved by departing from the regular sampling used to monitor the indirect domain, and relying instead on non-uniform sampling and iterative reconstruction algorithms. Alternatively, so-called "ultrafast" methods can compress the minimum number of scans involved in 2D NMR all the way to a minimum number of one, by spatially encoding the indirect domain information and subsequently recovering it via oscillating field gradients. Given ultrafast NMR's simultaneous recording of the indirect- and direct-domain data, this experiment couples the spectral constraints of these orthogonal domains - often calling for the use of strong acquisition gradients and large filter widths to fulfill the desired bandwidth and resolution demands along all spectral dimensions. This study discusses a way to alleviate these demands, and thereby enhance the method's performance and applicability, by combining spatial encoding with iterative reconstruction approaches. Examples of these new principles are given based on the compressed-sensed reconstruction of biomolecular 2D HSQC ultrafast NMR data, an approach that we show enables a decrease of the gradient strengths demanded in this type of experiments by up to 80%. Copyright © 2011 Elsevier Inc. All rights reserved.
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.
Directory of Open Access Journals (Sweden)
Tongfeng Zhang
2016-01-01
Full Text Available A one-dimensional (1D hybrid chaotic system is constructed by three different 1D chaotic maps in parallel-then-cascade fashion. The proposed chaotic map has larger key space and exhibits better uniform distribution property in some parametric range compared with existing 1D chaotic map. Meanwhile, with the combination of compressive sensing (CS and Fibonacci-Lucas transform (FLT, a novel image compression and encryption scheme is proposed with the advantages of the 1D hybrid chaotic map. The whole encryption procedure includes compression by compressed sensing (CS, scrambling with FLT, and diffusion after linear scaling. Bernoulli measurement matrix in CS is generated by the proposed 1D hybrid chaotic map due to its excellent uniform distribution. To enhance the security and complexity, transform kernel of FLT varies in each permutation round according to the generated chaotic sequences. Further, the key streams used in the diffusion process depend on the chaotic map as well as plain image, which could resist chosen plaintext attack (CPA. Experimental results and security analyses demonstrate the validity of our scheme in terms of high security and robustness against noise attack and cropping attack.
Energy Technology Data Exchange (ETDEWEB)
Park, Yeonok; Cho, Heemoon; Je, Uikyu; Cho, Hyosung, E-mail: hscho1@yonsei.ac.kr; Park, Chulkyu; Lim, Hyunwoo; Kim, Kyuseok; Kim, Guna; Park, Soyoung; Woo, Taeho; Choi, Sungil
2015-12-21
In this work, we have developed a prototype digital breast tomosynthesis (DBT) system which mainly consists of an x-ray generator (28 kV{sub p}, 7 mA s), a CMOS-type flat-panel detector (70-μm pixel size, 230.5×339 mm{sup 2} active area), and a rotational arm to move the x-ray generator in an arc. We employed a compressed-sensing (CS)-based reconstruction algorithm, rather than a common filtered-backprojection (FBP) one, for more accurate DBT reconstruction. Here the CS is a state-of-the-art mathematical theory for solving the inverse problems, which exploits the sparsity of the image with substantially high accuracy. We evaluated the reconstruction quality in terms of the detectability, the contrast-to-noise ratio (CNR), and the slice-sensitive profile (SSP) by using the mammographic accreditation phantom (Model 015, CIRS Inc.) and compared it to the FBP-based quality. The CS-based algorithm yielded much better image quality, preserving superior image homogeneity, edge sharpening, and cross-plane resolution, compared to the FBP-based one. - Highlights: • A prototype digital breast tomosynthesis (DBT) system is developed. • Compressed-sensing (CS) based reconstruction framework is employed. • We reconstructed high-quality DBT images by using the proposed reconstruction framework.
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Melli, Seyed Ali, E-mail: sem649@mail.usask.ca [Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK (Canada); Wahid, Khan A. [Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK (Canada); Babyn, Paul [Department of Medical Imaging, University of Saskatchewan, Saskatoon, SK (Canada); Montgomery, James [College of Medicine, University of Saskatchewan, Saskatoon, SK (Canada); Snead, Elisabeth [Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, SK (Canada); El-Gayed, Ali [College of Medicine, University of Saskatchewan, Saskatoon, SK (Canada); Pettitt, Murray; Wolkowski, Bailey [College of Agriculture and Bioresources, University of Saskatchewan, Saskatoon, SK (Canada); Wesolowski, Michal [Department of Medical Imaging, University of Saskatchewan, Saskatoon, SK (Canada)
2016-01-11
Synchrotron source propagation-based X-ray phase contrast computed tomography is increasingly used in pre-clinical imaging. However, it typically requires a large number of projections, and subsequently a large radiation dose, to produce high quality images. To improve the applicability of this imaging technique, reconstruction algorithms that can reduce the radiation dose and acquisition time without degrading image quality are needed. The proposed research focused on using a novel combination of Douglas–Rachford splitting and randomized Kaczmarz algorithms to solve large-scale total variation based optimization in a compressed sensing framework to reconstruct 2D images from a reduced number of projections. Visual assessment and quantitative performance evaluations of a synthetic abdomen phantom and real reconstructed image of an ex-vivo slice of canine prostate tissue demonstrate that the proposed algorithm is competitive in reconstruction process compared with other well-known algorithms. An additional potential benefit of reducing the number of projections would be reduction of time for motion artifact to occur if the sample moves during image acquisition. Use of this reconstruction algorithm to reduce the required number of projections in synchrotron source propagation-based X-ray phase contrast computed tomography is an effective form of dose reduction that may pave the way for imaging of in-vivo samples.
Potential benefits of remote sensing: Theoretical framework and empirical estimate
Eisgruber, L. M.
1972-01-01
A theoretical framwork is outlined for estimating social returns from research and application of remote sensing. The approximate dollar magnitude is given of a particular application of remote sensing, namely estimates of corn production, soybeans, and wheat. Finally, some comments are made on the limitations of this procedure and on the implications of results.
Deterministic matrices matching the compressed sensing phase transitions of Gaussian random matrices
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
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.
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Mathias Becquaert
2018-05-01
Full Text Available 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.
Millikan, Brian; Dutta, Aritra; Sun, Qiyu; Foroosh, Hassan
2017-01-01
Target detection of potential threats at night can be deployed on a costly infrared focal plane array with high resolution. Due to the compressibility of infrared image patches, the high resolution requirement could be reduced with target detection capability preserved. For this reason, a compressive midwave infrared imager (MWIR) with a low-resolution focal plane array has been developed. As the most probable coefficient indices of the support set of the infrared image patches could be learned from the training data, we develop stochastically trained least squares (STLS) for MWIR image reconstruction. Quadratic correlation filters (QCF) have been shown to be effective for target detection and there are several methods for designing a filter. Using the same measurement matrix as in STLS, we construct a compressed quadratic correlation filter (CQCF) employing filter designs for compressed infrared target detection. We apply CQCF to the U.S. Army Night Vision and Electronic Sensors Directorate dataset. Numerical simulations show that the recognition performance of our algorithm matches that of the standard full reconstruction methods, but at a fraction of the execution time.
Millikan, Brian
2017-05-02
Target detection of potential threats at night can be deployed on a costly infrared focal plane array with high resolution. Due to the compressibility of infrared image patches, the high resolution requirement could be reduced with target detection capability preserved. For this reason, a compressive midwave infrared imager (MWIR) with a low-resolution focal plane array has been developed. As the most probable coefficient indices of the support set of the infrared image patches could be learned from the training data, we develop stochastically trained least squares (STLS) for MWIR image reconstruction. Quadratic correlation filters (QCF) have been shown to be effective for target detection and there are several methods for designing a filter. Using the same measurement matrix as in STLS, we construct a compressed quadratic correlation filter (CQCF) employing filter designs for compressed infrared target detection. We apply CQCF to the U.S. Army Night Vision and Electronic Sensors Directorate dataset. Numerical simulations show that the recognition performance of our algorithm matches that of the standard full reconstruction methods, but at a fraction of the execution time.
A Web GIS Framework for Participatory Sensing Service: An Open Source-Based Implementation
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Yu Nakayama
2017-04-01
Full Text Available Participatory sensing is the process in which individuals or communities collect and analyze systematic data using mobile phones and cloud services. To efficiently develop participatory sensing services, some server-side technologies have been proposed. Although they provide a good platform for participatory sensing, they are not optimized for spatial data management and processing. For the purpose of spatial data collection and management, many web GIS approaches have been studied. However, they still have not focused on the optimal framework for participatory sensing services. This paper presents a web GIS framework for participatory sensing service (FPSS. The proposed FPSS enables an integrated deployment of spatial data capture, storage, and data management functions. In various types of participatory sensing experiments, users can collect and manage spatial data in a unified manner. This feature is realized by the optimized system architecture and use case based on the general requirements for participatory sensing. We developed an open source GIS-based implementation of the proposed framework, which can overcome financial difficulties that are one of the major problems of deploying sensing experiments. We confirmed with the prototype that participatory sensing experiments can be performed efficiently with the proposed FPSS.
Improved compressed sensing-based cone-beam CT reconstruction using adaptive prior image constraints
Lee, Ho; Xing, Lei; Davidi, Ran; Li, Ruijiang; Qian, Jianguo; Lee, Rena
2012-04-01
Volumetric cone-beam CT (CBCT) images are acquired repeatedly during a course of radiation therapy and a natural question to ask is whether CBCT images obtained earlier in the process can be utilized as prior knowledge to reduce patient imaging dose in subsequent scans. The purpose of this work is to develop an adaptive prior image constrained compressed sensing (APICCS) method to solve this problem. Reconstructed images using full projections are taken on the first day of radiation therapy treatment and are used as prior images. The subsequent scans are acquired using a protocol of sparse projections. In the proposed APICCS algorithm, the prior images are utilized as an initial guess and are incorporated into the objective function in the compressed sensing (CS)-based iterative reconstruction process. Furthermore, the prior information is employed to detect any possible mismatched regions between the prior and current images for improved reconstruction. For this purpose, the prior images and the reconstructed images are classified into three anatomical regions: air, soft tissue and bone. Mismatched regions are identified by local differences of the corresponding groups in the two classified sets of images. A distance transformation is then introduced to convert the information into an adaptive voxel-dependent relaxation map. In constructing the relaxation map, the matched regions (unchanged anatomy) between the prior and current images are assigned with smaller weight values, which are translated into less influence on the CS iterative reconstruction process. On the other hand, the mismatched regions (changed anatomy) are associated with larger values and the regions are updated more by the new projection data, thus avoiding any possible adverse effects of prior images. The APICCS approach was systematically assessed by using patient data acquired under standard and low-dose protocols for qualitative and quantitative comparisons. The APICCS method provides an
Massive-MIMO Sparse Uplink Channel Estimation Using Implicit Training and Compressed Sensing
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Babar Mansoor
2017-01-01
Full Text Available Massive multiple-input multiple-output (massive-MIMO is foreseen as a potential technology for future 5G cellular communication networks due to its substantial benefits in terms of increased spectral and energy efficiency. These advantages of massive-MIMO are a consequence of equipping the base station (BS with quite a large number of antenna elements, thus resulting in an aggressive spatial multiplexing. In order to effectively reap the benefits of massive-MIMO, an adequate estimate of the channel impulse response (CIR between each transmit–receive link is of utmost importance. It has been established in the literature that certain specific multipath propagation environments lead to a sparse structured CIR in spatial and/or delay domains. In this paper, implicit training and compressed sensing based CIR estimation techniques are proposed for the case of massive-MIMO sparse uplink channels. In the proposed superimposed training (SiT based techniques, a periodic and low power training sequence is superimposed (arithmetically added over the information sequence, thus avoiding any dedicated time/frequency slots for the training sequence. For the estimation of such massive-MIMO sparse uplink channels, two greedy pursuits based compressed sensing approaches are proposed, viz: SiT based stage-wise orthogonal matching pursuit (SiT-StOMP and gradient pursuit (SiT-GP. In order to demonstrate the validity of proposed techniques, a performance comparison in terms of normalized mean square error (NCMSE and bit error rate (BER is performed with a notable SiT based least squares (SiT-LS channel estimation technique. The effect of channels’ sparsity, training-to-information power ratio (TIR and signal-to-noise ratio (SNR on BER and NCMSE performance of proposed schemes is thoroughly studied. For a simulation scenario of: 4 × 64 massive-MIMO with a channel sparsity level of 80 % and signal-to-noise ratio (SNR of 10 dB , a performance gain of 18 dB and 13 d
Qin, W.; Yin, J.; Yao, H.
2013-12-01
On May 24th 2013 a Mw 8.3 normal faulting earthquake occurred at a depth of approximately 600 km beneath the sea of Okhotsk, Russia. It is a rare mega earthquake that ever occurred at such a great depth. We use the time-domain iterative backprojection (IBP) method [1] and also the frequency-domain compressive sensing (CS) technique[2] to investigate the rupture process and energy radiation of this mega earthquake. We currently use the teleseismic P-wave data from about 350 stations of USArray. IBP is an improved method of the traditional backprojection method, which more accurately locates subevents (energy burst) during earthquake rupture and determines the rupture speeds. The total rupture duration of this earthquake is about 35 s with a nearly N-S rupture direction. We find that the rupture is bilateral in the beginning 15 seconds with slow rupture speeds: about 2.5km/s for the northward rupture and about 2 km/s for the southward rupture. After that, the northward rupture stopped while the rupture towards south continued. The average southward rupture speed between 20-35 s is approximately 5 km/s, lower than the shear wave speed (about 5.5 km/s) at the hypocenter depth. The total rupture length is about 140km, in a nearly N-S direction, with a southward rupture length about 100 km and a northward rupture length about 40 km. We also use the CS method, a sparse source inversion technique, to study the frequency-dependent seismic radiation of this mega earthquake. We observe clear along-strike frequency dependence of the spatial and temporal distribution of seismic radiation and rupture process. The results from both methods are generally similar. In the next step, we'll use data from dense arrays in southwest China and also global stations for further analysis in order to more comprehensively study the rupture process of this deep mega earthquake. Reference [1] Yao H, Shearer P M, Gerstoft P. Subevent location and rupture imaging using iterative backprojection for
Cyclops: single-pixel imaging lidar system based on compressive sensing
Magalhães, F.; Correia, M. V.; Farahi, F.; Pereira do Carmo, J.; Araújo, F. M.
2017-11-01
Mars and the Moon are envisaged as major destinations of future space exploration missions in the upcoming decades. Imaging LIDARs are seen as a key enabling technology in the support of autonomous guidance, navigation and control operations, as they can provide very accurate, wide range, high-resolution distance measurements as required for the exploration missions. Imaging LIDARs can be used at critical stages of these exploration missions, such as descent and selection of safe landing sites, rendezvous and docking manoeuvres, or robotic surface navigation and exploration. Despite these devices have been commercially available and used for long in diverse metrology and ranging applications, their size, mass and power consumption are still far from being suitable and attractive for space exploratory missions. Here, we describe a compact Single-Pixel Imaging LIDAR System that is based on a compressive sensing technique. The application of the compressive codes to a DMD array enables compression of the spatial information, while the collection of timing histograms correlated to the pulsed laser source ensures image reconstruction at the ranged distances. Single-pixel cameras have been compared with raster scanning and array based counterparts in terms of noise performance, and proved to be superior. Since a single photodetector is used, a better SNR and higher reliability is expected in contrast with systems using large format photodetector arrays. Furthermore, the event of failure of one or more micromirror elements in the DMD does not prevent full reconstruction of the images. This brings additional robustness to the proposed 3D imaging LIDAR. The prototype that was implemented has three modes of operation. Range Finder: outputs the average distance between the system and the area of the target under illumination; Attitude Meter: provides the slope of the target surface based on distance measurements in three areas of the target; 3D Imager: produces 3D ranged
Directory of Open Access Journals (Sweden)
Shima Pakdaman Tirani
2016-01-01
Full Text Available Wireless Sensor Networks (WSNs consist of several sensor nodes with sensing, computation, and wireless communication capabilities. The energy constraint is one of the most important issues in these networks. Thus, the data-gathering process should be carefully designed to conserve the energy. In this situation, a load balancing strategy can enhance the resources utilization, and consequently, increase the network lifetime. Furthermore, recently, the sparse nature of data in WSNs has been motivated the use of the compressive sensing as an efficient data gathering technique. Using the compressive sensing theory significantly leads to decreasing the volume of the transmitted data. Taking the above challenges into account, the main goal of this paper is to jointly consider the compressive sensing method and the load-balancing in WSNs. In this regards, using the conventional network model, we analyze the network performance in several different states. These states challenge the sink location in term of the number of transmissions. Numerical results demonstrate the efficiency of the load-balancing in the network performance.
Fast and low-dose computed laminography using compressive sensing based technique
Abbas, Sajid; Park, Miran; Cho, Seungryong
2015-03-01
Computed laminography (CL) is well known for inspecting microstructures in the materials, weldments and soldering defects in high density packed components or multilayer printed circuit boards. The overload problem on x-ray tube and gross failure of the radio-sensitive electronics devices during a scan are among important issues in CL which needs to be addressed. The sparse-view CL can be one of the viable option to overcome such issues. In this work a numerical aluminum welding phantom was simulated to collect sparsely sampled projection data at only 40 views using a conventional CL scanning scheme i.e. oblique scan. A compressive-sensing inspired total-variation (TV) minimization algorithm was utilized to reconstruct the images. It is found that the images reconstructed using sparse view data are visually comparable with the images reconstructed using full scan data set i.e. at 360 views on regular interval. We have quantitatively confirmed that tiny structures such as copper and tungsten slags, and copper flakes in the reconstructed images from sparsely sampled data are comparable with the corresponding structure present in the fully sampled data case. A blurring effect can be seen near the edges of few pores at the bottom of the reconstructed images from sparsely sampled data, despite the overall image quality is reasonable for fast and low-dose NDT.
Fast and low-dose computed laminography using compressive sensing based technique
Energy Technology Data Exchange (ETDEWEB)
Abbas, Sajid, E-mail: scho@kaist.ac.kr; Park, Miran, E-mail: scho@kaist.ac.kr; Cho, Seungryong, E-mail: scho@kaist.ac.kr [Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 305-701 (Korea, Republic of)
2015-03-31
Computed laminography (CL) is well known for inspecting microstructures in the materials, weldments and soldering defects in high density packed components or multilayer printed circuit boards. The overload problem on x-ray tube and gross failure of the radio-sensitive electronics devices during a scan are among important issues in CL which needs to be addressed. The sparse-view CL can be one of the viable option to overcome such issues. In this work a numerical aluminum welding phantom was simulated to collect sparsely sampled projection data at only 40 views using a conventional CL scanning scheme i.e. oblique scan. A compressive-sensing inspired total-variation (TV) minimization algorithm was utilized to reconstruct the images. It is found that the images reconstructed using sparse view data are visually comparable with the images reconstructed using full scan data set i.e. at 360 views on regular interval. We have quantitatively confirmed that tiny structures such as copper and tungsten slags, and copper flakes in the reconstructed images from sparsely sampled data are comparable with the corresponding structure present in the fully sampled data case. A blurring effect can be seen near the edges of few pores at the bottom of the reconstructed images from sparsely sampled data, despite the overall image quality is reasonable for fast and low-dose NDT.
Fast and low-dose computed laminography using compressive sensing based technique
International Nuclear Information System (INIS)
Abbas, Sajid; Park, Miran; Cho, Seungryong
2015-01-01
Computed laminography (CL) is well known for inspecting microstructures in the materials, weldments and soldering defects in high density packed components or multilayer printed circuit boards. The overload problem on x-ray tube and gross failure of the radio-sensitive electronics devices during a scan are among important issues in CL which needs to be addressed. The sparse-view CL can be one of the viable option to overcome such issues. In this work a numerical aluminum welding phantom was simulated to collect sparsely sampled projection data at only 40 views using a conventional CL scanning scheme i.e. oblique scan. A compressive-sensing inspired total-variation (TV) minimization algorithm was utilized to reconstruct the images. It is found that the images reconstructed using sparse view data are visually comparable with the images reconstructed using full scan data set i.e. at 360 views on regular interval. We have quantitatively confirmed that tiny structures such as copper and tungsten slags, and copper flakes in the reconstructed images from sparsely sampled data are comparable with the corresponding structure present in the fully sampled data case. A blurring effect can be seen near the edges of few pores at the bottom of the reconstructed images from sparsely sampled data, despite the overall image quality is reasonable for fast and low-dose NDT
Visualization of Astronomical Nebulae via Distributed Multi-GPU Compressed Sensing Tomography.
Wenger, S; Ament, M; Guthe, S; Lorenz, D; Tillmann, A; Weiskopf, D; Magnor, M
2012-12-01
The 3D visualization of astronomical nebulae is a challenging problem since only a single 2D projection is observable from our fixed vantage point on Earth. We attempt to generate plausible and realistic looking volumetric visualizations via a tomographic approach that exploits the spherical or axial symmetry prevalent in some relevant types of nebulae. Different types of symmetry can be implemented by using different randomized distributions of virtual cameras. Our approach is based on an iterative compressed sensing reconstruction algorithm that we extend with support for position-dependent volumetric regularization and linear equality constraints. We present a distributed multi-GPU implementation that is capable of reconstructing high-resolution datasets from arbitrary projections. Its robustness and scalability are demonstrated for astronomical imagery from the Hubble Space Telescope. The resulting volumetric data is visualized using direct volume rendering. Compared to previous approaches, our method preserves a much higher amount of detail and visual variety in the 3D visualization, especially for objects with only approximate symmetry.
Detection of Defective Sensors in Phased Array Using Compressed Sensing and Hybrid Genetic Algorithm
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Shafqat Ullah Khan
2016-01-01
Full Text Available A compressed sensing based array diagnosis technique has been presented. This technique starts from collecting the measurements of the far-field pattern. The system linking the difference between the field measured using the healthy reference array and the field radiated by the array under test is solved using a genetic algorithm (GA, parallel coordinate descent (PCD algorithm, and then a hybridized GA with PCD algorithm. These algorithms are applied for fully and partially defective antenna arrays. The simulation results indicate that the proposed hybrid algorithm outperforms in terms of localization of element failure with a small number of measurements. In the proposed algorithm, the slow and early convergence of GA has been avoided by combining it with PCD algorithm. It has been shown that the hybrid GA-PCD algorithm provides an accurate diagnosis of fully and partially defective sensors as compared to GA or PCD alone. Different simulations have been provided to validate the performance of the designed algorithms in diversified scenarios.
Sana, Furrukh
2015-07-26
Recovering information on subsurface geological features, such as flow channels, holds significant importance for optimizing the productivity of oil reservoirs. The flow channels exhibit high permeability in contrast to low permeability rock formations in their surroundings, enabling formulation of a sparse field recovery problem. The Ensemble Kalman filter (EnKF) is a widely used technique for the estimation of subsurface parameters, such as permeability. However, the EnKF often fails to recover and preserve the channel structures during the estimation process. Compressed Sensing (CS) has shown to significantly improve the reconstruction quality when dealing with such problems. We propose a new scheme based on CS principles to enhance the reconstruction of subsurface geological features by transforming the EnKF estimation process to a sparse domain representing diverse geological structures. Numerical experiments suggest that the proposed scheme provides an efficient mechanism to incorporate and preserve structural information in the estimation process and results in significant enhancement in the recovery of flow channel structures.
Correlation between k-space sampling pattern and MTF in compressed sensing MRSI.
Heikal, A A; Wachowicz, K; Fallone, B G
2016-10-01
To investigate the relationship between the k-space sampling patterns used for compressed sensing MR spectroscopic imaging (CS-MRSI) and the modulation transfer function (MTF) of the metabolite maps. This relationship may allow the desired frequency content of the metabolite maps to be quantitatively tailored when designing an undersampling pattern. Simulations of a phantom were used to calculate the MTF of Nyquist sampled (NS) 32 × 32 MRSI, and four-times undersampled CS-MRSI reconstructions. The dependence of the CS-MTF on the k-space sampling pattern was evaluated for three sets of k-space sampling patterns generated using different probability distribution functions (PDFs). CS-MTFs were also evaluated for three more sets of patterns generated using a modified algorithm where the sampling ratios are constrained to adhere to PDFs. Strong visual correlation as well as high R 2 was found between the MTF of CS-MRSI and the product of the frequency-dependant sampling ratio and the NS 32 × 32 MTF. Also, PDF-constrained sampling patterns led to higher reproducibility of the CS-MTF, and stronger correlations to the above-mentioned product. The relationship established in this work provides the user with a theoretical solution for the MTF of CS MRSI that is both predictable and customizable to the user's needs.
Lin, Aaron C W; Strugnell, Wendy; Riley, Robyn; Schmitt, Benjamin; Zenge, Michael; Schmidt, Michaela; Morris, Norman R; Hamilton-Craig, Christian
2017-06-01
To assess the clinical feasibility of a compressed sensing cine magnetic resonance imaging (MRI) sequence of both high temporal and spatial resolution (CS_bSSFP) in comparison to a balanced steady-state free precession cine (bSSFP) sequence for reliable quantification of left ventricular (LV) volumes and mass. Segmented MRI cine images were acquired on a 1.5T scanner in 50 patients in the LV short-axis stack orientation using a retrospectively gated conventional bSSFP sequence (generalized autocalibrating partially parallel acquisition [GRAPPA] acceleration factor 2), followed by a prospectively triggered CS_bSSFP sequence with net acceleration factor of 8. Image quality was assessed by published criteria. Comparison of sequences was made in LV volumes and mass, image quality score, quantitative regional myocardial wall motion, and imaging time using Pearson's correlation, Bland-Altman and paired 2-tailed Student's t-test. Differences (bSSFP minus CS_bSSFP, mean ± SD) and Pearson's correlations were 14.8 ± 16.3 (P = 0.31) and r = 0.98 (P cine CS_bSSFP accurately and reliably quantitates LV volumes and mass, shortens acquisition times, and is clinically feasible. 1 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;45:1693-1699. © 2016 International Society for Magnetic Resonance in Medicine.
Compressed sensing reconstruction of cardiac cine MRI using golden angle spiral trajectories.
Tolouee, Azar; Alirezaie, Javad; Babyn, Paul
2015-11-01
In dynamic cardiac cine Magnetic Resonance Imaging (MRI), the spatiotemporal resolution is limited by the low imaging speed. Compressed sensing (CS) theory has been applied to improve the imaging speed and thus the spatiotemporal resolution. The purpose of this paper is to improve CS reconstruction of under sampled data by exploiting spatiotemporal sparsity and efficient spiral trajectories. We extend k-t sparse algorithm to spiral trajectories to achieve high spatio temporal resolutions in cardiac cine imaging. We have exploited spatiotemporal sparsity of cardiac cine MRI by applying a 2D+time wavelet-Fourier transform. For efficient coverage of k-space, we have used a modified version of multi shot (interleaved) spirals trajectories. In order to reduce incoherent aliasing artifact, we use different random undersampling pattern for each temporal frame. Finally, we have used nonuniform fast Fourier transform (NUFFT) algorithm to reconstruct the image from the non-uniformly acquired samples. The proposed approach was tested in simulated and cardiac cine MRI data. Results show that higher acceleration factors with improved image quality can be obtained with the proposed approach in comparison to the existing state-of-the-art method. The flexibility of the introduced method should allow it to be used not only for the challenging case of cardiac imaging, but also for other patient motion where the patient moves or breathes during acquisition. Copyright © 2015 Elsevier Inc. All rights reserved.
Overlapped block-based compressive sensing imaging on mobile handset devices
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Irene Manotas Gutiérrez
2014-01-01
Full Text Available Compressive Sensing (CS es una nueva técnica que simultáneamente comprime y muestrea una imagen tomando un conjunto de proyecciones aleatorias de una escena. Un algoritmo de optimización es empleado para reconstruir la imagen utilizando las proyecciones aleatorias. Diferentes algoritmos de optimización se han diseñado para obtener de manera eficiente una correcta reconstrucción de la señal original. En la práctica estos algoritmos se han restringido a implementaciones de CS en arquitecturas de alto rendimiento computacional, como computadores de escritorio o unidades de procesamiento gráfico, debido a el gran número de operaciones requeridas por el proceso de reconstrucción. Este trabajo extiende la aplicación de CS para ser implementado en una arquitectura con memoria y capacidad de procesamiento limitados como un dispositivo móvil. Específicamente, se describe un algoritmo basado en bloques sobrepuestos que permite reconstruir la imagen en un dispositivo móvil y se presenta un análisis del consumo de energía de los algoritmos utilizados. Los resultados muestran el tiempo computacional y la calidad de reconstrucción para imágenes de 128x128 y 256x256 píxeles.
Worst configurations (instantons) for compressed sensing over reals: a channel coding approach
International Nuclear Information System (INIS)
Chertkov, Michael; Chilappagari, Shashi K.; Vasic, Bane
2010-01-01
We consider Linear Programming (LP) solution of a Compressed Sensing (CS) problem over reals, also known as the Basis Pursuit (BasP) algorithm. The BasP allows interpretation as a channel-coding problem, and it guarantees the error-free reconstruction over reals for properly chosen measurement matrix and sufficiently sparse error vectors. In this manuscript, we examine how the BasP performs on a given measurement matrix and develop a technique to discover sparse vectors for which the BasP fails. The resulting algorithm is a generalization of our previous results on finding the most probable error-patterns, so called instantons, degrading performance of a finite size Low-Density Parity-Check (LDPC) code in the error-floor regime. The BasP fails when its output is different from the actual error-pattern. We design CS-Instanton Search Algorithm (ISA) generating a sparse vector, called CS-instanton, such that the BasP fails on the instanton, while its action on any modification of the CS-instanton decreasing a properly defined norm is successful. We also prove that, given a sufficiently dense random input for the error-vector, the CS-ISA converges to an instanton in a small finite number of steps. Performance of the CS-ISA is tested on example of a randomly generated 512 * 120 matrix, that outputs the shortest instanton (error vector) pattern of length 11.
Feedback Reduction in Broadcast and two Hop Multiuser Networks: A Compressed Sensing Approach
Shibli, Hussain J.
2013-05-21
In multiuser wireless networks, the base stations (BSs) rely on the channel state information (CSI) of the users to in order to perform user scheduling and downlink transmission. While the downlink channels can be easily estimated at all user terminals via a single broadcast, several key challenges are faced during uplink (feedback) transmission. Firstly, the noisy and fading feedback channels are usually unknown at the base station, and therefore, channel training is usually required from all users. Secondly, the amount of air-time required for feedback transmission grows linearly with the number of users. This domination of the network resources by feedback information leads to increased scheduling delay and outdated CSI at the BS. In this thesis, we tackle the above challenges and propose feedback reduction algorithms based on the theory of compressive sensing (CS). The proposed algorithms encompass both single and dual hop wireless networks, and; i) permit the BS to obtain CSI with acceptable recovery guarantees under substantially reduced feedback overhead, ii) are agnostic to the statistics of the feedback channels, and iii) utilize the apriori statistics of the additive noise to identify strong users. Numerical results show that the proposed algorithms are able to reduce the feedback overhead, improve detection at the BS, and achieve a sum-rate close to that obtained by noiseless dedicated feedback algorithms.
Scout-view assisted interior digital tomosynthesis (iDTS) based on compressed-sensing theory
Park, S. Y.; Kim, G. A.; Cho, H. S.; Seo, C. W.; Je, U. K.; Park, C. K.; Lim, H. W.; Kim, K. S.; Lee, D. Y.; Lee, H. W.; Kang, S. Y.; Park, J. E.; Woo, T. H.; Lee, M. S.
2017-12-01
Conventional digital tomosynthesis (DTS) based on the filtered-backprojection (FBP) reconstruction requires full field-of-view scan and also relatively dense projections, which results in still high dose for medical imaging purposes. In this work, to overcome these difficulties, we propose a new type of DTS examinations, the so-called scout-view assisted interior DTS (iDTS), in which the x-ray beam span covers only a small region-of-interest (ROI) containing target diagnosis with the help of some scout views and they are used in the reconstruction to add additional information to interior ROI otherwise absent with conventional iDTS reconstruction methods. We considered an effective iterative algorithm based on compressed-sensing theory, rather than the FBP-based algorithm, for more accurate iDTS reconstruction. We implemented the proposed algorithm, performed a systematic simulation and experiment, and investigated the image characteristics. We successfully reconstructed iDTS images of substantially high accuracy and no truncation artifacts by using the proposed method, preserving superior image homogeneity, edge sharpening, and in-plane spatial resolution.
Block compressed sensing for feedback reduction in relay-aided multiuser full duplex networks
Elkhalil, Khalil
2016-08-11
Opportunistic user selection is a simple technique that exploits the spatial diversity in multiuser relay-aided networks. Nonetheless, channel state information (CSI) from all users (and cooperating relays) is generally required at a central node in order to make selection decisions. Practically, CSI acquisition generates a great deal of feedback overhead that could result in significant transmission delays. In addition to this, the presence of a full-duplex cooperating relay corrupts the fed back CSI by additive noise and the relay\\'s loop (or self) interference. This could lead to transmission outages if user selection is based on inaccurate feedback information. In this paper, we propose an opportunistic full-duplex feedback algorithm that tackles the above challenges. We cast the problem of joint user signal-to-noise ratio (SNR) and the relay loop interference estimation at the base-station as a block sparse signal recovery problem in compressive sensing (CS). Using existing CS block recovery algorithms, the identity of the strong users is obtained and their corresponding SNRs are estimated. Numerical results show that the proposed technique drastically reduces the feedback overhead and achieves a rate close to that obtained by techniques that require dedicated error-free feedback from all users. Numerical results also show that there is a trade-off between the feedback interference and load, and for short coherence intervals, full-duplex feedback achieves higher throughput when compared to interference-free (half-duplex) feedback. © 2016 IEEE.
An effective approach to attenuate random noise based on compressive sensing and curvelet transform
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Liu, Wei; Cao, Siyuan; Zu, Shaohuan; Chen, Yangkang
2016-01-01
Random noise attenuation is an important step in seismic data processing. In this paper, we propose a novel denoising approach based on compressive sensing and the curvelet transform. We formulate the random noise attenuation problem as an L _1 norm regularized optimization problem. We propose to use the curvelet transform as the sparse transform in the optimization problem to regularize the sparse coefficients in order to separate signal and noise and to use the gradient projection for sparse reconstruction (GPSR) algorithm to solve the formulated optimization problem with an easy implementation and a fast convergence. We tested the performance of our proposed approach on both synthetic and field seismic data. Numerical results show that the proposed approach can effectively suppress the distortion near the edge of seismic events during the noise attenuation process and has high computational efficiency compared with the traditional curvelet thresholding and iterative soft thresholding based denoising methods. Besides, compared with f-x deconvolution, the proposed denoising method is capable of eliminating the random noise more effectively while preserving more useful signals. (paper)
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Dong Wang
2017-01-01
Full Text Available Purpose. Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI is used in cancer imaging to probe tumor vascular properties. Compressed sensing (CS theory makes it possible to recover MR images from randomly undersampled k-space data using nonlinear recovery schemes. The purpose of this paper is to quantitatively evaluate common temporal sparsity-promoting regularizers for CS DCE-MRI of the breast. Methods. We considered five ubiquitous temporal regularizers on 4.5x retrospectively undersampled Cartesian in vivo breast DCE-MRI data: Fourier transform (FT, Haar wavelet transform (WT, total variation (TV, second-order total generalized variation (TGVα2, and nuclear norm (NN. We measured the signal-to-error ratio (SER of the reconstructed images, the error in tumor mean, and concordance correlation coefficients (CCCs of the derived pharmacokinetic parameters Ktrans (volume transfer constant and ve (extravascular-extracellular volume fraction across a population of random sampling schemes. Results. NN produced the lowest image error (SER: 29.1, while TV/TGVα2 produced the most accurate Ktrans (CCC: 0.974/0.974 and ve (CCC: 0.916/0.917. WT produced the highest image error (SER: 21.8, while FT produced the least accurate Ktrans (CCC: 0.842 and ve (CCC: 0.799. Conclusion. TV/TGVα2 should be used as temporal constraints for CS DCE-MRI of the breast.
Peak reduction and clipping mitigation in OFDM by augmented compressive sensing
Al-Safadi, Ebrahim B.
2012-07-01
This work establishes the design, analysis, and fine-tuning of a peak-to-average-power-ratio (PAPR) reducing system, based on compressed sensing (CS) at the receiver of a peak-reducing sparse clipper applied to an orthogonal frequency-division multiplexing (OFDM) signal at the transmitter. By exploiting the sparsity of clipping events in the time domain relative to a predefined clipping threshold, the method depends on partially observing the frequency content of the clipping distortion over reserved tones to estimate the remaining distortion. The approach has the advantage of eliminating the computational complexity at the transmitter and reducing the overall complexity of the system compared to previous methods which incorporate pilots to cancel nonlinear distortion. Data-based augmented CS methods are also proposed that draw upon available phase and support information from data tones for enhanced estimation and cancelation of clipping noise. This enables signal recovery under more severe clipping scenarios and hence lower PAPR can be achieved compared to conventional CS techniques. © 2012 IEEE.
Peak reduction and clipping mitigation in OFDM by augmented compressive sensing
Al-Safadi, Ebrahim B.; Al-Naffouri, Tareq Y.
2012-01-01
This work establishes the design, analysis, and fine-tuning of a peak-to-average-power-ratio (PAPR) reducing system, based on compressed sensing (CS) at the receiver of a peak-reducing sparse clipper applied to an orthogonal frequency-division multiplexing (OFDM) signal at the transmitter. By exploiting the sparsity of clipping events in the time domain relative to a predefined clipping threshold, the method depends on partially observing the frequency content of the clipping distortion over reserved tones to estimate the remaining distortion. The approach has the advantage of eliminating the computational complexity at the transmitter and reducing the overall complexity of the system compared to previous methods which incorporate pilots to cancel nonlinear distortion. Data-based augmented CS methods are also proposed that draw upon available phase and support information from data tones for enhanced estimation and cancelation of clipping noise. This enables signal recovery under more severe clipping scenarios and hence lower PAPR can be achieved compared to conventional CS techniques. © 2012 IEEE.
A compressed sensing X-ray camera with a multilayer architecture
Wang, Zhehui; Iaroshenko, O.; Li, S.; Liu, T.; Parab, N.; Chen, W. W.; Chu, P.; Kenyon, G. T.; Lipton, R.; Sun, K.-X.
2018-01-01
Recent advances in compressed sensing theory and algorithms offer new possibilities for high-speed X-ray camera design. In many CMOS cameras, each pixel has an independent on-board circuit that includes an amplifier, noise rejection, signal shaper, an analog-to-digital converter (ADC), and optional in-pixel storage. When X-ray images are sparse, i.e., when one of the following cases is true: (a.) The number of pixels with true X-ray hits is much smaller than the total number of pixels; (b.) The X-ray information is redundant; or (c.) Some prior knowledge about the X-ray images exists, sparse sampling may be allowed. Here we first illustrate the feasibility of random on-board pixel sampling (ROPS) using an existing set of X-ray images, followed by a discussion about signal to noise as a function of pixel size. Next, we describe a possible circuit architecture to achieve random pixel access and in-pixel storage. The combination of a multilayer architecture, sparse on-chip sampling, and computational image techniques, is expected to facilitate the development and applications of high-speed X-ray camera technology.
A compressed sensing based 3D resistivity inversion algorithm for hydrogeological applications
Ranjan, Shashi; Kambhammettu, B. V. N. P.; Peddinti, Srinivasa Rao; Adinarayana, J.
2018-04-01
Image reconstruction from discrete electrical responses pose a number of computational and mathematical challenges. Application of smoothness constrained regularized inversion from limited measurements may fail to detect resistivity anomalies and sharp interfaces separated by hydro stratigraphic units. Under favourable conditions, compressed sensing (CS) can be thought of an alternative to reconstruct the image features by finding sparse solutions to highly underdetermined linear systems. This paper deals with the development of a CS assisted, 3-D resistivity inversion algorithm for use with hydrogeologists and groundwater scientists. CS based l1-regularized least square algorithm was applied to solve the resistivity inversion problem. Sparseness in the model update vector is introduced through block oriented discrete cosine transformation, with recovery of the signal achieved through convex optimization. The equivalent quadratic program was solved using primal-dual interior point method. Applicability of the proposed algorithm was demonstrated using synthetic and field examples drawn from hydrogeology. The proposed algorithm has outperformed the conventional (smoothness constrained) least square method in recovering the model parameters with much fewer data, yet preserving the sharp resistivity fronts separated by geologic layers. Resistivity anomalies represented by discrete homogeneous blocks embedded in contrasting geologic layers were better imaged using the proposed algorithm. In comparison to conventional algorithm, CS has resulted in an efficient (an increase in R2 from 0.62 to 0.78; a decrease in RMSE from 125.14 Ω-m to 72.46 Ω-m), reliable, and fast converging (run time decreased by about 25%) solution.
Sparse Channel Estimation for MIMO-OFDM Two-Way Relay Network with Compressed Sensing
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Aihua Zhang
2013-01-01
Full Text Available Accurate channel impulse response (CIR is required for equalization and can help improve communication service quality in next-generation wireless communication systems. An example of an advanced system is amplify-and-forward multiple-input multiple-output two-way relay network, which is modulated by orthogonal frequency-division multiplexing. Linear channel estimation methods, for example, least squares and expectation conditional maximization, have been proposed previously for the system. However, these methods do not take advantage of channel sparsity, and they decrease estimation performance. We propose a sparse channel estimation scheme, which is different from linear methods, at end users under the relay channel to enable us to exploit sparsity. First, we formulate the sparse channel estimation problem as a compressed sensing problem by using sparse decomposition theory. Second, the CIR is reconstructed by CoSaMP and OMP algorithms. Finally, computer simulations are conducted to confirm the superiority of the proposed methods over traditional linear channel estimation methods.
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Wei Ke
2017-01-01
Full Text Available In order to enhance the accuracy of sound source localization in noisy and reverberant environments, this paper proposes an adaptive sound source localization method based on distributed microphone arrays. Since sound sources lie at a few points in the discrete spatial domain, our method can exploit this inherent sparsity to convert the localization problem into a sparse recovery problem based on the compressive sensing (CS theory. In this method, a two-step discrete cosine transform- (DCT- based feature extraction approach is utilized to cover both short-time and long-time properties of acoustic signals and reduce the dimensions of the sparse model. In addition, an online dictionary learning (DL method is used to adjust the dictionary for matching the changes of audio signals, and then the sparse solution could better represent location estimations. Moreover, we propose an improved block-sparse reconstruction algorithm using approximate l0 norm minimization to enhance reconstruction performance for sparse signals in low signal-noise ratio (SNR conditions. The effectiveness of the proposed scheme is demonstrated by simulation results and experimental results where substantial improvement for localization performance can be obtained in the noisy and reverberant conditions.
Sana, Furrukh; Katterbauer, Klemens; Al-Naffouri, Tareq Y.; Hoteit, Ibrahim
2015-01-01
Recovering information on subsurface geological features, such as flow channels, holds significant importance for optimizing the productivity of oil reservoirs. The flow channels exhibit high permeability in contrast to low permeability rock formations in their surroundings, enabling formulation of a sparse field recovery problem. The Ensemble Kalman filter (EnKF) is a widely used technique for the estimation of subsurface parameters, such as permeability. However, the EnKF often fails to recover and preserve the channel structures during the estimation process. Compressed Sensing (CS) has shown to significantly improve the reconstruction quality when dealing with such problems. We propose a new scheme based on CS principles to enhance the reconstruction of subsurface geological features by transforming the EnKF estimation process to a sparse domain representing diverse geological structures. Numerical experiments suggest that the proposed scheme provides an efficient mechanism to incorporate and preserve structural information in the estimation process and results in significant enhancement in the recovery of flow channel structures.
FPGA Implementation of Real-Time Compressive Sensing with Partial Fourier Dictionary
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Yinghui Quan
2016-01-01
Full Text Available This paper presents a novel real-time compressive sensing (CS reconstruction which employs high density field-programmable gate array (FPGA for hardware acceleration. Traditionally, CS can be implemented using a high-level computer language in a personal computer (PC or multicore platforms, such as graphics processing units (GPUs and Digital Signal Processors (DSPs. However, reconstruction algorithms are computing demanding and software implementation of these algorithms is extremely slow and power consuming. In this paper, the orthogonal matching pursuit (OMP algorithm is refined to solve the sparse decomposition optimization for partial Fourier dictionary, which is always adopted in radar imaging and detection application. OMP reconstruction can be divided into two main stages: optimization which finds the closely correlated vectors and least square problem. For large scale dictionary, the implementation of correlation is time consuming since it often requires a large number of matrix multiplications. Also solving the least square problem always needs a scalable matrix decomposition operation. To solve these problems efficiently, the correlation optimization is implemented by fast Fourier transform (FFT and the large scale least square problem is implemented by Conjugate Gradient (CG technique, respectively. The proposed method is verified by FPGA (Xilinx Virtex-7 XC7VX690T realization, revealing its effectiveness in real-time applications.
Method for Multiple Targets Tracking in Cognitive Radar Based on Compressed Sensing
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Yang Jun
2016-02-01
Full Text Available A multiple targets cognitive radar tracking method based on Compressed Sensing (CS is proposed. In this method, the theory of CS is introduced to the case of cognitive radar tracking process in multiple targets scenario. The echo signal is sparsely expressed. The designs of sparse matrix and measurement matrix are accomplished by expressing the echo signal sparsely, and subsequently, the restruction of measurement signal under the down-sampling condition is realized. On the receiving end, after considering that the problems that traditional particle filter suffers from degeneracy, and require a large number of particles, the particle swarm optimization particle filter is used to track the targets. On the transmitting end, the Posterior Cramér-Rao Bounds (PCRB of the tracking accuracy is deduced, and the radar waveform parameters are further cognitively designed using PCRB. Simulation results show that the proposed method can not only reduce the data quantity, but also provide a better tracking performance compared with traditional method.
Zhao, Shengmei; Wang, Le; Liang, Wenqiang; Cheng, Weiwen; Gong, Longyan
2015-10-01
In this paper, we propose a high performance optical encryption (OE) scheme based on computational ghost imaging (GI) with QR code and compressive sensing (CS) technique, named QR-CGI-OE scheme. N random phase screens, generated by Alice, is a secret key and be shared with its authorized user, Bob. The information is first encoded by Alice with QR code, and the QR-coded image is then encrypted with the aid of computational ghost imaging optical system. Here, measurement results from the GI optical system's bucket detector are the encrypted information and be transmitted to Bob. With the key, Bob decrypts the encrypted information to obtain the QR-coded image with GI and CS techniques, and further recovers the information by QR decoding. The experimental and numerical simulated results show that the authorized users can recover completely the original image, whereas the eavesdroppers can not acquire any information about the image even the eavesdropping ratio (ER) is up to 60% at the given measurement times. For the proposed scheme, the number of bits sent from Alice to Bob are reduced considerably and the robustness is enhanced significantly. Meantime, the measurement times in GI system is reduced and the quality of the reconstructed QR-coded image is improved.
Continuous diffusion signal, EAP and ODF estimation via Compressive Sensing in diffusion MRI.
Merlet, Sylvain L; Deriche, Rachid
2013-07-01
In this paper, we exploit the ability of Compressed Sensing (CS) to recover the whole 3D Diffusion MRI (dMRI) signal from a limited number of samples while efficiently recovering important diffusion features such as the Ensemble Average Propagator (EAP) and the Orientation Distribution Function (ODF). Some attempts to use CS in estimating diffusion signals have been done recently. However, this was mainly an experimental insight of CS capabilities in dMRI and the CS theory has not been fully exploited. In this work, we also propose to study the impact of the sparsity, the incoherence and the RIP property on the reconstruction of diffusion signals. We show that an efficient use of the CS theory enables to drastically reduce the number of measurements commonly used in dMRI acquisitions. Only 20-30 measurements, optimally spread on several b-value shells, are shown to be necessary, which is less than previous attempts to recover the diffusion signal using CS. This opens an attractive perspective to measure the diffusion signals in white matter within a reduced acquisition time and shows that CS holds great promise and opens new and exciting perspectives in diffusion MRI (dMRI). Copyright © 2013 Elsevier B.V. All rights reserved.
Long-term surface EMG monitoring using K-means clustering and compressive sensing
Balouchestani, Mohammadreza; Krishnan, Sridhar
2015-05-01
In this work, we present an advanced K-means clustering algorithm based on Compressed Sensing theory (CS) in combination with the K-Singular Value Decomposition (K-SVD) method for Clustering of long-term recording of surface Electromyography (sEMG) signals. The long-term monitoring of sEMG signals aims at recording of the electrical activity produced by muscles which are very useful procedure for treatment and diagnostic purposes as well as for detection of various pathologies. The proposed algorithm is examined for three scenarios of sEMG signals including healthy person (sEMG-Healthy), a patient with myopathy (sEMG-Myopathy), and a patient with neuropathy (sEMG-Neuropathr), respectively. The proposed algorithm can easily scan large sEMG datasets of long-term sEMG recording. We test the proposed algorithm with Principal Component Analysis (PCA) and Linear Correlation Coefficient (LCC) dimensionality reduction methods. Then, the output of the proposed algorithm is fed to K-Nearest Neighbours (K-NN) and Probabilistic Neural Network (PNN) classifiers in order to calclute the clustering performance. The proposed algorithm achieves a classification accuracy of 99.22%. This ability allows reducing 17% of Average Classification Error (ACE), 9% of Training Error (TE), and 18% of Root Mean Square Error (RMSE). The proposed algorithm also reduces 14% clustering energy consumption compared to the existing K-Means clustering algorithm.
Block compressed sensing for feedback reduction in relay-aided multiuser full duplex networks
Elkhalil, Khalil; Eltayeb, Mohammed; Kammoun, Abla; Al-Naffouri, Tareq Y.; Bahrami, Hamid Reza
2016-01-01
Opportunistic user selection is a simple technique that exploits the spatial diversity in multiuser relay-aided networks. Nonetheless, channel state information (CSI) from all users (and cooperating relays) is generally required at a central node in order to make selection decisions. Practically, CSI acquisition generates a great deal of feedback overhead that could result in significant transmission delays. In addition to this, the presence of a full-duplex cooperating relay corrupts the fed back CSI by additive noise and the relay's loop (or self) interference. This could lead to transmission outages if user selection is based on inaccurate feedback information. In this paper, we propose an opportunistic full-duplex feedback algorithm that tackles the above challenges. We cast the problem of joint user signal-to-noise ratio (SNR) and the relay loop interference estimation at the base-station as a block sparse signal recovery problem in compressive sensing (CS). Using existing CS block recovery algorithms, the identity of the strong users is obtained and their corresponding SNRs are estimated. Numerical results show that the proposed technique drastically reduces the feedback overhead and achieves a rate close to that obtained by techniques that require dedicated error-free feedback from all users. Numerical results also show that there is a trade-off between the feedback interference and load, and for short coherence intervals, full-duplex feedback achieves higher throughput when compared to interference-free (half-duplex) feedback. © 2016 IEEE.
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.
International Nuclear Information System (INIS)
O’Connor, S M; Lynch, J P; Gilbert, A C
2014-01-01
Compressed sensing (CS) is a powerful new data acquisition paradigm that seeks to accurately reconstruct unknown sparse signals from very few (relative to the target signal dimension) random projections. The specific objective of this study is to save wireless sensor energy by using CS to simultaneously reduce data sampling rates, on-board storage requirements, and communication data payloads. For field-deployed low power wireless sensors that are often operated with limited energy sources, reduced communication translates directly into reduced power consumption and improved operational reliability. In this study, acceleration data from a multi-girder steel-concrete deck composite bridge are processed for the extraction of mode shapes. A wireless sensor node previously designed to perform traditional uniform, Nyquist rate sampling is modified to perform asynchronous, effectively sub-Nyquist rate sampling. The sub-Nyquist data are transmitted off-site to a computational server for reconstruction using the CoSaMP matching pursuit recovery algorithm and further processed for extraction of the structure’s mode shapes. The mode shape metric used for reconstruction quality is the modal assurance criterion (MAC), an indicator of the consistency between CS and traditional Nyquist acquired mode shapes. A comprehensive investigation of modal accuracy from a dense set of acceleration response data reveals that MAC values above 0.90 are obtained for the first four modes of a bridge structure when at least 20% of the original signal is sampled using the CS framework. Reduced data collection, storage and communication requirements are found to lead to substantial reductions in the energy requirements of wireless sensor networks at the expense of modal accuracy. Specifically, total energy reductions of 10–60% can be obtained for a sensor network with 10–100 sensor nodes, respectively. The reduced energy requirements of the CS sensor nodes are shown to directly result in
Benkert, Thomas; Feng, Li; Sodickson, Daniel K; Chandarana, Hersh; Block, Kai Tobias
2017-08-01
Conventional fat/water separation techniques require that patients hold breath during abdominal acquisitions, which often fails and limits the achievable spatial resolution and anatomic coverage. This work presents a novel approach for free-breathing volumetric fat/water separation. Multiecho data are acquired using a motion-robust radial stack-of-stars three-dimensional GRE sequence with bipolar readout. To obtain fat/water maps, a model-based reconstruction is used that accounts for the off-resonant blurring of fat and integrates both compressed sensing and parallel imaging. The approach additionally enables generation of respiration-resolved fat/water maps by detecting motion from k-space data and reconstructing different respiration states. Furthermore, an extension is described for dynamic contrast-enhanced fat-water-separated measurements. Uniform and robust fat/water separation is demonstrated in several clinical applications, including free-breathing noncontrast abdominal examination of adults and a pediatric subject with both motion-averaged and motion-resolved reconstructions, as well as in a noncontrast breast exam. Furthermore, dynamic contrast-enhanced fat/water imaging with high temporal resolution is demonstrated in the abdomen and breast. The described framework provides a viable approach for motion-robust fat/water separation and promises particular value for clinical applications that are currently limited by the breath-holding capacity or cooperation of patients. Magn Reson Med 78:565-576, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.
Chai, Xintao; Tang, Genyang; Peng, Ronghua; Liu, Shaoyong
2018-03-01
Full-waveform inversion (FWI) reconstructs the subsurface properties from acquired seismic data via minimization of the misfit between observed and simulated data. However, FWI suffers from considerable computational costs resulting from the numerical solution of the wave equation for each source at each iteration. To reduce the computational burden, constructing supershots by combining several sources (aka source encoding) allows mitigation of the number of simulations at each iteration, but it gives rise to crosstalk artifacts because of interference between the individual sources of the supershot. A modified Gauss-Newton FWI (MGNFWI) approach showed that as long as the difference between the initial and true models permits a sparse representation, the ℓ _1-norm constrained model updates suppress subsampling-related artifacts. However, the spectral-projected gradient ℓ _1 (SPGℓ _1) algorithm employed by MGNFWI is rather complicated that makes its implementation difficult. To facilitate realistic applications, we adapt a linearized Bregman (LB) method to sparsity-promoting FWI (SPFWI) because of the efficiency and simplicity of LB in the framework of ℓ _1-norm constrained optimization problem and compressive sensing. Numerical experiments performed with the BP Salt model, the Marmousi model and the BG Compass model verify the following points. The FWI result with LB solving ℓ _1-norm sparsity-promoting problem for the model update outperforms that generated by solving ℓ _2-norm problem in terms of crosstalk elimination and high-fidelity results. The simpler LB method performs comparably and even superiorly to the complicated SPGℓ _1 method in terms of computational efficiency and model quality, making the LB method a viable alternative for realistic implementations of SPFWI.
An L1-norm phase constraint for half-Fourier compressed sensing in 3D MR imaging.
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.
Compressed sensing of roller bearing fault based on multiple down-sampling strategy
International Nuclear Information System (INIS)
Wang, Huaqing; Ke, Yanliang; Luo, Ganggang; Tang, Gang
2016-01-01
Roller bearings are essential components of rotating machinery and are often exposed to complex operating conditions, which can easily lead to their failures. Thus, to ensure normal production and the safety of machine operators, it is essential to detect the failures as soon as possible. However, it is a major challenge to maintain a balance between detection efficiency and big data acquisition given the limitations of sampling theory. To overcome these limitations, we try to preserve the information pertaining to roller bearing failures using a sampling rate far below the Nyquist sampling rate, which can ease the pressure generated by the large-scale data. The big data of a faulty roller bearing’s vibration signals is firstly reduced by a down-sample strategy while preserving the fault features by selecting peaks to represent the data segments in time domain. However, a problem arises in that the fault features may be weaker than before, since the noise may be mistaken for the peaks when the noise is stronger than the vibration signals, which makes the fault features unable to be extracted by commonly-used envelope analysis. Here we employ compressive sensing theory to overcome this problem, which can make a signal enhancement and reduce the sample sizes further. Moreover, it is capable of detecting fault features from a small number of samples based on orthogonal matching pursuit approach, which can overcome the shortcomings of the multiple down-sample algorithm. Experimental results validate the effectiveness of the proposed technique in detecting roller bearing faults. (paper)
International Nuclear Information System (INIS)
Yang, Bingxin; Yuan, Min; Ma, Yide; Zhang, Jiuwen; Zhan, Kun
2015-01-01
Compressed sensing(CS) has been well applied to speed up imaging by exploring image sparsity over predefined basis functions or learnt dictionary. Firstly, the sparse representation is generally obtained in a single transform domain by using wavelet-like methods, which cannot produce optimal sparsity considering sparsity, data adaptivity and computational complexity. Secondly, most state-of-the-art reconstruction models seldom consider composite regularization upon the various structural features of images and transform coefficients sub-bands. Therefore, these two points lead to high sampling rates for reconstructing high-quality images. In this paper, an efficient composite sparsity structure is proposed. It learns adaptive dictionary from lowpass uniform discrete curvelet transform sub-band coefficients patches. Consistent with the sparsity structure, a novel composite regularization reconstruction model is developed to improve reconstruction results from highly undersampled k-space data. It is established via minimizing spatial image and lowpass sub-band coefficients total variation regularization, transform sub-bands coefficients l 1 sparse regularization and constraining k-space measurements fidelity. A new augmented Lagrangian method is then introduced to optimize the reconstruction model. It updates representation coefficients of lowpass sub-band coefficients over dictionary, transform sub-bands coefficients and k-space measurements upon the ideas of constrained split augmented Lagrangian shrinkage algorithm. Experimental results on in vivo data show that the proposed method obtains high-quality reconstructed images. The reconstructed images exhibit the least aliasing artifacts and reconstruction error among current CS MRI methods. The proposed sparsity structure can fit and provide hierarchical sparsity for magnetic resonance images simultaneously, bridging the gap between predefined sparse representation methods and explicit dictionary. The new augmented
Compressed sensing of roller bearing fault based on multiple down-sampling strategy
Wang, Huaqing; Ke, Yanliang; Luo, Ganggang; Tang, Gang
2016-02-01
Roller bearings are essential components of rotating machinery and are often exposed to complex operating conditions, which can easily lead to their failures. Thus, to ensure normal production and the safety of machine operators, it is essential to detect the failures as soon as possible. However, it is a major challenge to maintain a balance between detection efficiency and big data acquisition given the limitations of sampling theory. To overcome these limitations, we try to preserve the information pertaining to roller bearing failures using a sampling rate far below the Nyquist sampling rate, which can ease the pressure generated by the large-scale data. The big data of a faulty roller bearing’s vibration signals is firstly reduced by a down-sample strategy while preserving the fault features by selecting peaks to represent the data segments in time domain. However, a problem arises in that the fault features may be weaker than before, since the noise may be mistaken for the peaks when the noise is stronger than the vibration signals, which makes the fault features unable to be extracted by commonly-used envelope analysis. Here we employ compressive sensing theory to overcome this problem, which can make a signal enhancement and reduce the sample sizes further. Moreover, it is capable of detecting fault features from a small number of samples based on orthogonal matching pursuit approach, which can overcome the shortcomings of the multiple down-sample algorithm. Experimental results validate the effectiveness of the proposed technique in detecting roller bearing faults.
Zhang, Jian
2017-06-24
Traditional methods for image compressive sensing (CS) reconstruction solve a well-defined inverse problem that is based on a predefined CS model, which defines the underlying structure of the problem and is generally solved by employing convergent iterative solvers. These optimization-based CS methods face the challenge of choosing optimal transforms and tuning parameters in their solvers, while also suffering from high computational complexity in most cases. Recently, some deep network based CS algorithms have been proposed to improve CS reconstruction performance, while dramatically reducing time complexity as compared to optimization-based methods. Despite their impressive results, the proposed networks (either with fully-connected or repetitive convolutional layers) lack any structural diversity and they are trained as a black box, void of any insights from the CS domain. In this paper, we combine the merits of both types of CS methods: the structure insights of optimization-based method and the performance/speed of network-based ones. We propose a novel structured deep network, dubbed ISTA-Net, which is inspired by the Iterative Shrinkage-Thresholding Algorithm (ISTA) for optimizing a general $l_1$ norm CS reconstruction model. ISTA-Net essentially implements a truncated form of ISTA, where all ISTA-Net parameters are learned end-to-end to minimize a reconstruction error in training. Borrowing more insights from the optimization realm, we propose an accelerated version of ISTA-Net, dubbed FISTA-Net, which is inspired by the fast iterative shrinkage-thresholding algorithm (FISTA). Interestingly, this acceleration naturally leads to skip connections in the underlying network design. Extensive CS experiments demonstrate that the proposed ISTA-Net and FISTA-Net outperform existing optimization-based and network-based CS methods by large margins, while maintaining a fast runtime.
International Nuclear Information System (INIS)
Niu, Tianye; Fruhauf, Quentin; Petrongolo, Michael; Zhu, Lei; Ye, Xiaojing
2014-01-01
Recently, we proposed a new algorithm of accelerated barrier optimization compressed sensing (ABOCS) for iterative CT reconstruction. The previous implementation of ABOCS uses gradient projection (GP) with a Barzilai–Borwein (BB) step-size selection scheme (GP-BB) to search for the optimal solution. The algorithm does not converge stably due to its non-monotonic behavior. In this paper, we further improve the convergence of ABOCS using the unknown-parameter Nesterov (UPN) method and investigate the ABOCS reconstruction performance on clinical patient data. Comparison studies are carried out on reconstructions of computer simulation, a physical phantom and a head-and-neck patient. In all of these studies, the ABOCS results using UPN show more stable and faster convergence than those of the GP-BB method and a state-of-the-art Bregman-type method. As shown in the simulation study of the Shepp–Logan phantom, UPN achieves the same image quality as those of GP-BB and the Bregman-type methods, but reduces the iteration numbers by up to 50% and 90%, respectively. In the Catphan©600 phantom study, a high-quality image with relative reconstruction error (RRE) less than 3% compared to the full-view result is obtained using UPN with 17% projections (60 views). In the conventional filtered-backprojection reconstruction, the corresponding RRE is more than 15% on the same projection data. The superior performance of ABOCS with the UPN implementation is further demonstrated on the head-and-neck patient. Using 25% projections (91 views), the proposed method reduces the RRE from 21% as in the filtered backprojection (FBP) results to 7.3%. In conclusion, we propose UPN for ABOCS implementation. As compared to GP-BB and the Bregman-type methods, the new method significantly improves the convergence with higher stability and fewer iterations. (paper)
A compressed sensing approach for resolution improvement in fiber-bundle based endomicroscopy
Dumas, John P.; Lodhi, Muhammad A.; Bajwa, Waheed U.; Pierce, Mark C.
2018-02-01
Endomicroscopy techniques such as confocal, multi-photon, and wide-field imaging have all been demonstrated using coherent fiber-optic imaging bundles. While the narrow diameter and flexibility of fiber bundles is clinically advantageous, the number of resolvable points in an image is conventionally limited to the number of individual fibers within the bundle. We are introducing concepts from the compressed sensing (CS) field to fiber bundle based endomicroscopy, to allow images to be recovered with more resolvable points than fibers in the bundle. The distal face of the fiber bundle is treated as a low-resolution sensor with circular pixels (fibers) arranged in a hexagonal lattice. A spatial light modulator is located conjugate to the object and distal face, applying multiple high resolution masks to the intermediate image prior to propagation through the bundle. We acquire images of the proximal end of the bundle for each (known) mask pattern and then apply CS inversion algorithms to recover a single high-resolution image. We first developed a theoretical forward model describing image formation through the mask and fiber bundle. We then imaged objects through a rigid fiber bundle and demonstrate that our CS endomicroscopy architecture can recover intra-fiber details while filling inter-fiber regions with interpolation. Finally, we examine the relationship between reconstruction quality and the ratio of the number of mask elements to the number of fiber cores, finding that images could be generated with approximately 28,900 resolvable points for a 1,000 fiber region in our platform.
Directory of Open Access Journals (Sweden)
Mohammed Alawad
2015-03-01
Full Text Available This paper considers the problem of how to efficiently measure a large and complex information field with optimally few observations. Specifically, we investigate how to stochastically estimate modular criticality values in a large-scale digital circuit with a very limited number of measurements in order to minimize the total measurement efforts and time. We prove that, through sparsity-promoting transform domain regularization and by strategically integrating compressive sensing with Bayesian learning, more than 98% of the overall measurement accuracy can be achieved with fewer than 10% of measurements as required in a conventional approach that uses exhaustive measurements. Furthermore, we illustrate that the obtained criticality results can be utilized to selectively fortify large-scale digital circuits for operation with narrow voltage headrooms and in the presence of soft-errors rising at near threshold voltage levels, without excessive hardware overheads. Our numerical simulation results have shown that, by optimally allocating only 10% circuit redundancy, for some large-scale benchmark circuits, we can achieve more than a three-times reduction in its overall error probability, whereas if randomly distributing such 10% hardware resource, less than 2% improvements in the target circuit’s overall robustness will be observed. Finally, we conjecture that our proposed approach can be readily applied to estimate other essential properties of digital circuits that are critical to designing and analyzing them, such as the observability measure in reliability analysis and the path delay estimation in stochastic timing analysis. The only key requirement of our proposed methodology is that these global information fields exhibit a certain degree of smoothness, which is universally true for almost any physical phenomenon.
Combating Impairments in Multi-carrier Systems: A Compressed Sensing Approach
Al-Shuhail, Shamael
2015-05-01
Multi-carrier systems suffer from several impairments, and communication system engineers use powerful signal processing tools to combat these impairments and keep up with the capacity/rate demands. Compressed sensing (CS) is one such tool that allows recovering any sparse signal, requiring only a few measurements in a domain that is incoherent with the domain of sparsity. Almost all signals of interest have some degree of sparsity, and in this work we utilize the sparsity of impairments in orthogonal frequency division multiplexing (OFDM) and its variants (i.e., orthogonal frequency division multiplexing access (OFDMA) and single-carrier frequency-division multiple access (SC-FDMA)) to combat them using CS. We start with the problem of peak-to-average power ratio (PAPR) reduction in OFDM. OFDM signals suffer from high PAPR and clipping is the simplest PAPR reduction scheme. However, clipping introduces inband distortions that result in compromised performance and hence needs to be mitigated at the receiver. Due to the high PAPR nature of the OFDM signal, only a few instances are clipped, these clipping distortions can be recovered at the receiver by employing CS. We then extend the proposed clipping recovery scheme to an interleaved OFDMA system. Interleaved OFDMA presents a special structure that result in only self-inflicted clipping distortions. In this work, we prove that distortions do not spread over multiple users (while utilizing interleaved carrier assignment in OFDMA) and construct a CS system that recovers the clipping distortions on each user. Finally, we address the problem of narrowband interference (NBI) in SC-FDMA. Unlike OFDM and OFDMA systems, SC-FDMA does not suffer from high PAPR, but (as the data is encoded in time domain) is seriously vulnerable to information loss owing to NBI. Utilizing the sparse nature of NBI (in frequency domain) we combat its effect on SC-FDMA system by CS recovery.
Balouchestani, Mohammadreza
2017-05-01
Network traffic or data traffic in a Wireless Local Area Network (WLAN) is the amount of network packets moving across a wireless network from each wireless node to another wireless node, which provide the load of sampling in a wireless network. WLAN's Network traffic is the main component for network traffic measurement, network traffic control and simulation. Traffic classification technique is an essential tool for improving the Quality of Service (QoS) in different wireless networks in the complex applications such as local area networks, wireless local area networks, wireless personal area networks, wireless metropolitan area networks, and wide area networks. Network traffic classification is also an essential component in the products for QoS control in different wireless network systems and applications. Classifying network traffic in a WLAN allows to see what kinds of traffic we have in each part of the network, organize the various kinds of network traffic in each path into different classes in each path, and generate network traffic matrix in order to Identify and organize network traffic which is an important key for improving the QoS feature. To achieve effective network traffic classification, Real-time Network Traffic Classification (RNTC) algorithm for WLANs based on Compressed Sensing (CS) is presented in this paper. The fundamental goal of this algorithm is to solve difficult wireless network management problems. The proposed architecture allows reducing False Detection Rate (FDR) to 25% and Packet Delay (PD) to 15 %. The proposed architecture is also increased 10 % accuracy of wireless transmission, which provides a good background for establishing high quality wireless local area networks.
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.
CoP Sensing Framework on Web-Based Environment
Mustapha, S. M. F. D. Syed
The Web technologies and Web applications have shown similar high growth rate in terms of daily usages and user acceptance. The Web applications have not only penetrated in the traditional domains such as education and business but have also encroached into areas such as politics, social, lifestyle, and culture. The emergence of Web technologies has enabled Web access even to the person on the move through PDAs or mobile phones that are connected using Wi-Fi, HSDPA, or other communication protocols. These two phenomena are the inducement factors toward the need of building Web-based systems as the supporting tools in fulfilling many mundane activities. In doing this, one of the many focuses in research has been to look at the implementation challenges in building Web-based support systems in different types of environment. This chapter describes the implementation issues in building the community learning framework that can be supported on the Web-based platform. The Community of Practice (CoP) has been chosen as the community learning theory to be the case study and analysis as it challenges the creativity of the architectural design of the Web system in order to capture the presence of learning activities. The details of this chapter describe the characteristics of the CoP to understand the inherent intricacies in modeling in the Web-based environment, the evidences of CoP that need to be traced automatically in a slick manner such that the evidence-capturing process is unobtrusive, and the technologies needed to embrace a full adoption of Web-based support system for the community learning framework.
Goebel, Juliane; Nensa, Felix; Schemuth, Haemi P; Maderwald, Stefan; Gratz, Marcel; Quick, Harald H; Schlosser, Thomas; Nassenstein, Kai
2016-08-01
To assess two compressed sensing cine magnetic resonance imaging (MRI) sequences with high spatial or high temporal resolution in comparison to a reference steady-state free precession cine (SSFP) sequence for reliable quantification of left ventricular (LV) volumes. LV short axis stacks of two compressed sensing breath-hold cine sequences with high spatial resolution (SPARSE-SENSE HS: temporal resolution: 40 msec, in-plane resolution: 1.0 × 1.0 mm(2) ) and high temporal resolution (SPARSE-SENSE HT: temporal resolution: 11 msec, in-plane resolution: 1.7 × 1.7 mm(2) ) and of a reference cine SSFP sequence (standard SSFP: temporal resolution: 40 msec, in-plane resolution: 1.7 × 1.7 mm(2) ) were acquired in 16 healthy volunteers on a 1.5T MR system. LV parameters were analyzed semiautomatically twice by one reader and once by a second reader. The volumetric agreement between sequences was analyzed using paired t-test, Bland-Altman plots, and Passing-Bablock regression. Small differences were observed between standard SSFP and SPARSE-SENSE HS for stroke volume (SV; -7 ± 11 ml; P = 0.024), ejection fraction (EF; -2 ± 3%; P = 0.019), and myocardial mass (9 ± 9 g; P = 0.001), but not for end-diastolic volume (EDV; P = 0.079) and end-systolic volume (ESV; P = 0.266). No significant differences were observed between standard SSFP and SPARSE-SENSE HT regarding EDV (P = 0.956), SV (P = 0.088), and EF (P = 0.103), but for ESV (3 ± 5 ml; P = 0.039) and myocardial mass (8 ± 10 ml; P = 0.007). Bland-Altman analysis showed good agreement between the sequences (maximum bias ≤ -8%). Two compressed sensing cine sequences, one with high spatial resolution and one with high temporal resolution, showed good agreement with standard SSFP for LV volume assessment. J. Magn. Reson. Imaging 2016;44:366-374. © 2016 Wiley Periodicals, Inc.
Fast imaging of laboratory core floods using 3D compressed sensing RARE MRI.
Ramskill, N P; Bush, I; Sederman, A J; Mantle, M D; Benning, M; Anger, B C; Appel, M; Gladden, L F
2016-09-01
Three-dimensional (3D) imaging of the fluid distributions within the rock is essential to enable the unambiguous interpretation of core flooding data. Magnetic resonance imaging (MRI) has been widely used to image fluid saturation in rock cores; however, conventional acquisition strategies are typically too slow to capture the dynamic nature of the displacement processes that are of interest. Using Compressed Sensing (CS), it is possible to reconstruct a near-perfect image from significantly fewer measurements than was previously thought necessary, and this can result in a significant reduction in the image acquisition times. In the present study, a method using the Rapid Acquisition with Relaxation Enhancement (RARE) pulse sequence with CS to provide 3D images of the fluid saturation in rock core samples during laboratory core floods is demonstrated. An objective method using image quality metrics for the determination of the most suitable regularisation functional to be used in the CS reconstructions is reported. It is shown that for the present application, Total Variation outperforms the Haar and Daubechies3 wavelet families in terms of the agreement of their respective CS reconstructions with a fully-sampled reference image. Using the CS-RARE approach, 3D images of the fluid saturation in the rock core have been acquired in 16min. The CS-RARE technique has been applied to image the residual water saturation in the rock during a water-water displacement core flood. With a flow rate corresponding to an interstitial velocity of vi=1.89±0.03ftday(-1), 0.1 pore volumes were injected over the course of each image acquisition, a four-fold reduction when compared to a fully-sampled RARE acquisition. Finally, the 3D CS-RARE technique has been used to image the drainage of dodecane into the water-saturated rock in which the dynamics of the coalescence of discrete clusters of the non-wetting phase are clearly observed. The enhancement in the temporal resolution that has
Fuin, Niccolo; Catalano, Onofrio Antonio; Scipioni, Michele; Canjels, Lisanne P W; Izquierdo, David; Pedemonte, Stefano; Catana, Ciprian
2018-01-25
Purpose: We present an approach for concurrent reconstruction of respiratory motion compensated abdominal DCE-MRI and PET data in an integrated PET/MR scanner. The MR and PET reconstructions share the same motion vector fields (MVFs) derived from radial MR data; the approach is robust to changes in respiratory pattern and do not increase the total acquisition time. Methods: PET and DCE-MRI data of 12 oncological patients were simultaneously acquired for 6 minutes on an integrated PET/MR system after administration of 18 F-FDG and gadoterate meglumine. Golden-angle radial MR data were continuously acquired simultaneously with PET data and sorted into multiple motion phases based on a respiratory signal derived directly from the radial MR data. The resulting multidimensional dataset was reconstructed using a compressed sensing approach that exploits sparsity among respiratory phases. MVFs obtained using the full 6-minute (MC_6-min) and only the last 1 minute (MC_1-min) of data were incorporated into the PET reconstruction to obtain motion-corrected PET images and in an MR iterative reconstruction algorithm to produce a series of motion-corrected DCE-MRI images (moco_GRASP). The motion-correction methods (MC_6-min and MC_1-min) were evaluated by qualitative analysis of the MR images and quantitative analysis of maximum and mean standardized uptake values (SUV max , SUVmean), contrast, signal-to-noise ratio (SNR) and lesion volume in the PET images. Results: Motion corrected MC_6-min PET images demonstrated 30%, 23%, 34% and 18% increases in average SUV max , SUVmean, contrast and SNR, and an average 40% reduction in lesion volume with respect to the non-motion-corrected PET images. The changes in these figures of merit were smaller but still substantial for the MC_1-min protocol: 19%, 10%, 15% and 9% increases in average SUV max , SUVmean, contrast and SNR; and a 28% reduction in lesion volume. Moco_GRASP images were deemed of acceptable or better diagnostic image
Directory of Open Access Journals (Sweden)
Nuha A. S. Alwan
2013-01-01
Full Text Available Images with subband coding and threshold wavelet compression are transmitted over a Rayleigh communication channel with additive white Gaussian noise (AWGN, after quantization and 16-QAM modulation. A comparison is made between these two types of compression using both mean square error (MSE and structural similarity (SSIM image quality assessment (IQA criteria applied to the reconstructed image at the receiver. The two methods yielded comparable SSIM but different MSE measures. In this work, we justify our results which support previous findings in the literature that the MSE between two images is not indicative of structural similarity or the visibility of errors. It is found that it is difficult to reduce the pointwise errors in subband-compressed images (higher MSE. However, the compressed images provide comparable SSIM or perceived quality for both types of compression provided that the retained energy after compression is the same.
Gas Phase Sensing of Alcohols by Metal Organic Framework-Polymer Composite Materials
Sachdeva, S.; Koper, S.J.H.; Sabetghadam Esfahani, A.; Soccol, Dimitri; Gravesteijn, Dirk J.; Kapteijn, F.; Sudholter, E.J.R.; Gascon Sabate, J.; de Smet, L.C.P.M.
2017-01-01
Affinity layers play a crucial role in chemical sensors for the selective and sensitive detection of analytes. Here, we report the use of composite affinity layers containing Metal Organic Frameworks (MOFs) in a polymeric matrix for sensing purposes. Nanoparticles of NH_{2}-MIL-53(Al)
Gas Phase Sensing of Alcohols by Metal Organic Framework-Polymer Composite Materials
Sachdeva, Sumit; Koper, Sander J.H.; Sabetghadam, Anahid; Soccol, Dimitri; Gravesteijn, Dirk J.; Kapteijn, Freek; Sudhölter, Ernst J.R.; Gascon, Jorge; Smet, De Louis C.P.M.
2017-01-01
Affinity layers play a crucial role in chemical sensors for the selective and sensitive detection of analytes. Here, we report the use of composite affinity layers containing Metal Organic Frameworks (MOFs) in a polymeric matrix for sensing purposes. Nanoparticles of NH2-MIL-53(Al) were dispersed in
Gas phase sensing of alcohols by Metal Organic Framework – polymer composite materials
Sachdeva, S.; Koper, Sander J.H.; Sabetghadam, Anahid; Soccol, D.; Gravesteijn, Dirk J; Kapteijn, Freek; Sudholter, Ernst J.R.; Gascon, Jorge; de Smet, Louis C.P.M.
2017-01-01
Affinity layers play a crucial role in chemical sensors for the selective and sensitive detection of analytes. Here, we report the use of composite affinity layers containing Metal Organic Frameworks (MOFs) in a polymeric matrix for sensing purposes. Nanoparticles of NH2-MIL-53(Al) were dispersed in
A terbium(III)-organic framework for highly selective sensing of cytidine triphosphate.
Zhao, Xi Juan; He, Rong Xing; Li, Yuan Fang
2012-11-21
Highly selective sensing of cytidine triphosphate (CTP) against other triphosphate nucleosides including ATP, GTP and UTP is successfully achieved with a luminescent terbium(III)-organic framework (TbOF) of [Tb(2)(2,3-pzdc)(2)(ox)(H(2)O)(2)](n) (2,3-pzdc(2-) = 2,3-pyrazinedicarboxylate, ox(2-) = oxalate).
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.
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Chung-Liang Chang
2014-01-01
Full Text Available A compressive sensing based array processing method is proposed to lower the complexity, and computation load of array system and to maintain the robust antijam performance in global navigation satellite system (GNSS receiver. Firstly, the spatial and temporal compressed matrices are multiplied with array signal, which results in a small size array system. Secondly, the 2-dimensional (2D minimum variance distortionless response (MVDR beamformer is employed in proposed system to mitigate the narrowband and wideband interference simultaneously. The iterative process is performed to find optimal spatial and temporal gain vector by MVDR approach, which enhances the steering gain of direction of arrival (DOA of interest. Meanwhile, the null gain is set at DOA of interference. Finally, the simulated navigation signal is generated offline by the graphic user interface tool and employed in the proposed algorithm. The theoretical analysis results using the proposed algorithm are verified based on simulated results.
International Nuclear Information System (INIS)
Zhang, Leihong; Liang, Dong
2016-01-01
In order to solve the problem that reconstruction efficiency and precision is not high, in this paper different samples are selected to reconstruct spectral reflectance, and a new kind of spectral reflectance reconstruction method based on the algorithm of compressive sensing is provided. Four different color numbers of matte color cards such as the ColorChecker Color Rendition Chart and Color Checker SG, the copperplate paper spot color card of Panton, and the Munsell colors card are chosen as training samples, the spectral image is reconstructed respectively by the algorithm of compressive sensing and pseudo-inverse and Wiener, and the results are compared. These methods of spectral reconstruction are evaluated by root mean square error and color difference accuracy. The experiments show that the cumulative contribution rate and color difference of the Munsell colors card are better than those of the other three numbers of color cards in the same conditions of reconstruction, and the accuracy of the spectral reconstruction will be affected by the training sample of different numbers of color cards. The key technology of reconstruction means that the uniformity and representation of the training sample selection has important significance upon reconstruction. In this paper, the influence of the sample selection on the spectral image reconstruction is studied. The precision of the spectral reconstruction based on the algorithm of compressive sensing is higher than that of the traditional algorithm of spectral reconstruction. By the MATLAB simulation results, it can be seen that the spectral reconstruction precision and efficiency are affected by the different color numbers of the training sample. (paper)
DEFF Research Database (Denmark)
Oxvig, Christian Schou; Pedersen, Patrick Steffen; Arildsen, Thomas
2013-01-01
Reconstruction of an undersampled signal is at the root of compressive sensing: when is an algorithm capable of reconstructing the signal? what quality is achievable? and how much time does reconstruction require? We have considered the worst-case performance of the smoothed ℓ0 norm reconstruction...... algorithm in a noiseless setup. Through an empirical tuning of its parameters, we have improved the phase transition (capabilities) of the algorithm for fixed quality and required time. In this paper, we present simulation results that show a phase transition surpassing that of the theoretical ℓ1 approach......: the proposed modified algorithm obtains 1-norm phase transition with greatly reduced required computation time....
Compressed sensing for high-resolution nonlipid suppressed 1 H FID MRSI of the human brain at 9.4T.
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.
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Laisen Nie
2018-01-01
Full Text Available Wireless mesh network is prevalent for providing a decentralized access for users and other intelligent devices. Meanwhile, it can be employed as the infrastructure of the last few miles connectivity for various network applications, for example, Internet of Things (IoT and mobile networks. For a wireless mesh backbone network, it has obtained extensive attention because of its large capacity and low cost. Network traffic prediction is important for network planning and routing configurations that are implemented to improve the quality of service for users. This paper proposes a network traffic prediction method based on a deep learning architecture and the Spatiotemporal Compressive Sensing method. The proposed method first adopts discrete wavelet transform to extract the low-pass component of network traffic that describes the long-range dependence of itself. Then, a prediction model is built by learning a deep architecture based on the deep belief network from the extracted low-pass component. Otherwise, for the remaining high-pass component that expresses the gusty and irregular fluctuations of network traffic, the Spatiotemporal Compressive Sensing method is adopted to predict it. Based on the predictors of two components, we can obtain a predictor of network traffic. From the simulation, the proposed prediction method outperforms three existing methods.
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.
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Saghi, Zineb, E-mail: saghizineb@gmail.com [Department of Materials Science and Metallurgy, University of Cambridge, 27 Charles Babbage Road, Cambridge CB3 0FS (United Kingdom); Divitini, Giorgio [Department of Materials Science and Metallurgy, University of Cambridge, 27 Charles Babbage Road, Cambridge CB3 0FS (United Kingdom); Winter, Benjamin [Center for Nanoanalysis and Electron Microscopy (CENEM), Friedrich-Alexander-Universität Erlangen-Nürnberg, Cauerstraße 6, 91058 Erlangen (Germany); Leary, Rowan [Department of Materials Science and Metallurgy, University of Cambridge, 27 Charles Babbage Road, Cambridge CB3 0FS (United Kingdom); Spiecker, Erdmann [Center for Nanoanalysis and Electron Microscopy (CENEM), Friedrich-Alexander-Universität Erlangen-Nürnberg, Cauerstraße 6, 91058 Erlangen (Germany); Ducati, Caterina [Department of Materials Science and Metallurgy, University of Cambridge, 27 Charles Babbage Road, Cambridge CB3 0FS (United Kingdom); Midgley, Paul A., E-mail: pam33@cam.ac.uk [Department of Materials Science and Metallurgy, University of Cambridge, 27 Charles Babbage Road, Cambridge CB3 0FS (United Kingdom)
2016-01-15
Electron tomography is an invaluable method for 3D cellular imaging. The technique is, however, limited by the specimen geometry, with a loss of resolution due to a restricted tilt range, an increase in specimen thickness with tilt, and a resultant need for subjective and time-consuming manual segmentation. Here we show that 3D reconstructions of needle-shaped biological samples exhibit isotropic resolution, facilitating improved automated segmentation and feature detection. By using scanning transmission electron tomography, with small probe convergence angles, high spatial resolution is maintained over large depths of field and across the tilt range. Moreover, the application of compressed sensing methods to the needle data demonstrates how high fidelity reconstructions may be achieved with far fewer images (and thus greatly reduced dose) than needed by conventional methods. These findings open the door to high fidelity electron tomography over critically relevant length-scales, filling an important gap between existing 3D cellular imaging techniques. - Highlights: • On-axis electron tomography of a needle-shaped biological sample is presented. • A reconstruction with isotropic resolution is achieved. • Compressed sensing methods are compared to conventional reconstruction algorithms. • High fidelity reconstructions are achieved with greatly undersampled datasets.
International Nuclear Information System (INIS)
Saghi, Zineb; Divitini, Giorgio; Winter, Benjamin; Leary, Rowan; Spiecker, Erdmann; Ducati, Caterina; Midgley, Paul A.
2016-01-01
Electron tomography is an invaluable method for 3D cellular imaging. The technique is, however, limited by the specimen geometry, with a loss of resolution due to a restricted tilt range, an increase in specimen thickness with tilt, and a resultant need for subjective and time-consuming manual segmentation. Here we show that 3D reconstructions of needle-shaped biological samples exhibit isotropic resolution, facilitating improved automated segmentation and feature detection. By using scanning transmission electron tomography, with small probe convergence angles, high spatial resolution is maintained over large depths of field and across the tilt range. Moreover, the application of compressed sensing methods to the needle data demonstrates how high fidelity reconstructions may be achieved with far fewer images (and thus greatly reduced dose) than needed by conventional methods. These findings open the door to high fidelity electron tomography over critically relevant length-scales, filling an important gap between existing 3D cellular imaging techniques. - Highlights: • On-axis electron tomography of a needle-shaped biological sample is presented. • A reconstruction with isotropic resolution is achieved. • Compressed sensing methods are compared to conventional reconstruction algorithms. • High fidelity reconstructions are achieved with greatly undersampled datasets.
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.
International Nuclear Information System (INIS)
Wu, Dufan; Li, Liang; Zhang, Li
2013-01-01
In computed tomography (CT), incomplete data problems such as limited angle projections often cause artifacts in the reconstruction results. Additional prior knowledge of the image has shown the potential for better results, such as a prior image constrained compressed sensing algorithm. While a pre-full-scan of the same patient is not always available, massive well-reconstructed images of different patients can be easily obtained from clinical multi-slice helical CTs. In this paper, a feature constrained compressed sensing (FCCS) image reconstruction algorithm was proposed to improve the image quality by using the prior knowledge extracted from the clinical database. The database consists of instances which are similar to the target image but not necessarily the same. Robust principal component analysis is employed to retrieve features of the training images to sparsify the target image. The features form a low-dimensional linear space and a constraint on the distance between the image and the space is used. A bi-criterion convex program which combines the feature constraint and total variation constraint is proposed for the reconstruction procedure and a flexible method is adopted for a good solution. Numerical simulations on both the phantom and real clinical patient images were taken to validate our algorithm. Promising results are shown for limited angle problems. (paper)
International Nuclear Information System (INIS)
Guthier, C; Aschenbrenner, K P; Buergy, D; Ehmann, M; Wenz, F; Hesser, J W
2015-01-01
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. (paper)
Gong, Enhao; Huang, Feng; Ying, Kui; Wu, Wenchuan; Wang, Shi; Yuan, Chun
2015-02-01
A typical clinical MR examination includes multiple scans to acquire images with different contrasts for complementary diagnostic information. The multicontrast scheme requires long scanning time. The combination of partially parallel imaging and compressed sensing (CS-PPI) has been used to reconstruct accelerated scans. However, there are several unsolved problems in existing methods. The target of this work is to improve existing CS-PPI methods for multicontrast imaging, especially for two-dimensional imaging. If the same field of view is scanned in multicontrast imaging, there is significant amount of sharable information. It is proposed in this study to use manifold sharable information among multicontrast images to enhance CS-PPI in a sequential way. Coil sensitivity information and structure based adaptive regularization, which were extracted from previously reconstructed images, were applied to enhance the following reconstructions. The proposed method is called Parallel-imaging and compressed-sensing Reconstruction Of Multicontrast Imaging using SharablE information (PROMISE). Using L1 -SPIRiT as a CS-PPI example, results on multicontrast brain and carotid scans demonstrated that lower error level and better detail preservation can be achieved by exploiting manifold sharable information. Besides, the privilege of PROMISE still exists while there is interscan motion. Using the sharable information among multicontrast images can enhance CS-PPI with tolerance to motions. © 2014 Wiley Periodicals, Inc.
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.
International Nuclear Information System (INIS)
Leihong, Zhang; Dong, Liang; Bei, Li; Zilan, Pan; Dawei, Zhang; Xiuhua, Ma
2015-01-01
In this article, the algorithm of compressing sensing is used to improve the imaging resolution and realize ghost imaging via compressive sensing for a phase object based on the theoretical analysis of the lensless Fourier imaging of the algorithm of ghost imaging based on phase-shifting digital holography. The algorithm of ghost imaging via compressive sensing based on phase-shifting digital holography uses the bucket detector to measure the total light intensity of the interference and the four-step phase-shifting method is used to obtain the total light intensity of differential interference light. The experimental platform is built based on the software simulation, and the experimental results show that the algorithm of ghost imaging via compressive sensing based on phase-shifting digital holography can obtain the high-resolution phase distribution figure of the phase object. With the same sampling times, the phase clarity of the phase distribution figure obtained by the algorithm of ghost imaging via compressive sensing based on phase-shifting digital holography is higher than that obtained by the algorithm of ghost imaging based on phase-shift digital holography. In this article, this study further extends the application range of ghost imaging and obtains the phase distribution of the phase object. (letter)
2015-05-11
Of’ R BASE _fo • J.. J ) Oral PrC$&nlalton. published i Oral Presentatoon. not pub!ɝhed ) Vldoo ( 1 PO$ Iot ) Other exp!a.r Near-infrared compress...Micromirror Device (DMD) is a microelectromechanical (MEMS) device. A DMD consists of millions of electrostatically actuated micro- mirrors (or pixels
Accelerated high-frame-rate mouse heart cine-MRI using compressed sensing reconstruction
Motaal, Abdallah G.; Coolen, Bram F.; Abdurrachim, Desiree; Castro, Rui M.; Prompers, Jeanine J.; Florack, Luc M. J.; Nicolay, Klaas; Strijkers, Gustav J.
2013-01-01
We introduce a new protocol to obtain very high-frame-rate cinematographic (Cine) MRI movies of the beating mouse heart within a reasonable measurement time. The method is based on a self-gated accelerated fast low-angle shot (FLASH) acquisition and compressed sensi ng reconstruction. Key to our
Sensing and capture of toxic and hazardous gases and vapors by metal-organic frameworks.
Wang, Hao; Lustig, William P; Li, Jing
2018-03-13
Toxic and hazardous chemical species are ubiquitous, predominantly emitted by anthropogenic activities, and pose serious risks to human health and the environment. Thus, the sensing and subsequent capture of these chemicals, especially in the gas or vapor phase, are of extreme importance. To this end, metal-organic frameworks have attracted significant interest, as their high porosity and wide tunability make them ideal for both applications. These tailorable framework materials are particularly promising for the specific sensing and capture of targeted chemicals, as they can be designed to fit a diverse range of required conditions. This review will discuss the advantages of metal-organic frameworks in the sensing and capture of harmful gases and vapors, as well as principles and strategies guiding the design of these materials. Recent progress in the luminescent detection of aromatic and aliphatic volatile organic compounds, toxic gases, and chemical warfare agents will be summarized, and the adsorptive removal of fluorocarbons/chlorofluorocarbons, volatile radioactive species, toxic industrial gases and chemical warfare agents will be discussed.
EgoSENSE: A Framework for Context-Aware Mobile Applications Development
Directory of Open Access Journals (Sweden)
E. M. Milic
2017-08-01
Full Text Available This paper presents a context-aware mobile framework (or middleware, intended to support the implementation of context-aware mobile services. The overview of basic concepts, architecture and components of context-aware mobile framework is given. The mobile framework provide acquisition and management of context, where raw data sensed from physical (hardware sensors and virtual (software sensors are combined, processed and analyzed to provide high-level context and situation of the user to the mobile context-aware applications in near real-time. Using demo mobile health application, its most important components and functions, such as these supposed to detect urgent or alarming health conditions of a mobile user and to initiate appropriate actions demonstrated.
Czech Academy of Sciences Publication Activity Database
Slobodian, P.; Říha, Pavel; Olejník, R.; Matyáš, J.; Kovář, M.
2018-01-01
Roč. 271 (2018), s. 76-82 ISSN 0924-4247 R&D Projects: GA MŠk ED2.1.00/19.0409 Grant - others:Ministerstvo školství, mládeže a tělovýchovy (MŠMT)(CZ) LO1504; TBU in Zlin(CZ) IGA/CPS/2015/001 Institutional research plan: CEZ:AV0Z20600510 Keywords : compression force sensor * carbon nanotubes * polyurethane * polymer composite * nanocracks Subject RIV: BK - Fluid Dynamics OBOR OECD: Nano-processes (applications on nano-scale) Impact factor: 2.499, year: 2016
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.
Directory of Open Access Journals (Sweden)
Li Liechen
2016-02-01
Full Text Available A conformal sparse array based on combined Barker code is designed for airship platform. The performance of the designed array such as signal-to-noise ratio is analyzed. Using the hovering characteristics of the airship, interferometry operation can be applied on the real aperture imaging results of two pulses, which can eliminate the random backscatter phase and make the image sparse in the transform domain. Building the relationship between echo and transform coefficients, the Compressed Sensing (CS theory can be introduced to solve the formula and achieving imaging. The image quality of the proposed method can reach the image formed by the full array imaging. The simulation results show the effectiveness of the proposed method.
Thompson, Douglas; Hallquist, Aaron; Anderson, Hyrum
2017-10-17
The various embodiments presented herein relate to utilizing an operational single-channel radar to collect and process synthetic aperture radar (SAR) and ground moving target indicator (GMTI) imagery from a same set of radar returns. In an embodiment, data is collected by randomly staggering a slow-time pulse repetition interval (PRI) over a SAR aperture such that a number of transmitted pulses in the SAR aperture is preserved with respect to standard SAR, but many of the pulses are spaced very closely enabling movers (e.g., targets) to be resolved, wherein a relative velocity of the movers places them outside of the SAR ground patch. The various embodiments of image reconstruction can be based on compressed sensing inversion from undersampled data, which can be solved efficiently using such techniques as Bregman iteration. The various embodiments enable high-quality SAR reconstruction, and high-quality GMTI reconstruction from the same set of radar returns.
Directory of Open Access Journals (Sweden)
Yudong Zhang
2016-01-01
Full Text Available Aim. It can help improve the hospital throughput to accelerate magnetic resonance imaging (MRI scanning. Patients will benefit from less waiting time. Task. In the last decade, various rapid MRI techniques on the basis of compressed sensing (CS were proposed. However, both computation time and reconstruction quality of traditional CS-MRI did not meet the requirement of clinical use. Method. In this study, a novel method was proposed with the name of exponential wavelet iterative shrinkage-thresholding algorithm with random shift (abbreviated as EWISTARS. It is composed of three successful components: (i exponential wavelet transform, (ii iterative shrinkage-thresholding algorithm, and (iii random shift. Results. Experimental results validated that, compared to state-of-the-art approaches, EWISTARS obtained the least mean absolute error, the least mean-squared error, and the highest peak signal-to-noise ratio. Conclusion. EWISTARS is superior to state-of-the-art approaches.
Zhang, Yudong; Yang, Jiquan; Yang, Jianfei; Liu, Aijun; Sun, Ping
2016-01-01
Aim. It can help improve the hospital throughput to accelerate magnetic resonance imaging (MRI) scanning. Patients will benefit from less waiting time. Task. In the last decade, various rapid MRI techniques on the basis of compressed sensing (CS) were proposed. However, both computation time and reconstruction quality of traditional CS-MRI did not meet the requirement of clinical use. Method. In this study, a novel method was proposed with the name of exponential wavelet iterative shrinkage-thresholding algorithm with random shift (abbreviated as EWISTARS). It is composed of three successful components: (i) exponential wavelet transform, (ii) iterative shrinkage-thresholding algorithm, and (iii) random shift. Results. Experimental results validated that, compared to state-of-the-art approaches, EWISTARS obtained the least mean absolute error, the least mean-squared error, and the highest peak signal-to-noise ratio. Conclusion. EWISTARS is superior to state-of-the-art approaches. PMID:27066068
Directory of Open Access Journals (Sweden)
Francesco Bonavolontà
2014-10-01
Full Text Available The paper deals with the problem of improving the maximum sample rate of analog-to-digital converters (ADCs included in low cost wireless sensing nodes. To this aim, the authors propose an efficient acquisition strategy based on the combined use of high-resolution time-basis and compressive sampling. In particular, the high-resolution time-basis is adopted to provide a proper sequence of random sampling instants, and a suitable software procedure, based on compressive sampling approach, is exploited to reconstruct the signal of interest from the acquired samples. Thanks to the proposed strategy, the effective sample rate of the reconstructed signal can be as high as the frequency of the considered time-basis, thus significantly improving the inherent ADC sample rate. Several tests are carried out in simulated and real conditions to assess the performance of the proposed acquisition strategy in terms of reconstruction error. In particular, the results obtained in experimental tests with ADC included in actual 8- and 32-bits microcontrollers highlight the possibility of achieving effective sample rate up to 50 times higher than that of the original ADC sample rate.
International Nuclear Information System (INIS)
Xu, Lijun; Ren, Ying; Sun, Shijie; Cao, Zhang
2016-01-01
In this paper, an under-sampling method for wideband capacitance measurement was proposed by using the compressive sensing strategy. As the excitation signal is sparse in the frequency domain, the compressed sampling method that uses a random demodulator was adopted, which could greatly decrease the sampling rate. Besides, four switches were used to replace the multiplier in the random demodulator. As a result, not only the sampling rate can be much smaller than the signal excitation frequency, but also the circuit’s structure is simpler and its power consumption is lower. A hardware prototype was constructed to validate the method. In the prototype, an excitation voltage with a frequency up to 200 kHz was applied to a capacitance-to-voltage converter. The output signal of the converter was randomly modulated by a pseudo-random sequence through four switches. After a low-pass filter, the signal was sampled by an analog-to-digital converter at a sampling rate of 50 kHz, which was three times lower than the highest exciting frequency. The frequency and amplitude of the signal were then reconstructed to obtain the measured capacitance. Both theoretical analysis and experiments were carried out to show the feasibility of the proposed method and to evaluate the performance of the prototype, including its linearity, sensitivity, repeatability, accuracy and stability within a given measurement range. (paper)
Chen, Yuanfang; Lee, Gyu Myoung; Shu, Lei; Crespi, Noel
2016-01-01
The development of an efficient and cost-effective solution to solve a complex problem (e.g., dynamic detection of toxic gases) is an important research issue in the industrial applications of the Internet of Things (IoT). An industrial intelligent ecosystem enables the collection of massive data from the various devices (e.g., sensor-embedded wireless devices) dynamically collaborating with humans. Effectively collaborative analytics based on the collected massive data from humans and devices is quite essential to improve the efficiency of industrial production/service. In this study, we propose a collaborative sensing intelligence (CSI) framework, combining collaborative intelligence and industrial sensing intelligence. The proposed CSI facilitates the cooperativity of analytics with integrating massive spatio-temporal data from different sources and time points. To deploy the CSI for achieving intelligent and efficient industrial production/service, the key challenges and open issues are discussed, as well. PMID:26861345
Chen, Yuanfang; Lee, Gyu Myoung; Shu, Lei; Crespi, Noel
2016-02-06
The development of an efficient and cost-effective solution to solve a complex problem (e.g., dynamic detection of toxic gases) is an important research issue in the industrial applications of the Internet of Things (IoT). An industrial intelligent ecosystem enables the collection of massive data from the various devices (e.g., sensor-embedded wireless devices) dynamically collaborating with humans. Effectively collaborative analytics based on the collected massive data from humans and devices is quite essential to improve the efficiency of industrial production/service. In this study, we propose a collaborative sensing intelligence (CSI) framework, combining collaborative intelligence and industrial sensing intelligence. The proposed CSI facilitates the cooperativity of analytics with integrating massive spatio-temporal data from different sources and time points. To deploy the CSI for achieving intelligent and efficient industrial production/service, the key challenges and open issues are discussed, as well.
Directory of Open Access Journals (Sweden)
Yuanfang Chen
2016-02-01
Full Text Available The development of an efficient and cost-effective solution to solve a complex problem (e.g., dynamic detection of toxic gases is an important research issue in the industrial applications of the Internet of Things (IoT. An industrial intelligent ecosystem enables the collection of massive data from the various devices (e.g., sensor-embedded wireless devices dynamically collaborating with humans. Effectively collaborative analytics based on the collected massive data from humans and devices is quite essential to improve the efficiency of industrial production/service. In this study, we propose a collaborative sensing intelligence (CSI framework, combining collaborative intelligence and industrial sensing intelligence. The proposed CSI facilitates the cooperativity of analytics with integrating massive spatio-temporal data from different sources and time points. To deploy the CSI for achieving intelligent and efficient industrial production/service, the key challenges and open issues are discussed, as well.
Gu, Xiangping; Zhou, Xiaofeng; Sun, Yanjing
2018-02-28
Compressive sensing (CS)-based data gathering is a promising method to reduce energy consumption in wireless sensor networks (WSNs). Traditional CS-based data-gathering approaches require a large number of sensor nodes to participate in each CS measurement task, resulting in high energy consumption, and do not guarantee load balance. In this paper, we propose a sparser analysis that depends on modified diffusion wavelets, which exploit sensor readings' spatial correlation in WSNs. In particular, a novel data-gathering scheme with joint routing and CS is presented. A modified ant colony algorithm is adopted, where next hop node selection takes a node's residual energy and path length into consideration simultaneously. Moreover, in order to speed up the coverage rate and avoid the local optimal of the algorithm, an improved pheromone impact factor is put forward. More importantly, theoretical proof is given that the equivalent sensing matrix generated can satisfy the restricted isometric property (RIP). The simulation results demonstrate that the modified diffusion wavelets' sparsity affects the sensor signal and has better reconstruction performance than DFT. Furthermore, our data gathering with joint routing and CS can dramatically reduce the energy consumption of WSNs, balance the load, and prolong the network lifetime in comparison to state-of-the-art CS-based methods.
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.
International Nuclear Information System (INIS)
Shieh, C; Kipritidis, J; OBrien, R; Cooper, B; Kuncic, Z; Keall, P
2014-01-01
Purpose: The Feldkamp-Davis-Kress (FDK) algorithm currently used for clinical thoracic 4-dimensional (4D) cone-beam CT (CBCT) reconstruction suffers from noise and streaking artifacts due to projection under-sampling. Compressed sensing theory enables reconstruction of under-sampled datasets via total-variation (TV) minimization, but TV-minimization algorithms such as adaptive-steepest-descent-projection-onto-convex-sets (ASD-POCS) often converge slowly and are prone to over-smoothing anatomical details. These disadvantages can be overcome by incorporating general anatomical knowledge via anatomy segmentation. Based on this concept, we have developed an anatomical-adaptive compressed sensing (AACS) algorithm for thoracic 4D-CBCT reconstruction. Methods: AACS is based on the ASD-POCS framework, where each iteration consists of a TV-minimization step and a data fidelity constraint step. Prior to every AACS iteration, four major thoracic anatomical structures - soft tissue, lungs, bony anatomy, and pulmonary details - were segmented from the updated solution image. Based on the segmentation, an anatomical-adaptive weighting was applied to the TV-minimization step, so that TV-minimization was enhanced at noisy/streaky regions and suppressed at anatomical structures of interest. The image quality and convergence speed of AACS was compared to conventional ASD-POCS using an XCAT digital phantom and a patient scan. Results: For the XCAT phantom, the AACS image represented the ground truth better than the ASD-POCS image, giving a higher structural similarity index (0.93 vs. 0.84) and lower absolute difference (1.1*10 4 vs. 1.4*10 4 ). For the patient case, while both algorithms resulted in much less noise and streaking than FDK, the AACS image showed considerably better contrast and sharpness of the vessels, tumor, and fiducial marker than the ASD-POCS image. In addition, AACS converged over 50% faster than ASD-POCS in both cases. Conclusions: The proposed AACS algorithm
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Shieh, C; Kipritidis, J; OBrien, R; Cooper, B; Kuncic, Z; Keall, P [The University of Sydney, Sydney, New South Wales (Australia)
2014-06-15
Purpose: The Feldkamp-Davis-Kress (FDK) algorithm currently used for clinical thoracic 4-dimensional (4D) cone-beam CT (CBCT) reconstruction suffers from noise and streaking artifacts due to projection under-sampling. Compressed sensing theory enables reconstruction of under-sampled datasets via total-variation (TV) minimization, but TV-minimization algorithms such as adaptive-steepest-descent-projection-onto-convex-sets (ASD-POCS) often converge slowly and are prone to over-smoothing anatomical details. These disadvantages can be overcome by incorporating general anatomical knowledge via anatomy segmentation. Based on this concept, we have developed an anatomical-adaptive compressed sensing (AACS) algorithm for thoracic 4D-CBCT reconstruction. Methods: AACS is based on the ASD-POCS framework, where each iteration consists of a TV-minimization step and a data fidelity constraint step. Prior to every AACS iteration, four major thoracic anatomical structures - soft tissue, lungs, bony anatomy, and pulmonary details - were segmented from the updated solution image. Based on the segmentation, an anatomical-adaptive weighting was applied to the TV-minimization step, so that TV-minimization was enhanced at noisy/streaky regions and suppressed at anatomical structures of interest. The image quality and convergence speed of AACS was compared to conventional ASD-POCS using an XCAT digital phantom and a patient scan. Results: For the XCAT phantom, the AACS image represented the ground truth better than the ASD-POCS image, giving a higher structural similarity index (0.93 vs. 0.84) and lower absolute difference (1.1*10{sup 4} vs. 1.4*10{sup 4}). For the patient case, while both algorithms resulted in much less noise and streaking than FDK, the AACS image showed considerably better contrast and sharpness of the vessels, tumor, and fiducial marker than the ASD-POCS image. In addition, AACS converged over 50% faster than ASD-POCS in both cases. Conclusions: The proposed AACS
Motion compensated cine CMR of the fetal heart using radial undersampling and compressed sensing
Roy, Christopher W.; Seed, Mike; Kingdom, John C.; Macgowan, Christopher K.
2017-01-01
Background To develop and evaluate a reconstruction framework for high resolution time-resolved CMR of the fetal heart in the presence of motion. Methods Data were acquired using a golden angle radial trajectory in seven fetal subjects and reconstructed as real-time images to detect fetal movement. Data acquired during through-plane motion were discarded whereas in-plane motion was corrected. A fetal cardiac gating signal was extracted to sort the corrected data by cardiac phase, allowing rec...
A Framework for Sharing and Integrating Remote Sensing and GIS Models Based on Web Service
Chen, Zeqiang; Lin, Hui; Chen, Min; Liu, Deer; Bao, Ying; Ding, Yulin
2014-01-01
Sharing and integrating Remote Sensing (RS) and Geographic Information System/Science (GIS) models are critical for developing practical application systems. Facilitating model sharing and model integration is a problem for model publishers and model users, respectively. To address this problem, a framework based on a Web service for sharing and integrating RS and GIS models is proposed in this paper. The fundamental idea of the framework is to publish heterogeneous RS and GIS models into standard Web services for sharing and interoperation and then to integrate the RS and GIS models using Web services. For the former, a “black box” and a visual method are employed to facilitate the publishing of the models as Web services. For the latter, model integration based on the geospatial workflow and semantic supported marching method is introduced. Under this framework, model sharing and integration is applied for developing the Pearl River Delta water environment monitoring system. The results show that the framework can facilitate model sharing and model integration for model publishers and model users. PMID:24901016
A framework for sharing and integrating remote sensing and GIS models based on Web service.
Chen, Zeqiang; Lin, Hui; Chen, Min; Liu, Deer; Bao, Ying; Ding, Yulin
2014-01-01
Sharing and integrating Remote Sensing (RS) and Geographic Information System/Science (GIS) models are critical for developing practical application systems. Facilitating model sharing and model integration is a problem for model publishers and model users, respectively. To address this problem, a framework based on a Web service for sharing and integrating RS and GIS models is proposed in this paper. The fundamental idea of the framework is to publish heterogeneous RS and GIS models into standard Web services for sharing and interoperation and then to integrate the RS and GIS models using Web services. For the former, a "black box" and a visual method are employed to facilitate the publishing of the models as Web services. For the latter, model integration based on the geospatial workflow and semantic supported marching method is introduced. Under this framework, model sharing and integration is applied for developing the Pearl River Delta water environment monitoring system. The results show that the framework can facilitate model sharing and model integration for model publishers and model users.
Biomolecule-embedded metal-organic frameworks as an innovative sensing platform.
Kempahanumakkagari, Sureshkumar; Kumar, Vanish; Samaddar, Pallabi; Kumar, Pawan; Ramakrishnappa, Thippeswamy; Kim, Ki-Hyun
Technological advancements combined with materials research have led to the generation of enormous types of novel substrates and materials for use in various biological/medical, energy, and environmental applications. Lately, the embedding of biomolecules in novel and/or advanced materials (e.g., metal-organic frameworks (MOFs), nanoparticles, hydrogels, graphene, and their hybrid composites) has become a vital research area in the construction of an innovative platform for various applications including sensors (or biosensors), biofuel cells, and bioelectronic devices. Due to the intriguing properties of MOFs (e.g., framework architecture, topology, and optical properties), they have contributed considerably to recent progresses in enzymatic catalysis, antibody-antigen interactions, or many other related approaches. Here, we aim to describe the different strategies for the design and synthesis of diverse biomolecule-embedded MOFs for various sensing (e.g., optical, electrochemical, biological, and miscellaneous) techniques. Additionally, the benefits and future prospective of MOFs-based biomolecular immobilization as an innovative sensing platform are discussed along with the evaluation on their performance to seek for further development in this emerging research area. Copyright © 2018. Published by Elsevier Inc.
Chen, Ling; Ye, Jia-Wen; Wang, Hai-Ping; Pan, Mei; Yin, Shao-Yun; Wei, Zhang-Wen; Zhang, Lu-Yin; Wu, Kai; Fan, Ya-Nan; Su, Cheng-Yong
2017-06-01
A convenient, fast and selective water analysis method is highly desirable in industrial and detection processes. Here a robust microporous Zn-MOF (metal-organic framework, Zn(hpi2cf)(DMF)(H2O)) is assembled from a dual-emissive H2hpi2cf (5-(2-(5-fluoro-2-hydroxyphenyl)-4,5-bis(4-fluorophenyl)-1H-imidazol-1-yl)isophthalic acid) ligand that exhibits characteristic excited state intramolecular proton transfer (ESIPT). This Zn-MOF contains amphipathic micropores (behaviour. The interconversion between the hydrated and dehydrated phases can turn the ligand ESIPT process on or off, resulting in sensitive two-colour photoluminescence switching over cycles. Therefore, this Zn-MOF represents an excellent PL water-sensing material, showing a fast (on the order of seconds) and highly selective response to water on a molecular level. Furthermore, paper or in situ grown ZnO-based sensing films have been fabricated and applied in humidity sensing (RH<1%), detection of traces of water (<0.05% v/v) in various organic solvents, thermal imaging and as a thermometer.
Improved interior wall detection using designated dictionaries in compressive urban sensing problems
Lagunas, Eva; Amin, Moeness G.; Ahmad, Fauzia; Nájar, Montse
2013-05-01
In this paper, we address sparsity-based imaging of building interior structures for through-the-wall radar imaging and urban sensing applications. The proposed approach utilizes information about common building construction practices to form an appropriate sparse representation of the building layout. With a ground based SAR system, and considering that interior walls are either parallel or perpendicular to the exterior walls, the antenna at each position would receive reflections from the walls parallel to the radar's scan direction as well as from the corners between two meeting walls. We propose a two-step approach for wall detection and localization. In the first step, a dictionary of possible wall locations is used to recover the positions of both interior and exterior walls that are parallel to the scan direction. A follow-on step uses a dictionary of possible corner reflectors to locate wall-wall junctions along the detected wall segments, thereby determining the true wall extents and detecting walls perpendicular to the scan direction. The utility of the proposed approach is demonstrated using simulated data.
Basha, Tamer A; Akçakaya, Mehmet; Goddu, Beth; Berg, Sophie; Nezafat, Reza
2015-01-01
The aim of this study was to implement and evaluate an accelerated three-dimensional (3D) cine phase contrast MRI sequence by combining a randomly sampled 3D k-space acquisition sequence with an echo planar imaging (EPI) readout. An accelerated 3D cine phase contrast MRI sequence was implemented by combining EPI readout with randomly undersampled 3D k-space data suitable for compressed sensing (CS) reconstruction. The undersampled data were then reconstructed using low-dimensional structural self-learning and thresholding (LOST). 3D phase contrast MRI was acquired in 11 healthy adults using an overall acceleration of 7 (EPI factor of 3 and CS rate of 3). For comparison, a single two-dimensional (2D) cine phase contrast scan was also performed with sensitivity encoding (SENSE) rate 2 and approximately at the level of the pulmonary artery bifurcation. The stroke volume and mean velocity in both the ascending and descending aorta were measured and compared between two sequences using Bland-Altman plots. An average scan time of 3 min and 30 s, corresponding to an acceleration rate of 7, was achieved for 3D cine phase contrast scan with one direction flow encoding, voxel size of 2 × 2 × 3 mm(3) , foot-head coverage of 6 cm and temporal resolution of 30 ms. The mean velocity and stroke volume in both the ascending and descending aorta were statistically equivalent between the proposed 3D sequence and the standard 2D cine phase contrast sequence. The combination of EPI with a randomly undersampled 3D k-space sampling sequence using LOST reconstruction allows a seven-fold reduction in scan time of 3D cine phase contrast MRI without compromising blood flow quantification. Copyright © 2014 John Wiley & Sons, Ltd.
Uniform Recovery Bounds for Structured Random Matrices in Corrupted Compressed Sensing
Zhang, Peng; Gan, Lu; Ling, Cong; Sun, Sumei
2018-04-01
We study the problem of recovering an $s$-sparse signal $\\mathbf{x}^{\\star}\\in\\mathbb{C}^n$ from corrupted measurements $\\mathbf{y} = \\mathbf{A}\\mathbf{x}^{\\star}+\\mathbf{z}^{\\star}+\\mathbf{w}$, where $\\mathbf{z}^{\\star}\\in\\mathbb{C}^m$ is a $k$-sparse corruption vector whose nonzero entries may be arbitrarily large and $\\mathbf{w}\\in\\mathbb{C}^m$ is a dense noise with bounded energy. The aim is to exactly and stably recover the sparse signal with tractable optimization programs. In this paper, we prove the uniform recovery guarantee of this problem for two classes of structured sensing matrices. The first class can be expressed as the product of a unit-norm tight frame (UTF), a random diagonal matrix and a bounded columnwise orthonormal matrix (e.g., partial random circulant matrix). When the UTF is bounded (i.e. $\\mu(\\mathbf{U})\\sim1/\\sqrt{m}$), we prove that with high probability, one can recover an $s$-sparse signal exactly and stably by $l_1$ minimization programs even if the measurements are corrupted by a sparse vector, provided $m = \\mathcal{O}(s \\log^2 s \\log^2 n)$ and the sparsity level $k$ of the corruption is a constant fraction of the total number of measurements. The second class considers randomly sub-sampled orthogonal matrix (e.g., random Fourier matrix). We prove the uniform recovery guarantee provided that the corruption is sparse on certain sparsifying domain. Numerous simulation results are also presented to verify and complement the theoretical results.
Toward an enhanced Bayesian estimation framework for multiphase flow soft-sensing
International Nuclear Information System (INIS)
Luo, Xiaodong; Lorentzen, Rolf J; Stordal, Andreas S; Nævdal, Geir
2014-01-01
In this work the authors study the multiphase flow soft-sensing problem based on a previously established framework. There are three functional modules in this framework, namely, a transient well flow model that describes the response of certain physical variables in a well, for instance, temperature, velocity and pressure, to the flow rates entering and leaving the well zones; a Markov jump process that is designed to capture the potential abrupt changes in the flow rates; and an estimation method that is adopted to estimate the underlying flow rates based on the measurements from the physical sensors installed in the well. In the previous studies, the variances of the flow rates in the Markov jump process are chosen manually. To fill this gap, in the current work two automatic approaches are proposed in order to optimize the variance estimation. Through a numerical example, we show that, when the estimation framework is used in conjunction with these two proposed variance-estimation approaches, it can achieve reasonable performance in terms of matching both the measurements of the physical sensors and the true underlying flow rates. (paper)
Compressive sensing of signals generated in plastic scintillators in a novel J-PET instrument
International Nuclear Information System (INIS)
Raczyński, L.; Moskal, P.; Kowalski, P.; Wiślicki, W.; Bednarski, T.; Białas, P.; Czerwiński, E.; Gajos, A.; Kapłon, Ł.; Kochanowski, A.; Korcyl, G.; Kowal, J.; Kozik, T.; Krzemień, W.; Kubicz, E.; Niedźwiecki, Sz.; Pałka, M.; Rudy, Z.; Rundel, O.; Salabura, P.
2015-01-01
The J-PET scanner, which allows for single bed imaging of the whole human body, is currently under development at the Jagiellonian University. The discussed detector offers improvement of the Time of Flight (TOF) resolution due to the use of fast plastic scintillators and dedicated electronics allowing for sampling in the voltage domain of signals with durations of few nanoseconds. In this paper we show that recovery of the whole signal, based on only a few samples, is possible. In order to do that, we incorporate the training signals into the Tikhonov regularization framework and we perform the Principal Component Analysis decomposition, which is well known for its compaction properties. The method yields a simple closed form analytical solution that does not require iterative processing. Moreover, from the Bayes theory the properties of regularized solution, especially its covariance matrix, may be easily derived. This is the key to introduce and prove the formula for calculations of the signal recovery error. In this paper we show that an average recovery error is approximately inversely proportional to the number of acquired samples
Compressive sensing of signals generated in plastic scintillators in a novel J-PET instrument
Energy Technology Data Exchange (ETDEWEB)
Raczyński, L., E-mail: lech.raczynski@ncbj.gov.pl [Świerk Computing Centre, National Centre for Nuclear Research, 05-400 Otwock-Świerk (Poland); Moskal, P. [Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, 30-059 Cracow (Poland); Kowalski, P.; Wiślicki, W. [Świerk Computing Centre, National Centre for Nuclear Research, 05-400 Otwock-Świerk (Poland); Bednarski, T.; Białas, P.; Czerwiński, E.; Gajos, A. [Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, 30-059 Cracow (Poland); Kapłon, Ł. [Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, 30-059 Cracow (Poland); Institute of Metallurgy and Materials Science of Polish Academy of Sciences, Cracow (Poland); Kochanowski, A. [Faculty of Chemistry, Jagiellonian University, 30-060 Cracow (Poland); Korcyl, G.; Kowal, J.; Kozik, T.; Krzemień, W.; Kubicz, E.; Niedźwiecki, Sz.; Pałka, M.; Rudy, Z.; Rundel, O.; Salabura, P. [Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, 30-059 Cracow (Poland); and others
2015-06-21
The J-PET scanner, which allows for single bed imaging of the whole human body, is currently under development at the Jagiellonian University. The discussed detector offers improvement of the Time of Flight (TOF) resolution due to the use of fast plastic scintillators and dedicated electronics allowing for sampling in the voltage domain of signals with durations of few nanoseconds. In this paper we show that recovery of the whole signal, based on only a few samples, is possible. In order to do that, we incorporate the training signals into the Tikhonov regularization framework and we perform the Principal Component Analysis decomposition, which is well known for its compaction properties. The method yields a simple closed form analytical solution that does not require iterative processing. Moreover, from the Bayes theory the properties of regularized solution, especially its covariance matrix, may be easily derived. This is the key to introduce and prove the formula for calculations of the signal recovery error. In this paper we show that an average recovery error is approximately inversely proportional to the number of acquired samples.
Compressive sensing of signals generated in plastic scintillators in a novel J-PET instrument
Raczyński, L.; Moskal, P.; Kowalski, P.; Wiślicki, W.; Bednarski, T.; Białas, P.; Czerwiński, E.; Gajos, A.; Kapłon, Ł.; Kochanowski, A.; Korcyl, G.; Kowal, J.; Kozik, T.; Krzemień, W.; Kubicz, E.; Niedźwiecki, Sz.; Pałka, M.; Rudy, Z.; Rundel, O.; Salabura, P.; Sharma, N. G.; Silarski, M.; Słomski, A.; Smyrski, J.; Strzelecki, A.; Wieczorek, A.; Zieliński, M.; Zoń, N.
2015-06-01
The J-PET scanner, which allows for single bed imaging of the whole human body, is currently under development at the Jagiellonian University. The discussed detector offers improvement of the Time of Flight (TOF) resolution due to the use of fast plastic scintillators and dedicated electronics allowing for sampling in the voltage domain of signals with durations of few nanoseconds. In this paper we show that recovery of the whole signal, based on only a few samples, is possible. In order to do that, we incorporate the training signals into the Tikhonov regularization framework and we perform the Principal Component Analysis decomposition, which is well known for its compaction properties. The method yields a simple closed form analytical solution that does not require iterative processing. Moreover, from the Bayes theory the properties of regularized solution, especially its covariance matrix, may be easily derived. This is the key to introduce and prove the formula for calculations of the signal recovery error. In this paper we show that an average recovery error is approximately inversely proportional to the number of acquired samples.
Dual-wavelength OR-PAM with compressed sensing for cell tracking in a 3D cell culture system
Huang, Rou-Xuan; Fu, Ying; Liu, Wang; Ma, Yu-Ting; Hsieh, Bao-Yu; Chen, Shu-Ching; Sun, Mingjian; Li, Pai-Chi
2018-02-01
Monitoring dynamic interactions of T cells migrating toward tumor is beneficial to understand how cancer immunotherapy works. Optical-resolution photoacoustic microscope (OR-PAM) can provide not only high spatial resolution but also deeper penetration than conventional optical microscopy. With the aid of exogenous contrast agents, the dual-wavelength OR-PAM can be applied to map the distribution of CD8+ cytotoxic T lymphocytes (CTLs) with gold nanospheres (AuNS) under 523nm laser irradiation and Hepta1-6 tumor spheres with indocyanine green (ICG) under 800nm irradiation. However, at 1K laser PRF, it takes approximately 20 minutes to obtain a full sample volume of 160 × 160 × 150 μm3 . To increase the imaging rate, we propose a random non-uniform sparse sampling mechanism to achieve fast sparse photoacoustic data acquisition. The image recovery process is formulated as a low-rank matrix recovery (LRMR) based on compressed sensing (CS) theory. We show that it could be stably recovered via nuclear-norm minimization optimization problem to maintain image quality from a significantly fewer measurement. In this study, we use the dual-wavelength OR-PAM with CS to visualize T cell trafficking in a 3D culture system with higher temporal resolution. Data acquisition time is reduced by 40% in such sample volume where sampling density is 0.5. The imaging system reveals the potential to understand the dynamic cellular process for preclinical screening of anti-cancer drugs.
Directory of Open Access Journals (Sweden)
Rui Zhang
2018-01-01
Full Text Available This paper proposes a novel tamper detection, localization, and recovery scheme for encrypted images with Discrete Wavelet Transformation (DWT and Compressive Sensing (CS. The original image is first transformed into DWT domain and divided into important part, that is, low-frequency part, and unimportant part, that is, high-frequency part. For low-frequency part contains the main information of image, traditional chaotic encryption is employed. Then, high-frequency part is encrypted with CS to vacate space for watermark. The scheme takes the processed original image content as watermark, from which the characteristic digest values are generated. Comparing with the existing image authentication algorithms, the proposed scheme can realize not only tamper detection and localization but also tamper recovery. Moreover, tamper recovery is based on block division and the recovery accuracy varies with the contents that are possibly tampered. If either the watermark or low-frequency part is tampered, the recovery accuracy is 100%. The experimental results show that the scheme can not only distinguish the type of tamper and find the tampered blocks but also recover the main information of the original image. With great robustness and security, the scheme can adequately meet the need of secure image transmission under unreliable conditions.
Murphy, Mark; Alley, Marcus; Demmel, James; Keutzer, Kurt; Vasanawala, Shreyas; Lustig, Michael
2012-01-01
We present ℓ1-SPIRiT, a simple algorithm for auto calibrating parallel imaging (acPI) and compressed sensing (CS) that permits an efficient implementation with clinically-feasible runtimes. We propose a CS objective function that minimizes cross-channel joint sparsity in the Wavelet domain. Our reconstruction minimizes this objective via iterative soft-thresholding, and integrates naturally with iterative Self-Consistent Parallel Imaging (SPIRiT). Like many iterative MRI reconstructions, ℓ1-SPIRiT’s image quality comes at a high computational cost. Excessively long runtimes are a barrier to the clinical use of any reconstruction approach, and thus we discuss our approach to efficiently parallelizing ℓ1-SPIRiT and to achieving clinically-feasible runtimes. We present parallelizations of ℓ1-SPIRiT for both multi-GPU systems and multi-core CPUs, and discuss the software optimization and parallelization decisions made in our implementation. The performance of these alternatives depends on the processor architecture, the size of the image matrix, and the number of parallel imaging channels. Fundamentally, achieving fast runtime requires the correct trade-off between cache usage and parallelization overheads. We demonstrate image quality via a case from our clinical experimentation, using a custom 3DFT Spoiled Gradient Echo (SPGR) sequence with up to 8× acceleration via poisson-disc undersampling in the two phase-encoded directions. PMID:22345529
Kido, Tomoyuki; Kido, Teruhito; Nakamura, Masashi; Watanabe, Kouki; Schmidt, Michaela; Forman, Christoph; Mochizuki, Teruhito
2017-09-25
Compressed sensing (CS) cine magnetic resonance imaging (MRI) has the advantage of being inherently insensitive to respiratory motion. This study compared the accuracy of free-breathing (FB) CS and breath-hold (BH) standard cine MRI for left ventricular (LV) volume assessment.Methods and Results:Sixty-three patients underwent cine MRI with both techniques. Both types of images were acquired in stacks of 8 short-axis slices (temporal/spatial resolution, 41 ms/1.7×1.7×6 mm 3 ) and compared for ejection fraction, end-diastolic and systolic volumes, stroke volume, and LV mass. Both BH standard and FB CS cine MRI provided acceptable image quality for LV volumetric analysis (score ≥3) in all patients (4.7±0.5 and 3.7±0.5, respectively; Pcine MRI (median, IQR: BH standard, 83.8 mL, 64.7-102.7 mL; FB CS, 79.0 mL, 66.0-101.0 mL; P=0.0006). The total acquisition times for BH standard and FB CS cine MRI were 113±7 s and 24±4 s, respectively (Pcine MRI is a clinically useful alternative to BH standard cine MRI in patients with impaired BH capacity.
International Nuclear Information System (INIS)
Choi, Kihwan; Li, Ruijiang; Nam, Haewon; Xing, Lei
2014-01-01
As a solution to iterative CT image reconstruction, first-order methods are prominent for the large-scale capability and the fast convergence rate O(1/k 2 ). In practice, the CT system matrix with a large condition number may lead to slow convergence speed despite the theoretically promising upper bound. The aim of this study is to develop a Fourier-based scaling technique to enhance the convergence speed of first-order methods applied to CT image reconstruction. Instead of working in the projection domain, we transform the projection data and construct a data fidelity model in Fourier space. Inspired by the filtered backprojection formalism, the data are appropriately weighted in Fourier space. We formulate an optimization problem based on weighted least-squares in the Fourier space and total-variation (TV) regularization in image space for parallel-beam, fan-beam and cone-beam CT geometry. To achieve the maximum computational speed, the optimization problem is solved using a fast iterative shrinkage-thresholding algorithm with backtracking line search and GPU implementation of projection/backprojection. The performance of the proposed algorithm is demonstrated through a series of digital simulation and experimental phantom studies. The results are compared with the existing TV regularized techniques based on statistics-based weighted least-squares as well as basic algebraic reconstruction technique. The proposed Fourier-based compressed sensing (CS) method significantly improves both the image quality and the convergence rate compared to the existing CS techniques. (paper)
Cheng, Yih-Chun; Tsai, Pei-Yun; Huang, Ming-Hao
2016-05-19
Low-complexity compressed sensing (CS) techniques for monitoring electrocardiogram (ECG) signals in wireless body sensor network (WBSN) are presented. The prior probability of ECG sparsity in the wavelet domain is first exploited. Then, variable orthogonal multi-matching pursuit (vOMMP) algorithm that consists of two phases is proposed. In the first phase, orthogonal matching pursuit (OMP) algorithm is adopted to effectively augment the support set with reliable indices and in the second phase, the orthogonal multi-matching pursuit (OMMP) is employed to rescue the missing indices. The reconstruction performance is thus enhanced with the prior information and the vOMMP algorithm. Furthermore, the computation-intensive pseudo-inverse operation is simplified by the matrix-inversion-free (MIF) technique based on QR decomposition. The vOMMP-MIF CS decoder is then implemented in 90 nm CMOS technology. The QR decomposition is accomplished by two systolic arrays working in parallel. The implementation supports three settings for obtaining 40, 44, and 48 coefficients in the sparse vector. From the measurement result, the power consumption is 11.7 mW at 0.9 V and 12 MHz. Compared to prior chip implementations, our design shows good hardware efficiency and is suitable for low-energy applications.
Harris, Peter; Philip, Rachel; Robinson, Stephen; Wang, Lian
2016-01-01
Monitoring ocean acoustic noise has been the subject of considerable recent study, motivated by the desire to assess the impact of anthropogenic noise on marine life. A combination of measuring ocean sound using an acoustic sensor network and modelling sources of sound and sound propagation has been proposed as an approach to estimating the acoustic noise map within a region of interest. However, strategies for developing a monitoring network are not well established. In this paper, considerations for designing a network are investigated using a simulated scenario based on the measurement of sound from ships in a shipping lane. Using models for the sources of the sound and for sound propagation, a noise map is calculated and measurements of the noise map by a sensor network within the region of interest are simulated. A compressive sensing algorithm, which exploits the sparsity of the representation of the noise map in terms of the sources, is used to estimate the locations and levels of the sources and thence the entire noise map within the region of interest. It is shown that although the spatial resolution to which the sound sources can be identified is generally limited, estimates of aggregated measures of the noise map can be obtained that are more reliable compared with those provided by other approaches. PMID:27011187
Choi, Kihwan; Li, Ruijiang; Nam, Haewon; Xing, Lei
2014-06-21
As a solution to iterative CT image reconstruction, first-order methods are prominent for the large-scale capability and the fast convergence rate [Formula: see text]. In practice, the CT system matrix with a large condition number may lead to slow convergence speed despite the theoretically promising upper bound. The aim of this study is to develop a Fourier-based scaling technique to enhance the convergence speed of first-order methods applied to CT image reconstruction. Instead of working in the projection domain, we transform the projection data and construct a data fidelity model in Fourier space. Inspired by the filtered backprojection formalism, the data are appropriately weighted in Fourier space. We formulate an optimization problem based on weighted least-squares in the Fourier space and total-variation (TV) regularization in image space for parallel-beam, fan-beam and cone-beam CT geometry. To achieve the maximum computational speed, the optimization problem is solved using a fast iterative shrinkage-thresholding algorithm with backtracking line search and GPU implementation of projection/backprojection. The performance of the proposed algorithm is demonstrated through a series of digital simulation and experimental phantom studies. The results are compared with the existing TV regularized techniques based on statistics-based weighted least-squares as well as basic algebraic reconstruction technique. The proposed Fourier-based compressed sensing (CS) method significantly improves both the image quality and the convergence rate compared to the existing CS techniques.
Directory of Open Access Journals (Sweden)
Raczyński Lech
2016-03-01
Full Text Available Nowadays, in positron emission tomography (PET systems, a time of flight (TOF information is used to improve the image reconstruction process. In TOF-PET, fast detectors are able to measure the difference in the arrival time of the two gamma rays, with the precision enabling to shorten significantly a range along the line-of-response (LOR where the annihilation occurred. In the new concept, called J-PET scanner, gamma rays are detected in plastic scintillators. In a single strip of J-PET system, time values are obtained by probing signals in the amplitude domain. Owing to compressive sensing (CS theory, information about the shape and amplitude of the signals is recovered. In this paper, we demonstrate that based on the acquired signals parameters, a better signal normalization may be provided in order to improve the TOF resolution. The procedure was tested using large sample of data registered by a dedicated detection setup enabling sampling of signals with 50-ps intervals. Experimental setup provided irradiation of a chosen position in the plastic scintillator strip with annihilation gamma quanta.
Rank-defective millimeter-wave channel estimation based on subspace-compressive sensing
Directory of Open Access Journals (Sweden)
Majid Shakhsi Dastgahian
2016-11-01
Full Text Available Millimeter-wave communication (mmWC is considered as one of the pioneer candidates for 5G indoor and outdoor systems in E-band. To subdue the channel propagation characteristics in this band, high dimensional antenna arrays need to be deployed at both the base station (BS and mobile sets (MS. Unlike the conventional MIMO systems, Millimeter-wave (mmW systems lay away to employ the power predatory equipment such as ADC or RF chain in each branch of MIMO system because of hardware constraints. Such systems leverage to the hybrid precoding (combining architecture for downlink deployment. Because there is a large array at the transceiver, it is impossible to estimate the channel by conventional methods. This paper develops a new algorithm to estimate the mmW channel by exploiting the sparse nature of the channel. The main contribution is the representation of a sparse channel model and the exploitation of a modified approach based on Multiple Measurement Vector (MMV greedy sparse framework and subspace method of Multiple Signal Classification (MUSIC which work together to recover the indices of non-zero elements of an unknown channel matrix when the rank of the channel matrix is defected. In practical rank-defective channels, MUSIC fails, and we need to propose new extended MUSIC approaches based on subspace enhancement to compensate the limitation of MUSIC. Simulation results indicate that our proposed extended MUSIC algorithms will have proper performances and moderate computational speeds, and that they are even able to work in channels with an unknown sparsity level.
Directory of Open Access Journals (Sweden)
Ali Al-Khattawi
Full Text Available The work investigates the adhesive/cohesive molecular and physical interactions together with nanoscopic features of commonly used orally disintegrating tablet (ODT excipients microcrystalline cellulose (MCC and D-mannitol. This helps to elucidate the underlying physico-chemical and mechanical mechanisms responsible for powder densification and optimum product functionality. Atomic force microscopy (AFM contact mode analysis was performed to measure nano-adhesion forces and surface energies between excipient-drug particles (6-10 different particles per each pair. Moreover, surface topography images (100 nm2-10 µm2 and roughness data were acquired from AFM tapping mode. AFM data were related to ODT macro/microscopic properties obtained from SEM, FTIR, XRD, thermal analysis using DSC and TGA, disintegration testing, Heckel and tabletability profiles. The study results showed a good association between the adhesive molecular and physical forces of paired particles and the resultant densification mechanisms responsible for mechanical strength of tablets. MCC micro roughness was 3 times that of D-mannitol which explains the high hardness of MCC ODTs due to mechanical interlocking. Hydrogen bonding between MCC particles could not be established from both AFM and FTIR solid state investigation. On the contrary, D-mannitol produced fragile ODTs due to fragmentation of surface crystallites during compression attained from its weak crystal structure. Furthermore, AFM analysis has shown the presence of extensive micro fibril structures inhabiting nano pores which further supports the use of MCC as a disintegrant. Overall, excipients (and model drugs showed mechanistic behaviour on the nano/micro scale that could be related to the functionality of materials on the macro scale.
Al-khattawi, Ali; Alyami, Hamad; Townsend, Bill; Ma, Xianghong; Mohammed, Afzal R.
2014-01-01
The work investigates the adhesive/cohesive molecular and physical interactions together with nanoscopic features of commonly used orally disintegrating tablet (ODT) excipients microcrystalline cellulose (MCC) and D-mannitol. This helps to elucidate the underlying physico-chemical and mechanical mechanisms responsible for powder densification and optimum product functionality. Atomic force microscopy (AFM) contact mode analysis was performed to measure nano-adhesion forces and surface energies between excipient-drug particles (6-10 different particles per each pair). Moreover, surface topography images (100 nm2–10 µm2) and roughness data were acquired from AFM tapping mode. AFM data were related to ODT macro/microscopic properties obtained from SEM, FTIR, XRD, thermal analysis using DSC and TGA, disintegration testing, Heckel and tabletability profiles. The study results showed a good association between the adhesive molecular and physical forces of paired particles and the resultant densification mechanisms responsible for mechanical strength of tablets. MCC micro roughness was 3 times that of D-mannitol which explains the high hardness of MCC ODTs due to mechanical interlocking. Hydrogen bonding between MCC particles could not be established from both AFM and FTIR solid state investigation. On the contrary, D-mannitol produced fragile ODTs due to fragmentation of surface crystallites during compression attained from its weak crystal structure. Furthermore, AFM analysis has shown the presence of extensive micro fibril structures inhabiting nano pores which further supports the use of MCC as a disintegrant. Overall, excipients (and model drugs) showed mechanistic behaviour on the nano/micro scale that could be related to the functionality of materials on the macro scale. PMID:25025427
Cao, Anping
2018-03-09
Recently various porous organic frameworks (POFs, crystalline or amorphous materials) have been discovered, and used for a wide range of applications, including molecular separations and catalysis. Silicon nanowires (SiNWs) have been extensively studied for diverse applications, including as transistors, solar cells, lithium ion batteries and sensors. Here we demonstrate the functionalization of SiNW surfaces with POFs and explore its effect on the electrical sensing properties of SiNW-based devices. The surface modification by POFs was easily achieved by polycondensation on amine-modified SiNWs. Platinum nanoparticles were formed in these POFs by impregnation with chloroplatinic acid followed by chemical reduction. The final hybrid system showed highly enhanced sensitivity for methanol vapour detection. We envisage that the integration of SiNWs with POF selector layers, loaded with different metal nanoparticles will open up new avenues, not only in chemical and biosensing, but also in separations and catalysis.
Ling, Wei; Liew, Guoguang; Li, Ya; Hao, Yafeng; Pan, Huizhuo; Wang, Hanjie; Ning, Baoan; Xu, Hang; Huang, Xian
2018-06-01
The combination of novel materials with flexible electronic technology may yield new concepts of flexible electronic devices that effectively detect various biological chemicals to facilitate understanding of biological processes and conduct health monitoring. This paper demonstrates single- or multichannel implantable flexible sensors that are surface modified with conductive metal-organic frameworks (MOFs) such as copper-MOF and cobalt-MOF with large surface area, high porosity, and tunable catalysis capability. The sensors can monitor important nutriments such as ascorbicacid, glycine, l-tryptophan (l-Trp), and glucose with detection resolutions of 14.97, 0.71, 4.14, and 54.60 × 10 -6 m, respectively. In addition, they offer sensing capability even under extreme deformation and complex surrounding environment with continuous monitoring capability for 20 d due to minimized use of biological active chemicals. Experiments using live cells and animals indicate that the MOF-modified sensors are biologically safe to cells, and can detect l-Trp in blood and interstitial fluid. This work represents the first effort in integrating MOFs with flexible sensors to achieve highly specific and sensitive implantable electrochemical detection and may inspire appearance of more flexible electronic devices with enhanced capability in sensing, energy storage, and catalysis using various properties of MOFs. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Towards a framework for agent-based image analysis of remote-sensing data.
Hofmann, Peter; Lettmayer, Paul; Blaschke, Thomas; Belgiu, Mariana; Wegenkittl, Stefan; Graf, Roland; Lampoltshammer, Thomas Josef; Andrejchenko, Vera
2015-04-03
Object-based image analysis (OBIA) as a paradigm for analysing remotely sensed image data has in many cases led to spatially and thematically improved classification results in comparison to pixel-based approaches. Nevertheless, robust and transferable object-based solutions for automated image analysis capable of analysing sets of images or even large image archives without any human interaction are still rare. A major reason for this lack of robustness and transferability is the high complexity of image contents: Especially in very high resolution (VHR) remote-sensing data with varying imaging conditions or sensor characteristics, the variability of the objects' properties in these varying images is hardly predictable. The work described in this article builds on so-called rule sets. While earlier work has demonstrated that OBIA rule sets bear a high potential of transferability, they need to be adapted manually, or classification results need to be adjusted manually in a post-processing step. In order to automate these adaptation and adjustment procedures, we investigate the coupling, extension and integration of OBIA with the agent-based paradigm, which is exhaustively investigated in software engineering. The aims of such integration are (a) autonomously adapting rule sets and (b) image objects that can adopt and adjust themselves according to different imaging conditions and sensor characteristics. This article focuses on self-adapting image objects and therefore introduces a framework for agent-based image analysis (ABIA).
Directory of Open Access Journals (Sweden)
Vinh Quang Do
2018-05-01
Full Text Available This paper studies learning frameworks for energy-efficient data communications in an energy-harvesting cognitive radio network in which secondary users (SUs harvest energy from solar power while opportunistically accessing a licensed channel for data transmission. The SUs perform spectrum sensing individually, and send local decisions about the presence of the primary user (PU on the channel to a fusion center (FC. We first design a new cooperative spectrum-sensing technique based on a convolutional neural network in which the FC uses historical sensing data to train the network for classification problem. The system is assumed to operate in a time-slotted manner. At the beginning of each time slot, the FC uses the current local decisions as input for the trained network to decide whether the PU is active or not in that time slot. In addition, legitimate transmissions can be vulnerable to a hidden eavesdropper, which always passively listens to the communication. Therefore, we further propose a transfer learning actor–critic algorithm for an SU to decide its operation mode to increase the security level under the constraint of limited energy. In this approach, the SU directly interacts with the environment to learn its dynamics (i.e., an arrival of harvested energy; then, the SU can either stay idle to save energy or transmit to the FC secured data that are encrypted using a suitable private-key encryption method to maximize the long-term effective security level of the network. We finally present numerical simulation results under various configurations to evaluate our proposed schemes.
Montone, P.; Alessio, G.; Alfonsi, L.; Brunori, C.; Burrato, P.; Casula, G.; Cinti, F. R.; Civico, R.; Colini, L.; Cucci, L.; De Martini, P. M.; Falcucci, E.; Galadini, F.; Gaudiosi, G.; Gori, S.; Mariucci, M.; Moro, M.; Nappi, R.; Nardi, A.; Nave, R.; Pantosti, D.; Patera, A.; Pesci, A.; Pignone, M.; Pinzi, S.; Pucci, S.; Vannoli, P.; Venuti, A.; Villani, F.
2012-12-01
On May 20 2012 a Ml 5.9 seismic event hit the Emilia Po Plain area (northern Italy) triggering an intense earthquake activity along a broad area of the Plain. Nine days later, on May 29 a Ml 5.8 event occurred roughly 10 km to the SW of the first main shock; these events caused 26 victims and several injured and damages. The aftershock area extended for more than 50 km, in WNW-ESE direction, including five major aftershocks with 5.1≤Ml≤5.3 and more than two thousands of minor events. In general, the seismic sequence was confined in the upper 10 km of depth (ISIDe, http://iside.rm.ingv.it/). The focal mechanisms calculated for the main events and also for several M>4.5 aftershocks are almost all consistent with a compression (P-axes) N-S oriented due to thrust fault mechanisms. The two nodal planes, both E-W oriented, show a 40° southward and 60-70° northward dipping plane (QRCMT, Quick Regional Moment Tensors, http://autorcmt.bo.ingv.it/quicks.html), connected with the compressional regime of the area. From a tectonic point of view, the active Apennine thrust fronts, buried under the Po Plain Plio-Quaternary sediments, locally consist of three N-verging arcs. The most external structures, the active Ferrara and Mirandola thrusts and folds are responsible for the Emilia Romagna 2012 earthquake sequence. Just after the 20th May seismic event, the EMERGEO Working Group was active in surveying the epicentral area searching for coseismic geological effects. The survey lasted one month, involving about thirty researchers and technicians of the INGV in field and aerial investigations. Simultaneously, a laboratory-working group gathered, organized and interpreted the observations, processing them in the EMERGEO Information System (siE), on a GIS environment. The most common coseismic effects are: 1) liquefactions related to overpressure of aquifers hosted in buried and confined sand layers, occurring both as single cones or through several aligned vents forming
Hamid, A. H. A.; Rozan, M. Z. A.; Deris, S.; Ibrahim, R.; Abdullah, W. S. W.; Rahman, A. A.; Yunus, M. N. M.
2016-01-01
The evolution of current Radiation and Nuclear Emergency Planning Framework (RANEPF) simulator emphasizes on the human factors to be analyzed and interpreted according to the stakeholder's tacit and explicit knowledge. These human factor criteria are analyzed and interpreted according to the "sense making theory" and Disaster Emergency Response Management Information System (DERMIS) design premises. These criteria are corroborated by the statistical criteria. In recent findings, there were no differences of distributions among the stakeholders according to gender and organizational expertise. These criteria are incrementally accepted and agreed the research elements indicated in the respective emergency planning frameworks and simulator (i.e. 78.18 to 84.32, p-value <0.05). This paper suggested these human factors criteria in the associated analyses and theoretical perspectives to be further acomodated in the future simulator development. This development is in conjunction with the proposed hypothesis building of the process factors and responses diagram. We proposed that future work which implies the additional functionality of the simulator, as strategized, condensed and concise, comprehensive public disaster preparedness and intervention guidelines, to be a useful and efficient computer simulation.
International Nuclear Information System (INIS)
Hamid, A. H. A.; Rozan, M. Z. A.; Ibrahim, R.; Deris, S.; Abdullah, W. S. W.; Yunus, M. N. M.; Rahman, A. A.
2016-01-01
The evolution of current Radiation and Nuclear Emergency Planning Framework (RANEPF) simulator emphasizes on the human factors to be analyzed and interpreted according to the stakeholder’s tacit and explicit knowledge. These human factor criteria are analyzed and interpreted according to the “sense making theory” and Disaster Emergency Response Management Information System (DERMIS) design premises. These criteria are corroborated by the statistical criteria. In recent findings, there were no differences of distributions among the stakeholders according to gender and organizational expertise. These criteria are incrementally accepted and agreed the research elements indicated in the respective emergency planning frameworks and simulator (i.e. 78.18 to 84.32, p-value <0.05). This paper suggested these human factors criteria in the associated analyses and theoretical perspectives to be further acomodated in the future simulator development. This development is in conjunction with the proposed hypothesis building of the process factors and responses diagram. We proposed that future work which implies the additional functionality of the simulator, as strategized, condensed and concise, comprehensive public disaster preparedness and intervention guidelines, to be a useful and efficient computer simulation
A three-dimensional metal–organic framework for selective sensing of nitroaromatic compounds
Energy Technology Data Exchange (ETDEWEB)
Tian, Dan; Chen, Rong-Ying; Xu, Jian; Bu, Xian-He, E-mail: buxh@nankai.edu.cn [Department of Chemistry, Collaborative Innovation Center of Chemical Science and Engineering, and Tianjin Key Lab of Metal and Molecule-based Material Chemistry, Nankai University, Tianjin 300071 (China); Li, Yun-Wu [Department of Chemistry, Collaborative Innovation Center of Chemical Science and Engineering, and Tianjin Key Lab of Metal and Molecule-based Material Chemistry, Nankai University, Tianjin 300071 (China); School of Chemistry and Chemical Engineering and Shandong Provincial Key Laboratory of Chemical Energy Storage and Novel Cell Technology, Liaocheng University, Liaocheng 252000 (China)
2014-12-01
A 3D metal–organic framework [NH{sub 2}(CH{sub 3}){sub 2}][Cd{sub 6}(L){sub 4}(DMF){sub 6}(HCOO)](DMF = N,N-dimethylformamide) (1) has been synthesized using a tripodal ligand H{sub 3}L (2,4,6-tris[1-(3-carboxylphenoxy)ylmethyl]mesitylene). The obtained complex exhibits a 3D framework containing hexanuclear (Cd{sub 6}) building units formed by two trinuclear (Cd{sub 3}) clusters that are connected via HCOO{sup −} anions. For complex 1, the participation of the fluorescent ligand H{sub 3}L not only gives rise to a strong photoluminescence emission as expected, but more interestingly, that ligand originated characteristic band could be quenched selectively by nitrobenzene with a low detection limit, showing its potential as a highly sensitive and selective sensor for nitrobenzene. Based on an electron transfer quenching mechanism, the fluorescence sensing ability of 1 is also applicable for other electron-deficient nitroaromatic compounds with high selectivity and sensitivity, i.e., 1,4-dinitrobenzene, 1,3-dinitrobenzene, 2,4-dinitrotoluene, and 4-nitrotoluene, suggesting 1 a promising fluorescence sensor for detecting and recognizing the same kind of chemicals.
A three-dimensional metal–organic framework for selective sensing of nitroaromatic compounds
Directory of Open Access Journals (Sweden)
Dan Tian
2014-12-01
Full Text Available A 3D metal–organic framework [NH2(CH32][Cd6(L4(DMF6(HCOO](DMF = N,N-dimethylformamide (1 has been synthesized using a tripodal ligand H3L (2,4,6-tris[1-(3-carboxylphenoxyylmethyl]mesitylene. The obtained complex exhibits a 3D framework containing hexanuclear {Cd6} building units formed by two trinuclear {Cd3} clusters that are connected via HCOO− anions. For complex 1, the participation of the fluorescent ligand H3L not only gives rise to a strong photoluminescence emission as expected, but more interestingly, that ligand originated characteristic band could be quenched selectively by nitrobenzene with a low detection limit, showing its potential as a highly sensitive and selective sensor for nitrobenzene. Based on an electron transfer quenching mechanism, the fluorescence sensing ability of 1 is also applicable for other electron-deficient nitroaromatic compounds with high selectivity and sensitivity, i.e., 1,4-dinitrobenzene, 1,3-dinitrobenzene, 2,4-dinitrotoluene, and 4-nitrotoluene, suggesting 1 a promising fluorescence sensor for detecting and recognizing the same kind of chemicals.
Energy Technology Data Exchange (ETDEWEB)
Hamid, A. H. A., E-mail: amyhamijah@gmail.com, E-mail: amyhamijah@nm.gov.my [Faculty of Computing, Universiti Teknologi Malaysia (UTM), Skudai, 81310 Johor Bahru, Johor (Malaysia); Universiti Malaysia Kelantan (UMK), Pengkalan Chepa, 16100 Kota Bharu, Kelantan (Malaysia); Rozan, M. Z. A., E-mail: drmohdzaidi@gmail.com; Ibrahim, R. [Faculty of Computing, Universiti Teknologi Malaysia (UTM), Skudai, 81310 Johor Bahru, Johor (Malaysia); Deris, S. [Universiti Malaysia Kelantan (UMK), Pengkalan Chepa, 16100 Kota Bharu, Kelantan (Malaysia); Abdullah, W. S. W.; Yunus, M. N. M. [Malaysian Nuclear Agency (NM), Bangi, 43000 Kajang, Selangor (Malaysia); Rahman, A. A. [Faculty of Medicine and Health Sciences, Universiti Putra Malaysia (UPM), 43400 Serdang, Selangor (Malaysia)
2016-01-22
The evolution of current Radiation and Nuclear Emergency Planning Framework (RANEPF) simulator emphasizes on the human factors to be analyzed and interpreted according to the stakeholder’s tacit and explicit knowledge. These human factor criteria are analyzed and interpreted according to the “sense making theory” and Disaster Emergency Response Management Information System (DERMIS) design premises. These criteria are corroborated by the statistical criteria. In recent findings, there were no differences of distributions among the stakeholders according to gender and organizational expertise. These criteria are incrementally accepted and agreed the research elements indicated in the respective emergency planning frameworks and simulator (i.e. 78.18 to 84.32, p-value <0.05). This paper suggested these human factors criteria in the associated analyses and theoretical perspectives to be further acomodated in the future simulator development. This development is in conjunction with the proposed hypothesis building of the process factors and responses diagram. We proposed that future work which implies the additional functionality of the simulator, as strategized, condensed and concise, comprehensive public disaster preparedness and intervention guidelines, to be a useful and efficient computer simulation.
An algorithm for hyperspectral remote sensing of aerosols: 1. Development of theoretical framework
International Nuclear Information System (INIS)
Hou, Weizhen; Wang, Jun; Xu, Xiaoguang; Reid, Jeffrey S.; Han, Dong
2016-01-01
This paper describes the first part of a series of investigations to develop algorithms for simultaneous retrieval of aerosol parameters and surface reflectance from a newly developed hyperspectral instrument, the GEOstationary Trace gas and Aerosol Sensor Optimization (GEO-TASO), by taking full advantage of available hyperspectral measurement information in the visible bands. We describe the theoretical framework of an inversion algorithm for the hyperspectral remote sensing of the aerosol optical properties, in which major principal components (PCs) for surface reflectance is assumed known, and the spectrally dependent aerosol refractive indices are assumed to follow a power-law approximation with four unknown parameters (two for real and two for imaginary part of refractive index). New capabilities for computing the Jacobians of four Stokes parameters of reflected solar radiation at the top of the atmosphere with respect to these unknown aerosol parameters and the weighting coefficients for each PC of surface reflectance are added into the UNified Linearized Vector Radiative Transfer Model (UNL-VRTM), which in turn facilitates the optimization in the inversion process. Theoretical derivations of the formulas for these new capabilities are provided, and the analytical solutions of Jacobians are validated against the finite-difference calculations with relative error less than 0.2%. Finally, self-consistency check of the inversion algorithm is conducted for the idealized green-vegetation and rangeland surfaces that were spectrally characterized by the U.S. Geological Survey digital spectral library. It shows that the first six PCs can yield the reconstruction of spectral surface reflectance with errors less than 1%. Assuming that aerosol properties can be accurately characterized, the inversion yields a retrieval of hyperspectral surface reflectance with an uncertainty of 2% (and root-mean-square error of less than 0.003), which suggests self-consistency in the
Zhu, Chengcheng; Tian, Bing; Chen, Luguang; Eisenmenger, Laura; Raithel, Esther; Forman, Christoph; Ahn, Sinyeob; Laub, Gerhard; Liu, Qi; Lu, Jianping; Liu, Jing; Hess, Christopher; Saloner, David
2018-06-01
Develop and optimize an accelerated, high-resolution (0.5 mm isotropic) 3D black blood MRI technique to reduce scan time for whole-brain intracranial vessel wall imaging. A 3D accelerated T 1 -weighted fast-spin-echo prototype sequence using compressed sensing (CS-SPACE) was developed at 3T. Both the acquisition [echo train length (ETL), under-sampling factor] and reconstruction parameters (regularization parameter, number of iterations) were first optimized in 5 healthy volunteers. Ten patients with a variety of intracranial vascular disease presentations (aneurysm, atherosclerosis, dissection, vasculitis) were imaged with SPACE and optimized CS-SPACE, pre and post Gd contrast. Lumen/wall area, wall-to-lumen contrast ratio (CR), enhancement ratio (ER), sharpness, and qualitative scores (1-4) by two radiologists were recorded. The optimized CS-SPACE protocol has ETL 60, 20% k-space under-sampling, 0.002 regularization factor with 20 iterations. In patient studies, CS-SPACE and conventional SPACE had comparable image scores both pre- (3.35 ± 0.85 vs. 3.54 ± 0.65, p = 0.13) and post-contrast (3.72 ± 0.58 vs. 3.53 ± 0.57, p = 0.15), but the CS-SPACE acquisition was 37% faster (6:48 vs. 10:50). CS-SPACE agreed with SPACE for lumen/wall area, ER measurements and sharpness, but marginally reduced the CR. In the evaluation of intracranial vascular disease, CS-SPACE provides a substantial reduction in scan time compared to conventional T 1 -weighted SPACE while maintaining good image quality.
Chai, Xintao; Wang, Shangxu; Tang, Genyang; Meng, Xiangcui
2017-10-01
The anelastic effects of subsurface media decrease the amplitude and distort the phase of propagating wave. These effects, also referred to as the earth's Q filtering effects, diminish seismic resolution. Ignoring anelastic effects during seismic imaging process generates an image with reduced amplitude and incorrect position of reflectors, especially for highly absorptive media. The numerical instability and the expensive computational cost are major concerns when compensating for anelastic effects during migration. We propose a stable and efficient Q-compensated imaging methodology with compressive sensing, sparsity-promoting, and preconditioning. The stability is achieved by using the Born operator for forward modeling and the adjoint operator for back propagating the residual wavefields. Constructing the attenuation-compensated operators by reversing the sign of attenuation operator is avoided. The method proposed is always stable. To reduce the computational cost that is proportional to the number of wave-equation to be solved (thereby the number of frequencies, source experiments, and iterations), we first subsample over both frequencies and source experiments. We mitigate the artifacts caused by the dimensionality reduction via promoting sparsity of the imaging solutions. We further employ depth- and Q-preconditioning operators to accelerate the convergence rate of iterative migration. We adopt a relatively simple linearized Bregman method to solve the sparsity-promoting imaging problem. Singular value decomposition analysis of the forward operator reveals that attenuation increases the condition number of migration operator, making the imaging problem more ill-conditioned. The visco-acoustic imaging problem converges slower than the acoustic case. The stronger the attenuation, the slower the convergence rate. The preconditioning strategy evidently decreases the condition number of migration operator, which makes the imaging problem less ill-conditioned and
Energy Technology Data Exchange (ETDEWEB)
Ray, Jaideep; Lee, Jina; Lefantzi, Sophia; Yadav, Vineet; Michalak, Anna M.; van Bloemen Waanders, Bart Gustaaf; McKenna, Sean Andrew
2013-09-01
The estimation of fossil-fuel CO2 emissions (ffCO2) from limited ground-based and satellite measurements of CO2 concentrations will form a key component of the monitoring of treaties aimed at the abatement of greenhouse gas emissions. The limited nature of the measured data leads to a severely-underdetermined estimation problem. If the estimation is performed at fine spatial resolutions, it can also be computationally expensive. In order to enable such estimations, advances are needed in the spatial representation of ffCO2 emissions, scalable inversion algorithms and the identification of observables to measure. To that end, we investigate parsimonious spatial parameterizations of ffCO2 emissions which can be used in atmospheric inversions. We devise and test three random field models, based on wavelets, Gaussian kernels and covariance structures derived from easily-observed proxies of human activity. In doing so, we constructed a novel inversion algorithm, based on compressive sensing and sparse reconstruction, to perform the estimation. We also address scalable ensemble Kalman filters as an inversion mechanism and quantify the impact of Gaussian assumptions inherent in them. We find that the assumption does not impact the estimates of mean ffCO2 source strengths appreciably, but a comparison with Markov chain Monte Carlo estimates show significant differences in the variance of the source strengths. Finally, we study if the very different spatial natures of biogenic and ffCO2 emissions can be used to estimate them, in a disaggregated fashion, solely from CO2 concentration measurements, without extra information from products of incomplete combustion e.g., CO. We find that this is possible during the winter months, though the errors can be as large as 50%.
Barth, Ruud; Hemming, Jochen; Henten, van E.J.
2016-01-01
A modular software framework design that allows flexible implementation of eye-in-hand sensing and motion control for agricultural robotics in dense vegetation is reported. Harvesting robots in cultivars with dense vegetation require multiple viewpoints and on-line trajectory adjustments in order
Nunes, David; Tran, Thanh-Dien; Raposo, Duarte; Pinto, André; Gomes, André; Silva, Jorge Sá
2012-01-01
As the Internet evolved, social networks (such as Facebook) have bloomed and brought together an astonishing number of users. Mashing up mobile phones and sensors with these social environments enables the creation of people-centric sensing systems which have great potential for expanding our current social networking usage. However, such systems also have many associated technical challenges, such as privacy concerns, activity detection mechanisms or intermittent connectivity, as well as limitations due to the heterogeneity of sensor nodes and networks. Considering the openness of the Web 2.0, good technical solutions for these cases consist of frameworks that expose sensing data and functionalities as common Web-Services. This paper presents our RESTful Web Service-based model for people-centric sensing frameworks, which uses sensors and mobile phones to detect users' activities and locations, sharing this information amongst the user's friends within a social networking site. We also present some screenshot results of our experimental prototype.
Sidebotham, M; Fenwick, J; Carter, A; Gamble, J
2015-01-01
developing a student's sense of capability, purpose, resourcefulness, identity and connectedness (five-senses of success) are key factors that may be important in predicting student satisfaction and progression within their university program. the study aimed to examine the expectations and experiences of second and third year midwifery students enroled in a Bachelor of Midwifery program and identify barriers and enablers to success. a descriptive exploratory qualitative design was used. Fifty-six students enroled in either year 2 or 3 of the Bachelor of Midwifery program in SE Queensland participated in an anonymous survey using open-ended questions. In addition, 16 students participated in two year-level focus groups. Template analysis, using the Five Senses Framework, was used to analyse the data set. early exposure to 'hands on' clinical midwifery practice as well as continuity of care experiences provided students with an opportunity to link theory to practice and increased their perception of capability as they transitioned through the program. Students' sense of identity, purpose, resourcefulness, and capability was strongly influenced by the programs embedded meta-values, including a 'woman centred' approach. In addition, a student's ability to form strong positive relationships with women, peers, lecturers and supportive clinicians was central to developing connections and ultimately a sense of success. A sense of connection not only fostered an ongoing belief that challenges could be overcome but that students themselves could initiate or influence change. the five senses framework provided a useful lens through which to analyse the student experience. Key factors to student satisfaction and retention within a Bachelor of Midwifery program include: a clearly articulated midwifery philosophy, strategies to promote student connectedness including the use of social media, and further development of clinicians' skills in preceptorship, clinical teaching and
Energy Technology Data Exchange (ETDEWEB)
Lappa, Marcello, E-mail: marcello.lappa@strath.ac.uk
2016-05-15
The relevance of non-equilibrium phenomena, nonlinear behavior, gravitational effects and fluid compressibility in a wide range of problems related to high-temperature gas-dynamics, especially in thermal, mechanical and nuclear engineering, calls for a concerted approach using the tools of the kinetic theory of gases, statistical physics, quantum mechanics, thermodynamics and mathematical modeling in synergy with advanced numerical strategies for the solution of the Navier–Stokes equations. The reason behind such a need is that in many instances of relevance in this field one witnesses a departure from canonical models and the resulting inadequacy of standard CFD approaches, especially those traditionally used to deal with thermal (buoyancy) convection problems. Starting from microscopic considerations and typical concepts of molecular dynamics, passing through the Boltzmann equation and its known solutions, we show how it is possible to remove past assumptions and elaborate an algorithm capable of targeting the broadest range of applications. Moving beyond the Boussinesq approximation, the Sutherland law and the principle of energy equipartition, the resulting method allows most of the fluid properties (density, viscosity, thermal conductivity, heat capacity and diffusivity, etc.) to be derived in a rational and natural way while keeping empirical contamination to the minimum. Special attention is deserved as well to the well-known pressure issue. With the application of the socalled multiple pressure variables concept and a projection-like numerical approach, difficulties with such a term in the momentum equation are circumvented by allowing the hydrodynamic pressure to decouple from its thermodynamic counterpart. The final result is a flexible and modular framework that on the one hand is able to account for all the molecule (translational, rotational and vibrational) degrees of freedom and their effective excitation, and on the other hand can guarantee
Energy Technology Data Exchange (ETDEWEB)
Zhou, Pingping [College of Sciences, Agricultural University of Hebei, Baoding (China)
2017-05-18
The Cd{sup II} compound, namely [Cd(Tppa)(SO{sub 4})(H{sub 2}O)]{sub n} (1) [Tppa = tris(4-(pyridyl)phenyl) amine], was synthesized by the reaction of CdSO{sub 4}.8H{sub 2}O and Tppa under solvothermal conditions. Single crystal X-ray diffraction analysis revealed that compound 1 features a 3D porous framework based on 1D inorganic -[Cd-SO{sub 4}-Cd]{sub n}- chains. Topological analysis reveals that compound 1 represents a trinodal (3,4,6)-connected topological network with the point symbol of {6.7"2}{sub 2}{6"4.7.10}{6"4.7"5.8"4.10"2}. Gas adsorption properties investigations indicate that compound 1 exhibits moderate adsorption capacities for light hydrocarbons at room temperature. Luminescence property studies revealed that this Cd{sup II} compound exhibits high fluorescence sensitivity for sensing of CS{sub 2} molecule. (copyright 2017 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim)
Directory of Open Access Journals (Sweden)
Gang Wang
2018-05-01
Full Text Available 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.
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.
Zeolite-like Metal–Organic Framework (MOF) Encaged Pt(II)-Porphyrin for Anion-Selective Sensing
Masih, Dilshad
2018-03-26
The selectivity and sensitivity of sensors are of great interest to the materials chemistry community, and a lot of effort is now devoted to improving these characteristics. More specifically, the selective sensing of anions is one of the largest challenges impeding the sensing-research area due to their similar physical and chemical behaviors. In this work, platinum–metalated porphyrin (Pt(II)TMPyP) was successfully encapsulated in a rho-type zeolite-like metal–organic framework (rho-ZMOF) and applied for anion-selective sensing. The sensing activity and selectivity of the MOF-encaged Pt(II)TMPyP for various anions in aqueous and methanolic media were compared to that of the free (nonencapsulated) Pt(II)TMPyP. While the photoinduced triplet-state electron transfer of Pt(II)TMPyP showed a very low detection limit for anions with no selectivity, the Pt(II)TMPyP encapsulated in the rho-ZMOF framework possessed a unique chemical structure to overcome such limitations. This new approach has the potential for use in other complex sensing applications, including biosensors, which require ion selectivity.
A Webgis Framework for Disseminating Processed Remotely Sensed on Land Cover Transformations
Caradonna, Grazia; Novelli, Antonio; Tarantino, Eufemia; Cefalo, Raffaela; Fratino, Umberto
2016-06-01
Mediterranean regions have experienced significant soil degradation over the past decades. In this context, careful land observation using satellite data is crucial for understanding the long-term usage patterns of natural resources and facilitating their sustainable management to monitor and evaluate the potential degradation. Given the environmental and political interest on this problem, there is urgent need for a centralized repository and mechanism to share geospatial data, information and maps of land change. Geospatial data collecting is one of the most important task for many users because there are significant barriers in accessing and using data. This limit could be overcome by implementing a WebGIS through a combination of existing free and open source software for geographic information systems (FOSS4G). In this paper we preliminary discuss methods for collecting raster data in a geodatabase by processing open multi-temporal and multi-scale satellite data aimed at retrieving indicators for land degradation phenomenon (i.e. land cover/land use analysis, vegetation indices, trend analysis, etc.). Then we describe a methodology for designing a WebGIS framework in order to disseminate information through maps for territory monitoring. Basic WebGIS functions were extended with the help of POSTGIS database and OpenLayers libraries. Geoserver was customized to set up and enhance the website functions developing various advanced queries using PostgreSQL and innovative tools to carry out efficiently multi-layer overlay analysis. The end-product is a simple system that provides the opportunity not only to consult interactively but also download processed remote sensing data.
Advancing knowledge on practice change: linking facilitation to the senses framework.
Cooper, Julie; Meyer, Julienne; Holman, Cheryl
2013-06-01
To explore the facilitating factors that enabled staff on a rehabilitation ward for older people engage in change activities. The importance of facilitation in practice change is widely acknowledged; however, little nursing research has taken place in relation to its nature. Following identification in the early phases of an action research study that learned helplessness states and the use of socially structured defence techniques were preventing staff on a rehabilitation ward for older people from engaging in practice development, some change was achieved. What facilitated this to take place needed to be explored. An action research approach was used. Data gained from 13 in-depth interviews with staff and managers together with three years of researcher field notes were analysed using thematic analysis. The continuous presence and neutrality of the researcher who worked together with staff on their issues of concern using a flexible ward-based approach, combined with giving staff the opportunity to explore what it was like for them working in this area, were considered key in helping staff to engage with change. Analysis of findings suggests that the senses framework presents a theoretical approach to facilitation that can help staff move out of learned helplessness states and reduce the need for the use of socially structured defence techniques. This study identifies a facilitation approach that enabled staff to engage with practice change. Although carried out in the UK, its findings have wider relevance through the application of a theoretical perspective for practice change facilitation that has not before been considered in this literature, and which is likely to be of interest to those involved in practice change internationally. © 2013 Blackwell Publishing Ltd.
Du, Yaran; Li, Xiqian; Lv, Xueju; Jia, Qiong
2017-09-13
Free bilirubin, a key biomarker for jaundice, was detected with a newly designed fluorescent postsynthetically modified metal organic framework (MOF) (UIO-66-PSM) sensor. UiO-66-PSM was prepared based on the aldimine condensation reaction of UiO-66-NH 2 with 2,3,4-trihydroxybenzaldehyde. The fluorescence of UIO-66-PSM could be effectively quenched by free bilirubin via a fluorescent resonant energy transfer process, thus achieving its recognition of free bilirubin. It was the first attempt to design a MOF-based fluorescent probe for sensing free bilirubin. The probe exhibited fast response time, low detection limit, wide linear range, and high selectivity toward free bilirubin. The sensing system enabled the monitor of free bilirubin in real human serum. Hence, the reported free bilirubin sensing platform has potential applications for clinical diagnosis of jaundice.
Energy Technology Data Exchange (ETDEWEB)
Park, Yeonok; Je, Uikyu; Cho, Hyosung, E-mail: hscho1@yonsei.ac.kr; Hong, Daeki; Park, Chulkyu; Cho, Heemoon; Choi, Sungil; Woo, Taeho
2015-03-21
In this work, we performed a feasibility study for image reconstruction in a circular digital tomosynthesis (CDTS) from limited-scan angle data based on compressed-sensing (CS) theory. Here, the X-ray source moves along an arc within a limited-scan angle (≤ 180°) on a circular path set perpendicularly to the axial direction during the image acquisition. This geometry, compared to full-angle (360°) scan geometry, allows imaging system to be designed more compactly and gives better tomographic quality than conventional linear digital tomosynthesis (DTS). We implemented an efficient CS-based reconstruction algorithm for the proposed geometry and performed systematic simulations to investigate the image characteristics. We successfully reconstructed CDTS images with incomplete projections acquired at several selected limited-scan angles of 45°, 90°, 135°, and 180° for a given tomographic angle of 80° and evaluated the reconstruction quality. Our simulation results indicate that the proposed method can provide superior tomographic quality for axial view and even for the other views (i.e., sagittal and coronal), as in computed tomography, to conventional DTS. - Highlights: • Image reconstruction is done in circular digital tomosynthesis (CDTS). • The designed geometry allows imaging system to be the better image. • An efficient compressed-sensing (CS)-based reconstruction algorithm is performed. • Proposed method can provide superior tomographic quality for the axial view.
A systematic framework for Monte Carlo simulation of remote sensing errors map in carbon assessments
S. Healey; P. Patterson; S. Urbanski
2014-01-01
Remotely sensed observations can provide unique perspective on how management and natural disturbance affect carbon stocks in forests. However, integration of these observations into formal decision support will rely upon improved uncertainty accounting. Monte Carlo (MC) simulations offer a practical, empirical method of accounting for potential remote sensing errors...
DMello, Marilyn Esclance; Sundaram, Nalini G; Kalidindi, Suresh Babu
2018-05-03
Metal-organic frameworks (MOFs) are widely known for their record storage capacities of small gas molecules (H 2 , CO 2 , and CH 4 ). Assembly of such porous materials onto well-known chemiresistive gas sensing elements such as SnO 2 could be an attractive prospect to achieve novel sensing properties as this affects the surface chemistry of SnO 2 . Cobalt-imidazole based ZIF-67 MOF was grown onto preformed SnO 2 nanoparticles to realize core-shell like architecture and explored for greenhouse gas CO 2 sensing. CO 2 sensing over SnO 2 is a challenge because its interaction with SnO 2 surface is minimal. The ZIF-67 coating over SnO 2 improved the response of SnO 2 up to 12-fold (for 50 % CO 2 ). The SnO 2 @ZIF-67 also showed a response of 16.5±2.1 % for 5000 ppm CO 2 (threshold limit value (TLV)) at 205 °C, one of the best values reported for a SnO 2 -based sensor. The observed novel CO 2 sensing characteristics are assigned to electronic structure changes at the interface of ZIF-67 and SnO 2 . © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.
Scheele, C. J.; Huang, Q.
2016-12-01
In the past decade, the rise in social media has led to the development of a vast number of social media services and applications. Disaster management represents one of such applications leveraging massive data generated for event detection, response, and recovery. In order to find disaster relevant social media data, current approaches utilize natural language processing (NLP) methods based on keywords, or machine learning algorithms relying on text only. However, these approaches cannot be perfectly accurate due to the variability and uncertainty in language used on social media. To improve current methods, the enhanced text-mining framework is proposed to incorporate location information from social media and authoritative remote sensing datasets for detecting disaster relevant social media posts, which are determined by assessing the textual content using common text mining methods and how the post relates spatiotemporally to the disaster event. To assess the framework, geo-tagged Tweets were collected for three different spatial and temporal disaster events: hurricane, flood, and tornado. Remote sensing data and products for each event were then collected using RealEarthTM. Both Naive Bayes and Logistic Regression classifiers were used to compare the accuracy within the enhanced text-mining framework. Finally, the accuracies from the enhanced text-mining framework were compared to the current text-only methods for each of the case study disaster events. The results from this study address the need for more authoritative data when using social media in disaster management applications.
International Nuclear Information System (INIS)
Park, Chan Wook; Lee, Sung Su
2008-01-01
Two-phase compressible flow fields of air-water are investigated numerically in the fixed Eulerian grid framework. The phase interface is captured via volume fractions of ech phase. A way to model two phase compressible flows as a single phase one is found based on an equivalent equation of states of Tait's type for a multiphase cell. The equivalent single phase field is discretized using the Roe's approximate Riemann solver. Two approaches are tried to suppress the pressure oscillation phenomena at the phase interface, a passive advection of volume fraction and a direct pressure relaxation with the compressible form of volume fraction equation. The direct pressure equalizing method suppresses pressure oscillation successfully and generates sharp discontinuities, transmitting and reflecting acoustic waves naturally at the phase interface. In discretizing the compressible form of volume fraction equation, phase interfaces are geometrically reconstructed to minimize the numerical diffusion of volume fraction and relevant variables. The motion of a projectile in a water-filled tube which is fired by the release of highly pressurized air is simulated presuming the flow field as a two dimensional one, and several design factors affecting the projectile movement are investigated
Cao, Anping; Shan, Meixia; Paltrinieri, Laura; Evers, Wiel H.; Chu, Liangyong; Poltorak, Lukasz; Klootwijk, Johan H.; Seoane, Beatriz; Gascon, Jorge; Sudhö lter, Ernst J. R.; de Smet, Louis C. P. M.
2018-01-01
studied for diverse applications, including as transistors, solar cells, lithium ion batteries and sensors. Here we demonstrate the functionalization of SiNW surfaces with POFs and explore its effect on the electrical sensing properties of Si
Directory of Open Access Journals (Sweden)
Jorge Sá Silva
2012-02-01
Full Text Available As the Internet evolved, social networks (such as Facebook have bloomed and brought together an astonishing number of users. Mashing up mobile phones and sensors with these social environments enables the creation of people-centric sensing systems which have great potential for expanding our current social networking usage. However, such systems also have many associated technical challenges, such as privacy concerns, activity detection mechanisms or intermittent connectivity, as well as limitations due to the heterogeneity of sensor nodes and networks. Considering the openness of the Web 2.0, good technical solutions for these cases consist of frameworks that expose sensing data and functionalities as common Web-Services. This paper presents our RESTful Web Service-based model for people-centric sensing frameworks, which uses sensors and mobile phones to detect users’ activities and locations, sharing this information amongst the user’s friends within a social networking site. We also present some screenshot results of our experimental prototype.
An Interactive Web-Based Analysis Framework for Remote Sensing Cloud Computing
Wang, X. Z.; Zhang, H. M.; Zhao, J. H.; Lin, Q. H.; Zhou, Y. C.; Li, J. H.
2015-07-01
Spatiotemporal data, especially remote sensing data, are widely used in ecological, geographical, agriculture, and military research and applications. With the development of remote sensing technology, more and more remote sensing data are accumulated and stored in the cloud. An effective way for cloud users to access and analyse these massive spatiotemporal data in the web clients becomes an urgent issue. In this paper, we proposed a new scalable, interactive and web-based cloud computing solution for massive remote sensing data analysis. We build a spatiotemporal analysis platform to provide the end-user with a safe and convenient way to access massive remote sensing data stored in the cloud. The lightweight cloud storage system used to store public data and users' private data is constructed based on open source distributed file system. In it, massive remote sensing data are stored as public data, while the intermediate and input data are stored as private data. The elastic, scalable, and flexible cloud computing environment is built using Docker, which is a technology of open-source lightweight cloud computing container in the Linux operating system. In the Docker container, open-source software such as IPython, NumPy, GDAL, and Grass GIS etc., are deployed. Users can write scripts in the IPython Notebook web page through the web browser to process data, and the scripts will be submitted to IPython kernel to be executed. By comparing the performance of remote sensing data analysis tasks executed in Docker container, KVM virtual machines and physical machines respectively, we can conclude that the cloud computing environment built by Docker makes the greatest use of the host system resources, and can handle more concurrent spatial-temporal computing tasks. Docker technology provides resource isolation mechanism in aspects of IO, CPU, and memory etc., which offers security guarantee when processing remote sensing data in the IPython Notebook. Users can write
International Nuclear Information System (INIS)
Shim, J.H.; Kang, M.; Lee, Y.; Lee, C.
2012-01-01
Nanoporous ruthenium oxide frameworks (L 2 -eRuO) were electrodeposited on gold substrates by repetitive potential cycling in solutions of ruthenium(III) ions in the presence of reverse neutral micelles. The L 2 -eRuO was characterized in terms of direct oxidation of glucose and potentiometric response to pH values. The surface structures and morphologies of the L 2 -eRuO were characterized by scanning electron microscopy, energy dispersive X-ray spectroscopy, Raman spectroscopy, and high-resolution transmission electron microscopy. Their surface area was estimated via underpotential deposition of copper. L 2-eRuO-modified electrodes showed a 17-fold higher sensitivity (40 μA mM -1 cm -2 towards glucose in 0-4 mM concentration in solution of pH 7.4) than a RuO electrode prepared in the absence of reverse micelles. Potential interferents such as ascorbic acid, 4-acetamidophenol, uric acid and dopamine displayed no effect. The new electrode also revealed improved potentiometric response to pH changes compared to a platinum electrode of the same type. (author)
Directory of Open Access Journals (Sweden)
Wooyeon Sunwoo
2017-01-01
Full Text Available Runoff prediction in limited-data areas is vital for hydrological applications, such as the design of infrastructure and flood defenses, runoff forecasting, and water management. Rainfall–runoff models may be useful for simulation of runoff generation, particularly event-based models, which offer a practical modeling scheme because of their simplicity. However, there is a need to reduce the uncertainties related to the estimation of the initial wetness condition (IWC prior to a rainfall event. Soil moisture is one of the most important variables in rainfall–runoff modeling, and remotely sensed soil moisture is recognized as an effective way to improve the accuracy of runoff prediction. In this study, the IWC was evaluated based on remotely sensed soil moisture by using the Soil Conservation Service-Curve Number (SCS-CN method, which is one of the representative event-based models used for reducing the uncertainty of runoff prediction. Four proxy variables for the IWC were determined from the measurements of total rainfall depth (API5, ground-based soil moisture (SSMinsitu, remotely sensed surface soil moisture (SSM, and soil water index (SWI provided by the advanced scatterometer (ASCAT. To obtain a robust IWC framework, this study consists of two main parts: the validation of remotely sensed soil moisture, and the evaluation of runoff prediction using four proxy variables with a set of rainfall–runoff events in the East Asian monsoon region. The results showed an acceptable agreement between remotely sensed soil moisture (SSM and SWI and ground based soil moisture data (SSMinsitu. In the proxy variable analysis, the SWI indicated the optimal value among the proposed proxy variables. In the runoff prediction analysis considering various infiltration conditions, the SSM and SWI proxy variables significantly reduced the runoff prediction error as compared with API5 by 60% and 66%, respectively. Moreover, the proposed IWC framework with
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
Directory of Open Access Journals (Sweden)
Manoel Afonso Pereira de Lima
2016-09-01
Full Text Available Structural Health Monitoring (SHM is an important technique used to preserve many types of structures in the short and long run, using sensor networks to continuously gather the desired data. However, this causes a strong impact in the data size to be stored and processed. A common solution to this is using compression algorithms, where the level of data compression should be adequate enough to allow the correct damage identification. In this work, we use the data sets from a laboratory three-story structure to evaluate the performance of common compression algorithms which, then, are combined with damage detection algorithms used in SHM. We also analyze how the use of Independent Component Analysis, a common technique to reduce noise in raw data, can assist the detection performance. The results showed that Piecewise Linear Histogram combined with Nonlinear PCA have the best trade-off between compression and detection for small error thresholds while Adaptive PCA with Principal Component Analysis perform better with higher values.
Shahtahmassebi, Amir Reza; Song, Jie; Zheng, Qing; Blackburn, George Alan; Wang, Ke; Huang, Ling Yan; Pan, Yi; Moore, Nathan; Shahtahmassebi, Golnaz; Sadrabadi Haghighi, Reza; Deng, Jing Song
2016-04-01
A substantial body of literature has accumulated on the topic of using remotely sensed data to map impervious surfaces which are widely recognized as an important indicator of urbanization. However, the remote sensing of impervious surface growth has not been successfully addressed. This study proposes a new framework for deriving and summarizing urban expansion and re-densification using time series of impervious surface fractions (ISFs) derived from remotely sensed imagery. This approach integrates multiple endmember spectral mixture analysis (MESMA), analysis of regression residuals, spatial statistics (Getis_Ord) and urban growth theories; hence, the framework is abbreviated as MRGU. The performance of MRGU was compared with commonly used change detection techniques in order to evaluate the effectiveness of the approach. The results suggested that the ISF regression residuals were optimal for detecting impervious surface changes while Getis_Ord was effective for mapping hotspot regions in the regression residuals image. Moreover, the MRGU outputs agreed with the mechanisms proposed in several existing urban growth theories, but importantly the outputs enable the refinement of such models by explicitly accounting for the spatial distribution of both expansion and re-densification mechanisms. Based on Landsat data, the MRGU is somewhat restricted in its ability to measure re-densification in the urban core but this may be improved through the use of higher spatial resolution satellite imagery. The paper ends with an assessment of the present gaps in remote sensing of impervious surface growth and suggests some solutions. The application of impervious surface fractions in urban change detection is a stimulating new research idea which is driving future research with new models and algorithms.
Hromadka, J.; Tokay, B.; James, S.; Korposh, S.
2017-04-01
An optical fibre long period grating (LPG) modified with a thin film of HKUST-1, a material from metal organic framework (MOF) family, was employed for the detection of carbon dioxide. The sensing mechanism is based on the measurement of the change of the refractive index (RI) of the coating that is induced by the penetration of CO2 molecules into the HKUST-1 pores. The responses of the resonance bands in the transmission spectrum of an LPG modified with 40 layers of HKUST-1 upon exposure to carbon dioxide in mixture with nitrogen were investigated.
AN INTERACTIVE WEB-BASED ANALYSIS FRAMEWORK FOR REMOTE SENSING CLOUD COMPUTING
Directory of Open Access Journals (Sweden)
X. Z. Wang
2015-07-01
Full Text Available Spatiotemporal data, especially remote sensing data, are widely used in ecological, geographical, agriculture, and military research and applications. With the development of remote sensing technology, more and more remote sensing data are accumulated and stored in the cloud. An effective way for cloud users to access and analyse these massive spatiotemporal data in the web clients becomes an urgent issue. In this paper, we proposed a new scalable, interactive and web-based cloud computing solution for massive remote sensing data analysis. We build a spatiotemporal analysis platform to provide the end-user with a safe and convenient way to access massive remote sensing data stored in the cloud. The lightweight cloud storage system used to store public data and users’ private data is constructed based on open source distributed file system. In it, massive remote sensing data are stored as public data, while the intermediate and input data are stored as private data. The elastic, scalable, and flexible cloud computing environment is built using Docker, which is a technology of open-source lightweight cloud computing container in the Linux operating system. In the Docker container, open-source software such as IPython, NumPy, GDAL, and Grass GIS etc., are deployed. Users can write scripts in the IPython Notebook web page through the web browser to process data, and the scripts will be submitted to IPython kernel to be executed. By comparing the performance of remote sensing data analysis tasks executed in Docker container, KVM virtual machines and physical machines respectively, we can conclude that the cloud computing environment built by Docker makes the greatest use of the host system resources, and can handle more concurrent spatial-temporal computing tasks. Docker technology provides resource isolation mechanism in aspects of IO, CPU, and memory etc., which offers security guarantee when processing remote sensing data in the IPython Notebook
Single exposure optically compressed imaging and visualization using random aperture coding
Energy Technology Data Exchange (ETDEWEB)
Stern, A [Electro Optical Unit, Ben Gurion University of the Negev, Beer-Sheva 84105 (Israel); Rivenson, Yair [Department of Electrical and Computer Engineering, Ben Gurion University of the Negev, Beer-Sheva 84105 (Israel); Javidi, Bahrain [Department of Electrical and Computer Engineering, University of Connecticut, Storrs, Connecticut 06269-1157 (United States)], E-mail: stern@bgu.ac.il
2008-11-01
The common approach in digital imaging follows the sample-then-compress framework. According to this approach, in the first step as many pixels as possible are captured and in the second step the captured image is compressed by digital means. The recently introduced theory of compressed sensing provides the mathematical foundation necessary to combine these two steps in a single one, that is, to compress the information optically before it is recorded. In this paper we overview and extend an optical implementation of compressed sensing theory that we have recently proposed. With this new imaging approach the compression is accomplished inherently in the optical acquisition step. The primary feature of this imaging approach is a randomly encoded aperture realized by means of a random phase screen. The randomly encoded aperture implements random projection of the object field in the image plane. Using a single exposure, a randomly encoded image is captured which can be decoded by proper decoding algorithm.
Je, U K; Cho, H M; Hong, D K; Cho, H S; Park, Y O; Park, C K; Kim, K S; Lim, H W; Kim, G A; Park, S Y; Woo, T H; Cho, S I
2016-01-01
In this work, we propose a practical method that can combine the two functionalities of dental panoramic and cone-beam CT (CBCT) features in one by using a single panoramic detector. We implemented a CS-based reconstruction algorithm for the proposed method and performed a systematic simulation to demonstrate its viability for 3D dental X-ray imaging. We successfully reconstructed volumetric images of considerably high accuracy by using a panoramic detector having an active area of 198.4 mm × 6.4 mm and evaluated the reconstruction quality as a function of the pitch (p) and the angle step (Δθ). Our simulation results indicate that the CS-based reconstruction almost completely recovered the phantom structures, as in CBCT, for p≤2.0 and θ≤6°, indicating that it seems very promising for accurate image reconstruction even for large-pitch and few-view data. We expect the proposed method to be applicable to developing a cost-effective, volumetric dental X-ray imaging system. Copyright © 2015 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Szi-Wen Chen
Full Text Available In this paper, a reweighted ℓ1-minimization based Compressed Sensing (CS algorithm incorporating the Integral Pulse Frequency Modulation (IPFM model for spectral estimation of HRV is introduced. Knowing as a novel sensing/sampling paradigm, the theory of CS asserts certain signals that are considered sparse or compressible can be possibly reconstructed from substantially fewer measurements than those required by traditional methods. Our study aims to employ a novel reweighted ℓ1-minimization CS method for deriving the spectrum of the modulating signal of IPFM model from incomplete RR measurements for HRV assessments. To evaluate the performance of HRV spectral estimation, a quantitative measure, referred to as the Percent Error Power (PEP that measures the percentage of difference between the true spectrum and the spectrum derived from the incomplete RR dataset, was used. We studied the performance of spectral reconstruction from incomplete simulated and real HRV signals by experimentally truncating a number of RR data accordingly in the top portion, in the bottom portion, and in a random order from the original RR column vector. As a result, for up to 20% data truncation/loss the proposed reweighted ℓ1-minimization CS method produced, on average, 2.34%, 2.27%, and 4.55% PEP in the top, bottom, and random data-truncation cases, respectively, on Autoregressive (AR model derived simulated HRV signals. Similarly, for up to 20% data loss the proposed method produced 5.15%, 4.33%, and 0.39% PEP in the top, bottom, and random data-truncation cases, respectively, on a real HRV database drawn from PhysioNet. Moreover, results generated by a number of intensive numerical experiments all indicated that the reweighted ℓ1-minimization CS method always achieved the most accurate and high-fidelity HRV spectral estimates in every aspect, compared with the ℓ1-minimization based method and Lomb's method used for estimating the spectrum of HRV from
International Nuclear Information System (INIS)
Fritz, Jan; Thawait, Gaurav K.; Fritz, Benjamin; Raithel, Esther; Nittka, Mathias; Gilson, Wesley D.; Mont, Michael A.
2016-01-01
Compressed sensing (CS) acceleration has been theorized for slice encoding for metal artifact correction (SEMAC), but has not been shown to be feasible. Therefore, we tested the hypothesis that CS-SEMAC is feasible for MRI of metal-on-metal hip resurfacing implants. Following prospective institutional review board approval, 22 subjects with metal-on-metal hip resurfacing implants underwent 1.5 T MRI. We compared CS-SEMAC prototype, high-bandwidth TSE, and SEMAC sequences with acquisition times of 4-5, 4-5 and 10-12 min, respectively. Outcome measures included bone-implant interfaces, image quality, periprosthetic structures, artifact size, and signal- and contrast-to-noise ratios (SNR and CNR). Using Friedman, repeated measures analysis of variances, and Cohen's weighted kappa tests, Bonferroni-corrected p-values of 0.005 and less were considered statistically significant. There was no statistical difference of outcomes measures of SEMAC and CS-SEMAC images. Visibility of implant-bone interfaces and pseudocapsule as well as fat suppression and metal reduction were ''adequate'' to ''good'' on CS-SEMAC and ''non-diagnostic'' to ''adequate'' on high-BW TSE (p < 0.001, respectively). SEMAC and CS-SEMAC showed mild blur and ripple artifacts. The metal artifact size was 63 % larger for high-BW TSE as compared to SEMAC and CS-SEMAC (p < 0.0001, respectively). CNRs were sufficiently high and statistically similar, with the exception of CNR of fluid and muscle and CNR of fluid and tendon, which were higher on intermediate-weighted high-BW TSE (p < 0.005, respectively). Compressed sensing acceleration enables the time-neutral use of SEMAC for MRI of metal-on-metal hip resurfacing implants when compared to high-BW TSE and image quality similar to conventional SEMAC. (orig.)
Energy Technology Data Exchange (ETDEWEB)
Fritz, Jan; Thawait, Gaurav K. [Johns Hopkins University School of Medicine, Russell H. Morgan Department of Radiology and Radiological Science, Section of Musculoskeletal Radiology, Baltimore, MD (United States); Fritz, Benjamin [University of Freiburg, Department of Radiology, Freiburg im Breisgau (Germany); Raithel, Esther; Nittka, Mathias [Siemens Healthcare GmbH, Erlangen (Germany); Gilson, Wesley D. [Siemens Healthcare USA, Inc., Baltimore, MD (United States); Mont, Michael A. [Cleveland Clinic Foundation, Department of Orthopedic Surgery, Cleveland, OH (United States)
2016-10-15
Compressed sensing (CS) acceleration has been theorized for slice encoding for metal artifact correction (SEMAC), but has not been shown to be feasible. Therefore, we tested the hypothesis that CS-SEMAC is feasible for MRI of metal-on-metal hip resurfacing implants. Following prospective institutional review board approval, 22 subjects with metal-on-metal hip resurfacing implants underwent 1.5 T MRI. We compared CS-SEMAC prototype, high-bandwidth TSE, and SEMAC sequences with acquisition times of 4-5, 4-5 and 10-12 min, respectively. Outcome measures included bone-implant interfaces, image quality, periprosthetic structures, artifact size, and signal- and contrast-to-noise ratios (SNR and CNR). Using Friedman, repeated measures analysis of variances, and Cohen's weighted kappa tests, Bonferroni-corrected p-values of 0.005 and less were considered statistically significant. There was no statistical difference of outcomes measures of SEMAC and CS-SEMAC images. Visibility of implant-bone interfaces and pseudocapsule as well as fat suppression and metal reduction were ''adequate'' to ''good'' on CS-SEMAC and ''non-diagnostic'' to ''adequate'' on high-BW TSE (p < 0.001, respectively). SEMAC and CS-SEMAC showed mild blur and ripple artifacts. The metal artifact size was 63 % larger for high-BW TSE as compared to SEMAC and CS-SEMAC (p < 0.0001, respectively). CNRs were sufficiently high and statistically similar, with the exception of CNR of fluid and muscle and CNR of fluid and tendon, which were higher on intermediate-weighted high-BW TSE (p < 0.005, respectively). Compressed sensing acceleration enables the time-neutral use of SEMAC for MRI of metal-on-metal hip resurfacing implants when compared to high-BW TSE and image quality similar to conventional SEMAC. (orig.)
Tamondong, A.; Cruz, C.; Ticman, T.; Peralta, R.; Go, G. A.; Vergara, M.; Estabillo, M. S.; Cadalzo, I. E.; Jalbuena, R.; Blanco, A.
2016-06-01
Remote sensing has been an effective technology in mapping natural resources by reducing the costs and field data gathering time and bringing in timely information. With the launch of several earth observation satellites, an increase in the availability of satellite imageries provides an immense selection of data for the users. The Philippines has recently embarked in a program which will enable the gathering of LiDAR data in the whole country. The capacity of the Philippines to take advantage of these advancements and opportunities is lacking. There is a need to transfer the knowledge of remote sensing technology to other institutions to better utilize the available data. Being an archipelagic country with approximately 36,000 kilometers of coastline, and most of its people depending on its coastal resources, remote sensing is an optimal choice in mapping such resources. A project involving fifteen (15) state universities and colleges and higher education institutions all over the country headed by the University of the Philippines Training Center for Applied Geodesy and Photogrammetry and funded by the Department of Science and Technology was formed to carry out the task of capacity building in mapping the country's coastal resources using LiDAR and other remotely sensed datasets. This paper discusses the accomplishments and the future activities of the project.
CSIR Research Space (South Africa)
De Klerk, HM
2016-11-01
Full Text Available Traditionally, to map environmental features using remote sensing, practitioners will use training data to develop models on various satellite data sets using a number of classification approaches and use test data to select a single ‘best performer...
A data fusion framework for floodplain analysis using GIS and remotely sensed data
Necsoiu, Dorel Marius
Throughout history floods have been part of the human experience. They are recurring phenomena that form a necessary and enduring feature of all river basin and lowland coastal systems. In an average year, they benefit millions of people who depend on them. In the more developed countries, major floods can be the largest cause of economic losses from natural disasters, and are also a major cause of disaster-related deaths in the less developed countries. Flood disaster mitigation research was conducted to determine how remotely sensed data can effectively be used to produce accurate flood plain maps (FPMs), and to identify/quantify the sources of error associated with such data. Differences were analyzed between flood maps produced by an automated remote sensing analysis tailored to the available satellite remote sensing datasets (rFPM), the 100-year flooded areas "predicted" by the Flood Insurance Rate Maps, and FPMs based on DEM and hydrological data (aFPM). Landuse/landcover was also examined to determine its influence on rFPM errors. These errors were identified and the results were integrated in a GIS to minimize landuse/landcover effects. Two substantial flood events were analyzed. These events were selected because of their similar characteristics (i.e., the existence of FIRM or Q3 data; flood data which included flood peaks, rating curves, and flood profiles; and DEM and remote sensing imagery). Automatic feature extraction was determined to be an important component for successful flood analysis. A process network, in conjunction with domain specific information, was used to map raw remotely sensed data onto a representation that is more compatible with a GIS data model. From a practical point of view, rFPM provides a way to automatically match existing data models to the type of remote sensing data available for each event under investigation. Overall, results showed how remote sensing could contribute to the complex problem of flood management by
Han, Xiaopeng; Huang, Xin; Li, Jiayi; Li, Yansheng; Yang, Michael Ying; Gong, Jianya
2018-04-01
In recent years, the availability of high-resolution imagery has enabled more detailed observation of the Earth. However, it is imperative to simultaneously achieve accurate interpretation and preserve the spatial details for the classification of such high-resolution data. To this aim, we propose the edge-preservation multi-classifier relearning framework (EMRF). This multi-classifier framework is made up of support vector machine (SVM), random forest (RF), and sparse multinomial logistic regression via variable splitting and augmented Lagrangian (LORSAL) classifiers, considering their complementary characteristics. To better characterize complex scenes of remote sensing images, relearning based on landscape metrics is proposed, which iteratively quantizes both the landscape composition and spatial configuration by the use of the initial classification results. In addition, a novel tri-training strategy is proposed to solve the over-smoothing effect of relearning by means of automatic selection of training samples with low classification certainties, which always distribute in or near the edge areas. Finally, EMRF flexibly combines the strengths of relearning and tri-training via the classification certainties calculated by the probabilistic output of the respective classifiers. It should be noted that, in order to achieve an unbiased evaluation, we assessed the classification accuracy of the proposed framework using both edge and non-edge test samples. The experimental results obtained with four multispectral high-resolution images confirm the efficacy of the proposed framework, in terms of both edge and non-edge accuracy.
Kim, Ki-Joong; Lu, Ping; Culp, Jeffrey T; Ohodnicki, Paul R
2018-02-23
Integration of optical fiber with sensitive thin films offers great potential for the realization of novel chemical sensing platforms. In this study, we present a simple design strategy and high performance of nanoporous metal-organic framework (MOF) based optical gas sensors, which enables detection of a wide range of concentrations of small molecules based upon extremely small differences in refractive indices as a function of analyte adsorption within the MOF framework. Thin and compact MOF films can be uniformly formed and tightly bound on the surface of etched optical fiber through a simple solution method which is critical for manufacturability of MOF-based sensor devices. The resulting sensors show high sensitivity/selectivity to CO 2 gas relative to other small gases (H 2 , N 2 , O 2 , and CO) with rapid (optical fiber platform which results in an amplification of inherent optical absorption present within the MOF-based sensing layer with increasing values of effective refractive index associated with adsorption of gases.
Energy Technology Data Exchange (ETDEWEB)
Zhang, Xiu-Mei, E-mail: zhangxiumeilb@126.com; Li, Peng; Gao, Wei; Liu, Feng; Liu, Jie-Ping
2016-12-15
Three lanthanide metal-organic frameworks (Ln-MOFs), [Ln(TZI)(H{sub 2}O){sub 4}]·3H{sub 2}O (Ln=Gd (1) and Tb (2) and Dy (3), H{sub 3}TZI=5-(1H-tetrazol-5-yl)isophthalic acid), have been synthesized under hydrothermal conditions. Single crystal X-ray diffraction reveals that 1–3 are isostructural and display a 1D double chain based on dinuclear motifs with (μ-COO){sub 2} double bridges. Magnetic studies indicate antiferromagnetic interactions in 1, ferromagnetic interactions in 2 and 3. Furthermore, compound 3 displays a slow relaxation behavior. Compound 2 exhibits intense characteristic green emission of Tb(III) ions in the solid state, which can be observed by the naked eye under UV light. Interestingly, 2 can selectively sense Pb{sup 2+} and Fe{sup 3+} ions through luminescence enhancement and quenching, respectively. The luminescence quenching mechanisms have been investigated in detail. The study on luminescence Ln-MOFs as a probe for sensing Pb{sup 2+} and Fe{sup 3+} ions is exceedingly rare example. - Graphical abstract: Three Ln-MOFs were successfully synthesized using a 5-(1H-tetrazol-5-yl)isophthalic acid ligand. They displays different magnetic behavior. Especially, the Dy(III) compound slow relaxation behavior. Interestingly, the Tb(III) compound can selectively sense Pb{sup 2+} and Fe{sup 3+} ions through luminescence enhancement and quenching, respectively. - Highlights: • Three Ln-MOFs with tetrazolate dicarboxylate ligand. • Dy(III) compound displays slow relaxation behavior. • The Tb(III) compound shows highly selective luminescence sensing of the Fe{sup 3+} and Pb{sup 2+} ions.
Kirschbaum, Dalia; Stanley, Thomas
2016-04-01
Remote sensing data offers the unique perspective to provide situational awareness of hydrometeorological hazards over large areas in a way that is impossible to achieve with in situ data. Recent work has shown that rainfall-triggered landslides, while typically local hazards that occupy small spatial areas, can be approximated over regional or global scales in near real-time. This work presents a regional and global approach to approximating potential landslide activity using the landslide hazard assessment for situational awareness (LHASA) model. This system couples remote sensing data, including Global Precipitation Measurement rainfall data, Shuttle Radar Topography Mission and other surface variables to estimate where and when landslide activity may be likely. This system also evaluates the effectiveness of quantitative precipitation estimates from the Goddard Earth Observing System Model, Version 5 to provide a 24 forecast of potential landslide activity. Preliminary results of the LHASA model and implications for are presented for a regional version of this system in Central America as well as a prototype global approach.
Development of a Remote-Sensing Based Framework for Mapping Drought over North America
Hain, C.; Anderson, M. C.; Zhan, X.; Gao, F.; Svoboda, M.; Wardlow, B.; Mladenova, I. E.
2012-12-01
This presentation will address the development of a multi-scale drought monitoring tool for North America based on remotely sensed estimates of evapotranspiration. The North American continent represents a broad range in vegetation and climate conditions, from the boreal forests in Canada to the arid deserts in Mexico. This domain also encompasses a range in constraints limiting vegetation growth, with a gradient from radiation/energy limitation in the north to moisture limits in the south. This feasibility study over NA will provide a valuable test bed for future implementation world-wide in support of proposed global drought monitoring and early warning efforts. The Evaporative Stress Index (ESI) represents anomalies in the ratio of actual-to-potential ET (fPET), generated with the thermal remote sensing based Atmosphere-Land Exchange Inverse (ALEXI) surface energy balance model and associated disaggregation algorithm, DisALEXI demonstrated that ESI maps over the continental US (CONUS) show good correspondence with standard drought metrics and with patterns of antecedent precipitation, but can be generated at significantly higher spatial resolution due to a limited reliance on ground observations. Unique behavior is observed in the ESI in regions where the evaporative flux is enhanced by moisture sources decoupled from local rainfall, for example in areas where drought impacts are being mitigated by intense irrigation or shallow water tables. As such, the ESI is a measure of actual stress rather than potential for stress, and has physical relevance to projected crop development. Because precipitation is not used in construction of the ESI, this index provides an independent assessment of drought conditions and will have particular utility for real-time monitoring in regions with sparse rainfall data or significant delays in meteorological reporting. The North American ESI product will be quantitatively compared with spatiotemporal patterns in the NADM, and with
Clapham, David E; Miller, Christopher
2011-12-06
The exceptionally high temperature sensitivity of certain transient receptor potential (TRP) family ion channels is the molecular basis of hot and cold sensation in sensory neurons. The laws of thermodynamics dictate that opening of these specialized TRP channels must involve an unusually large conformational standard-state enthalpy, ΔH(o): positive ΔH(o) for heat-activated and negative ΔH(o) for cold-activated TRPs. However, the molecular source of such high-enthalpy changes has eluded neurobiologists and biophysicists. Here we offer a general, unifying mechanism for both hot and cold activation that recalls long-appreciated principles of protein folding. We suggest that TRP channel gating is accompanied by large changes in molar heat capacity, ΔC(P). This postulate, along with the laws of thermodynamics and independent of mechanistic detail, leads to the conclusion that hot- and cold-sensing TRPs operate by identical conformational changes.
Directory of Open Access Journals (Sweden)
M. Schneider
2010-12-01
Full Text Available We show that the near infrared solar absorption spectra recorded in the framework of the Total Carbon Column Observing Network (TCCON can be used to derive the vertical distribution of tropospheric water vapour. The resolution of the TCCON spectra of 0.02 cm^{−1} is sufficient for retrieving lower and middle/upper tropospheric water vapour concentrations with a vertical resolution of about 3 and 8 km, respectively. We document the good quality of the remotely-sensed profiles by comparisons with coincident in-situ Vaisala RS92 radiosonde measurements. Due to the high measurement frequency, the TCCON water vapour profile data offer novel opportunities for estimating the water vapour variability at different timescales and altitudes.
Dual-Emitting UiO-66(Zr&Eu) Metal-Organic Framework Films for Ratiometric Temperature Sensing.
Feng, Ji-Fei; Liu, Tian-Fu; Shi, Jianlin; Gao, Shui-Ying; Cao, Rong
2018-06-20
A novel dual-emitting metal-organic framework based on Zr and Eu, named as UiO-66(Zr&Eu), was built using a clever strategy based on secondary building units. With the use of polymers, the obtained UiO-66(Zr&Eu) was subsequently deposited as thin films that can be utilized as smart thermometers. The UiO-66(Zr&Eu) polymer films can be used for the detection of temperature changes in the range of 237-337 K due to the energy transfer between the lanthanide ions (Eu in clusters) and the luminescent ligands, and the relative sensitivity reaches 4.26% K -1 at 337 K. Moreover, the sensitivity can be improved to 19.67% K -1 by changing the film thickness. In addition, the temperature-sensing performance of the films is superior to that of the powders, and the sensor can be reused 3 times without loss of performance.
Zhong, Yanfei; Han, Xiaobing; Zhang, Liangpei
2018-04-01
Multi-class geospatial object detection from high spatial resolution (HSR) remote sensing imagery is attracting increasing attention in a wide range of object-related civil and engineering applications. However, the distribution of objects in HSR remote sensing imagery is location-variable and complicated, and how to accurately detect the objects in HSR remote sensing imagery is a critical problem. Due to the powerful feature extraction and representation capability of deep learning, the deep learning based region proposal generation and object detection integrated framework has greatly promoted the performance of multi-class geospatial object detection for HSR remote sensing imagery. However, due to the translation caused by the convolution operation in the convolutional neural network (CNN), although the performance of the classification stage is seldom influenced, the localization accuracies of the predicted bounding boxes in the detection stage are easily influenced. The dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage has not been addressed for HSR remote sensing imagery, and causes position accuracy problems for multi-class geospatial object detection with region proposal generation and object detection. In order to further improve the performance of the region proposal generation and object detection integrated framework for HSR remote sensing imagery object detection, a position-sensitive balancing (PSB) framework is proposed in this paper for multi-class geospatial object detection from HSR remote sensing imagery. The proposed PSB framework takes full advantage of the fully convolutional network (FCN), on the basis of a residual network, and adopts the PSB framework to solve the dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage. In addition, a pre-training mechanism is utilized to accelerate the training procedure
Heacock, Laura; Gao, Yiming; Heller, Samantha L; Melsaether, Amy N; Babb, James S; Block, Tobias K; Otazo, Ricardo; Kim, Sungheon G; Moy, Linda
2017-06-01
To compare a novel multicoil compressed sensing technique with flexible temporal resolution, golden-angle radial sparse parallel (GRASP), to conventional fat-suppressed spoiled three-dimensional (3D) gradient-echo (volumetric interpolated breath-hold examination, VIBE) MRI in evaluating the conspicuity of benign and malignant breast lesions. Between March and August 2015, 121 women (24-84 years; mean, 49.7 years) with 180 biopsy-proven benign and malignant lesions were imaged consecutively at 3.0 Tesla in a dynamic contrast-enhanced (DCE) MRI exam using sagittal T1-weighted fat-suppressed 3D VIBE in this Health Insurance Portability and Accountability Act-compliant, retrospective study. Subjects underwent MRI-guided breast biopsy (mean, 13 days [1-95 days]) using GRASP DCE-MRI, a fat-suppressed radial "stack-of-stars" 3D FLASH sequence with golden-angle ordering. Three readers independently evaluated breast lesions on both sequences. Statistical analysis included mixed models with generalized estimating equations, kappa-weighted coefficients and Fisher's exact test. All lesions demonstrated good conspicuity on VIBE and GRASP sequences (4.28 ± 0.81 versus 3.65 ± 1.22), with no significant difference in lesion detection (P = 0.248). VIBE had slightly higher lesion conspicuity than GRASP for all lesions, with VIBE 12.6% (0.63/5.0) more conspicuous (P < 0.001). Masses and nonmass enhancement (NME) were more conspicuous on VIBE (P < 0.001), with a larger difference for NME (14.2% versus 9.4% more conspicuous). Malignant lesions were more conspicuous than benign lesions (P < 0.001) on both sequences. GRASP DCE-MRI, a multicoil compressed sensing technique with high spatial resolution and flexible temporal resolution, has near-comparable performance to conventional VIBE imaging for breast lesion evaluation. 3 Technical Efficacy: Stage 3 J. MAGN. RESON. IMAGING 2017;45:1746-1752. © 2016 International Society for Magnetic Resonance in Medicine.
Zhou, Ruixi; Huang, Wei; Yang, Yang; Chen, Xiao; Weller, Daniel S; Kramer, Christopher M; Kozerke, Sebastian; Salerno, Michael
2018-02-01
Cardiovascular magnetic resonance (CMR) stress perfusion imaging provides important diagnostic and prognostic information in coronary artery disease (CAD). Current clinical sequences have limited temporal and/or spatial resolution, and incomplete heart coverage. Techniques such as k-t principal component analysis (PCA) or k-t sparcity and low rank structure (SLR), which rely on the high degree of spatiotemporal correlation in first-pass perfusion data, can significantly accelerate image acquisition mitigating these problems. However, in the presence of respiratory motion, these techniques can suffer from significant degradation of image quality. A number of techniques based on non-rigid registration have been developed. However, to first approximation, breathing motion predominantly results in rigid motion of the heart. To this end, a simple robust motion correction strategy is proposed for k-t accelerated and compressed sensing (CS) perfusion imaging. A simple respiratory motion compensation (MC) strategy for k-t accelerated and compressed-sensing CMR perfusion imaging to selectively correct respiratory motion of the heart was implemented based on linear k-space phase shifts derived from rigid motion registration of a region-of-interest (ROI) encompassing the heart. A variable density Poisson disk acquisition strategy was used to minimize coherent aliasing in the presence of respiratory motion, and images were reconstructed using k-t PCA and k-t SLR with or without motion correction. The strategy was evaluated in a CMR-extended cardiac torso digital (XCAT) phantom and in prospectively acquired first-pass perfusion studies in 12 subjects undergoing clinically ordered CMR studies. Phantom studies were assessed using the Structural Similarity Index (SSIM) and Root Mean Square Error (RMSE). In patient studies, image quality was scored in a blinded fashion by two experienced cardiologists. In the phantom experiments, images reconstructed with the MC strategy had higher
Bai, Shouli; Liu, Chengyao; Luo, Ruixian; Chen, Aifan
2018-04-01
The SnO2/NiO composites were synthesized by hydrothermal followed by calcination using metal-organic framework (MOF) consisting of the ligand of p-benzene-dicarboxylic acid (PTA) and the Sn and Ni center ions as sacriﬁcial templates. The structure and morphology of Sn/Ni-based MOF and SnO2/NiO composites were characterized by XRD, SEM, TEM, FT-IR, TG, XPS and Brunauer-Emmett-Teller analysis. Sensing experiments reveal that the SnO2/NiO composite with the molar ratio of 9:1 not only exhibits the highest response of 14.03 that is 3 times higher than pristine SnO2 to triethylamine at 70 °C, but also shows good selectivity. Such excellent performance is attributed to the MOF-driven strategy and the formation of p-n heterojunctions, because the metal ions can be highly dispersed and separated in the MOFs and can prevent the metal ions aggregation during the MOF decomposition process. The work is a novel route for synthesis of gas sensing material.
Directory of Open Access Journals (Sweden)
Shaobo Li
2018-03-01
Full Text Available Rapid perception and processing of critical monitoring events are essential to ensure healthy operation of Internet of Manufacturing Things (IoMT-based manufacturing processes. In this paper, we proposed a framework (active sensing and processing architecture (ASPIE for active sensing and processing of critical events in IoMT-based manufacturing based on the characteristics of IoMT architecture as well as its perception model. A relation model of complex events in manufacturing processes, together with related operators and unified XML-based semantic definitions, are developed to effectively process the complex event big data. A template based processing method for complex events is further introduced to conduct complex event matching using the Apriori frequent item mining algorithm. To evaluate the proposed models and methods, we developed a software platform based on ASPIE for a local chili sauce manufacturing company, which demonstrated the feasibility and effectiveness of the proposed methods for active perception and processing of complex events in IoMT-based manufacturing.
A 3D Ag(I) metal-organic framework for sensing luminescence and photocatalytic activities
Chang, Hai-Ning; Liu, Li-Wei; Hao, Zeng Chuan; Cui, Guang-Hua
2018-03-01
A fluorescent metal-organic framework (MOF), [Ag(btx)0.5(DCTP)0.5]n (1) (H2DCTP = 2,5-dichloroterephthalic acid and btx = 1,4-bis(1,2,4-triazol-1-ylmethyl)benzene) has been hydrothermally synthesized and characterized by IR spectroscopy, elemental analysis, powder X-ray diffractions and thermogravimetry. The framework of 1 can be classified as a binodal (4,4)-connected PtS topological network. The fluorescence explorations demonstrated that 1 shows selective and sensitive detection towards Cr2O72- with high quenching efficiency of 1.92 × 104 M-1. The rapid and efficient response of 1 to Cr2O72- ion suggests that this material could be used as a luminescent sensor for Cr2O72- ion. Meanwhile the photocatalytic properties of 1 for the degradation of RhB have also been investigated under UV radiation. The possible photocatalytic mechanisms were also speculated. Hence, 1 can become multi-functional material in sensitive detection and effective removal of some environment pollutants in industrial waste water solutions.
Directory of Open Access Journals (Sweden)
Jeffrey D. Poston
2017-11-01
Full Text Available Counting the number of occupants in building areas over time—occupancy tracking—provides valuable information for responding to emergencies, optimizing thermal conditions or managing personnel. This capability is distinct from tracking individual building occupants as they move within a building, has lower complexity than conventional tracking algorithms require, and avoids privacy concerns that tracking individuals may pose. The approach proposed here is a novel combination of data analytics applied to measurements from a building’s structural dynamics sensors (e.g., accelerometers or geophones. Specifically, measurements of footstep-generated structural waves provide evidence of occupancy in a building area. These footstep vibrations can be distinguished from other vibrations, and, once identified, the footsteps can be located. These locations, in turn, form the starting point of estimating occupancy in an area. In order to provide a meaningful occupancy count, however, it is first necessary to associate discrete footsteps with individuals. The proposed framework incorporates a tractable algorithm for this association task. The proposed algorithms operate online, updating occupancy count over time as new footsteps are detected. Experiments with measurements from a public building illustrate the operation of the proposed framework. This approach offers an advantage over others based on conventional technologies by avoiding the cost of a separate sensor system devoted to occupancy tracking.
Cloud-based crowd sensing: a framework for location-based crowd analyzer and advisor
Aishwarya, K. C.; Nambi, A.; Hudson, S.; Nadesh, R. K.
2017-11-01
Cloud computing is an emerging field of computer science to integrate and explore large and powerful computing systems and storages for personal and also for enterprise requirements. Mobile Cloud Computing is the inheritance of this concept towards mobile hand-held devices. Crowdsensing, or to be precise, Mobile Crowdsensing is the process of sharing resources from an available group of mobile handheld devices that support sharing of different resources such as data, memory and bandwidth to perform a single task for collective reasons. In this paper, we propose a framework to use Crowdsensing and perform a crowd analyzer and advisor whether the user can go to the place or not. This is an ongoing research and is a new concept to which the direction of cloud computing has shifted and is viable for more expansion in the near future.
Mehmood, Irfan; Sajjad, Muhammad; Baik, Sung Wook
2014-09-15
Wireless capsule endoscopy (WCE) has great advantages over traditional endoscopy because it is portable and easy to use, especially in remote monitoring health-services. However, during the WCE process, the large amount of captured video data demands a significant deal of computation to analyze and retrieve informative video frames. In order to facilitate efficient WCE data collection and browsing task, we present a resource- and bandwidth-aware WCE video summarization framework that extracts the representative keyframes of the WCE video contents by removing redundant and non-informative frames. For redundancy elimination, we use Jeffrey-divergence between color histograms and inter-frame Boolean series-based correlation of color channels. To remove non-informative frames, multi-fractal texture features are extracted to assist the classification using an ensemble-based classifier. Owing to the limited WCE resources, it is impossible for the WCE system to perform computationally intensive video summarization tasks. To resolve computational challenges, mobile-cloud architecture is incorporated, which provides resizable computing capacities by adaptively offloading video summarization tasks between the client and the cloud server. The qualitative and quantitative results are encouraging and show that the proposed framework saves information transmission cost and bandwidth, as well as the valuable time of data analysts in browsing remote sensing data.
Directory of Open Access Journals (Sweden)
Irfan Mehmood
2014-09-01
Full Text Available Wireless capsule endoscopy (WCE has great advantages over traditional endoscopy because it is portable and easy to use, especially in remote monitoring health-services. However, during the WCE process, the large amount of captured video data demands a significant deal of computation to analyze and retrieve informative video frames. In order to facilitate efficient WCE data collection and browsing task, we present a resource- and bandwidth-aware WCE video summarization framework that extracts the representative keyframes of the WCE video contents by removing redundant and non-informative frames. For redundancy elimination, we use Jeffrey-divergence between color histograms and inter-frame Boolean series-based correlation of color channels. To remove non-informative frames, multi-fractal texture features are extracted to assist the classification using an ensemble-based classifier. Owing to the limited WCE resources, it is impossible for the WCE system to perform computationally intensive video summarization tasks. To resolve computational challenges, mobile-cloud architecture is incorporated, which provides resizable computing capacities by adaptively offloading video summarization tasks between the client and the cloud server. The qualitative and quantitative results are encouraging and show that the proposed framework saves information transmission cost and bandwidth, as well as the valuable time of data analysts in browsing remote sensing data.
Mehmood, Irfan; Sajjad, Muhammad; Baik, Sung Wook
2014-01-01
Wireless capsule endoscopy (WCE) has great advantages over traditional endoscopy because it is portable and easy to use, especially in remote monitoring health-services. However, during the WCE process, the large amount of captured video data demands a significant deal of computation to analyze and retrieve informative video frames. In order to facilitate efficient WCE data collection and browsing task, we present a resource- and bandwidth-aware WCE video summarization framework that extracts the representative keyframes of the WCE video contents by removing redundant and non-informative frames. For redundancy elimination, we use Jeffrey-divergence between color histograms and inter-frame Boolean series-based correlation of color channels. To remove non-informative frames, multi-fractal texture features are extracted to assist the classification using an ensemble-based classifier. Owing to the limited WCE resources, it is impossible for the WCE system to perform computationally intensive video summarization tasks. To resolve computational challenges, mobile-cloud architecture is incorporated, which provides resizable computing capacities by adaptively offloading video summarization tasks between the client and the cloud server. The qualitative and quantitative results are encouraging and show that the proposed framework saves information transmission cost and bandwidth, as well as the valuable time of data analysts in browsing remote sensing data. PMID:25225874
Directory of Open Access Journals (Sweden)
Mohamad Kashef
2009-11-01
Full Text Available This study engages the planning and urban design literature as well as social theory to develop a nuanced understanding of issues related to neighborhood form and sense of community. The study analyzes the meaning of community from economic, social, and cultural perspectives. It contextualizes the New Urbanism use of physical design as a subtext for community within a broader theoretical context. The study revisits the New Urbanism design principle regarding the interface between the private and public realm and its relationship to the idea of place and social attachment. While questioning the relevance of the New Urbanism planning agenda to U.S. metropolitan formations, the study discussions underline the value of its design formula for the social life of residential neighborhoods. The multidisciplinary approach of this study unravels some of the confusion over sociospatial dialectics in general, and community and built environment in particular. It opens the door for further cross-disciplinary research aimed at understanding and potentially optimizing the relationship between people and their built environments.
Park, S. Y.; Kim, G. A.; Cho, H. S.; Park, C. K.; Lee, D. Y.; Lim, H. W.; Lee, H. W.; Kim, K. S.; Kang, S. Y.; Park, J. E.; Kim, W. S.; Jeon, D. H.; Je, U. K.; Woo, T. H.; Oh, J. E.
2018-02-01
In recent digital tomosynthesis (DTS), iterative reconstruction methods are often used owing to the potential to provide multiplanar images of superior image quality to conventional filtered-backprojection (FBP)-based methods. However, they require enormous computational cost in the iterative process, which has still been an obstacle to put them to practical use. In this work, we propose a new DTS reconstruction method incorporated with a dual-resolution voxelization scheme in attempt to overcome these difficulties, in which the voxels outside a small region-of-interest (ROI) containing target diagnosis are binned by 2 × 2 × 2 while the voxels inside the ROI remain unbinned. We considered a compressed-sensing (CS)-based iterative algorithm with a dual-constraint strategy for more accurate DTS reconstruction. We implemented the proposed algorithm and performed a systematic simulation and experiment to demonstrate its viability. Our results indicate that the proposed method seems to be effective for reducing computational cost considerably in iterative DTS reconstruction, keeping the image quality inside the ROI not much degraded. A binning size of 2 × 2 × 2 required only about 31.9% computational memory and about 2.6% reconstruction time, compared to those for no binning case. The reconstruction quality was evaluated in terms of the root-mean-square error (RMSE), the contrast-to-noise ratio (CNR), and the universal-quality index (UQI).
Directory of Open Access Journals (Sweden)
Juwei Zhang
2017-03-01
Full Text Available Electromagnetic methods are commonly employed to detect wire rope discontinuities. However, determining the residual strength of wire rope based on the quantitative recognition of discontinuities remains problematic. We have designed a prototype device based on the residual magnetic field (RMF of ferromagnetic materials, which overcomes the disadvantages associated with in-service inspections, such as large volume, inconvenient operation, low precision, and poor portability by providing a relatively small and lightweight device with improved detection precision. A novel filtering system consisting of the Hilbert-Huang transform and compressed sensing wavelet filtering is presented. Digital image processing was applied to achieve the localization and segmentation of defect RMF images. The statistical texture and invariant moment characteristics of the defect images were extracted as the input of a radial basis function neural network. Experimental results show that the RMF device can detect defects in various types of wire rope and prolong the service life of test equipment by reducing the friction between the detection device and the wire rope by accommodating a high lift-off distance.
Fluorescent molecule incorporated metal-organic framework for fluoride sensing in aqueous solution
Energy Technology Data Exchange (ETDEWEB)
Zhao, Xudong, E-mail: zhaoxd_tykj@163.com; Wang, Yuanyang; Hao, Xiuli; Liu, Wen, E-mail: 13700509372@163.com
2017-04-30
Highlights: • Fluorescein sodium was successfully encapsulated in UiO-66 via in-situ synthesis. • FS@UiO-66 is one of the few Zr-MOF-based probes for fluoride so far. • FS@UiO-66 is a highly effective, fast-response and naked-eye sensor for fluoride. - Abstract: In this work, the fluorescent molecule (fluorescein sodium, FS) was successfully incorporated in the zirconium-based MOF (UiO-66) via in-situ synthesis method, which can be confirmed by FTIR spectra and fluorescence microscopic images. Based on this in-situ synthesis strategy, FS molecule can be immobilized tightly in the framework. Furthermore, the resulting FS@UiO-66 demonstrates to be a highly selective, real-time and naked-eye chemical sensor for fluoride in aqueous solution, which is mainly due to the release of FS molecule from FS@UiO-66 into the aqueous solution. Meanwhile, to the best of our knowledge, such Zr-MOF-based fluoride sensor is very rare so far. These results provide a promising approach to rationally design novel MOF-based fluorescent sensor for the target molecules.
Metal-Organic Frameworks for Sensing Applications in the Gas Phase
Directory of Open Access Journals (Sweden)
Sabine Achmann
2009-03-01
Full Text Available Several metal-organic framework (MOF materials were under investigated to test their applicability as sensor materials for impedimetric gas sensors. The materials were tested in a temperature range of 120 °C - 240 °C with varying concentrations of O2, CO2, C3H8, NO, H2, ethanol and methanol in the gas atmosphere and under different test gas humidity conditions. Different sensor configurations were studied in a frequency range of 1 Hz -1 MHz and time-continuous measurements were performed at 1 Hz. The materials did not show any impedance response to O2, CO2, C3H8, NO, or H2 in the gas atmospheres, although for some materials a significant impedance decrease was induced by a change of the ethanol or methanol concentration in the gas phase. Moreover, pronounced promising and reversible changes in the electric properties of a special MOF material were monitored under varying humidity, with a linear response curve at 120 °C. Further investigations were carried out with differently doped MOF materials of this class, to evaluate the influence of special dopants on the sensor effect.
Mishra, V.; Cruise, J.; Mecikalski, J. R.
2017-12-01
Much effort has been expended recently on the assimilation of remotely sensed soil moisture into operational land surface models (LSM). These efforts have normally been focused on the use of data derived from the microwave bands and results have often shown that improvements to model simulations have been limited due to the fact that microwave signals only penetrate the top 2-5 cm of the soil surface. It is possible that model simulations could be further improved through the introduction of geostationary satellite thermal infrared (TIR) based root zone soil moisture in addition to the microwave deduced surface estimates. In this study, root zone soil moisture estimates from the TIR based Atmospheric Land Exchange Inverse (ALEXI) model were merged with NASA Soil Moisture Active Passive (SMAP) based surface estimates through the application of informational entropy. Entropy can be used to characterize the movement of moisture within the vadose zone and accounts for both advection and diffusion processes. The Principle of Maximum Entropy (POME) can be used to derive complete soil moisture profiles and, fortuitously, only requires a surface boundary condition as well as the overall mean moisture content of the soil column. A lower boundary can be considered a soil parameter or obtained from the LSM itself. In this study, SMAP provided the surface boundary while ALEXI supplied the mean and the entropy integral was used to tie the two together and produce the vertical profile. However, prior to the merging, the coarse resolution (9 km) SMAP data were downscaled to the finer resolution (4.7 km) ALEXI grid. The disaggregation scheme followed the Soil Evaporative Efficiency approach and again, all necessary inputs were available from the TIR model. The profiles were then assimilated into a standard agricultural crop model (Decision Support System for Agrotechnology, DSSAT) via the ensemble Kalman Filter. The study was conducted over the Southeastern United States for the
Anh, N. K.; Liou, Y. A.; Li, M. H.
2016-12-01
The motivation for this study is assessment of the eco-environment vulnerability based on four independent determinants: hydro-meteorology, topography, land resources, and human activities. An assessment framework is proposed to assess the vulnerable eco-environment by using 16 variables with 6 of them constructed from Landsat 8 satellite images. The remaining variables were extracted from digital maps. Each variable was evaluated and spatially mapped with the aid of an analytical hierarchy process (AHP) and geographical information system (GIS). The Thua Thien - Hue Province that has been experiencing natural disasters and urbanization in the recent decades is selected as our study area. An eco-environmental vulnerability map is assorted into six vulnerable levels consisting of potential, slight, light, medium, heavy, and very heavy vulnerabilities, representing 14%, 27%, 17%, 26%, 13%, 3% of the study area, respectively. It is found that heavy and very heavy vulnerable areas appear mainly in the low and medium lands with high intensification of social-economic activities and often suffer from flooding. Tiny percentages of medium and heavy vulnerable levels occur in high land areas probably caused by agricultural practices in highlands, slash and burn cultivation and removal of natural forests with new plantation forests and these regions are usually influenced by landslides, flash flooding. Based on our results, three ecological zones requiring different development and protection solutions are proposed to restore local eco-environment toward sustainable development. Our findings support the idea that eco-environmental vulnerability is driven by anthropogenic processes and enhanced by natural disaster in the Thua Thien-Hue Province.
Novel dansyl-appended calix[4]arene frameworks: fluorescence properties and mercury sensing.
Pandey, Shubha; Azam, Amir; Pandey, Siddharth; Chawla, H M
2009-01-21
Covalently-attached fluorophores may impart enhanced chemosensing capabilities to calixarene frameworks. Synthesis and characterization of six novel dansyl-appended calix[4]arenes, namely, H/Dan4, NO2/Dan4, H/(OH)2Dan2, H/(Ester)2(Dan)2, t-Bu/(OH)2Dan2, and t-Bu/(Ester)2Dan2, containing two or four dansyl moieties are reported. Among these, fluorescence intensity of NO2/Dan4 is observed to decrease significantly in the presence Hg2+ in the solution. Based on the decrease in fluorescence, a limit of detection for Hg2+ of 20 ppb is obtained. NO2/Dan4 as a chemosensing agent for Hg2+ shows excellent selectivity and adequate reversibility. Complexation of NO2/Dan4 with Hg2+ is investigated using fluorescence spectroscopy and is observed to be 2:1. The formation constant of (NO2/Dan4)2Hg2+ is estimated to be 5.2(+/- 0.8) x 10(10) M(-2) at ambient conditions. These observations are traced to the fact that while all other dansyl-appended calix[4]arenes show cone conformation in the solution, NO2/Dan4 is in the 1,3-alternate conformation. Stokes shift versus solvent orientational polarizability for NO2/Dan4 also indicates the difference in the ground- to excited-state dipole moment of this compound to be the maximum among all six, rendering it most sensitive to its environment. Fluorescence emission of NO2/Dan4 in nonpolar chloroform, polar-aprotic acetonitrile, and polar-protic ethanol is observed to be different than that of the rest of the dansyl-appended compounds as well.
International Nuclear Information System (INIS)
Wang, Li; Yang, Han; He, Juan; Zhang, Yayun; Yu, Jie; Song, Yonghai
2016-01-01
Graphical abstract: A simple, sensitive and effective method to detect hydrogen peroxide based on a hybrid Cu-hemin metal-organic-frameworks (MOFs)/chitosan-functionalized reduced graphene oxide (CS-rGO) nanocomposite was achieved via Cu-hemin MOFs constructing with CS-rGO in room temperature. The Cu-hemin MOFs/CS-rGO nanomaterials exhibited a unique peroxidase-like activity and good electrical conductivity as well as some novel properties. And the as-prepared electrode resulted in a perfect electrochemical performance towards reduction of hydrogen peroxide which was superior to natural enzymes and some inorganic mimic enzymes. - Highlights: • A hybrid Cu-hemin MOF/CS-rGO with a unique peroxidase-like activity was prepared. • The CS-rGO improved electrical conductivity of the nanocomposites greatly. • The 3D porous structure enhanced the catalytic activity of hemin for H 2 O 2 . • A novel sensitive electrochemical biosensing for H 2 O 2 detection was achieved. - Abstract: Herein, a Cu-hemin metal-organic-frameworks (MOFs)/chitosan (CS)-reduced graphene oxide (CS-rGO) nanocomposite with unique peroxidase-like bioactivity and good electrical conductivity was prepared for electrochemical H 2 O 2 sensing for the first time. The prepared Cu-hemin MOFs/CS-rGO nanocomposites were well characterized by scanning electron microscopy, energy dispersive X-ray spectroscopy, thermogravimetric analysis, Fourier transform infrared spectroscopy, X-ray powder diffraction, UV–vis spectroscopy and electrochemical techniques. The results showed that after the Cu-hemin MOFs were formed on the CS-rGO surface, the crystalline structure of the Cu-hemin MOFs was kept while the size of Cu-hemin MOFs was decreased and the electrical conductivity of the nanocomposites was enhanced greatly as compared with that of Cu-hemin MOFs. The unique peroxidase-like bioactivity and good electrical conductivity as well as some novel properties of Cu-hemin MOFs/CS-rGO nanocomposites resulted in
Dynamic mode decomposition for compressive system identification
Bai, Zhe; Kaiser, Eurika; Proctor, Joshua L.; Kutz, J. Nathan; Brunton, Steven L.
2017-11-01
Dynamic mode decomposition has emerged as a leading technique to identify spatiotemporal coherent structures from high-dimensional data. In this work, we integrate and unify two recent innovations that extend DMD to systems with actuation and systems with heavily subsampled measurements. When combined, these methods yield a novel framework for compressive system identification, where it is possible to identify a low-order model from limited input-output data and reconstruct the associated full-state dynamic modes with compressed sensing, providing interpretability of the state of the reduced-order model. When full-state data is available, it is possible to dramatically accelerate downstream computations by first compressing the data. We demonstrate this unified framework on simulated data of fluid flow past a pitching airfoil, investigating the effects of sensor noise, different types of measurements (e.g., point sensors, Gaussian random projections, etc.), compression ratios, and different choices of actuation (e.g., localized, broadband, etc.). This example provides a challenging and realistic test-case for the proposed method, and results indicate that the dominant coherent structures and dynamics are well characterized even with heavily subsampled data.
Küstner, Thomas; Würslin, Christian; Schwartz, Martin; Martirosian, Petros; Gatidis, Sergios; Brendle, Cornelia; Seith, Ferdinand; Schick, Fritz; Schwenzer, Nina F; Yang, Bin; Schmidt, Holger
2017-08-01
To enable fast and flexible high-resolution four-dimensional (4D) MRI of periodic thoracic/abdominal motion for motion visualization or motion-corrected imaging. We proposed a Cartesian three-dimensional k-space sampling scheme that acquires a random combination of k-space lines in the ky/kz plane. A partial Fourier-like constraint compacts the sampling space to one half of k-space. The central k-space line is periodically acquired to allow an extraction of a self-navigated respiration signal used to populate a k-space of multiple breathing positions. The randomness of the acquisition (induced by periodic breathing pattern) yields a subsampled k-space that is reconstructed using compressed sensing. Local image evaluations (coefficient of variation and slope steepness through organs) reveal information about motion resolvability. Image quality is inspected by a blinded reading. Sequence and reconstruction method are made publicly available. The method is able to capture and reconstruct 4D images with high image quality and motion resolution within a short scan time of less than 2 min. These findings are supported by restricted-isometry-property analysis, local image evaluation, and blinded reading. The proposed method provides a clinical feasible setup to capture periodic respiratory motion with a fast acquisition protocol and can be extended by further surrogate signals to capture additional periodic motions. Retrospective parametrization allows for flexible tuning toward the targeted applications. Magn Reson Med 78:632-644, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.
Chandarana, Hersh; Block, Tobias Kai; Ream, Justin; Mikheev, Artem; Sigal, Samuel H; Otazo, Ricardo; Rusinek, Henry
2015-02-01
The purpose of this study was to estimate perfusion metrics in healthy and cirrhotic liver with pharmacokinetic modeling of high-temporal resolution reconstruction of continuously acquired free-breathing gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid-enhanced acquisition in patients undergoing clinically indicated liver magnetic resonance imaging. In this Health Insurance Portability and Accountability Act-compliant prospective study, 9 cirrhotic and 10 noncirrhotic patients underwent clinical magnetic resonance imaging, which included continuously acquired radial stack-of-stars 3-dimensional gradient recalled echo sequence with golden-angle ordering scheme in free breathing during contrast injection. A total of 1904 radial spokes were acquired continuously in 318 to 340 seconds. High-temporal resolution data sets were formed by grouping 13 spokes per frame for temporal resolution of 2.2 to 2.4 seconds, which were reconstructed using the golden-angle radial sparse parallel technique that combines compressed sensing and parallel imaging. High-temporal resolution reconstructions were evaluated by a board-certified radiologist to generate gadolinium concentration-time curves in the aorta (arterial input function), portal vein (venous input function), and liver, which were fitted to dual-input dual-compartment model to estimate liver perfusion metrics that were compared between cirrhotic and noncirrhotic livers. The cirrhotic livers had significantly lower total plasma flow (70.1 ± 10.1 versus 103.1 ± 24.3 mL/min per 100 mL; P The mean transit time was higher in the cirrhotic livers (24.4 ± 4.7 versus 15.7 ± 3.4 seconds; P the hepatocellular uptake rate was lower (3.03 ± 2.1 versus 6.53 ± 2.4 100/min; P < 0.05). Liver perfusion metrics can be estimated from free-breathing dynamic acquisition performed for every clinical examination without additional contrast injection or time. This is a novel paradigm for dynamic liver imaging.
A Water-Stable Proton-Conductive Barium(II)-Organic Framework for Ammonia Sensing at High Humidity.
Guo, Kaimeng; Zhao, Lili; Yu, Shihang; Zhou, Wenyan; Li, Zifeng; Li, Gang
2018-06-07
In view of environmental protection and the need for early prediction of major diseases, it is necessary to accurately monitor the change of trace ammonia concentration in air or in exhaled breath. However, the adoption of proton-conductive metal-organic frameworks (MOFs) as smart sensors in this field is limited by a lack of ultrasensitive gas-detecting performance at high relative humidity (RH). Here, the pellet fabrication of a water-stable proton-conductive MOF, Ba( o-CbPhH 2 IDC)(H 2 O) 4 ] n (1) ( o-CbPhH 4 IDC = 2-(2-carboxylphenyl)-1 H-imidazole-4,5-dicarboxylic acid) is reported. The MOF 1 displays enhanced sensitivity and selectivity to NH 3 gas at high RHs (>85%) and 30 °C, and the sensing mechanism is suggested. The electrochemical impedance gas sensor fabricated by MOF 1 is a promising sensor for ammonia at mild temperature and high RHs.
Tau Siesakul, Bamrung; Gkoktsi, Kyriaki; Giaralis, Agathoklis
2015-05-01
Motivated by the need to reduce monetary and energy consumption costs of wireless sensor networks in undertaking output-only/operational modal analysis of engineering structures, this paper considers a multi-coset analog-toinformation converter for structural system identification from acceleration response signals of white noise excited linear damped structures sampled at sub-Nyquist rates. The underlying natural frequencies, peak gains in the frequency domain, and critical damping ratios of the vibrating structures are estimated directly from the sub-Nyquist measurements and, therefore, the computationally demanding signal reconstruction step is by-passed. This is accomplished by first employing a power spectrum blind sampling (PSBS) technique for multi-band wide sense stationary stochastic processes in conjunction with deterministic non-uniform multi-coset sampling patterns derived from solving a weighted least square optimization problem. Next, modal properties are derived by the standard frequency domain peak picking algorithm. Special attention is focused on assessing the potential of the adopted PSBS technique, which poses no sparsity requirements to the sensed signals, to derive accurate estimates of modal structural system properties from noisy sub- Nyquist measurements. To this aim, sub-Nyquist sampled acceleration response signals corrupted by various levels of additive white noise pertaining to a benchmark space truss structure with closely spaced natural frequencies are obtained within an efficient Monte Carlo simulation-based framework. Accurate estimates of natural frequencies and reasonable estimates of local peak spectral ordinates and critical damping ratios are derived from measurements sampled at about 70% below the Nyquist rate and for SNR as low as 0db demonstrating that the adopted approach enjoys noise immunity.
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.
International Nuclear Information System (INIS)
Van Gorp, Jetse S; Bakker, Chris J G; Bouwman, Job G; Zijlstra, Frank; Seevinck, Peter R; Smink, Jouke
2015-01-01
In this study, we explore the potential of compressed sensing (CS) accelerated broadband 3D phase-encoded turbo spin-echo (3D-PE-TSE) for the purpose of geometrically undistorted imaging in the presence of field inhomogeneities. To achieve this goal 3D-PE-SE and 3D-PE-TSE sequences with broadband rf pulses and dedicated undersampling patterns were implemented on a clinical scanner. Additionally, a 3D multi-spectral spin-echo (ms3D-SE) sequence was implemented for reference purposes. First, we demonstrated the influence of susceptibility induced off-resonance effects on the spatial encoding of broadband 3D-SE, ms3D-SE, 3D-PE-SE and 3D-PE-TSE using a grid phantom containing a titanium implant (Δχ = 182 ppm) with x-ray CT as a gold standard. These experiments showed that the spatial encoding of 3D-PE-(T)SE was unaffected by susceptibility induced off-resonance effects, which caused geometrical distortions and/or signal hyper-intensities in broadband 3D-SE and, to a lesser extent, in ms3D-SE frequency encoded methods. Additionally, an SNR analysis was performed and the temporally resolved signal of 3D-PE-(T)SE sequences was exploited to retrospectively decrease the acquisition bandwidth and obtain field offset maps. The feasibility of CS acceleration was studied retrospectively and prospectively for the 3D-PE-SE sequence using an existing CS algorithm adapted for the reconstruction of 3D data with undersampling in all three phase encoded dimensions. CS was combined with turbo-acceleration by variable density undersampling and spherical stepwise T 2 weighting by randomly sorting consecutive echoes in predefined spherical k-space layers. The CS-TSE combination resulted in an overall acceleration factor of 60, decreasing the original 3D-PE-SE scan time from 7 h to 7 min. Finally, CS accelerated 3D-PE-TSE in vivo images of a titanium screw were obtained within 10 min using a micro-coil demonstrating the feasibility of geometrically undistorted MRI near severe
Henninger, B; Raithel, E; Kranewitter, C; Steurer, M; Jaschke, W; Kremser, C
2018-05-01
To prospectively evaluate a prototypical 3D turbo-spin-echo proton-density-weighted sequence with compressed sensing and free-stop scan mode for preventing motion artefacts (3D-PD-CS-SPACE free-stop) for knee imaging in a clinical setting. 80 patients underwent 3T magnetic resonance imaging (MRI) of the knee with our 2D routine protocol and with 3D-PD-CS-SPACE free-stop. In case of a scan-stop caused by motion (images are calculated nevertheless) the sequence was repeated without free-stop mode. All scans were evaluated by 2 radiologists concerning image quality of the 3D-PD-CS-SPACE (with and without free-stop). Important knee structures were further assessed in a lesion based analysis and compared to our reference 2D-PD-fs sequences. Image quality of the 3D-PD-CS-SPACE free-stop was found optimal in 47/80, slightly compromised in 21/80, moderately in 10/80 and severely in 2/80. In 29/80, the free-stop scan mode stopped the 3D-PD-CS-SPACE due to subject motion with a slight increase of image quality at longer effective acquisition times. Compared to the 3D-PD-CS-SPACE with free-stop, the image quality of the acquired 3D-PD-CS-SPACE without free-stop was found equal in 6/29, slightly improved in 13/29, improved with equal contours in 8/29, and improved with sharper contours in 2/29. The lesion based analysis showed a high agreement between the results from the 3D-PD-CS-SPACE free-stop and our 2D-PD-fs routine protocol (overall agreement 96.25%-100%, Cohen's Kappa 0.883-1, p SPACE free-stop is a reliable alternative for standard 2D-PD-fs protocols with acceptable acquisition times. Copyright © 2018 Elsevier B.V. All rights reserved.
Dietz, Bryson; Yip, Eugene; Yun, Jihyun; Fallone, B Gino; Wachowicz, Keith
2017-08-01
This work presents a real-time dynamic image reconstruction technique, which combines compressed sensing and principal component analysis (CS-PCA), to achieve real-time adaptive radiotherapy with the use of a linac-magnetic resonance imaging system. Six retrospective fully sampled dynamic data sets of patients diagnosed with non-small-cell lung cancer were used to investigate the CS-PCA algorithm. Using a database of fully sampled k-space, principal components (PC's) were calculated to aid in the reconstruction of undersampled images. Missing k-space data were calculated by projecting the current undersampled k-space data onto the PC's to generate the corresponding PC weights. The weighted PC's were summed together, and the missing k-space was iteratively updated. To gain insight into how the reconstruction might proceed at lower fields, 6× noise was added to the 3T data to investigate how the algorithm handles noisy data. Acceleration factors ranging from 2 to 10× were investigated using CS-PCA and Split Bregman CS for comparison. Metrics to determine the reconstruction quality included the normalized mean square error (NMSE), as well as the dice coefficients (DC) and centroid displacement of the tumor segmentations. Our results demonstrate that CS-PCA performed superior than CS alone. The CS-PCA patient averaged DC for 3T and 6× noise added data remained above 0.9 for acceleration factors up to 10×. The patient averaged NMSE gradually increased with increasing acceleration; however, it remained below 0.06 up to an acceleration factor of 10× for both 3T and 6× noise added data. The CS-PCA reconstruction speed ranged from 5 to 20 ms (Intel i7-4710HQ CPU @ 2.5 GHz), depending on the chosen parameters. A real-time reconstruction technique was developed for adaptive radiotherapy using a Linac-MRI system. Our CS-PCA algorithm can achieve tumor contours with DC greater than 0.9 and NMSE less than 0.06 at acceleration factors of up to, and including, 10×. The
Zhang, Huiting; Xie, Junshuai; Xiao, Sa; Zhao, Xiuchao; Zhang, Ming; Shi, Lei; Wang, Ke; Wu, Guangyao; Sun, Xianping; Ye, Chaohui; Zhou, Xin
2018-05-04
To demonstrate the feasibility of compressed sensing (CS) to accelerate the acquisition of hyperpolarized (HP) 129 Xe multi-b diffusion MRI for quantitative assessments of lung microstructural morphometry. Six healthy subjects and six chronic obstructive pulmonary disease (COPD) subjects underwent HP 129 Xe multi-b diffusion MRI (b = 0, 10, 20, 30, and 40 s/cm 2 ). First, a fully sampled (FS) acquisition of HP 129 Xe multi-b diffusion MRI was conducted in one healthy subject. The acquired FS dataset was retrospectively undersampled in the phase encoding direction, and an optimal twofold undersampled pattern was then obtained by minimizing mean absolute error (MAE) between retrospective CS (rCS) and FS MR images. Next, the FS and CS acquisitions during separate breath holds were performed on five healthy subjects (including the above one). Additionally, the FS and CS synchronous acquisitions during a single breath hold were performed on the sixth healthy subject and one COPD subject. However, only CS acquisitions were conducted in the rest of the five COPD subjects. Finally, all the acquired FS, rCS and CS MR images were used to obtain morphometric parameters, including acinar duct radius (R), acinar lumen radius (r), alveolar sleeve depth (h), mean linear intercept (L m ), and surface-to-volume ratio (SVR). The Wilcoxon signed-rank test and the Bland-Altman plot were employed to assess the fidelity of the CS reconstruction. Moreover, the t-test was used to demonstrate the effectiveness of the multi-b diffusion MRI with CS in clinical applications. The retrospective results demonstrated that there was no statistically significant difference between rCS and FS measurements using the Wilcoxon signed-rank test (P > 0.05). Good agreement between measurements obtained with the CS and FS acquisitions during separate breath holds was demonstrated in Bland-Altman plots of slice differences. Specifically, the mean biases of the R, r, h, L m , and SVR between the CS and
Jaggi, S.
1993-01-01
A study is conducted to investigate the effects and advantages of data compression techniques on multispectral imagery data acquired by NASA's airborne scanners at the Stennis Space Center. The first technique used was vector quantization. The vector is defined in the multispectral imagery context as an array of pixels from the same location from each channel. The error obtained in substituting the reconstructed images for the original set is compared for different compression ratios. Also, the eigenvalues of the covariance matrix obtained from the reconstructed data set are compared with the eigenvalues of the original set. The effects of varying the size of the vector codebook on the quality of the compression and on subsequent classification are also presented. The output data from the Vector Quantization algorithm was further compressed by a lossless technique called Difference-mapped Shift-extended Huffman coding. The overall compression for 7 channels of data acquired by the Calibrated Airborne Multispectral Scanner (CAMS), with an RMS error of 15.8 pixels was 195:1 (0.41 bpp) and with an RMS error of 3.6 pixels was 18:1 (.447 bpp). The algorithms were implemented in software and interfaced with the help of dedicated image processing boards to an 80386 PC compatible computer. Modules were developed for the task of image compression and image analysis. Also, supporting software to perform image processing for visual display and interpretation of the compressed/classified images was developed.
Wang, Meng; Guo, Lin; Cao, Dapeng
2018-03-06
Rapid and selective sensing of sulfur dioxide (SO 2 ) gas has attracted more and more attention because SO 2 not only causes environmental pollution but also severely affects the health of human beings. Here we report an amino-functionalized luminescent metal-organic framework (MOF) material (i.e., MOF-5-NH 2 ) and further investigate its sensing property for SO 2 gas and its derivatives as a luminescent probe. The results indicate that the MOF-5-NH 2 probe can selectively and sensitively sense SO 2 derivatives (i.e., SO 3 2- ) in real time by a luminescence turn-on effect with a lower detection limit of 0.168 ppm and a response time of less than 15 s. Importantly, the luminescence turn-on phenomenon can be observed by the naked eye. We also assembled MOF-5-NH 2 into a test paper to achieve the aim of portable detection, and the lower-limit concentration of the test paper for sensing SO 2 in real time was found to be about 0.05 ppm. Moreover, MOF-5-NH 2 also shows good anti-interference ability, strong luminescence stability, and reusability, which means that this material is an excellent sensing candidate. The amino functionalization may also provide a modification strategy to design luminescent sensors for other atmospheric pollutants.
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 (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 (XD-reconstructed CS-VIBE) significantly reduced motion artifacts (P 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.
Babbitt, Wm Randall; Barber, Zeb W; Renner, Christoffer
2011-12-15
Compressive sampling has been previously proposed as a technique for sampling radar returns and determining sparse range profiles with a reduced number of measurements compared to conventional techniques. By employing modulation on both transmission and reception, compressive sensing in ranging is extended to the direct measurement of range profiles without intermediate measurement of the return waveform. This compressive ranging approach enables the use of pseudorandom binary transmit waveforms and return modulation, along with low-bandwidth optical detectors to yield high-resolution ranging information. A proof-of-concept experiment is presented. With currently available compact, off-the-shelf electronics and photonics, such as high data rate binary pattern generators and high-bandwidth digital optical modulators, compressive laser ranging can readily achieve subcentimeter resolution in a compact, lightweight package.
DNABIT Compress - Genome compression algorithm.
Rajarajeswari, Pothuraju; Apparao, Allam
2011-01-22
Data compression is concerned with how information is organized in data. Efficient storage means removal of redundancy from the data being stored in the DNA molecule. Data compression algorithms remove redundancy and are used to understand biologically important molecules. We present a compression algorithm, "DNABIT Compress" for DNA sequences based on a novel algorithm of assigning binary bits for smaller segments of DNA bases to compress both repetitive and non repetitive DNA sequence. Our proposed algorithm achieves the best compression ratio for DNA sequences for larger genome. Significantly better compression results show that "DNABIT Compress" algorithm is the best among the remaining compression algorithms. While achieving the best compression ratios for DNA sequences (Genomes),our new DNABIT Compress algorithm significantly improves the running time of all previous DNA compression programs. Assigning binary bits (Unique BIT CODE) for (Exact Repeats, Reverse Repeats) fragments of DNA sequence is also a unique concept introduced in this algorithm for the first time in DNA compression. This proposed new algorithm could achieve the best compression ratio as much as 1.58 bits/bases where the existing best methods could not achieve a ratio less than 1.72 bits/bases.
Call your health insurance or prescription plan: Find out if they pay for compression stockings. Ask if your durable medical equipment benefit pays for compression stockings. Get a prescription from your doctor. Find a medical equipment store where they can ...
Kido, Tomoyuki; Kido, Teruhito; Nakamura, Masashi; Watanabe, Kouki; Schmidt, Michaela; Forman, Christoph; Mochizuki, Teruhito
2016-08-24
Cardiovascular cine magnetic resonance (CMR) accelerated by compressed sensing (CS) is used to assess left ventricular (LV) function. However, it is difficult for prospective CS cine CMR to capture the complete end-diastolic phase, which can lead to underestimation of the end-diastolic volume (EDV), stroke volume (SV), and ejection fraction (EF), compared to retrospective standard cine CMR. This prospective study aimed to evaluate the diagnostic quality and accuracy of single-breath-hold full cardiac cycle CS cine CMR, acquired over two heart beats, to quantify LV volume in comparison to multi-breath-hold standard cine CMR. Eighty-one participants underwent standard segmented breath-hold cine and CS real-time cine CMR examinations to obtain a stack of eight contiguous short-axis images with same high spatial (1.7 × 1.7 mm(2)) and temporal resolution (41 ms). Two radiologists independently performed qualitative analysis of image quality (score, 1 [i.e., "nondiagnostic"] to 5 [i.e., "excellent"]) and quantitative analysis of the LV volume measurements. The total examination time was 113 ± 7 s for standard cine CMR and 24 ± 4 s for CS cine CMR (p cine image quality was slightly lower than standard cine (4.8 ± 0.5 for standard vs. 4.4 ± 0.5 for CS; p cine were above 4 (i.e., good). No significant differences existed between standard and CS cine MR for all quantitative LV measurements. The mean differences with 95 % confidence interval (CI), based on Bland-Altman analysis, were 1.3 mL (95 % CI, -14.6 - 17.2) for LV end-diastolic volume, 0.2 mL (95 % CI, -9.8 to10.3) for LV end-systolic volume, 1.1 mL (95 % CI, -10.5 to 12.7) for LV stroke volume, 1.0 g (95 % CI, -11.2 to 13.3) for LV mass, and 0.4 % (95 % CI, -4.8 - 5.6) for LV ejection fraction. The interobserver and intraobserver variability for CS cine MR ranged from -4.8 - 1.6 % and from -7.3 - 9.3 %, respectively, with slopes of the regressions ranging 0.88-1.0 and 0
Choi, Sunghoon; Lee, Haenghwa; Lee, Donghoon; Choi, Seungyeon; Lee, Chang-Lae; Kwon, Woocheol; Shin, Jungwook; Seo, Chang-Woo; Kim, Hee-Joung
2018-05-01
This work describes the hardware and software developments of a prototype chest digital tomosynthesis (CDT) R/F system. The purpose of this study was to validate the developed system for its possible clinical application on low-dose chest tomosynthesis imaging. The prototype CDT R/F system was operated by carefully controlling the electromechanical subsystems through a synchronized interface. Once a command signal was delivered by the user, a tomosynthesis sweep started to acquire 81 projection views (PVs) in a limited angular range of ±20°. Among the full projection dataset of 81 images, several sets of 21 (quarter view) and 41 (half view) images with equally spaced angle steps were selected to represent a sparse view condition. GPU-accelerated and total-variation (TV) regularization strategy-based compressed sensing (CS) image reconstruction was implemented. The imaged objects were a flat-field using a copper filter to measure the noise power spectrum (NPS), a Catphan ® CTP682 quality assurance (QA) phantom to measure a task-based modulation transfer function (MTF T ask ) of three different cylinders' edge, and an anthropomorphic chest phantom with inserted lung nodules. The authors also verified the accelerated computing power over CPU programming by checking the elapsed time required for the CS method. The resultant absorbed and effective doses that were delivered to the chest phantom from two-view digital radiographic projections, helical computed tomography (CT), and the prototype CDT system were compared. The prototype CDT system was successfully operated, showing little geometric error with fast rise and fall times of R/F x-ray pulse less than 2 and 10 ms, respectively. The in-plane NPS presented essential symmetric patterns as predicted by the central slice theorem. The NPS images from 21 PVs were provided quite different pattern against 41 and 81 PVs due to aliased noise. The voxel variance values which summed all NPS intensities were inversely
Zeolite-like Metal–Organic Framework (MOF) Encaged Pt(II)-Porphyrin for Anion-Selective Sensing
Masih, Dilshad; Chernikova, Valeriya; Shekhah, Osama; Eddaoudi, Mohamed; Mohammed, Omar F.
2018-01-01
The selectivity and sensitivity of sensors are of great interest to the materials chemistry community, and a lot of effort is now devoted to improving these characteristics. More specifically, the selective sensing of anions is one of the largest
Energy Technology Data Exchange (ETDEWEB)
Jin, Jun-Cheng [Key Laboratory of Synthetic and Natural Functional Molecule Chemistry of the Ministry of Education, Shaanxi Key Laboratory of Physico-Inorganic Chemistry, College of Chemistry & Materials Science, Northwest University, Xi' an 710069 (China); Technology Promotion Center of Nano Composite Material of Biomimetic Sensor and Detecting Technology, Preparation and Application, Anhui Provincial Laboratory West Anhui University, Anhui 237012 (China); Guo, Rui-Li; Zhang, Wen-Yan [Key Laboratory of Synthetic and Natural Functional Molecule Chemistry of the Ministry of Education, Shaanxi Key Laboratory of Physico-Inorganic Chemistry, College of Chemistry & Materials Science, Northwest University, Xi' an 710069 (China); Jiang, Chen [Technology Promotion Center of Nano Composite Material of Biomimetic Sensor and Detecting Technology, Preparation and Application, Anhui Provincial Laboratory West Anhui University, Anhui 237012 (China); Wang, Yao-Yu, E-mail: wyaoyu@nwu.edu.cn [Key Laboratory of Synthetic and Natural Functional Molecule Chemistry of the Ministry of Education, Shaanxi Key Laboratory of Physico-Inorganic Chemistry, College of Chemistry & Materials Science, Northwest University, Xi' an 710069 (China)
2016-11-15
A systematic study has been conducted on a novel luminescent metal-organic framework, ([Zn(bpyp)(L-OH)]·DMF·2H{sub 2}O){sub n} (1), to explore its sensing mechanisms to Fe{sup 3+}. Structure analyses show that compound 1 exist pyridine N atoms and -OH groups on the pore surface for specific sensing of metal ions via Lewis acid-base interactions. On this consideration, the quenching mechanisms are studied and the processes are controlled by multiple mechanisms in which dynamic and static mechanisms are calculated, achieving the quantification evaluation of the quenching process. This work not only achieves the quantitative evaluation of the luminescence quenching but also provides certain insights into the quenching process, and the possible mechanisms explored in this work may inspire future research and design of target luminescent metal-organic frameworks (LMOFs) with specific functions. - Graphical abstract: A systematic study has been conducted on a novel luminescent metal-organic framework to explore its sensing mechanisms to Fe{sup 3+}. The quenching mechanisms are studied and the processes are controlled by multiple mechanisms in which dynamic and static mechanisms are calculated, achieving the quantification evaluation of the quenching process. - Highlights: • A novel porous luminescent MOF containing uncoordinated groups in interlayer channels was successfully synthesized. • The compound 1 can exhibit significant luminescent sensitivity to Fe{sup 3+}, which make its good candidate as luminescent sensor. • The corresponding dynamic and static quenching constants are calculated, achieving the quantification evaluation of the quenching process.
Compressed normalized block difference for object tracking
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.
With the rapid development of small imaging sensors and unmanned aerial vehicles (UAVs), remote sensing is undergoing a revolution with greatly increased spatial and temporal resolutions. While more relevant detail becomes available, it is a challenge to analyze the large number of images to extract...
Generalized massive optimal data compression
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.
Mittler, Jessica N; Martsolf, Grant R; Telenko, Shannon J; Scanlon, Dennis P
2013-03-01
Policymakers and practitioners continue to pursue initiatives designed to engage individuals in their health and health care despite discordant views and mixed evidence regarding the ability to cultivate greater individual engagement that improves Americans' health and well-being and helps manage health care costs. There is limited and mixed evidence regarding the value of different interventions. Based on our involvement in evaluating various community-based consumer engagement initiatives and a targeted literature review of models of behavior change, we identified the need for a framework to classify the universe of consumer engagement initiatives toward advancing policymakers' and practitioners' knowledge of their value and fit in various contexts. We developed a framework that expanded our conceptualization of consumer engagement, building on elements of two common models, the individually focused transtheoretical model of behavior and the broader, multilevel social ecological model. Finally, we applied this framework to one community's existing consumer engagement program. Consumer engagement in health and health care refers to the performance of specific behaviors ("engaged behaviors") and/or an individual's capacity and motivation to perform these behaviors ("activation"). These two dimensions are related but distinct and thus should be differentiated. The framework creates four classification schemas, by (1) targeted behavior types (self-management, health care encounter, shopping, and health behaviors) and by (2) individual, (3) group, and (4) community dimensions. Our example illustrates that the framework can systematically classify a variety of consumer engagement programs, and that this exercise and resulting characterization can provide a structured way to consider the program and how its components fit program goals both individually and collectively. Applying the framework could help advance the field by making policymakers and practitioners aware
Duhn, Iris; Fleer, Marilyn; Harrison, Linda
2016-01-01
This article focuses on the "Relational Agency Framework" (RAF), an analytical tool developed for an Australian review and evaluation study of an early years' policy initiative. We explore Anne Edward's concepts of "relational expertise", "building common knowledge" and "relational agency" to explore how…
DEFF Research Database (Denmark)
Xu, Hui; Iversen, Bo Brummerstedt
of graphene, Since the discovery of graphene, series of two-dimensional (2-D) nanosheets materials such as metal oxides, metal hydroxides, transition metal chalcogenides (TMDs), boron nitride (BN) and black phosphorus have been of great interests, and have been extensively investigated for applications...... in electronics, lithium-ion batteries, catalysis and mechanical properties, etc. 2-D MOF nanosheets materials, as a new member of the 2-D nanomaterials family, are still at the very early stage. However, to the best of our knowledge, the 2-D MOF nanosheets materials for luminescent sensing have been rarely...
A biological compression model and its applications.
Cao, Minh Duc; Dix, Trevor I; Allison, Lloyd
2011-01-01
A biological compression model, expert model, is presented which is superior to existing compression algorithms in both compression performance and speed. The model is able to compress whole eukaryotic genomes. Most importantly, the model provides a framework for knowledge discovery from biological data. It can be used for repeat element discovery, sequence alignment and phylogenetic analysis. We demonstrate that the model can handle statistically biased sequences and distantly related sequences where conventional knowledge discovery tools often fail.
Wang, Ying; Zhu, Yingjing; Binyam, Atsebeha; Liu, Misha; Wu, Yinan; Li, Fengting
2016-12-15
A label-free sensing strategy based on the enzyme-mimicking activity of MOF was demonstrated for colorimetric detection of biomolecules. Firstly obvious blue color was observed due to the high efficiency of peroxidase-like catalytic activity of Fe-MIL-88A (an ion-based MOF material) toward 3,3',5,5'-tetramethylbenzidine (TMB). Then in the presence of target biomolecule and corresponding aptamer, the mimetic activity of Fe-MIL-88A can be strongly inhibited and used directly to realize the colorimetric detection. On the basis of the interesting findings, we designed a straightforward, label-free and sensitive colorimetric method for biomolecule detection by using the enzyme mimetic property of MOF coupling with molecular recognition element. Compared with the existed publications, our work breaks the routine way by setting up an inorganic-organic MOF-aptamer hybrid platform for colorimetric determination of biomolecules, expanding the targets scope from H2O2 or glucose to biomolecules. As a proof of concept, thrombin and thrombin aptamer was used as a model analyte. The limit of detection of 10nM can be achieved with naked eyes and ultrahigh selectivity of thrombin toward numerous interfering substances with 10-fold concentration was demonstrated significantly. Of note, the method was further applied for the detection of thrombin in human serum samples, showing the results in agreement with those values obtained in an immobilization buffer by the colorimetric method. This inorganic-organic MOF-aptamer sensing strategy may in principle be universally applicable for the detection of a range of environmental or biomedical molecules of interests. Copyright © 2016 Elsevier B.V. All rights reserved.
Directory of Open Access Journals (Sweden)
M. F. McCabe
2005-01-01
Full Text Available Characterising the development of evapotranspiration through time is a difficult task, particularly when utilising remote sensing data, because retrieved information is often spatially dense, but temporally sparse. Techniques to expand these essentially instantaneous measures are not only limited, they are restricted by the general paucity of information describing the spatial distribution and temporal evolution of evaporative patterns. In a novel approach, temporal changes in land surface temperatures, derived from NOAA-AVHRR imagery and a generalised split-window algorithm, are used as a calibration variable in a simple land surface scheme (TOPUP and combined within the Generalised Likelihood Uncertainty Estimation (GLUE methodology to provide estimates of areal evapotranspiration at the pixel scale. Such an approach offers an innovative means of transcending the patch or landscape scale of SVAT type models, to spatially distributed estimates of model output. The resulting spatial and temporal patterns of land surface fluxes and surface resistance are used to more fully understand the hydro-ecological trends observed across a study catchment in eastern Australia. The modelling approach is assessed by comparing predicted cumulative evapotranspiration values with surface fluxes determined from Bowen ratio systems and using auxiliary information such as in-situ soil moisture measurements and depth to groundwater to corroborate observed responses.
SmartHome: a domotic framework based on smart sensing and actuator network to reduce energy wastes
Santamaria, Amilcare Francesco; De Rango, Floriano; Falbo, Domenico; Barletta, Domenico
2014-05-01
Domestic environment and human interaction with services supplied by domotic devices is going to be a very interesting application field. With a domotic system is possible to achieve great interaction between human beings, environments and smart devices. The enhancing of these interactions is the main goal of this work whose intent is to improve the classic concept of domotics. The framework we developed can be used for several application fields such as lighting, heating, conditioning or water management and energy consumption. In particular, the proposed system can optimize energy consumptions by rising awareness to users that have full control of their house and the possibility to save money and reduce the impact of the energetic consumes to the earth, matching the new "green" motto requirements. In this way, the overall system wants to match the central concept of Internet Of Things (IoT) as well. From this point of view a complex automation system with smart devices make possible a more efficient way to produce, follow and manage domotic policies. Following the spread of IoT, for this work we designed and implemented new plug-and-play and ready-to-use smart devices that are part of a complex automation system that offers a user-friendly web application and allows users to control and interact with different plans of their house in order to make life more comfortable and be aware of their energy consumptions. Control and awareness arc the two key points that led us to develop the proposed system.
Development of a compressive sampling hyperspectral imager prototype
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".
Energy Technology Data Exchange (ETDEWEB)
Liu, Fenfen; He, Juan; Zeng, Mulang; Hao, Juan; Guo, Qiaohui; Song, Yonghai; Wang, Li, E-mail: lwanggroup@aliyun.com [Jiangxi Normal University, Key Laboratory of Functional Small Organic Molecule, Ministry of Education, College of Chemistry and Chemical Engineering (China)
2016-05-15
In this work, a facile strategy to synthesize Cu–hemin metal-organic frameworks (MOFs) with peroxidase-like activity was reported. The prepared Cu–hemin MOFs were characterized by various techniques such as scanning electron microscopy, transmission electron microscopy, X-ray powder diffraction, Fourier transform infrared spectroscopy, UV–visible absorbance spectra, and so on. The results showed that the prepared Cu–hemin MOFs looked like a ball-flower with an average diameter of 10 μm and provided a large specific surface area. The Cu–hemin MOFs possessing peroxidase-like activity could be used to catalyze the peroxidase substrate of 3,3,5,5-tetramethylbenzidine in the presence of H{sub 2}O{sub 2}, which was employed to detect H{sub 2}O{sub 2} quantitatively with the linear range from 1.0 μM to 1.0 mM and the detection limit was 0.42 μM. Furthermore, with the additional help of glucose oxidase, a sensitive and selective method to detect glucose was developed by using the Cu–hemin MOFs as catalyst and the linear range was from 10.0 μM to 3.0 mM and the detection limit was 6.9 μM. This work informs researchers of the advantages of MOFs for preparing biomimetic catalysts and extends the functionality of MOFs for biosensor application.Graphical Abstract.
Sun, Qilin
2017-04-01
High resolution transient/3D imaging technology is of high interest in both scientific research and commercial application. Nowadays, all of the transient imaging methods suffer from low resolution or time consuming mechanical scanning. We proposed a new method based on TCSPC and Compressive Sensing to achieve a high resolution transient imaging with a several seconds capturing process. Picosecond laser sends a serious of equal interval pulse while synchronized SPAD camera\\'s detecting gate window has a precise phase delay at each cycle. After capturing enough points, we are able to make up a whole signal. By inserting a DMD device into the system, we are able to modulate all the frames of data using binary random patterns to reconstruct a super resolution transient/3D image later. Because the low fill factor of SPAD sensor will make a compressive sensing scenario ill-conditioned, We designed and fabricated a diffractive microlens array. We proposed a new CS reconstruction algorithm which is able to denoise at the same time for the measurements suffering from Poisson noise. Instead of a single SPAD senor, we chose a SPAD array because it can drastically reduce the requirement for the number of measurements and its reconstruction time. Further more, it not easy to reconstruct a high resolution image with only one single sensor while for an array, it just needs to reconstruct small patches and a few measurements. In this thesis, we evaluated the reconstruction methods using both clean measurements and the version corrupted by Poisson noise. The results show how the integration over the layers influence the image quality and our algorithm works well while the measurements suffer from non-trival Poisson noise. It\\'s a breakthrough in the areas of both transient imaging and compressive sensing.
Energy Technology Data Exchange (ETDEWEB)
Harrington, Joe [Sertco Industries, Inc., Okemah, OK (United States); Vazquez, Daniel [Hoerbiger Service Latin America Inc., Deerfield Beach, FL (United States); Jacobs, Denis Richard [Hoerbiger do Brasil Industria de Equipamentos, Cajamar, SP (Brazil)
2012-07-01
Over time, all wells experience a natural decline in oil and gas production. In gas wells, the major problems are liquid loading and low downhole differential pressures which negatively impact total gas production. As a form of artificial lift, w