Iterative PSF Estimation and Its Application to Shift Invariant and Variant Blur Reduction

Directory of Open Access Journals (Sweden)

Seung-Won Jung

2009-01-01

Full Text Available Among image restoration approaches, image deconvolution has been considered a powerful solution. In image deconvolution, a point spread function (PSF, which describes the blur of the image, needs to be determined. Therefore, in this paper, we propose an iterative PSF estimation algorithm which is able to estimate an accurate PSF. In real-world motion-blurred images, a simple parametric model of the PSF fails when a camera moves in an arbitrary direction with an inconsistent speed during an exposure time. Moreover, the PSF normally changes with spatial location. In order to accurately estimate the complex PSF of a real motion blurred image, we iteratively update the PSF by using a directional spreading operator. The directional spreading is applied to the PSF when it reduces the amount of the blur and the restoration artifacts. Then, to generalize the proposed technique to the linear shift variant (LSV model, a piecewise invariant approach is adopted by the proposed image segmentation method. Experimental results show that the proposed method effectively estimates the PSF and restores the degraded images.

2D sparse array transducer optimization for 3D ultrasound imaging

International Nuclear Information System (INIS)

Choi, Jae Hoon; Park, Kwan Kyu

2014-01-01

A 3D ultrasound image is desired in many medical examinations. However, the implementation of a 2D array, which is needed for a 3D image, is challenging with respect to fabrication, interconnection and cabling. A 2D sparse array, which needs fewer elements than a dense array, is a realistic way to achieve 3D images. Because the number of ways the elements can be placed in an array is extremely large, a method for optimizing the array configuration is needed. Previous research placed the target point far from the transducer array, making it impossible to optimize the array in the operating range. In our study, we focused on optimizing a 2D sparse array transducer for 3D imaging by using a simulated annealing method. We compared the far-field optimization method with the near-field optimization method by analyzing a point-spread function (PSF). The resolution of the optimized sparse array is comparable to that of the dense array.

Nuclear safety research project (PSF). 1999 annual report

International Nuclear Information System (INIS)

Muehl, B.

2000-08-01

The reactor safety R and D work of the Karlsruhe Research Centre (FZK) has been part of the Nuclear Safety Research Project (PSF) since 1990. The present annual report summarizes the R and D results of PSF during 1999. The research tasks cover three main topics: Light Water Reactor safety, innovative systems, and studies related to the transmutation of actinides. The importance of the Light Water Reactor safety, however, has decreased during the last year in favour of the transmutation of actinides. Numerous institutes of the research centre contribute to the PSF programme, as well as several external partners. The tasks are coordinated in agreement with internal and external working groups. The contributions to this report, which are either written in German or in English, correspond to the status of early/mid 2000. (orig.) [de

Alpha Matting with KL-Divergence Based Sparse Sampling.

Science.gov (United States)

Karacan, Levent; Erdem, Aykut; Erdem, Erkut

2017-06-22

In this paper, we present a new sampling-based alpha matting approach for the accurate estimation of foreground and background layers of an image. Previous sampling-based methods typically rely on certain heuristics in collecting representative samples from known regions, and thus their performance deteriorates if the underlying assumptions are not satisfied. To alleviate this, we take an entirely new approach and formulate sampling as a sparse subset selection problem where we propose to pick a small set of candidate samples that best explains the unknown pixels. Moreover, we describe a new dissimilarity measure for comparing two samples which is based on KLdivergence between the distributions of features extracted in the vicinity of the samples. The proposed framework is general and could be easily extended to video matting by additionally taking temporal information into account in the sampling process. Evaluation on standard benchmark datasets for image and video matting demonstrates that our approach provides more accurate results compared to the state-of-the-art methods.

Sample size reduction in groundwater surveys via sparse data assimilation

KAUST Repository

Hussain, Z.; Muhammad, A.

2013-01-01

In this paper, we focus on sparse signal recovery methods for data assimilation in groundwater models. The objective of this work is to exploit the commonly understood spatial sparsity in hydrodynamic models and thereby reduce the number of measurements to image a dynamic groundwater profile. To achieve this we employ a Bayesian compressive sensing framework that lets us adaptively select the next measurement to reduce the estimation error. An extension to the Bayesian compressive sensing framework is also proposed which incorporates the additional model information to estimate system states from even lesser measurements. Instead of using cumulative imaging-like measurements, such as those used in standard compressive sensing, we use sparse binary matrices. This choice of measurements can be interpreted as randomly sampling only a small subset of dug wells at each time step, instead of sampling the entire grid. Therefore, this framework offers groundwater surveyors a significant reduction in surveying effort without compromising the quality of the survey. © 2013 IEEE.

Sample size reduction in groundwater surveys via sparse data assimilation

KAUST Repository

Hussain, Z.

2013-04-01

In this paper, we focus on sparse signal recovery methods for data assimilation in groundwater models. The objective of this work is to exploit the commonly understood spatial sparsity in hydrodynamic models and thereby reduce the number of measurements to image a dynamic groundwater profile. To achieve this we employ a Bayesian compressive sensing framework that lets us adaptively select the next measurement to reduce the estimation error. An extension to the Bayesian compressive sensing framework is also proposed which incorporates the additional model information to estimate system states from even lesser measurements. Instead of using cumulative imaging-like measurements, such as those used in standard compressive sensing, we use sparse binary matrices. This choice of measurements can be interpreted as randomly sampling only a small subset of dug wells at each time step, instead of sampling the entire grid. Therefore, this framework offers groundwater surveyors a significant reduction in surveying effort without compromising the quality of the survey. © 2013 IEEE.

Iterative PSF Estimation and Its Application to Shift Invariant and Variant Blur Reduction

OpenAIRE

Seung-Won Jung; Byeong-Doo Choi; Sung-Jea Ko

2009-01-01

Among image restoration approaches, image deconvolution has been considered a powerful solution. In image deconvolution, a point spread function (PSF), which describes the blur of the image, needs to be determined. Therefore, in this paper, we propose an iterative PSF estimation algorithm which is able to estimate an accurate PSF. In real-world motion-blurred images, a simple parametric model of the PSF fails when a camera moves in an arbitrary direction with an inconsistent speed during an e...

Sampling of finite elements for sparse recovery in large scale 3D electrical impedance tomography

International Nuclear Information System (INIS)

Javaherian, Ashkan; Moeller, Knut; Soleimani, Manuchehr

2015-01-01

This study proposes a method to improve performance of sparse recovery inverse solvers in 3D electrical impedance tomography (3D EIT), especially when the volume under study contains small-sized inclusions, e.g. 3D imaging of breast tumours. Initially, a quadratic regularized inverse solver is applied in a fast manner with a stopping threshold much greater than the optimum. Based on assuming a fixed level of sparsity for the conductivity field, finite elements are then sampled via applying a compressive sensing (CS) algorithm to the rough blurred estimation previously made by the quadratic solver. Finally, a sparse inverse solver is applied solely to the sampled finite elements, with the solution to the CS as its initial guess. The results show the great potential of the proposed CS-based sparse recovery in improving accuracy of sparse solution to the large-size 3D EIT. (paper)

Synthesis of atmospheric turbulence point spread functions by sparse and redundant representations

Science.gov (United States)

Hunt, Bobby R.; Iler, Amber L.; Bailey, Christopher A.; Rucci, Michael A.

2018-02-01

Atmospheric turbulence is a fundamental problem in imaging through long slant ranges, horizontal-range paths, or uplooking astronomical cases through the atmosphere. An essential characterization of atmospheric turbulence is the point spread function (PSF). Turbulence images can be simulated to study basic questions, such as image quality and image restoration, by synthesizing PSFs of desired properties. In this paper, we report on a method to synthesize PSFs of atmospheric turbulence. The method uses recent developments in sparse and redundant representations. From a training set of measured atmospheric PSFs, we construct a dictionary of "basis functions" that characterize the atmospheric turbulence PSFs. A PSF can be synthesized from this dictionary by a properly weighted combination of dictionary elements. We disclose an algorithm to synthesize PSFs from the dictionary. The algorithm can synthesize PSFs in three orders of magnitude less computing time than conventional wave optics propagation methods. The resulting PSFs are also shown to be statistically representative of the turbulence conditions that were used to construct the dictionary.

Quadrature demodulation based circuit implementation of pulse stream for ultrasonic signal FRI sparse sampling

International Nuclear Information System (INIS)

Shoupeng, Song; Zhou, Jiang

2017-01-01

Converting ultrasonic signal to ultrasonic pulse stream is the key step of finite rate of innovation (FRI) sparse sampling. At present, ultrasonic pulse-stream-forming techniques are mainly based on digital algorithms. No hardware circuit that can achieve it has been reported. This paper proposes a new quadrature demodulation (QD) based circuit implementation method for forming an ultrasonic pulse stream. Elaborating on FRI sparse sampling theory, the process of ultrasonic signal is explained, followed by a discussion and analysis of ultrasonic pulse-stream-forming methods. In contrast to ultrasonic signal envelope extracting techniques, a quadrature demodulation method (QDM) is proposed. Simulation experiments were performed to determine its performance at various signal-to-noise ratios (SNRs). The circuit was then designed, with mixing module, oscillator, low pass filter (LPF), and root of square sum module. Finally, application experiments were carried out on pipeline sample ultrasonic flaw testing. The experimental results indicate that the QDM can accurately convert ultrasonic signal to ultrasonic pulse stream, and reverse the original signal information, such as pulse width, amplitude, and time of arrival. This technique lays the foundation for ultrasonic signal FRI sparse sampling directly with hardware circuitry. (paper)

LWR surveillance dosimetry improvement program: PSF metallurgical blind test results

International Nuclear Information System (INIS)

Kam, F.B.K.; Stallmann, F.W.; Guthrie, G.; McElroy, W.N.

1985-01-01

The ORR-PSF benchmark experiment was designed to simulate the surveillance capsule-pressure vessel configuration in power reactors and to test the validity of procedures which determine the radiation damage in the vessel from test results in the surveillance capsule. The PSF metallurgical blind test was initiated to give participants an opportunity to test their current embrittlement prediction methodologies. Experimental results were withheld from the participants except for the type of information which is normally contained in surveillance reports. Preliminary analysis of the PSF metallurgical blind test results shows that: (1) current prediction methodologies, as used by the PSF Blind Test participants, are adequate, falling within +- 20 0 C of the measured values for Δ NDT. None of the different methods is clearly superior; (2) the proposed revision of Reg. Guide 1.99 (Rev. 2) gives a better representation of the fluence and chemistry dependency of Δ NDT than the current version (Rev. 1); and (3) fluence rate effects can be seen but not quantified. Fluence spectral effects are too small to be detectable in this experiment. (orig.)

Sparse Power-Law Network Model for Reliable Statistical Predictions Based on Sampled Data

Directory of Open Access Journals (Sweden)

Alexander P. Kartun-Giles

2018-04-01

Full Text Available A projective network model is a model that enables predictions to be made based on a subsample of the network data, with the predictions remaining unchanged if a larger sample is taken into consideration. An exchangeable model is a model that does not depend on the order in which nodes are sampled. Despite a large variety of non-equilibrium (growing and equilibrium (static sparse complex network models that are widely used in network science, how to reconcile sparseness (constant average degree with the desired statistical properties of projectivity and exchangeability is currently an outstanding scientific problem. Here we propose a network process with hidden variables which is projective and can generate sparse power-law networks. Despite the model not being exchangeable, it can be closely related to exchangeable uncorrelated networks as indicated by its information theory characterization and its network entropy. The use of the proposed network process as a null model is here tested on real data, indicating that the model offers a promising avenue for statistical network modelling.

A structured sparse regression method for estimating isoform expression level from multi-sample RNA-seq data.

Science.gov (United States)

Zhang, L; Liu, X J

2016-06-03

With the rapid development of next-generation high-throughput sequencing technology, RNA-seq has become a standard and important technique for transcriptome analysis. For multi-sample RNA-seq data, the existing expression estimation methods usually deal with each single-RNA-seq sample, and ignore that the read distributions are consistent across multiple samples. In the current study, we propose a structured sparse regression method, SSRSeq, to estimate isoform expression using multi-sample RNA-seq data. SSRSeq uses a non-parameter model to capture the general tendency of non-uniformity read distribution for all genes across multiple samples. Additionally, our method adds a structured sparse regularization, which not only incorporates the sparse specificity between a gene and its corresponding isoform expression levels, but also reduces the effects of noisy reads, especially for lowly expressed genes and isoforms. Four real datasets were used to evaluate our method on isoform expression estimation. Compared with other popular methods, SSRSeq reduced the variance between multiple samples, and produced more accurate isoform expression estimations, and thus more meaningful biological interpretations.

Preparation of PANI/PSF conductive composite films and their characteristic

Institute of Scientific and Technical Information of China (English)

Yang Yuying; Shang Xiuli; Kong Chao; Zhao Hongxiao; Hu Zhong'ai

2006-01-01

Polyaniline (PANI)/polysulfone (PSF) composite films are successfully prepared by phase separation and one-step in-situ polymerization.It is found that the head-on face (in contact with solution) of the films is green while the back face is white.The chemical component and the surface morphology of both surfaces of the films are characterized by FT-IR spectra and SEM,respectively.The effect of the polymerization temperature,time and concentration of the reactants on the electrical properties of the films are discussed in details.The thermo-oxidative degradation of the films is studied by thermogravimetric analysis (TGA).The results indicate that the thermal stability of the PANI/PSF films is higher than that of the pure PSF film.

CT Image Sequence Restoration Based on Sparse and Low-Rank Decomposition

Science.gov (United States)

Gou, Shuiping; Wang, Yueyue; Wang, Zhilong; Peng, Yong; Zhang, Xiaopeng; Jiao, Licheng; Wu, Jianshe

2013-01-01

Blurry organ boundaries and soft tissue structures present a major challenge in biomedical image restoration. In this paper, we propose a low-rank decomposition-based method for computed tomography (CT) image sequence restoration, where the CT image sequence is decomposed into a sparse component and a low-rank component. A new point spread function of Weiner filter is employed to efficiently remove blur in the sparse component; a wiener filtering with the Gaussian PSF is used to recover the average image of the low-rank component. And then we get the recovered CT image sequence by combining the recovery low-rank image with all recovery sparse image sequence. Our method achieves restoration results with higher contrast, sharper organ boundaries and richer soft tissue structure information, compared with existing CT image restoration methods. The robustness of our method was assessed with numerical experiments using three different low-rank models: Robust Principle Component Analysis (RPCA), Linearized Alternating Direction Method with Adaptive Penalty (LADMAP) and Go Decomposition (GoDec). Experimental results demonstrated that the RPCA model was the most suitable for the small noise CT images whereas the GoDec model was the best for the large noisy CT images. PMID:24023764

Non-parametric PSF estimation from celestial transit solar images using blind deconvolution

Directory of Open Access Journals (Sweden)

González Adriana

2016-01-01

Full Text Available Context: Characterization of instrumental effects in astronomical imaging is important in order to extract accurate physical information from the observations. The measured image in a real optical instrument is usually represented by the convolution of an ideal image with a Point Spread Function (PSF. Additionally, the image acquisition process is also contaminated by other sources of noise (read-out, photon-counting. The problem of estimating both the PSF and a denoised image is called blind deconvolution and is ill-posed. Aims: We propose a blind deconvolution scheme that relies on image regularization. Contrarily to most methods presented in the literature, our method does not assume a parametric model of the PSF and can thus be applied to any telescope. Methods: Our scheme uses a wavelet analysis prior model on the image and weak assumptions on the PSF. We use observations from a celestial transit, where the occulting body can be assumed to be a black disk. These constraints allow us to retain meaningful solutions for the filter and the image, eliminating trivial, translated, and interchanged solutions. Under an additive Gaussian noise assumption, they also enforce noise canceling and avoid reconstruction artifacts by promoting the whiteness of the residual between the blurred observations and the cleaned data. Results: Our method is applied to synthetic and experimental data. The PSF is estimated for the SECCHI/EUVI instrument using the 2007 Lunar transit, and for SDO/AIA using the 2012 Venus transit. Results show that the proposed non-parametric blind deconvolution method is able to estimate the core of the PSF with a similar quality to parametric methods proposed in the literature. We also show that, if these parametric estimations are incorporated in the acquisition model, the resulting PSF outperforms both the parametric and non-parametric methods.

Analysis of Operators Comments on the PSF Questionnaire of the Task Complexity Experiment 2003/2004

Energy Technology Data Exchange (ETDEWEB)

Torralba, B.; Martinez-Arias, R.

2007-07-01

Human Reliability Analysis (HRA) methods usually take into account the effect of Performance Shaping Factors (PSF). Therefore, the adequate treatment of PSFs in HRA of Probabilistic Safety Assessment (PSA) models has a crucial importance. There is an important need for collecting PSF data based on simulator experiments. During the task complexity experiment 2003-2004, carried out in the BWR simulator of Halden Man-Machine Laboratory (HAMMLAB), there was a data collection on PSF by means of a PSF Questionnaire. Seven crews (composed of shift supervisor, reactor operator and turbine operator) from Swedish Nuclear Power Plants participated in the experiment. The PSF Questionnaire collected data on the factors: procedures, training and experience, indications, controls, team management, team communication, individual work practice, available time for the tasks, number of tasks or information load, masking and seriousness. The main statistical significant results are presented on Performance Shaping Factors data collection and analysis of the task complexity experiment 2003/2004 (HWR-810). The analysis of the comments about PSFs, which were provided by operators on the PSF Questionnaire, is described. It has been summarised the comments provided for each PSF on the scenarios, using a content analysis technique. (Author)

Analysis of Operators Comments on the PSF Questionnaire of the Task Complexity Experiment 2003/2004

International Nuclear Information System (INIS)

Torralba, B.; Martinez-Arias, R.

2007-01-01

Human Reliability Analysis (HRA) methods usually take into account the effect of Performance Shaping Factors (PSF). Therefore, the adequate treatment of PSFs in HRA of Probabilistic Safety Assessment (PSA) models has a crucial importance. There is an important need for collecting PSF data based on simulator experiments. During the task complexity experiment 2003-2004, carried out in the BWR simulator of Halden Man-Machine Laboratory (HAMMLAB), there was a data collection on PSF by means of a PSF Questionnaire. Seven crews (composed of shift supervisor, reactor operator and turbine operator) from Swedish Nuclear Power Plants participated in the experiment. The PSF Questionnaire collected data on the factors: procedures, training and experience, indications, controls, team management, team communication, individual work practice, available time for the tasks, number of tasks or information load, masking and seriousness. The main statistical significant results are presented on Performance Shaping Factors data collection and analysis of the task complexity experiment 2003/2004 (HWR-810). The analysis of the comments about PSFs, which were provided by operators on the PSF Questionnaire, is described. It has been summarised the comments provided for each PSF on the scenarios, using a content analysis technique. (Author)

A PSF-shape-based beamforming strategy for robust 2D motion estimation in ultrafast data

NARCIS (Netherlands)

Saris, Anne E.C.M.; Fekkes, Stein; Nillesen, Maartje; Hansen, Hendrik H.G.; de Korte, Chris L.

2018-01-01

This paper presents a framework for motion estimation in ultrafast ultrasound data. It describes a novel approach for determining the sampling grid for ultrafast data based on the system's point-spread-function (PSF). As a consequence, the cross-correlation functions (CCF) used in the speckle

Sparse-sampling with time-encoded (TICO) stimulated Raman scattering for fast image acquisition

Science.gov (United States)

Hakert, Hubertus; Eibl, Matthias; Karpf, Sebastian; Huber, Robert

2017-07-01

Modern biomedical imaging modalities aim to provide researchers a multimodal contrast for a deeper insight into a specimen under investigation. A very promising technique is stimulated Raman scattering (SRS) microscopy, which can unveil the chemical composition of a sample with a very high specificity. Although the signal intensities are enhanced manifold to achieve a faster acquisition of images if compared to standard Raman microscopy, there is a trade-off between specificity and acquisition speed. Commonly used SRS concepts either probe only very few Raman transitions as the tuning of the applied laser sources is complicated or record whole spectra with a spectrometer based setup. While the first approach is fast, it reduces the specificity and the spectrometer approach records whole spectra -with energy differences where no Raman information is present-, which limits the acquisition speed. Therefore, we present a new approach based on the TICO-Raman concept, which we call sparse-sampling. The TICO-sparse-sampling setup is fully electronically controllable and allows probing of only the characteristic peaks of a Raman spectrum instead of always acquiring a whole spectrum. By reducing the spectral points to the relevant peaks, the acquisition time can be greatly reduced compared to a uniformly, equidistantly sampled Raman spectrum while the specificity and the signal to noise ratio (SNR) are maintained. Furthermore, all laser sources are completely fiber based. The synchronized detection enables a full resolution of the Raman signal, whereas the analogue and digital balancing allows shot noise limited detection. First imaging results with polystyrene (PS) and polymethylmethacrylate (PMMA) beads confirm the advantages of TICO sparse-sampling. We achieved a pixel dwell time as low as 35 μs for an image differentiating both species. The mechanical properties of the applied voice coil stage for scanning the sample currently limits even faster acquisition.

In-flight PSF calibration of the NuSTAR hard X-ray optics

DEFF Research Database (Denmark)

An, Hongjun; Madsen, Kristin K.; Westergaard, Niels J.

2014-01-01

We present results of the point spread function (PSF) calibration of the hard X-ray optics of the Nuclear Spectroscopic Telescope Array (NuSTAR). Immediately post-launch, NuSTAR has observed bright point sources such as Cyg X-1, Vela X-1, and Her X-1 for the PSF calibration. We use the point source...... observations taken at several off-axis angles together with a ray-trace model to characterize the in-orbit angular response, and find that the ray-trace model alone does not fit the observed event distributions and applying empirical corrections to the ray-trace model improves the fit significantly. We...... describe the corrections applied to the ray-trace model and show that the uncertainties in the enclosed energy fraction (EEF) of the new PSF model is less than or similar to 3 for extraction apertures of R greater than or similar to 60" with no significant energy dependence. We also show that the PSF...

Implementation and Application of PSF-Based EPI Distortion Correction to High Field Animal Imaging

Directory of Open Access Journals (Sweden)

Dominik Paul

2009-01-01

Full Text Available The purpose of this work is to demonstrate the functionality and performance of a PSF-based geometric distortion correction for high-field functional animal EPI. The EPI method was extended to measure the PSF and a postprocessing chain was implemented in Matlab for offline distortion correction. The correction procedure was applied to phantom and in vivo imaging of mice and rats at 9.4T using different SE-EPI and DWI-EPI protocols. Results show the significant improvement in image quality for single- and multishot EPI. Using a reduced FOV in the PSF encoding direction clearly reduced the acquisition time for PSF data by an acceleration factor of 2 or 4, without affecting the correction quality.

PSF blind test SSC, SPVC, and SVBC physics-dosimetry-metallurgy data packages

International Nuclear Information System (INIS)

1984-01-01

Information is presented concerning the final PSF radiometric data; calculated spectral fluences and dosimeter activities for the metallurgical blind test irradiations at the ORR-PSF; fabrication data package for HEDL dosimetry in the ORNL Poolside Facility LWR pressure vessel mock-up irradiation; SSC-1; NUREG-CR-3457; and NUREG-CR-3295

Murine hematopoietic stem cell dormancy controlled by induction of a novel short form of PSF1 by histone deacetylase inhibitors

International Nuclear Information System (INIS)

Han, Yinglu; Gong, Zhi-Yuan; Takakura, Nobuyuki

2015-01-01

Hematopoietic stem cells (HSCs) can survive long-term in a state of dormancy. Little is known about how histone deacetylase inhibitors (HDACi) affect HSC kinetics. Here, we use trichostatin A (TSA), a histone deacetylase inhibitor, to enforce histone acetylation and show that this suppresses cell cycle entry by dormant HSCs. Previously, we found that haploinsufficiency of PSF1, a DNA replication factor, led to attenuation of the bone marrow (BM) HSC pool size and lack of acute proliferation after 5-FU ablation. Because PSF1 protein is present in CD34 + transiently amplifying HSCs but not in CD34 − long-term reconstituting-HSCs which are resting in a dormant state, we analyzed the relationship between dormancy and PSF1 expression, and how a histone deacetylase inhibitor affects this. We found that CD34 + HSCs produce long functional PSF1 (PSF1a) but CD34 − HSCs produce a shorter possibly non-functional PSF1 (PSF1b, c, dominantly PSF1c). Using PSF1a-overexpressing NIH-3T3 cells in which the endogenous PSF1 promoter is suppressed, we found that TSA treatment promotes production of the shorter form of PSF1 possibly by inducing recruitment of E2F family factors upstream of the PSF1 transcription start site. Our data document one mechanism by which histone deacetylase inhibitors affect the dormancy of HSCs by regulating the DNA replication factor PSF1. - Highlights: • Hematopoetic stem cell dormancy is controlled by histone deacetylation inhibitors. • Dormancy of HSCs is associated with a shorter form of non-functional PSF1. • Histone deacetylase inhibitors suppress PSF1 promoter activity

Murine hematopoietic stem cell dormancy controlled by induction of a novel short form of PSF1 by histone deacetylase inhibitors

Energy Technology Data Exchange (ETDEWEB)

Han, Yinglu; Gong, Zhi-Yuan [Department of Signal Transduction, Research Institute for Microbial Diseases, Osaka University, 3-1 Yamada-oka, Suita, Osaka 565-0871 (Japan); Takakura, Nobuyuki, E-mail: ntakaku@biken.osaka-u.ac.jp [Department of Signal Transduction, Research Institute for Microbial Diseases, Osaka University, 3-1 Yamada-oka, Suita, Osaka 565-0871 (Japan); Japan Science Technology Agency, CREST, K' s Gobancho, 7, Gobancho, Chiyoda-ku, Tokyo 102-0076 (Japan)

2015-06-10

Hematopoietic stem cells (HSCs) can survive long-term in a state of dormancy. Little is known about how histone deacetylase inhibitors (HDACi) affect HSC kinetics. Here, we use trichostatin A (TSA), a histone deacetylase inhibitor, to enforce histone acetylation and show that this suppresses cell cycle entry by dormant HSCs. Previously, we found that haploinsufficiency of PSF1, a DNA replication factor, led to attenuation of the bone marrow (BM) HSC pool size and lack of acute proliferation after 5-FU ablation. Because PSF1 protein is present in CD34{sup +} transiently amplifying HSCs but not in CD34{sup −} long-term reconstituting-HSCs which are resting in a dormant state, we analyzed the relationship between dormancy and PSF1 expression, and how a histone deacetylase inhibitor affects this. We found that CD34{sup +} HSCs produce long functional PSF1 (PSF1a) but CD34{sup −} HSCs produce a shorter possibly non-functional PSF1 (PSF1b, c, dominantly PSF1c). Using PSF1a-overexpressing NIH-3T3 cells in which the endogenous PSF1 promoter is suppressed, we found that TSA treatment promotes production of the shorter form of PSF1 possibly by inducing recruitment of E2F family factors upstream of the PSF1 transcription start site. Our data document one mechanism by which histone deacetylase inhibitors affect the dormancy of HSCs by regulating the DNA replication factor PSF1. - Highlights: • Hematopoetic stem cell dormancy is controlled by histone deacetylation inhibitors. • Dormancy of HSCs is associated with a shorter form of non-functional PSF1. • Histone deacetylase inhibitors suppress PSF1 promoter activity.

PSF Estimation of Space-Variant Ultra-Wide Field of View Imaging Systems

Directory of Open Access Journals (Sweden)

Petr Janout

2017-02-01

Full Text Available Ultra-wide-field of view (UWFOV imaging systems are affected by various aberrations, most of which are highly angle-dependent. A description of UWFOV imaging systems, such as microscopy optics, security camera systems and other special space-variant imaging systems, is a difficult task that can be achieved by estimating the Point Spread Function (PSF of the system. This paper proposes a novel method for modeling the space-variant PSF of an imaging system using the Zernike polynomials wavefront description. The PSF estimation algorithm is based on obtaining field-dependent expansion coefficients of the Zernike polynomials by fitting real image data of the analyzed imaging system using an iterative approach in an initial estimate of the fitting parameters to ensure convergence robustness. The method is promising as an alternative to the standard approach based on Shack–Hartmann interferometry, since the estimate of the aberration coefficients is processed directly in the image plane. This approach is tested on simulated and laboratory-acquired image data that generally show good agreement. The resulting data are compared with the results of other modeling methods. The proposed PSF estimation method provides around 5% accuracy of the optical system model.

Polarization behaviour of polyvinylidenefluoride-polysulfone (PVDF: PSF) blends under high field and high temperature condition

Science.gov (United States)

Shrivas, Sandhya; Patel, Swarnim; Dubey, R. K.; Keller, J. M.

2018-05-01

Thermally stimulated discharge currents of PVDF: PSF blend samples in ratio 80:20 and 95:05 prepared by the solution cast technique have been studied as a function of polarizing field and polarizing temperature, the temperature corresponding to a peak in TSDC is found to be independent of polarizing field but dependent on the polarizing temperature.

Validation of PSF-based 3D reconstruction for myocardial blood flow measurements with Rb-82 PET

DEFF Research Database (Denmark)

Tolbod, Lars Poulsen; Christensen, Nana Louise; Møller, Lone W.

images, filtered backprojection (FBP). Furthermore, since myocardial segmentation might be affected by image quality, two different approaches to segmentation implemented in standard software (Carimas (Turku PET Centre) and QPET (Cedar Sinai)) are utilized. Method:14 dynamic rest-stress Rb-82 patient......-scans performed on a GE Discovery 690 PET/CT were included. Images were reconstructed in an isotropic matrix (3.27x3.27x3.27 mm) using PSF (SharpIR: 3 iterations and 21 subsets) and FBP (FORE FBP) with the same edge-preserving filter (3D Butterworth: cut-off 10 mm, power 10). Analysis: The dynamic PET......Aim:The use of PSF-based 3D reconstruction algorithms (PSF) is desirable in most clinical PET-exams due to their superior image quality. Rb-82 cardiac PET is inherently noisy due to short half-life and prompt gammas and would presumably benefit from PSF. However, the quantitative behavior of PSF...

Preparation, characterisation and critical flux determination of graphene oxide blended polysulfone (PSf) membranes in an MBR system.

Science.gov (United States)

Ravishankar, Harish; Roddick, Felicity; Navaratna, Dimuth; Jegatheesan, Veeriah

2018-05-01

Microfiltration membranes having different blends of graphene-oxide (GO) (0-1 wt%) and Polysulfone (PSf) (15-20 wt%) were prepared using the classical non-solvent induced phase inversion process. The prepared membranes were characterised for their structural morphology, surface properties, mechanical strength, porosity and pure water flux. Based on the initial characterisation results, four membranes (15 wt% PSf, 15 wt% PSf + 0.25 wt% GO, 15 wt% PSf + 1 wt% GO and 20 wt% PSf + 1 wt% GO) were chosen for critical flux study, that was conducted using flux-step method in a lab scale MBR system. In order to study the application potential of GO blended membranes, the critical flux of each membrane was evaluated in two operational modes i.e., continuous and intermittent modes with backwash. The membranes with maximal GO concentration (15 wt% PSf + 1 wt% GO and 20 wt% PSf + 1 wt% GO) showed higher critical flux (16.5, 12.8 L/m 2 h and 19, 15 L/m 2 h for continuous and intermittent mode, respectively). It was observed that the operational modes did not have a significant effect on the critical flux of the membranes with low GO concentration (15 wt% PSf and 15 wt% PSf + 0.25 wt% GO), indicating a minimal of 1 wt% GO was required for an observable effect that favoured intermittent mode of operation. Through these results, ideal operating condition was arrived (i.e., flux maintained at 6.4 L/m 2 h operated under intermittent mode) and the membranes 15 wt% PSf and 15 wt% PSf + 1 wt% GO were studied for their long-term operation. The positive effect of GO on filtration time, cleaning frequency and against fouling was demonstrated through long term TMP profile of the membranes, indicating the suitability of GO blended membrane for real time wastewater treatment. Copyright © 2018 Elsevier Ltd. All rights reserved.

MEASURING X-RAY VARIABILITY IN FAINT/SPARSELY SAMPLED ACTIVE GALACTIC NUCLEI

Energy Technology Data Exchange (ETDEWEB)

Allevato, V. [Department of Physics, University of Helsinki, Gustaf Haellstroemin katu 2a, FI-00014 Helsinki (Finland); Paolillo, M. [Department of Physical Sciences, University Federico II, via Cinthia 6, I-80126 Naples (Italy); Papadakis, I. [Department of Physics and Institute of Theoretical and Computational Physics, University of Crete, 71003 Heraklion (Greece); Pinto, C. [SRON Netherlands Institute for Space Research, Sorbonnelaan 2, 3584-CA Utrecht (Netherlands)

2013-07-01

We study the statistical properties of the normalized excess variance of variability process characterized by a ''red-noise'' power spectral density (PSD), as in the case of active galactic nuclei (AGNs). We perform Monte Carlo simulations of light curves, assuming both a continuous and a sparse sampling pattern and various signal-to-noise ratios (S/Ns). We show that the normalized excess variance is a biased estimate of the variance even in the case of continuously sampled light curves. The bias depends on the PSD slope and on the sampling pattern, but not on the S/N. We provide a simple formula to account for the bias, which yields unbiased estimates with an accuracy better than 15%. We show that the normalized excess variance estimates based on single light curves (especially for sparse sampling and S/N < 3) are highly uncertain (even if corrected for bias) and we propose instead the use of an ''ensemble estimate'', based on multiple light curves of the same object, or on the use of light curves of many objects. These estimates have symmetric distributions, known errors, and can also be corrected for biases. We use our results to estimate the ability to measure the intrinsic source variability in current data, and show that they could also be useful in the planning of the observing strategy of future surveys such as those provided by X-ray missions studying distant and/or faint AGN populations and, more in general, in the estimation of the variability amplitude of sources that will result from future surveys such as Pan-STARRS and LSST.

SD-SEM: sparse-dense correspondence for 3D reconstruction of microscopic samples.

Science.gov (United States)

Baghaie, Ahmadreza; Tafti, Ahmad P; Owen, Heather A; D'Souza, Roshan M; Yu, Zeyun

2017-06-01

Scanning electron microscopy (SEM) imaging has been a principal component of many studies in biomedical, mechanical, and materials sciences since its emergence. Despite the high resolution of captured images, they remain two-dimensional (2D). In this work, a novel framework using sparse-dense correspondence is introduced and investigated for 3D reconstruction of stereo SEM images. SEM micrographs from microscopic samples are captured by tilting the specimen stage by a known angle. The pair of SEM micrographs is then rectified using sparse scale invariant feature transform (SIFT) features/descriptors and a contrario RANSAC for matching outlier removal to ensure a gross horizontal displacement between corresponding points. This is followed by dense correspondence estimation using dense SIFT descriptors and employing a factor graph representation of the energy minimization functional and loopy belief propagation (LBP) as means of optimization. Given the pixel-by-pixel correspondence and the tilt angle of the specimen stage during the acquisition of micrographs, depth can be recovered. Extensive tests reveal the strength of the proposed method for high-quality reconstruction of microscopic samples. Copyright © 2017 Elsevier Ltd. All rights reserved.

PSF interlaboratory comparison

International Nuclear Information System (INIS)

Kellogg, L.S.; Lippincott, E.P.

1982-01-01

Two experiments for interlaboratory verification of radiometric analysis methods have been conducted with dosimeters irradiated in the Oak Ridge Research Reactor (ORR) Poolside Facility (PSF) Surveillance Dosimeter Measurement Facility (SDMF). In a preliminary analysis of data supplied by the six participants, biases as large as 60% were observed which could lead to errors of this general magnitude in reported surveillance capsule fluence values. As a result of these comparisons, problems were identified and the spread in final values was greatly reduced. Relative agreement among the final results reported by four of the laboratories now appears to be satisfactory (the non-fission dosimeter results generally falling within +-5% and the fission dosimeter results within +-10%), but improvement is required in order to routinely meet Reactor Vessel Material Surveillance Program goals

Model-based PSF and MTF estimation and validation from skeletal clinical CT images.

Science.gov (United States)

Pakdel, Amirreza; Mainprize, James G; Robert, Normand; Fialkov, Jeffery; Whyne, Cari M

2014-01-01

A method was developed to correct for systematic errors in estimating the thickness of thin bones due to image blurring in CT images using bone interfaces to estimate the point-spread-function (PSF). This study validates the accuracy of the PSFs estimated using said method from various clinical CT images featuring cortical bones. Gaussian PSFs, characterized by a different extent in the z (scan) direction than in the x and y directions were obtained using our method from 11 clinical CT scans of a cadaveric craniofacial skeleton. These PSFs were estimated for multiple combinations of scanning parameters and reconstruction methods. The actual PSF for each scan setting was measured using the slanted-slit technique within the image slice plane and the longitudinal axis. The Gaussian PSF and the corresponding modulation transfer function (MTF) are compared against the actual PSF and MTF for validation. The differences (errors) between the actual and estimated full-width half-max (FWHM) of the PSFs were 0.09 ± 0.05 and 0.14 ± 0.11 mm for the xy and z axes, respectively. The overall errors in the predicted frequencies measured at 75%, 50%, 25%, 10%, and 5% MTF levels were 0.06 ± 0.07 and 0.06 ± 0.04 cycles/mm for the xy and z axes, respectively. The accuracy of the estimates was dependent on whether they were reconstructed with a standard kernel (Toshiba's FC68, mean error of 0.06 ± 0.05 mm, MTF mean error 0.02 ± 0.02 cycles/mm) or a high resolution bone kernel (Toshiba's FC81, PSF FWHM error 0.12 ± 0.03 mm, MTF mean error 0.09 ± 0.08 cycles/mm). The method is accurate in 3D for an image reconstructed using a standard reconstruction kernel, which conforms to the Gaussian PSF assumption but less accurate when using a high resolution bone kernel. The method is a practical and self-contained means of estimating the PSF in clinical CT images featuring cortical bones, without the need phantoms or any prior knowledge about the scanner-specific parameters.

Model-based PSF and MTF estimation and validation from skeletal clinical CT images

International Nuclear Information System (INIS)

Pakdel, Amirreza; Mainprize, James G.; Robert, Normand; Fialkov, Jeffery; Whyne, Cari M.

2014-01-01

Purpose: A method was developed to correct for systematic errors in estimating the thickness of thin bones due to image blurring in CT images using bone interfaces to estimate the point-spread-function (PSF). This study validates the accuracy of the PSFs estimated using said method from various clinical CT images featuring cortical bones. Methods: Gaussian PSFs, characterized by a different extent in the z (scan) direction than in the x and y directions were obtained using our method from 11 clinical CT scans of a cadaveric craniofacial skeleton. These PSFs were estimated for multiple combinations of scanning parameters and reconstruction methods. The actual PSF for each scan setting was measured using the slanted-slit technique within the image slice plane and the longitudinal axis. The Gaussian PSF and the corresponding modulation transfer function (MTF) are compared against the actual PSF and MTF for validation. Results: The differences (errors) between the actual and estimated full-width half-max (FWHM) of the PSFs were 0.09 ± 0.05 and 0.14 ± 0.11 mm for the xy and z axes, respectively. The overall errors in the predicted frequencies measured at 75%, 50%, 25%, 10%, and 5% MTF levels were 0.06 ± 0.07 and 0.06 ± 0.04 cycles/mm for the xy and z axes, respectively. The accuracy of the estimates was dependent on whether they were reconstructed with a standard kernel (Toshiba's FC68, mean error of 0.06 ± 0.05 mm, MTF mean error 0.02 ± 0.02 cycles/mm) or a high resolution bone kernel (Toshiba's FC81, PSF FWHM error 0.12 ± 0.03 mm, MTF mean error 0.09 ± 0.08 cycles/mm). Conclusions: The method is accurate in 3D for an image reconstructed using a standard reconstruction kernel, which conforms to the Gaussian PSF assumption but less accurate when using a high resolution bone kernel. The method is a practical and self-contained means of estimating the PSF in clinical CT images featuring cortical bones, without the need phantoms or any prior knowledge about the

Model-based PSF and MTF estimation and validation from skeletal clinical CT images

Energy Technology Data Exchange (ETDEWEB)

Pakdel, Amirreza [Sunnybrook Research Institute, Toronto, Ontario M4N 3M5, Canada and Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario M5S 3M2 (Canada); Mainprize, James G.; Robert, Normand [Sunnybrook Research Institute, Toronto, Ontario M4N 3M5 (Canada); Fialkov, Jeffery [Division of Plastic Surgery, Sunnybrook Health Sciences Center, Toronto, Ontario M4N 3M5, Canada and Department of Surgery, University of Toronto, Toronto, Ontario M5S 3M2 (Canada); Whyne, Cari M., E-mail: cari.whyne@sunnybrook.ca [Sunnybrook Research Institute, Toronto, Ontario M4N 3M5, Canada and Department of Surgery, Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario M5S 3M2 (Canada)

2014-01-15

Purpose: A method was developed to correct for systematic errors in estimating the thickness of thin bones due to image blurring in CT images using bone interfaces to estimate the point-spread-function (PSF). This study validates the accuracy of the PSFs estimated using said method from various clinical CT images featuring cortical bones. Methods: Gaussian PSFs, characterized by a different extent in the z (scan) direction than in the x and y directions were obtained using our method from 11 clinical CT scans of a cadaveric craniofacial skeleton. These PSFs were estimated for multiple combinations of scanning parameters and reconstruction methods. The actual PSF for each scan setting was measured using the slanted-slit technique within the image slice plane and the longitudinal axis. The Gaussian PSF and the corresponding modulation transfer function (MTF) are compared against the actual PSF and MTF for validation. Results: The differences (errors) between the actual and estimated full-width half-max (FWHM) of the PSFs were 0.09 ± 0.05 and 0.14 ± 0.11 mm for the xy and z axes, respectively. The overall errors in the predicted frequencies measured at 75%, 50%, 25%, 10%, and 5% MTF levels were 0.06 ± 0.07 and 0.06 ± 0.04 cycles/mm for the xy and z axes, respectively. The accuracy of the estimates was dependent on whether they were reconstructed with a standard kernel (Toshiba's FC68, mean error of 0.06 ± 0.05 mm, MTF mean error 0.02 ± 0.02 cycles/mm) or a high resolution bone kernel (Toshiba's FC81, PSF FWHM error 0.12 ± 0.03 mm, MTF mean error 0.09 ± 0.08 cycles/mm). Conclusions: The method is accurate in 3D for an image reconstructed using a standard reconstruction kernel, which conforms to the Gaussian PSF assumption but less accurate when using a high resolution bone kernel. The method is a practical and self-contained means of estimating the PSF in clinical CT images featuring cortical bones, without the need phantoms or any prior knowledge

Online sparse representation for remote sensing compressed-sensed video sampling

Science.gov (United States)

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.

Use of the probability of stone formation (PSF) score to assess stone forming risk and treatment response in a cohort of Brazilian stone formers

OpenAIRE

Turney, Benjamin; Robertson, William; Wiseman, Oliver; Amaro, Carmen Regina P. R.; Leitão, Victor A.; Silva, Isabela Leme da; Amaro, João Luiz

2014-01-01

Introduction: The aim was to confirm that PSF (probability of stone formation) changed appropriately following medical therapy on recurrent stone formers.Materials and Methods: Data were collected on 26 Brazilian stone-formers. A baseline 24-hour urine collection was performed prior to treatment. Details of the medical treatment initiated for stone-disease were recorded. A PSF calculation was performed on the 24 hour urine sample using the 7 urinary parameters required: voided volume, oxalate...

HIV-1 pre-mRNA commitment to Rev mediated export through PSF and Matrin 3

International Nuclear Information System (INIS)

Kula, Anna; Gharu, Lavina; Marcello, Alessandro

2013-01-01

Human immunodeficiency virus gene expression and replication are regulated at several levels. Incompletely spliced viral RNAs and full-length genomic RNA contain the RRE element and are bound by the viral trans-acting protein Rev to be transported out of the nucleus. Previously we found that the nuclear matrix protein MATR3 was a cofactor of Rev-mediated RNA export. Here we show that the pleiotropic protein PSF binds viral RNA and is associated with MATR3. PSF is involved in the maintenance of a pool of RNA available for Rev activity. However, while Rev and PSF bind the viral pre-mRNA at the site of viral transcription, MATR3 interacts at a subsequent step. We propose that PSF and MATR3 define a novel pathway for RRE-containing HIV-1 RNAs that is hijacked by the viral Rev protein.

Effect of Evaporation Time on Separation Performance of Polysulfone/Cellulose Acetate (PSF/CA) Membrane

Science.gov (United States)

Syahbanu, Intan; Piluharto, Bambang; Khairi, Syahrul; Sudarko

2018-01-01

Polysulfone and cellulose acetate are common material in separation. In this research, polysulfone/cellulose actetate (PSF/CA) blend membrane was prepared. The aim of this research was to study effect of evaporation time in casting of PSF/CA membrane and its performance in filtration. CA was obtained by acetylation process of bacterial cellulose (BC) from fermentation of coconut water. Fourier Transform Infra Red (FTIR) Spectroscopy was used to examine functional groups of BC, CA and commercial cellulose acetate. Subtitution of acetyl groups determined by titration method. Blend membranes were prepared through phase inversion technique in which composition of PSF/PEG/CA/NMP(%w) was 15/5/5/75. Polyethyleneglycol (PEG) and N-methyl-2-pyrrolidone (NMP) were act as pore forming agent and solvent, respectively. Variation of evaporation times were used as parameter to examine water uptake, flux, and morphology of PSF/CA blend membranes. FTIR spectra of CA show characteristic peak of acetyl group at 1220 cm-1 indicated that BC was acetylated succesfully. Degree of subtitution of BCA was found at 2.62. Highest water flux was performed at 2 bar obtained at 106.31 L.m-2.h-1 at 0 minute variation, and decrease as increasing evaporation time. Morphology of PSF/BCA blend membranes were investigated by Scanning Electron Microscopy (SEM) showed that porous asymetric membrane were formed.

Discriminative sparse coding on multi-manifolds

KAUST Repository

Wang, J.J.-Y.; Bensmail, H.; Yao, N.; Gao, Xin

2013-01-01

Sparse coding has been popularly used as an effective data representation method in various applications, such as computer vision, medical imaging and bioinformatics. However, the conventional sparse coding algorithms and their manifold-regularized variants (graph sparse coding and Laplacian sparse coding), learn codebooks and codes in an unsupervised manner and neglect class information that is available in the training set. To address this problem, we propose a novel discriminative sparse coding method based on multi-manifolds, that learns discriminative class-conditioned codebooks and sparse codes from both data feature spaces and class labels. First, the entire training set is partitioned into multiple manifolds according to the class labels. Then, we formulate the sparse coding as a manifold-manifold matching problem and learn class-conditioned codebooks and codes to maximize the manifold margins of different classes. Lastly, we present a data sample-manifold matching-based strategy to classify the unlabeled data samples. Experimental results on somatic mutations identification and breast tumor classification based on ultrasonic images demonstrate the efficacy of the proposed data representation and classification approach. 2013 The Authors. All rights reserved.

Discriminative sparse coding on multi-manifolds

KAUST Repository

Wang, J.J.-Y.

2013-09-26

Sparse coding has been popularly used as an effective data representation method in various applications, such as computer vision, medical imaging and bioinformatics. However, the conventional sparse coding algorithms and their manifold-regularized variants (graph sparse coding and Laplacian sparse coding), learn codebooks and codes in an unsupervised manner and neglect class information that is available in the training set. To address this problem, we propose a novel discriminative sparse coding method based on multi-manifolds, that learns discriminative class-conditioned codebooks and sparse codes from both data feature spaces and class labels. First, the entire training set is partitioned into multiple manifolds according to the class labels. Then, we formulate the sparse coding as a manifold-manifold matching problem and learn class-conditioned codebooks and codes to maximize the manifold margins of different classes. Lastly, we present a data sample-manifold matching-based strategy to classify the unlabeled data samples. Experimental results on somatic mutations identification and breast tumor classification based on ultrasonic images demonstrate the efficacy of the proposed data representation and classification approach. 2013 The Authors. All rights reserved.

Experimental evaluation and basis function optimization of the spatially variant image-space PSF on the Ingenuity PET/MR scanner

International Nuclear Information System (INIS)

Kotasidis, Fotis A.; Zaidi, Habib

2014-01-01

superposition and keeping the image representation error to a minimum, is feasible, with the parameter combination range depending upon the scanner's intrinsic resolution characteristics. Conclusions: Using the printed point source array as a MR compatible methodology for experimentally measuring the scanner's PSF, the system's spatially variant resolution properties were successfully evaluated in image space. Overall the PET subsystem exhibits excellent resolution characteristics mainly due to the fact that the raw data are not under-sampled/rebinned, enabling the spatial resolution to be dictated by the scanner's intrinsic resolution and the image reconstruction parameters. Due to the impact of these parameters on the resolution properties of the reconstructed images, the image space PSF varies both under spatial transformations and due to basis function parameter selection. Nonetheless, for a range of basis function parameters, the image space PSF remains unaffected, with the range depending on the scanner's intrinsic resolution properties

Experimental evaluation and basis function optimization of the spatially variant image-space PSF on the Ingenuity PET/MR scanner

Energy Technology Data Exchange (ETDEWEB)

Kotasidis, Fotis A., E-mail: Fotis.Kotasidis@unige.ch [Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland and Wolfson Molecular Imaging Centre, MAHSC, University of Manchester, Manchester M20 3LJ (United Kingdom); Zaidi, Habib [Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva (Switzerland); Geneva Neuroscience Centre, Geneva University, CH-1205 Geneva (Switzerland); Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, 9700 RB (Netherlands)

2014-06-15

function superposition and keeping the image representation error to a minimum, is feasible, with the parameter combination range depending upon the scanner's intrinsic resolution characteristics. Conclusions: Using the printed point source array as a MR compatible methodology for experimentally measuring the scanner's PSF, the system's spatially variant resolution properties were successfully evaluated in image space. Overall the PET subsystem exhibits excellent resolution characteristics mainly due to the fact that the raw data are not under-sampled/rebinned, enabling the spatial resolution to be dictated by the scanner's intrinsic resolution and the image reconstruction parameters. Due to the impact of these parameters on the resolution properties of the reconstructed images, the image space PSF varies both under spatial transformations and due to basis function parameter selection. Nonetheless, for a range of basis function parameters, the image space PSF remains unaffected, with the range depending on the scanner's intrinsic resolution properties.

A Unifying model of perfusion and motion applied to reconstruction of sparsely sampled free-breathing myocardial perfusion MRI

DEFF Research Database (Denmark)

Pedersen, Henrik; Ólafsdóttir, Hildur; Larsen, Rasmus

2010-01-01

The clinical potential of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is currently limited by respiratory induced motion of the heart. This paper presents a unifying model of perfusion and motion in which respiratory motion becomes an integral part of myocardial perfusion...... quantification. Hence, the need for tedious manual motion correction prior to perfusion quantification is avoided. In addition, we demonstrate that the proposed framework facilitates the process of reconstructing DCEMRI from sparsely sampled data in the presence of respiratory motion. The paper focuses primarily...... on the underlying theory of the proposed framework, but shows in vivo results of respiratory motion correction and simulation results of reconstructing sparsely sampled data....

Multilevel sparse functional principal component analysis.

Science.gov (United States)

Di, Chongzhi; Crainiceanu, Ciprian M; Jank, Wolfgang S

2014-01-29

We consider analysis of sparsely sampled multilevel functional data, where the basic observational unit is a function and data have a natural hierarchy of basic units. An example is when functions are recorded at multiple visits for each subject. Multilevel functional principal component analysis (MFPCA; Di et al. 2009) was proposed for such data when functions are densely recorded. Here we consider the case when functions are sparsely sampled and may contain only a few observations per function. We exploit the multilevel structure of covariance operators and achieve data reduction by principal component decompositions at both between and within subject levels. We address inherent methodological differences in the sparse sampling context to: 1) estimate the covariance operators; 2) estimate the functional principal component scores; 3) predict the underlying curves. Through simulations the proposed method is able to discover dominating modes of variations and reconstruct underlying curves well even in sparse settings. Our approach is illustrated by two applications, the Sleep Heart Health Study and eBay auctions.

Relationship between line spread function (LSF), or slice sensitivity profile (SSP), and point spread function (PSF) in CT image system

International Nuclear Information System (INIS)

Ohkubo, Masaki; Wada, Shinichi; Kobayashi, Teiji; Lee, Yongbum; Tsai, Du-Yih

2004-01-01

In the CT image system, we revealed the relationship between line spread function (LSF), or slice sensitivity profile (SSP), and point spread function (PSF). In the system, the following equation has been reported; I(x,y)=O(x,y) ** PSF(x,y), in which I(x,y) and O(x,y) are CT image and object function, respectively, and ** is 2-dimensional convolution. In the same way, the following 3-dimensional expression applies; I'(x,y,z)=O'(x,y,z) *** PSF'(x,y,z), in which z-axis is the direction perpendicular to the x/y-scan plane. We defined that the CT image system was separable, when the above two equations could be transformed into following equations; I(x,y)=[O(x,y) * LSF x (x)] * LSF y (y) and I'(x,y,z) =[O'(x,y,z) * SSP(z)] ** PSF(x,y), respectively, in which LSF x (x) and LSF y (y) are LSFs in x- and y-direction, respectively. Previous reports for the LSF and SSP are considered to assume the separable-system. Under the condition of separable-system, we derived following equations; PSF(x,y)=LSF x (x) ·LSF y (y) and PSF'(x,y,z)=PSF(x,y)·SSP(z). They were validated by the computer-simulations. When the study based on 1-dimensional functions of LSF and SSP are expanded to that based on 2- or 3-dimensional functions of PSF, derived equations must be required. (author)

Uncertainty Analysis Based on Sparse Grid Collocation and Quasi-Monte Carlo Sampling with Application in Groundwater Modeling

Science.gov (United States)

Zhang, G.; Lu, D.; Ye, M.; Gunzburger, M.

2011-12-01

Markov Chain Monte Carlo (MCMC) methods have been widely used in many fields of uncertainty analysis to estimate the posterior distributions of parameters and credible intervals of predictions in the Bayesian framework. However, in practice, MCMC may be computationally unaffordable due to slow convergence and the excessive number of forward model executions required, especially when the forward model is expensive to compute. Both disadvantages arise from the curse of dimensionality, i.e., the posterior distribution is usually a multivariate function of parameters. Recently, sparse grid method has been demonstrated to be an effective technique for coping with high-dimensional interpolation or integration problems. Thus, in order to accelerate the forward model and avoid the slow convergence of MCMC, we propose a new method for uncertainty analysis based on sparse grid interpolation and quasi-Monte Carlo sampling. First, we construct a polynomial approximation of the forward model in the parameter space by using the sparse grid interpolation. This approximation then defines an accurate surrogate posterior distribution that can be evaluated repeatedly at minimal computational cost. Second, instead of using MCMC, a quasi-Monte Carlo method is applied to draw samples in the parameter space. Then, the desired probability density function of each prediction is approximated by accumulating the posterior density values of all the samples according to the prediction values. Our method has the following advantages: (1) the polynomial approximation of the forward model on the sparse grid provides a very efficient evaluation of the surrogate posterior distribution; (2) the quasi-Monte Carlo method retains the same accuracy in approximating the PDF of predictions but avoids all disadvantages of MCMC. The proposed method is applied to a controlled numerical experiment of groundwater flow modeling. The results show that our method attains the same accuracy much more efficiently

A PSF-Shape-Based Beamforming Strategy for Robust 2D Motion Estimation in Ultrafast Data

OpenAIRE

Anne E. C. M. Saris; Stein Fekkes; Maartje M. Nillesen; Hendrik H. G. Hansen; Chris L. de Korte

2018-01-01

This paper presents a framework for motion estimation in ultrafast ultrasound data. It describes a novel approach for determining the sampling grid for ultrafast data based on the system’s point-spread-function (PSF). As a consequence, the cross-correlation functions (CCF) used in the speckle tracking (ST) algorithm will have circular-shaped peaks, which can be interpolated using a 2D interpolation method to estimate subsample displacements. Carotid artery wall motion and parabolic blood flow...

Sparse feature learning for instrument identification: Effects of sampling and pooling methods.

Science.gov (United States)

Han, Yoonchang; Lee, Subin; Nam, Juhan; Lee, Kyogu

2016-05-01

Feature learning for music applications has recently received considerable attention from many researchers. This paper reports on the sparse feature learning algorithm for musical instrument identification, and in particular, focuses on the effects of the frame sampling techniques for dictionary learning and the pooling methods for feature aggregation. To this end, two frame sampling techniques are examined that are fixed and proportional random sampling. Furthermore, the effect of using onset frame was analyzed for both of proposed sampling methods. Regarding summarization of the feature activation, a standard deviation pooling method is used and compared with the commonly used max- and average-pooling techniques. Using more than 47 000 recordings of 24 instruments from various performers, playing styles, and dynamics, a number of tuning parameters are experimented including the analysis frame size, the dictionary size, and the type of frequency scaling as well as the different sampling and pooling methods. The results show that the combination of proportional sampling and standard deviation pooling achieve the best overall performance of 95.62% while the optimal parameter set varies among the instrument classes.

A method of PSF generation for 3D brightfield deconvolution.

Science.gov (United States)

Tadrous, P J

2010-02-01

This paper addresses the problem of 3D deconvolution of through focus widefield microscope datasets (Z-stacks). One of the most difficult stages in brightfield deconvolution is finding the point spread function. A theoretically calculated point spread function (called a 'synthetic PSF' in this paper) requires foreknowledge of many system parameters and still gives only approximate results. A point spread function measured from a sub-resolution bead suffers from low signal-to-noise ratio, compounded in the brightfield setting (by contrast to fluorescence) by absorptive, refractive and dispersal effects. This paper describes a method of point spread function estimation based on measurements of a Z-stack through a thin sample. This Z-stack is deconvolved by an idealized point spread function derived from the same Z-stack to yield a point spread function of high signal-to-noise ratio that is also inherently tailored to the imaging system. The theory is validated by a practical experiment comparing the non-blind 3D deconvolution of the yeast Saccharomyces cerevisiae with the point spread function generated using the method presented in this paper (called the 'extracted PSF') to a synthetic point spread function. Restoration of both high- and low-contrast brightfield structures is achieved with fewer artefacts using the extracted point spread function obtained with this method. Furthermore the deconvolution progresses further (more iterations are allowed before the error function reaches its nadir) with the extracted point spread function compared to the synthetic point spread function indicating that the extracted point spread function is a better fit to the brightfield deconvolution model than the synthetic point spread function.

Supervised Transfer Sparse Coding

KAUST Repository

Al-Shedivat, Maruan

2014-07-27

A combination of the sparse coding and transfer learn- ing techniques was shown to be accurate and robust in classification tasks where training and testing objects have a shared feature space but are sampled from differ- ent underlying distributions, i.e., belong to different do- mains. The key assumption in such case is that in spite of the domain disparity, samples from different domains share some common hidden factors. Previous methods often assumed that all the objects in the target domain are unlabeled, and thus the training set solely comprised objects from the source domain. However, in real world applications, the target domain often has some labeled objects, or one can always manually label a small num- ber of them. In this paper, we explore such possibil- ity and show how a small number of labeled data in the target domain can significantly leverage classifica- tion accuracy of the state-of-the-art transfer sparse cod- ing methods. We further propose a unified framework named supervised transfer sparse coding (STSC) which simultaneously optimizes sparse representation, domain transfer and classification. Experimental results on three applications demonstrate that a little manual labeling and then learning the model in a supervised fashion can significantly improve classification accuracy.

LWR surveillance dosimetry improvement program: PSF metallurgical blind test results

International Nuclear Information System (INIS)

Kam, F.B.K.; Maerker, R.E.; Stallmann, F.W.

1984-01-01

The metallurgical irradiation experiment at the Oak Ridge Research Reactor Poolside Facility (ORR-PSF) was designed as a benchmark to test the accuracy of radiation embrittlement predictions in the pressure vessel wall of light water reactors on the basis of results from surveillance capsules. The PSF metallurgical Blind Test is concerned with the simulated surveillance capsule (SSC) and the simulated pressure vessel capsule (SPVC). The data from the ORR-PSF benchmark experiment are the basis for comparison with the predictions made by participants of the metallurgical ''Blind Test''. The Blind Test required the participants to predict the embrittlement of the irradiated specimen based only on dosimetry and metallurgical data from the SSC1 capsule. This exercise included both the prediction of damage fluence and the prediction of embrittlement based on the predicted fluence. A variety of prediction methodologies was used by the participants. No glaring biases or other deficiencies were found, but neither were any of the methods clearly superior to the others. Closer analysis shows a rather complex and poorly understood relation between fluence and material damage. Many prediction formulas can give an adequate approximation, but further improvement of the prediction methodology is unlikely at this time given the many unknown factors. Instead, attention should be focused on determining realistic uncertainties for the predicted material changes. The Blind Test comparisons provide some clues for the size of these uncertainties. In particular, higher uncertainties must be assigned to materials whose chemical composition lies outside the data set for which the prediction formula was obtained. 16 references, 14 figures, 5 tables

Fast shading correction for cone beam CT in radiation therapy via sparse sampling on planning CT.

Science.gov (United States)

Shi, Linxi; Tsui, Tiffany; Wei, Jikun; Zhu, Lei

2017-05-01

The image quality of cone beam computed tomography (CBCT) is limited by severe shading artifacts, hindering its quantitative applications in radiation therapy. In this work, we propose an image-domain shading correction method using planning CT (pCT) as prior information which is highly adaptive to clinical environment. We propose to perform shading correction via sparse sampling on pCT. The method starts with a coarse mapping between the first-pass CBCT images obtained from the Varian TrueBeam system and the pCT. The scatter correction method embedded in the Varian commercial software removes some image errors but the CBCT images still contain severe shading artifacts. The difference images between the mapped pCT and the CBCT are considered as shading errors, but only sparse shading samples are selected for correction using empirical constraints to avoid carrying over false information from pCT. A Fourier-Transform-based technique, referred to as local filtration, is proposed to efficiently process the sparse data for effective shading correction. The performance of the proposed method is evaluated on one anthropomorphic pelvis phantom and 17 patients, who were scheduled for radiation therapy. (The codes of the proposed method and sample data can be downloaded from https://sites.google.com/view/linxicbct) RESULTS: The proposed shading correction substantially improves the CBCT image quality on both the phantom and the patients to a level close to that of the pCT images. On the phantom, the spatial nonuniformity (SNU) difference between CBCT and pCT is reduced from 74 to 1 HU. The root of mean square difference of SNU between CBCT and pCT is reduced from 83 to 10 HU on the pelvis patients, and from 101 to 12 HU on the thorax patients. The robustness of the proposed shading correction is fully investigated with simulated registration errors between CBCT and pCT on the phantom and mis-registration on patients. The sparse sampling scheme of our method successfully

ORNL evaluation of the ORR-PSF metallurgical experiment and blind test

International Nuclear Information System (INIS)

Stallmann, F.W.

1984-01-01

A methodology is described to evaluate the dosimetry and metallurgical data from the two-year ORR-PSF metallurgical irradiation experiment. The first step is to obtain a three-dimensional map of damage exposure parameter values based on neutron transport calculations and dosimetry measurements which are obtained by means of the LSL-M2 adjustment procedure. Metallurgical test data are then combined with damage parameter, temperature, and chemistry information to determine the correlation between radiation and steel embrittlement in reactor pressure vessels including estimates for the uncertainties. Statistical procedures for the evaluation of Charpy data, developed earlier, are used for this investigation. The data obtained in this investigation provide a benchmark against which the predictions of the PSF Blind Test can be compared. The results of this investigation and the Blind Test comparison are discussed

Porosity estimation by semi-supervised learning with sparsely available labeled samples

Science.gov (United States)

Lima, Luiz Alberto; Görnitz, Nico; Varella, Luiz Eduardo; Vellasco, Marley; Müller, Klaus-Robert; Nakajima, Shinichi

2017-09-01

This paper addresses the porosity estimation problem from seismic impedance volumes and porosity samples located in a small group of exploratory wells. Regression methods, trained on the impedance as inputs and the porosity as output labels, generally suffer from extremely expensive (and hence sparsely available) porosity samples. To optimally make use of the valuable porosity data, a semi-supervised machine learning method was proposed, Transductive Conditional Random Field Regression (TCRFR), showing good performance (Görnitz et al., 2017). TCRFR, however, still requires more labeled data than those usually available, which creates a gap when applying the method to the porosity estimation problem in realistic situations. In this paper, we aim to fill this gap by introducing two graph-based preprocessing techniques, which adapt the original TCRFR for extremely weakly supervised scenarios. Our new method outperforms the previous automatic estimation methods on synthetic data and provides a comparable result to the manual labored, time-consuming geostatistics approach on real data, proving its potential as a practical industrial tool.

Desempenho do PSF no Sul e no Nordeste do Brasil: avaliação institucional e epidemiológica da Atenção Básica à Saúde Performance of the PSF in the Brazilian South and Northeast: institutional and epidemiological Assessment of Primary Health Care

Directory of Open Access Journals (Sweden)

Luiz Augusto Facchini

2006-09-01

sampled domiciles. The coverage provided by PSF between 1999 and 2004 increased more in the Northeast than in the South. Less than half of the workers were admitted through open public selection and the work under contract was more frequent in the PSF than in the traditional UBS. These findings suggest a performance of primary health care still very distant from the prescriptions of the Unified Health System. Less than half of the potential users frequented the UBS in their area. The offer of health actions, their use and the contact through programmed actions were more adequate in the PSF.

A random sampling approach for robust estimation of tissue-to-plasma ratio from extremely sparse data.

Science.gov (United States)

Chu, Hui-May; Ette, Ene I

2005-09-02

his study was performed to develop a new nonparametric approach for the estimation of robust tissue-to-plasma ratio from extremely sparsely sampled paired data (ie, one sample each from plasma and tissue per subject). Tissue-to-plasma ratio was estimated from paired/unpaired experimental data using independent time points approach, area under the curve (AUC) values calculated with the naïve data averaging approach, and AUC values calculated using sampling based approaches (eg, the pseudoprofile-based bootstrap [PpbB] approach and the random sampling approach [our proposed approach]). The random sampling approach involves the use of a 2-phase algorithm. The convergence of the sampling/resampling approaches was investigated, as well as the robustness of the estimates produced by different approaches. To evaluate the latter, new data sets were generated by introducing outlier(s) into the real data set. One to 2 concentration values were inflated by 10% to 40% from their original values to produce the outliers. Tissue-to-plasma ratios computed using the independent time points approach varied between 0 and 50 across time points. The ratio obtained from AUC values acquired using the naive data averaging approach was not associated with any measure of uncertainty or variability. Calculating the ratio without regard to pairing yielded poorer estimates. The random sampling and pseudoprofile-based bootstrap approaches yielded tissue-to-plasma ratios with uncertainty and variability. However, the random sampling approach, because of the 2-phase nature of its algorithm, yielded more robust estimates and required fewer replications. Therefore, a 2-phase random sampling approach is proposed for the robust estimation of tissue-to-plasma ratio from extremely sparsely sampled data.

Enhancement of antibacterial activity in nanofillers incorporated PSF/PVP membranes

Science.gov (United States)

Pramila, P.; Gopalakrishnan, N.

2018-04-01

An attempt has been made to investigate the nanofillers incorporated polysulfone (PSF) and polyvinylpyrrolidone (PVP) polymer membranes prepared by phase inversion method. Initially, the nanofillers, viz, Zinc Oxide (ZnO) nanoparticle, Graphene Oxide-Zinc Oxide (GO-ZnO) nanocomposite were synthesized and then directly incorporated into PSF/PVP blend during the preparation of membranes. The prepared membranes have been subjected to FE-SEM, AFM, BET, contact angle, tensile test and anti-bacterial studies. Significant membrane morphologies and nanoporous properties have been observed by FE-SEM and BET, respectively. It has been observed that hydrophilicity, mechanical strength and water permeability of the ZnO and GO-ZnO incorporated membranes were enhanced than bare membrane. Antibacterial activity was assessed by measuring the inhibition zones formed around the membrane by disc-diffusion method using Escherichia coli (gram-negative) as a model bacterium. Again, it has been observed that nanofillers incorporated membrane exhibits high antibacterial performance compared to bare membrane.

A single-photon ecat reconstruction procedure based on a PSF model

International Nuclear Information System (INIS)

Ying-Lie, O.

1984-01-01

Emission Computed Axial Tomography (ECAT) has been applied in nuclear medicine for the past few years. Owing to attenuation and scatter along the ray path, adequate correction methods are required. In this thesis, a correction method for attenuation, detector response and Compton scatter has been proposed. The method developed is based on a PSF model. The parameters of the models were derived by fitting experimental and simulation data. Because of its flexibility, a Monte Carlo simulation method has been employed. Using the PSF models, it was found that the ECAT problem can be described by the added modified equation. Application of the reconstruction procedure on simulation data yield satisfactory results. The algorithm tends to amplify noise and distortion in the data, however. Therefore, the applicability of the method on patient studies remain to be seen. (Auth.)

Structure-aware Local Sparse Coding for Visual Tracking

KAUST Repository

Qi, Yuankai

2018-01-24

Sparse coding has been applied to visual tracking and related vision problems with demonstrated success in recent years. Existing tracking methods based on local sparse coding sample patches from a target candidate and sparsely encode these using a dictionary consisting of patches sampled from target template images. The discriminative strength of existing methods based on local sparse coding is limited as spatial structure constraints among the template patches are not exploited. To address this problem, we propose a structure-aware local sparse coding algorithm which encodes a target candidate using templates with both global and local sparsity constraints. For robust tracking, we show local regions of a candidate region should be encoded only with the corresponding local regions of the target templates that are the most similar from the global view. Thus, a more precise and discriminative sparse representation is obtained to account for appearance changes. To alleviate the issues with tracking drifts, we design an effective template update scheme. Extensive experiments on challenging image sequences demonstrate the effectiveness of the proposed algorithm against numerous stateof- the-art methods.

SparseM: A Sparse Matrix Package for R *

Directory of Open Access Journals (Sweden)

Roger Koenker

2003-02-01

Full Text Available SparseM provides some basic R functionality for linear algebra with sparse matrices. Use of the package is illustrated by a family of linear model fitting functions that implement least squares methods for problems with sparse design matrices. Significant performance improvements in memory utilization and computational speed are possible for applications involving large sparse matrices.

Modification of PSf/SPSf Blended Porous Support for Improving the Reverse Osmosis Performance of Aromatic Polyamide Thin Film Composite Membranes

Directory of Open Access Journals (Sweden)

Li-Fen Liu

2018-06-01

Full Text Available In this study, modification of polysulfone (PSf/sulfonated polysulfone (SPSf blended porous ultrafiltration (UF support membranes was proposed to improve the reverse osmosis (RO performance of aromatic polyamide thin film composite (TFC membranes. The synergistic effects of solvent, polymer concentration, and SPSf doping content in the casting solution were investigated systematically on the properties of both porous supports and RO membranes. SEM and AFM were combined to characterize the physical properties of the membranes, including surface pore natures (porosity, mean pore radius, surface morphology, and section structure. A contact angle meter was used to analyze the membrane surface hydrophilicity. Permeate experiments were carried out to evaluate the separation performances of the membranes. The results showed that the PSf/SPSf blended porous support modified with 6 wt % SPSf in the presence of DMF and 14 wt % PSf had higher porosity, bigger pore diameter, and a rougher and more hydrophilic surface, which was more beneficial for fabrication of a polyamide TFC membrane with favorable reverse osmosis performance. This modified PSf/SPSf support endowed the RO membrane with a more hydrophilic surface, higher water flux (about 1.2 times, as well as a slight increase in salt rejection than the nascent PSf support. In a word, this work provides a new facile method to improve the separation performance of polyamide TFC RO membranes via the modification of conventional PSf porous support with SPSf.

Performance measurement of PSF modeling reconstruction (True X) on Siemens Biograph TruePoint TrueV PET/CT.

Science.gov (United States)

Lee, Young Sub; Kim, Jin Su; Kim, Kyeong Min; Kang, Joo Hyun; Lim, Sang Moo; Kim, Hee-Joung

2014-05-01

The Siemens Biograph TruePoint TrueV (B-TPTV) positron emission tomography (PET) scanner performs 3D PET reconstruction using a system matrix with point spread function (PSF) modeling (called the True X reconstruction). PET resolution was dramatically improved with the True X method. In this study, we assessed the spatial resolution and image quality on a B-TPTV PET scanner. In addition, we assessed the feasibility of animal imaging with a B-TPTV PET and compared it with a microPET R4 scanner. Spatial resolution was measured at center and at 8 cm offset from the center in transverse plane with warm background activity. True X, ordered subset expectation maximization (OSEM) without PSF modeling, and filtered back-projection (FBP) reconstruction methods were used. Percent contrast (% contrast) and percent background variability (% BV) were assessed according to NEMA NU2-2007. The recovery coefficient (RC), non-uniformity, spill-over ratio (SOR), and PET imaging of the Micro Deluxe Phantom were assessed to compare image quality of B-TPTV PET with that of the microPET R4. When True X reconstruction was used, spatial resolution was RC with True X reconstruction was higher than that with the FBP method and the OSEM without PSF modeling method on the microPET R4. The non-uniformity with True X reconstruction was higher than that with FBP and OSEM without PSF modeling on microPET R4. SOR with True X reconstruction was better than that with FBP or OSEM without PSF modeling on the microPET R4. This study assessed the performance of the True X reconstruction. Spatial resolution with True X reconstruction was improved by 45 % and its % contrast was significantly improved compared to those with the conventional OSEM without PSF modeling reconstruction algorithm. The noise level was higher than that with the other reconstruction algorithm. Therefore, True X reconstruction should be used with caution when quantifying PET data.

A PSF-Shape-Based Beamforming Strategy for Robust 2D Motion Estimation in Ultrafast Data

Directory of Open Access Journals (Sweden)

Anne E. C. M. Saris

2018-03-01

Full Text Available This paper presents a framework for motion estimation in ultrafast ultrasound data. It describes a novel approach for determining the sampling grid for ultrafast data based on the system’s point-spread-function (PSF. As a consequence, the cross-correlation functions (CCF used in the speckle tracking (ST algorithm will have circular-shaped peaks, which can be interpolated using a 2D interpolation method to estimate subsample displacements. Carotid artery wall motion and parabolic blood flow simulations together with rotating disk experiments using a Verasonics Vantage 256 are used for performance evaluation. Zero-degree plane wave data were acquired using an ATL L5-12 (fc = 9 MHz transducer for a range of pulse repetition frequencies (PRFs, resulting in 0–600 µm inter-frame displacements. The proposed methodology was compared to data beamformed on a conventionally spaced grid, combined with the commonly used 1D parabolic interpolation. The PSF-shape-based beamforming grid combined with 2D cubic interpolation showed the most accurate and stable performance with respect to the full range of inter-frame displacements, both for the assessment of blood flow and vessel wall dynamics. The proposed methodology can be used as a protocolled way to beamform ultrafast data and obtain accurate estimates of tissue motion.

Population Pharmacokinetics of Gemcitabine and dFdU in Pancreatic Cancer Patients Using an Optimal Design, Sparse Sampling Approach.

Science.gov (United States)

Serdjebi, Cindy; Gattacceca, Florence; Seitz, Jean-François; Fein, Francine; Gagnière, Johan; François, Eric; Abakar-Mahamat, Abakar; Deplanque, Gael; Rachid, Madani; Lacarelle, Bruno; Ciccolini, Joseph; Dahan, Laetitia

2017-06-01

Gemcitabine remains a pillar in pancreatic cancer treatment. However, toxicities are frequently observed. Dose adjustment based on therapeutic drug monitoring might help decrease the occurrence of toxicities. In this context, this work aims at describing the pharmacokinetics (PK) of gemcitabine and its metabolite dFdU in pancreatic cancer patients and at identifying the main sources of their PK variability using a population PK approach, despite a sparse sampled-population and heterogeneous administration and sampling protocols. Data from 38 patients were included in the analysis. The 3 optimal sampling times were determined using KineticPro and the population PK analysis was performed on Monolix. Available patient characteristics, including cytidine deaminase (CDA) status, were tested as covariates. Correlation between PK parameters and occurrence of severe hematological toxicities was also investigated. A two-compartment model best fitted the gemcitabine and dFdU PK data (volume of distribution and clearance for gemcitabine: V1 = 45 L and CL1 = 4.03 L/min; for dFdU: V2 = 36 L and CL2 = 0.226 L/min). Renal function was found to influence gemcitabine clearance, and body surface area to impact the volume of distribution of dFdU. However, neither CDA status nor the occurrence of toxicities was correlated to PK parameters. Despite sparse sampling and heterogeneous administration and sampling protocols, population and individual PK parameters of gemcitabine and dFdU were successfully estimated using Monolix population PK software. The estimated parameters were consistent with previously published results. Surprisingly, CDA activity did not influence gemcitabine PK, which was explained by the absence of CDA-deficient patients enrolled in the study. This work suggests that even sparse data are valuable to estimate population and individual PK parameters in patients, which will be usable to individualize the dose for an optimized benefit to risk ratio.

Correlating PSf Support Physicochemical Properties with the Formation of Piperazine-Based Polyamide and Evaluating the Resultant Nanofiltration Membrane Performance

Directory of Open Access Journals (Sweden)

Micah Belle Marie Yap Ang

2017-10-01

Full Text Available Membrane support properties influence the performance of thin-film composite nanofiltration membranes. We fabricated several polysulfone (PSf supports. The physicochemical properties of PSf were altered by adding polyethylene glycol (PEG of varying molecular weights (200–35,000 g/mol. This alteration facilitated the formation of a thin polyamide layer on the PSf surface during the interfacial polymerization reaction involving an aqueous solution of piperazine containing 4-aminobenzoic acid and an organic solution of trimesoyl chloride. Attenuated total reflectance-Fourier transform infrared validated the presence of PEG in the membrane support. Scanning electron microscopy and atomic force microscopy illustrated that the thin-film polyamide layer morphology transformed from a rough to a smooth surface. A cross-flow filtration test indicated that a thin-film composite polyamide membrane comprising a PSf support (TFC-PEG20k with a low surface porosity, small pore size, and suitable hydrophilicity delivered the highest water flux and separation efficiency (J = 81.1 ± 6.4 L·m−2·h−1, RNa2SO4 = 91.1% ± 1.8%, and RNaCl = 35.7% ± 3.1% at 0.60 MPa. This membrane had a molecular weight cutoff of 292 g/mol and also a high rejection for negatively charged dyes. Therefore, a PSf support exhibiting suitable physicochemical properties endowed a thin-film composite polyamide membrane with high performance.

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.

Sparse Representation Based SAR Vehicle Recognition along with Aspect Angle

Directory of Open Access Journals (Sweden)

Xiangwei Xing

2014-01-01

Full Text Available As a method of representing the test sample with few training samples from an overcomplete dictionary, sparse representation classification (SRC has attracted much attention in synthetic aperture radar (SAR automatic target recognition (ATR recently. In this paper, we develop a novel SAR vehicle recognition method based on sparse representation classification along with aspect information (SRCA, in which the correlation between the vehicle’s aspect angle and the sparse representation vector is exploited. The detailed procedure presented in this paper can be summarized as follows. Initially, the sparse representation vector of a test sample is solved by sparse representation algorithm with a principle component analysis (PCA feature-based dictionary. Then, the coefficient vector is projected onto a sparser one within a certain range of the vehicle’s aspect angle. Finally, the vehicle is classified into a certain category that minimizes the reconstruction error with the novel sparse representation vector. Extensive experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR dataset and the results demonstrate that the proposed method performs robustly under the variations of depression angle and target configurations, as well as incomplete observation.

PERIOD ESTIMATION FOR SPARSELY SAMPLED QUASI-PERIODIC LIGHT CURVES APPLIED TO MIRAS

Energy Technology Data Exchange (ETDEWEB)

He, Shiyuan; Huang, Jianhua Z.; Long, James [Department of Statistics, Texas A and M University, College Station, TX (United States); Yuan, Wenlong; Macri, Lucas M., E-mail: lmacri@tamu.edu [George P. and Cynthia W. Mitchell Institute for Fundamental Physics and Astronomy, Department of Physics and Astronomy, Texas A and M University, College Station, TX (United States)

2016-12-01

We develop a nonlinear semi-parametric Gaussian process model to estimate periods of Miras with sparsely sampled light curves. The model uses a sinusoidal basis for the periodic variation and a Gaussian process for the stochastic changes. We use maximum likelihood to estimate the period and the parameters of the Gaussian process, while integrating out the effects of other nuisance parameters in the model with respect to a suitable prior distribution obtained from earlier studies. Since the likelihood is highly multimodal for period, we implement a hybrid method that applies the quasi-Newton algorithm for Gaussian process parameters and search the period/frequency parameter space over a dense grid. A large-scale, high-fidelity simulation is conducted to mimic the sampling quality of Mira light curves obtained by the M33 Synoptic Stellar Survey. The simulated data set is publicly available and can serve as a testbed for future evaluation of different period estimation methods. The semi-parametric model outperforms an existing algorithm on this simulated test data set as measured by period recovery rate and quality of the resulting period–luminosity relations.

Accuracy of lung nodule density on HRCT: analysis by PSF-based image simulation.

Science.gov (United States)

Ohno, Ken; Ohkubo, Masaki; Marasinghe, Janaka C; Murao, Kohei; Matsumoto, Toru; Wada, Shinichi

2012-11-08

A computed tomography (CT) image simulation technique based on the point spread function (PSF) was applied to analyze the accuracy of CT-based clinical evaluations of lung nodule density. The PSF of the CT system was measured and used to perform the lung nodule image simulation. Then, the simulated image was resampled at intervals equal to the pixel size and the slice interval found in clinical high-resolution CT (HRCT) images. On those images, the nodule density was measured by placing a region of interest (ROI) commonly used for routine clinical practice, and comparing the measured value with the true value (a known density of object function used in the image simulation). It was quantitatively determined that the measured nodule density depended on the nodule diameter and the image reconstruction parameters (kernel and slice thickness). In addition, the measured density fluctuated, depending on the offset between the nodule center and the image voxel center. This fluctuation was reduced by decreasing the slice interval (i.e., with the use of overlapping reconstruction), leading to a stable density evaluation. Our proposed method of PSF-based image simulation accompanied with resampling enables a quantitative analysis of the accuracy of CT-based evaluations of lung nodule density. These results could potentially reveal clinical misreadings in diagnosis, and lead to more accurate and precise density evaluations. They would also be of value for determining the optimum scan and reconstruction parameters, such as image reconstruction kernels and slice thicknesses/intervals.

Feature selection and multi-kernel learning for sparse representation on a manifold

KAUST Repository

Wang, Jim Jing-Yan

2014-03-01

Sparse representation has been widely studied as a part-based data representation method and applied in many scientific and engineering fields, such as bioinformatics and medical imaging. It seeks to represent a data sample as a sparse linear combination of some basic items in a dictionary. Gao etal. (2013) recently proposed Laplacian sparse coding by regularizing the sparse codes with an affinity graph. However, due to the noisy features and nonlinear distribution of the data samples, the affinity graph constructed directly from the original feature space is not necessarily a reliable reflection of the intrinsic manifold of the data samples. To overcome this problem, we integrate feature selection and multiple kernel learning into the sparse coding on the manifold. To this end, unified objectives are defined for feature selection, multiple kernel learning, sparse coding, and graph regularization. By optimizing the objective functions iteratively, we develop novel data representation algorithms with feature selection and multiple kernel learning respectively. Experimental results on two challenging tasks, N-linked glycosylation prediction and mammogram retrieval, demonstrate that the proposed algorithms outperform the traditional sparse coding methods. © 2013 Elsevier Ltd.

Feature selection and multi-kernel learning for sparse representation on a manifold.

Science.gov (United States)

Wang, Jim Jing-Yan; Bensmail, Halima; Gao, Xin

2014-03-01

Sparse representation has been widely studied as a part-based data representation method and applied in many scientific and engineering fields, such as bioinformatics and medical imaging. It seeks to represent a data sample as a sparse linear combination of some basic items in a dictionary. Gao et al. (2013) recently proposed Laplacian sparse coding by regularizing the sparse codes with an affinity graph. However, due to the noisy features and nonlinear distribution of the data samples, the affinity graph constructed directly from the original feature space is not necessarily a reliable reflection of the intrinsic manifold of the data samples. To overcome this problem, we integrate feature selection and multiple kernel learning into the sparse coding on the manifold. To this end, unified objectives are defined for feature selection, multiple kernel learning, sparse coding, and graph regularization. By optimizing the objective functions iteratively, we develop novel data representation algorithms with feature selection and multiple kernel learning respectively. Experimental results on two challenging tasks, N-linked glycosylation prediction and mammogram retrieval, demonstrate that the proposed algorithms outperform the traditional sparse coding methods. Copyright © 2013 Elsevier Ltd. All rights reserved.

Sparse dictionary for synthetic transmit aperture medical ultrasound imaging.

Science.gov (United States)

Wang, Ping; Jiang, Jin-Yang; Li, Na; Luo, Han-Wu; Li, Fang; Cui, Shi-Gang

2017-07-01

It is possible to recover a signal below the Nyquist sampling limit using a compressive sensing technique in ultrasound imaging. However, the reconstruction enabled by common sparse transform approaches does not achieve satisfactory results. Considering the ultrasound echo signal's features of attenuation, repetition, and superposition, a sparse dictionary with the emission pulse signal is proposed. Sparse coefficients in the proposed dictionary have high sparsity. Images reconstructed with this dictionary were compared with those obtained with the three other common transforms, namely, discrete Fourier transform, discrete cosine transform, and discrete wavelet transform. The performance of the proposed dictionary was analyzed via a simulation and experimental data. The mean absolute error (MAE) was used to quantify the quality of the reconstructions. Experimental results indicate that the MAE associated with the proposed dictionary was always the smallest, the reconstruction time required was the shortest, and the lateral resolution and contrast of the reconstructed images were also the closest to the original images. The proposed sparse dictionary performed better than the other three sparse transforms. With the same sampling rate, the proposed dictionary achieved excellent reconstruction quality.

Evaluation of a Class of Simple and Effective Uncertainty Methods for Sparse Samples of Random Variables and Functions

Energy Technology Data Exchange (ETDEWEB)

Romero, Vicente [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Bonney, Matthew [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Schroeder, Benjamin [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Weirs, V. Gregory [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2017-11-01

When very few samples of a random quantity are available from a source distribution of unknown shape, it is usually not possible to accurately infer the exact distribution from which the data samples come. Under-estimation of important quantities such as response variance and failure probabilities can result. For many engineering purposes, including design and risk analysis, we attempt to avoid under-estimation with a strategy to conservatively estimate (bound) these types of quantities -- without being overly conservative -- when only a few samples of a random quantity are available from model predictions or replicate experiments. This report examines a class of related sparse-data uncertainty representation and inference approaches that are relatively simple, inexpensive, and effective. Tradeoffs between the methods' conservatism, reliability, and risk versus number of data samples (cost) are quantified with multi-attribute metrics use d to assess method performance for conservative estimation of two representative quantities: central 95% of response; and 10^-4 probability of exceeding a response threshold in a tail of the distribution. Each method's performance is characterized with 10,000 random trials on a large number of diverse and challenging distributions. The best method and number of samples to use in a given circumstance depends on the uncertainty quantity to be estimated, the PDF character, and the desired reliability of bounding the true value. On the basis of this large data base and study, a strategy is proposed for selecting the method and number of samples for attaining reasonable credibility levels in bounding these types of quantities when sparse samples of random variables or functions are available from experiments or simulations.

Near-field acoustic holography using sparse regularization and compressive sampling principles.

Science.gov (United States)

Chardon, Gilles; Daudet, Laurent; Peillot, Antoine; Ollivier, François; Bertin, Nancy; Gribonval, Rémi

2012-09-01

Regularization of the inverse problem is a complex issue when using near-field acoustic holography (NAH) techniques to identify the vibrating sources. This paper shows that, for convex homogeneous plates with arbitrary boundary conditions, alternative regularization schemes can be developed based on the sparsity of the normal velocity of the plate in a well-designed basis, i.e., the possibility to approximate it as a weighted sum of few elementary basis functions. In particular, these techniques can handle discontinuities of the velocity field at the boundaries, which can be problematic with standard techniques. This comes at the cost of a higher computational complexity to solve the associated optimization problem, though it remains easily tractable with out-of-the-box software. Furthermore, this sparsity framework allows us to take advantage of the concept of compressive sampling; under some conditions on the sampling process (here, the design of a random array, which can be numerically and experimentally validated), it is possible to reconstruct the sparse signals with significantly less measurements (i.e., microphones) than classically required. After introducing the different concepts, this paper presents numerical and experimental results of NAH with two plate geometries, and compares the advantages and limitations of these sparsity-based techniques over standard Tikhonov regularization.

Sparse Frequency Waveform Design for Radar-Embedded Communication

Directory of Open Access Journals (Sweden)

Chaoyun Mai

2016-01-01

Full Text Available According to the Tag application with function of covert communication, a method for sparse frequency waveform design based on radar-embedded communication is proposed. Firstly, sparse frequency waveforms are designed based on power spectral density fitting and quasi-Newton method. Secondly, the eigenvalue decomposition of the sparse frequency waveform sequence is used to get the dominant space. Finally the communication waveforms are designed through the projection of orthogonal pseudorandom vectors in the vertical subspace. Compared with the linear frequency modulation waveform, the sparse frequency waveform can further improve the bandwidth occupation of communication signals, thus achieving higher communication rate. A certain correlation exists between the reciprocally orthogonal communication signals samples and the sparse frequency waveform, which guarantees the low SER (signal error rate and LPI (low probability of intercept. The simulation results verify the effectiveness of this method.

Spatial resolution of the HRRT PET scanner using 3D-OSEM PSF reconstruction

DEFF Research Database (Denmark)

Olesen, Oline Vinter; Sibomana, Merence; Keller, Sune Høgild

2009-01-01

The spatial resolution of the Siemens High Resolution Research Tomograph (HRRT) dedicated brain PET scanner installed at Copenhagen University Hospital (Rigshospitalet) was measured using a point-source phantom with high statistics. Further, it was demonstrated how the newly developed 3D-OSEM PSF...

Noise and signal properties in PSF-based fully 3D PET image reconstruction: an experimental evaluation

International Nuclear Information System (INIS)

Tong, S; Alessio, A M; Kinahan, P E

2010-01-01

The addition of accurate system modeling in PET image reconstruction results in images with distinct noise texture and characteristics. In particular, the incorporation of point spread functions (PSF) into the system model has been shown to visually reduce image noise, but the noise properties have not been thoroughly studied. This work offers a systematic evaluation of noise and signal properties in different combinations of reconstruction methods and parameters. We evaluate two fully 3D PET reconstruction algorithms: (1) OSEM with exact scanner line of response modeled (OSEM+LOR), (2) OSEM with line of response and a measured point spread function incorporated (OSEM+LOR+PSF), in combination with the effects of four post-reconstruction filtering parameters and 1-10 iterations, representing a range of clinically acceptable settings. We used a modified NEMA image quality (IQ) phantom, which was filled with 68 Ge and consisted of six hot spheres of different sizes with a target/background ratio of 4:1. The phantom was scanned 50 times in 3D mode on a clinical system to provide independent noise realizations. Data were reconstructed with OSEM+LOR and OSEM+LOR+PSF using different reconstruction parameters, and our implementations of the algorithms match the vendor's product algorithms. With access to multiple realizations, background noise characteristics were quantified with four metrics. Image roughness and the standard deviation image measured the pixel-to-pixel variation; background variability and ensemble noise quantified the region-to-region variation. Image roughness is the image noise perceived when viewing an individual image. At matched iterations, the addition of PSF leads to images with less noise defined as image roughness (reduced by 35% for unfiltered data) and as the standard deviation image, while it has no effect on background variability or ensemble noise. In terms of signal to noise performance, PSF-based reconstruction has a 7% improvement in

Distributed fiber sparse-wideband vibration sensing by sub-Nyquist additive random sampling

Science.gov (United States)

Zhang, Jingdong; Zheng, Hua; Zhu, Tao; Yin, Guolu; Liu, Min; Bai, Yongzhong; Qu, Dingrong; Qiu, Feng; Huang, Xianbing

2018-05-01

The round trip time of the light pulse limits the maximum detectable vibration frequency response range of phase-sensitive optical time domain reflectometry ({\\phi}-OTDR). Unlike the uniform laser pulse interval in conventional {\\phi}-OTDR, we randomly modulate the pulse interval, so that an equivalent sub-Nyquist additive random sampling (sNARS) is realized for every sensing point of the long interrogation fiber. For an {\\phi}-OTDR system with 10 km sensing length, the sNARS method is optimized by theoretical analysis and Monte Carlo simulation, and the experimental results verify that a wide-band spars signal can be identified and reconstructed. Such a method can broaden the vibration frequency response range of {\\phi}-OTDR, which is of great significance in sparse-wideband-frequency vibration signal detection, such as rail track monitoring and metal defect detection.

Hierarchical Bayesian sparse image reconstruction with application to MRFM.

Science.gov (United States)

Dobigeon, Nicolas; Hero, Alfred O; Tourneret, Jean-Yves

2009-09-01

This paper presents a hierarchical Bayesian model to reconstruct sparse images when the observations are obtained from linear transformations and corrupted by an additive white Gaussian noise. Our hierarchical Bayes model is well suited to such naturally sparse image applications as it seamlessly accounts for properties such as sparsity and positivity of the image via appropriate Bayes priors. We propose a prior that is based on a weighted mixture of a positive exponential distribution and a mass at zero. The prior has hyperparameters that are tuned automatically by marginalization over the hierarchical Bayesian model. To overcome the complexity of the posterior distribution, a Gibbs sampling strategy is proposed. The Gibbs samples can be used to estimate the image to be recovered, e.g., by maximizing the estimated posterior distribution. In our fully Bayesian approach, the posteriors of all the parameters are available. Thus, our algorithm provides more information than other previously proposed sparse reconstruction methods that only give a point estimate. The performance of the proposed hierarchical Bayesian sparse reconstruction method is illustrated on synthetic data and real data collected from a tobacco virus sample using a prototype MRFM instrument.

In Defense of Sparse Tracking: Circulant Sparse Tracker

KAUST Repository

Zhang, Tianzhu; Bibi, Adel Aamer; Ghanem, Bernard

2016-01-01

Sparse representation has been introduced to visual tracking by finding the best target candidate with minimal reconstruction error within the particle filter framework. However, most sparse representation based trackers have high computational cost, less than promising tracking performance, and limited feature representation. To deal with the above issues, we propose a novel circulant sparse tracker (CST), which exploits circulant target templates. Because of the circulant structure property, CST has the following advantages: (1) It can refine and reduce particles using circular shifts of target templates. (2) The optimization can be efficiently solved entirely in the Fourier domain. (3) High dimensional features can be embedded into CST to significantly improve tracking performance without sacrificing much computation time. Both qualitative and quantitative evaluations on challenging benchmark sequences demonstrate that CST performs better than all other sparse trackers and favorably against state-of-the-art methods.

In Defense of Sparse Tracking: Circulant Sparse Tracker

KAUST Repository

Zhang, Tianzhu

2016-12-13

Sparse representation has been introduced to visual tracking by finding the best target candidate with minimal reconstruction error within the particle filter framework. However, most sparse representation based trackers have high computational cost, less than promising tracking performance, and limited feature representation. To deal with the above issues, we propose a novel circulant sparse tracker (CST), which exploits circulant target templates. Because of the circulant structure property, CST has the following advantages: (1) It can refine and reduce particles using circular shifts of target templates. (2) The optimization can be efficiently solved entirely in the Fourier domain. (3) High dimensional features can be embedded into CST to significantly improve tracking performance without sacrificing much computation time. Both qualitative and quantitative evaluations on challenging benchmark sequences demonstrate that CST performs better than all other sparse trackers and favorably against state-of-the-art methods.

Sparse PCA with Oracle Property.

Science.gov (United States)

Gu, Quanquan; Wang, Zhaoran; Liu, Han

In this paper, we study the estimation of the k -dimensional sparse principal subspace of covariance matrix Σ in the high-dimensional setting. We aim to recover the oracle principal subspace solution, i.e., the principal subspace estimator obtained assuming the true support is known a priori. To this end, we propose a family of estimators based on the semidefinite relaxation of sparse PCA with novel regularizations. In particular, under a weak assumption on the magnitude of the population projection matrix, one estimator within this family exactly recovers the true support with high probability, has exact rank- k , and attains a [Formula: see text] statistical rate of convergence with s being the subspace sparsity level and n the sample size. Compared to existing support recovery results for sparse PCA, our approach does not hinge on the spiked covariance model or the limited correlation condition. As a complement to the first estimator that enjoys the oracle property, we prove that, another estimator within the family achieves a sharper statistical rate of convergence than the standard semidefinite relaxation of sparse PCA, even when the previous assumption on the magnitude of the projection matrix is violated. We validate the theoretical results by numerical experiments on synthetic datasets.

Machine-learned Identification of RR Lyrae Stars from Sparse, Multi-band Data: The PS1 Sample

Science.gov (United States)

Sesar, Branimir; Hernitschek, Nina; Mitrović, Sandra; Ivezić, Željko; Rix, Hans-Walter; Cohen, Judith G.; Bernard, Edouard J.; Grebel, Eva K.; Martin, Nicolas F.; Schlafly, Edward F.; Burgett, William S.; Draper, Peter W.; Flewelling, Heather; Kaiser, Nick; Kudritzki, Rolf P.; Magnier, Eugene A.; Metcalfe, Nigel; Tonry, John L.; Waters, Christopher

2017-05-01

RR Lyrae stars may be the best practical tracers of Galactic halo (sub-)structure and kinematics. The PanSTARRS1 (PS1) 3π survey offers multi-band, multi-epoch, precise photometry across much of the sky, but a robust identification of RR Lyrae stars in this data set poses a challenge, given PS1's sparse, asynchronous multi-band light curves (≲ 12 epochs in each of five bands, taken over a 4.5 year period). We present a novel template fitting technique that uses well-defined and physically motivated multi-band light curves of RR Lyrae stars, and demonstrate that we get accurate period estimates, precise to 2 s in > 80 % of cases. We augment these light-curve fits with other features from photometric time-series and provide them to progressively more detailed machine-learned classification models. From these models, we are able to select the widest (three-fourths of the sky) and deepest (reaching 120 kpc) sample of RR Lyrae stars to date. The PS1 sample of ˜45,000 RRab stars is pure (90%) and complete (80% at 80 kpc) at high galactic latitudes. It also provides distances that are precise to 3%, measured with newly derived period-luminosity relations for optical/near-infrared PS1 bands. With the addition of proper motions from Gaia and radial velocity measurements from multi-object spectroscopic surveys, we expect the PS1 sample of RR Lyrae stars to become the premier source for studying the structure, kinematics, and the gravitational potential of the Galactic halo. The techniques presented in this study should translate well to other sparse, multi-band data sets, such as those produced by the Dark Energy Survey and the upcoming Large Synoptic Survey Telescope Galactic plane sub-survey.

Exhaustive Search for Sparse Variable Selection in Linear Regression

Science.gov (United States)

Igarashi, Yasuhiko; Takenaka, Hikaru; Nakanishi-Ohno, Yoshinori; Uemura, Makoto; Ikeda, Shiro; Okada, Masato

2018-04-01

We propose a K-sparse exhaustive search (ES-K) method and a K-sparse approximate exhaustive search method (AES-K) for selecting variables in linear regression. With these methods, K-sparse combinations of variables are tested exhaustively assuming that the optimal combination of explanatory variables is K-sparse. By collecting the results of exhaustively computing ES-K, various approximate methods for selecting sparse variables can be summarized as density of states. With this density of states, we can compare different methods for selecting sparse variables such as relaxation and sampling. For large problems where the combinatorial explosion of explanatory variables is crucial, the AES-K method enables density of states to be effectively reconstructed by using the replica-exchange Monte Carlo method and the multiple histogram method. Applying the ES-K and AES-K methods to type Ia supernova data, we confirmed the conventional understanding in astronomy when an appropriate K is given beforehand. However, we found the difficulty to determine K from the data. Using virtual measurement and analysis, we argue that this is caused by data shortage.

Fast Markov chain Monte Carlo sampling for sparse Bayesian inference in high-dimensional inverse problems using L1-type priors

International Nuclear Information System (INIS)

Lucka, Felix

2012-01-01

Sparsity has become a key concept for solving of high-dimensional inverse problems using variational regularization techniques. Recently, using similar sparsity-constraints in the Bayesian framework for inverse problems by encoding them in the prior distribution has attracted attention. Important questions about the relation between regularization theory and Bayesian inference still need to be addressed when using sparsity promoting inversion. A practical obstacle for these examinations is the lack of fast posterior sampling algorithms for sparse, high-dimensional Bayesian inversion. Accessing the full range of Bayesian inference methods requires being able to draw samples from the posterior probability distribution in a fast and efficient way. This is usually done using Markov chain Monte Carlo (MCMC) sampling algorithms. In this paper, we develop and examine a new implementation of a single component Gibbs MCMC sampler for sparse priors relying on L1-norms. We demonstrate that the efficiency of our Gibbs sampler increases when the level of sparsity or the dimension of the unknowns is increased. This property is contrary to the properties of the most commonly applied Metropolis–Hastings (MH) sampling schemes. We demonstrate that the efficiency of MH schemes for L1-type priors dramatically decreases when the level of sparsity or the dimension of the unknowns is increased. Practically, Bayesian inversion for L1-type priors using MH samplers is not feasible at all. As this is commonly believed to be an intrinsic feature of MCMC sampling, the performance of our Gibbs sampler also challenges common beliefs about the applicability of sample based Bayesian inference. (paper)

Is multidetector CT-based bone mineral density and quantitative bone microstructure assessment at the spine still feasible using ultra-low tube current and sparse sampling?

International Nuclear Information System (INIS)

Mei, Kai; Kopp, Felix K.; Schwaiger, Benedikt J.; Gersing, Alexandra S.; Sauter, Andreas; Muenzel, Daniela; Rummeny, Ernst J.; Bippus, Rolf; Koehler, Thomas; Fehringer, Andreas; Pfeiffer, Franz; Kirschke, Jan S.; Noel, Peter B.; Baum, Thomas

2017-01-01

Osteoporosis diagnosis using multidetector CT (MDCT) is limited to relatively high radiation exposure. We investigated the effect of simulated ultra-low-dose protocols on in-vivo bone mineral density (BMD) and quantitative trabecular bone assessment. Institutional review board approval was obtained. Twelve subjects with osteoporotic vertebral fractures and 12 age- and gender-matched controls undergoing routine thoracic and abdominal MDCT were included (average effective dose: 10 mSv). Ultra-low radiation examinations were achieved by simulating lower tube currents and sparse samplings at 50%, 25% and 10% of the original dose. BMD and trabecular bone parameters were extracted in T10-L5. Except for BMD measurements in sparse sampling data, absolute values of all parameters derived from ultra-low-dose data were significantly different from those derived from original dose images (p<0.05). BMD, apparent bone fraction and trabecular thickness were still consistently lower in subjects with than in those without fractures (p<0.05). In ultra-low-dose scans, BMD and microstructure parameters were able to differentiate subjects with and without vertebral fractures, suggesting osteoporosis diagnosis is feasible. However, absolute values differed from original values. BMD from sparse sampling appeared to be more robust. This dose-dependency of parameters should be considered for future clinical use. (orig.)

Is multidetector CT-based bone mineral density and quantitative bone microstructure assessment at the spine still feasible using ultra-low tube current and sparse sampling?

Energy Technology Data Exchange (ETDEWEB)

Mei, Kai; Kopp, Felix K.; Schwaiger, Benedikt J.; Gersing, Alexandra S.; Sauter, Andreas; Muenzel, Daniela; Rummeny, Ernst J. [Klinikum rechts der Isar, Technische Universitaet Muenchen, Department of Diagnostic and Interventional Radiology, Munich (Germany); Bippus, Rolf [Research Laboratories, Philips GmbH Innovative Technologies, Hamburg (Germany); Koehler, Thomas [Research Laboratories, Philips GmbH Innovative Technologies, Hamburg (Germany); Technische Universitaet Muenchen, TUM Institute for Advanced Studies, Garching (Germany); Fehringer, Andreas [Technische Universitaet Muenchen, Lehrstuhl fuer Biomedizinische Physik, Garching (Germany); Pfeiffer, Franz [Klinikum rechts der Isar, Technische Universitaet Muenchen, Department of Diagnostic and Interventional Radiology, Munich (Germany); Technische Universitaet Muenchen, TUM Institute for Advanced Studies, Garching (Germany); Technische Universitaet Muenchen, Lehrstuhl fuer Biomedizinische Physik, Garching (Germany); Kirschke, Jan S. [Klinikum rechts der Isar, Technische Universitaet Muenchen, Section of Diagnostic and Interventional Neuroradiology, Munich (Germany); Noel, Peter B. [Klinikum rechts der Isar, Technische Universitaet Muenchen, Department of Diagnostic and Interventional Radiology, Munich (Germany); Technische Universitaet Muenchen, Lehrstuhl fuer Biomedizinische Physik, Garching (Germany); Baum, Thomas [Klinikum rechts der Isar, Technische Universitaet Muenchen, Department of Diagnostic and Interventional Radiology, Munich (Germany); Klinikum rechts der Isar, Technische Universitaet Muenchen, Section of Diagnostic and Interventional Neuroradiology, Munich (Germany)

2017-12-15

Osteoporosis diagnosis using multidetector CT (MDCT) is limited to relatively high radiation exposure. We investigated the effect of simulated ultra-low-dose protocols on in-vivo bone mineral density (BMD) and quantitative trabecular bone assessment. Institutional review board approval was obtained. Twelve subjects with osteoporotic vertebral fractures and 12 age- and gender-matched controls undergoing routine thoracic and abdominal MDCT were included (average effective dose: 10 mSv). Ultra-low radiation examinations were achieved by simulating lower tube currents and sparse samplings at 50%, 25% and 10% of the original dose. BMD and trabecular bone parameters were extracted in T10-L5. Except for BMD measurements in sparse sampling data, absolute values of all parameters derived from ultra-low-dose data were significantly different from those derived from original dose images (p<0.05). BMD, apparent bone fraction and trabecular thickness were still consistently lower in subjects with than in those without fractures (p<0.05). In ultra-low-dose scans, BMD and microstructure parameters were able to differentiate subjects with and without vertebral fractures, suggesting osteoporosis diagnosis is feasible. However, absolute values differed from original values. BMD from sparse sampling appeared to be more robust. This dose-dependency of parameters should be considered for future clinical use. (orig.)

Karlsruhe Research Center, Nuclear Safety Research Project (PSF). Annual report 1994

International Nuclear Information System (INIS)

Hueper, R.

1995-08-01

The reactor safety R and D work of the Karlsruhe Research Centre (FZKA) has been part of the Nuclear Safety Research Projet (PSF) since 1990. The present annual report 1994 summarizes the R and D results. The research tasks are coordinated in agreement with internal and external working groups. The contributions to this report correspond to the status of early 1995. An abstract in English precedes each of them, whenever the respective article is written in German. (orig.) [de

The application of sparse linear prediction dictionary to compressive sensing in speech signals

Directory of Open Access Journals (Sweden)

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.

An Improved Sparse Representation over Learned Dictionary Method for Seizure Detection.

Science.gov (United States)

Li, Junhui; Zhou, Weidong; Yuan, Shasha; Zhang, Yanli; Li, Chengcheng; Wu, Qi

2016-02-01

Automatic seizure detection has played an important role in the monitoring, diagnosis and treatment of epilepsy. In this paper, a patient specific method is proposed for seizure detection in the long-term intracranial electroencephalogram (EEG) recordings. This seizure detection method is based on sparse representation with online dictionary learning and elastic net constraint. The online learned dictionary could sparsely represent the testing samples more accurately, and the elastic net constraint which combines the 11-norm and 12-norm not only makes the coefficients sparse but also avoids over-fitting problem. First, the EEG signals are preprocessed using wavelet filtering and differential filtering, and the kernel function is applied to make the samples closer to linearly separable. Then the dictionaries of seizure and nonseizure are respectively learned from original ictal and interictal training samples with online dictionary optimization algorithm to compose the training dictionary. After that, the test samples are sparsely coded over the learned dictionary and the residuals associated with ictal and interictal sub-dictionary are calculated, respectively. Eventually, the test samples are classified as two distinct categories, seizure or nonseizure, by comparing the reconstructed residuals. The average segment-based sensitivity of 95.45%, specificity of 99.08%, and event-based sensitivity of 94.44% with false detection rate of 0.23/h and average latency of -5.14 s have been achieved with our proposed method.

Structural Sparse Tracking

KAUST Repository

Zhang, Tianzhu

2015-06-01

Sparse representation has been applied to visual tracking by finding the best target candidate with minimal reconstruction error by use of target templates. However, most sparse representation based trackers only consider holistic or local representations and do not make full use of the intrinsic structure among and inside target candidates, thereby making the representation less effective when similar objects appear or under occlusion. In this paper, we propose a novel Structural Sparse Tracking (SST) algorithm, which not only exploits the intrinsic relationship among target candidates and their local patches to learn their sparse representations jointly, but also preserves the spatial layout structure among the local patches inside each target candidate. We show that our SST algorithm accommodates most existing sparse trackers with the respective merits. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed SST algorithm performs favorably against several state-of-the-art methods.

Uncovering Transcriptional Regulatory Networks by Sparse Bayesian Factor Model

Directory of Open Access Journals (Sweden)

Qi Yuan(Alan

2010-01-01

Full Text Available Abstract The problem of uncovering transcriptional regulation by transcription factors (TFs based on microarray data is considered. A novel Bayesian sparse correlated rectified factor model (BSCRFM is proposed that models the unknown TF protein level activity, the correlated regulations between TFs, and the sparse nature of TF-regulated genes. The model admits prior knowledge from existing database regarding TF-regulated target genes based on a sparse prior and through a developed Gibbs sampling algorithm, a context-specific transcriptional regulatory network specific to the experimental condition of the microarray data can be obtained. The proposed model and the Gibbs sampling algorithm were evaluated on the simulated systems, and results demonstrated the validity and effectiveness of the proposed approach. The proposed model was then applied to the breast cancer microarray data of patients with Estrogen Receptor positive ( status and Estrogen Receptor negative ( status, respectively.

Magnetic Resonance Super-resolution Imaging Measurement with Dictionary-optimized Sparse Learning

Directory of Open Access Journals (Sweden)

Li Jun-Bao

2017-06-01

Full Text Available Magnetic Resonance Super-resolution Imaging Measurement (MRIM is an effective way of measuring materials. MRIM has wide applications in physics, chemistry, biology, geology, medical and material science, especially in medical diagnosis. It is feasible to improve the resolution of MR imaging through increasing radiation intensity, but the high radiation intensity and the longtime of magnetic field harm the human body. Thus, in the practical applications the resolution of hardware imaging reaches the limitation of resolution. Software-based super-resolution technology is effective to improve the resolution of image. This work proposes a framework of dictionary-optimized sparse learning based MR super-resolution method. The framework is to solve the problem of sample selection for dictionary learning of sparse reconstruction. The textural complexity-based image quality representation is proposed to choose the optimal samples for dictionary learning. Comprehensive experiments show that the dictionary-optimized sparse learning improves the performance of sparse representation.

Two-dimensional sparse wavenumber recovery for guided wavefields

Science.gov (United States)

Sabeti, Soroosh; Harley, Joel B.

2018-04-01

The multi-modal and dispersive behavior of guided waves is often characterized by their dispersion curves, which describe their frequency-wavenumber behavior. In prior work, compressive sensing based techniques, such as sparse wavenumber analysis (SWA), have been capable of recovering dispersion curves from limited data samples. A major limitation of SWA, however, is the assumption that the structure is isotropic. As a result, SWA fails when applied to composites and other anisotropic structures. There have been efforts to address this issue in the literature, but they either are not easily generalizable or do not sufficiently express the data. In this paper, we enhance the existing approaches by employing a two-dimensional wavenumber model to account for direction-dependent velocities in anisotropic media. We integrate this model with tools from compressive sensing to reconstruct a wavefield from incomplete data. Specifically, we create a modified two-dimensional orthogonal matching pursuit algorithm that takes an undersampled wavefield image, with specified unknown elements, and determines its sparse wavenumber characteristics. We then recover the entire wavefield from the sparse representations obtained with our small number of data samples.

Feature selection and multi-kernel learning for sparse representation on a manifold

KAUST Repository

Wang, Jim Jing-Yan; Bensmail, Halima; Gao, Xin

2014-01-01

combination of some basic items in a dictionary. Gao etal. (2013) recently proposed Laplacian sparse coding by regularizing the sparse codes with an affinity graph. However, due to the noisy features and nonlinear distribution of the data samples, the affinity

X-ray computed tomography using curvelet sparse regularization.

Science.gov (United States)

Wieczorek, Matthias; Frikel, Jürgen; Vogel, Jakob; Eggl, Elena; Kopp, Felix; Noël, Peter B; Pfeiffer, Franz; Demaret, Laurent; Lasser, Tobias

2015-04-01

Reconstruction of x-ray computed tomography (CT) data remains a mathematically challenging problem in medical imaging. Complementing the standard analytical reconstruction methods, sparse regularization is growing in importance, as it allows inclusion of prior knowledge. The paper presents a method for sparse regularization based on the curvelet frame for the application to iterative reconstruction in x-ray computed tomography. In this work, the authors present an iterative reconstruction approach based on the alternating direction method of multipliers using curvelet sparse regularization. Evaluation of the method is performed on a specifically crafted numerical phantom dataset to highlight the method's strengths. Additional evaluation is performed on two real datasets from commercial scanners with different noise characteristics, a clinical bone sample acquired in a micro-CT and a human abdomen scanned in a diagnostic CT. The results clearly illustrate that curvelet sparse regularization has characteristic strengths. In particular, it improves the restoration and resolution of highly directional, high contrast features with smooth contrast variations. The authors also compare this approach to the popular technique of total variation and to traditional filtered backprojection. The authors conclude that curvelet sparse regularization is able to improve reconstruction quality by reducing noise while preserving highly directional features.

Sparse linear models: Variational approximate inference and Bayesian experimental design

International Nuclear Information System (INIS)

Seeger, Matthias W

2009-01-01

A wide range of problems such as signal reconstruction, denoising, source separation, feature selection, and graphical model search are addressed today by posterior maximization for linear models with sparsity-favouring prior distributions. The Bayesian posterior contains useful information far beyond its mode, which can be used to drive methods for sampling optimization (active learning), feature relevance ranking, or hyperparameter estimation, if only this representation of uncertainty can be approximated in a tractable manner. In this paper, we review recent results for variational sparse inference, and show that they share underlying computational primitives. We discuss how sampling optimization can be implemented as sequential Bayesian experimental design. While there has been tremendous recent activity to develop sparse estimation, little attendance has been given to sparse approximate inference. In this paper, we argue that many problems in practice, such as compressive sensing for real-world image reconstruction, are served much better by proper uncertainty approximations than by ever more aggressive sparse estimation algorithms. Moreover, since some variational inference methods have been given strong convex optimization characterizations recently, theoretical analysis may become possible, promising new insights into nonlinear experimental design.

Sparse linear models: Variational approximate inference and Bayesian experimental design

Energy Technology Data Exchange (ETDEWEB)

Seeger, Matthias W [Saarland University and Max Planck Institute for Informatics, Campus E1.4, 66123 Saarbruecken (Germany)

2009-12-01

A wide range of problems such as signal reconstruction, denoising, source separation, feature selection, and graphical model search are addressed today by posterior maximization for linear models with sparsity-favouring prior distributions. The Bayesian posterior contains useful information far beyond its mode, which can be used to drive methods for sampling optimization (active learning), feature relevance ranking, or hyperparameter estimation, if only this representation of uncertainty can be approximated in a tractable manner. In this paper, we review recent results for variational sparse inference, and show that they share underlying computational primitives. We discuss how sampling optimization can be implemented as sequential Bayesian experimental design. While there has been tremendous recent activity to develop sparse estimation, little attendance has been given to sparse approximate inference. In this paper, we argue that many problems in practice, such as compressive sensing for real-world image reconstruction, are served much better by proper uncertainty approximations than by ever more aggressive sparse estimation algorithms. Moreover, since some variational inference methods have been given strong convex optimization characterizations recently, theoretical analysis may become possible, promising new insights into nonlinear experimental design.

Robust nonhomogeneous training samples detection method for space-time adaptive processing radar using sparse-recovery with knowledge-aided

Science.gov (United States)

Li, Zhihui; Liu, Hanwei; Zhang, Yongshun; Guo, Yiduo

2017-10-01

The performance of space-time adaptive processing (STAP) may degrade significantly when some of the training samples are contaminated by the signal-like components (outliers) in nonhomogeneous clutter environments. To remove the training samples contaminated by outliers in nonhomogeneous clutter environments, a robust nonhomogeneous training samples detection method using the sparse-recovery (SR) with knowledge-aided (KA) is proposed. First, the reduced-dimension (RD) overcomplete spatial-temporal steering dictionary is designed with the prior knowledge of system parameters and the possible target region. Then, the clutter covariance matrix (CCM) of cell under test is efficiently estimated using a modified focal underdetermined system solver (FOCUSS) algorithm, where a RD overcomplete spatial-temporal steering dictionary is applied. Third, the proposed statistics are formed by combining the estimated CCM with the generalized inner products (GIP) method, and the contaminated training samples can be detected and removed. Finally, several simulation results validate the effectiveness of the proposed KA-SR-GIP method.

Assessing human error during collecting a hydrocarbon sample of ...

African Journals Online (AJOL)

This paper reports the assessment method of the hydrocarbon sample collection standard operation procedure (SOP) using THERP. The Performance Shaping Factors (PSF) from THERP analyzed and assessed the human errors during collecting a hydrocarbon sample of a petrochemical refinery plant. Twenty-two ...

Sparse PDF Volumes for Consistent Multi-Resolution Volume Rendering

KAUST Repository

Sicat, Ronell Barrera

2014-12-31

This paper presents a new multi-resolution volume representation called sparse pdf volumes, which enables consistent multi-resolution volume rendering based on probability density functions (pdfs) of voxel neighborhoods. These pdfs are defined in the 4D domain jointly comprising the 3D volume and its 1D intensity range. Crucially, the computation of sparse pdf volumes exploits data coherence in 4D, resulting in a sparse representation with surprisingly low storage requirements. At run time, we dynamically apply transfer functions to the pdfs using simple and fast convolutions. Whereas standard low-pass filtering and down-sampling incur visible differences between resolution levels, the use of pdfs facilitates consistent results independent of the resolution level used. We describe the efficient out-of-core computation of large-scale sparse pdf volumes, using a novel iterative simplification procedure of a mixture of 4D Gaussians. Finally, our data structure is optimized to facilitate interactive multi-resolution volume rendering on GPUs.

An algorithm for 3D target scatterer feature estimation from sparse SAR apertures

Science.gov (United States)

Jackson, Julie Ann; Moses, Randolph L.

2009-05-01

We present an algorithm for extracting 3D canonical scattering features from complex targets observed over sparse 3D SAR apertures. The algorithm begins with complex phase history data and ends with a set of geometrical features describing the scene. The algorithm provides a pragmatic approach to initialization of a nonlinear feature estimation scheme, using regularization methods to deconvolve the point spread function and obtain sparse 3D images. Regions of high energy are detected in the sparse images, providing location initializations for scattering center estimates. A single canonical scattering feature, corresponding to a geometric shape primitive, is fit to each region via nonlinear optimization of fit error between the regularized data and parametric canonical scattering models. Results of the algorithm are presented using 3D scattering prediction data of a simple scene for both a densely-sampled and a sparsely-sampled SAR measurement aperture.

Technical innovation in dynamic contrast-enhanced magnetic resonance imaging of musculoskeletal tumors: an MR angiographic sequence using a sparse k-space sampling strategy.

Science.gov (United States)

Fayad, Laura M; Mugera, Charles; Soldatos, Theodoros; Flammang, Aaron; del Grande, Filippo

2013-07-01

We demonstrate the clinical use of an MR angiography sequence performed with sparse k-space sampling (MRA), as a method for dynamic contrast-enhanced (DCE)-MRI, and apply it to the assessment of sarcomas for treatment response. Three subjects with sarcomas (2 with osteosarcoma, 1 with high-grade soft tissue sarcomas) underwent MRI after neoadjuvant therapy/prior to surgery, with conventional MRI (T1-weighted, fluid-sensitive, static post-contrast T1-weighted sequences) and DCE-MRI (MRA, time resolution = 7-10 s, TR/TE 2.4/0.9 ms, FOV 40 cm(2)). Images were reviewed by two observers in consensus who recorded image quality (1 = diagnostic, no significant artifacts, 2 = diagnostic, 75 % with good response, >75 % with poor response). DCE-MRI findings were concordant with histological response (arterial enhancement with poor response, no arterial enhancement with good response). Unlike conventional DCE-MRI sequences, an MRA sequence with sparse k-space sampling is easily integrated into a routine musculoskeletal tumor MRI protocol, with high diagnostic quality. In this preliminary work, tumor enhancement characteristics by DCE-MRI were used to assess treatment response.

Diagnosis and prognosis of Ostheoarthritis by texture analysis using sparse linear models

DEFF Research Database (Denmark)

Marques, Joselene; Clemmensen, Line Katrine Harder; Dam, Erik

We present a texture analysis methodology that combines uncommitted machine-learning techniques and sparse feature transformation methods in a fully automatic framework. We compare the performances of a partial least squares (PLS) forward feature selection strategy to a hard threshold sparse PLS...... algorithm and a sparse linear discriminant model. The texture analysis framework was applied to diagnosis of knee osteoarthritis (OA) and prognosis of cartilage loss. For this investigation, a generic texture feature bank was extracted from magnetic resonance images of tibial knee bone. The features were...... used as input to the sparse algorithms, which dened the best features to retain in the model. To cope with the limited number of samples, the data was evaluated using 10 fold cross validation (CV). The diagnosis evaluation using sparse PLS reached a generalization area-under-the-ROC curve (AUC) of 0...

Transport Properties, Mechanical Behavior, Thermal and Chemical Resistance of Asymmetric Flat Sheet Membrane Prepared from PSf/PVDF Blended Membrane on Gauze Supporting Layer

Directory of Open Access Journals (Sweden)

Nita Kusumawati

2018-05-01

Full Text Available Asymmetric polysulfone (PSf membrane is prepared using phase inversion method and blending with polyvinylidene fluoride (PVDF on the gauze solid support. Casting solution composition optimization has been done to get PSf/PVDF membrane with best characteristics and permeability. The result shows that blending on PSf with PVDF polymer using phase inversion method has been very helpful in creating an asymmetric porous membrane. Increased level of PVDF in casting solution has increased the formation of asymmetry structure and corresponding flux membrane. The result from thermal test using Differential Scanning Calorimetry (DSC-Thermal Gravimetric Analysis (TGA shows the resistance of the membrane to temperature 460 °C. Membrane resistance against acid looks from undetectable changes on infrared spectra after immersion process in H2SO4 6–98 v/v%. While membrane color changes from white to brownish and black is detected after the immersion process in sodium hydroxide (NaOH 0.15–80 w/v%.

Fast Sparse Coding for Range Data Denoising with Sparse Ridges Constraint

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Zhi Gao

2018-05-01

Full Text Available Light detection and ranging (LiDAR sensors have been widely deployed on intelligent systems such as unmanned ground vehicles (UGVs and unmanned aerial vehicles (UAVs to perform localization, obstacle detection, and navigation tasks. Thus, research into range data processing with competitive performance in terms of both accuracy and efficiency has attracted increasing attention. Sparse coding has revolutionized signal processing and led to state-of-the-art performance in a variety of applications. However, dictionary learning, which plays the central role in sparse coding techniques, is computationally demanding, resulting in its limited applicability in real-time systems. In this study, we propose sparse coding algorithms with a fixed pre-learned ridge dictionary to realize range data denoising via leveraging the regularity of laser range measurements in man-made environments. Experiments on both synthesized data and real data demonstrate that our method obtains accuracy comparable to that of sophisticated sparse coding methods, but with much higher computational efficiency.

Fast Sparse Coding for Range Data Denoising with Sparse Ridges Constraint.

Science.gov (United States)

Gao, Zhi; Lao, Mingjie; Sang, Yongsheng; Wen, Fei; Ramesh, Bharath; Zhai, Ruifang

2018-05-06

Light detection and ranging (LiDAR) sensors have been widely deployed on intelligent systems such as unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) to perform localization, obstacle detection, and navigation tasks. Thus, research into range data processing with competitive performance in terms of both accuracy and efficiency has attracted increasing attention. Sparse coding has revolutionized signal processing and led to state-of-the-art performance in a variety of applications. However, dictionary learning, which plays the central role in sparse coding techniques, is computationally demanding, resulting in its limited applicability in real-time systems. In this study, we propose sparse coding algorithms with a fixed pre-learned ridge dictionary to realize range data denoising via leveraging the regularity of laser range measurements in man-made environments. Experiments on both synthesized data and real data demonstrate that our method obtains accuracy comparable to that of sophisticated sparse coding methods, but with much higher computational efficiency.

When sparse coding meets ranking: a joint framework for learning sparse codes and ranking scores

KAUST Repository

Wang, Jim Jing-Yan

2017-06-28

Sparse coding, which represents a data point as a sparse reconstruction code with regard to a dictionary, has been a popular data representation method. Meanwhile, in database retrieval problems, learning the ranking scores from data points plays an important role. Up to now, these two problems have always been considered separately, assuming that data coding and ranking are two independent and irrelevant problems. However, is there any internal relationship between sparse coding and ranking score learning? If yes, how to explore and make use of this internal relationship? In this paper, we try to answer these questions by developing the first joint sparse coding and ranking score learning algorithm. To explore the local distribution in the sparse code space, and also to bridge coding and ranking problems, we assume that in the neighborhood of each data point, the ranking scores can be approximated from the corresponding sparse codes by a local linear function. By considering the local approximation error of ranking scores, the reconstruction error and sparsity of sparse coding, and the query information provided by the user, we construct a unified objective function for learning of sparse codes, the dictionary and ranking scores. We further develop an iterative algorithm to solve this optimization problem.

Resolution optimization with irregularly sampled Fourier data

International Nuclear Information System (INIS)

Ferrara, Matthew; Parker, Jason T; Cheney, Margaret

2013-01-01

Image acquisition systems such as synthetic aperture radar (SAR) and magnetic resonance imaging often measure irregularly spaced Fourier samples of the desired image. In this paper we show the relationship between sample locations, their associated backprojection weights, and image resolution as characterized by the resulting point spread function (PSF). Two new methods for computing data weights, based on different optimization criteria, are proposed. The first method, which solves a maximal-eigenvector problem, optimizes a PSF-derived resolution metric which is shown to be equivalent to the volume of the Cramer–Rao (positional) error ellipsoid in the uniform-weight case. The second approach utilizes as its performance metric the Frobenius error between the PSF operator and the ideal delta function, and is an extension of a previously reported algorithm. Our proposed extension appropriately regularizes the weight estimates in the presence of noisy data and eliminates the superfluous issue of image discretization in the choice of data weights. The Frobenius-error approach results in a Tikhonov-regularized inverse problem whose Tikhonov weights are dependent on the locations of the Fourier data as well as the noise variance. The two new methods are compared against several state-of-the-art weighting strategies for synthetic multistatic point-scatterer data, as well as an ‘interrupted SAR’ dataset representative of in-band interference commonly encountered in very high frequency radar applications. (paper)

Low-Rank Sparse Coding for Image Classification

KAUST Repository

Zhang, Tianzhu; Ghanem, Bernard; Liu, Si; Xu, Changsheng; Ahuja, Narendra

2013-01-01

In this paper, we propose a low-rank sparse coding (LRSC) method that exploits local structure information among features in an image for the purpose of image-level classification. LRSC represents densely sampled SIFT descriptors, in a spatial neighborhood, collectively as low-rank, sparse linear combinations of code words. As such, it casts the feature coding problem as a low-rank matrix learning problem, which is different from previous methods that encode features independently. This LRSC has a number of attractive properties. (1) It encourages sparsity in feature codes, locality in codebook construction, and low-rankness for spatial consistency. (2) LRSC encodes local features jointly by considering their low-rank structure information, and is computationally attractive. We evaluate the LRSC by comparing its performance on a set of challenging benchmarks with that of 7 popular coding and other state-of-the-art methods. Our experiments show that by representing local features jointly, LRSC not only outperforms the state-of-the-art in classification accuracy but also improves the time complexity of methods that use a similar sparse linear representation model for feature coding.

Low-Rank Sparse Coding for Image Classification

KAUST Repository

Zhang, Tianzhu

2013-12-01

In this paper, we propose a low-rank sparse coding (LRSC) method that exploits local structure information among features in an image for the purpose of image-level classification. LRSC represents densely sampled SIFT descriptors, in a spatial neighborhood, collectively as low-rank, sparse linear combinations of code words. As such, it casts the feature coding problem as a low-rank matrix learning problem, which is different from previous methods that encode features independently. This LRSC has a number of attractive properties. (1) It encourages sparsity in feature codes, locality in codebook construction, and low-rankness for spatial consistency. (2) LRSC encodes local features jointly by considering their low-rank structure information, and is computationally attractive. We evaluate the LRSC by comparing its performance on a set of challenging benchmarks with that of 7 popular coding and other state-of-the-art methods. Our experiments show that by representing local features jointly, LRSC not only outperforms the state-of-the-art in classification accuracy but also improves the time complexity of methods that use a similar sparse linear representation model for feature coding.

SU-D-207A-05: Investigating Sparse-Sampled MRI for Motion Management in Thoracic Radiotherapy

International Nuclear Information System (INIS)

Sabouri, P; Sawant, A; Arai, T

2016-01-01

Purpose: Sparse sampling and reconstruction-based MRI techniques represent an attractive strategy to achieve sufficiently high image acquisition speed while maintaining image quality for the task of radiotherapy guidance. In this study, we examine rapid dynamic MRI using a sparse sampling sequence k-t BLAST in capturing motion-induced, cycle-to-cycle variations in tumor position. We investigate the utility of long-term MRI-based motion monitoring as a means of better characterizing respiration-induced tumor motion compared to a single-cycle 4DCT. Methods: An MRI-compatible, programmable, deformable lung motion phantom with eleven 1.5 ml water marker tubes was placed inside a 3.0 T whole-body MR scanner (Philips Ingenia). The phantom was programmed with 10 lung tumor motion traces previously recorded using the Synchrony system. 2D+t image sequences of a coronal slice were acquired using a balanced-SSFP sequence combined with k-t BLAST (accn=3, resolution=0.66×0.66×5 mm3; acquisition time = 110 ms/slice). kV fluoroscopic (ground truth) and 4DCT imaging was performed with the same phantom setup and motion trajectories. Marker positions in all three modalities were segmented and tracked using an opensource deformable image registration package, NiftyReg. Results: Marker trajectories obtained from rapid MRI exhibited <1 mm error compared to kv Fluoro trajectories in the presence of complex motion including baseline shifts and changes in respiratory amplitude, indicating the ability of MRI to monitor motion with adequate geometric fidelity for the purpose of radiotherapy guidance. In contrast, the trajectory derived from 4DCT exhibited significant errors up to 6 mm due to cycle-to-cycle variations and baseline shifts. Consequently, 4DCT was found to underestimate the range of marker motion by as much as 50%. Conclusion: Dynamic MRI is a promising tool for radiotherapy motion management as it permits for longterm, dose-free, soft-tissue-based monitoring of motion

SU-D-207A-05: Investigating Sparse-Sampled MRI for Motion Management in Thoracic Radiotherapy

Energy Technology Data Exchange (ETDEWEB)

Sabouri, P; Sawant, A [University of Maryland School of Medicine, Baltimore, MD (United States); Arai, T [University of Texas Southwestern Medical Center, Dallas, TX (United States)

2016-06-15

Purpose: Sparse sampling and reconstruction-based MRI techniques represent an attractive strategy to achieve sufficiently high image acquisition speed while maintaining image quality for the task of radiotherapy guidance. In this study, we examine rapid dynamic MRI using a sparse sampling sequence k-t BLAST in capturing motion-induced, cycle-to-cycle variations in tumor position. We investigate the utility of long-term MRI-based motion monitoring as a means of better characterizing respiration-induced tumor motion compared to a single-cycle 4DCT. Methods: An MRI-compatible, programmable, deformable lung motion phantom with eleven 1.5 ml water marker tubes was placed inside a 3.0 T whole-body MR scanner (Philips Ingenia). The phantom was programmed with 10 lung tumor motion traces previously recorded using the Synchrony system. 2D+t image sequences of a coronal slice were acquired using a balanced-SSFP sequence combined with k-t BLAST (accn=3, resolution=0.66×0.66×5 mm3; acquisition time = 110 ms/slice). kV fluoroscopic (ground truth) and 4DCT imaging was performed with the same phantom setup and motion trajectories. Marker positions in all three modalities were segmented and tracked using an opensource deformable image registration package, NiftyReg. Results: Marker trajectories obtained from rapid MRI exhibited <1 mm error compared to kv Fluoro trajectories in the presence of complex motion including baseline shifts and changes in respiratory amplitude, indicating the ability of MRI to monitor motion with adequate geometric fidelity for the purpose of radiotherapy guidance. In contrast, the trajectory derived from 4DCT exhibited significant errors up to 6 mm due to cycle-to-cycle variations and baseline shifts. Consequently, 4DCT was found to underestimate the range of marker motion by as much as 50%. Conclusion: Dynamic MRI is a promising tool for radiotherapy motion management as it permits for longterm, dose-free, soft-tissue-based monitoring of motion

IMAGE ANALYSIS FOR COSMOLOGY: RESULTS FROM THE GREAT10 STAR CHALLENGE

International Nuclear Information System (INIS)

Kitching, T. D.; Heymans, C.; Rowe, B.; Witherick, D.; Gill, M.; Massey, R.; Courbin, F.; Gentile, M.; Meylan, G.; Georgatzis, K.; Gruen, D.; Kilbinger, M.; Li, G. L.; Mariglis, A. P.; Storkey, A.; Xin, B.

2013-01-01

We present the results from the first public blind point-spread function (PSF) reconstruction challenge, the GRavitational lEnsing Accuracy Testing 2010 (GREAT10) Star Challenge. Reconstruction of a spatially varying PSF, sparsely sampled by stars, at non-star positions is a critical part in the image analysis for weak lensing where inaccuracies in the modeled ellipticity e and size R 2 can impact the ability to measure the shapes of galaxies. This is of importance because weak lensing is a particularly sensitive probe of dark energy and can be used to map the mass distribution of large scale structure. Participants in the challenge were presented with 27,500 stars over 1300 images subdivided into 26 sets, where in each set a category change was made in the type or spatial variation of the PSF. Thirty submissions were made by nine teams. The best methods reconstructed the PSF with an accuracy of σ(e) ≈ 2.5 × 10 –4 and σ(R 2 )/R 2 ≈ 7.4 × 10 –4 . For a fixed pixel scale, narrower PSFs were found to be more difficult to model than larger PSFs, and the PSF reconstruction was severely degraded with the inclusion of an atmospheric turbulence model (although this result is likely to be a strong function of the amplitude of the turbulence power spectrum).

Integrative analysis of multiple diverse omics datasets by sparse group multitask regression

Directory of Open Access Journals (Sweden)

Dongdong eLin

2014-10-01

Full Text Available A variety of high throughput genome-wide assays enable the exploration of genetic risk factors underlying complex traits. Although these studies have remarkable impact on identifying susceptible biomarkers, they suffer from issues such as limited sample size and low reproducibility. Combining individual studies of different genetic levels/platforms has the promise to improve the power and consistency of biomarker identification. In this paper, we propose a novel integrative method, namely sparse group multitask regression, for integrating diverse omics datasets, platforms and populations to identify risk genes/factors of complex diseases. This method combines multitask learning with sparse group regularization, which will: 1 treat the biomarker identification in each single study as a task and then combine them by multitask learning; 2 group variables from all studies for identifying significant genes; 3 enforce sparse constraint on groups of variables to overcome the ‘small sample, but large variables’ problem. We introduce two sparse group penalties: sparse group lasso and sparse group ridge in our multitask model, and provide an effective algorithm for each model. In addition, we propose a significance test for the identification of potential risk genes. Two simulation studies are performed to evaluate the performance of our integrative method by comparing it with conventional meta-analysis method. The results show that our sparse group multitask method outperforms meta-analysis method significantly. In an application to our osteoporosis studies, 7 genes are identified as significant genes by our method and are found to have significant effects in other three independent studies for validation. The most significant gene SOD2 has been identified in our previous osteoporosis study involving the same expression dataset. Several other genes such as TREML2, HTR1E and GLO1 are shown to be novel susceptible genes for osteoporosis, as confirmed

Miniature Laboratory for Detecting Sparse Biomolecules

Science.gov (United States)

Lin, Ying; Yu, Nan

2005-01-01

A miniature laboratory system has been proposed for use in the field to detect sparsely distributed biomolecules. By emphasizing concentration and sorting of specimens prior to detection, the underlying system concept would make it possible to attain high detection sensitivities without the need to develop ever more sensitive biosensors. The original purpose of the proposal is to aid the search for signs of life on a remote planet by enabling the detection of specimens as sparse as a few molecules or microbes in a large amount of soil, dust, rocks, water/ice, or other raw sample material. Some version of the system could prove useful on Earth for remote sensing of biological contamination, including agents of biological warfare. Processing in this system would begin with dissolution of the raw sample material in a sample-separation vessel. The solution in the vessel would contain floating microscopic magnetic beads coated with substances that could engage in chemical reactions with various target functional groups that are parts of target molecules. The chemical reactions would cause the targeted molecules to be captured on the surfaces of the beads. By use of a controlled magnetic field, the beads would be concentrated in a specified location in the vessel. Once the beads were thus concentrated, the rest of the solution would be discarded. This procedure would obviate the filtration steps and thereby also eliminate the filter-clogging difficulties of typical prior sample-concentration schemes. For ferrous dust/soil samples, the dissolution would be done first in a separate vessel before the solution is transferred to the microbead-containing vessel.

Preparation, characterization and gas permeation study of PSf/MgO nanocomposite membrane

Directory of Open Access Journals (Sweden)

S. M. Momeni

2013-09-01

Full Text Available Nanocomposite membranes composed of polymer and inorganic nanoparticles are a novel method to enhance gas separation performance. In this study, membranes were fabricated from polysulfone (PSf containing magnesium oxide (MgO nanoparticles and gas permeation properties of the resulting membranes were investigated. Membranes were prepared by solution blending and phase inversion methods. Morphology of the membranes, void formations, MgO distribution and aggregates were observed by SEM analysis. Furthermore, thermal stability, residual solvent in the membrane film and structural ruination of membranes were analyzed by thermal gravimetric analysis (TGA. The effects of MgO nanoparticles on the glass transition temperature (Tg of the prepared nanocomposites were studied by differential scanning calorimetry (DSC. The Tg of nanocomposite membranes increased with MgO loading. Fourier transform infrared (FTIR spectra of nanocomposite membranes were analyzed to identify the variations of the bonds. The results obtained from gas permeation experiments with a constant pressure setup showed that adding MgO nanoparticles to the polymeric membrane structure increased the permeability of the membranes. At 30 wt% MgO loading, the CO2 permeability was enhanced from 25.75×10-16 to 47.12×10-16 mol.m/(m².s.Pa and the CO2/CH4 selectivity decreased from 30.84 to 25.65 when compared with pure PSf. For H2, the permeability was enhanced from 44.05×10-16 to 67.3×10-16 mol.m/(m².s.Pa, whereas the H2/N2 selectivity decreased from 47.11 to 33.58.

Technical innovation in dynamic contrast-enhanced magnetic resonance imaging of musculoskeletal tumors: an MR angiographic sequence using a sparse k-space sampling strategy

International Nuclear Information System (INIS)

Fayad, Laura M.; Mugera, Charles; Grande, Filippo del; Soldatos, Theodoros; Flammang, Aaron

2013-01-01

We demonstrate the clinical use of an MR angiography sequence performed with sparse k-space sampling (MRA), as a method for dynamic contrast-enhanced (DCE)-MRI, and apply it to the assessment of sarcomas for treatment response. Three subjects with sarcomas (2 with osteosarcoma, 1 with high-grade soft tissue sarcomas) underwent MRI after neoadjuvant therapy/prior to surgery, with conventional MRI (T1-weighted, fluid-sensitive, static post-contrast T1-weighted sequences) and DCE-MRI (MRA, time resolution = 7-10 s, TR/TE 2.4/0.9 ms, FOV 40 cm 2 ). Images were reviewed by two observers in consensus who recorded image quality (1 = diagnostic, no significant artifacts, 2 = diagnostic, 75 % with good response, >75 % with poor response). DCE-MRI findings were concordant with histological response (arterial enhancement with poor response, no arterial enhancement with good response). Unlike conventional DCE-MRI sequences, an MRA sequence with sparse k-space sampling is easily integrated into a routine musculoskeletal tumor MRI protocol, with high diagnostic quality. In this preliminary work, tumor enhancement characteristics by DCE-MRI were used to assess treatment response. (orig.)

Real-time cardiac magnetic resonance cine imaging with sparse sampling and iterative reconstruction for left-ventricular measures: Comparison with gold-standard segmented steady-state free precession.

Science.gov (United States)

Camargo, Gabriel C; Erthal, Fernanda; Sabioni, Leticia; Penna, Filipe; Strecker, Ralph; Schmidt, Michaela; Zenge, Michael O; Lima, Ronaldo de S L; Gottlieb, Ilan

2017-05-01

Segmented cine imaging with a steady-state free-precession sequence (Cine-SSFP) is currently the gold standard technique for measuring ventricular volumes and mass, but due to multi breath-hold (BH) requirements, it is prone to misalignment of consecutive slices, time consuming and dependent on respiratory capacity. Real-time cine avoids those limitations, but poor spatial and temporal resolution of conventional sequences has prevented its routine application. We sought to examine the accuracy and feasibility of a newly developed real-time sequence with aggressive under-sampling of k-space using sparse sampling and iterative reconstruction (Cine-RT). Stacks of short-axis cines were acquired covering both ventricles in a 1.5T system using gold standard Cine-SSFP and Cine-RT. Acquisition parameters for Cine-SSFP were: acquisition matrix of 224×196, temporal resolution of 39ms, retrospective gating, with an average of 8 heartbeats per slice and 1-2 slices/BH. For Cine-RT: acquisition matrix of 224×196, sparse sampling net acceleration factor of 11.3, temporal resolution of 41ms, prospective gating, real-time acquisition of 1 heart-beat/slice and all slices in one BH. LV contours were drawn at end diastole and systole to derive LV volumes and mass. Forty-one consecutive patients (15 male; 41±17years) in sinus rhythm were successfully included. All images from Cine-SSFP and Cine-RT were considered to have excellent quality. Cine-RT-derived LV volumes and mass were slightly underestimated but strongly correlated with gold standard Cine-SSFP. Inter- and intra-observer analysis presented similar results between both sequences. Cine-RT featuring sparse sampling and iterative reconstruction can achieve spatial and temporal resolution equivalent to Cine-SSFP, providing excellent image quality, with similar precision measurements and highly correlated and only slightly underestimated volume and mass values. Copyright © 2017 Elsevier Inc. All rights reserved.

Sparse structure regularized ranking

KAUST Repository

Wang, Jim Jing-Yan; Sun, Yijun; Gao, Xin

2014-01-01

Learning ranking scores is critical for the multimedia database retrieval problem. In this paper, we propose a novel ranking score learning algorithm by exploring the sparse structure and using it to regularize ranking scores. To explore the sparse

Sparse structure regularized ranking

KAUST Repository

Wang, Jim Jing-Yan

2014-04-17

Learning ranking scores is critical for the multimedia database retrieval problem. In this paper, we propose a novel ranking score learning algorithm by exploring the sparse structure and using it to regularize ranking scores. To explore the sparse structure, we assume that each multimedia object could be represented as a sparse linear combination of all other objects, and combination coefficients are regarded as a similarity measure between objects and used to regularize their ranking scores. Moreover, we propose to learn the sparse combination coefficients and the ranking scores simultaneously. A unified objective function is constructed with regard to both the combination coefficients and the ranking scores, and is optimized by an iterative algorithm. Experiments on two multimedia database retrieval data sets demonstrate the significant improvements of the propose algorithm over state-of-the-art ranking score learning algorithms.

Turbulent flows over sparse canopies

Science.gov (United States)

Sharma, Akshath; García-Mayoral, Ricardo

2018-04-01

Turbulent flows over sparse and dense canopies exerting a similar drag force on the flow are investigated using Direct Numerical Simulations. The dense canopies are modelled using a homogeneous drag force, while for the sparse canopy, the geometry of the canopy elements is represented. It is found that on using the friction velocity based on the local shear at each height, the streamwise velocity fluctuations and the Reynolds stress within the sparse canopy are similar to those from a comparable smooth-wall case. In addition, when scaled with the local friction velocity, the intensity of the off-wall peak in the streamwise vorticity for sparse canopies also recovers a value similar to a smooth-wall. This indicates that the sparse canopy does not significantly disturb the near-wall turbulence cycle, but causes its rescaling to an intensity consistent with a lower friction velocity within the canopy. In comparison, the dense canopy is found to have a higher damping effect on the turbulent fluctuations. For the case of the sparse canopy, a peak in the spectral energy density of the wall-normal velocity, and Reynolds stress is observed, which may indicate the formation of Kelvin-Helmholtz-like instabilities. It is also found that a sparse canopy is better modelled by a homogeneous drag applied on the mean flow alone, and not the turbulent fluctuations.

Image Super-Resolution Algorithm Based on an Improved Sparse Autoencoder

Directory of Open Access Journals (Sweden)

Detian Huang

2018-01-01

Full Text Available Due to the limitations of the resolution of the imaging system and the influence of scene changes and other factors, sometimes only low-resolution images can be acquired, which cannot satisfy the practical application’s requirements. To improve the quality of low-resolution images, a novel super-resolution algorithm based on an improved sparse autoencoder is proposed. Firstly, in the training set preprocessing stage, the high- and low-resolution image training sets are constructed, respectively, by using high-frequency information of the training samples as the characterization, and then the zero-phase component analysis whitening technique is utilized to decorrelate the formed joint training set to reduce its redundancy. Secondly, a constructed sparse regularization term is added to the cost function of the traditional sparse autoencoder to further strengthen the sparseness constraint on the hidden layer. Finally, in the dictionary learning stage, the improved sparse autoencoder is adopted to achieve unsupervised dictionary learning to improve the accuracy and stability of the dictionary. Experimental results validate that the proposed algorithm outperforms the existing algorithms both in terms of the subjective visual perception and the objective evaluation indices, including the peak signal-to-noise ratio and the structural similarity measure.

Control of the ORR-PSF pressure-vessel surveillance irradiation experiment temperature

International Nuclear Information System (INIS)

Miller, L.F.

1982-01-01

Control of the Oak Ridge Research Reactor Pool Side Facility (ORR-PSF) pressure vessel surveillance irradiation experiment temperature is implemented by digital computer control of electrical heaters under fixed cooling conditions. Cooling is accomplished with continuous flows of water in pipes between specimen sets and of helium-neon gas in the specimen set housings. Control laws are obtained from solutions of the discrete-time Riccati equation and are implemented with direct digital control of solid state relays in the electrical heater circuit. Power dissipated by the heaters is determined by variac settings and the percent of time that the solid state relays allow power to be supplied to the heaters. Control demands are updated every forty seconds

Volumetric CT with sparse detector arrays (and application to Si-strip photon counters).

Science.gov (United States)

Sisniega, A; Zbijewski, W; Stayman, J W; Xu, J; Taguchi, K; Fredenberg, E; Lundqvist, Mats; Siewerdsen, J H

2016-01-07

Novel x-ray medical imaging sensors, such as photon counting detectors (PCDs) and large area CCD and CMOS cameras can involve irregular and/or sparse sampling of the detector plane. Application of such detectors to CT involves undersampling that is markedly different from the commonly considered case of sparse angular sampling. This work investigates volumetric sampling in CT systems incorporating sparsely sampled detectors with axial and helical scan orbits and evaluates performance of model-based image reconstruction (MBIR) with spatially varying regularization in mitigating artifacts due to sparse detector sampling. Volumetric metrics of sampling density and uniformity were introduced. Penalized-likelihood MBIR with a spatially varying penalty that homogenized resolution by accounting for variations in local sampling density (i.e. detector gaps) was evaluated. The proposed methodology was tested in simulations and on an imaging bench based on a Si-strip PCD (total area 5 cm  ×  25 cm) consisting of an arrangement of line sensors separated by gaps of up to 2.5 mm. The bench was equipped with translation/rotation stages allowing a variety of scanning trajectories, ranging from a simple axial acquisition to helical scans with variable pitch. Statistical (spherical clutter) and anthropomorphic (hand) phantoms were considered. Image quality was compared to that obtained with a conventional uniform penalty in terms of structural similarity index (SSIM), image uniformity, spatial resolution, contrast, and noise. Scan trajectories with intermediate helical width (~10 mm longitudinal distance per 360° rotation) demonstrated optimal tradeoff between the average sampling density and the homogeneity of sampling throughout the volume. For a scan trajectory with 10.8 mm helical width, the spatially varying penalty resulted in significant visual reduction of sampling artifacts, confirmed by a 10% reduction in minimum SSIM (from 0.88 to 0.8) and a 40

Sparse Regression by Projection and Sparse Discriminant Analysis

KAUST Repository

Qi, Xin

2015-04-03

© 2015, © American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America. Recent years have seen active developments of various penalized regression methods, such as LASSO and elastic net, to analyze high-dimensional data. In these approaches, the direction and length of the regression coefficients are determined simultaneously. Due to the introduction of penalties, the length of the estimates can be far from being optimal for accurate predictions. We introduce a new framework, regression by projection, and its sparse version to analyze high-dimensional data. The unique nature of this framework is that the directions of the regression coefficients are inferred first, and the lengths and the tuning parameters are determined by a cross-validation procedure to achieve the largest prediction accuracy. We provide a theoretical result for simultaneous model selection consistency and parameter estimation consistency of our method in high dimension. This new framework is then generalized such that it can be applied to principal components analysis, partial least squares, and canonical correlation analysis. We also adapt this framework for discriminant analysis. Compared with the existing methods, where there is relatively little control of the dependency among the sparse components, our method can control the relationships among the components. We present efficient algorithms and related theory for solving the sparse regression by projection problem. Based on extensive simulations and real data analysis, we demonstrate that our method achieves good predictive performance and variable selection in the regression setting, and the ability to control relationships between the sparse components leads to more accurate classification. In supplementary materials available online, the details of the algorithms and theoretical proofs, and R codes for all simulation studies are provided.

Sparse distributed memory overview

Science.gov (United States)

Raugh, Mike

1990-01-01

The Sparse Distributed Memory (SDM) project is investigating the theory and applications of massively parallel computing architecture, called sparse distributed memory, that will support the storage and retrieval of sensory and motor patterns characteristic of autonomous systems. The immediate objectives of the project are centered in studies of the memory itself and in the use of the memory to solve problems in speech, vision, and robotics. Investigation of methods for encoding sensory data is an important part of the research. Examples of NASA missions that may benefit from this work are Space Station, planetary rovers, and solar exploration. Sparse distributed memory offers promising technology for systems that must learn through experience and be capable of adapting to new circumstances, and for operating any large complex system requiring automatic monitoring and control. Sparse distributed memory is a massively parallel architecture motivated by efforts to understand how the human brain works. Sparse distributed memory is an associative memory, able to retrieve information from cues that only partially match patterns stored in the memory. It is able to store long temporal sequences derived from the behavior of a complex system, such as progressive records of the system's sensory data and correlated records of the system's motor controls.

In-place sparse suffix sorting

DEFF Research Database (Denmark)

Prezza, Nicola

2018-01-01

information regarding the lexicographical order of a size-b subset of all n text suffixes is often needed. Such information can be stored space-efficiently (in b words) in the sparse suffix array (SSA). The SSA and its relative sparse LCP array (SLCP) can be used as a space-efficient substitute of the sparse...... suffix tree. Very recently, Gawrychowski and Kociumaka [11] showed that the sparse suffix tree (and therefore SSA and SLCP) can be built in asymptotically optimal O(b) space with a Monte Carlo algorithm running in O(n) time. The main reason for using the SSA and SLCP arrays in place of the sparse suffix...... tree is, however, their reduced space of b words each. This leads naturally to the quest for in-place algorithms building these arrays. Franceschini and Muthukrishnan [8] showed that the full suffix array can be built in-place and in optimal running time. On the other hand, finding sub-quadratic in...

Enhancing adaptive sparse grid approximations and improving refinement strategies using adjoint-based a posteriori error estimates

Science.gov (United States)

Jakeman, J. D.; Wildey, T.

2015-01-01

In this paper we present an algorithm for adaptive sparse grid approximations of quantities of interest computed from discretized partial differential equations. We use adjoint-based a posteriori error estimates of the physical discretization error and the interpolation error in the sparse grid to enhance the sparse grid approximation and to drive adaptivity of the sparse grid. Utilizing these error estimates provides significantly more accurate functional values for random samples of the sparse grid approximation. We also demonstrate that alternative refinement strategies based upon a posteriori error estimates can lead to further increases in accuracy in the approximation over traditional hierarchical surplus based strategies. Throughout this paper we also provide and test a framework for balancing the physical discretization error with the stochastic interpolation error of the enhanced sparse grid approximation.

Enhancing adaptive sparse grid approximations and improving refinement strategies using adjoint-based a posteriori error estimates

International Nuclear Information System (INIS)

Jakeman, J.D.; Wildey, T.

2015-01-01

In this paper we present an algorithm for adaptive sparse grid approximations of quantities of interest computed from discretized partial differential equations. We use adjoint-based a posteriori error estimates of the physical discretization error and the interpolation error in the sparse grid to enhance the sparse grid approximation and to drive adaptivity of the sparse grid. Utilizing these error estimates provides significantly more accurate functional values for random samples of the sparse grid approximation. We also demonstrate that alternative refinement strategies based upon a posteriori error estimates can lead to further increases in accuracy in the approximation over traditional hierarchical surplus based strategies. Throughout this paper we also provide and test a framework for balancing the physical discretization error with the stochastic interpolation error of the enhanced sparse grid approximation

Classification of multispectral or hyperspectral satellite imagery using clustering of sparse approximations on sparse representations in learned dictionaries obtained using efficient convolutional sparse coding

Science.gov (United States)

Moody, Daniela; Wohlberg, Brendt

2018-01-02

An approach for land cover classification, seasonal and yearly change detection and monitoring, and identification of changes in man-made features may use a clustering of sparse approximations (CoSA) on sparse representations in learned dictionaries. The learned dictionaries may be derived using efficient convolutional sparse coding to build multispectral or hyperspectral, multiresolution dictionaries that are adapted to regional satellite image data. Sparse image representations of images over the learned dictionaries may be used to perform unsupervised k-means clustering into land cover categories. The clustering process behaves as a classifier in detecting real variability. This approach may combine spectral and spatial textural characteristics to detect geologic, vegetative, hydrologic, and man-made features, as well as changes in these features over time.

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

Science.gov (United States)

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.

TH-EF-BRA-08: A Novel Technique for Estimating Volumetric Cine MRI (VC-MRI) From Multi-Slice Sparsely Sampled Cine Images Using Motion Modeling and Free Form Deformation

International Nuclear Information System (INIS)

Harris, W; Yin, F; Wang, C; Chang, Z; Cai, J; Zhang, Y; Ren, L

2016-01-01

Purpose: To develop a technique to estimate on-board VC-MRI using multi-slice sparsely-sampled cine images, patient prior 4D-MRI, motion-modeling and free-form deformation for real-time 3D target verification of lung radiotherapy. Methods: A previous method has been developed to generate on-board VC-MRI by deforming prior MRI images based on a motion model(MM) extracted from prior 4D-MRI and a single-slice on-board 2D-cine image. In this study, free-form deformation(FD) was introduced to correct for errors in the MM when large anatomical changes exist. Multiple-slice sparsely-sampled on-board 2D-cine images located within the target are used to improve both the estimation accuracy and temporal resolution of VC-MRI. The on-board 2D-cine MRIs are acquired at 20–30frames/s by sampling only 10% of the k-space on Cartesian grid, with 85% of that taken at the central k-space. The method was evaluated using XCAT(computerized patient model) simulation of lung cancer patients with various anatomical and respirational changes from prior 4D-MRI to onboard volume. The accuracy was evaluated using Volume-Percent-Difference(VPD) and Center-of-Mass-Shift(COMS) of the estimated tumor volume. Effects of region-of-interest(ROI) selection, 2D-cine slice orientation, slice number and slice location on the estimation accuracy were evaluated. Results: VCMRI estimated using 10 sparsely-sampled sagittal 2D-cine MRIs achieved VPD/COMS of 9.07±3.54%/0.45±0.53mm among all scenarios based on estimation with ROI_MM-ROI_FD. The FD optimization improved estimation significantly for scenarios with anatomical changes. Using ROI-FD achieved better estimation than global-FD. Changing the multi-slice orientation to axial, coronal, and axial/sagittal orthogonal reduced the accuracy of VCMRI to VPD/COMS of 19.47±15.74%/1.57±2.54mm, 20.70±9.97%/2.34±0.92mm, and 16.02±13.79%/0.60±0.82mm, respectively. Reducing the number of cines to 8 enhanced temporal resolution of VC-MRI by 25% while

TH-EF-BRA-08: A Novel Technique for Estimating Volumetric Cine MRI (VC-MRI) From Multi-Slice Sparsely Sampled Cine Images Using Motion Modeling and Free Form Deformation

Energy Technology Data Exchange (ETDEWEB)

Harris, W; Yin, F; Wang, C; Chang, Z; Cai, J; Zhang, Y; Ren, L [Duke University Medical Center, Durham, NC (United States)

2016-06-15

Purpose: To develop a technique to estimate on-board VC-MRI using multi-slice sparsely-sampled cine images, patient prior 4D-MRI, motion-modeling and free-form deformation for real-time 3D target verification of lung radiotherapy. Methods: A previous method has been developed to generate on-board VC-MRI by deforming prior MRI images based on a motion model(MM) extracted from prior 4D-MRI and a single-slice on-board 2D-cine image. In this study, free-form deformation(FD) was introduced to correct for errors in the MM when large anatomical changes exist. Multiple-slice sparsely-sampled on-board 2D-cine images located within the target are used to improve both the estimation accuracy and temporal resolution of VC-MRI. The on-board 2D-cine MRIs are acquired at 20–30frames/s by sampling only 10% of the k-space on Cartesian grid, with 85% of that taken at the central k-space. The method was evaluated using XCAT(computerized patient model) simulation of lung cancer patients with various anatomical and respirational changes from prior 4D-MRI to onboard volume. The accuracy was evaluated using Volume-Percent-Difference(VPD) and Center-of-Mass-Shift(COMS) of the estimated tumor volume. Effects of region-of-interest(ROI) selection, 2D-cine slice orientation, slice number and slice location on the estimation accuracy were evaluated. Results: VCMRI estimated using 10 sparsely-sampled sagittal 2D-cine MRIs achieved VPD/COMS of 9.07±3.54%/0.45±0.53mm among all scenarios based on estimation with ROI-MM-ROI-FD. The FD optimization improved estimation significantly for scenarios with anatomical changes. Using ROI-FD achieved better estimation than global-FD. Changing the multi-slice orientation to axial, coronal, and axial/sagittal orthogonal reduced the accuracy of VCMRI to VPD/COMS of 19.47±15.74%/1.57±2.54mm, 20.70±9.97%/2.34±0.92mm, and 16.02±13.79%/0.60±0.82mm, respectively. Reducing the number of cines to 8 enhanced temporal resolution of VC-MRI by 25% while

Designing sparse sensing matrix for compressive sensing to reconstruct high resolution medical images

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.

Locality-preserving sparse representation-based classification in hyperspectral imagery

Science.gov (United States)

Gao, Lianru; Yu, Haoyang; Zhang, Bing; Li, Qingting

2016-10-01

This paper proposes to combine locality-preserving projections (LPP) and sparse representation (SR) for hyperspectral image classification. The LPP is first used to reduce the dimensionality of all the training and testing data by finding the optimal linear approximations to the eigenfunctions of the Laplace Beltrami operator on the manifold, where the high-dimensional data lies. Then, SR codes the projected testing pixels as sparse linear combinations of all the training samples to classify the testing pixels by evaluating which class leads to the minimum approximation error. The integration of LPP and SR represents an innovative contribution to the literature. The proposed approach, called locality-preserving SR-based classification, addresses the imbalance between high dimensionality of hyperspectral data and the limited number of training samples. Experimental results on three real hyperspectral data sets demonstrate that the proposed approach outperforms the original counterpart, i.e., SR-based classification.

Discrete Sparse Coding.

Science.gov (United States)

Exarchakis, Georgios; Lücke, Jörg

2017-11-01

Sparse coding algorithms with continuous latent variables have been the subject of a large number of studies. However, discrete latent spaces for sparse coding have been largely ignored. In this work, we study sparse coding with latents described by discrete instead of continuous prior distributions. We consider the general case in which the latents (while being sparse) can take on any value of a finite set of possible values and in which we learn the prior probability of any value from data. This approach can be applied to any data generated by discrete causes, and it can be applied as an approximation of continuous causes. As the prior probabilities are learned, the approach then allows for estimating the prior shape without assuming specific functional forms. To efficiently train the parameters of our probabilistic generative model, we apply a truncated expectation-maximization approach (expectation truncation) that we modify to work with a general discrete prior. We evaluate the performance of the algorithm by applying it to a variety of tasks: (1) we use artificial data to verify that the algorithm can recover the generating parameters from a random initialization, (2) use image patches of natural images and discuss the role of the prior for the extraction of image components, (3) use extracellular recordings of neurons to present a novel method of analysis for spiking neurons that includes an intuitive discretization strategy, and (4) apply the algorithm on the task of encoding audio waveforms of human speech. The diverse set of numerical experiments presented in this letter suggests that discrete sparse coding algorithms can scale efficiently to work with realistic data sets and provide novel statistical quantities to describe the structure of the data.

Sparse BLIP: BLind Iterative Parallel imaging reconstruction using compressed sensing.

Science.gov (United States)

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.

A flexible framework for sparse simultaneous component based data integration

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Van Deun Katrijn

2011-11-01

Full Text Available Abstract 1 Background High throughput data are complex and methods that reveal structure underlying the data are most useful. Principal component analysis, frequently implemented as a singular value decomposition, is a popular technique in this respect. Nowadays often the challenge is to reveal structure in several sources of information (e.g., transcriptomics, proteomics that are available for the same biological entities under study. Simultaneous component methods are most promising in this respect. However, the interpretation of the principal and simultaneous components is often daunting because contributions of each of the biomolecules (transcripts, proteins have to be taken into account. 2 Results We propose a sparse simultaneous component method that makes many of the parameters redundant by shrinking them to zero. It includes principal component analysis, sparse principal component analysis, and ordinary simultaneous component analysis as special cases. Several penalties can be tuned that account in different ways for the block structure present in the integrated data. This yields known sparse approaches as the lasso, the ridge penalty, the elastic net, the group lasso, sparse group lasso, and elitist lasso. In addition, the algorithmic results can be easily transposed to the context of regression. Metabolomics data obtained with two measurement platforms for the same set of Escherichia coli samples are used to illustrate the proposed methodology and the properties of different penalties with respect to sparseness across and within data blocks. 3 Conclusion Sparse simultaneous component analysis is a useful method for data integration: First, simultaneous analyses of multiple blocks offer advantages over sequential and separate analyses and second, interpretation of the results is highly facilitated by their sparseness. The approach offered is flexible and allows to take the block structure in different ways into account. As such

A flexible framework for sparse simultaneous component based data integration.

Science.gov (United States)

Van Deun, Katrijn; Wilderjans, Tom F; van den Berg, Robert A; Antoniadis, Anestis; Van Mechelen, Iven

2011-11-15

High throughput data are complex and methods that reveal structure underlying the data are most useful. Principal component analysis, frequently implemented as a singular value decomposition, is a popular technique in this respect. Nowadays often the challenge is to reveal structure in several sources of information (e.g., transcriptomics, proteomics) that are available for the same biological entities under study. Simultaneous component methods are most promising in this respect. However, the interpretation of the principal and simultaneous components is often daunting because contributions of each of the biomolecules (transcripts, proteins) have to be taken into account. We propose a sparse simultaneous component method that makes many of the parameters redundant by shrinking them to zero. It includes principal component analysis, sparse principal component analysis, and ordinary simultaneous component analysis as special cases. Several penalties can be tuned that account in different ways for the block structure present in the integrated data. This yields known sparse approaches as the lasso, the ridge penalty, the elastic net, the group lasso, sparse group lasso, and elitist lasso. In addition, the algorithmic results can be easily transposed to the context of regression. Metabolomics data obtained with two measurement platforms for the same set of Escherichia coli samples are used to illustrate the proposed methodology and the properties of different penalties with respect to sparseness across and within data blocks. Sparse simultaneous component analysis is a useful method for data integration: First, simultaneous analyses of multiple blocks offer advantages over sequential and separate analyses and second, interpretation of the results is highly facilitated by their sparseness. The approach offered is flexible and allows to take the block structure in different ways into account. As such, structures can be found that are exclusively tied to one data platform

Dificuldades do trabalho médico no PSF Difficulties of medical working at the family health program

Directory of Open Access Journals (Sweden)

Fernanda Gaspar Antonini Vasconcelos

2011-01-01

Full Text Available O presente estudo tem como objetivos identificar o perfil dos médicos que atuam ou atuaram no PSF, suas principais dificuldades e levantar a porcentagem de equipes de saúde da família sem médico no município de São Paulo. Para isso, foi utilizado um questionário baseado nas principais falas do estudo de Capozzolo, coletadas de janeiro até maio de 2008, e dados da atenção básica de outubro até dezembro de 2007. Os principais resultados incluem um tempo menor que cinco anos de formação para a maioria dos entrevistados e afinidade pelo PSF como motivação para o trabalho. As principais dificuldades referem-se à alta demanda, alta incidência de casos complexos, dificuldade de referenciamento, perfil de divisão do tempo não condizente com as necessidades de saúde e falta de incentivo à especialização. Os dados da atenção básica demonstraram que a Coordenadoria Leste foi a que mais sofreu falta de médicos no período analisado, mantendo índices em torno de 20% e 40%; existência de um aumento no déficit com a aproximação do final do ano e a manutenção dos déficits em algumas unidades.This study aims to identify the profile of doctors who act or acted in PSF, its main difficulties and raise the percentage of teams of family health without doctor in the city of São Paulo. For this was used a questionnaire based on keywords of the study of Capozzolo collected from January to May 2008, and data of the Primary Care from October until December 2007. The main results include a time less than 5 years of training for most of the interviewees and affinity by the PSF as motivation for work. Some of the main difficulties are the high demand, high incidence of complex cases, difficulty of listings, profile division of time is not consistent with health needs and lack of incentive to specialization. The figures for Primary Care demonstrated that the coordination East had the highest absence of experienced doctors in the period

Group sparse canonical correlation analysis for genomic data integration.

Science.gov (United States)

Lin, Dongdong; Zhang, Jigang; Li, Jingyao; Calhoun, Vince D; Deng, Hong-Wen; Wang, Yu-Ping

2013-08-12

The emergence of high-throughput genomic datasets from different sources and platforms (e.g., gene expression, single nucleotide polymorphisms (SNP), and copy number variation (CNV)) has greatly enhanced our understandings of the interplay of these genomic factors as well as their influences on the complex diseases. It is challenging to explore the relationship between these different types of genomic data sets. In this paper, we focus on a multivariate statistical method, canonical correlation analysis (CCA) method for this problem. Conventional CCA method does not work effectively if the number of data samples is significantly less than that of biomarkers, which is a typical case for genomic data (e.g., SNPs). Sparse CCA (sCCA) methods were introduced to overcome such difficulty, mostly using penalizations with l-1 norm (CCA-l1) or the combination of l-1and l-2 norm (CCA-elastic net). However, they overlook the structural or group effect within genomic data in the analysis, which often exist and are important (e.g., SNPs spanning a gene interact and work together as a group). We propose a new group sparse CCA method (CCA-sparse group) along with an effective numerical algorithm to study the mutual relationship between two different types of genomic data (i.e., SNP and gene expression). We then extend the model to a more general formulation that can include the existing sCCA models. We apply the model to feature/variable selection from two data sets and compare our group sparse CCA method with existing sCCA methods on both simulation and two real datasets (human gliomas data and NCI60 data). We use a graphical representation of the samples with a pair of canonical variates to demonstrate the discriminating characteristic of the selected features. Pathway analysis is further performed for biological interpretation of those features. The CCA-sparse group method incorporates group effects of features into the correlation analysis while performs individual feature

Solving Sparse Polynomial Optimization Problems with Chordal Structure Using the Sparse, Bounded-Degree Sum-of-Squares Hierarchy

NARCIS (Netherlands)

Marandi, Ahmadreza; de Klerk, Etienne; Dahl, Joachim

The sparse bounded degree sum-of-squares (sparse-BSOS) hierarchy of Weisser, Lasserre and Toh [arXiv:1607.01151,2016] constructs a sequence of lower bounds for a sparse polynomial optimization problem. Under some assumptions, it is proven by the authors that the sequence converges to the optimal

IMAGE ANALYSIS FOR COSMOLOGY: RESULTS FROM THE GREAT10 STAR CHALLENGE

Energy Technology Data Exchange (ETDEWEB)

Kitching, T. D.; Heymans, C. [Mullard Space Science Laboratory, University College London, Holmbury St. Mary, Dorking, Surrey RH5 6NT (United Kingdom); Rowe, B.; Witherick, D. [Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT (United Kingdom); Gill, M. [Center for Cosmology and AstroParticle Physics, Physics Department, The Ohio State University, Columbus, OH (United States); Massey, R. [Institute for Computational Cosmology, Durham University, South Road, Durham DH1 3LE (United Kingdom); Courbin, F.; Gentile, M.; Meylan, G. [Laboratoire d' Astrophysique, Ecole Polytechnique Federale de Lausanne (EPFL) (Switzerland); Georgatzis, K. [Department of Information and Computer Science, Aalto University, P.O. Box 15400, FI-00076 Aalto (Finland); Gruen, D. [Department of Physics and Astronomy, 209 South 33rd Street, University of Pennsylvania, Philadelphia, PA 19104 (United States); Kilbinger, M. [Excellence Cluster Universe, Boltzmannstr. 2, D-85748 Garching (Germany); Li, G. L. [Purple Mountain Observatory, 2 West Beijing Road, Nanjing 210008 (China); Mariglis, A. P.; Storkey, A. [School of Informatics, University of Edinburgh, 10 Crichton Street, Edinburgh EH8 9AB (United Kingdom); Xin, B., E-mail: t.kitching@ucl.ac.uk [Department of Physics, Purdue University, 525 Northwestern Avenue, West Lafayette, IN 47907 (United States)

2013-04-01

We present the results from the first public blind point-spread function (PSF) reconstruction challenge, the GRavitational lEnsing Accuracy Testing 2010 (GREAT10) Star Challenge. Reconstruction of a spatially varying PSF, sparsely sampled by stars, at non-star positions is a critical part in the image analysis for weak lensing where inaccuracies in the modeled ellipticity e and size R {sup 2} can impact the ability to measure the shapes of galaxies. This is of importance because weak lensing is a particularly sensitive probe of dark energy and can be used to map the mass distribution of large scale structure. Participants in the challenge were presented with 27,500 stars over 1300 images subdivided into 26 sets, where in each set a category change was made in the type or spatial variation of the PSF. Thirty submissions were made by nine teams. The best methods reconstructed the PSF with an accuracy of {sigma}(e) Almost-Equal-To 2.5 Multiplication-Sign 10{sup -4} and {sigma}(R {sup 2})/R {sup 2} Almost-Equal-To 7.4 Multiplication-Sign 10{sup -4}. For a fixed pixel scale, narrower PSFs were found to be more difficult to model than larger PSFs, and the PSF reconstruction was severely degraded with the inclusion of an atmospheric turbulence model (although this result is likely to be a strong function of the amplitude of the turbulence power spectrum).

Deep ensemble learning of sparse regression models for brain disease diagnosis.

Science.gov (United States)

Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

2017-04-01

Recent studies on brain imaging analysis witnessed the core roles of machine learning techniques in computer-assisted intervention for brain disease diagnosis. Of various machine-learning techniques, sparse regression models have proved their effectiveness in handling high-dimensional data but with a small number of training samples, especially in medical problems. In the meantime, deep learning methods have been making great successes by outperforming the state-of-the-art performances in various applications. In this paper, we propose a novel framework that combines the two conceptually different methods of sparse regression and deep learning for Alzheimer's disease/mild cognitive impairment diagnosis and prognosis. Specifically, we first train multiple sparse regression models, each of which is trained with different values of a regularization control parameter. Thus, our multiple sparse regression models potentially select different feature subsets from the original feature set; thereby they have different powers to predict the response values, i.e., clinical label and clinical scores in our work. By regarding the response values from our sparse regression models as target-level representations, we then build a deep convolutional neural network for clinical decision making, which thus we call 'Deep Ensemble Sparse Regression Network.' To our best knowledge, this is the first work that combines sparse regression models with deep neural network. In our experiments with the ADNI cohort, we validated the effectiveness of the proposed method by achieving the highest diagnostic accuracies in three classification tasks. We also rigorously analyzed our results and compared with the previous studies on the ADNI cohort in the literature. Copyright © 2017 Elsevier B.V. All rights reserved.

Image Classification Based on Convolutional Denoising Sparse Autoencoder

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Shuangshuang Chen

2017-01-01

Full Text Available Image classification aims to group images into corresponding semantic categories. Due to the difficulties of interclass similarity and intraclass variability, it is a challenging issue in computer vision. In this paper, an unsupervised feature learning approach called convolutional denoising sparse autoencoder (CDSAE is proposed based on the theory of visual attention mechanism and deep learning methods. Firstly, saliency detection method is utilized to get training samples for unsupervised feature learning. Next, these samples are sent to the denoising sparse autoencoder (DSAE, followed by convolutional layer and local contrast normalization layer. Generally, prior in a specific task is helpful for the task solution. Therefore, a new pooling strategy—spatial pyramid pooling (SPP fused with center-bias prior—is introduced into our approach. Experimental results on the common two image datasets (STL-10 and CIFAR-10 demonstrate that our approach is effective in image classification. They also demonstrate that none of these three components: local contrast normalization, SPP fused with center-prior, and l2 vector normalization can be excluded from our proposed approach. They jointly improve image representation and classification performance.

Bayesian Inference Methods for Sparse Channel Estimation

DEFF Research Database (Denmark)

Pedersen, Niels Lovmand

2013-01-01

This thesis deals with sparse Bayesian learning (SBL) with application to radio channel estimation. As opposed to the classical approach for sparse signal representation, we focus on the problem of inferring complex signals. Our investigations within SBL constitute the basis for the development...... of Bayesian inference algorithms for sparse channel estimation. Sparse inference methods aim at finding the sparse representation of a signal given in some overcomplete dictionary of basis vectors. Within this context, one of our main contributions to the field of SBL is a hierarchical representation...... analysis of the complex prior representation, where we show that the ability to induce sparse estimates of a given prior heavily depends on the inference method used and, interestingly, whether real or complex variables are inferred. We also show that the Bayesian estimators derived from the proposed...

Semi-inner-products in Banach Spaces with Applications to Regularized Learning, Sampling, and Sparse Approximation

Science.gov (United States)

2016-03-13

7.00 8.00 Praveen K. Yenduri, Anna C. Gilbert, Jun Zhang. Integrate-and-fire neuron modeled as a low-rate sparse time-encoding device, 2012 Third...International Conference on Intelligent Control and Information Processing (ICICIP). 15-JUL- 12, Dalian, China. : , Praveen K. Yenduri, Anna C. Gilbert

When sparse coding meets ranking: a joint framework for learning sparse codes and ranking scores

KAUST Repository

Wang, Jim Jing-Yan; Cui, Xuefeng; Yu, Ge; Guo, Lili; Gao, Xin

2017-01-01

Sparse coding, which represents a data point as a sparse reconstruction code with regard to a dictionary, has been a popular data representation method. Meanwhile, in database retrieval problems, learning the ranking scores from data points plays

Method for estimating modulation transfer function from sample images.

Science.gov (United States)

Saiga, Rino; Takeuchi, Akihisa; Uesugi, Kentaro; Terada, Yasuko; Suzuki, Yoshio; Mizutani, Ryuta

2018-02-01

The modulation transfer function (MTF) represents the frequency domain response of imaging modalities. Here, we report a method for estimating the MTF from sample images. Test images were generated from a number of images, including those taken with an electron microscope and with an observation satellite. These original images were convolved with point spread functions (PSFs) including those of circular apertures. The resultant test images were subjected to a Fourier transformation. The logarithm of the squared norm of the Fourier transform was plotted against the squared distance from the origin. Linear correlations were observed in the logarithmic plots, indicating that the PSF of the test images can be approximated with a Gaussian. The MTF was then calculated from the Gaussian-approximated PSF. The obtained MTF closely coincided with the MTF predicted from the original PSF. The MTF of an x-ray microtomographic section of a fly brain was also estimated with this method. The obtained MTF showed good agreement with the MTF determined from an edge profile of an aluminum test object. We suggest that this approach is an alternative way of estimating the MTF, independently of the image type. Copyright © 2017 Elsevier Ltd. All rights reserved.

Improved Sparse Channel Estimation for Cooperative Communication Systems

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Guan Gui

2012-01-01

Full Text Available Accurate channel state information (CSI is necessary at receiver for coherent detection in amplify-and-forward (AF cooperative communication systems. To estimate the channel, traditional methods, that is, least squares (LS and least absolute shrinkage and selection operator (LASSO, are based on assumptions of either dense channel or global sparse channel. However, LS-based linear method neglects the inherent sparse structure information while LASSO-based sparse channel method cannot take full advantage of the prior information. Based on the partial sparse assumption of the cooperative channel model, we propose an improved channel estimation method with partial sparse constraint. At first, by using sparse decomposition theory, channel estimation is formulated as a compressive sensing problem. Secondly, the cooperative channel is reconstructed by LASSO with partial sparse constraint. Finally, numerical simulations are carried out to confirm the superiority of proposed methods over global sparse channel estimation methods.

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.

Sparse Image Reconstruction in Computed Tomography

DEFF Research Database (Denmark)

Jørgensen, Jakob Sauer

In recent years, increased focus on the potentially harmful effects of x-ray computed tomography (CT) scans, such as radiation-induced cancer, has motivated research on new low-dose imaging techniques. Sparse image reconstruction methods, as studied for instance in the field of compressed sensing...... applications. This thesis takes a systematic approach toward establishing quantitative understanding of conditions for sparse reconstruction to work well in CT. A general framework for analyzing sparse reconstruction methods in CT is introduced and two sets of computational tools are proposed: 1...... contributions to a general set of computational characterization tools. Thus, the thesis contributions help advance sparse reconstruction methods toward routine use in...

Sparse Regression by Projection and Sparse Discriminant Analysis

KAUST Repository

Qi, Xin; Luo, Ruiyan; Carroll, Raymond J.; Zhao, Hongyu

2015-01-01

predictions. We introduce a new framework, regression by projection, and its sparse version to analyze high-dimensional data. The unique nature of this framework is that the directions of the regression coefficients are inferred first, and the lengths

Duplex scanning using sparse data sequences

DEFF Research Database (Denmark)

Møllenbach, S. K.; Jensen, Jørgen Arendt

2008-01-01

reconstruction of the missing samples possible. The periodic pattern has the length T = M + A samples, where M are for B-mode and A for velocity estimation. The missing samples can now be reconstructed using a filter bank. One filter bank reconstructs one missing sample, so the number of filter banks corresponds...... to M. The number of sub filters in every filter bank is the same as A. Every sub filter contains fractional delay (FD) filter and an interpolation function. Many different sequences can be selected to adapt the B-mode frame rate needed. The drawback of the method is that the maximum velocity detectable......, the fprf and the resolution are 15 MHz, 3.5 kHz, and 12 bit sample (8 kHz and 16 bit for the Carotid artery). The resulting data contains 8000 RF lines with 128 samples at a depth of 45 mm for the vein and 50 mm for Aorta. Sparse sequences are constructed from the full data sequences to have both...

Sparse decompositions in 'incoherent' dictionaries

DEFF Research Database (Denmark)

Gribonval, R.; Nielsen, Morten

2003-01-01

a unique sparse representation in such a dictionary. In particular, it is proved that the result of Donoho and Huo, concerning the replacement of a combinatorial optimization problem with a linear programming problem when searching for sparse representations, has an analog for dictionaries that may...

Data analysis in high-dimensional sparse spaces

DEFF Research Database (Denmark)

Clemmensen, Line Katrine Harder

classification techniques for high-dimensional problems are presented: Sparse discriminant analysis, sparse mixture discriminant analysis and orthogonality constrained support vector machines. The first two introduces sparseness to the well known linear and mixture discriminant analysis and thereby provide low...... are applied to classifications of fish species, ear canal impressions used in the hearing aid industry, microbiological fungi species, and various cancerous tissues and healthy tissues. In addition, novel applications of sparse regressions (also called the elastic net) to the medical, concrete, and food...

A sparse-grid isogeometric solver

KAUST Repository

Beck, Joakim; Sangalli, Giancarlo; Tamellini, Lorenzo

2018-01-01

Isogeometric Analysis (IGA) typically adopts tensor-product splines and NURBS as a basis for the approximation of the solution of PDEs. In this work, we investigate to which extent IGA solvers can benefit from the so-called sparse-grids construction in its combination technique form, which was first introduced in the early 90’s in the context of the approximation of high-dimensional PDEs.The tests that we report show that, in accordance to the literature, a sparse-grid construction can indeed be useful if the solution of the PDE at hand is sufficiently smooth. Sparse grids can also be useful in the case of non-smooth solutions when some a-priori knowledge on the location of the singularities of the solution can be exploited to devise suitable non-equispaced meshes. Finally, we remark that sparse grids can be seen as a simple way to parallelize pre-existing serial IGA solvers in a straightforward fashion, which can be beneficial in many practical situations.

A sparse-grid isogeometric solver

KAUST Repository

Beck, Joakim

2018-02-28

Isogeometric Analysis (IGA) typically adopts tensor-product splines and NURBS as a basis for the approximation of the solution of PDEs. In this work, we investigate to which extent IGA solvers can benefit from the so-called sparse-grids construction in its combination technique form, which was first introduced in the early 90’s in the context of the approximation of high-dimensional PDEs.The tests that we report show that, in accordance to the literature, a sparse-grid construction can indeed be useful if the solution of the PDE at hand is sufficiently smooth. Sparse grids can also be useful in the case of non-smooth solutions when some a-priori knowledge on the location of the singularities of the solution can be exploited to devise suitable non-equispaced meshes. Finally, we remark that sparse grids can be seen as a simple way to parallelize pre-existing serial IGA solvers in a straightforward fashion, which can be beneficial in many practical situations.

Deconvolving the Nucleus of Centaurus A Using Chandra PSF Library

Science.gov (United States)

Karovska, Margarita

2000-01-01

Centaurus A (NGC 5128) is a giant early-type galaxy containing the nearest (at 3.5 Mpc) radio-bright Active Galactic Nucleus (AGN). Cen A was observed with the High Resolution Camera (HRC) on the Chandra X-ray Observatory on several occasions since the launch in July 1999. The high-angular resolution (less than 0.5 arcsecond) Chandra/HRC images reveal X ray multi-scale structures in this object with unprecedented detail and clarity, including the bright nucleus believed to be associated with a supermassive black hole. We explored the spatial extent of the Cen A nucleus using deconvolution techniques on the full resolution Chandra images. Model point spread functions (PSFs) were derived from the standard Chandra raytrace PSF library as well as unresolved point sources observed with Chandra. The deconvolved images show that the Cen A nucleus is resolved and asymmetric. We discuss several possible causes of this extended emission and of the asymmetries.

Joint Group Sparse PCA for Compressed Hyperspectral Imaging.

Science.gov (United States)

Khan, Zohaib; Shafait, Faisal; Mian, Ajmal

2015-12-01

A sparse principal component analysis (PCA) seeks a sparse linear combination of input features (variables), so that the derived features still explain most of the variations in the data. A group sparse PCA introduces structural constraints on the features in seeking such a linear combination. Collectively, the derived principal components may still require measuring all the input features. We present a joint group sparse PCA (JGSPCA) algorithm, which forces the basic coefficients corresponding to a group of features to be jointly sparse. Joint sparsity ensures that the complete basis involves only a sparse set of input features, whereas the group sparsity ensures that the structural integrity of the features is maximally preserved. We evaluate the JGSPCA algorithm on the problems of compressed hyperspectral imaging and face recognition. Compressed sensing results show that the proposed method consistently outperforms sparse PCA and group sparse PCA in reconstructing the hyperspectral scenes of natural and man-made objects. The efficacy of the proposed compressed sensing method is further demonstrated in band selection for face recognition.

Aspect-Aided Dynamic Non-Negative Sparse Representation-Based Microwave Image Classification

Directory of Open Access Journals (Sweden)

Xinzheng Zhang

2016-09-01

Full Text Available Classification of target microwave images is an important application in much areas such as security, surveillance, etc. With respect to the task of microwave image classification, a recognition algorithm based on aspect-aided dynamic non-negative least square (ADNNLS sparse representation is proposed. Firstly, an aspect sector is determined, the center of which is the estimated aspect angle of the testing sample. The training samples in the aspect sector are divided into active atoms and inactive atoms by smooth self-representative learning. Secondly, for each testing sample, the corresponding active atoms are selected dynamically, thereby establishing dynamic dictionary. Thirdly, the testing sample is represented with ℓ 1 -regularized non-negative sparse representation under the corresponding dynamic dictionary. Finally, the class label of the testing sample is identified by use of the minimum reconstruction error. Verification of the proposed algorithm was conducted using the Moving and Stationary Target Acquisition and Recognition (MSTAR database which was acquired by synthetic aperture radar. Experiment results validated that the proposed approach was able to capture the local aspect characteristics of microwave images effectively, thereby improving the classification performance.

SU-E-I-45: Reconstruction of CT Images From Sparsely-Sampled Data Using the Logarithmic Barrier Method

International Nuclear Information System (INIS)

Xu, H

2014-01-01

Purpose: To develop and investigate whether the logarithmic barrier (LB) method can result in high-quality reconstructed CT images using sparsely-sampled noisy projection data Methods: The objective function is typically formulated as the sum of the total variation (TV) and a data fidelity (DF) term with a parameter λ that governs the relative weight between them. Finding the optimized value of λ is a critical step for this approach to give satisfactory results. The proposed LB method avoid using λ by constructing the objective function as the sum of the TV and a log function whose augment is the DF term. Newton's method was used to solve the optimization problem. The algorithm was coded in MatLab2013b. Both Shepp-Logan phantom and a patient lung CT image were used for demonstration of the algorithm. Measured data were simulated by calculating the projection data using radon transform. A Poisson noise model was used to account for the simulated detector noise. The iteration stopped when the difference of the current TV and the previous one was less than 1%. Results: Shepp-Logan phantom reconstruction study shows that filtered back-projection (FBP) gives high streak artifacts for 30 and 40 projections. Although visually the streak artifacts are less pronounced for 64 and 90 projections in FBP, the 1D pixel profiles indicate that FBP gives noisier reconstructed pixel values than LB does. A lung image reconstruction is presented. It shows that use of 64 projections gives satisfactory reconstructed image quality with regard to noise suppression and sharp edge preservation. Conclusion: This study demonstrates that the logarithmic barrier method can be used to reconstruct CT images from sparsely-amped data. The number of projections around 64 gives a balance between the over-smoothing of the sharp demarcation and noise suppression. Future study may extend to CBCT reconstruction and improvement on computation speed

Parallel Sparse Matrix - Vector Product

DEFF Research Database (Denmark)

Alexandersen, Joe; Lazarov, Boyan Stefanov; Dammann, Bernd

This technical report contains a case study of a sparse matrix-vector product routine, implemented for parallel execution on a compute cluster with both pure MPI and hybrid MPI-OpenMP solutions. C++ classes for sparse data types were developed and the report shows how these class can be used...

Multi-threaded Sparse Matrix Sparse Matrix Multiplication for Many-Core and GPU Architectures.

Energy Technology Data Exchange (ETDEWEB)

Deveci, Mehmet [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Trott, Christian Robert [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Rajamanickam, Sivasankaran [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2018-01-01

Sparse Matrix-Matrix multiplication is a key kernel that has applications in several domains such as scientific computing and graph analysis. Several algorithms have been studied in the past for this foundational kernel. In this paper, we develop parallel algorithms for sparse matrix- matrix multiplication with a focus on performance portability across different high performance computing architectures. The performance of these algorithms depend on the data structures used in them. We compare different types of accumulators in these algorithms and demonstrate the performance difference between these data structures. Furthermore, we develop a meta-algorithm, kkSpGEMM, to choose the right algorithm and data structure based on the characteristics of the problem. We show performance comparisons on three architectures and demonstrate the need for the community to develop two phase sparse matrix-matrix multiplication implementations for efficient reuse of the data structures involved.

Sparse approximation with bases

CERN Document Server

2015-01-01

This book systematically presents recent fundamental results on greedy approximation with respect to bases. Motivated by numerous applications, the last decade has seen great successes in studying nonlinear sparse approximation. Recent findings have established that greedy-type algorithms are suitable methods of nonlinear approximation in both sparse approximation with respect to bases and sparse approximation with respect to redundant systems. These insights, combined with some previous fundamental results, form the basis for constructing the theory of greedy approximation. Taking into account the theoretical and practical demand for this kind of theory, the book systematically elaborates a theoretical framework for greedy approximation and its applications.  The book addresses the needs of researchers working in numerical mathematics, harmonic analysis, and functional analysis. It quickly takes the reader from classical results to the latest frontier, but is written at the level of a graduate course and do...

Efficient convolutional sparse coding

Science.gov (United States)

Wohlberg, Brendt

2017-06-20

Computationally efficient algorithms may be applied for fast dictionary learning solving the convolutional sparse coding problem in the Fourier domain. More specifically, efficient convolutional sparse coding may be derived within an alternating direction method of multipliers (ADMM) framework that utilizes fast Fourier transforms (FFT) to solve the main linear system in the frequency domain. Such algorithms may enable a significant reduction in computational cost over conventional approaches by implementing a linear solver for the most critical and computationally expensive component of the conventional iterative algorithm. The theoretical computational cost of the algorithm may be reduced from O(M.sup.3N) to O(MN log N), where N is the dimensionality of the data and M is the number of elements in the dictionary. This significant improvement in efficiency may greatly increase the range of problems that can practically be addressed via convolutional sparse representations.

Hyperspectral Unmixing with Robust Collaborative Sparse Regression

Directory of Open Access Journals (Sweden)

Chang Li

2016-07-01

Full Text Available Recently, sparse unmixing (SU of hyperspectral data has received particular attention for analyzing remote sensing images. However, most SU methods are based on the commonly admitted linear mixing model (LMM, which ignores the possible nonlinear effects (i.e., nonlinearity. In this paper, we propose a new method named robust collaborative sparse regression (RCSR based on the robust LMM (rLMM for hyperspectral unmixing. The rLMM takes the nonlinearity into consideration, and the nonlinearity is merely treated as outlier, which has the underlying sparse property. The RCSR simultaneously takes the collaborative sparse property of the abundance and sparsely distributed additive property of the outlier into consideration, which can be formed as a robust joint sparse regression problem. The inexact augmented Lagrangian method (IALM is used to optimize the proposed RCSR. The qualitative and quantitative experiments on synthetic datasets and real hyperspectral images demonstrate that the proposed RCSR is efficient for solving the hyperspectral SU problem compared with the other four state-of-the-art algorithms.

Model's sparse representation based on reduced mixed GMsFE basis methods

Energy Technology Data Exchange (ETDEWEB)

Jiang, Lijian, E-mail: ljjiang@hnu.edu.cn [Institute of Mathematics, Hunan University, Changsha 410082 (China); Li, Qiuqi, E-mail: qiuqili@hnu.edu.cn [College of Mathematics and Econometrics, Hunan University, Changsha 410082 (China)

2017-06-01

In this paper, we propose a model's sparse representation based on reduced mixed generalized multiscale finite element (GMsFE) basis methods for elliptic PDEs with random inputs. A typical application for the elliptic PDEs is the flow in heterogeneous random porous media. Mixed generalized multiscale finite element method (GMsFEM) is one of the accurate and efficient approaches to solve the flow problem in a coarse grid and obtain the velocity with local mass conservation. When the inputs of the PDEs are parameterized by the random variables, the GMsFE basis functions usually depend on the random parameters. This leads to a large number degree of freedoms for the mixed GMsFEM and substantially impacts on the computation efficiency. In order to overcome the difficulty, we develop reduced mixed GMsFE basis methods such that the multiscale basis functions are independent of the random parameters and span a low-dimensional space. To this end, a greedy algorithm is used to find a set of optimal samples from a training set scattered in the parameter space. Reduced mixed GMsFE basis functions are constructed based on the optimal samples using two optimal sampling strategies: basis-oriented cross-validation and proper orthogonal decomposition. Although the dimension of the space spanned by the reduced mixed GMsFE basis functions is much smaller than the dimension of the original full order model, the online computation still depends on the number of coarse degree of freedoms. To significantly improve the online computation, we integrate the reduced mixed GMsFE basis methods with sparse tensor approximation and obtain a sparse representation for the model's outputs. The sparse representation is very efficient for evaluating the model's outputs for many instances of parameters. To illustrate the efficacy of the proposed methods, we present a few numerical examples for elliptic PDEs with multiscale and random inputs. In particular, a two-phase flow model in

Image fusion using sparse overcomplete feature dictionaries

Science.gov (United States)

Brumby, Steven P.; Bettencourt, Luis; Kenyon, Garrett T.; Chartrand, Rick; Wohlberg, Brendt

2015-10-06

Approaches for deciding what individuals in a population of visual system "neurons" are looking for using sparse overcomplete feature dictionaries are provided. A sparse overcomplete feature dictionary may be learned for an image dataset and a local sparse representation of the image dataset may be built using the learned feature dictionary. A local maximum pooling operation may be applied on the local sparse representation to produce a translation-tolerant representation of the image dataset. An object may then be classified and/or clustered within the translation-tolerant representation of the image dataset using a supervised classification algorithm and/or an unsupervised clustering algorithm.

Risk programming and sparse data: how to get more reliable results

NARCIS (Netherlands)

Hardaker, J.B.; Lien, G.; Asseldonk, van M.A.P.M.; Richardson, W.; Hegrenes, A.

2008-01-01

Because relevant historical data for farms are inevitably sparse, most risk programming studies rely on few observations. We discuss how to use available information to derive an appropriate multivariate distribution function that can be sampled for a more complete representation of the possible

Manifold regularization for sparse unmixing of hyperspectral images.

Science.gov (United States)

Liu, Junmin; Zhang, Chunxia; Zhang, Jiangshe; Li, Huirong; Gao, Yuelin

2016-01-01

Recently, sparse unmixing has been successfully applied to spectral mixture analysis of remotely sensed hyperspectral images. Based on the assumption that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance, unmixing of each mixed pixel in the scene is to find an optimal subset of signatures in a very large spectral library, which is cast into the framework of sparse regression. However, traditional sparse regression models, such as collaborative sparse regression , ignore the intrinsic geometric structure in the hyperspectral data. In this paper, we propose a novel model, called manifold regularized collaborative sparse regression , by introducing a manifold regularization to the collaborative sparse regression model. The manifold regularization utilizes a graph Laplacian to incorporate the locally geometrical structure of the hyperspectral data. An algorithm based on alternating direction method of multipliers has been developed for the manifold regularized collaborative sparse regression model. Experimental results on both the simulated and real hyperspectral data sets have demonstrated the effectiveness of our proposed model.

Enhancing Scalability of Sparse Direct Methods

International Nuclear Information System (INIS)

Li, Xiaoye S.; Demmel, James; Grigori, Laura; Gu, Ming; Xia, Jianlin; Jardin, Steve; Sovinec, Carl; Lee, Lie-Quan

2007-01-01

TOPS is providing high-performance, scalable sparse direct solvers, which have had significant impacts on the SciDAC applications, including fusion simulation (CEMM), accelerator modeling (COMPASS), as well as many other mission-critical applications in DOE and elsewhere. Our recent developments have been focusing on new techniques to overcome scalability bottleneck of direct methods, in both time and memory. These include parallelizing symbolic analysis phase and developing linear-complexity sparse factorization methods. The new techniques will make sparse direct methods more widely usable in large 3D simulations on highly-parallel petascale computers

Regression with Sparse Approximations of Data

DEFF Research Database (Denmark)

Noorzad, Pardis; Sturm, Bob L.

2012-01-01

We propose sparse approximation weighted regression (SPARROW), a method for local estimation of the regression function that uses sparse approximation with a dictionary of measurements. SPARROW estimates the regression function at a point with a linear combination of a few regressands selected...... by a sparse approximation of the point in terms of the regressors. We show SPARROW can be considered a variant of \\(k\\)-nearest neighbors regression (\\(k\\)-NNR), and more generally, local polynomial kernel regression. Unlike \\(k\\)-NNR, however, SPARROW can adapt the number of regressors to use based...

Sparse adaptive filters for echo cancellation

CERN Document Server

Paleologu, Constantin

2011-01-01

Adaptive filters with a large number of coefficients are usually involved in both network and acoustic echo cancellation. Consequently, it is important to improve the convergence rate and tracking of the conventional algorithms used for these applications. This can be achieved by exploiting the sparseness character of the echo paths. Identification of sparse impulse responses was addressed mainly in the last decade with the development of the so-called ``proportionate''-type algorithms. The goal of this book is to present the most important sparse adaptive filters developed for echo cancellati

PTB-associated splicing factor (PSF) functions as a repressor of STAT6-mediated IG{epsilon} gene transcription by recruitment of HDAC1

DEFF Research Database (Denmark)

Dong, Lijie; Zhang, Xinyu; Fu, Xiao

2010-01-01

understood. Here we identified by proteomic approach that PTB-associated splicing factor (PSF) interacts with STAT6. In cells the interaction required IL-4 stimulation and was observed both with endogenous and ectopically expressed proteins. The ligand dependency of the interaction suggested involvement...

Parallel sparse direct solver for integrated circuit simulation

CERN Document Server

Chen, Xiaoming; Yang, Huazhong

2017-01-01

This book describes algorithmic methods and parallelization techniques to design a parallel sparse direct solver which is specifically targeted at integrated circuit simulation problems. The authors describe a complete flow and detailed parallel algorithms of the sparse direct solver. They also show how to improve the performance by simple but effective numerical techniques. The sparse direct solver techniques described can be applied to any SPICE-like integrated circuit simulator and have been proven to be high-performance in actual circuit simulation. Readers will benefit from the state-of-the-art parallel integrated circuit simulation techniques described in this book, especially the latest parallel sparse matrix solution techniques. · Introduces complicated algorithms of sparse linear solvers, using concise principles and simple examples, without complex theory or lengthy derivations; · Describes a parallel sparse direct solver that can be adopted to accelerate any SPICE-like integrated circuit simulato...

Biclustering via Sparse Singular Value Decomposition

KAUST Repository

Lee, Mihee

2010-02-16

Sparse singular value decomposition (SSVD) is proposed as a new exploratory analysis tool for biclustering or identifying interpretable row-column associations within high-dimensional data matrices. SSVD seeks a low-rank, checkerboard structured matrix approximation to data matrices. The desired checkerboard structure is achieved by forcing both the left- and right-singular vectors to be sparse, that is, having many zero entries. By interpreting singular vectors as regression coefficient vectors for certain linear regressions, sparsity-inducing regularization penalties are imposed to the least squares regression to produce sparse singular vectors. An efficient iterative algorithm is proposed for computing the sparse singular vectors, along with some discussion of penalty parameter selection. A lung cancer microarray dataset and a food nutrition dataset are used to illustrate SSVD as a biclustering method. SSVD is also compared with some existing biclustering methods using simulated datasets. © 2010, The International Biometric Society.

A coarse-to-fine approach for medical hyperspectral image classification with sparse representation

Science.gov (United States)

Chang, Lan; Zhang, Mengmeng; Li, Wei

2017-10-01

A coarse-to-fine approach with sparse representation is proposed for medical hyperspectral image classification in this work. Segmentation technique with different scales is employed to exploit edges of the input image, where coarse super-pixel patches provide global classification information while fine ones further provide detail information. Different from common RGB image, hyperspectral image has multi bands to adjust the cluster center with more high precision. After segmentation, each super pixel is classified by recently-developed sparse representation-based classification (SRC), which assigns label for testing samples in one local patch by means of sparse linear combination of all the training samples. Furthermore, segmentation with multiple scales is employed because single scale is not suitable for complicate distribution of medical hyperspectral imagery. Finally, classification results for different sizes of super pixel are fused by some fusion strategy, offering at least two benefits: (1) the final result is obviously superior to that of segmentation with single scale, and (2) the fusion process significantly simplifies the choice of scales. Experimental results using real medical hyperspectral images demonstrate that the proposed method outperforms the state-of-the-art SRC.

Robust Face Recognition Via Gabor Feature and Sparse Representation

Directory of Open Access Journals (Sweden)

Hao Yu-Juan

2016-01-01

Full Text Available Sparse representation based on compressed sensing theory has been widely used in the field of face recognition, and has achieved good recognition results. but the face feature extraction based on sparse representation is too simple, and the sparse coefficient is not sparse. In this paper, we improve the classification algorithm based on the fusion of sparse representation and Gabor feature, and then improved algorithm for Gabor feature which overcomes the problem of large dimension of the vector dimension, reduces the computation and storage cost, and enhances the robustness of the algorithm to the changes of the environment.The classification efficiency of sparse representation is determined by the collaborative representation,we simplify the sparse constraint based on L1 norm to the least square constraint, which makes the sparse coefficients both positive and reduce the complexity of the algorithm. Experimental results show that the proposed method is robust to illumination, facial expression and pose variations of face recognition, and the recognition rate of the algorithm is improved.

Sparse Learning with Stochastic Composite Optimization.

Science.gov (United States)

Zhang, Weizhong; Zhang, Lijun; Jin, Zhongming; Jin, Rong; Cai, Deng; Li, Xuelong; Liang, Ronghua; He, Xiaofei

2017-06-01

In this paper, we study Stochastic Composite Optimization (SCO) for sparse learning that aims to learn a sparse solution from a composite function. Most of the recent SCO algorithms have already reached the optimal expected convergence rate O(1/λT), but they often fail to deliver sparse solutions at the end either due to the limited sparsity regularization during stochastic optimization (SO) or due to the limitation in online-to-batch conversion. Even when the objective function is strongly convex, their high probability bounds can only attain O(√{log(1/δ)/T}) with δ is the failure probability, which is much worse than the expected convergence rate. To address these limitations, we propose a simple yet effective two-phase Stochastic Composite Optimization scheme by adding a novel powerful sparse online-to-batch conversion to the general Stochastic Optimization algorithms. We further develop three concrete algorithms, OptimalSL, LastSL and AverageSL, directly under our scheme to prove the effectiveness of the proposed scheme. Both the theoretical analysis and the experiment results show that our methods can really outperform the existing methods at the ability of sparse learning and at the meantime we can improve the high probability bound to approximately O(log(log(T)/δ)/λT).

Shearlets and Optimally Sparse Approximations

DEFF Research Database (Denmark)

Kutyniok, Gitta; Lemvig, Jakob; Lim, Wang-Q

2012-01-01

Multivariate functions are typically governed by anisotropic features such as edges in images or shock fronts in solutions of transport-dominated equations. One major goal both for the purpose of compression as well as for an efficient analysis is the provision of optimally sparse approximations...... optimally sparse approximations of this model class in 2D as well as 3D. Even more, in contrast to all other directional representation systems, a theory for compactly supported shearlet frames was derived which moreover also satisfy this optimality benchmark. This chapter shall serve as an introduction...... to and a survey about sparse approximations of cartoon-like images by band-limited and also compactly supported shearlet frames as well as a reference for the state-of-the-art of this research field....

Characterizing spatiotemporal information loss in sparse-sampling-based dynamic MRI for monitoring respiration-induced tumor motion in radiotherapy

Energy Technology Data Exchange (ETDEWEB)

Arai, Tatsuya J. [Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas 75390 (United States); Nofiele, Joris; Yuan, Qing [Department of Radiology, UT Southwestern Medical Center, Dallas, Texas 75390 (United States); Madhuranthakam, Ananth J.; Pedrosa, Ivan; Chopra, Rajiv [Department of Radiology, UT Southwestern Medical Center, Dallas, Texas 75390 (United States); Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, Texas 75390 (United States); Sawant, Amit, E-mail: amit.sawant@utsouthwestern.edu [Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas 75390 (United States); Department of Radiology, UT Southwestern Medical Center, Dallas, Texas 75390 (United States); Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, 21201 (United States)

2016-06-15

Purpose: Sparse-sampling and reconstruction techniques represent an attractive strategy to achieve faster image acquisition speeds, while maintaining adequate spatial resolution and signal-to-noise ratio in rapid magnetic resonance imaging (MRI). The authors investigate the use of one such sequence, broad-use linear acquisition speed-up technique (k-t BLAST) in monitoring tumor motion for thoracic and abdominal radiotherapy and examine the potential trade-off between increased sparsification (to increase imaging speed) and the potential loss of “true” information due to greater reliance on a priori information. Methods: Lung tumor motion trajectories in the superior–inferior direction, previously recorded from ten lung cancer patients, were replayed using a motion phantom module driven by an MRI-compatible motion platform. Eppendorf test tubes filled with water which serve as fiducial markers were placed in the phantom. The modeled rigid and deformable motions were collected in a coronal image slice using balanced fast field echo in conjunction with k-t BLAST. Root mean square (RMS) error was used as a metric of spatial accuracy as measured trajectories were compared to input data. The loss of spatial information was characterized for progressively increasing acceleration factor from 1 to 16; the resultant sampling frequency was increased approximately from 2.5 to 19 Hz when the principal direction of the motion was set along frequency encoding direction. In addition to the phantom study, respiration-induced tumor motions were captured from two patients (kidney tumor and lung tumor) at 13 Hz over 49 s to demonstrate the impact of high speed motion monitoring over multiple breathing cycles. For each subject, the authors compared the tumor centroid trajectory as well as the deformable motion during free breathing. Results: In the rigid and deformable phantom studies, the RMS error of target tracking at the acquisition speed of 19 Hz was approximately 0.3–0

A sparse version of IGA solvers

KAUST Repository

Beck, Joakim; Sangalli, Giancarlo; Tamellini, Lorenzo

2017-01-01

Isogeometric Analysis (IGA) typically adopts tensor-product splines and NURBS as a basis for the approximation of the solution of PDEs. In this work, we investigate to which extent IGA solvers can benefit from the so-called sparse-grids construction in its combination technique form, which was first introduced in the early 90s in the context of the approximation of high-dimensional PDEs. The tests that we report show that, in accordance to the literature, a sparse grids construction can indeed be useful if the solution of the PDE at hand is sufficiently smooth. Sparse grids can also be useful in the case of non-smooth solutions when some a-priori knowledge on the location of the singularities of the solution can be exploited to devise suitable non-equispaced meshes. Finally, we remark that sparse grids can be seen as a simple way to parallelize pre-existing serial IGA solvers in a straightforward fashion, which can be beneficial in many practical situations.

A sparse version of IGA solvers

KAUST Repository

Beck, Joakim

2017-07-30

Isogeometric Analysis (IGA) typically adopts tensor-product splines and NURBS as a basis for the approximation of the solution of PDEs. In this work, we investigate to which extent IGA solvers can benefit from the so-called sparse-grids construction in its combination technique form, which was first introduced in the early 90s in the context of the approximation of high-dimensional PDEs. The tests that we report show that, in accordance to the literature, a sparse grids construction can indeed be useful if the solution of the PDE at hand is sufficiently smooth. Sparse grids can also be useful in the case of non-smooth solutions when some a-priori knowledge on the location of the singularities of the solution can be exploited to devise suitable non-equispaced meshes. Finally, we remark that sparse grids can be seen as a simple way to parallelize pre-existing serial IGA solvers in a straightforward fashion, which can be beneficial in many practical situations.

A Modified Sparse Representation Method for Facial Expression Recognition

Directory of Open Access Journals (Sweden)

Wei Wang

2016-01-01

Full Text Available In this paper, we carry on research on a facial expression recognition method, which is based on modified sparse representation recognition (MSRR method. On the first stage, we use Haar-like+LPP to extract feature and reduce dimension. On the second stage, we adopt LC-K-SVD (Label Consistent K-SVD method to train the dictionary, instead of adopting directly the dictionary from samples, and add block dictionary training into the training process. On the third stage, stOMP (stagewise orthogonal matching pursuit method is used to speed up the convergence of OMP (orthogonal matching pursuit. Besides, a dynamic regularization factor is added to iteration process to suppress noises and enhance accuracy. We verify the proposed method from the aspect of training samples, dimension, feature extraction and dimension reduction methods and noises in self-built database and Japan’s JAFFE and CMU’s CK database. Further, we compare this sparse method with classic SVM and RVM and analyze the recognition effect and time efficiency. The result of simulation experiment has shown that the coefficient of MSRR method contains classifying information, which is capable of improving the computing speed and achieving a satisfying recognition result.

Language Recognition via Sparse Coding

Science.gov (United States)

2016-09-08

explanation is that sparse coding can achieve a near-optimal approximation of much complicated nonlinear relationship through local and piecewise linear...training examples, where x(i) ∈ RN is the ith example in the batch. Optionally, X can be normalized and whitened before sparse coding for better result...normalized input vectors are then ZCA- whitened [20]. Em- pirically, we choose ZCA- whitening over PCA- whitening , and there is no dimensionality reduction

Sparse seismic imaging using variable projection

NARCIS (Netherlands)

Aravkin, Aleksandr Y.; Tu, Ning; van Leeuwen, Tristan

2013-01-01

We consider an important class of signal processing problems where the signal of interest is known to be sparse, and can be recovered from data given auxiliary information about how the data was generated. For example, a sparse Green's function may be recovered from seismic experimental data using

Tunable Sparse Network Coding for Multicast Networks

DEFF Research Database (Denmark)

Feizi, Soheil; Roetter, Daniel Enrique Lucani; Sørensen, Chres Wiant

2014-01-01

This paper shows the potential and key enabling mechanisms for tunable sparse network coding, a scheme in which the density of network coded packets varies during a transmission session. At the beginning of a transmission session, sparsely coded packets are transmitted, which benefits decoding...... complexity. At the end of a transmission, when receivers have accumulated degrees of freedom, coding density is increased. We propose a family of tunable sparse network codes (TSNCs) for multicast erasure networks with a controllable trade-off between completion time performance to decoding complexity...... a mechanism to perform efficient Gaussian elimination over sparse matrices going beyond belief propagation but maintaining low decoding complexity. Supporting simulation results are provided showing the trade-off between decoding complexity and completion time....

Sparse representation based image interpolation with nonlocal autoregressive modeling.

Science.gov (United States)

Dong, Weisheng; Zhang, Lei; Lukac, Rastislav; Shi, Guangming

2013-04-01

Sparse representation is proven to be a promising approach to image super-resolution, where the low-resolution (LR) image is usually modeled as the down-sampled version of its high-resolution (HR) counterpart after blurring. When the blurring kernel is the Dirac delta function, i.e., the LR image is directly down-sampled from its HR counterpart without blurring, the super-resolution problem becomes an image interpolation problem. In such cases, however, the conventional sparse representation models (SRM) become less effective, because the data fidelity term fails to constrain the image local structures. In natural images, fortunately, many nonlocal similar patches to a given patch could provide nonlocal constraint to the local structure. In this paper, we incorporate the image nonlocal self-similarity into SRM for image interpolation. More specifically, a nonlocal autoregressive model (NARM) is proposed and taken as the data fidelity term in SRM. We show that the NARM-induced sampling matrix is less coherent with the representation dictionary, and consequently makes SRM more effective for image interpolation. Our extensive experimental results demonstrate that the proposed NARM-based image interpolation method can effectively reconstruct the edge structures and suppress the jaggy/ringing artifacts, achieving the best image interpolation results so far in terms of PSNR as well as perceptual quality metrics such as SSIM and FSIM.

Desempenho do PSF no Sul e no Nordeste do Brasil: avaliação institucional e epidemiológica da Atenção Básica à Saúde

Directory of Open Access Journals (Sweden)

Luiz Augusto Facchini

Full Text Available A pesquisa, desenvolvida dentro dos Estudos de Linha de Base do Proesf analisou o desempenho do Programa Saúde da Família (PSF em 41 municípios dos Estados de Alagoas, Paraíba, Pernambuco, Piauí, Rio Grande do Norte, Rio Grande do Sul e Santa Catarina. Utilizou delineamento transversal, com grupo de comparação externo (atenção básica tradicional. Entrevistou 41 presidentes de Conselhos Municipais de Saúde, 29 secretários municipais de Saúde e 32 coordenadores de Atenção Básica. Foram caracterizados a estrutura e o processo de trabalho em 234 Unidades Básicas de Saúde (UBS, incluindo 4.749 trabalhadores de saúde; 4.079 crianças; 3.945 mulheres; 4.060 adultos e 4.006 idosos. O controle de qualidade alcançou 6% dos domicílios amostrados. A cobertura do PSF de 1999 a 2004 cresceu mais no Nordeste do que no Sul. Menos da metade dos trabalhadores ingressaram por concurso público e o trabalho precário foi maior no PSF do que em UBS tradicionais. Os achados sugerem um desempenho da Atenção Básica à Saúde (ABS ainda distante das prescrições do SUS. Menos da metade da demanda potencial utilizou a UBS de sua área de abrangência. A oferta de ações de saúde, a sua utilização e o contato por ações programáticas foram mais adequados no PSF.

SU-E-I-45: Reconstruction of CT Images From Sparsely-Sampled Data Using the Logarithmic Barrier Method

Energy Technology Data Exchange (ETDEWEB)

Xu, H [Department of Radiation Oncology, Dalhousie University, Halifax, NS (Canada)

2014-06-01

Purpose: To develop and investigate whether the logarithmic barrier (LB) method can result in high-quality reconstructed CT images using sparsely-sampled noisy projection data Methods: The objective function is typically formulated as the sum of the total variation (TV) and a data fidelity (DF) term with a parameter λ that governs the relative weight between them. Finding the optimized value of λ is a critical step for this approach to give satisfactory results. The proposed LB method avoid using λ by constructing the objective function as the sum of the TV and a log function whose augment is the DF term. Newton's method was used to solve the optimization problem. The algorithm was coded in MatLab2013b. Both Shepp-Logan phantom and a patient lung CT image were used for demonstration of the algorithm. Measured data were simulated by calculating the projection data using radon transform. A Poisson noise model was used to account for the simulated detector noise. The iteration stopped when the difference of the current TV and the previous one was less than 1%. Results: Shepp-Logan phantom reconstruction study shows that filtered back-projection (FBP) gives high streak artifacts for 30 and 40 projections. Although visually the streak artifacts are less pronounced for 64 and 90 projections in FBP, the 1D pixel profiles indicate that FBP gives noisier reconstructed pixel values than LB does. A lung image reconstruction is presented. It shows that use of 64 projections gives satisfactory reconstructed image quality with regard to noise suppression and sharp edge preservation. Conclusion: This study demonstrates that the logarithmic barrier method can be used to reconstruct CT images from sparsely-amped data. The number of projections around 64 gives a balance between the over-smoothing of the sharp demarcation and noise suppression. Future study may extend to CBCT reconstruction and improvement on computation speed.

Efficient Sparse Signal Transmission over a Lossy Link Using Compressive Sensing

Directory of Open Access Journals (Sweden)

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.

Structural Sparse Tracking

KAUST Repository

Zhang, Tianzhu; Yang, Ming-Hsuan; Ahuja, Narendra; Ghanem, Bernard; Yan, Shuicheng; Xu, Changsheng; Liu, Si

2015-01-01

candidate. We show that our SST algorithm accommodates most existing sparse trackers with the respective merits. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed SST algorithm performs

Technique detection software for Sparse Matrices

Directory of Open Access Journals (Sweden)

KHAN Muhammad Taimoor

2009-12-01

Full Text Available Sparse storage formats are techniques for storing and processing the sparse matrix data efficiently. The performance of these storage formats depend upon the distribution of non-zeros, within the matrix in different dimensions. In order to have better results we need a technique that suits best the organization of data in a particular matrix. So the decision of selecting a better technique is the main step towards improving the system's results otherwise the efficiency can be decreased. The purpose of this research is to help identify the best storage format in case of reduced storage size and high processing efficiency for a sparse matrix.

Sparse Representations of Hyperspectral Images

KAUST Repository

Swanson, Robin J.

2015-11-23

Hyperspectral image data has long been an important tool for many areas of sci- ence. The addition of spectral data yields significant improvements in areas such as object and image classification, chemical and mineral composition detection, and astronomy. Traditional capture methods for hyperspectral data often require each wavelength to be captured individually, or by sacrificing spatial resolution. Recently there have been significant improvements in snapshot hyperspectral captures using, in particular, compressed sensing methods. As we move to a compressed sensing image formation model the need for strong image priors to shape our reconstruction, as well as sparse basis become more important. Here we compare several several methods for representing hyperspectral images including learned three dimensional dictionaries, sparse convolutional coding, and decomposable nonlocal tensor dictionaries. Addi- tionally, we further explore their parameter space to identify which parameters provide the most faithful and sparse representations.

Sparse Representations of Hyperspectral Images

KAUST Repository

Swanson, Robin J.

2015-01-01

Hyperspectral image data has long been an important tool for many areas of sci- ence. The addition of spectral data yields significant improvements in areas such as object and image classification, chemical and mineral composition detection, and astronomy. Traditional capture methods for hyperspectral data often require each wavelength to be captured individually, or by sacrificing spatial resolution. Recently there have been significant improvements in snapshot hyperspectral captures using, in particular, compressed sensing methods. As we move to a compressed sensing image formation model the need for strong image priors to shape our reconstruction, as well as sparse basis become more important. Here we compare several several methods for representing hyperspectral images including learned three dimensional dictionaries, sparse convolutional coding, and decomposable nonlocal tensor dictionaries. Addi- tionally, we further explore their parameter space to identify which parameters provide the most faithful and sparse representations.

Supervised Convolutional Sparse Coding

KAUST Repository

Affara, Lama Ahmed

2018-04-08

Convolutional Sparse Coding (CSC) is a well-established image representation model especially suited for image restoration tasks. In this work, we extend the applicability of this model by proposing a supervised approach to convolutional sparse coding, which aims at learning discriminative dictionaries instead of purely reconstructive ones. We incorporate a supervised regularization term into the traditional unsupervised CSC objective to encourage the final dictionary elements to be discriminative. Experimental results show that using supervised convolutional learning results in two key advantages. First, we learn more semantically relevant filters in the dictionary and second, we achieve improved image reconstruction on unseen data.

A Hybrid Algorithm for Period Analysis from Multiband Data with Sparse and Irregular Sampling for Arbitrary Light-curve Shapes

Science.gov (United States)

Saha, Abhijit; Vivas, A. Katherina

2017-12-01

Ongoing and future surveys with repeat imaging in multiple bands are producing (or will produce) time-spaced measurements of brightness, resulting in the identification of large numbers of variable sources in the sky. A large fraction of these are periodic variables: compilations of these are of scientific interest for a variety of purposes. Unavoidably, the data sets from many such surveys not only have sparse sampling, but also have embedded frequencies in the observing cadence that beat against the natural periodicities of any object under investigation. Such limitations can make period determination ambiguous and uncertain. For multiband data sets with asynchronous measurements in multiple passbands, we wish to maximally use the information on periodicity in a manner that is agnostic of differences in the light-curve shapes across the different channels. Given large volumes of data, computational efficiency is also at a premium. This paper develops and presents a computationally economic method for determining periodicity that combines the results from two different classes of period-determination algorithms. The underlying principles are illustrated through examples. The effectiveness of this approach for combining asynchronously sampled measurements in multiple observables that share an underlying fundamental frequency is also demonstrated.

Massive Asynchronous Parallelization of Sparse Matrix Factorizations

Energy Technology Data Exchange (ETDEWEB)

Chow, Edmond [Georgia Inst. of Technology, Atlanta, GA (United States)

2018-01-08

Solving sparse problems is at the core of many DOE computational science applications. We focus on the challenge of developing sparse algorithms that can fully exploit the parallelism in extreme-scale computing systems, in particular systems with massive numbers of cores per node. Our approach is to express a sparse matrix factorization as a large number of bilinear constraint equations, and then solving these equations via an asynchronous iterative method. The unknowns in these equations are the matrix entries of the factorization that is desired.

Karlsruhe Research Center, Nuclear Safety Research Project (PSF). Annual report 1994; Forschungszentrum Karlsruhe, Projekt Nukleare Sicherheitsforschung. Jahrsbericht 1994

Energy Technology Data Exchange (ETDEWEB)

Hueper, R. [ed.

1995-08-01

The reactor safety R and D work of the Karlsruhe Research Centre (FZKA) has been part of the Nuclear Safety Research Projet (PSF) since 1990. The present annual report 1994 summarizes the R and D results. The research tasks are coordinated in agreement with internal and external working groups. The contributions to this report correspond to the status of early 1995. An abstract in English precedes each of them, whenever the respective article is written in German. (orig.) [Deutsch] Seit Beginn 1990 sind die F+E-Arbeiten des Forschungszentrum Karlsruhe (FZKA) zur Reaktorsicherheit im Projekt Nukleare Sicherheitsforschung (PSF) zusammengefasst. Der vorliegende Jahresbericht 1994 enthaelt Beitraege zu aktuellen Fragen der Sicherheit von Leichtwasserreaktoren und innovativen Systemen sowie der Umwandlung von minoren Aktiniden. Die konkreten Forschungsthemen und -vorhaben werden mit internen und externen Fachgremien laufend abgestimmt. An den beschriebenen Arbeiten sind die folgenden Institute und Abteilungen des FZKA beteiligt: Institut fuer Materialforschung IMF I, II, III; Institut fuer Neutronenphysik und Reaktortechnik INR; Institut fuer Angewandte Thermo- und Fluiddynamik IATF; Institut fuer Reaktorsicherheit IRS; Hauptabteilung Ingenieurtechnik HIT; Hauptabteilung Versuchstechnik HVT sowie vom KfK beauftragte externe Institutionen. Die einzelnen Beitraege stellen den Stand der Arbeiten zum Fruehjahr 1995 dar und sind entsprechend dem F+E-Programm 1994 numeriert. Den in deutscher Sprache verfassten Beitraege sind Kurzfassungen in englischer Sprache vorangestellt. (orig.)

Learning Low-Rank Class-Specific Dictionary and Sparse Intra-Class Variant Dictionary for Face Recognition

Science.gov (United States)

Tang, Xin; Feng, Guo-can; Li, Xiao-xin; Cai, Jia-xin

2015-01-01

Face recognition is challenging especially when the images from different persons are similar to each other due to variations in illumination, expression, and occlusion. If we have sufficient training images of each person which can span the facial variations of that person under testing conditions, sparse representation based classification (SRC) achieves very promising results. However, in many applications, face recognition often encounters the small sample size problem arising from the small number of available training images for each person. In this paper, we present a novel face recognition framework by utilizing low-rank and sparse error matrix decomposition, and sparse coding techniques (LRSE+SC). Firstly, the low-rank matrix recovery technique is applied to decompose the face images per class into a low-rank matrix and a sparse error matrix. The low-rank matrix of each individual is a class-specific dictionary and it captures the discriminative feature of this individual. The sparse error matrix represents the intra-class variations, such as illumination, expression changes. Secondly, we combine the low-rank part (representative basis) of each person into a supervised dictionary and integrate all the sparse error matrix of each individual into a within-individual variant dictionary which can be applied to represent the possible variations between the testing and training images. Then these two dictionaries are used to code the query image. The within-individual variant dictionary can be shared by all the subjects and only contribute to explain the lighting conditions, expressions, and occlusions of the query image rather than discrimination. At last, a reconstruction-based scheme is adopted for face recognition. Since the within-individual dictionary is introduced, LRSE+SC can handle the problem of the corrupted training data and the situation that not all subjects have enough samples for training. Experimental results show that our method achieves the

Learning Low-Rank Class-Specific Dictionary and Sparse Intra-Class Variant Dictionary for Face Recognition.

Science.gov (United States)

Tang, Xin; Feng, Guo-Can; Li, Xiao-Xin; Cai, Jia-Xin

2015-01-01

Face recognition is challenging especially when the images from different persons are similar to each other due to variations in illumination, expression, and occlusion. If we have sufficient training images of each person which can span the facial variations of that person under testing conditions, sparse representation based classification (SRC) achieves very promising results. However, in many applications, face recognition often encounters the small sample size problem arising from the small number of available training images for each person. In this paper, we present a novel face recognition framework by utilizing low-rank and sparse error matrix decomposition, and sparse coding techniques (LRSE+SC). Firstly, the low-rank matrix recovery technique is applied to decompose the face images per class into a low-rank matrix and a sparse error matrix. The low-rank matrix of each individual is a class-specific dictionary and it captures the discriminative feature of this individual. The sparse error matrix represents the intra-class variations, such as illumination, expression changes. Secondly, we combine the low-rank part (representative basis) of each person into a supervised dictionary and integrate all the sparse error matrix of each individual into a within-individual variant dictionary which can be applied to represent the possible variations between the testing and training images. Then these two dictionaries are used to code the query image. The within-individual variant dictionary can be shared by all the subjects and only contribute to explain the lighting conditions, expressions, and occlusions of the query image rather than discrimination. At last, a reconstruction-based scheme is adopted for face recognition. Since the within-individual dictionary is introduced, LRSE+SC can handle the problem of the corrupted training data and the situation that not all subjects have enough samples for training. Experimental results show that our method achieves the

Learning Low-Rank Class-Specific Dictionary and Sparse Intra-Class Variant Dictionary for Face Recognition.

Directory of Open Access Journals (Sweden)

Xin Tang

Full Text Available Face recognition is challenging especially when the images from different persons are similar to each other due to variations in illumination, expression, and occlusion. If we have sufficient training images of each person which can span the facial variations of that person under testing conditions, sparse representation based classification (SRC achieves very promising results. However, in many applications, face recognition often encounters the small sample size problem arising from the small number of available training images for each person. In this paper, we present a novel face recognition framework by utilizing low-rank and sparse error matrix decomposition, and sparse coding techniques (LRSE+SC. Firstly, the low-rank matrix recovery technique is applied to decompose the face images per class into a low-rank matrix and a sparse error matrix. The low-rank matrix of each individual is a class-specific dictionary and it captures the discriminative feature of this individual. The sparse error matrix represents the intra-class variations, such as illumination, expression changes. Secondly, we combine the low-rank part (representative basis of each person into a supervised dictionary and integrate all the sparse error matrix of each individual into a within-individual variant dictionary which can be applied to represent the possible variations between the testing and training images. Then these two dictionaries are used to code the query image. The within-individual variant dictionary can be shared by all the subjects and only contribute to explain the lighting conditions, expressions, and occlusions of the query image rather than discrimination. At last, a reconstruction-based scheme is adopted for face recognition. Since the within-individual dictionary is introduced, LRSE+SC can handle the problem of the corrupted training data and the situation that not all subjects have enough samples for training. Experimental results show that our

Storage of sparse files using parallel log-structured file system

Science.gov (United States)

Bent, John M.; Faibish, Sorin; Grider, Gary; Torres, Aaron

2017-11-07

A sparse file is stored without holes by storing a data portion of the sparse file using a parallel log-structured file system; and generating an index entry for the data portion, the index entry comprising a logical offset, physical offset and length of the data portion. The holes can be restored to the sparse file upon a reading of the sparse file. The data portion can be stored at a logical end of the sparse file. Additional storage efficiency can optionally be achieved by (i) detecting a write pattern for a plurality of the data portions and generating a single patterned index entry for the plurality of the patterned data portions; and/or (ii) storing the patterned index entries for a plurality of the sparse files in a single directory, wherein each entry in the single directory comprises an identifier of a corresponding sparse file.

Sparse image representation for jet neutron and gamma tomography

Energy Technology Data Exchange (ETDEWEB)

Craciunescu, T. [EURATOM-MEdC Association, Institute for Laser, Plasma and Radiation Physics, Bucharest (Romania); Kiptily, V. [EURATOM/CCFE Association, Culham Science Centre, Abingdon (United Kingdom); Murari, A. [Consorzio RFX, Associazione EURATOM-ENEA per la Fusione, Padova (Italy); Tiseanu, I.; Zoita, V. [EURATOM-MEdC Association, Institute for Laser, Plasma and Radiation Physics, Bucharest (Romania)

2013-10-15

Highlights: •A new tomographic method for the reconstruction of the 2-D neutron and gamma emissivity on JET. •The method is based on the sparse representation of the reconstructed image in an over-complete dictionary. •Several techniques, based on a priori information are used to regularize this highly limited data set tomographic problem. •The proposed method provides good reconstructions in terms of shapes and resolution. -- Abstract: The JET gamma/neutron profile monitor plasma coverage of the emissive region enables tomographic reconstruction. However, due to the availability of only two projection angles and to the coarse sampling, tomography is a highly limited data set problem. A new reconstruction method, based on the sparse representation of the reconstructed image in an over-complete dictionary, has been developed and applied to JET neutron/gamma tomography. The method has been tested on JET experimental data and significant results are presented. The proposed method provides good reconstructions in terms of shapes and resolution.

Sparse reconstruction using distribution agnostic bayesian matching pursuit

KAUST Repository

Masood, Mudassir

2013-11-01

A fast matching pursuit method using a Bayesian approach is introduced for sparse signal recovery. This method performs Bayesian estimates of sparse signals even when the signal prior is non-Gaussian or unknown. It is agnostic on signal statistics and utilizes a priori statistics of additive noise and the sparsity rate of the signal, which are shown to be easily estimated from data if not available. The method utilizes a greedy approach and order-recursive updates of its metrics to find the most dominant sparse supports to determine the approximate minimum mean-square error (MMSE) estimate of the sparse signal. Simulation results demonstrate the power and robustness of our proposed estimator. © 2013 IEEE.

Image understanding using sparse representations

CERN Document Server

Thiagarajan, Jayaraman J; Turaga, Pavan; Spanias, Andreas

2014-01-01

Image understanding has been playing an increasingly crucial role in several inverse problems and computer vision. Sparse models form an important component in image understanding, since they emulate the activity of neural receptors in the primary visual cortex of the human brain. Sparse methods have been utilized in several learning problems because of their ability to provide parsimonious, interpretable, and efficient models. Exploiting the sparsity of natural signals has led to advances in several application areas including image compression, denoising, inpainting, compressed sensing, blin

Sparse regularization for force identification using dictionaries

Science.gov (United States)

Qiao, Baijie; Zhang, Xingwu; Wang, Chenxi; Zhang, Hang; Chen, Xuefeng

2016-04-01

The classical function expansion method based on minimizing l2-norm of the response residual employs various basis functions to represent the unknown force. Its difficulty lies in determining the optimum number of basis functions. Considering the sparsity of force in the time domain or in other basis space, we develop a general sparse regularization method based on minimizing l1-norm of the coefficient vector of basis functions. The number of basis functions is adaptively determined by minimizing the number of nonzero components in the coefficient vector during the sparse regularization process. First, according to the profile of the unknown force, the dictionary composed of basis functions is determined. Second, a sparsity convex optimization model for force identification is constructed. Third, given the transfer function and the operational response, Sparse reconstruction by separable approximation (SpaRSA) is developed to solve the sparse regularization problem of force identification. Finally, experiments including identification of impact and harmonic forces are conducted on a cantilever thin plate structure to illustrate the effectiveness and applicability of SpaRSA. Besides the Dirac dictionary, other three sparse dictionaries including Db6 wavelets, Sym4 wavelets and cubic B-spline functions can also accurately identify both the single and double impact forces from highly noisy responses in a sparse representation frame. The discrete cosine functions can also successfully reconstruct the harmonic forces including the sinusoidal, square and triangular forces. Conversely, the traditional Tikhonov regularization method with the L-curve criterion fails to identify both the impact and harmonic forces in these cases.

Improved success of sparse matrix protein crystallization screening with heterogeneous nucleating agents.

Directory of Open Access Journals (Sweden)

Anil S Thakur

2007-10-01

Full Text Available Crystallization is a major bottleneck in the process of macromolecular structure determination by X-ray crystallography. Successful crystallization requires the formation of nuclei and their subsequent growth to crystals of suitable size. Crystal growth generally occurs spontaneously in a supersaturated solution as a result of homogenous nucleation. However, in a typical sparse matrix screening experiment, precipitant and protein concentration are not sampled extensively, and supersaturation conditions suitable for nucleation are often missed.We tested the effect of nine potential heterogenous nucleating agents on crystallization of ten test proteins in a sparse matrix screen. Several nucleating agents induced crystal formation under conditions where no crystallization occurred in the absence of the nucleating agent. Four nucleating agents: dried seaweed; horse hair; cellulose and hydroxyapatite, had a considerable overall positive effect on crystallization success. This effect was further enhanced when these nucleating agents were used in combination with each other.Our results suggest that the addition of heterogeneous nucleating agents increases the chances of crystal formation when using sparse matrix screens.

Sparse inpainting and isotropy

Energy Technology Data Exchange (ETDEWEB)

Feeney, Stephen M.; McEwen, Jason D.; Peiris, Hiranya V. [Department of Physics and Astronomy, University College London, Gower Street, London, WC1E 6BT (United Kingdom); Marinucci, Domenico; Cammarota, Valentina [Department of Mathematics, University of Rome Tor Vergata, via della Ricerca Scientifica 1, Roma, 00133 (Italy); Wandelt, Benjamin D., E-mail: s.feeney@imperial.ac.uk, E-mail: marinucc@axp.mat.uniroma2.it, E-mail: jason.mcewen@ucl.ac.uk, E-mail: h.peiris@ucl.ac.uk, E-mail: wandelt@iap.fr, E-mail: cammarot@axp.mat.uniroma2.it [Kavli Institute for Theoretical Physics, Kohn Hall, University of California, 552 University Road, Santa Barbara, CA, 93106 (United States)

2014-01-01

Sparse inpainting techniques are gaining in popularity as a tool for cosmological data analysis, in particular for handling data which present masked regions and missing observations. We investigate here the relationship between sparse inpainting techniques using the spherical harmonic basis as a dictionary and the isotropy properties of cosmological maps, as for instance those arising from cosmic microwave background (CMB) experiments. In particular, we investigate the possibility that inpainted maps may exhibit anisotropies in the behaviour of higher-order angular polyspectra. We provide analytic computations and simulations of inpainted maps for a Gaussian isotropic model of CMB data, suggesting that the resulting angular trispectrum may exhibit small but non-negligible deviations from isotropy.

Object tracking by occlusion detection via structured sparse learning

KAUST Repository

Zhang, Tianzhu

2013-06-01

Sparse representation based methods have recently drawn much attention in visual tracking due to good performance against illumination variation and occlusion. They assume the errors caused by image variations can be modeled as pixel-wise sparse. However, in many practical scenarios these errors are not truly pixel-wise sparse but rather sparsely distributed in a structured way. In fact, pixels in error constitute contiguous regions within the object\\'s track. This is the case when significant occlusion occurs. To accommodate for non-sparse occlusion in a given frame, we assume that occlusion detected in previous frames can be propagated to the current one. This propagated information determines which pixels will contribute to the sparse representation of the current track. In other words, pixels that were detected as part of an occlusion in the previous frame will be removed from the target representation process. As such, this paper proposes a novel tracking algorithm that models and detects occlusion through structured sparse learning. We test our tracker on challenging benchmark sequences, such as sports videos, which involve heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that our tracker consistently outperforms the state-of-the-art. © 2013 IEEE.

Sparse modeling applied to patient identification for safety in medical physics applications

Science.gov (United States)

Lewkowitz, Stephanie

Every scheduled treatment at a radiation therapy clinic involves a series of safety protocol to ensure the utmost patient care. Despite safety protocol, on a rare occasion an entirely preventable medical event, an accident, may occur. Delivering a treatment plan to the wrong patient is preventable, yet still is a clinically documented error. This research describes a computational method to identify patients with a novel machine learning technique to combat misadministration. The patient identification program stores face and fingerprint data for each patient. New, unlabeled data from those patients are categorized according to the library. The categorization of data by this face-fingerprint detector is accomplished with new machine learning algorithms based on Sparse Modeling that have already begun transforming the foundation of Computer Vision. Previous patient recognition software required special subroutines for faces and different tailored subroutines for fingerprints. In this research, the same exact model is used for both fingerprints and faces, without any additional subroutines and even without adjusting the two hyperparameters. Sparse modeling is a powerful tool, already shown utility in the areas of super-resolution, denoising, inpainting, demosaicing, and sub-nyquist sampling, i.e. compressed sensing. Sparse Modeling is possible because natural images are inherently sparse in some bases, due to their inherent structure. This research chooses datasets of face and fingerprint images to test the patient identification model. The model stores the images of each dataset as a basis (library). One image at a time is removed from the library, and is classified by a sparse code in terms of the remaining library. The Locally Competitive Algorithm, a truly neural inspired Artificial Neural Network, solves the computationally difficult task of finding the sparse code for the test image. The components of the sparse representation vector are summed by ℓ1 pooling

Epileptic Seizure Detection with Log-Euclidean Gaussian Kernel-Based Sparse Representation.

Science.gov (United States)

Yuan, Shasha; Zhou, Weidong; Wu, Qi; Zhang, Yanli

2016-05-01

Epileptic seizure detection plays an important role in the diagnosis of epilepsy and reducing the massive workload of reviewing electroencephalography (EEG) recordings. In this work, a novel algorithm is developed to detect seizures employing log-Euclidean Gaussian kernel-based sparse representation (SR) in long-term EEG recordings. Unlike the traditional SR for vector data in Euclidean space, the log-Euclidean Gaussian kernel-based SR framework is proposed for seizure detection in the space of the symmetric positive definite (SPD) matrices, which form a Riemannian manifold. Since the Riemannian manifold is nonlinear, the log-Euclidean Gaussian kernel function is applied to embed it into a reproducing kernel Hilbert space (RKHS) for performing SR. The EEG signals of all channels are divided into epochs and the SPD matrices representing EEG epochs are generated by covariance descriptors. Then, the testing samples are sparsely coded over the dictionary composed by training samples utilizing log-Euclidean Gaussian kernel-based SR. The classification of testing samples is achieved by computing the minimal reconstructed residuals. The proposed method is evaluated on the Freiburg EEG dataset of 21 patients and shows its notable performance on both epoch-based and event-based assessments. Moreover, this method handles multiple channels of EEG recordings synchronously which is more speedy and efficient than traditional seizure detection methods.

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....

A Sparse Approximate Inverse Preconditioner for Nonsymmetric Linear Systems

Czech Academy of Sciences Publication Activity Database

Benzi, M.; Tůma, Miroslav

1998-01-01

Roč. 19, č. 3 (1998), s. 968-994 ISSN 1064-8275 R&D Projects: GA ČR GA201/93/0067; GA AV ČR IAA230401 Keywords : large sparse systems * interative methods * preconditioning * approximate inverse * sparse linear systems * sparse matrices * incomplete factorizations * conjugate gradient -type methods Subject RIV: BA - General Mathematics Impact factor: 1.378, year: 1998

SPARSE FARADAY ROTATION MEASURE SYNTHESIS

International Nuclear Information System (INIS)

Andrecut, M.; Stil, J. M.; Taylor, A. R.

2012-01-01

Faraday rotation measure synthesis is a method for analyzing multichannel polarized radio emissions, and it has emerged as an important tool in the study of Galactic and extragalactic magnetic fields. The method requires the recovery of the Faraday dispersion function from measurements restricted to limited wavelength ranges, which is an ill-conditioned deconvolution problem. Here, we discuss a recovery method that assumes a sparse approximation of the Faraday dispersion function in an overcomplete dictionary of functions. We discuss the general case when both thin and thick components are included in the model, and we present the implementation of a greedy deconvolution algorithm. We illustrate the method with several numerical simulations that emphasize the effect of the covered range and sampling resolution in the Faraday depth space, and the effect of noise on the observed data.

Annealing optimization in the process of making membrane PSF19%DMFEVA2 for wastewater treatment of palm oil mill effluent

Science.gov (United States)

Said, A. A.; Mustafa

2018-02-01

A small proportion of the Palm Oil Mill Effluent (POME) treatment has used its wastewater to converted to methane gas which will then be converted again into electrical energy. However, for Palm Oil Mill whose has a value of Chemical Oxygen Demand in its wastewater is less than 60.000 mg / L this can’t so that the purpose wastewater treatment only to reach the standard that can be safe to dispose into the environment. Wastewater treatment systems that are general applied by Palm Oil Mill especially in North Sumatera are aerobic and anaerobic, this method takes a relatively long time due to very dependent on microbial activity. An alternative method for wastewater treatment offered is membrane technology because the process is much more effective, the time is relatively short, and expected to give more optimal result. The optimum membrane obtained is PSF19%DMFEVA2T75 membrane,while the parameter condition of the permeate analysis produced in the treatment of POME wastewater with membrane PSF19%DMFEVA2T75 obtained at pH = 7.0; TSS = 148 mg / L; BOD = 149 mg / L; And COD = 252 mg / L. The results obtained is accordance with the standard of the quality of POME.

Implementation of PSF engineering in high-resolution 3D microscopy imaging with a LCoS (reflective) SLM

Science.gov (United States)

King, Sharon V.; Doblas, Ana; Patwary, Nurmohammed; Saavedra, Genaro; Martínez-Corral, Manuel; Preza, Chrysanthe

2014-03-01

Wavefront coding techniques are currently used to engineer unique point spread functions (PSFs) that enhance existing microscope modalities or create new ones. Previous work in this field demonstrated that simulated intensity PSFs encoded with a generalized cubic phase mask (GCPM) are invariant to spherical aberration or misfocus; dependent on parameter selection. Additional work demonstrated that simulated PSFs encoded with a squared cubic phase mask (SQUBIC) produce a depth invariant focal spot for application in confocal scanning microscopy. Implementation of PSF engineering theory with a liquid crystal on silicon (LCoS) spatial light modulator (SLM) enables validation of WFC phase mask designs and parameters by manipulating optical wavefront properties with a programmable diffractive element. To validate and investigate parameters of the GCPM and SQUBIC WFC masks, we implemented PSF engineering in an upright microscope modified with a dual camera port and a LCoS SLM. We present measured WFC PSFs and compare them to simulated PSFs through analysis of their effect on the microscope imaging system properties. Experimentally acquired PSFs show the same intensity distribution as simulation for the GCPM phase mask, the SQUBIC-mask and the well-known and characterized cubic-phase mask (CPM), first applied to high NA microscopy by Arnison et al.10, for extending depth of field. These measurements provide experimental validation of new WFC masks and demonstrate the use of the LCoS SLM as a WFC design tool. Although efficiency improvements are needed, this application of LCoS technology renders the microscope capable of switching among multiple WFC modes.

Structure-based bayesian sparse reconstruction

KAUST Repository

Quadeer, Ahmed Abdul

2012-12-01

Sparse signal reconstruction algorithms have attracted research attention due to their wide applications in various fields. In this paper, we present a simple Bayesian approach that utilizes the sparsity constraint and a priori statistical information (Gaussian or otherwise) to obtain near optimal estimates. In addition, we make use of the rich structure of the sensing matrix encountered in many signal processing applications to develop a fast sparse recovery algorithm. The computational complexity of the proposed algorithm is very low compared with the widely used convex relaxation methods as well as greedy matching pursuit techniques, especially at high sparsity. © 1991-2012 IEEE.

Greedy vs. L1 convex optimization in sparse coding

DEFF Research Database (Denmark)

Ren, Huamin; Pan, Hong; Olsen, Søren Ingvor

2015-01-01

Sparse representation has been applied successfully in many image analysis applications, including abnormal event detection, in which a baseline is to learn a dictionary from the training data and detect anomalies from its sparse codes. During this procedure, sparse codes which can be achieved...... solutions. Considering the property of abnormal event detection, i.e., only normal videos are used as training data due to practical reasons, effective codes in classification application may not perform well in abnormality detection. Therefore, we compare the sparse codes and comprehensively evaluate...... their performance from various aspects to better understand their applicability, including computation time, reconstruction error, sparsity, detection...

A sparse matrix based full-configuration interaction algorithm

International Nuclear Information System (INIS)

Rolik, Zoltan; Szabados, Agnes; Surjan, Peter R.

2008-01-01

We present an algorithm related to the full-configuration interaction (FCI) method that makes complete use of the sparse nature of the coefficient vector representing the many-electron wave function in a determinantal basis. Main achievements of the presented sparse FCI (SFCI) algorithm are (i) development of an iteration procedure that avoids the storage of FCI size vectors; (ii) development of an efficient algorithm to evaluate the effect of the Hamiltonian when both the initial and the product vectors are sparse. As a result of point (i) large disk operations can be skipped which otherwise may be a bottleneck of the procedure. At point (ii) we progress by adopting the implementation of the linear transformation by Olsen et al. [J. Chem Phys. 89, 2185 (1988)] for the sparse case, getting the algorithm applicable to larger systems and faster at the same time. The error of a SFCI calculation depends only on the dropout thresholds for the sparse vectors, and can be tuned by controlling the amount of system memory passed to the procedure. The algorithm permits to perform FCI calculations on single node workstations for systems previously accessible only by supercomputers

Evaluation of fast highly undersampled contrast-enhanced MR angiography (sparse CE-MRA) in intracranial applications - initial study

International Nuclear Information System (INIS)

Gratz, Marcel; Quick, Harald H.; Schlamann, Marc; Goericke, Sophia; Maderwald, Stefan

2017-01-01

To assess the image quality of sparsely sampled contrast-enhanced MR angiography (sparse CE-MRA) providing high spatial resolution and whole-head coverage. Twenty-three patients scheduled for contrast-enhanced MR imaging of the head, (N = 19 with intracranial pathologies, N = 9 with vascular diseases), were included. Sparse CE-MRA at 3 Tesla was conducted using a single dose of contrast agent. Two neuroradiologists independently evaluated the data regarding vascular visibility and diagnostic value of overall 24 parameters and vascular segments on a 5-point ordinary scale (5 = very good, 1 = insufficient vascular visibility). Contrast bolus timing and the resulting arterio-venous overlap was also evaluated. Where available (N = 9), sparse CE-MRA was compared to intracranial Time-of-Flight MRA. The overall rating across all patients for sparse CE-MRA was 3.50 ± 1.07. Direct influence of the contrast bolus timing on the resulting image quality was observed. Overall mean vascular visibility and image quality across different features was rated good to intermediate (3.56 ± 0.95). The average performance of intracranial Time-of-Flight was rated 3.84 ± 0.87 across all patients and 3.54 ± 0.62 across all features. Sparse CE-MRA provides high-quality 3D MRA with high spatial resolution and whole-head coverage within short acquisition time. Accurate contrast bolus timing is mandatory. (orig.)

Evaluation of fast highly undersampled contrast-enhanced MR angiography (sparse CE-MRA) in intracranial applications - initial study

Energy Technology Data Exchange (ETDEWEB)

Gratz, Marcel; Quick, Harald H. [University of Duisburg-Essen, Erwin L. Hahn Institute for MR Imaging, Essen (Germany); University Hospital Essen, High Field and Hybrid MR Imaging, Essen (Germany); Schlamann, Marc [University Hospital Giessen and Marburg GmbH, Neuroradiology, Giessen (Germany); University Hospital Essen, Department of Diagnostic and Interventional Radiology and Neuroradiology, Essen (Germany); Goericke, Sophia [University Hospital Essen, Department of Diagnostic and Interventional Radiology and Neuroradiology, Essen (Germany); Maderwald, Stefan [University of Duisburg-Essen, Erwin L. Hahn Institute for MR Imaging, Essen (Germany)

2017-03-15

To assess the image quality of sparsely sampled contrast-enhanced MR angiography (sparse CE-MRA) providing high spatial resolution and whole-head coverage. Twenty-three patients scheduled for contrast-enhanced MR imaging of the head, (N = 19 with intracranial pathologies, N = 9 with vascular diseases), were included. Sparse CE-MRA at 3 Tesla was conducted using a single dose of contrast agent. Two neuroradiologists independently evaluated the data regarding vascular visibility and diagnostic value of overall 24 parameters and vascular segments on a 5-point ordinary scale (5 = very good, 1 = insufficient vascular visibility). Contrast bolus timing and the resulting arterio-venous overlap was also evaluated. Where available (N = 9), sparse CE-MRA was compared to intracranial Time-of-Flight MRA. The overall rating across all patients for sparse CE-MRA was 3.50 ± 1.07. Direct influence of the contrast bolus timing on the resulting image quality was observed. Overall mean vascular visibility and image quality across different features was rated good to intermediate (3.56 ± 0.95). The average performance of intracranial Time-of-Flight was rated 3.84 ± 0.87 across all patients and 3.54 ± 0.62 across all features. Sparse CE-MRA provides high-quality 3D MRA with high spatial resolution and whole-head coverage within short acquisition time. Accurate contrast bolus timing is mandatory. (orig.)

An in-depth study of sparse codes on abnormality detection

DEFF Research Database (Denmark)

Ren, Huamin; Pan, Hong; Olsen, Søren Ingvor

2016-01-01

Sparse representation has been applied successfully in abnormal event detection, in which the baseline is to learn a dictionary accompanied by sparse codes. While much emphasis is put on discriminative dictionary construction, there are no comparative studies of sparse codes regarding abnormality...... are carried out from various angles to better understand the applicability of sparse codes, including computation time, reconstruction error, sparsity, detection accuracy, and their performance combining various detection methods. The experiment results show that combining OMP codes with maximum coordinate...

Sparse Principal Component Analysis in Medical Shape Modeling

DEFF Research Database (Denmark)

Sjöstrand, Karl; Stegmann, Mikkel Bille; Larsen, Rasmus

2006-01-01

Principal component analysis (PCA) is a widely used tool in medical image analysis for data reduction, model building, and data understanding and exploration. While PCA is a holistic approach where each new variable is a linear combination of all original variables, sparse PCA (SPCA) aims...... analysis in medicine. Results for three different data sets are given in relation to standard PCA and sparse PCA by simple thresholding of sufficiently small loadings. Focus is on a recent algorithm for computing sparse principal components, but a review of other approaches is supplied as well. The SPCA...

Sparse reconstruction using distribution agnostic bayesian matching pursuit

KAUST Repository

Masood, Mudassir; Al-Naffouri, Tareq Y.

2013-01-01

A fast matching pursuit method using a Bayesian approach is introduced for sparse signal recovery. This method performs Bayesian estimates of sparse signals even when the signal prior is non-Gaussian or unknown. It is agnostic on signal statistics

User's Manual for PCSMS (Parallel Complex Sparse Matrix Solver). Version 1.

Science.gov (United States)

Reddy, C. J.

2000-01-01

PCSMS (Parallel Complex Sparse Matrix Solver) is a computer code written to make use of the existing real sparse direct solvers to solve complex, sparse matrix linear equations. PCSMS converts complex matrices into real matrices and use real, sparse direct matrix solvers to factor and solve the real matrices. The solution vector is reconverted to complex numbers. Though, this utility is written for Silicon Graphics (SGI) real sparse matrix solution routines, it is general in nature and can be easily modified to work with any real sparse matrix solver. The User's Manual is written to make the user acquainted with the installation and operation of the code. Driver routines are given to aid the users to integrate PCSMS routines in their own codes.

SparseBeads data: benchmarking sparsity-regularized computed tomography

Science.gov (United States)

Jørgensen, Jakob S.; Coban, Sophia B.; Lionheart, William R. B.; McDonald, Samuel A.; Withers, Philip J.

2017-12-01

Sparsity regularization (SR) such as total variation (TV) minimization allows accurate image reconstruction in x-ray computed tomography (CT) from fewer projections than analytical methods. Exactly how few projections suffice and how this number may depend on the image remain poorly understood. Compressive sensing connects the critical number of projections to the image sparsity, but does not cover CT, however empirical results suggest a similar connection. The present work establishes for real CT data a connection between gradient sparsity and the sufficient number of projections for accurate TV-regularized reconstruction. A collection of 48 x-ray CT datasets called SparseBeads was designed for benchmarking SR reconstruction algorithms. Beadpacks comprising glass beads of five different sizes as well as mixtures were scanned in a micro-CT scanner to provide structured datasets with variable image sparsity levels, number of projections and noise levels to allow the systematic assessment of parameters affecting performance of SR reconstruction algorithms6. Using the SparseBeads data, TV-regularized reconstruction quality was assessed as a function of numbers of projections and gradient sparsity. The critical number of projections for satisfactory TV-regularized reconstruction increased almost linearly with the gradient sparsity. This establishes a quantitative guideline from which one may predict how few projections to acquire based on expected sample sparsity level as an aid in planning of dose- or time-critical experiments. The results are expected to hold for samples of similar characteristics, i.e. consisting of few, distinct phases with relatively simple structure. Such cases are plentiful in porous media, composite materials, foams, as well as non-destructive testing and metrology. For samples of other characteristics the proposed methodology may be used to investigate similar relations.

Parallel transposition of sparse data structures

DEFF Research Database (Denmark)

Wang, Hao; Liu, Weifeng; Hou, Kaixi

2016-01-01

Many applications in computational sciences and social sciences exploit sparsity and connectivity of acquired data. Even though many parallel sparse primitives such as sparse matrix-vector (SpMV) multiplication have been extensively studied, some other important building blocks, e.g., parallel tr...... transposition in the latest vendor-supplied library on an Intel multicore CPU platform, and the MergeTrans approach achieves on average of 3.4-fold (up to 11.7-fold) speedup on an Intel Xeon Phi many-core processor....

Numerical solution of large sparse linear systems

International Nuclear Information System (INIS)

Meurant, Gerard; Golub, Gene.

1982-02-01

This note is based on one of the lectures given at the 1980 CEA-EDF-INRIA Numerical Analysis Summer School whose aim is the study of large sparse linear systems. The main topics are solving least squares problems by orthogonal transformation, fast Poisson solvers and solution of sparse linear system by iterative methods with a special emphasis on preconditioned conjuguate gradient method [fr

Sparse and stable Markowitz portfolios.

Science.gov (United States)

Brodie, Joshua; Daubechies, Ingrid; De Mol, Christine; Giannone, Domenico; Loris, Ignace

2009-07-28

We consider the problem of portfolio selection within the classical Markowitz mean-variance framework, reformulated as a constrained least-squares regression problem. We propose to add to the objective function a penalty proportional to the sum of the absolute values of the portfolio weights. This penalty regularizes (stabilizes) the optimization problem, encourages sparse portfolios (i.e., portfolios with only few active positions), and allows accounting for transaction costs. Our approach recovers as special cases the no-short-positions portfolios, but does allow for short positions in limited number. We implement this methodology on two benchmark data sets constructed by Fama and French. Using only a modest amount of training data, we construct portfolios whose out-of-sample performance, as measured by Sharpe ratio, is consistently and significantly better than that of the naïve evenly weighted portfolio.

Sparse Source EEG Imaging with the Variational Garrote

DEFF Research Database (Denmark)

Hansen, Sofie Therese; Stahlhut, Carsten; Hansen, Lars Kai

2013-01-01

EEG imaging, the estimation of the cortical source distribution from scalp electrode measurements, poses an extremely ill-posed inverse problem. Recent work by Delorme et al. (2012) supports the hypothesis that distributed source solutions are sparse. We show that direct search for sparse solutions...

Deploying temporary networks for upscaling of sparse network stations

Science.gov (United States)

Coopersmith, Evan J.; Cosh, Michael H.; Bell, Jesse E.; Kelly, Victoria; Hall, Mark; Palecki, Michael A.; Temimi, Marouane

2016-10-01

Soil observations networks at the national scale play an integral role in hydrologic modeling, drought assessment, agricultural decision support, and our ability to understand climate change. Understanding soil moisture variability is necessary to apply these measurements to model calibration, business and consumer applications, or even human health issues. The installation of soil moisture sensors as sparse, national networks is necessitated by limited financial resources. However, this results in the incomplete sampling of the local heterogeneity of soil type, vegetation cover, topography, and the fine spatial distribution of precipitation events. To this end, temporary networks can be installed in the areas surrounding a permanent installation within a sparse network. The temporary networks deployed in this study provide a more representative average at the 3 km and 9 km scales, localized about the permanent gauge. The value of such temporary networks is demonstrated at test sites in Millbrook, New York and Crossville, Tennessee. The capacity of a single U.S. Climate Reference Network (USCRN) sensor set to approximate the average of a temporary network at the 3 km and 9 km scales using a simple linear scaling function is tested. The capacity of a temporary network to provide reliable estimates with diminishing numbers of sensors, the temporal stability of those networks, and ultimately, the relationship of the variability of those networks to soil moisture conditions at the permanent sensor are investigated. In this manner, this work demonstrates the single-season installation of a temporary network as a mechanism to characterize the soil moisture variability at a permanent gauge within a sparse network.

Low-count PET image restoration using sparse representation

Science.gov (United States)

Li, Tao; Jiang, Changhui; Gao, Juan; Yang, Yongfeng; Liang, Dong; Liu, Xin; Zheng, Hairong; Hu, Zhanli

2018-04-01

In the field of positron emission tomography (PET), reconstructed images are often blurry and contain noise. These problems are primarily caused by the low resolution of projection data. Solving this problem by improving hardware is an expensive solution, and therefore, we attempted to develop a solution based on optimizing several related algorithms in both the reconstruction and image post-processing domains. As sparse technology is widely used, sparse prediction is increasingly applied to solve this problem. In this paper, we propose a new sparse method to process low-resolution PET images. Two dictionaries (D1 for low-resolution PET images and D2 for high-resolution PET images) are learned from a group real PET image data sets. Among these two dictionaries, D1 is used to obtain a sparse representation for each patch of the input PET image. Then, a high-resolution PET image is generated from this sparse representation using D2. Experimental results indicate that the proposed method exhibits a stable and superior ability to enhance image resolution and recover image details. Quantitatively, this method achieves better performance than traditional methods. This proposed strategy is a new and efficient approach for improving the quality of PET images.

Sparse covariance estimation in heterogeneous samples.

Science.gov (United States)

Rodríguez, Abel; Lenkoski, Alex; Dobra, Adrian

Standard Gaussian graphical models implicitly assume that the conditional independence among variables is common to all observations in the sample. However, in practice, observations are usually collected from heterogeneous populations where such an assumption is not satisfied, leading in turn to nonlinear relationships among variables. To address such situations we explore mixtures of Gaussian graphical models; in particular, we consider both infinite mixtures and infinite hidden Markov models where the emission distributions correspond to Gaussian graphical models. Such models allow us to divide a heterogeneous population into homogenous groups, with each cluster having its own conditional independence structure. As an illustration, we study the trends in foreign exchange rate fluctuations in the pre-Euro era.

Experimental characterization of the Green’s function in a room using sparse reconstruction principles

DEFF Research Database (Denmark)

Fernandez Grande, Efren; Christiansen, Rasmus Ellebæk

2017-01-01

Measuring the Green’s function over the entire volume of a room would typically require an unfeasible number of measurements, due to requirements on spatial sampling. To alleviate the need for excessive measurements, sparse reconstruction methods can be employed, as they make it possible to recon...

A sparse electromagnetic imaging scheme using nonlinear landweber iterations

KAUST Repository

Desmal, Abdulla; Bagci, Hakan

2015-01-01

Development and use of electromagnetic inverse scattering techniques for imagining sparse domains have been on the rise following the recent advancements in solving sparse optimization problems. Existing techniques rely on iteratively converting

Scalable group level probabilistic sparse factor analysis

DEFF Research Database (Denmark)

Hinrich, Jesper Løve; Nielsen, Søren Føns Vind; Riis, Nicolai Andre Brogaard

2017-01-01

Many data-driven approaches exist to extract neural representations of functional magnetic resonance imaging (fMRI) data, but most of them lack a proper probabilistic formulation. We propose a scalable group level probabilistic sparse factor analysis (psFA) allowing spatially sparse maps, component...... pruning using automatic relevance determination (ARD) and subject specific heteroscedastic spatial noise modeling. For task-based and resting state fMRI, we show that the sparsity constraint gives rise to components similar to those obtained by group independent component analysis. The noise modeling...... shows that noise is reduced in areas typically associated with activation by the experimental design. The psFA model identifies sparse components and the probabilistic setting provides a natural way to handle parameter uncertainties. The variational Bayesian framework easily extends to more complex...

Fast wavelet based sparse approximate inverse preconditioner

Energy Technology Data Exchange (ETDEWEB)

Wan, W.L. [Univ. of California, Los Angeles, CA (United States)

1996-12-31

Incomplete LU factorization is a robust preconditioner for both general and PDE problems but unfortunately not easy to parallelize. Recent study of Huckle and Grote and Chow and Saad showed that sparse approximate inverse could be a potential alternative while readily parallelizable. However, for special class of matrix A that comes from elliptic PDE problems, their preconditioners are not optimal in the sense that independent of mesh size. A reason may be that no good sparse approximate inverse exists for the dense inverse matrix. Our observation is that for this kind of matrices, its inverse entries typically have piecewise smooth changes. We can take advantage of this fact and use wavelet compression techniques to construct a better sparse approximate inverse preconditioner. We shall show numerically that our approach is effective for this kind of matrices.

Local posterior concentration rate for multilevel sparse sequences

NARCIS (Netherlands)

Belitser, E.N.; Nurushev, N.

2017-01-01

We consider empirical Bayesian inference in the many normal means model in the situation when the high-dimensional mean vector is multilevel sparse, that is,most of the entries of the parameter vector are some fixed values. For instance, the traditional sparse signal is a particular case (with one

Face recognition based on two-dimensional discriminant sparse preserving projection

Science.gov (United States)

Zhang, Dawei; Zhu, Shanan

2018-04-01

In this paper, a supervised dimensionality reduction algorithm named two-dimensional discriminant sparse preserving projection (2DDSPP) is proposed for face recognition. In order to accurately model manifold structure of data, 2DDSPP constructs within-class affinity graph and between-class affinity graph by the constrained least squares (LS) and l1 norm minimization problem, respectively. Based on directly operating on image matrix, 2DDSPP integrates graph embedding (GE) with Fisher criterion. The obtained projection subspace preserves within-class neighborhood geometry structure of samples, while keeping away samples from different classes. The experimental results on the PIE and AR face databases show that 2DDSPP can achieve better recognition performance.

Sparse modeling of spatial environmental variables associated with asthma.

Science.gov (United States)

Chang, Timothy S; Gangnon, Ronald E; David Page, C; Buckingham, William R; Tandias, Aman; Cowan, Kelly J; Tomasallo, Carrie D; Arndt, Brian G; Hanrahan, Lawrence P; Guilbert, Theresa W

2015-02-01

Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin's Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5-50years over a three-year period. Each patient's home address was geocoded to one of 3456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin's geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors. Copyright © 2014 Elsevier Inc. All rights reserved.

Analog system for computing sparse codes

Science.gov (United States)

Rozell, Christopher John; Johnson, Don Herrick; Baraniuk, Richard Gordon; Olshausen, Bruno A.; Ortman, Robert Lowell

2010-08-24

A parallel dynamical system for computing sparse representations of data, i.e., where the data can be fully represented in terms of a small number of non-zero code elements, and for reconstructing compressively sensed images. The system is based on the principles of thresholding and local competition that solves a family of sparse approximation problems corresponding to various sparsity metrics. The system utilizes Locally Competitive Algorithms (LCAs), nodes in a population continually compete with neighboring units using (usually one-way) lateral inhibition to calculate coefficients representing an input in an over complete dictionary.

Efficient Pseudorecursive Evaluation Schemes for Non-adaptive Sparse Grids

KAUST Repository

Buse, Gerrit; Pflü ger, Dirk; Jacob, Riko

2014-01-01

In this work we propose novel algorithms for storing and evaluating sparse grid functions, operating on regular (not spatially adaptive), yet potentially dimensionally adaptive grid types. Besides regular sparse grids our approach includes truncated

Homotopic non-local regularized reconstruction from sparse positron emission tomography measurements

International Nuclear Information System (INIS)

Wong, Alexander; Liu, Chenyi; Wang, Xiao Yu; Fieguth, Paul; Bie, Hongxia

2015-01-01

Positron emission tomography scanners collect measurements of a patient’s in vivo radiotracer distribution. The system detects pairs of gamma rays emitted indirectly by a positron-emitting radionuclide (tracer), which is introduced into the body on a biologically active molecule, and the tomograms must be reconstructed from projections. The reconstruction of tomograms from the acquired PET data is an inverse problem that requires regularization. The use of tightly packed discrete detector rings, although improves signal-to-noise ratio, are often associated with high costs of positron emission tomography systems. Thus a sparse reconstruction, which would be capable of overcoming the noise effect while allowing for a reduced number of detectors, would have a great deal to offer. In this study, we introduce and investigate the potential of a homotopic non-local regularization reconstruction framework for effectively reconstructing positron emission tomograms from such sparse measurements. Results obtained using the proposed approach are compared with traditional filtered back-projection as well as expectation maximization reconstruction with total variation regularization. A new reconstruction method was developed for the purpose of improving the quality of positron emission tomography reconstruction from sparse measurements. We illustrate that promising reconstruction performance can be achieved for the proposed approach even at low sampling fractions, which allows for the use of significantly fewer detectors and have the potential to reduce scanner costs

Occlusion detection via structured sparse learning for robust object tracking

KAUST Repository

Zhang, Tianzhu

2014-01-01

Sparse representation based methods have recently drawn much attention in visual tracking due to good performance against illumination variation and occlusion. They assume the errors caused by image variations can be modeled as pixel-wise sparse. However, in many practical scenarios, these errors are not truly pixel-wise sparse but rather sparsely distributed in a structured way. In fact, pixels in error constitute contiguous regions within the object’s track. This is the case when significant occlusion occurs. To accommodate for nonsparse occlusion in a given frame, we assume that occlusion detected in previous frames can be propagated to the current one. This propagated information determines which pixels will contribute to the sparse representation of the current track. In other words, pixels that were detected as part of an occlusion in the previous frame will be removed from the target representation process. As such, this paper proposes a novel tracking algorithm that models and detects occlusion through structured sparse learning. We test our tracker on challenging benchmark sequences, such as sports videos, which involve heavy occlusion, drastic illumination changes, and large pose variations. Extensive experimental results show that our proposed tracker consistently outperforms the state-of-the-art trackers.

Functional fixedness in a technologically sparse culture.

Science.gov (United States)

German, Tim P; Barrett, H Clark

2005-01-01

Problem solving can be inefficient when the solution requires subjects to generate an atypical function for an object and the object's typical function has been primed. Subjects become "fixed" on the design function of the object, and problem solving suffers relative to control conditions in which the object's function is not demonstrated. In the current study, such functional fixedness was demonstrated in a sample of adolescents (mean age of 16 years) among the Shuar of Ecuadorian Amazonia, whose technologically sparse culture provides limited access to large numbers of artifacts with highly specialized functions. This result suggests that design function may universally be the core property of artifact concepts in human semantic memory.

Estimation of kinship coefficient in structured and admixed populations using sparse sequencing data.

Directory of Open Access Journals (Sweden)

Jinzhuang Dou

2017-09-01

Full Text Available Knowledge of biological relatedness between samples is important for many genetic studies. In large-scale human genetic association studies, the estimated kinship is used to remove cryptic relatedness, control for family structure, and estimate trait heritability. However, estimation of kinship is challenging for sparse sequencing data, such as those from off-target regions in target sequencing studies, where genotypes are largely uncertain or missing. Existing methods often assume accurate genotypes at a large number of markers across the genome. We show that these methods, without accounting for the genotype uncertainty in sparse sequencing data, can yield a strong downward bias in kinship estimation. We develop a computationally efficient method called SEEKIN to estimate kinship for both homogeneous samples and heterogeneous samples with population structure and admixture. Our method models genotype uncertainty and leverages linkage disequilibrium through imputation. We test SEEKIN on a whole exome sequencing dataset (WES of Singapore Chinese and Malays, which involves substantial population structure and admixture. We show that SEEKIN can accurately estimate kinship coefficient and classify genetic relatedness using off-target sequencing data down sampled to ~0.15X depth. In application to the full WES dataset without down sampling, SEEKIN also outperforms existing methods by properly analyzing shallow off-target data (~0.75X. Using both simulated and real phenotypes, we further illustrate how our method improves estimation of trait heritability for WES studies.

SparseLeap: Efficient Empty Space Skipping for Large-Scale Volume Rendering

KAUST Repository

Hadwiger, Markus; Al-Awami, Ali K.; Beyer, Johanna; Agus, Marco; Pfister, Hanspeter

2017-01-01

Recent advances in data acquisition produce volume data of very high resolution and large size, such as terabyte-sized microscopy volumes. These data often contain many fine and intricate structures, which pose huge challenges for volume rendering, and make it particularly important to efficiently skip empty space. This paper addresses two major challenges: (1) The complexity of large volumes containing fine structures often leads to highly fragmented space subdivisions that make empty regions hard to skip efficiently. (2) The classification of space into empty and non-empty regions changes frequently, because the user or the evaluation of an interactive query activate a different set of objects, which makes it unfeasible to pre-compute a well-adapted space subdivision. We describe the novel SparseLeap method for efficient empty space skipping in very large volumes, even around fine structures. The main performance characteristic of SparseLeap is that it moves the major cost of empty space skipping out of the ray-casting stage. We achieve this via a hybrid strategy that balances the computational load between determining empty ray segments in a rasterization (object-order) stage, and sampling non-empty volume data in the ray-casting (image-order) stage. Before ray-casting, we exploit the fast hardware rasterization of GPUs to create a ray segment list for each pixel, which identifies non-empty regions along the ray. The ray-casting stage then leaps over empty space without hierarchy traversal. Ray segment lists are created by rasterizing a set of fine-grained, view-independent bounding boxes. Frame coherence is exploited by re-using the same bounding boxes unless the set of active objects changes. We show that SparseLeap scales better to large, sparse data than standard octree empty space skipping.

SparseLeap: Efficient Empty Space Skipping for Large-Scale Volume Rendering

KAUST Repository

Hadwiger, Markus

2017-08-28

Recent advances in data acquisition produce volume data of very high resolution and large size, such as terabyte-sized microscopy volumes. These data often contain many fine and intricate structures, which pose huge challenges for volume rendering, and make it particularly important to efficiently skip empty space. This paper addresses two major challenges: (1) The complexity of large volumes containing fine structures often leads to highly fragmented space subdivisions that make empty regions hard to skip efficiently. (2) The classification of space into empty and non-empty regions changes frequently, because the user or the evaluation of an interactive query activate a different set of objects, which makes it unfeasible to pre-compute a well-adapted space subdivision. We describe the novel SparseLeap method for efficient empty space skipping in very large volumes, even around fine structures. The main performance characteristic of SparseLeap is that it moves the major cost of empty space skipping out of the ray-casting stage. We achieve this via a hybrid strategy that balances the computational load between determining empty ray segments in a rasterization (object-order) stage, and sampling non-empty volume data in the ray-casting (image-order) stage. Before ray-casting, we exploit the fast hardware rasterization of GPUs to create a ray segment list for each pixel, which identifies non-empty regions along the ray. The ray-casting stage then leaps over empty space without hierarchy traversal. Ray segment lists are created by rasterizing a set of fine-grained, view-independent bounding boxes. Frame coherence is exploited by re-using the same bounding boxes unless the set of active objects changes. We show that SparseLeap scales better to large, sparse data than standard octree empty space skipping.

Learning sparse generative models of audiovisual signals

OpenAIRE

Monaci, Gianluca; Sommer, Friedrich T.; Vandergheynst, Pierre

2008-01-01

This paper presents a novel framework to learn sparse represen- tations for audiovisual signals. An audiovisual signal is modeled as a sparse sum of audiovisual kernels. The kernels are bimodal functions made of synchronous audio and video components that can be positioned independently and arbitrarily in space and time. We design an algorithm capable of learning sets of such audiovi- sual, synchronous, shift-invariant functions by alternatingly solving a coding and a learning pr...

Towards the low-dose characterization of beam sensitive nanostructures via implementation of sparse image acquisition in scanning transmission electron microscopy

International Nuclear Information System (INIS)

Hwang, Sunghwan; Han, Chang Wan; Ortalan, Volkan; Venkatakrishnan, Singanallur V; Bouman, Charles A

2017-01-01

Scanning transmission electron microscopy (STEM) has been successfully utilized to investigate atomic structure and chemistry of materials with atomic resolution. However, STEM’s focused electron probe with a high current density causes the electron beam damages including radiolysis and knock-on damage when the focused probe is exposed onto the electron-beam sensitive materials. Therefore, it is highly desirable to decrease the electron dose used in STEM for the investigation of biological/organic molecules, soft materials and nanomaterials in general. With the recent emergence of novel sparse signal processing theories, such as compressive sensing and model-based iterative reconstruction, possibilities of operating STEM under a sparse acquisition scheme to reduce the electron dose have been opened up. In this paper, we report our recent approach to implement a sparse acquisition in STEM mode executed by a random sparse-scan and a signal processing algorithm called model-based iterative reconstruction (MBIR). In this method, a small portion, such as 5% of randomly chosen unit sampling areas (i.e. electron probe positions), which corresponds to pixels of a STEM image, within the region of interest (ROI) of the specimen are scanned with an electron probe to obtain a sparse image. Sparse images are then reconstructed using the MBIR inpainting algorithm to produce an image of the specimen at the original resolution that is consistent with an image obtained using conventional scanning methods. Experimental results for down to 5% sampling show consistency with the full STEM image acquired by the conventional scanning method. Although, practical limitations of the conventional STEM instruments, such as internal delays of the STEM control electronics and the continuous electron gun emission, currently hinder to achieve the full potential of the sparse acquisition STEM in realizing the low dose imaging condition required for the investigation of beam-sensitive materials

Support agnostic Bayesian matching pursuit for block sparse signals

KAUST Repository

Masood, Mudassir

2013-05-01

A fast matching pursuit method using a Bayesian approach is introduced for block-sparse signal recovery. This method performs Bayesian estimates of block-sparse signals even when the distribution of active blocks is non-Gaussian or unknown. It is agnostic to the distribution of active blocks in the signal and utilizes a priori statistics of additive noise and the sparsity rate of the signal, which are shown to be easily estimated from data and no user intervention is required. The method requires a priori knowledge of block partition and utilizes a greedy approach and order-recursive updates of its metrics to find the most dominant sparse supports to determine the approximate minimum mean square error (MMSE) estimate of the block-sparse signal. Simulation results demonstrate the power and robustness of our proposed estimator. © 2013 IEEE.

Integrative sparse principal component analysis of gene expression data.

Science.gov (United States)

Liu, Mengque; Fan, Xinyan; Fang, Kuangnan; Zhang, Qingzhao; Ma, Shuangge

2017-12-01

In the analysis of gene expression data, dimension reduction techniques have been extensively adopted. The most popular one is perhaps the PCA (principal component analysis). To generate more reliable and more interpretable results, the SPCA (sparse PCA) technique has been developed. With the "small sample size, high dimensionality" characteristic of gene expression data, the analysis results generated from a single dataset are often unsatisfactory. Under contexts other than dimension reduction, integrative analysis techniques, which jointly analyze the raw data of multiple independent datasets, have been developed and shown to outperform "classic" meta-analysis and other multidatasets techniques and single-dataset analysis. In this study, we conduct integrative analysis by developing the iSPCA (integrative SPCA) method. iSPCA achieves the selection and estimation of sparse loadings using a group penalty. To take advantage of the similarity across datasets and generate more accurate results, we further impose contrasted penalties. Different penalties are proposed to accommodate different data conditions. Extensive simulations show that iSPCA outperforms the alternatives under a wide spectrum of settings. The analysis of breast cancer and pancreatic cancer data further shows iSPCA's satisfactory performance. © 2017 WILEY PERIODICALS, INC.

Preconditioned Inexact Newton for Nonlinear Sparse Electromagnetic Imaging

KAUST Repository

Desmal, Abdulla

2014-05-04

Newton-type algorithms have been extensively studied in nonlinear microwave imaging due to their quadratic convergence rate and ability to recover images with high contrast values. In the past, Newton methods have been implemented in conjunction with smoothness promoting optimization/regularization schemes. However, this type of regularization schemes are known to perform poorly when applied in imagining domains with sparse content or sharp variations. In this work, an inexact Newton algorithm is formulated and implemented in conjunction with a linear sparse optimization scheme. A novel preconditioning technique is proposed to increase the convergence rate of the optimization problem. Numerical results demonstrate that the proposed framework produces sharper and more accurate images when applied in sparse/sparsified domains.

Preconditioned Inexact Newton for Nonlinear Sparse Electromagnetic Imaging

KAUST Repository

Desmal, Abdulla

2014-01-06

Newton-type algorithms have been extensively studied in nonlinear microwave imaging due to their quadratic convergence rate and ability to recover images with high contrast values. In the past, Newton methods have been implemented in conjunction with smoothness promoting optimization/regularization schemes. However, this type of regularization schemes are known to perform poorly when applied in imagining domains with sparse content or sharp variations. In this work, an inexact Newton algorithm is formulated and implemented in conjunction with a linear sparse optimization scheme. A novel preconditioning technique is proposed to increase the convergence rate of the optimization problem. Numerical results demonstrate that the proposed framework produces sharper and more accurate images when applied in sparse/sparsified domains.

Electromagnetic Formation Flight (EMFF) for Sparse Aperture Arrays

Science.gov (United States)

Kwon, Daniel W.; Miller, David W.; Sedwick, Raymond J.

2004-01-01

Traditional methods of actuating spacecraft in sparse aperture arrays use propellant as a reaction mass. For formation flying systems, propellant becomes a critical consumable which can be quickly exhausted while maintaining relative orientation. Additional problems posed by propellant include optical contamination, plume impingement, thermal emission, and vibration excitation. For these missions where control of relative degrees of freedom is important, we consider using a system of electromagnets, in concert with reaction wheels, to replace the consumables. Electromagnetic Formation Flight sparse apertures, powered by solar energy, are designed differently from traditional propulsion systems, which are based on V. This paper investigates the design of sparse apertures both inside and outside the Earth's gravity field.

Preconditioned Inexact Newton for Nonlinear Sparse Electromagnetic Imaging

KAUST Repository

Desmal, Abdulla; Bagci, Hakan

2014-01-01

Newton-type algorithms have been extensively studied in nonlinear microwave imaging due to their quadratic convergence rate and ability to recover images with high contrast values. In the past, Newton methods have been implemented in conjunction with smoothness promoting optimization/regularization schemes. However, this type of regularization schemes are known to perform poorly when applied in imagining domains with sparse content or sharp variations. In this work, an inexact Newton algorithm is formulated and implemented in conjunction with a linear sparse optimization scheme. A novel preconditioning technique is proposed to increase the convergence rate of the optimization problem. Numerical results demonstrate that the proposed framework produces sharper and more accurate images when applied in sparse/sparsified domains.

A comprehensive study of sparse codes on abnormality detection

DEFF Research Database (Denmark)

Ren, Huamin; Pan, Hong; Olsen, Søren Ingvor

2017-01-01

Sparse representation has been applied successfully in abnor-mal event detection, in which the baseline is to learn a dic-tionary accompanied by sparse codes. While much empha-sis is put on discriminative dictionary construction, there areno comparative studies of sparse codes regarding abnormal-ity...... detection. We comprehensively study two types of sparsecodes solutions - greedy algorithms and convex L1-norm so-lutions - and their impact on abnormality detection perfor-mance. We also propose our framework of combining sparsecodes with different detection methods. Our comparative ex-periments are carried...

Support agnostic Bayesian matching pursuit for block sparse signals

KAUST Repository

Masood, Mudassir; Al-Naffouri, Tareq Y.

2013-01-01

priori knowledge of block partition and utilizes a greedy approach and order-recursive updates of its metrics to find the most dominant sparse supports to determine the approximate minimum mean square error (MMSE) estimate of the block-sparse signal

SU-E-T-499: Comparison of Measured Tissue Phantom Ratios (TPR) Against Calculated From Percent Depth Doses (PDD) with and Without Peak Scatter Factor (PSF) in 6MV Open Beam

International Nuclear Information System (INIS)

Narayanasamy, G; Cruz, W; Gutierrez, Alonso; Mavroidis, Panayiotis; Papanikolaou, N; Stathakis, S; Breton, C

2014-01-01

Purpose: To examine the accuracy of measured tissue phantom ratios (TPR) values with TPR calculated from percentage depth dose (PDD) with and without peak scatter fraction (PSF) correction. Methods: For 6MV open beam, TPR and PDD values were measured using PTW Semiflex (31010) ionization field and reference chambers (0.125cc volume) in a PTW MP3-M water tank. PDD curves were measured at SSD of 100cm for 7 square fields from 3cm to 30cm. The TPR values were measured up to 22cm depth for the same fields by continuous water draining method with ionization chamber static at 100cm from source. A comparison study was performed between the (a) measured TPR, (b) TPR calculated from PDD without PSF, (c) TPR calculated from PDD with PSF and (d) clinical TPR from RadCalc (ver 6.2, Sun Nuclear Corp). Results: There is a field size, depth dependence on TPR values. For 10cmx10cm, the differences in surface dose (DDs), dose at 10cm depth (DD10) <0.5%; differences in dmax (Ddmax) <2mm for the 4 methods. The corresponding values for 30cmx30cm are DDs, DD10 <0.2% and Ddmax<3mm. Even though for 3cmx3cm field, DDs and DD10 <1% and Ddmax<1mm, the calculated TPR values with and without PSF correction differed by 2% at >20cm depth. In all field sizes at depths>28cm, (d) clinical TPR values are larger than that from (b) and (c) by >3%. Conclusion: Measured TPR in method (a) differ from calculated TPR in methods (b) and (c) to within 1% for depths < 28cm in all 7 fields in open 6MV beam. The dmax values are within 3mm of each other. The largest deviation of >3% was observed in clinical TPR values in method (d) for all fields at depths < 28cm

Selectivity and sparseness in randomly connected balanced networks.

Directory of Open Access Journals (Sweden)

Cengiz Pehlevan

Full Text Available Neurons in sensory cortex show stimulus selectivity and sparse population response, even in cases where no strong functionally specific structure in connectivity can be detected. This raises the question whether selectivity and sparseness can be generated and maintained in randomly connected networks. We consider a recurrent network of excitatory and inhibitory spiking neurons with random connectivity, driven by random projections from an input layer of stimulus selective neurons. In this architecture, the stimulus-to-stimulus and neuron-to-neuron modulation of total synaptic input is weak compared to the mean input. Surprisingly, we show that in the balanced state the network can still support high stimulus selectivity and sparse population response. In the balanced state, strong synapses amplify the variation in synaptic input and recurrent inhibition cancels the mean. Functional specificity in connectivity emerges due to the inhomogeneity caused by the generative statistical rule used to build the network. We further elucidate the mechanism behind and evaluate the effects of model parameters on population sparseness and stimulus selectivity. Network response to mixtures of stimuli is investigated. It is shown that a balanced state with unselective inhibition can be achieved with densely connected input to inhibitory population. Balanced networks exhibit the "paradoxical" effect: an increase in excitatory drive to inhibition leads to decreased inhibitory population firing rate. We compare and contrast selectivity and sparseness generated by the balanced network to randomly connected unbalanced networks. Finally, we discuss our results in light of experiments.

SPARSE ELECTROMAGNETIC IMAGING USING NONLINEAR LANDWEBER ITERATIONS

KAUST Repository

Desmal, Abdulla

2015-07-29

A scheme for efficiently solving the nonlinear electromagnetic inverse scattering problem on sparse investigation domains is described. The proposed scheme reconstructs the (complex) dielectric permittivity of an investigation domain from fields measured away from the domain itself. Least-squares data misfit between the computed scattered fields, which are expressed as a nonlinear function of the permittivity, and the measured fields is constrained by the L0/L1-norm of the solution. The resulting minimization problem is solved using nonlinear Landweber iterations, where at each iteration a thresholding function is applied to enforce the sparseness-promoting L0/L1-norm constraint. The thresholded nonlinear Landweber iterations are applied to several two-dimensional problems, where the ``measured\\'\\' fields are synthetically generated or obtained from actual experiments. These numerical experiments demonstrate the accuracy, efficiency, and applicability of the proposed scheme in reconstructing sparse profiles with high permittivity values.

Vector sparse representation of color image using quaternion matrix analysis.

Science.gov (United States)

Xu, Yi; Yu, Licheng; Xu, Hongteng; Zhang, Hao; Nguyen, Truong

2015-04-01

Traditional sparse image models treat color image pixel as a scalar, which represents color channels separately or concatenate color channels as a monochrome image. In this paper, we propose a vector sparse representation model for color images using quaternion matrix analysis. As a new tool for color image representation, its potential applications in several image-processing tasks are presented, including color image reconstruction, denoising, inpainting, and super-resolution. The proposed model represents the color image as a quaternion matrix, where a quaternion-based dictionary learning algorithm is presented using the K-quaternion singular value decomposition (QSVD) (generalized K-means clustering for QSVD) method. It conducts the sparse basis selection in quaternion space, which uniformly transforms the channel images to an orthogonal color space. In this new color space, it is significant that the inherent color structures can be completely preserved during vector reconstruction. Moreover, the proposed sparse model is more efficient comparing with the current sparse models for image restoration tasks due to lower redundancy between the atoms of different color channels. The experimental results demonstrate that the proposed sparse image model avoids the hue bias issue successfully and shows its potential as a general and powerful tool in color image analysis and processing domain.

Sparse Image Reconstruction on the Sphere: Analysis and Synthesis.

Science.gov (United States)

Wallis, Christopher G R; Wiaux, Yves; McEwen, Jason D

2017-11-01

We develop techniques to solve ill-posed inverse problems on the sphere by sparse regularization, exploiting sparsity in both axisymmetric and directional scale-discretized wavelet space. Denoising, inpainting, and deconvolution problems and combinations thereof, are considered as examples. Inverse problems are solved in both the analysis and synthesis settings, with a number of different sampling schemes. The most effective approach is that with the most restricted solution-space, which depends on the interplay between the adopted sampling scheme, the selection of the analysis/synthesis problem, and any weighting of the l 1 norm appearing in the regularization problem. More efficient sampling schemes on the sphere improve reconstruction fidelity by restricting the solution-space and also by improving sparsity in wavelet space. We apply the technique to denoise Planck 353-GHz observations, improving the ability to extract the structure of Galactic dust emission, which is important for studying Galactic magnetism.

Intelligent condition monitoring method for bearing faults from highly compressed measurements using sparse over-complete features

Science.gov (United States)

Ahmed, H. O. A.; Wong, M. L. D.; Nandi, A. K.

2018-01-01

Condition classification of rolling element bearings in rotating machines is important to prevent the breakdown of industrial machinery. A considerable amount of literature has been published on bearing faults classification. These studies aim to determine automatically the current status of a roller element bearing. Of these studies, methods based on compressed sensing (CS) have received some attention recently due to their ability to allow one to sample below the Nyquist sampling rate. This technology has many possible uses in machine condition monitoring and has been investigated as a possible approach for fault detection and classification in the compressed domain, i.e., without reconstructing the original signal. However, previous CS based methods have been found to be too weak for highly compressed data. The present paper explores computationally, for the first time, the effects of sparse autoencoder based over-complete sparse representations on the classification performance of highly compressed measurements of bearing vibration signals. For this study, the CS method was used to produce highly compressed measurements of the original bearing dataset. Then, an effective deep neural network (DNN) with unsupervised feature learning algorithm based on sparse autoencoder is used for learning over-complete sparse representations of these compressed datasets. Finally, the fault classification is achieved using two stages, namely, pre-training classification based on stacked autoencoder and softmax regression layer form the deep net stage (the first stage), and re-training classification based on backpropagation (BP) algorithm forms the fine-tuning stage (the second stage). The experimental results show that the proposed method is able to achieve high levels of accuracy even with extremely compressed measurements compared with the existing techniques.

Fast convolutional sparse coding using matrix inversion lemma

Czech Academy of Sciences Publication Activity Database

Šorel, Michal; Šroubek, Filip

2016-01-01

Roč. 55, č. 1 (2016), s. 44-51 ISSN 1051-2004 R&D Projects: GA ČR GA13-29225S Institutional support: RVO:67985556 Keywords : Convolutional sparse coding * Feature learning * Deconvolution networks * Shift-invariant sparse coding Subject RIV: JD - Computer Applications, Robotics Impact factor: 2.337, year: 2016 http://library.utia.cas.cz/separaty/2016/ZOI/sorel-0459332.pdf

Network and adaptive sampling

CERN Document Server

Chaudhuri, Arijit

2014-01-01

Combining the two statistical techniques of network sampling and adaptive sampling, this book illustrates the advantages of using them in tandem to effectively capture sparsely located elements in unknown pockets. It shows how network sampling is a reliable guide in capturing inaccessible entities through linked auxiliaries. The text also explores how adaptive sampling is strengthened in information content through subsidiary sampling with devices to mitigate unmanageable expanding sample sizes. Empirical data illustrates the applicability of both methods.

A PSF photometry tool for NASA's Kepler, K2, and TESS missions

Science.gov (United States)

Cardoso, Jose Vinicius De Miranda; Barentsen, Geert; Hedges, Christina L.; Gully-Santiago, Michael A.; Cody, Ann Marie; Montet, Ben

2018-01-01

NASA's Kepler and K2 missions have impacted all areas of astrophysics in unique and important ways by delivering high-precision time series data on asteroids, stars, and galaxies. For example, both the official Kepler pipeline and the various community-owned pipelines have been successful at discovering a myriad of transiting exoplanets around a wide range of stellar types. However, the existing pipelines tend to focus on studying isolated stars using simple aperture photometry, and often perform sub-optimally in crowded fields where objects are blended. To address this issue, we present a Point Spread Function (PSF) photometry toolkit for Kepler and K2 which is able to extract light curves from crowded regions, such as the Beehive Cluster, the Lagoon Nebula, and the M67 globular cluster, which were all recently observed by Kepler. We present a detailed discussion on the theory, the practical use, and demonstrate our tool on various levels of crowding. Finally, we discuss the future use of the tool on data from the TESS mission. The code is open source and available on GitHub as part of the PyKE toolkit for Kepler/K2 data analysis.

Structure-based bayesian sparse reconstruction

KAUST Repository

Quadeer, Ahmed Abdul; Al-Naffouri, Tareq Y.

2012-01-01

Sparse signal reconstruction algorithms have attracted research attention due to their wide applications in various fields. In this paper, we present a simple Bayesian approach that utilizes the sparsity constraint and a priori statistical

Binary Sparse Phase Retrieval via Simulated Annealing

Directory of Open Access Journals (Sweden)

Wei Peng

2016-01-01

Full Text Available This paper presents the Simulated Annealing Sparse PhAse Recovery (SASPAR algorithm for reconstructing sparse binary signals from their phaseless magnitudes of the Fourier transform. The greedy strategy version is also proposed for a comparison, which is a parameter-free algorithm. Sufficient numeric simulations indicate that our method is quite effective and suggest the binary model is robust. The SASPAR algorithm seems competitive to the existing methods for its efficiency and high recovery rate even with fewer Fourier measurements.

Comparison of PSF maxima and minima of multiple annuli coded aperture (MACA) and complementary multiple annuli coded aperture (CMACA) systems

Science.gov (United States)

Ratnam, Challa; Lakshmana Rao, Vadlamudi; Lachaa Goud, Sivagouni

2006-10-01

In the present paper, and a series of papers to follow, the Fourier analytical properties of multiple annuli coded aperture (MACA) and complementary multiple annuli coded aperture (CMACA) systems are investigated. First, the transmission function for MACA and CMACA is derived using Fourier methods and, based on the Fresnel-Kirchoff diffraction theory, the formulae for the point spread function are formulated. The PSF maxima and minima are calculated for both the MACA and CMACA systems. The dependence of these properties on the number of zones is studied and reported in this paper.

Comparison of PSF maxima and minima of multiple annuli coded aperture (MACA) and complementary multiple annuli coded aperture (CMACA) systems

International Nuclear Information System (INIS)

Ratnam, Challa; Rao, Vadlamudi Lakshmana; Goud, Sivagouni Lachaa

2006-01-01

In the present paper, and a series of papers to follow, the Fourier analytical properties of multiple annuli coded aperture (MACA) and complementary multiple annuli coded aperture (CMACA) systems are investigated. First, the transmission function for MACA and CMACA is derived using Fourier methods and, based on the Fresnel-Kirchoff diffraction theory, the formulae for the point spread function are formulated. The PSF maxima and minima are calculated for both the MACA and CMACA systems. The dependence of these properties on the number of zones is studied and reported in this paper

Confidence of model based shape reconstruction from sparse data

DEFF Research Database (Denmark)

Baka, N.; de Bruijne, Marleen; Reiber, J. H. C.

2010-01-01

Statistical shape models (SSM) are commonly applied for plausible interpolation of missing data in medical imaging. However, when fitting a shape model to sparse information, many solutions may fit the available data. In this paper we derive a constrained SSM to fit noisy sparse input landmarks...

Risk programming and sparse data: how to get more reliable results

OpenAIRE

Hardaker, J. Brian; Lien, Gudbrand D.; Van Asseldonk, Marcel A.P.M.; Richardson, James W.; Hegrenes, Agnar

2008-01-01

Because relevant historical data for farms are inevitably sparse, most risk programming studies rely on few observations. We discuss how to use available information to derive an appropriate multivariate distribution function that can be sampled for a more complete representation of the possible risks in riskbased models. For the particular example of a Norwegian mixed livestock and crop farm, the solution is shown to be unstable with few states, although the cost of picking a sub-optimal pla...

Proportionate Minimum Error Entropy Algorithm for Sparse System Identification

Directory of Open Access Journals (Sweden)

Zongze Wu

2015-08-01

Full Text Available Sparse system identification has received a great deal of attention due to its broad applicability. The proportionate normalized least mean square (PNLMS algorithm, as a popular tool, achieves excellent performance for sparse system identification. In previous studies, most of the cost functions used in proportionate-type sparse adaptive algorithms are based on the mean square error (MSE criterion, which is optimal only when the measurement noise is Gaussian. However, this condition does not hold in most real-world environments. In this work, we use the minimum error entropy (MEE criterion, an alternative to the conventional MSE criterion, to develop the proportionate minimum error entropy (PMEE algorithm for sparse system identification, which may achieve much better performance than the MSE based methods especially in heavy-tailed non-Gaussian situations. Moreover, we analyze the convergence of the proposed algorithm and derive a sufficient condition that ensures the mean square convergence. Simulation results confirm the excellent performance of the new algorithm.

Ordering sparse matrices for cache-based systems

International Nuclear Information System (INIS)

Biswas, Rupak; Oliker, Leonid

2001-01-01

The Conjugate Gradient (CG) algorithm is the oldest and best-known Krylov subspace method used to solve sparse linear systems. Most of the coating-point operations within each CG iteration is spent performing sparse matrix-vector multiplication (SPMV). We examine how various ordering and partitioning strategies affect the performance of CG and SPMV when different programming paradigms are used on current commercial cache-based computers. However, a multithreaded implementation on the cacheless Cray MTA demonstrates high efficiency and scalability without any special ordering or partitioning

P-SPARSLIB: A parallel sparse iterative solution package

Energy Technology Data Exchange (ETDEWEB)

Saad, Y. [Univ. of Minnesota, Minneapolis, MN (United States)

1994-12-31

Iterative methods are gaining popularity in engineering and sciences at a time where the computational environment is changing rapidly. P-SPARSLIB is a project to build a software library for sparse matrix computations on parallel computers. The emphasis is on iterative methods and the use of distributed sparse matrices, an extension of the domain decomposition approach to general sparse matrices. One of the goals of this project is to develop a software package geared towards specific applications. For example, the author will test the performance and usefulness of P-SPARSLIB modules on linear systems arising from CFD applications. Equally important is the goal of portability. In the long run, the author wishes to ensure that this package is portable on a variety of platforms, including SIMD environments and shared memory environments.

Sparse representation, modeling and learning in visual recognition theory, algorithms and applications

CERN Document Server

Cheng, Hong

2015-01-01

This unique text/reference presents a comprehensive review of the state of the art in sparse representations, modeling and learning. The book examines both the theoretical foundations and details of algorithm implementation, highlighting the practical application of compressed sensing research in visual recognition and computer vision. Topics and features: provides a thorough introduction to the fundamentals of sparse representation, modeling and learning, and the application of these techniques in visual recognition; describes sparse recovery approaches, robust and efficient sparse represen

Design Patterns for Sparse-Matrix Computations on Hybrid CPU/GPU Platforms

Directory of Open Access Journals (Sweden)

Valeria Cardellini

2014-01-01

Full Text Available We apply object-oriented software design patterns to develop code for scientific software involving sparse matrices. Design patterns arise when multiple independent developments produce similar designs which converge onto a generic solution. We demonstrate how to use design patterns to implement an interface for sparse matrix computations on NVIDIA GPUs starting from PSBLAS, an existing sparse matrix library, and from existing sets of GPU kernels for sparse matrices. We also compare the throughput of the PSBLAS sparse matrix–vector multiplication on two platforms exploiting the GPU with that obtained by a CPU-only PSBLAS implementation. Our experiments exhibit encouraging results regarding the comparison between CPU and GPU executions in double precision, obtaining a speedup of up to 35.35 on NVIDIA GTX 285 with respect to AMD Athlon 7750, and up to 10.15 on NVIDIA Tesla C2050 with respect to Intel Xeon X5650.

An Efficient GPU General Sparse Matrix-Matrix Multiplication for Irregular Data

DEFF Research Database (Denmark)

Liu, Weifeng; Vinter, Brian

2014-01-01

General sparse matrix-matrix multiplication (SpGEMM) is a fundamental building block for numerous applications such as algebraic multigrid method, breadth first search and shortest path problem. Compared to other sparse BLAS routines, an efficient parallel SpGEMM algorithm has to handle extra...... irregularity from three aspects: (1) the number of the nonzero entries in the result sparse matrix is unknown in advance, (2) very expensive parallel insert operations at random positions in the result sparse matrix dominate the execution time, and (3) load balancing must account for sparse data in both input....... Load balancing builds on the number of the necessary arithmetic operations on the nonzero entries and is guaranteed in all stages. Compared with the state-of-the-art GPU SpGEMM methods in the CUSPARSE library and the CUSP library and the latest CPU SpGEMM method in the Intel Math Kernel Library, our...

Comparison of Methods for Sparse Representation of Musical Signals

DEFF Research Database (Denmark)

Endelt, Line Ørtoft; la Cour-Harbo, Anders

2005-01-01

by a number of sparseness measures and results are shown on the ℓ1 norm of the coefficients, using a dictionary containing a Dirac basis, a Discrete Cosine Transform, and a Wavelet Packet. Evaluated only on the sparseness Matching Pursuit is the best method, and it is also relatively fast....

Joint-2D-SL0 Algorithm for Joint Sparse Matrix Reconstruction

Directory of Open Access Journals (Sweden)

Dong Zhang

2017-01-01

Full Text Available Sparse matrix reconstruction has a wide application such as DOA estimation and STAP. However, its performance is usually restricted by the grid mismatch problem. In this paper, we revise the sparse matrix reconstruction model and propose the joint sparse matrix reconstruction model based on one-order Taylor expansion. And it can overcome the grid mismatch problem. Then, we put forward the Joint-2D-SL0 algorithm which can solve the joint sparse matrix reconstruction problem efficiently. Compared with the Kronecker compressive sensing method, our proposed method has a higher computational efficiency and acceptable reconstruction accuracy. Finally, simulation results validate the superiority of the proposed method.

Discussion of CoSA: Clustering of Sparse Approximations

Energy Technology Data Exchange (ETDEWEB)

Armstrong, Derek Elswick [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2017-03-07

The purpose of this talk is to discuss the possible applications of CoSA (Clustering of Sparse Approximations) to the exploitation of HSI (HyperSpectral Imagery) data. CoSA is presented by Moody et al. in the Journal of Applied Remote Sensing (“Land cover classification in multispectral imagery using clustering of sparse approximations over learned feature dictionaries”, Vol. 8, 2014) and is based on machine learning techniques.

Recruiting a special sample with sparse resources: lessons from a study of Gulf War veterans.

Science.gov (United States)

Erickson, Laura C; Ritchie, Janis B; Javors, Jennifer M; Golomb, Beatrice A

2013-01-01

Recruitment is the most common failure point for clinical studies, with recruitment failure adversely affecting science, dollar costs, human capital, and the ethical risk-benefit trade-off to study participants. Added problems attend recruitment of special and/or challenging candidate populations, particularly in settings of sparse recruitment resources. Obstacles to study recruitment and participation of ill Gulf War veterans (GWVs) include health barriers, work and family obligations, mistrust of the medical/scientific community, and challenges to identifying/reaching potential participants. We sought to identify and implement a minimal-cost multipronged recruitment approach for a small single-site (recruitment settings and larger multisite studies. Categories of recruitment approach included directed as well as general media, collaborations with support groups/interest groups, local free advertising resources (Craigslist and Backpage), physician outreach, Internet-based approaches, and referrals from study participants and screenees. We describe the subcategories and yield of each approach within each approach. Each approach contributed candidates to the final recruitment tally, with the largest fractional contribution by directed media (52%). Among the remainder, no other individual approach was clearly dominant (largest contribution: 13%). Special population subsamples present special challenges; all approaches cited may not be useful in all settings and subpopulations. A multipronged suite of minimal-cost approaches led to successful recruitment to target for this single-site clinical trial for a special population with significant recruitment challenges. It additionally yielded a nation-wide corpus of several hundred individuals interested in participation in future studies of GWVs. While certain approaches produced disproportionate yield, it was not possible to predict these a priori. We suggest that this model, which incorporates a suite of approaches, and

Facial Expression Recognition via Non-Negative Least-Squares Sparse Coding

Directory of Open Access Journals (Sweden)

Ying Chen

2014-05-01

Full Text Available Sparse coding is an active research subject in signal processing, computer vision, and pattern recognition. A novel method of facial expression recognition via non-negative least squares (NNLS sparse coding is presented in this paper. The NNLS sparse coding is used to form a facial expression classifier. To testify the performance of the presented method, local binary patterns (LBP and the raw pixels are extracted for facial feature representation. Facial expression recognition experiments are conducted on the Japanese Female Facial Expression (JAFFE database. Compared with other widely used methods such as linear support vector machines (SVM, sparse representation-based classifier (SRC, nearest subspace classifier (NSC, K-nearest neighbor (KNN and radial basis function neural networks (RBFNN, the experiment results indicate that the presented NNLS method performs better than other used methods on facial expression recognition tasks.

Sparse Representation Based Multi-Instance Learning for Breast Ultrasound Image Classification

Directory of Open Access Journals (Sweden)

Lu Bing

2017-01-01

Full Text Available We propose a novel method based on sparse representation for breast ultrasound image classification under the framework of multi-instance learning (MIL. After image enhancement and segmentation, concentric circle is used to extract the global and local features for improving the accuracy in diagnosis and prediction. The classification problem of ultrasound image is converted to sparse representation based MIL problem. Each instance of a bag is represented as a sparse linear combination of all basis vectors in the dictionary, and then the bag is represented by one feature vector which is obtained via sparse representations of all instances within the bag. The sparse and MIL problem is further converted to a conventional learning problem that is solved by relevance vector machine (RVM. Results of single classifiers are combined to be used for classification. Experimental results on the breast cancer datasets demonstrate the superiority of the proposed method in terms of classification accuracy as compared with state-of-the-art MIL methods.

Sparse Representation Based Multi-Instance Learning for Breast Ultrasound Image Classification.

Science.gov (United States)

Bing, Lu; Wang, Wei

2017-01-01

We propose a novel method based on sparse representation for breast ultrasound image classification under the framework of multi-instance learning (MIL). After image enhancement and segmentation, concentric circle is used to extract the global and local features for improving the accuracy in diagnosis and prediction. The classification problem of ultrasound image is converted to sparse representation based MIL problem. Each instance of a bag is represented as a sparse linear combination of all basis vectors in the dictionary, and then the bag is represented by one feature vector which is obtained via sparse representations of all instances within the bag. The sparse and MIL problem is further converted to a conventional learning problem that is solved by relevance vector machine (RVM). Results of single classifiers are combined to be used for classification. Experimental results on the breast cancer datasets demonstrate the superiority of the proposed method in terms of classification accuracy as compared with state-of-the-art MIL methods.

Joint sparse representation for robust multimodal biometrics recognition.

Science.gov (United States)

Shekhar, Sumit; Patel, Vishal M; Nasrabadi, Nasser M; Chellappa, Rama

2014-01-01

Traditional biometric recognition systems rely on a single biometric signature for authentication. While the advantage of using multiple sources of information for establishing the identity has been widely recognized, computational models for multimodal biometrics recognition have only recently received attention. We propose a multimodal sparse representation method, which represents the test data by a sparse linear combination of training data, while constraining the observations from different modalities of the test subject to share their sparse representations. Thus, we simultaneously take into account correlations as well as coupling information among biometric modalities. A multimodal quality measure is also proposed to weigh each modality as it gets fused. Furthermore, we also kernelize the algorithm to handle nonlinearity in data. The optimization problem is solved using an efficient alternative direction method. Various experiments show that the proposed method compares favorably with competing fusion-based methods.

Droplet Image Super Resolution Based on Sparse Representation and Kernel Regression

Science.gov (United States)

Zou, Zhenzhen; Luo, Xinghong; Yu, Qiang

2018-05-01

Microgravity and containerless conditions, which are produced via electrostatic levitation combined with a drop tube, are important when studying the intrinsic properties of new metastable materials. Generally, temperature and image sensors can be used to measure the changes of sample temperature, morphology and volume. Then, the specific heat, surface tension, viscosity changes and sample density can be obtained. Considering that the falling speed of the material sample droplet is approximately 31.3 m/s when it reaches the bottom of a 50-meter-high drop tube, a high-speed camera with a collection rate of up to 106 frames/s is required to image the falling droplet. However, at the high-speed mode, very few pixels, approximately 48-120, will be obtained in each exposure time, which results in low image quality. Super-resolution image reconstruction is an algorithm that provides finer details than the sampling grid of a given imaging device by increasing the number of pixels per unit area in the image. In this work, we demonstrate the application of single image-resolution reconstruction in the microgravity and electrostatic levitation for the first time. Here, using the image super-resolution method based on sparse representation, a low-resolution droplet image can be reconstructed. Employed Yang's related dictionary model, high- and low-resolution image patches were combined with dictionary training, and high- and low-resolution-related dictionaries were obtained. The online double-sparse dictionary training algorithm was used in the study of related dictionaries and overcome the shortcomings of the traditional training algorithm with small image patch. During the stage of image reconstruction, the algorithm of kernel regression is added, which effectively overcomes the shortcomings of the Yang image's edge blurs.

Robust Visual Tracking Via Consistent Low-Rank Sparse Learning

KAUST Repository

Zhang, Tianzhu

2014-06-19

Object tracking is the process of determining the states of a target in consecutive video frames based on properties of motion and appearance consistency. In this paper, we propose a consistent low-rank sparse tracker (CLRST) that builds upon the particle filter framework for tracking. By exploiting temporal consistency, the proposed CLRST algorithm adaptively prunes and selects candidate particles. By using linear sparse combinations of dictionary templates, the proposed method learns the sparse representations of image regions corresponding to candidate particles jointly by exploiting the underlying low-rank constraints. In addition, the proposed CLRST algorithm is computationally attractive since temporal consistency property helps prune particles and the low-rank minimization problem for learning joint sparse representations can be efficiently solved by a sequence of closed form update operations. We evaluate the proposed CLRST algorithm against 14 state-of-the-art tracking methods on a set of 25 challenging image sequences. Experimental results show that the CLRST algorithm performs favorably against state-of-the-art tracking methods in terms of accuracy and execution time.

Efficient collaborative sparse channel estimation in massive MIMO

KAUST Repository

Masood, Mudassir

2015-08-12

We propose a method for estimation of sparse frequency selective channels within MIMO-OFDM systems. These channels are independently sparse and share a common support. The method estimates the impulse response for each channel observed by the antennas at the receiver. Estimation is performed in a coordinated manner by sharing minimal information among neighboring antennas to achieve results better than many contemporary methods. Simulations demonstrate the superior performance of the proposed method.

Efficient collaborative sparse channel estimation in massive MIMO

KAUST Repository

Masood, Mudassir; Afify, Laila H.; Al-Naffouri, Tareq Y.

2015-01-01

We propose a method for estimation of sparse frequency selective channels within MIMO-OFDM systems. These channels are independently sparse and share a common support. The method estimates the impulse response for each channel observed by the antennas at the receiver. Estimation is performed in a coordinated manner by sharing minimal information among neighboring antennas to achieve results better than many contemporary methods. Simulations demonstrate the superior performance of the proposed method.

Sparse dictionary learning of resting state fMRI networks.

Science.gov (United States)

Eavani, Harini; Filipovych, Roman; Davatzikos, Christos; Satterthwaite, Theodore D; Gur, Raquel E; Gur, Ruben C

2012-07-02

Research in resting state fMRI (rsfMRI) has revealed the presence of stable, anti-correlated functional subnetworks in the brain. Task-positive networks are active during a cognitive process and are anti-correlated with task-negative networks, which are active during rest. In this paper, based on the assumption that the structure of the resting state functional brain connectivity is sparse, we utilize sparse dictionary modeling to identify distinct functional sub-networks. We propose two ways of formulating the sparse functional network learning problem that characterize the underlying functional connectivity from different perspectives. Our results show that the whole-brain functional connectivity can be concisely represented with highly modular, overlapping task-positive/negative pairs of sub-networks.

Regularized generalized eigen-decomposition with applications to sparse supervised feature extraction and sparse discriminant analysis

DEFF Research Database (Denmark)

Han, Xixuan; Clemmensen, Line Katrine Harder

2015-01-01

We propose a general technique for obtaining sparse solutions to generalized eigenvalue problems, and call it Regularized Generalized Eigen-Decomposition (RGED). For decades, Fisher's discriminant criterion has been applied in supervised feature extraction and discriminant analysis, and it is for...

A performance study of sparse Cholesky factorization on INTEL iPSC/860

Science.gov (United States)

Zubair, M.; Ghose, M.

1992-01-01

The problem of Cholesky factorization of a sparse matrix has been very well investigated on sequential machines. A number of efficient codes exist for factorizing large unstructured sparse matrices. However, there is a lack of such efficient codes on parallel machines in general, and distributed machines in particular. Some of the issues that are critical to the implementation of sparse Cholesky factorization on a distributed memory parallel machine are ordering, partitioning and mapping, load balancing, and ordering of various tasks within a processor. Here, we focus on the effect of various partitioning schemes on the performance of sparse Cholesky factorization on the Intel iPSC/860. Also, a new partitioning heuristic for structured as well as unstructured sparse matrices is proposed, and its performance is compared with other schemes.

Fast sparsely synchronized brain rhythms in a scale-free neural network.

Science.gov (United States)

Kim, Sang-Yoon; Lim, Woochang

2015-08-01

We consider a directed version of the Barabási-Albert scale-free network model with symmetric preferential attachment with the same in- and out-degrees and study the emergence of sparsely synchronized rhythms for a fixed attachment degree in an inhibitory population of fast-spiking Izhikevich interneurons. Fast sparsely synchronized rhythms with stochastic and intermittent neuronal discharges are found to appear for large values of J (synaptic inhibition strength) and D (noise intensity). For an intensive study we fix J at a sufficiently large value and investigate the population states by increasing D. For small D, full synchronization with the same population-rhythm frequency fp and mean firing rate (MFR) fi of individual neurons occurs, while for large D partial synchronization with fp>〈fi〉 (〈fi〉: ensemble-averaged MFR) appears due to intermittent discharge of individual neurons; in particular, the case of fp>4〈fi〉 is referred to as sparse synchronization. For the case of partial and sparse synchronization, MFRs of individual neurons vary depending on their degrees. As D passes a critical value D* (which is determined by employing an order parameter), a transition to unsynchronization occurs due to the destructive role of noise to spoil the pacing between sparse spikes. For Dsparse synchronization do contributions of individual neuronal dynamics to population synchronization change depending on their degrees, unlike in the case of full synchronization. Consequently, dynamics of individual neurons reveal the inhomogeneous network structure for the case of partial and sparse synchronization, which is in contrast to the case of

Fast sparsely synchronized brain rhythms in a scale-free neural network

Science.gov (United States)

Kim, Sang-Yoon; Lim, Woochang

2015-08-01

We consider a directed version of the Barabási-Albert scale-free network model with symmetric preferential attachment with the same in- and out-degrees and study the emergence of sparsely synchronized rhythms for a fixed attachment degree in an inhibitory population of fast-spiking Izhikevich interneurons. Fast sparsely synchronized rhythms with stochastic and intermittent neuronal discharges are found to appear for large values of J (synaptic inhibition strength) and D (noise intensity). For an intensive study we fix J at a sufficiently large value and investigate the population states by increasing D . For small D , full synchronization with the same population-rhythm frequency fp and mean firing rate (MFR) fi of individual neurons occurs, while for large D partial synchronization with fp> ( : ensemble-averaged MFR) appears due to intermittent discharge of individual neurons; in particular, the case of fp>4 is referred to as sparse synchronization. For the case of partial and sparse synchronization, MFRs of individual neurons vary depending on their degrees. As D passes a critical value D* (which is determined by employing an order parameter), a transition to unsynchronization occurs due to the destructive role of noise to spoil the pacing between sparse spikes. For D sparse synchronization do contributions of individual neuronal dynamics to population synchronization change depending on their degrees, unlike in the case of full synchronization. Consequently, dynamics of individual neurons reveal the inhomogeneous network structure for the case of partial and sparse synchronization, which is in contrast to the case of statistically homogeneous

Avaliação da saúde bucal das gestantes atendidas no PSF Adirbal Corralo na cidade Passo Fundo-RS

OpenAIRE

Carlos Alberto Rech; Patrícia Manfio

2016-01-01

O presente trabalho tem por objetivo analisar as condições e percepções de saúde bucal das gestantes que freqüentam o grupo de gestantes do PSF Adirbal Corralo na cidade de Passo Fundo-RS. Trata-se de um estudo quantitativo com abordagem descritiva. Para a coleta de dados foram utilizados questionários acerca da saúde bucal das gestantes, procurando observar quantas vezes e como é feita a escovação, o atendimento odontológico, as orientações odontológicas pré-natais e também exame clínico ver...

l1- and l2-Norm Joint Regularization Based Sparse Signal Reconstruction Scheme

Directory of Open Access Journals (Sweden)

Chanzi Liu

2016-01-01

Full Text Available Many problems in signal processing and statistical inference involve finding sparse solution to some underdetermined linear system of equations. This is also the application condition of compressive sensing (CS which can find the sparse solution from the measurements far less than the original signal. In this paper, we propose l1- and l2-norm joint regularization based reconstruction framework to approach the original l0-norm based sparseness-inducing constrained sparse signal reconstruction problem. Firstly, it is shown that, by employing the simple conjugate gradient algorithm, the new formulation provides an effective framework to deduce the solution as the original sparse signal reconstruction problem with l0-norm regularization item. Secondly, the upper reconstruction error limit is presented for the proposed sparse signal reconstruction framework, and it is unveiled that a smaller reconstruction error than l1-norm relaxation approaches can be realized by using the proposed scheme in most cases. Finally, simulation results are presented to validate the proposed sparse signal reconstruction approach.

Image fusion via nonlocal sparse K-SVD dictionary learning.

Science.gov (United States)

Li, Ying; Li, Fangyi; Bai, Bendu; Shen, Qiang

2016-03-01

Image fusion aims to merge two or more images captured via various sensors of the same scene to construct a more informative image by integrating their details. Generally, such integration is achieved through the manipulation of the representations of the images concerned. Sparse representation plays an important role in the effective description of images, offering a great potential in a variety of image processing tasks, including image fusion. Supported by sparse representation, in this paper, an approach for image fusion by the use of a novel dictionary learning scheme is proposed. The nonlocal self-similarity property of the images is exploited, not only at the stage of learning the underlying description dictionary but during the process of image fusion. In particular, the property of nonlocal self-similarity is combined with the traditional sparse dictionary. This results in an improved learned dictionary, hereafter referred to as the nonlocal sparse K-SVD dictionary (where K-SVD stands for the K times singular value decomposition that is commonly used in the literature), and abbreviated to NL_SK_SVD. The performance of the NL_SK_SVD dictionary is applied for image fusion using simultaneous orthogonal matching pursuit. The proposed approach is evaluated with different types of images, and compared with a number of alternative image fusion techniques. The resultant superior fused images using the present approach demonstrates the efficacy of the NL_SK_SVD dictionary in sparse image representation.

Sparse trees and shrubs confers a high biodiversity to pastures: Case study on spiders from Transylvania.

Science.gov (United States)

Gallé, Róbert; Urák, István; Nikolett, Gallé-Szpisjak; Hartel, Tibor

2017-01-01

The integration of food production and biodiversity conservation represents a key challenge for sustainability. Several studies suggest that even small structural elements in the landscape can make a substantial contribution to the overall biodiversity value of the agricultural landscapes. Pastures can have high biodiversity potential. However, their intensive and monofunctional use typically erodes its natural capital, including biodiversity. Here we address the ecological value of fine scale structural elements represented by sparsely scattered trees and shrubs for the spider communities in a moderately intensively grazed pasture in Transylvania, Eastern Europe. The pasture was grazed with sheep, cattle and buffalo (ca 1 Livestock Unit ha-1) and no chemical fertilizers were applied. Sampling sites covered the open pasture as well as the existing fine-scale heterogeneity created by scattered trees and shrub. 40 sampling locations each being represented by three 1 m2 quadrats were situated in a stratified design while assuring spatial independency of sampling locations. We identified 140 species of spiders, out of which 18 were red listed and four were new for the Romanian fauna. Spider species assemblages of open pasture, scattered trees, trees and shrubs and the forest edge were statistically distinct. Our study shows that sparsely scattered mature woody vegetation and shrubs substantially increases the ecological value of managed pastures. The structural complexity provided by scattered trees and shrubs makes possible the co-occurrence of high spider diversity with a moderately high intensity grazing possible in this wood-pasture. Our results are in line with recent empirical research showing that sparse trees and shrubs increases the biodiversity potential of pastures managed for commodity production.

Detection of Pitting in Gears Using a Deep Sparse Autoencoder

Directory of Open Access Journals (Sweden)

Yongzhi Qu

2017-05-01

Full Text Available In this paper; a new method for gear pitting fault detection is presented. The presented method is developed based on a deep sparse autoencoder. The method integrates dictionary learning in sparse coding into a stacked autoencoder network. Sparse coding with dictionary learning is viewed as an adaptive feature extraction method for machinery fault diagnosis. An autoencoder is an unsupervised machine learning technique. A stacked autoencoder network with multiple hidden layers is considered to be a deep learning network. The presented method uses a stacked autoencoder network to perform the dictionary learning in sparse coding and extract features from raw vibration data automatically. These features are then used to perform gear pitting fault detection. The presented method is validated with vibration data collected from gear tests with pitting faults in a gearbox test rig and compared with an existing deep learning-based approach.

In-Storage Embedded Accelerator for Sparse Pattern Processing

OpenAIRE

Jun, Sang-Woo; Nguyen, Huy T.; Gadepally, Vijay N.; Arvind

2016-01-01

We present a novel architecture for sparse pattern processing, using flash storage with embedded accelerators. Sparse pattern processing on large data sets is the essence of applications such as document search, natural language processing, bioinformatics, subgraph matching, machine learning, and graph processing. One slice of our prototype accelerator is capable of handling up to 1TB of data, and experiments show that it can outperform C/C++ software solutions on a 16-core system at a fracti...

Process Knowledge Discovery Using Sparse Principal Component Analysis

DEFF Research Database (Denmark)

Gao, Huihui; Gajjar, Shriram; Kulahci, Murat

2016-01-01

As the goals of ensuring process safety and energy efficiency become ever more challenging, engineers increasingly rely on data collected from such processes for informed decision making. During recent decades, extracting and interpreting valuable process information from large historical data sets...... SPCA approach that helps uncover the underlying process knowledge regarding variable relations. This approach systematically determines the optimal sparse loadings for each sparse PC while improving interpretability and minimizing information loss. The salient features of the proposed approach...

Sparse representations and compressive sensing for imaging and vision

CERN Document Server

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.

Massively parallel sparse matrix function calculations with NTPoly

Science.gov (United States)

Dawson, William; Nakajima, Takahito

2018-04-01

We present NTPoly, a massively parallel library for computing the functions of sparse, symmetric matrices. The theory of matrix functions is a well developed framework with a wide range of applications including differential equations, graph theory, and electronic structure calculations. One particularly important application area is diagonalization free methods in quantum chemistry. When the input and output of the matrix function are sparse, methods based on polynomial expansions can be used to compute matrix functions in linear time. We present a library based on these methods that can compute a variety of matrix functions. Distributed memory parallelization is based on a communication avoiding sparse matrix multiplication algorithm. OpenMP task parallellization is utilized to implement hybrid parallelization. We describe NTPoly's interface and show how it can be integrated with programs written in many different programming languages. We demonstrate the merits of NTPoly by performing large scale calculations on the K computer.

Deformable segmentation via sparse representation and dictionary learning.

Science.gov (United States)

Zhang, Shaoting; Zhan, Yiqiang; Metaxas, Dimitris N

2012-10-01

"Shape" and "appearance", the two pillars of a deformable model, complement each other in object segmentation. In many medical imaging applications, while the low-level appearance information is weak or mis-leading, shape priors play a more important role to guide a correct segmentation, thanks to the strong shape characteristics of biological structures. Recently a novel shape prior modeling method has been proposed based on sparse learning theory. Instead of learning a generative shape model, shape priors are incorporated on-the-fly through the sparse shape composition (SSC). SSC is robust to non-Gaussian errors and still preserves individual shape characteristics even when such characteristics is not statistically significant. Although it seems straightforward to incorporate SSC into a deformable segmentation framework as shape priors, the large-scale sparse optimization of SSC has low runtime efficiency, which cannot satisfy clinical requirements. In this paper, we design two strategies to decrease the computational complexity of SSC, making a robust, accurate and efficient deformable segmentation system. (1) When the shape repository contains a large number of instances, which is often the case in 2D problems, K-SVD is used to learn a more compact but still informative shape dictionary. (2) If the derived shape instance has a large number of vertices, which often appears in 3D problems, an affinity propagation method is used to partition the surface into small sub-regions, on which the sparse shape composition is performed locally. Both strategies dramatically decrease the scale of the sparse optimization problem and hence speed up the algorithm. Our method is applied on a diverse set of biomedical image analysis problems. Compared to the original SSC, these two newly-proposed modules not only significant reduce the computational complexity, but also improve the overall accuracy. Copyright © 2012 Elsevier B.V. All rights reserved.

Sparseness- and continuity-constrained seismic imaging

Science.gov (United States)

Herrmann, Felix J.

2005-04-01

Non-linear solution strategies to the least-squares seismic inverse-scattering problem with sparseness and continuity constraints are proposed. Our approach is designed to (i) deal with substantial amounts of additive noise (SNR formulating the solution of the seismic inverse problem in terms of an optimization problem. During the optimization, sparseness on the basis and continuity along the reflectors are imposed by jointly minimizing the l1- and anisotropic diffusion/total-variation norms on the coefficients and reflectivity, respectively. [Joint work with Peyman P. Moghaddam was carried out as part of the SINBAD project, with financial support secured through ITF (the Industry Technology Facilitator) from the following organizations: BG Group, BP, ExxonMobil, and SHELL. Additional funding came from the NSERC Discovery Grants 22R81254.

Fast method of sparse acquisition and reconstruction of view and illumination dependent datasets

Czech Academy of Sciences Publication Activity Database

Filip, Jiří; Vávra, Radomír

2013-01-01

Roč. 37, č. 5 (2013), s. 376-388 ISSN 0097-8493 R&D Projects: GA ČR GAP103/11/0335 Grant - others:EC ERG (European Reintegration Grant) FP7(BE) 239294 Institutional support: RVO:67985556 Keywords : apparent BRDF * measurement * reconstruction * sparse sampling * portable setup Subject RIV: BD - Theory of Information Impact factor: 1.029, year: 2013 http://library.utia.cas.cz/separaty/2013/RO/filip-0392214.pdf

Combinatorial Algorithms for Computing Column Space Bases ThatHave Sparse Inverses

Energy Technology Data Exchange (ETDEWEB)

Pinar, Ali; Chow, Edmond; Pothen, Alex

2005-03-18

This paper presents a combinatorial study on the problem ofconstructing a sparse basis forthe null-space of a sparse, underdetermined, full rank matrix, A. Such a null-space is suitable forsolving solving many saddle point problems. Our approach is to form acolumn space basis of A that has a sparse inverse, by selecting suitablecolumns of A. This basis is then used to form a sparse null-space basisin fundamental form. We investigate three different algorithms forcomputing the column space basis: Two greedy approaches that rely onmatching, and a third employing a divide and conquer strategy implementedwith hypergraph partitioning followed by the greedy approach. We alsodiscuss the complexity of selecting a column basis when it is known thata block diagonal basis exists with a small given block size.

Hyperspectral Image Classification Based on the Combination of Spatial-spectral Feature and Sparse Representation

Directory of Open Access Journals (Sweden)

YANG Zhaoxia

2015-07-01

Full Text Available In order to avoid the problem of being over-dependent on high-dimensional spectral feature in the traditional hyperspectral image classification, a novel approach based on the combination of spatial-spectral feature and sparse representation is proposed in this paper. Firstly, we extract the spatial-spectral feature by reorganizing the local image patch with the first d principal components(PCs into a vector representation, followed by a sorting scheme to make the vector invariant to local image rotation. Secondly, we learn the dictionary through a supervised method, and use it to code the features from test samples afterwards. Finally, we embed the resulting sparse feature coding into the support vector machine(SVM for hyperspectral image classification. Experiments using three hyperspectral data show that the proposed method can effectively improve the classification accuracy comparing with traditional classification methods.

Efficient implementations of block sparse matrix operations on shared memory vector machines

International Nuclear Information System (INIS)

Washio, T.; Maruyama, K.; Osoda, T.; Doi, S.; Shimizu, F.

2000-01-01

In this paper, we propose vectorization and shared memory-parallelization techniques for block-type random sparse matrix operations in finite element (FEM) applications. Here, a block corresponds to unknowns on one node in the FEM mesh and we assume that the block size is constant over the mesh. First, we discuss some basic vectorization ideas (the jagged diagonal (JAD) format and the segmented scan algorithm) for the sparse matrix-vector product. Then, we extend these ideas to the shared memory parallelization. After that, we show that the techniques can be applied not only to the sparse matrix-vector product but also to the sparse matrix-matrix product, the incomplete or complete sparse LU factorization and preconditioning. Finally, we report the performance evaluation results obtained on an NEC SX-4 shared memory vector machine for linear systems in some FEM applications. (author)

A Projected Conjugate Gradient Method for Sparse Minimax Problems

DEFF Research Database (Denmark)

Madsen, Kaj; Jonasson, Kristjan

1993-01-01

A new method for nonlinear minimax problems is presented. The method is of the trust region type and based on sequential linear programming. It is a first order method that only uses first derivatives and does not approximate Hessians. The new method is well suited for large sparse problems...... as it only requires that software for sparse linear programming and a sparse symmetric positive definite equation solver are available. On each iteration a special linear/quadratic model of the function is minimized, but contrary to the usual practice in trust region methods the quadratic model is only...... with the method are presented. In fact, we find that the number of iterations required is comparable to that of state-of-the-art quasi-Newton codes....

Identification of MIMO systems with sparse transfer function coefficients

Science.gov (United States)

Qiu, Wanzhi; Saleem, Syed Khusro; Skafidas, Efstratios

2012-12-01

We study the problem of estimating transfer functions of multivariable (multiple-input multiple-output--MIMO) systems with sparse coefficients. We note that subspace identification methods are powerful and convenient tools in dealing with MIMO systems since they neither require nonlinear optimization nor impose any canonical form on the systems. However, subspace-based methods are inefficient for systems with sparse transfer function coefficients since they work on state space models. We propose a two-step algorithm where the first step identifies the system order using the subspace principle in a state space format, while the second step estimates coefficients of the transfer functions via L1-norm convex optimization. The proposed algorithm retains good features of subspace methods with improved noise-robustness for sparse systems.

MULTISCALE SPARSE APPEARANCE MODELING AND SIMULATION OF PATHOLOGICAL DEFORMATIONS

Directory of Open Access Journals (Sweden)

Rami Zewail

2017-08-01

Full Text Available Machine learning and statistical modeling techniques has drawn much interest within the medical imaging research community. However, clinically-relevant modeling of anatomical structures continues to be a challenging task. This paper presents a novel method for multiscale sparse appearance modeling in medical images with application to simulation of pathological deformations in X-ray images of human spine. The proposed appearance model benefits from the non-linear approximation power of Contourlets and its ability to capture higher order singularities to achieve a sparse representation while preserving the accuracy of the statistical model. Independent Component Analysis is used to extract statistical independent modes of variations from the sparse Contourlet-based domain. The new model is then used to simulate clinically-relevant pathological deformations in radiographic images.

An Adaptive Sparse Grid Algorithm for Elliptic PDEs with Lognormal Diffusion Coefficient

KAUST Repository

Nobile, Fabio

2016-03-18

In this work we build on the classical adaptive sparse grid algorithm (T. Gerstner and M. Griebel, Dimension-adaptive tensor-product quadrature), obtaining an enhanced version capable of using non-nested collocation points, and supporting quadrature and interpolation on unbounded sets. We also consider several profit indicators that are suitable to drive the adaptation process. We then use such algorithm to solve an important test case in Uncertainty Quantification problem, namely the Darcy equation with lognormal permeability random field, and compare the results with those obtained with the quasi-optimal sparse grids based on profit estimates, which we have proposed in our previous works (cf. e.g. Convergence of quasi-optimal sparse grids approximation of Hilbert-valued functions: application to random elliptic PDEs). To treat the case of rough permeability fields, in which a sparse grid approach may not be suitable, we propose to use the adaptive sparse grid quadrature as a control variate in a Monte Carlo simulation. Numerical results show that the adaptive sparse grids have performances similar to those of the quasi-optimal sparse grids and are very effective in the case of smooth permeability fields. Moreover, their use as control variate in a Monte Carlo simulation allows to tackle efficiently also problems with rough coefficients, significantly improving the performances of a standard Monte Carlo scheme.

Orthogonal sparse linear discriminant analysis

Science.gov (United States)

Liu, Zhonghua; Liu, Gang; Pu, Jiexin; Wang, Xiaohong; Wang, Haijun

2018-03-01

Linear discriminant analysis (LDA) is a linear feature extraction approach, and it has received much attention. On the basis of LDA, researchers have done a lot of research work on it, and many variant versions of LDA were proposed. However, the inherent problem of LDA cannot be solved very well by the variant methods. The major disadvantages of the classical LDA are as follows. First, it is sensitive to outliers and noises. Second, only the global discriminant structure is preserved, while the local discriminant information is ignored. In this paper, we present a new orthogonal sparse linear discriminant analysis (OSLDA) algorithm. The k nearest neighbour graph is first constructed to preserve the locality discriminant information of sample points. Then, L2,1-norm constraint on the projection matrix is used to act as loss function, which can make the proposed method robust to outliers in data points. Extensive experiments have been performed on several standard public image databases, and the experiment results demonstrate the performance of the proposed OSLDA algorithm.

Sparse principal component analysis in medical shape modeling

Science.gov (United States)

Sjöstrand, Karl; Stegmann, Mikkel B.; Larsen, Rasmus

2006-03-01

Principal component analysis (PCA) is a widely used tool in medical image analysis for data reduction, model building, and data understanding and exploration. While PCA is a holistic approach where each new variable is a linear combination of all original variables, sparse PCA (SPCA) aims at producing easily interpreted models through sparse loadings, i.e. each new variable is a linear combination of a subset of the original variables. One of the aims of using SPCA is the possible separation of the results into isolated and easily identifiable effects. This article introduces SPCA for shape analysis in medicine. Results for three different data sets are given in relation to standard PCA and sparse PCA by simple thresholding of small loadings. Focus is on a recent algorithm for computing sparse principal components, but a review of other approaches is supplied as well. The SPCA algorithm has been implemented using Matlab and is available for download. The general behavior of the algorithm is investigated, and strengths and weaknesses are discussed. The original report on the SPCA algorithm argues that the ordering of modes is not an issue. We disagree on this point and propose several approaches to establish sensible orderings. A method that orders modes by decreasing variance and maximizes the sum of variances for all modes is presented and investigated in detail.

Large-region acoustic source mapping using a movable array and sparse covariance fitting.

Science.gov (United States)

Zhao, Shengkui; Tuna, Cagdas; Nguyen, Thi Ngoc Tho; Jones, Douglas L

2017-01-01

Large-region acoustic source mapping is important for city-scale noise monitoring. Approaches using a single-position measurement scheme to scan large regions using small arrays cannot provide clean acoustic source maps, while deploying large arrays spanning the entire region of interest is prohibitively expensive. A multiple-position measurement scheme is applied to scan large regions at multiple spatial positions using a movable array of small size. Based on the multiple-position measurement scheme, a sparse-constrained multiple-position vectorized covariance matrix fitting approach is presented. In the proposed approach, the overall sample covariance matrix of the incoherent virtual array is first estimated using the multiple-position array data and then vectorized using the Khatri-Rao (KR) product. A linear model is then constructed for fitting the vectorized covariance matrix and a sparse-constrained reconstruction algorithm is proposed for recovering source powers from the model. The user parameter settings are discussed. The proposed approach is tested on a 30 m × 40 m region and a 60 m × 40 m region using simulated and measured data. Much cleaner acoustic source maps and lower sound pressure level errors are obtained compared to the beamforming approaches and the previous sparse approach [Zhao, Tuna, Nguyen, and Jones, Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP) (2016)].

Adequação de recursos humanos ao PSF: percepção de formandos de dois modelos de formação acadêmica em odontologia The Family Health Program (FHP and human resources: perceptions of students from two different dentistry schools

Directory of Open Access Journals (Sweden)

Heriberto Fiúza Sanchez

2008-04-01

Full Text Available O Programa Saúde da Família - PSF foi instituído pelo governo federal objetivando reverter o modelo assistencial. Os recursos humanos envolvidos devem estar preparados para alcançar os objetivos que o PSF propõe. O propósito desse artigo foi avaliar os desejos, percepções e preparo de acadêmicos de Odontologia, em relação aos princípios do PSF, de duas diferentes Faculdades de Odontologia, aqui denominadas Faculdades 1 e 2. Buscou-se ainda analisar se as faculdades tiveram potencial transformador sobre os acadêmicos, graduando-os com compromisso social e sensibilidade humanitária, considerados importantes para aqueles que querem trabalhar no PSF. Questionários individuais foram aplicados por um único pesquisador. As respostas foram analisadas pelo programa Epi-Info. Os resultados mostraram que prevalece entre os acadêmicos o desejo de trabalhar no PSF por razões ligadas às dificuldades do mercado de trabalho e os mesmos citam freqüentemente a técnica como a principal característica necessária a um dentista para que o mesmo atue no PSF. Por outro lado, diferenças estatisticamente significativas foram encontradas entre os acadêmicos, apontando uma provável influência do Estágio Supervisionado, ministrado sob a forma de internato rural, sobre a formação do acadêmico da Faculdade 1, possivelmente habilitando-o melhor para o PSF.The purpose of this study was to evaluate the perceptions and opinions of dental students from two different Dentistry Schools in Brazil, both known here as Dentistry Schools 1 and 2 about the Family Health Program - FHP. The study analyzed if the Dentistry Schools had any influence on the students, graduating professionals with humanitarian and social sensibility, which are considered very important prerequisites for those who wish to work on this governmental health program, as well as searching for professional expectation of the students. Individual questionnaires were applied by only one

Inference algorithms and learning theory for Bayesian sparse factor analysis

International Nuclear Information System (INIS)

Rattray, Magnus; Sharp, Kevin; Stegle, Oliver; Winn, John

2009-01-01

Bayesian sparse factor analysis has many applications; for example, it has been applied to the problem of inferring a sparse regulatory network from gene expression data. We describe a number of inference algorithms for Bayesian sparse factor analysis using a slab and spike mixture prior. These include well-established Markov chain Monte Carlo (MCMC) and variational Bayes (VB) algorithms as well as a novel hybrid of VB and Expectation Propagation (EP). For the case of a single latent factor we derive a theory for learning performance using the replica method. We compare the MCMC and VB/EP algorithm results with simulated data to the theoretical prediction. The results for MCMC agree closely with the theory as expected. Results for VB/EP are slightly sub-optimal but show that the new algorithm is effective for sparse inference. In large-scale problems MCMC is infeasible due to computational limitations and the VB/EP algorithm then provides a very useful computationally efficient alternative.

Inference algorithms and learning theory for Bayesian sparse factor analysis

Energy Technology Data Exchange (ETDEWEB)

Rattray, Magnus; Sharp, Kevin [School of Computer Science, University of Manchester, Manchester M13 9PL (United Kingdom); Stegle, Oliver [Max-Planck-Institute for Biological Cybernetics, Tuebingen (Germany); Winn, John, E-mail: magnus.rattray@manchester.ac.u [Microsoft Research Cambridge, Roger Needham Building, Cambridge, CB3 0FB (United Kingdom)

2009-12-01

Bayesian sparse factor analysis has many applications; for example, it has been applied to the problem of inferring a sparse regulatory network from gene expression data. We describe a number of inference algorithms for Bayesian sparse factor analysis using a slab and spike mixture prior. These include well-established Markov chain Monte Carlo (MCMC) and variational Bayes (VB) algorithms as well as a novel hybrid of VB and Expectation Propagation (EP). For the case of a single latent factor we derive a theory for learning performance using the replica method. We compare the MCMC and VB/EP algorithm results with simulated data to the theoretical prediction. The results for MCMC agree closely with the theory as expected. Results for VB/EP are slightly sub-optimal but show that the new algorithm is effective for sparse inference. In large-scale problems MCMC is infeasible due to computational limitations and the VB/EP algorithm then provides a very useful computationally efficient alternative.

Pairwise Constraint-Guided Sparse Learning for Feature Selection.

Science.gov (United States)

Liu, Mingxia; Zhang, Daoqiang

2016-01-01

Feature selection aims to identify the most informative features for a compact and accurate data representation. As typical supervised feature selection methods, Lasso and its variants using L1-norm-based regularization terms have received much attention in recent studies, most of which use class labels as supervised information. Besides class labels, there are other types of supervised information, e.g., pairwise constraints that specify whether a pair of data samples belong to the same class (must-link constraint) or different classes (cannot-link constraint). However, most of existing L1-norm-based sparse learning methods do not take advantage of the pairwise constraints that provide us weak and more general supervised information. For addressing that problem, we propose a pairwise constraint-guided sparse (CGS) learning method for feature selection, where the must-link and the cannot-link constraints are used as discriminative regularization terms that directly concentrate on the local discriminative structure of data. Furthermore, we develop two variants of CGS, including: 1) semi-supervised CGS that utilizes labeled data, pairwise constraints, and unlabeled data and 2) ensemble CGS that uses the ensemble of pairwise constraint sets. We conduct a series of experiments on a number of data sets from University of California-Irvine machine learning repository, a gene expression data set, two real-world neuroimaging-based classification tasks, and two large-scale attribute classification tasks. Experimental results demonstrate the efficacy of our proposed methods, compared with several established feature selection methods.

Universal Regularizers For Robust Sparse Coding and Modeling

OpenAIRE

Ramirez, Ignacio; Sapiro, Guillermo

2010-01-01

Sparse data models, where data is assumed to be well represented as a linear combination of a few elements from a dictionary, have gained considerable attention in recent years, and their use has led to state-of-the-art results in many signal and image processing tasks. It is now well understood that the choice of the sparsity regularization term is critical in the success of such models. Based on a codelength minimization interpretation of sparse coding, and using tools from universal coding...

Efficient coordinated recovery of sparse channels in massive MIMO

KAUST Repository

Masood, Mudassir

2015-01-01

This paper addresses the problem of estimating sparse channels in massive MIMO-OFDM systems. Most wireless channels are sparse in nature with large delay spread. In addition, these channels as observed by multiple antennas in a neighborhood have approximately common support. The sparsity and common support properties are attractive when it comes to the efficient estimation of large number of channels in massive MIMO systems. Moreover, to avoid pilot contamination and to achieve better spectral efficiency, it is important to use a small number of pilots. We present a novel channel estimation approach which utilizes the sparsity and common support properties to estimate sparse channels and requires a small number of pilots. Two algorithms based on this approach have been developed that perform Bayesian estimates of sparse channels even when the prior is non-Gaussian or unknown. Neighboring antennas share among each other their beliefs about the locations of active channel taps to perform estimation. The coordinated approach improves channel estimates and also reduces the required number of pilots. Further improvement is achieved by the data-aided version of the algorithm. Extensive simulation results are provided to demonstrate the performance of the proposed algorithms.

Robust Fringe Projection Profilometry via Sparse Representation.

Science.gov (United States)

Budianto; Lun, Daniel P K

2016-04-01

In this paper, a robust fringe projection profilometry (FPP) algorithm using the sparse dictionary learning and sparse coding techniques is proposed. When reconstructing the 3D model of objects, traditional FPP systems often fail to perform if the captured fringe images have a complex scene, such as having multiple and occluded objects. It introduces great difficulty to the phase unwrapping process of an FPP system that can result in serious distortion in the final reconstructed 3D model. For the proposed algorithm, it encodes the period order information, which is essential to phase unwrapping, into some texture patterns and embeds them to the projected fringe patterns. When the encoded fringe image is captured, a modified morphological component analysis and a sparse classification procedure are performed to decode and identify the embedded period order information. It is then used to assist the phase unwrapping process to deal with the different artifacts in the fringe images. Experimental results show that the proposed algorithm can significantly improve the robustness of an FPP system. It performs equally well no matter the fringe images have a simple or complex scene, or are affected due to the ambient lighting of the working environment.

Sparse DOA estimation with polynomial rooting

DEFF Research Database (Denmark)

Xenaki, Angeliki; Gerstoft, Peter; Fernandez Grande, Efren

2015-01-01

Direction-of-arrival (DOA) estimation involves the localization of a few sources from a limited number of observations on an array of sensors. Thus, DOA estimation can be formulated as a sparse signal reconstruction problem and solved efficiently with compressive sensing (CS) to achieve highresol......Direction-of-arrival (DOA) estimation involves the localization of a few sources from a limited number of observations on an array of sensors. Thus, DOA estimation can be formulated as a sparse signal reconstruction problem and solved efficiently with compressive sensing (CS) to achieve...... highresolution imaging. Utilizing the dual optimal variables of the CS optimization problem, it is shown with Monte Carlo simulations that the DOAs are accurately reconstructed through polynomial rooting (Root-CS). Polynomial rooting is known to improve the resolution in several other DOA estimation methods...

A General Sparse Tensor Framework for Electronic Structure Theory.

Science.gov (United States)

Manzer, Samuel; Epifanovsky, Evgeny; Krylov, Anna I; Head-Gordon, Martin

2017-03-14

Linear-scaling algorithms must be developed in order to extend the domain of applicability of electronic structure theory to molecules of any desired size. However, the increasing complexity of modern linear-scaling methods makes code development and maintenance a significant challenge. A major contributor to this difficulty is the lack of robust software abstractions for handling block-sparse tensor operations. We therefore report the development of a highly efficient symbolic block-sparse tensor library in order to provide access to high-level software constructs to treat such problems. Our implementation supports arbitrary multi-dimensional sparsity in all input and output tensors. We avoid cumbersome machine-generated code by implementing all functionality as a high-level symbolic C++ language library and demonstrate that our implementation attains very high performance for linear-scaling sparse tensor contractions.

Joint Sparse Recovery With Semisupervised MUSIC

Science.gov (United States)

Wen, Zaidao; Hou, Biao; Jiao, Licheng

2017-05-01

Discrete multiple signal classification (MUSIC) with its low computational cost and mild condition requirement becomes a significant noniterative algorithm for joint sparse recovery (JSR). However, it fails in rank defective problem caused by coherent or limited amount of multiple measurement vectors (MMVs). In this letter, we provide a novel sight to address this problem by interpreting JSR as a binary classification problem with respect to atoms. Meanwhile, MUSIC essentially constructs a supervised classifier based on the labeled MMVs so that its performance will heavily depend on the quality and quantity of these training samples. From this viewpoint, we develop a semisupervised MUSIC (SS-MUSIC) in the spirit of machine learning, which declares that the insufficient supervised information in the training samples can be compensated from those unlabeled atoms. Instead of constructing a classifier in a fully supervised manner, we iteratively refine a semisupervised classifier by exploiting the labeled MMVs and some reliable unlabeled atoms simultaneously. Through this way, the required conditions and iterations can be greatly relaxed and reduced. Numerical experimental results demonstrate that SS-MUSIC can achieve much better recovery performances than other MUSIC extended algorithms as well as some typical greedy algorithms for JSR in terms of iterations and recovery probability.

Fluorescence microscopy point spread function model accounting for aberrations due to refractive index variability within a specimen.

Science.gov (United States)

Ghosh, Sreya; Preza, Chrysanthe

2015-07-01

A three-dimensional (3-D) point spread function (PSF) model for wide-field fluorescence microscopy, suitable for imaging samples with variable refractive index (RI) in multilayered media, is presented. This PSF model is a key component for accurate 3-D image restoration of thick biological samples, such as lung tissue. Microscope- and specimen-derived parameters are combined with a rigorous vectorial formulation to obtain a new PSF model that accounts for additional aberrations due to specimen RI variability. Experimental evaluation and verification of the PSF model was accomplished using images from 175-nm fluorescent beads in a controlled test sample. Fundamental experimental validation of the advantage of using improved PSFs in depth-variant restoration was accomplished by restoring experimental data from beads (6  μm in diameter) mounted in a sample with RI variation. In the investigated study, improvement in restoration accuracy in the range of 18 to 35% was observed when PSFs from the proposed model were used over restoration using PSFs from an existing model. The new PSF model was further validated by showing that its prediction compares to an experimental PSF (determined from 175-nm beads located below a thick rat lung slice) with a 42% improved accuracy over the current PSF model prediction.

Low-rank and sparse modeling for visual analysis

CERN Document Server

Fu, Yun

2014-01-01

This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding and learning among unconstrained visual data. The book includes chapters covering multiple emerging topics in this new field. It links multiple popular research fields in Human-Centered Computing, Social Media, Image Classification, Pattern Recognition, Computer Vision, Big Data, and Human-Computer Interaction. Contains an overview of the low-rank and sparse modeling techniques for visual analysis by examining both theoretical analysis and real-world applic

Image Quality Assessment via Quality-aware Group Sparse Coding

Directory of Open Access Journals (Sweden)

Minglei Tong

2014-12-01

Full Text Available Image quality assessment has been attracting growing attention at an accelerated pace over the past decade, in the fields of image processing, vision and machine learning. In particular, general purpose blind image quality assessment is technically challenging and lots of state-of-the-art approaches have been developed to solve this problem, most under the supervised learning framework where the human scored samples are needed for training a regression model. In this paper, we propose an unsupervised learning approach that work without the human label. In the off-line stage, our method trains a dictionary covering different levels of image quality patch atoms across the training samples without knowing the human score, where each atom is associated with a quality score induced from the reference image; at the on-line stage, given each image patch, our method performs group sparse coding to encode the sample, such that the sample quality can be estimated from the few labeled atoms whose encoding coefficients are nonzero. Experimental results on the public dataset show the promising performance of our approach and future research direction is also discussed.

Direction-of-Arrival Estimation Based on Sparse Recovery with Second-Order Statistics

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H. Chen

2015-04-01

Full Text Available Traditional direction-of-arrival (DOA estimation techniques perform Nyquist-rate sampling of the received signals and as a result they require high storage. To reduce sampling ratio, we introduce level-crossing (LC sampling which captures samples whenever the signal crosses predetermined reference levels, and the LC-based analog-to-digital converter (LC ADC has been shown to efficiently sample certain classes of signals. In this paper, we focus on the DOA estimation problem by using second-order statistics based on the LC samplings recording on one sensor, along with the synchronous samplings of the another sensors, a sparse angle space scenario can be found by solving an $ell_1$ minimization problem, giving the number of sources and their DOA's. The experimental results show that our proposed method, when compared with some existing norm-based constrained optimization compressive sensing (CS algorithms, as well as subspace method, improves the DOA estimation performance, while using less samples when compared with Nyquist-rate sampling and reducing sensor activity especially for long time silence signal.

Real-time SPARSE-SENSE cardiac cine MR imaging: optimization of image reconstruction and sequence validation.

Science.gov (United States)

Goebel, Juliane; Nensa, Felix; Bomas, Bettina; Schemuth, Haemi P; Maderwald, Stefan; Gratz, Marcel; Quick, Harald H; Schlosser, Thomas; Nassenstein, Kai

2016-12-01

Improved real-time cardiac magnetic resonance (CMR) sequences have currently been introduced, but so far only limited practical experience exists. This study aimed at image reconstruction optimization and clinical validation of a new highly accelerated real-time cine SPARSE-SENSE sequence. Left ventricular (LV) short-axis stacks of a real-time free-breathing SPARSE-SENSE sequence with high spatiotemporal resolution and of a standard segmented cine SSFP sequence were acquired at 1.5 T in 11 volunteers and 15 patients. To determine the optimal iterations, all volunteers' SPARSE-SENSE images were reconstructed using 10-200 iterations, and contrast ratios, image entropies, and reconstruction times were assessed. Subsequently, the patients' SPARSE-SENSE images were reconstructed with the clinically optimal iterations. LV volumetric values were evaluated and compared between both sequences. Sufficient image quality and acceptable reconstruction times were achieved when using 80 iterations. Bland-Altman plots and Passing-Bablok regression showed good agreement for all volumetric parameters. 80 iterations are recommended for iterative SPARSE-SENSE image reconstruction in clinical routine. Real-time cine SPARSE-SENSE yielded comparable volumetric results as the current standard SSFP sequence. Due to its intrinsic low image acquisition times, real-time cine SPARSE-SENSE imaging with iterative image reconstruction seems to be an attractive alternative for LV function analysis. • A highly accelerated real-time CMR sequence using SPARSE-SENSE was evaluated. • SPARSE-SENSE allows free breathing in real-time cardiac cine imaging. • For clinically optimal SPARSE-SENSE image reconstruction, 80 iterations are recommended. • Real-time SPARSE-SENSE imaging yielded comparable volumetric results as the reference SSFP sequence. • The fast SPARSE-SENSE sequence is an attractive alternative to standard SSFP sequences.

Face recognition from unconstrained three-dimensional face images using multitask sparse representation

Science.gov (United States)

Bentaieb, Samia; Ouamri, Abdelaziz; Nait-Ali, Amine; Keche, Mokhtar

2018-01-01

We propose and evaluate a three-dimensional (3D) face recognition approach that applies the speeded up robust feature (SURF) algorithm to the depth representation of shape index map, under real-world conditions, using only a single gallery sample for each subject. First, the 3D scans are preprocessed, then SURF is applied on the shape index map to find interest points and their descriptors. Each 3D face scan is represented by keypoints descriptors, and a large dictionary is built from all the gallery descriptors. At the recognition step, descriptors of a probe face scan are sparsely represented by the dictionary. A multitask sparse representation classification is used to determine the identity of each probe face. The feasibility of the approach that uses the SURF algorithm on the shape index map for face identification/authentication is checked through an experimental investigation conducted on Bosphorus, University of Milano Bicocca, and CASIA 3D datasets. It achieves an overall rank one recognition rate of 97.75%, 80.85%, and 95.12%, respectively, on these datasets.

Security-enhanced phase encryption assisted by nonlinear optical correlation via sparse phase

International Nuclear Information System (INIS)

Chen, Wen; Chen, Xudong; Wang, Xiaogang

2015-01-01

We propose a method for security-enhanced phase encryption assisted by a nonlinear optical correlation via a sparse phase. Optical configurations are established based on a phase retrieval algorithm for embedding an input image and the secret data into phase-only masks. We found that when one or a few phase-only masks generated during data hiding are sparse, it is possible to integrate these sparse masks into those phase-only masks generated during the encoding of the input image. Synthesized phase-only masks are used for the recovery, and sparse distributions (i.e., binary maps) for generating the incomplete phase-only masks are considered as additional parameters for the recovery of secret data. It is difficult for unauthorized receivers to know that a useful phase has been sparsely distributed in the finally generated phase-only masks for secret-data recovery. Only when the secret data are correctly verified can the input image obtained with valid keys be claimed as targeted information. (paper)

Single and Multiple Object Tracking Using a Multi-Feature Joint Sparse Representation.

Science.gov (United States)

Hu, Weiming; Li, Wei; Zhang, Xiaoqin; Maybank, Stephen

2015-04-01

In this paper, we propose a tracking algorithm based on a multi-feature joint sparse representation. The templates for the sparse representation can include pixel values, textures, and edges. In the multi-feature joint optimization, noise or occlusion is dealt with using a set of trivial templates. A sparse weight constraint is introduced to dynamically select the relevant templates from the full set of templates. A variance ratio measure is adopted to adaptively adjust the weights of different features. The multi-feature template set is updated adaptively. We further propose an algorithm for tracking multi-objects with occlusion handling based on the multi-feature joint sparse reconstruction. The observation model based on sparse reconstruction automatically focuses on the visible parts of an occluded object by using the information in the trivial templates. The multi-object tracking is simplified into a joint Bayesian inference. The experimental results show the superiority of our algorithm over several state-of-the-art tracking algorithms.

Genomic epidemiology of a major Mycobacterium tuberculosis outbreak: Retrospective cohort study in a low incidence setting using sparse time-series sampling

DEFF Research Database (Denmark)

Folkvardsen, Dorte Bek; Norman, Anders; Andersen, Åse Bengård

2017-01-01

cases belonging to this outbreak via routine MIRU-VNTR typing. Here, we present a retrospective analysis of the C2/1112-15 dataset, based on whole-genome data from a sparse time-series consisting of five randomly selected isolates from each of the 23 years. Even if these data are derived from only 12...

Sparse Representation Based Binary Hypothesis Model for Hyperspectral Image Classification

Directory of Open Access Journals (Sweden)

Yidong Tang

2016-01-01

Full Text Available The sparse representation based classifier (SRC and its kernel version (KSRC have been employed for hyperspectral image (HSI classification. However, the state-of-the-art SRC often aims at extended surface objects with linear mixture in smooth scene and assumes that the number of classes is given. Considering the small target with complex background, a sparse representation based binary hypothesis (SRBBH model is established in this paper. In this model, a query pixel is represented in two ways, which are, respectively, by background dictionary and by union dictionary. The background dictionary is composed of samples selected from the local dual concentric window centered at the query pixel. Thus, for each pixel the classification issue becomes an adaptive multiclass classification problem, where only the number of desired classes is required. Furthermore, the kernel method is employed to improve the interclass separability. In kernel space, the coding vector is obtained by using kernel-based orthogonal matching pursuit (KOMP algorithm. Then the query pixel can be labeled by the characteristics of the coding vectors. Instead of directly using the reconstruction residuals, the different impacts the background dictionary and union dictionary have on reconstruction are used for validation and classification. It enhances the discrimination and hence improves the performance.

Low-rank sparse learning for robust visual tracking

KAUST Repository

Zhang, Tianzhu

2012-01-01

In this paper, we propose a new particle-filter based tracking algorithm that exploits the relationship between particles (candidate targets). By representing particles as sparse linear combinations of dictionary templates, this algorithm capitalizes on the inherent low-rank structure of particle representations that are learned jointly. As such, it casts the tracking problem as a low-rank matrix learning problem. This low-rank sparse tracker (LRST) has a number of attractive properties. (1) Since LRST adaptively updates dictionary templates, it can handle significant changes in appearance due to variations in illumination, pose, scale, etc. (2) The linear representation in LRST explicitly incorporates background templates in the dictionary and a sparse error term, which enables LRST to address the tracking drift problem and to be robust against occlusion respectively. (3) LRST is computationally attractive, since the low-rank learning problem can be efficiently solved as a sequence of closed form update operations, which yield a time complexity that is linear in the number of particles and the template size. We evaluate the performance of LRST by applying it to a set of challenging video sequences and comparing it to 6 popular tracking methods. Our experiments show that by representing particles jointly, LRST not only outperforms the state-of-the-art in tracking accuracy but also significantly improves the time complexity of methods that use a similar sparse linear representation model for particles [1]. © 2012 Springer-Verlag.

Group-sparse representation with dictionary learning for medical image denoising and fusion.

Science.gov (United States)

Li, Shutao; Yin, Haitao; Fang, Leyuan

2012-12-01

Recently, sparse representation has attracted a lot of interest in various areas. However, the standard sparse representation does not consider the intrinsic structure, i.e., the nonzero elements occur in clusters, called group sparsity. Furthermore, there is no dictionary learning method for group sparse representation considering the geometrical structure of space spanned by atoms. In this paper, we propose a novel dictionary learning method, called Dictionary Learning with Group Sparsity and Graph Regularization (DL-GSGR). First, the geometrical structure of atoms is modeled as the graph regularization. Then, combining group sparsity and graph regularization, the DL-GSGR is presented, which is solved by alternating the group sparse coding and dictionary updating. In this way, the group coherence of learned dictionary can be enforced small enough such that any signal can be group sparse coded effectively. Finally, group sparse representation with DL-GSGR is applied to 3-D medical image denoising and image fusion. Specifically, in 3-D medical image denoising, a 3-D processing mechanism (using the similarity among nearby slices) and temporal regularization (to perverse the correlations across nearby slices) are exploited. The experimental results on 3-D image denoising and image fusion demonstrate the superiority of our proposed denoising and fusion approaches.

Sparse electromagnetic imaging using nonlinear iterative shrinkage thresholding

KAUST Repository

Desmal, Abdulla; Bagci, Hakan

2015-01-01

A sparse nonlinear electromagnetic imaging scheme is proposed for reconstructing dielectric contrast of investigation domains from measured fields. The proposed approach constructs the optimization problem by introducing the sparsity constraint to the data misfit between the scattered fields expressed as a nonlinear function of the contrast and the measured fields and solves it using the nonlinear iterative shrinkage thresholding algorithm. The thresholding is applied to the result of every nonlinear Landweber iteration to enforce the sparsity constraint. Numerical results demonstrate the accuracy and efficiency of the proposed method in reconstructing sparse dielectric profiles.

Sparse electromagnetic imaging using nonlinear iterative shrinkage thresholding

KAUST Repository

Desmal, Abdulla

2015-04-13

A sparse nonlinear electromagnetic imaging scheme is proposed for reconstructing dielectric contrast of investigation domains from measured fields. The proposed approach constructs the optimization problem by introducing the sparsity constraint to the data misfit between the scattered fields expressed as a nonlinear function of the contrast and the measured fields and solves it using the nonlinear iterative shrinkage thresholding algorithm. The thresholding is applied to the result of every nonlinear Landweber iteration to enforce the sparsity constraint. Numerical results demonstrate the accuracy and efficiency of the proposed method in reconstructing sparse dielectric profiles.

Learning dictionaries of sparse codes of 3D movements of body joints for real-time human activity understanding.

Science.gov (United States)

Qi, Jin; Yang, Zhiyong

2014-01-01

Real-time human activity recognition is essential for human-robot interactions for assisted healthy independent living. Most previous work in this area is performed on traditional two-dimensional (2D) videos and both global and local methods have been used. Since 2D videos are sensitive to changes of lighting condition, view angle, and scale, researchers begun to explore applications of 3D information in human activity understanding in recently years. Unfortunately, features that work well on 2D videos usually don't perform well on 3D videos and there is no consensus on what 3D features should be used. Here we propose a model of human activity recognition based on 3D movements of body joints. Our method has three steps, learning dictionaries of sparse codes of 3D movements of joints, sparse coding, and classification. In the first step, space-time volumes of 3D movements of body joints are obtained via dense sampling and independent component analysis is then performed to construct a dictionary of sparse codes for each activity. In the second step, the space-time volumes are projected to the dictionaries and a set of sparse histograms of the projection coefficients are constructed as feature representations of the activities. Finally, the sparse histograms are used as inputs to a support vector machine to recognize human activities. We tested this model on three databases of human activities and found that it outperforms the state-of-the-art algorithms. Thus, this model can be used for real-time human activity recognition in many applications.

Learning dictionaries of sparse codes of 3D movements of body joints for real-time human activity understanding.

Directory of Open Access Journals (Sweden)

Jin Qi

Full Text Available Real-time human activity recognition is essential for human-robot interactions for assisted healthy independent living. Most previous work in this area is performed on traditional two-dimensional (2D videos and both global and local methods have been used. Since 2D videos are sensitive to changes of lighting condition, view angle, and scale, researchers begun to explore applications of 3D information in human activity understanding in recently years. Unfortunately, features that work well on 2D videos usually don't perform well on 3D videos and there is no consensus on what 3D features should be used. Here we propose a model of human activity recognition based on 3D movements of body joints. Our method has three steps, learning dictionaries of sparse codes of 3D movements of joints, sparse coding, and classification. In the first step, space-time volumes of 3D movements of body joints are obtained via dense sampling and independent component analysis is then performed to construct a dictionary of sparse codes for each activity. In the second step, the space-time volumes are projected to the dictionaries and a set of sparse histograms of the projection coefficients are constructed as feature representations of the activities. Finally, the sparse histograms are used as inputs to a support vector machine to recognize human activities. We tested this model on three databases of human activities and found that it outperforms the state-of-the-art algorithms. Thus, this model can be used for real-time human activity recognition in many applications.

Efficient spectral estimation by MUSIC and ESPRIT with application to sparse FFT

Directory of Open Access Journals (Sweden)

Daniel ePotts

2016-02-01

Full Text Available In spectral estimation, one has to determine all parameters of an exponential sum for finitely many (noisysampled data of this exponential sum.Frequently used methods for spectral estimation are MUSIC (MUltiple SIgnal Classification and ESPRIT (Estimation of Signal Parameters viaRotational Invariance Technique.For a trigonometric polynomial of large sparsity, we present a new sparse fast Fourier transform byshifted sampling and using MUSIC resp. ESPRIT, where the ESPRIT based method has lower computational cost.Later this technique is extended to a new reconstruction of a multivariate trigonometric polynomial of large sparsity for given (noisy values sampled on a reconstructing rank-1 lattice. Numerical experiments illustrate thehigh performance of these procedures.

On the Automatic Parallelization of Sparse and Irregular Fortran Programs

Directory of Open Access Journals (Sweden)

Yuan Lin

1999-01-01

Full Text Available Automatic parallelization is usually believed to be less effective at exploiting implicit parallelism in sparse/irregular programs than in their dense/regular counterparts. However, not much is really known because there have been few research reports on this topic. In this work, we have studied the possibility of using an automatic parallelizing compiler to detect the parallelism in sparse/irregular programs. The study with a collection of sparse/irregular programs led us to some common loop patterns. Based on these patterns new techniques were derived that produced good speedups when manually applied to our benchmark codes. More importantly, these parallelization methods can be implemented in a parallelizing compiler and can be applied automatically.

Split-Bregman-based sparse-view CT reconstruction

Energy Technology Data Exchange (ETDEWEB)

Vandeghinste, Bert; Vandenberghe, Stefaan [Ghent Univ. (Belgium). Medical Image and Signal Processing (MEDISIP); Goossens, Bart; Pizurica, Aleksandra; Philips, Wilfried [Ghent Univ. (Belgium). Image Processing and Interpretation Research Group (IPI); Beenhouwer, Jan de [Ghent Univ. (Belgium). Medical Image and Signal Processing (MEDISIP); Antwerp Univ., Wilrijk (Belgium). The Vision Lab; Staelens, Steven [Ghent Univ. (Belgium). Medical Image and Signal Processing (MEDISIP); Antwerp Univ., Edegem (Belgium). Molecular Imaging Centre Antwerp

2011-07-01

Total variation minimization has been extensively researched for image denoising and sparse view reconstruction. These methods show superior denoising performance for simple images with little texture, but result in texture information loss when applied to more complex images. It could thus be beneficial to use other regularizers within medical imaging. We propose a general regularization method, based on a split-Bregman approach. We show results for this framework combined with a total variation denoising operator, in comparison to ASD-POCS. We show that sparse-view reconstruction and noise regularization is possible. This general method will allow us to investigate other regularizers in the context of regularized CT reconstruction, and decrease the acquisition times in {mu}CT. (orig.)

Enhanced bacterial affinity of PVDF membrane: its application as improved sea water sampling tool for environmental monitoring.

Science.gov (United States)

Kumar, Sweta Binod; Sharnagat, Preeti; Manna, Paramita; Bhattacharya, Amit; Haldar, Soumya

2017-02-01

Isolation of diversified bacteria from seawater is a major challenge in the field of environmental microbiology. In the present study, an attempt has been made to select specific membrane with improved property of attaching diversified bacteria. Initially, different concentrations (15, 18, and 20% W/W) of polysulfone (PSF) were used to check their affinity for the attachment of selected gram-positive (Bacillus subtilis) and gram-negative (Escherichia coli) bacteria. Among these, 20% W/W PSF showed maximum attachment. Therefore, membrane prepared with other materials such as polyvinylidene fluoride (PVDF) and polyether sulfone (PES) were used with the same concentration (20% W/W) to check their improved bacterial attachment property. Comparative study of bacterial attachment on three different membranes revealed that PVDF possessed the highest affinity towards both the groups of bacteria. This property was confirmed by different analytical methods viz. contact angle, atomic force microscopy, zeta potential, and flux study and further validated with seawater samples collected from seven sites of western coast and Lakshadweep island of India, using Biolog EcoPlate™. All the samples showed that bacterial richness and diversity was high in PVDF membrane in comparison to surrounding seawater samples. Interestingly, affinity for more diversified bacteria was reported to be higher in water sample with less turbidity and low bacteria load. This finding can facilitate the development of PVDF (20% W/W) membrane as a simple, cheap, and less labor intensive environmental sampling tool for the isolation of diversified bacteria from seawater sample wih different physiochemical properties. Graphical abstract ᅟ.

A framework for general sparse matrix-matrix multiplication on GPUs and heterogeneous processors

DEFF Research Database (Denmark)

Liu, Weifeng; Vinter, Brian

2015-01-01

General sparse matrix-matrix multiplication (SpGEMM) is a fundamental building block for numerous applications such as algebraic multigrid method (AMG), breadth first search and shortest path problem. Compared to other sparse BLAS routines, an efficient parallel SpGEMM implementation has to handle...... extra irregularity from three aspects: (1) the number of nonzero entries in the resulting sparse matrix is unknown in advance, (2) very expensive parallel insert operations at random positions in the resulting sparse matrix dominate the execution time, and (3) load balancing must account for sparse data...... memory space and efficiently utilizes the very limited on-chip scratchpad memory. Parallel insert operations of the nonzero entries are implemented through the GPU merge path algorithm that is experimentally found to be the fastest GPU merge approach. Load balancing builds on the number of necessary...

Dissecting high-dimensional phenotypes with bayesian sparse factor analysis of genetic covariance matrices.

Science.gov (United States)

Runcie, Daniel E; Mukherjee, Sayan

2013-07-01

Quantitative genetic studies that model complex, multivariate phenotypes are important for both evolutionary prediction and artificial selection. For example, changes in gene expression can provide insight into developmental and physiological mechanisms that link genotype and phenotype. However, classical analytical techniques are poorly suited to quantitative genetic studies of gene expression where the number of traits assayed per individual can reach many thousand. Here, we derive a Bayesian genetic sparse factor model for estimating the genetic covariance matrix (G-matrix) of high-dimensional traits, such as gene expression, in a mixed-effects model. The key idea of our model is that we need consider only G-matrices that are biologically plausible. An organism's entire phenotype is the result of processes that are modular and have limited complexity. This implies that the G-matrix will be highly structured. In particular, we assume that a limited number of intermediate traits (or factors, e.g., variations in development or physiology) control the variation in the high-dimensional phenotype, and that each of these intermediate traits is sparse - affecting only a few observed traits. The advantages of this approach are twofold. First, sparse factors are interpretable and provide biological insight into mechanisms underlying the genetic architecture. Second, enforcing sparsity helps prevent sampling errors from swamping out the true signal in high-dimensional data. We demonstrate the advantages of our model on simulated data and in an analysis of a published Drosophila melanogaster gene expression data set.

Optimal Couple Projections for Domain Adaptive Sparse Representation-based Classification.

Science.gov (United States)

Zhang, Guoqing; Sun, Huaijiang; Porikli, Fatih; Liu, Yazhou; Sun, Quansen

2017-08-29

In recent years, sparse representation based classification (SRC) is one of the most successful methods and has been shown impressive performance in various classification tasks. However, when the training data has a different distribution than the testing data, the learned sparse representation may not be optimal, and the performance of SRC will be degraded significantly. To address this problem, in this paper, we propose an optimal couple projections for domain-adaptive sparse representation-based classification (OCPD-SRC) method, in which the discriminative features of data in the two domains are simultaneously learned with the dictionary that can succinctly represent the training and testing data in the projected space. OCPD-SRC is designed based on the decision rule of SRC, with the objective to learn coupled projection matrices and a common discriminative dictionary such that the between-class sparse reconstruction residuals of data from both domains are maximized, and the within-class sparse reconstruction residuals of data are minimized in the projected low-dimensional space. Thus, the resulting representations can well fit SRC and simultaneously have a better discriminant ability. In addition, our method can be easily extended to multiple domains and can be kernelized to deal with the nonlinear structure of data. The optimal solution for the proposed method can be efficiently obtained following the alternative optimization method. Extensive experimental results on a series of benchmark databases show that our method is better or comparable to many state-of-the-art methods.

Completing sparse and disconnected protein-protein network by deep learning.

Science.gov (United States)

Huang, Lei; Liao, Li; Wu, Cathy H

2018-03-22

and multiple heterogeneous data sources. Tested by the yeast data with six heterogeneous feature kernels, the results show our method can further improve the prediction performance by up to 2%, which is very close to an upper bound that is obtained by an Approximate Bayesian Computation based sampling method. The proposed evolution deep neural network, coupled with regularized Laplacian kernel, is an effective tool in completing sparse and disconnected PPI networks and in facilitating integration of heterogeneous data sources.

A Counterexample on Sample-Path Optimality in Stable Markov Decision Chains with the Average Reward Criterion

Czech Academy of Sciences Publication Activity Database

Cavazos-Cadena, R.; Montes-de-Oca, R.; Sladký, Karel

2014-01-01

Roč. 163, č. 2 (2014), s. 674-684 ISSN 0022-3239 Grant - others:PSF Organization(US) 012/300/02; CONACYT (México) and ASCR (Czech Republic)(MX) 171396 Institutional support: RVO:67985556 Keywords : Strong sample-path optimality * Lyapunov function condition * Stationary policy * Expected average reward criterion Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 1.509, year: 2014 http://library.utia.cas.cz/separaty/2014/E/sladky-0432661.pdf

Sparse matrix test collections

Energy Technology Data Exchange (ETDEWEB)

Duff, I.

1996-12-31

This workshop will discuss plans for coordinating and developing sets of test matrices for the comparison and testing of sparse linear algebra software. We will talk of plans for the next release (Release 2) of the Harwell-Boeing Collection and recent work on improving the accessibility of this Collection and others through the World Wide Web. There will only be three talks of about 15 to 20 minutes followed by a discussion from the floor.

JiTTree: A Just-in-Time Compiled Sparse GPU Volume Data Structure

KAUST Repository

Labschutz, Matthias

2015-08-12

Sparse volume data structures enable the efficient representation of large but sparse volumes in GPU memory for computation and visualization. However, the choice of a specific data structure for a given data set depends on several factors, such as the memory budget, the sparsity of the data, and data access patterns. In general, there is no single optimal sparse data structure, but a set of several candidates with individual strengths and drawbacks. One solution to this problem are hybrid data structures which locally adapt themselves to the sparsity. However, they typically suffer from increased traversal overhead which limits their utility in many applications. This paper presents JiTTree, a novel sparse hybrid volume data structure that uses just-in-time compilation to overcome these problems. By combining multiple sparse data structures and reducing traversal overhead we leverage their individual advantages. We demonstrate that hybrid data structures adapt well to a large range of data sets. They are especially superior to other sparse data structures for data sets that locally vary in sparsity. Possible optimization criteria are memory, performance and a combination thereof. Through just-in-time (JIT) compilation, JiTTree reduces the traversal overhead of the resulting optimal data structure. As a result, our hybrid volume data structure enables efficient computations on the GPU, while being superior in terms of memory usage when compared to non-hybrid data structures.

JiTTree: A Just-in-Time Compiled Sparse GPU Volume Data Structure

KAUST Repository

Labschutz, Matthias; Bruckner, Stefan; Groller, M. Eduard; Hadwiger, Markus; Rautek, Peter

2015-01-01

Sparse volume data structures enable the efficient representation of large but sparse volumes in GPU memory for computation and visualization. However, the choice of a specific data structure for a given data set depends on several factors, such as the memory budget, the sparsity of the data, and data access patterns. In general, there is no single optimal sparse data structure, but a set of several candidates with individual strengths and drawbacks. One solution to this problem are hybrid data structures which locally adapt themselves to the sparsity. However, they typically suffer from increased traversal overhead which limits their utility in many applications. This paper presents JiTTree, a novel sparse hybrid volume data structure that uses just-in-time compilation to overcome these problems. By combining multiple sparse data structures and reducing traversal overhead we leverage their individual advantages. We demonstrate that hybrid data structures adapt well to a large range of data sets. They are especially superior to other sparse data structures for data sets that locally vary in sparsity. Possible optimization criteria are memory, performance and a combination thereof. Through just-in-time (JIT) compilation, JiTTree reduces the traversal overhead of the resulting optimal data structure. As a result, our hybrid volume data structure enables efficient computations on the GPU, while being superior in terms of memory usage when compared to non-hybrid data structures.

JiTTree: A Just-in-Time Compiled Sparse GPU Volume Data Structure.

Science.gov (United States)

Labschütz, Matthias; Bruckner, Stefan; Gröller, M Eduard; Hadwiger, Markus; Rautek, Peter

2016-01-01

Sparse volume data structures enable the efficient representation of large but sparse volumes in GPU memory for computation and visualization. However, the choice of a specific data structure for a given data set depends on several factors, such as the memory budget, the sparsity of the data, and data access patterns. In general, there is no single optimal sparse data structure, but a set of several candidates with individual strengths and drawbacks. One solution to this problem are hybrid data structures which locally adapt themselves to the sparsity. However, they typically suffer from increased traversal overhead which limits their utility in many applications. This paper presents JiTTree, a novel sparse hybrid volume data structure that uses just-in-time compilation to overcome these problems. By combining multiple sparse data structures and reducing traversal overhead we leverage their individual advantages. We demonstrate that hybrid data structures adapt well to a large range of data sets. They are especially superior to other sparse data structures for data sets that locally vary in sparsity. Possible optimization criteria are memory, performance and a combination thereof. Through just-in-time (JIT) compilation, JiTTree reduces the traversal overhead of the resulting optimal data structure. As a result, our hybrid volume data structure enables efficient computations on the GPU, while being superior in terms of memory usage when compared to non-hybrid data structures.

Compact data structure and scalable algorithms for the sparse grid technique

KAUST Repository

Murarasu, Alin

2011-01-01

The sparse grid discretization technique enables a compressed representation of higher-dimensional functions. In its original form, it relies heavily on recursion and complex data structures, thus being far from well-suited for GPUs. In this paper, we describe optimizations that enable us to implement compression and decompression, the crucial sparse grid algorithms for our application, on Nvidia GPUs. The main idea consists of a bijective mapping between the set of points in a multi-dimensional sparse grid and a set of consecutive natural numbers. The resulting data structure consumes a minimum amount of memory. For a 10-dimensional sparse grid with approximately 127 million points, it consumes up to 30 times less memory than trees or hash tables which are typically used. Compared to a sequential CPU implementation, the speedups achieved on GPU are up to 17 for compression and up to 70 for decompression, respectively. We show that the optimizations are also applicable to multicore CPUs. Copyright © 2011 ACM.

Multisnapshot Sparse Bayesian Learning for DOA

DEFF Research Database (Denmark)

Gerstoft, Peter; Mecklenbrauker, Christoph F.; Xenaki, Angeliki

2016-01-01

The directions of arrival (DOA) of plane waves are estimated from multisnapshot sensor array data using sparse Bayesian learning (SBL). The prior for the source amplitudes is assumed independent zero-mean complex Gaussian distributed with hyperparameters, the unknown variances (i.e., the source...

Continuous speech recognition with sparse coding

CSIR Research Space (South Africa)

Smit, WJ

2009-04-01

Full Text Available generative model. The spike train is classified by making use of a spike train model and dynamic programming. It is computationally expensive to find a sparse code. We use an iterative subset selection algorithm with quadratic programming for this process...

A density functional for sparse matter

DEFF Research Database (Denmark)

Langreth, D.C.; Lundqvist, Bengt; Chakarova-Kack, S.D.

2009-01-01

forces in molecules, to adsorbed molecules, like benzene, naphthalene, phenol and adenine on graphite, alumina and metals, to polymer and carbon nanotube (CNT) crystals, and hydrogen storage in graphite and metal-organic frameworks (MOFs), and to the structure of DNA and of DNA with intercalators......Sparse matter is abundant and has both strong local bonds and weak nonbonding forces, in particular nonlocal van der Waals (vdW) forces between atoms separated by empty space. It encompasses a broad spectrum of systems, like soft matter, adsorption systems and biostructures. Density-functional...... theory (DFT), long since proven successful for dense matter, seems now to have come to a point, where useful extensions to sparse matter are available. In particular, a functional form, vdW-DF (Dion et al 2004 Phys. Rev. Lett. 92 246401; Thonhauser et al 2007 Phys. Rev. B 76 125112), has been proposed...

Sparse learning of stochastic dynamical equations

Science.gov (United States)

Boninsegna, Lorenzo; Nüske, Feliks; Clementi, Cecilia

2018-06-01

With the rapid increase of available data for complex systems, there is great interest in the extraction of physically relevant information from massive datasets. Recently, a framework called Sparse Identification of Nonlinear Dynamics (SINDy) has been introduced to identify the governing equations of dynamical systems from simulation data. In this study, we extend SINDy to stochastic dynamical systems which are frequently used to model biophysical processes. We prove the asymptotic correctness of stochastic SINDy in the infinite data limit, both in the original and projected variables. We discuss algorithms to solve the sparse regression problem arising from the practical implementation of SINDy and show that cross validation is an essential tool to determine the right level of sparsity. We demonstrate the proposed methodology on two test systems, namely, the diffusion in a one-dimensional potential and the projected dynamics of a two-dimensional diffusion process.

A novel method to design sparse linear arrays for ultrasonic phased array.

Science.gov (United States)

Yang, Ping; Chen, Bin; Shi, Ke-Ren

2006-12-22

In ultrasonic phased array testing, a sparse array can increase the resolution by enlarging the aperture without adding system complexity. Designing a sparse array involves choosing the best or a better configuration from a large number of candidate arrays. We firstly designed sparse arrays by using a genetic algorithm, but found that the arrays have poor performance and poor consistency. So, a method based on the Minimum Redundancy Linear Array was then adopted. Some elements are determined by the minimum-redundancy array firstly in order to ensure spatial resolution and then a genetic algorithm is used to optimize the remaining elements. Sparse arrays designed by this method have much better performance and consistency compared to the arrays designed only by a genetic algorithm. Both simulation and experiment confirm the effectiveness.

Multiple instance learning tracking method with local sparse representation

KAUST Repository

Xie, Chengjun

2013-10-01

When objects undergo large pose change, illumination variation or partial occlusion, most existed visual tracking algorithms tend to drift away from targets and even fail in tracking them. To address this issue, in this study, the authors propose an online algorithm by combining multiple instance learning (MIL) and local sparse representation for tracking an object in a video system. The key idea in our method is to model the appearance of an object by local sparse codes that can be formed as training data for the MIL framework. First, local image patches of a target object are represented as sparse codes with an overcomplete dictionary, where the adaptive representation can be helpful in overcoming partial occlusion in object tracking. Then MIL learns the sparse codes by a classifier to discriminate the target from the background. Finally, results from the trained classifier are input into a particle filter framework to sequentially estimate the target state over time in visual tracking. In addition, to decrease the visual drift because of the accumulative errors when updating the dictionary and classifier, a two-step object tracking method combining a static MIL classifier with a dynamical MIL classifier is proposed. Experiments on some publicly available benchmarks of video sequences show that our proposed tracker is more robust and effective than others. © The Institution of Engineering and Technology 2013.

Discriminative object tracking via sparse representation and online dictionary learning.

Science.gov (United States)

Xie, Yuan; Zhang, Wensheng; Li, Cuihua; Lin, Shuyang; Qu, Yanyun; Zhang, Yinghua

2014-04-01

We propose a robust tracking algorithm based on local sparse coding with discriminative dictionary learning and new keypoint matching schema. This algorithm consists of two parts: the local sparse coding with online updated discriminative dictionary for tracking (SOD part), and the keypoint matching refinement for enhancing the tracking performance (KP part). In the SOD part, the local image patches of the target object and background are represented by their sparse codes using an over-complete discriminative dictionary. Such discriminative dictionary, which encodes the information of both the foreground and the background, may provide more discriminative power. Furthermore, in order to adapt the dictionary to the variation of the foreground and background during the tracking, an online learning method is employed to update the dictionary. The KP part utilizes refined keypoint matching schema to improve the performance of the SOD. With the help of sparse representation and online updated discriminative dictionary, the KP part are more robust than the traditional method to reject the incorrect matches and eliminate the outliers. The proposed method is embedded into a Bayesian inference framework for visual tracking. Experimental results on several challenging video sequences demonstrate the effectiveness and robustness of our approach.

Free-breathing Sparse Sampling Cine MR Imaging with Iterative Reconstruction for the Assessment of Left Ventricular Function and Mass at 3.0 T.

Science.gov (United States)

Sudarski, Sonja; Henzler, Thomas; Haubenreisser, Holger; Dösch, Christina; Zenge, Michael O; Schmidt, Michaela; Nadar, Mariappan S; Borggrefe, Martin; Schoenberg, Stefan O; Papavassiliu, Theano

2017-01-01

Purpose To prospectively evaluate the accuracy of left ventricle (LV) analysis with a two-dimensional real-time cine true fast imaging with steady-state precession (trueFISP) magnetic resonance (MR) imaging sequence featuring sparse data sampling with iterative reconstruction (SSIR) performed with and without breath-hold (BH) commands at 3.0 T. Materials and Methods Ten control subjects (mean age, 35 years; range, 25-56 years) and 60 patients scheduled to undergo a routine cardiac examination that included LV analysis (mean age, 58 years; range, 20-86 years) underwent a fully sampled segmented multiple BH cine sequence (standard of reference) and a prototype undersampled SSIR sequence performed during a single BH and during free breathing (non-BH imaging). Quantitative analysis of LV function and mass was performed. Linear regression, Bland-Altman analysis, and paired t testing were performed. Results Similar to the results in control subjects, analysis of the 60 patients showed excellent correlation with the standard of reference for single-BH SSIR (r = 0.93-0.99) and non-BH SSIR (r = 0.92-0.98) for LV ejection fraction (EF), volume, and mass (P 3.0 T is noninferior to the standard of reference irrespective of BH commands. LV mass, however, is overestimated with SSIR. © RSNA, 2016 Online supplemental material is available for this article.

Building Input Adaptive Parallel Applications: A Case Study of Sparse Grid Interpolation

KAUST Repository

Murarasu, Alin; Weidendorfer, Josef

2012-01-01

bring a substantial contribution to the speedup. By identifying common patterns in the input data, we propose new algorithms for sparse grid interpolation that accelerate the state-of-the-art non-specialized version. Sparse grid interpolation

Jointly-check iterative decoding algorithm for quantum sparse graph codes

International Nuclear Information System (INIS)

Jun-Hu, Shao; Bao-Ming, Bai; Wei, Lin; Lin, Zhou

2010-01-01

For quantum sparse graph codes with stabilizer formalism, the unavoidable girth-four cycles in their Tanner graphs greatly degrade the iterative decoding performance with a standard belief-propagation (BP) algorithm. In this paper, we present a jointly-check iterative algorithm suitable for decoding quantum sparse graph codes efficiently. Numerical simulations show that this modified method outperforms the standard BP algorithm with an obvious performance improvement. (general)

Rotational image deblurring with sparse matrices

DEFF Research Database (Denmark)

Hansen, Per Christian; Nagy, James G.; Tigkos, Konstantinos

2014-01-01

We describe iterative deblurring algorithms that can handle blur caused by a rotation along an arbitrary axis (including the common case of pure rotation). Our algorithms use a sparse-matrix representation of the blurring operation, which allows us to easily handle several different boundary...

Normalization for sparse encoding of odors by a wide-field interneuron.

Science.gov (United States)

Papadopoulou, Maria; Cassenaer, Stijn; Nowotny, Thomas; Laurent, Gilles

2011-05-06

Sparse coding presents practical advantages for sensory representations and memory storage. In the insect olfactory system, the representation of general odors is dense in the antennal lobes but sparse in the mushroom bodies, only one synapse downstream. In locusts, this transformation relies on the oscillatory structure of antennal lobe output, feed-forward inhibitory circuits, intrinsic properties of mushroom body neurons, and connectivity between antennal lobe and mushroom bodies. Here we show the existence of a normalizing negative-feedback loop within the mushroom body to maintain sparse output over a wide range of input conditions. This loop consists of an identifiable "giant" nonspiking inhibitory interneuron with ubiquitous connectivity and graded release properties.

Random On-Board Pixel Sampling (ROPS) X-Ray Camera

Energy Technology Data Exchange (ETDEWEB)

Wang, Zhehui [Los Alamos; Iaroshenko, O. [Los Alamos; Li, S. [Los Alamos; Liu, T. [Fermilab; Parab, N. [Argonne (main); Chen, W. W. [Purdue U.; Chu, P. [Los Alamos; Kenyon, G. [Los Alamos; Lipton, R. [Fermilab; Sun, K.-X. [Nevada U., Las Vegas

2017-09-25

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

Sparse Representation Denoising for Radar High Resolution Range Profiling

Directory of Open Access Journals (Sweden)

Min Li

2014-01-01

Full Text Available Radar high resolution range profile has attracted considerable attention in radar automatic target recognition. In practice, radar return is usually contaminated by noise, which results in profile distortion and recognition performance degradation. To deal with this problem, in this paper, a novel denoising method based on sparse representation is proposed to remove the Gaussian white additive noise. The return is sparsely described in the Fourier redundant dictionary and the denoising problem is described as a sparse representation model. Noise level of the return, which is crucial to the denoising performance but often unknown, is estimated by performing subspace method on the sliding subsequence correlation matrix. Sliding window process enables noise level estimation using only one observation sequence, not only guaranteeing estimation efficiency but also avoiding the influence of profile time-shift sensitivity. Experimental results show that the proposed method can effectively improve the signal-to-noise ratio of the return, leading to a high-quality profile.

The Real-Valued Sparse Direction of Arrival (DOA Estimation Based on the Khatri-Rao Product

Directory of Open Access Journals (Sweden)

Tao Chen

2016-05-01

Full Text Available There is a problem that complex operation which leads to a heavy calculation burden is required when the direction of arrival (DOA of a sparse signal is estimated by using the array covariance matrix. The solution of the multiple measurement vectors (MMV model is difficult. In this paper, a real-valued sparse DOA estimation algorithm based on the Khatri-Rao (KR product called the L1-RVSKR is proposed. The proposed algorithm is based on the sparse representation of the array covariance matrix. The array covariance matrix is transformed to a real-valued matrix via a unitary transformation so that a real-valued sparse model is achieved. The real-valued sparse model is vectorized for transforming to a single measurement vector (SMV model, and a new virtual overcomplete dictionary is constructed according to the KR product’s property. Finally, the sparse DOA estimation is solved by utilizing the idea of a sparse representation of array covariance vectors (SRACV. The simulation results demonstrate the superior performance and the low computational complexity of the proposed algorithm.

A modified sparse reconstruction method for three-dimensional synthetic aperture radar image

Science.gov (United States)

Zhang, Ziqiang; Ji, Kefeng; Song, Haibo; Zou, Huanxin

2018-03-01

There is an increasing interest in three-dimensional Synthetic Aperture Radar (3-D SAR) imaging from observed sparse scattering data. However, the existing 3-D sparse imaging method requires large computing times and storage capacity. In this paper, we propose a modified method for the sparse 3-D SAR imaging. The method processes the collection of noisy SAR measurements, usually collected over nonlinear flight paths, and outputs 3-D SAR imagery. Firstly, the 3-D sparse reconstruction problem is transformed into a series of 2-D slices reconstruction problem by range compression. Then the slices are reconstructed by the modified SL0 (smoothed l0 norm) reconstruction algorithm. The improved algorithm uses hyperbolic tangent function instead of the Gaussian function to approximate the l0 norm and uses the Newton direction instead of the steepest descent direction, which can speed up the convergence rate of the SL0 algorithm. Finally, numerical simulation results are given to demonstrate the effectiveness of the proposed algorithm. It is shown that our method, compared with existing 3-D sparse imaging method, performs better in reconstruction quality and the reconstruction time.

Porting of the DBCSR library for Sparse Matrix-Matrix Multiplications to Intel Xeon Phi systems

OpenAIRE

Bethune, Iain; Gloess, Andeas; Hutter, Juerg; Lazzaro, Alfio; Pabst, Hans; Reid, Fiona

2017-01-01

Multiplication of two sparse matrices is a key operation in the simulation of the electronic structure of systems containing thousands of atoms and electrons. The highly optimized sparse linear algebra library DBCSR (Distributed Block Compressed Sparse Row) has been specifically designed to efficiently perform such sparse matrix-matrix multiplications. This library is the basic building block for linear scaling electronic structure theory and low scaling correlated methods in CP2K. It is para...

Synthesizing spatiotemporally sparse smartphone sensor data for bridge modal identification

Science.gov (United States)

Ozer, Ekin; Feng, Maria Q.

2016-08-01

Smartphones as vibration measurement instruments form a large-scale, citizen-induced, and mobile wireless sensor network (WSN) for system identification and structural health monitoring (SHM) applications. Crowdsourcing-based SHM is possible with a decentralized system granting citizens with operational responsibility and control. Yet, citizen initiatives introduce device mobility, drastically changing SHM results due to uncertainties in the time and the space domains. This paper proposes a modal identification strategy that fuses spatiotemporally sparse SHM data collected by smartphone-based WSNs. Multichannel data sampled with the time and the space independence is used to compose the modal identification parameters such as frequencies and mode shapes. Structural response time history can be gathered by smartphone accelerometers and converted into Fourier spectra by the processor units. Timestamp, data length, energy to power conversion address temporal variation, whereas spatial uncertainties are reduced by geolocation services or determining node identity via QR code labels. Then, parameters collected from each distributed network component can be extended to global behavior to deduce modal parameters without the need of a centralized and synchronous data acquisition system. The proposed method is tested on a pedestrian bridge and compared with a conventional reference monitoring system. The results show that the spatiotemporally sparse mobile WSN data can be used to infer modal parameters despite non-overlapping sensor operation schedule.

Sparse canonical methods for biological data integration: application to a cross-platform study

Directory of Open Access Journals (Sweden)

Robert-Granié Christèle

2009-01-01

Full Text Available Abstract Background In the context of systems biology, few sparse approaches have been proposed so far to integrate several data sets. It is however an important and fundamental issue that will be widely encountered in post genomic studies, when simultaneously analyzing transcriptomics, proteomics and metabolomics data using different platforms, so as to understand the mutual interactions between the different data sets. In this high dimensional setting, variable selection is crucial to give interpretable results. We focus on a sparse Partial Least Squares approach (sPLS to handle two-block data sets, where the relationship between the two types of variables is known to be symmetric. Sparse PLS has been developed either for a regression or a canonical correlation framework and includes a built-in procedure to select variables while integrating data. To illustrate the canonical mode approach, we analyzed the NCI60 data sets, where two different platforms (cDNA and Affymetrix chips were used to study the transcriptome of sixty cancer cell lines. Results We compare the results obtained with two other sparse or related canonical correlation approaches: CCA with Elastic Net penalization (CCA-EN and Co-Inertia Analysis (CIA. The latter does not include a built-in procedure for variable selection and requires a two-step analysis. We stress the lack of statistical criteria to evaluate canonical correlation methods, which makes biological interpretation absolutely necessary to compare the different gene selections. We also propose comprehensive graphical representations of both samples and variables to facilitate the interpretation of the results. Conclusion sPLS and CCA-EN selected highly relevant genes and complementary findings from the two data sets, which enabled a detailed understanding of the molecular characteristics of several groups of cell lines. These two approaches were found to bring similar results, although they highlighted the same

Fast Solution in Sparse LDA for Binary Classification

Science.gov (United States)

Moghaddam, Baback

2010-01-01

An algorithm that performs sparse linear discriminant analysis (Sparse-LDA) finds near-optimal solutions in far less time than the prior art when specialized to binary classification (of 2 classes). Sparse-LDA is a type of feature- or variable- selection problem with numerous applications in statistics, machine learning, computer vision, computational finance, operations research, and bio-informatics. Because of its combinatorial nature, feature- or variable-selection problems are NP-hard or computationally intractable in cases involving more than 30 variables or features. Therefore, one typically seeks approximate solutions by means of greedy search algorithms. The prior Sparse-LDA algorithm was a greedy algorithm that considered the best variable or feature to add/ delete to/ from its subsets in order to maximally discriminate between multiple classes of data. The present algorithm is designed for the special but prevalent case of 2-class or binary classification (e.g. 1 vs. 0, functioning vs. malfunctioning, or change versus no change). The present algorithm provides near-optimal solutions on large real-world datasets having hundreds or even thousands of variables or features (e.g. selecting the fewest wavelength bands in a hyperspectral sensor to do terrain classification) and does so in typical computation times of minutes as compared to days or weeks as taken by the prior art. Sparse LDA requires solving generalized eigenvalue problems for a large number of variable subsets (represented by the submatrices of the input within-class and between-class covariance matrices). In the general (fullrank) case, the amount of computation scales at least cubically with the number of variables and thus the size of the problems that can be solved is limited accordingly. However, in binary classification, the principal eigenvalues can be found using a special analytic formula, without resorting to costly iterative techniques. The present algorithm exploits this analytic

Sparse Localization with a Mobile Beacon Based on LU Decomposition in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Chunhui Zhao

2015-09-01

Full Text Available Node localization is the core in wireless sensor network. It can be solved by powerful beacons, which are equipped with global positioning system devices to know their location information. In this article, we present a novel sparse localization approach with a mobile beacon based on LU decomposition. Our scheme firstly translates node localization problem into a 1-sparse vector recovery problem by establishing sparse localization model. Then, LU decomposition pre-processing is adopted to solve the problem that measurement matrix does not meet the re¬stricted isometry property. Later, the 1-sparse vector can be exactly recovered by compressive sensing. Finally, as the 1-sparse vector is approximate sparse, weighted Cen¬troid scheme is introduced to accurately locate the node. Simulation and analysis show that our scheme has better localization performance and lower requirement for the mobile beacon than MAP+GC, MAP-M, and MAP-MN schemes. In addition, the obstacles and DOI have little effect on the novel scheme, and it has great localization performance under low SNR, thus, the scheme proposed is robust.

Robust visual tracking via multiscale deep sparse networks

Science.gov (United States)

Wang, Xin; Hou, Zhiqiang; Yu, Wangsheng; Xue, Yang; Jin, Zefenfen; Dai, Bo

2017-04-01

In visual tracking, deep learning with offline pretraining can extract more intrinsic and robust features. It has significant success solving the tracking drift in a complicated environment. However, offline pretraining requires numerous auxiliary training datasets and is considerably time-consuming for tracking tasks. To solve these problems, a multiscale sparse networks-based tracker (MSNT) under the particle filter framework is proposed. Based on the stacked sparse autoencoders and rectifier linear unit, the tracker has a flexible and adjustable architecture without the offline pretraining process and exploits the robust and powerful features effectively only through online training of limited labeled data. Meanwhile, the tracker builds four deep sparse networks of different scales, according to the target's profile type. During tracking, the tracker selects the matched tracking network adaptively in accordance with the initial target's profile type. It preserves the inherent structural information more efficiently than the single-scale networks. Additionally, a corresponding update strategy is proposed to improve the robustness of the tracker. Extensive experimental results on a large scale benchmark dataset show that the proposed method performs favorably against state-of-the-art methods in challenging environments.

Efficient MATLAB computations with sparse and factored tensors.

Energy Technology Data Exchange (ETDEWEB)

Bader, Brett William; Kolda, Tamara Gibson (Sandia National Lab, Livermore, CA)

2006-12-01

In this paper, the term tensor refers simply to a multidimensional or N-way array, and we consider how specially structured tensors allow for efficient storage and computation. First, we study sparse tensors, which have the property that the vast majority of the elements are zero. We propose storing sparse tensors using coordinate format and describe the computational efficiency of this scheme for various mathematical operations, including those typical to tensor decomposition algorithms. Second, we study factored tensors, which have the property that they can be assembled from more basic components. We consider two specific types: a Tucker tensor can be expressed as the product of a core tensor (which itself may be dense, sparse, or factored) and a matrix along each mode, and a Kruskal tensor can be expressed as the sum of rank-1 tensors. We are interested in the case where the storage of the components is less than the storage of the full tensor, and we demonstrate that many elementary operations can be computed using only the components. All of the efficiencies described in this paper are implemented in the Tensor Toolbox for MATLAB.

Ground and space-based separate PSF photometry of Pluto and Charon from New Horizons and Magellan

Science.gov (United States)

Zangari, Amanda M.; Stern, S. A.; Young, L. A.; Weaver, H. A.; Olkin, C.; Buratti, B. J.; Spencer, J.; Ennico, K.

2013-10-01

While Pluto and Charon are easily resolvable in some space-based telescopes, ground-based imaging of Pluto and Charon can yield separate PSF photometry in excellent seeing. We present B and Sloan g', r', i', and z' separate photometry of Pluto and Charon taken at the Magellan Clay telescope using LDSS-3. In 2011, observations were made on 7, 8, 9, 19, and 20 March, at 9:00 UT, covering sub-Earth longitudes 130°, 74°, 17°, 175° and 118°. The solar phase angle ranged from 1.66-1.68° to 1.76-1.77°. In 2012, observations were made on February 28, 29 and March 1 at 9:00 UT covering longitudes 342°, 110° and 53° and on May 30 and 31 at 9:30 UT and 7:00 UT, covering longitudes 358° and 272°. Solar phase angles were 1.53-1.56° and 0.89°-0.90° degrees. All longitudes use the convention of zero at the sub-Charon longitude and decrease in time. Seeing ranged from 0.46 to 1.26 arcsecond. We find that the mean rotationally-averaged Charon-to-Pluto light ratio is 0.142±0.003 for Sloan r',i' and z'. Charon is brighter in B and g', with a light ratio of 0.182±0.003 and 0.178±0.002 respectively. Additionally, we present separate PSF photometry of Pluto and Charon from New Horizons images taken by the LORRI instrument on 1 and 3 July 2013 at 17:00 UT and 23:00 UT, sub-Earth longitude 251° and 125°. We find that the rotation-dependent variations in the light ratio are consistent with earlier estimates such as those from Buie et al. 2010, AJ 139, 1117-1127. However, at a solar phase angle of 10.9°, Charon appears 0.25 magnitudes fainter relative to Pluto at the same rotational phase than measurements from the ground with the largest possible solar phase angle. Thus we provide the first estimate of a Pluto phase curve beyond 2°. These results represent some of the first Pluto science from New Horizons. This work has been funded in part by NASA Planetary Astronomy Grant NNX10AB27G and NSF Award 0707609 to MIT and by NASA's New Horizons mission to Pluto.

Subspace Based Blind Sparse Channel Estimation

DEFF Research Database (Denmark)

Hayashi, Kazunori; Matsushima, Hiroki; Sakai, Hideaki

2012-01-01

The paper proposes a subspace based blind sparse channel estimation method using 1–2 optimization by replacing the 2–norm minimization in the conventional subspace based method by the 1–norm minimization problem. Numerical results confirm that the proposed method can significantly improve...

Sparse grid techniques for particle-in-cell schemes

Science.gov (United States)

Ricketson, L. F.; Cerfon, A. J.

2017-02-01

We propose the use of sparse grids to accelerate particle-in-cell (PIC) schemes. By using the so-called ‘combination technique’ from the sparse grids literature, we are able to dramatically increase the size of the spatial cells in multi-dimensional PIC schemes while paying only a slight penalty in grid-based error. The resulting increase in cell size allows us to reduce the statistical noise in the simulation without increasing total particle number. We present initial proof-of-principle results from test cases in two and three dimensions that demonstrate the new scheme’s efficiency, both in terms of computation time and memory usage.

Adjusting for sampling variability in sparse data: geostatistical approaches to disease mapping.

Science.gov (United States)

Hampton, Kristen H; Serre, Marc L; Gesink, Dionne C; Pilcher, Christopher D; Miller, William C

2011-10-06

Disease maps of crude rates from routinely collected health data indexed at a small geographical resolution pose specific statistical problems due to the sparse nature of the data. Spatial smoothers allow areas to borrow strength from neighboring regions to produce a more stable estimate of the areal value. Geostatistical smoothers are able to quantify the uncertainty in smoothed rate estimates without a high computational burden. In this paper, we introduce a uniform model extension of Bayesian Maximum Entropy (UMBME) and compare its performance to that of Poisson kriging in measures of smoothing strength and estimation accuracy as applied to simulated data and the real data example of HIV infection in North Carolina. The aim is to produce more reliable maps of disease rates in small areas to improve identification of spatial trends at the local level. In all data environments, Poisson kriging exhibited greater smoothing strength than UMBME. With the simulated data where the true latent rate of infection was known, Poisson kriging resulted in greater estimation accuracy with data that displayed low spatial autocorrelation, while UMBME provided more accurate estimators with data that displayed higher spatial autocorrelation. With the HIV data, UMBME performed slightly better than Poisson kriging in cross-validatory predictive checks, with both models performing better than the observed data model with no smoothing. Smoothing methods have different advantages depending upon both internal model assumptions that affect smoothing strength and external data environments, such as spatial correlation of the observed data. Further model comparisons in different data environments are required to provide public health practitioners with guidelines needed in choosing the most appropriate smoothing method for their particular health dataset.

Reducing computational costs in large scale 3D EIT by using a sparse Jacobian matrix with block-wise CGLS reconstruction

International Nuclear Information System (INIS)

Yang, C L; Wei, H Y; Soleimani, M; Adler, A

2013-01-01

Electrical impedance tomography (EIT) is a fast and cost-effective technique to provide a tomographic conductivity image of a subject from boundary current–voltage data. This paper proposes a time and memory efficient method for solving a large scale 3D EIT inverse problem using a parallel conjugate gradient (CG) algorithm. The 3D EIT system with a large number of measurement data can produce a large size of Jacobian matrix; this could cause difficulties in computer storage and the inversion process. One of challenges in 3D EIT is to decrease the reconstruction time and memory usage, at the same time retaining the image quality. Firstly, a sparse matrix reduction technique is proposed using thresholding to set very small values of the Jacobian matrix to zero. By adjusting the Jacobian matrix into a sparse format, the element with zeros would be eliminated, which results in a saving of memory requirement. Secondly, a block-wise CG method for parallel reconstruction has been developed. The proposed method has been tested using simulated data as well as experimental test samples. Sparse Jacobian with a block-wise CG enables the large scale EIT problem to be solved efficiently. Image quality measures are presented to quantify the effect of sparse matrix reduction in reconstruction results. (paper)

Reducing computational costs in large scale 3D EIT by using a sparse Jacobian matrix with block-wise CGLS reconstruction.

Science.gov (United States)

Yang, C L; Wei, H Y; Adler, A; Soleimani, M

2013-06-01

Electrical impedance tomography (EIT) is a fast and cost-effective technique to provide a tomographic conductivity image of a subject from boundary current-voltage data. This paper proposes a time and memory efficient method for solving a large scale 3D EIT inverse problem using a parallel conjugate gradient (CG) algorithm. The 3D EIT system with a large number of measurement data can produce a large size of Jacobian matrix; this could cause difficulties in computer storage and the inversion process. One of challenges in 3D EIT is to decrease the reconstruction time and memory usage, at the same time retaining the image quality. Firstly, a sparse matrix reduction technique is proposed using thresholding to set very small values of the Jacobian matrix to zero. By adjusting the Jacobian matrix into a sparse format, the element with zeros would be eliminated, which results in a saving of memory requirement. Secondly, a block-wise CG method for parallel reconstruction has been developed. The proposed method has been tested using simulated data as well as experimental test samples. Sparse Jacobian with a block-wise CG enables the large scale EIT problem to be solved efficiently. Image quality measures are presented to quantify the effect of sparse matrix reduction in reconstruction results.

Regression analysis of sparse asynchronous longitudinal data.

Science.gov (United States)

Cao, Hongyuan; Zeng, Donglin; Fine, Jason P

2015-09-01

We consider estimation of regression models for sparse asynchronous longitudinal observations, where time-dependent responses and covariates are observed intermittently within subjects. Unlike with synchronous data, where the response and covariates are observed at the same time point, with asynchronous data, the observation times are mismatched. Simple kernel-weighted estimating equations are proposed for generalized linear models with either time invariant or time-dependent coefficients under smoothness assumptions for the covariate processes which are similar to those for synchronous data. For models with either time invariant or time-dependent coefficients, the estimators are consistent and asymptotically normal but converge at slower rates than those achieved with synchronous data. Simulation studies evidence that the methods perform well with realistic sample sizes and may be superior to a naive application of methods for synchronous data based on an ad hoc last value carried forward approach. The practical utility of the methods is illustrated on data from a study on human immunodeficiency virus.

Information filtering in sparse online systems: recommendation via semi-local diffusion.

Science.gov (United States)

Zeng, Wei; Zeng, An; Shang, Ming-Sheng; Zhang, Yi-Cheng

2013-01-01

With the rapid growth of the Internet and overwhelming amount of information and choices that people are confronted with, recommender systems have been developed to effectively support users' decision-making process in the online systems. However, many recommendation algorithms suffer from the data sparsity problem, i.e. the user-object bipartite networks are so sparse that algorithms cannot accurately recommend objects for users. This data sparsity problem makes many well-known recommendation algorithms perform poorly. To solve the problem, we propose a recommendation algorithm based on the semi-local diffusion process on the user-object bipartite network. The simulation results on two sparse datasets, Amazon and Bookcross, show that our method significantly outperforms the state-of-the-art methods especially for those small-degree users. Two personalized semi-local diffusion methods are proposed which further improve the recommendation accuracy. Finally, our work indicates that sparse online systems are essentially different from the dense online systems, so it is necessary to reexamine former algorithms and conclusions based on dense data in sparse systems.

Codesign of Beam Pattern and Sparse Frequency Waveforms for MIMO Radar

Directory of Open Access Journals (Sweden)

Chaoyun Mai

2015-01-01

Full Text Available Multiple-input multiple-output (MIMO radar takes the advantages of high degrees of freedom for beam pattern design and waveform optimization, because each antenna in centralized MIMO radar system can transmit different signal waveforms. When continuous band is divided into several pieces, sparse frequency radar waveforms play an important role due to the special pattern of the sparse spectrum. In this paper, we start from the covariance matrix of the transmitted waveform and extend the concept of sparse frequency design to the study of MIMO radar beam pattern. With this idea in mind, we first solve the problem of semidefinite constraint by optimization tools and get the desired covariance matrix of the ideal beam pattern. Then, we use the acquired covariance matrix and generalize the objective function by adding the constraint of both constant modulus of the signals and corresponding spectrum. Finally, we solve the objective function by the cyclic algorithm and obtain the sparse frequency MIMO radar waveforms with desired beam pattern. The simulation results verify the effectiveness of this method.

High-SNR spectrum measurement based on Hadamard encoding and sparse reconstruction

Science.gov (United States)

Wang, Zhaoxin; Yue, Jiang; Han, Jing; Li, Long; Jin, Yong; Gao, Yuan; Li, Baoming

2017-12-01

The denoising capabilities of the H-matrix and cyclic S-matrix based on the sparse reconstruction, employed in the Pixel of Focal Plane Coded Visible Spectrometer for spectrum measurement are investigated, where the spectrum is sparse in a known basis. In the measurement process, the digital micromirror device plays an important role, which implements the Hadamard coding. In contrast with Hadamard transform spectrometry, based on the shift invariability, this spectrometer may have the advantage of a high efficiency. Simulations and experiments show that the nonlinear solution with a sparse reconstruction has a better signal-to-noise ratio than the linear solution and the H-matrix outperforms the cyclic S-matrix whether the reconstruction method is nonlinear or linear.

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.

Comparison of sparse point distribution models

DEFF Research Database (Denmark)

Erbou, Søren Gylling Hemmingsen; Vester-Christensen, Martin; Larsen, Rasmus

2010-01-01

This paper compares several methods for obtaining sparse and compact point distribution models suited for data sets containing many variables. These are evaluated on a database consisting of 3D surfaces of a section of the pelvic bone obtained from CT scans of 33 porcine carcasses. The superior m...

Efficient Techniques of Sparse Signal Analysis for Enhanced Recovery of Information in Biomedical Engineering and Geosciences

KAUST Repository

Sana, Furrukh

2016-11-01

Sparse signals are abundant among both natural and man-made signals. Sparsity implies that the signal essentially resides in a small dimensional subspace. The sparsity of the signal can be exploited to improve its recovery from limited and noisy observations. Traditional estimation algorithms generally lack the ability to take advantage of signal sparsity. This dissertation considers several problems in the areas of biomedical engineering and geosciences with the aim of enhancing the recovery of information by exploiting the underlying sparsity in the problem. The objective is to overcome the fundamental bottlenecks, both in terms of estimation accuracies and required computational resources. In the first part of dissertation, we present a high precision technique for the monitoring of human respiratory movements by exploiting the sparsity of wireless ultra-wideband signals. The proposed technique provides a novel methodology of overcoming the Nyquist sampling constraint and enables robust performance in the presence of noise and interferences. We also present a comprehensive framework for the important problem of extracting the fetal electrocardiogram (ECG) signals from abdominal ECG recordings of pregnant women. The multiple measurement vectors approach utilized for this purpose provides an efficient mechanism of exploiting the common structure of ECG signals, when represented in sparse transform domains, and allows leveraging information from multiple ECG electrodes under a joint estimation formulation. In the second part of dissertation, we adopt sparse signal processing principles for improved information recovery in large-scale subsurface reservoir characterization problems. We propose multiple new algorithms for sparse representation of the subsurface geological structures, incorporation of useful prior information in the estimation process, and for reducing computational complexities of the problem. The techniques presented here enable significantly

A Multiobjective Sparse Feature Learning Model for Deep Neural Networks.

Science.gov (United States)

Gong, Maoguo; Liu, Jia; Li, Hao; Cai, Qing; Su, Linzhi

2015-12-01

Hierarchical deep neural networks are currently popular learning models for imitating the hierarchical architecture of human brain. Single-layer feature extractors are the bricks to build deep networks. Sparse feature learning models are popular models that can learn useful representations. But most of those models need a user-defined constant to control the sparsity of representations. In this paper, we propose a multiobjective sparse feature learning model based on the autoencoder. The parameters of the model are learnt by optimizing two objectives, reconstruction error and the sparsity of hidden units simultaneously to find a reasonable compromise between them automatically. We design a multiobjective induced learning procedure for this model based on a multiobjective evolutionary algorithm. In the experiments, we demonstrate that the learning procedure is effective, and the proposed multiobjective model can learn useful sparse features.

Uniform sparse bounds for discrete quadratic phase Hilbert transforms

Science.gov (United States)

Kesler, Robert; Arias, Darío Mena

2017-09-01

For each α \\in T consider the discrete quadratic phase Hilbert transform acting on finitely supported functions f : Z → C according to H^{α }f(n):= \\sum _{m ≠ 0} e^{iα m^2} f(n - m)/m. We prove that, uniformly in α \\in T , there is a sparse bound for the bilinear form for every pair of finitely supported functions f,g : Z→ C . The sparse bound implies several mapping properties such as weighted inequalities in an intersection of Muckenhoupt and reverse Hölder classes.

Sparse Matrix for ECG Identification with Two-Lead Features

Directory of Open Access Journals (Sweden)

Kuo-Kun Tseng

2015-01-01

Full Text Available Electrocardiograph (ECG human identification has the potential to improve biometric security. However, improvements in ECG identification and feature extraction are required. Previous work has focused on single lead ECG signals. Our work proposes a new algorithm for human identification by mapping two-lead ECG signals onto a two-dimensional matrix then employing a sparse matrix method to process the matrix. And that is the first application of sparse matrix techniques for ECG identification. Moreover, the results of our experiments demonstrate the benefits of our approach over existing methods.

A Non-static Data Layout Enhancing Parallelism and Vectorization in Sparse Grid Algorithms

KAUST Repository

Buse, Gerrit

2012-06-01

The name sparse grids denotes a highly space-efficient, grid-based numerical technique to approximate high-dimensional functions. Although employed in a broad spectrum of applications from different fields, there have only been few tries to use it in real time visualization (e.g. [1]), due to complex data structures and long algorithm runtime. In this work we present a novel approach inspired by principles of I/0-efficient algorithms. Locally applied coefficient permutations lead to improved cache performance and facilitate the use of vector registers for our sparse grid benchmark problem hierarchization. Based on the compact data structure proposed for regular sparse grids in [2], we developed a new algorithm that outperforms existing implementations on modern multi-core systems by a factor of 37 for a grid size of 127 million points. For larger problems the speedup is even increasing, and with execution times below 1 s, sparse grids are well-suited for visualization applications. Furthermore, we point out how a broad class of sparse grid algorithms can benefit from our approach. © 2012 IEEE.

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

Science.gov (United States)

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.

Sparse sampling and reconstruction for electron and scanning probe microscope imaging

Science.gov (United States)

Anderson, Hyrum; Helms, Jovana; Wheeler, Jason W.; Larson, Kurt W.; Rohrer, Brandon R.

2015-07-28

Systems and methods for conducting electron or scanning probe microscopy are provided herein. In a general embodiment, the systems and methods for conducting electron or scanning probe microscopy with an undersampled data set include: driving an electron beam or probe to scan across a sample and visit a subset of pixel locations of the sample that are randomly or pseudo-randomly designated; determining actual pixel locations on the sample that are visited by the electron beam or probe; and processing data collected by detectors from the visits of the electron beam or probe at the actual pixel locations and recovering a reconstructed image of the sample.

SPARSE: quadratic time simultaneous alignment and folding of RNAs without sequence-based heuristics

Science.gov (United States)

Will, Sebastian; Otto, Christina; Miladi, Milad; Möhl, Mathias; Backofen, Rolf

2015-01-01

Motivation: RNA-Seq experiments have revealed a multitude of novel ncRNAs. The gold standard for their analysis based on simultaneous alignment and folding suffers from extreme time complexity of O(n6). Subsequently, numerous faster ‘Sankoff-style’ approaches have been suggested. Commonly, the performance of such methods relies on sequence-based heuristics that restrict the search space to optimal or near-optimal sequence alignments; however, the accuracy of sequence-based methods breaks down for RNAs with sequence identities below 60%. Alignment approaches like LocARNA that do not require sequence-based heuristics, have been limited to high complexity (≥ quartic time). Results: Breaking this barrier, we introduce the novel Sankoff-style algorithm ‘sparsified prediction and alignment of RNAs based on their structure ensembles (SPARSE)’, which runs in quadratic time without sequence-based heuristics. To achieve this low complexity, on par with sequence alignment algorithms, SPARSE features strong sparsification based on structural properties of the RNA ensembles. Following PMcomp, SPARSE gains further speed-up from lightweight energy computation. Although all existing lightweight Sankoff-style methods restrict Sankoff’s original model by disallowing loop deletions and insertions, SPARSE transfers the Sankoff algorithm to the lightweight energy model completely for the first time. Compared with LocARNA, SPARSE achieves similar alignment and better folding quality in significantly less time (speedup: 3.7). At similar run-time, it aligns low sequence identity instances substantially more accurate than RAF, which uses sequence-based heuristics. Availability and implementation: SPARSE is freely available at http://www.bioinf.uni-freiburg.de/Software/SPARSE. Contact: backofen@informatik.uni-freiburg.de Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25838465

A Framework for Final Drive Simultaneous Failure Diagnosis Based on Fuzzy Entropy and Sparse Bayesian Extreme Learning Machine

Directory of Open Access Journals (Sweden)

Qing Ye

2015-01-01

Full Text Available This research proposes a novel framework of final drive simultaneous failure diagnosis containing feature extraction, training paired diagnostic models, generating decision threshold, and recognizing simultaneous failure modes. In feature extraction module, adopt wavelet package transform and fuzzy entropy to reduce noise interference and extract representative features of failure mode. Use single failure sample to construct probability classifiers based on paired sparse Bayesian extreme learning machine which is trained only by single failure modes and have high generalization and sparsity of sparse Bayesian learning approach. To generate optimal decision threshold which can convert probability output obtained from classifiers into final simultaneous failure modes, this research proposes using samples containing both single and simultaneous failure modes and Grid search method which is superior to traditional techniques in global optimization. Compared with other frequently used diagnostic approaches based on support vector machine and probability neural networks, experiment results based on F1-measure value verify that the diagnostic accuracy and efficiency of the proposed framework which are crucial for simultaneous failure diagnosis are superior to the existing approach.

Greedy Algorithms for Nonnegativity-Constrained Simultaneous Sparse Recovery

Science.gov (United States)

Kim, Daeun; Haldar, Justin P.

2016-01-01

This work proposes a family of greedy algorithms to jointly reconstruct a set of vectors that are (i) nonnegative and (ii) simultaneously sparse with a shared support set. The proposed algorithms generalize previous approaches that were designed to impose these constraints individually. Similar to previous greedy algorithms for sparse recovery, the proposed algorithms iteratively identify promising support indices. In contrast to previous approaches, the support index selection procedure has been adapted to prioritize indices that are consistent with both the nonnegativity and shared support constraints. Empirical results demonstrate for the first time that the combined use of simultaneous sparsity and nonnegativity constraints can substantially improve recovery performance relative to existing greedy algorithms that impose less signal structure. PMID:26973368

SPARSE ELECTROMAGNETIC IMAGING USING NONLINEAR LANDWEBER ITERATIONS

KAUST Repository

Desmal, Abdulla; Bagci, Hakan

2015-01-01

minimization problem is solved using nonlinear Landweber iterations, where at each iteration a thresholding function is applied to enforce the sparseness-promoting L0/L1-norm constraint. The thresholded nonlinear Landweber iterations are applied to several two

Multiuser TOA Estimation Algorithm in DS-CDMA Sparse Channel for Radiolocation

Science.gov (United States)

Kim, Sunwoo

This letter considers multiuser time delay estimation in a sparse channel environment for radiolocation. The generalized successive interference cancellation (GSIC) algorithm is used to eliminate the multiple access interference (MAI). To adapt GSIC to sparse channels the alternating maximization (AM) algorithm is considered, and the continuous time delay of each path is estimated without requiring a priori known data sequences.

Adjusting for sampling variability in sparse data: geostatistical approaches to disease mapping

Directory of Open Access Journals (Sweden)

Pilcher Christopher D

2011-10-01

Full Text Available Abstract Background Disease maps of crude rates from routinely collected health data indexed at a small geographical resolution pose specific statistical problems due to the sparse nature of the data. Spatial smoothers allow areas to borrow strength from neighboring regions to produce a more stable estimate of the areal value. Geostatistical smoothers are able to quantify the uncertainty in smoothed rate estimates without a high computational burden. In this paper, we introduce a uniform model extension of Bayesian Maximum Entropy (UMBME and compare its performance to that of Poisson kriging in measures of smoothing strength and estimation accuracy as applied to simulated data and the real data example of HIV infection in North Carolina. The aim is to produce more reliable maps of disease rates in small areas to improve identification of spatial trends at the local level. Results In all data environments, Poisson kriging exhibited greater smoothing strength than UMBME. With the simulated data where the true latent rate of infection was known, Poisson kriging resulted in greater estimation accuracy with data that displayed low spatial autocorrelation, while UMBME provided more accurate estimators with data that displayed higher spatial autocorrelation. With the HIV data, UMBME performed slightly better than Poisson kriging in cross-validatory predictive checks, with both models performing better than the observed data model with no smoothing. Conclusions Smoothing methods have different advantages depending upon both internal model assumptions that affect smoothing strength and external data environments, such as spatial correlation of the observed data. Further model comparisons in different data environments are required to provide public health practitioners with guidelines needed in choosing the most appropriate smoothing method for their particular health dataset.

Pulse-Width-Modulation of Neutral-Point-Clamped Sparse Matrix Converter

DEFF Research Database (Denmark)

Loh, P.C.; Blaabjerg, Frede; Gao, F.

2007-01-01

input current and output voltage can be achieved with minimized rectification switching loss, rendering the sparse matrix converter as a competitive choice for interfacing the utility grid to (e.g.) defense facilities that require a different frequency supply. As an improvement, sparse matrix converter...... with improved waveform quality. Performances and practicalities of the designed schemes are verified in simulation and experimentally using an implemented laboratory prototype with some representative results captured and presented in the paper....

Learning a Nonnegative Sparse Graph for Linear Regression.

Science.gov (United States)

Fang, Xiaozhao; Xu, Yong; Li, Xuelong; Lai, Zhihui; Wong, Wai Keung

2015-09-01

Previous graph-based semisupervised learning (G-SSL) methods have the following drawbacks: 1) they usually predefine the graph structure and then use it to perform label prediction, which cannot guarantee an overall optimum and 2) they only focus on the label prediction or the graph structure construction but are not competent in handling new samples. To this end, a novel nonnegative sparse graph (NNSG) learning method was first proposed. Then, both the label prediction and projection learning were integrated into linear regression. Finally, the linear regression and graph structure learning were unified within the same framework to overcome these two drawbacks. Therefore, a novel method, named learning a NNSG for linear regression was presented, in which the linear regression and graph learning were simultaneously performed to guarantee an overall optimum. In the learning process, the label information can be accurately propagated via the graph structure so that the linear regression can learn a discriminative projection to better fit sample labels and accurately classify new samples. An effective algorithm was designed to solve the corresponding optimization problem with fast convergence. Furthermore, NNSG provides a unified perceptiveness for a number of graph-based learning methods and linear regression methods. The experimental results showed that NNSG can obtain very high classification accuracy and greatly outperforms conventional G-SSL methods, especially some conventional graph construction methods.

Nonlinear spike-and-slab sparse coding for interpretable image encoding.

Directory of Open Access Journals (Sweden)

Jacquelyn A Shelton

Full Text Available Sparse coding is a popular approach to model natural images but has faced two main challenges: modelling low-level image components (such as edge-like structures and their occlusions and modelling varying pixel intensities. Traditionally, images are modelled as a sparse linear superposition of dictionary elements, where the probabilistic view of this problem is that the coefficients follow a Laplace or Cauchy prior distribution. We propose a novel model that instead uses a spike-and-slab prior and nonlinear combination of components. With the prior, our model can easily represent exact zeros for e.g. the absence of an image component, such as an edge, and a distribution over non-zero pixel intensities. With the nonlinearity (the nonlinear max combination rule, the idea is to target occlusions; dictionary elements correspond to image components that can occlude each other. There are major consequences of the model assumptions made by both (nonlinear approaches, thus the main goal of this paper is to isolate and highlight differences between them. Parameter optimization is analytically and computationally intractable in our model, thus as a main contribution we design an exact Gibbs sampler for efficient inference which we can apply to higher dimensional data using latent variable preselection. Results on natural and artificial occlusion-rich data with controlled forms of sparse structure show that our model can extract a sparse set of edge-like components that closely match the generating process, which we refer to as interpretable components. Furthermore, the sparseness of the solution closely follows the ground-truth number of components/edges in the images. The linear model did not learn such edge-like components with any level of sparsity. This suggests that our model can adaptively well-approximate and characterize the meaningful generation process.

Efficient sparse matrix-matrix multiplication for computing periodic responses by shooting method on Intel Xeon Phi

Science.gov (United States)

Stoykov, S.; Atanassov, E.; Margenov, S.

2016-10-01

Many of the scientific applications involve sparse or dense matrix operations, such as solving linear systems, matrix-matrix products, eigensolvers, etc. In what concerns structural nonlinear dynamics, the computations of periodic responses and the determination of stability of the solution are of primary interest. Shooting method iswidely used for obtaining periodic responses of nonlinear systems. The method involves simultaneously operations with sparse and dense matrices. One of the computationally expensive operations in the method is multiplication of sparse by dense matrices. In the current work, a new algorithm for sparse matrix by dense matrix products is presented. The algorithm takes into account the structure of the sparse matrix, which is obtained by space discretization of the nonlinear Mindlin's plate equation of motion by the finite element method. The algorithm is developed to use the vector engine of Intel Xeon Phi coprocessors. It is compared with the standard sparse matrix by dense matrix algorithm and the one developed by Intel MKL and it is shown that by considering the properties of the sparse matrix better algorithms can be developed.

Compressed sensing & sparse filtering

CERN Document Server

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. Apart from compressed sensing this book contains other related app

Example-Based Image Colorization Using Locality Consistent Sparse Representation.

Science.gov (United States)

Bo Li; Fuchen Zhao; Zhuo Su; Xiangguo Liang; Yu-Kun Lai; Rosin, Paul L

2017-11-01

Image colorization aims to produce a natural looking color image from a given gray-scale image, which remains a challenging problem. In this paper, we propose a novel example-based image colorization method exploiting a new locality consistent sparse representation. Given a single reference color image, our method automatically colorizes the target gray-scale image by sparse pursuit. For efficiency and robustness, our method operates at the superpixel level. We extract low-level intensity features, mid-level texture features, and high-level semantic features for each superpixel, which are then concatenated to form its descriptor. The collection of feature vectors for all the superpixels from the reference image composes the dictionary. We formulate colorization of target superpixels as a dictionary-based sparse reconstruction problem. Inspired by the observation that superpixels with similar spatial location and/or feature representation are likely to match spatially close regions from the reference image, we further introduce a locality promoting regularization term into the energy formulation, which substantially improves the matching consistency and subsequent colorization results. Target superpixels are colorized based on the chrominance information from the dominant reference superpixels. Finally, to further improve coherence while preserving sharpness, we develop a new edge-preserving filter for chrominance channels with the guidance from the target gray-scale image. To the best of our knowledge, this is the first work on sparse pursuit image colorization from single reference images. Experimental results demonstrate that our colorization method outperforms the state-of-the-art methods, both visually and quantitatively using a user study.

Compressive sampling by artificial neural networks for video

Science.gov (United States)

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

2011-06-01

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

Dose-shaping using targeted sparse optimization

International Nuclear Information System (INIS)

Sayre, George A.; Ruan, Dan

2013-01-01

Purpose: Dose volume histograms (DVHs) are common tools in radiation therapy treatment planning to characterize plan quality. As statistical metrics, DVHs provide a compact summary of the underlying plan at the cost of losing spatial information: the same or similar dose-volume histograms can arise from substantially different spatial dose maps. This is exactly the reason why physicians and physicists scrutinize dose maps even after they satisfy all DVH endpoints numerically. However, up to this point, little has been done to control spatial phenomena, such as the spatial distribution of hot spots, which has significant clinical implications. To this end, the authors propose a novel objective function that enables a more direct tradeoff between target coverage, organ-sparing, and planning target volume (PTV) homogeneity, and presents our findings from four prostate cases, a pancreas case, and a head-and-neck case to illustrate the advantages and general applicability of our method.Methods: In designing the energy minimization objective (E tot sparse ), the authors utilized the following robust cost functions: (1) an asymmetric linear well function to allow differential penalties for underdose, relaxation of prescription dose, and overdose in the PTV; (2) a two-piece linear function to heavily penalize high dose and mildly penalize low and intermediate dose in organs-at risk (OARs); and (3) a total variation energy, i.e., the L 1 norm applied to the first-order approximation of the dose gradient in the PTV. By minimizing a weighted sum of these robust costs, general conformity to dose prescription and dose-gradient prescription is achieved while encouraging prescription violations to follow a Laplace distribution. In contrast, conventional quadratic objectives are associated with a Gaussian distribution of violations, which is less forgiving to large violations of prescription than the Laplace distribution. As a result, the proposed objective E tot sparse improves

Dose-shaping using targeted sparse optimization.

Science.gov (United States)

Sayre, George A; Ruan, Dan

2013-07-01

Dose volume histograms (DVHs) are common tools in radiation therapy treatment planning to characterize plan quality. As statistical metrics, DVHs provide a compact summary of the underlying plan at the cost of losing spatial information: the same or similar dose-volume histograms can arise from substantially different spatial dose maps. This is exactly the reason why physicians and physicists scrutinize dose maps even after they satisfy all DVH endpoints numerically. However, up to this point, little has been done to control spatial phenomena, such as the spatial distribution of hot spots, which has significant clinical implications. To this end, the authors propose a novel objective function that enables a more direct tradeoff between target coverage, organ-sparing, and planning target volume (PTV) homogeneity, and presents our findings from four prostate cases, a pancreas case, and a head-and-neck case to illustrate the advantages and general applicability of our method. In designing the energy minimization objective (E tot (sparse)), the authors utilized the following robust cost functions: (1) an asymmetric linear well function to allow differential penalties for underdose, relaxation of prescription dose, and overdose in the PTV; (2) a two-piece linear function to heavily penalize high dose and mildly penalize low and intermediate dose in organs-at risk (OARs); and (3) a total variation energy, i.e., the L1 norm applied to the first-order approximation of the dose gradient in the PTV. By minimizing a weighted sum of these robust costs, general conformity to dose prescription and dose-gradient prescription is achieved while encouraging prescription violations to follow a Laplace distribution. In contrast, conventional quadratic objectives are associated with a Gaussian distribution of violations, which is less forgiving to large violations of prescription than the Laplace distribution. As a result, the proposed objective E tot (sparse) improves tradeoff between

Feature based omnidirectional sparse visual path following

OpenAIRE

Goedemé, Toon; Tuytelaars, Tinne; Van Gool, Luc; Vanacker, Gerolf; Nuttin, Marnix

2005-01-01

Goedemé T., Tuytelaars T., Van Gool L., Vanacker G., Nuttin M., ''Feature based omnidirectional sparse visual path following'', Proceedings IEEE/RSJ international conference on intelligent robots and systems - IROS2005, pp. 1003-1008, August 2-6, 2005, Edmonton, Alberta, Canada.

A Non-static Data Layout Enhancing Parallelism and Vectorization in Sparse Grid Algorithms

KAUST Repository

Buse, Gerrit; Pfluger, Dirk; Murarasu, Alin; Jacob, Riko

2012-01-01

performance and facilitate the use of vector registers for our sparse grid benchmark problem hierarchization. Based on the compact data structure proposed for regular sparse grids in [2], we developed a new algorithm that outperforms existing implementations

Balanced and sparse Tamo-Barg codes

KAUST Repository

Halbawi, Wael; Duursma, Iwan; Dau, Hoang; Hassibi, Babak

2017-01-01

We construct balanced and sparse generator matrices for Tamo and Barg's Locally Recoverable Codes (LRCs). More specifically, for a cyclic Tamo-Barg code of length n, dimension k and locality r, we show how to deterministically construct a generator matrix where the number of nonzeros in any two columns differs by at most one, and where the weight of every row is d + r - 1, where d is the minimum distance of the code. Since LRCs are designed mainly for distributed storage systems, the results presented in this work provide a computationally balanced and efficient encoding scheme for these codes. The balanced property ensures that the computational effort exerted by any storage node is essentially the same, whilst the sparse property ensures that this effort is minimal. The work presented in this paper extends a similar result previously established for Reed-Solomon (RS) codes, where it is now known that any cyclic RS code possesses a generator matrix that is balanced as described, but is sparsest, meaning that each row has d nonzeros.

Balanced and sparse Tamo-Barg codes

KAUST Repository

Halbawi, Wael

2017-08-29

We construct balanced and sparse generator matrices for Tamo and Barg\\'s Locally Recoverable Codes (LRCs). More specifically, for a cyclic Tamo-Barg code of length n, dimension k and locality r, we show how to deterministically construct a generator matrix where the number of nonzeros in any two columns differs by at most one, and where the weight of every row is d + r - 1, where d is the minimum distance of the code. Since LRCs are designed mainly for distributed storage systems, the results presented in this work provide a computationally balanced and efficient encoding scheme for these codes. The balanced property ensures that the computational effort exerted by any storage node is essentially the same, whilst the sparse property ensures that this effort is minimal. The work presented in this paper extends a similar result previously established for Reed-Solomon (RS) codes, where it is now known that any cyclic RS code possesses a generator matrix that is balanced as described, but is sparsest, meaning that each row has d nonzeros.

Pedestrian detection from thermal images: A sparse representation based approach

Science.gov (United States)

Qi, Bin; John, Vijay; Liu, Zheng; Mita, Seiichi

2016-05-01

Pedestrian detection, a key technology in computer vision, plays a paramount role in the applications of advanced driver assistant systems (ADASs) and autonomous vehicles. The objective of pedestrian detection is to identify and locate people in a dynamic environment so that accidents can be avoided. With significant variations introduced by illumination, occlusion, articulated pose, and complex background, pedestrian detection is a challenging task for visual perception. Different from visible images, thermal images are captured and presented with intensity maps based objects' emissivity, and thus have an enhanced spectral range to make human beings perceptible from the cool background. In this study, a sparse representation based approach is proposed for pedestrian detection from thermal images. We first adopted the histogram of sparse code to represent image features and then detect pedestrian with the extracted features in an unimodal and a multimodal framework respectively. In the unimodal framework, two types of dictionaries, i.e. joint dictionary and individual dictionary, are built by learning from prepared training samples. In the multimodal framework, a weighted fusion scheme is proposed to further highlight the contributions from features with higher separability. To validate the proposed approach, experiments were conducted to compare with three widely used features: Haar wavelets (HWs), histogram of oriented gradients (HOG), and histogram of phase congruency (HPC) as well as two classification methods, i.e. AdaBoost and support vector machine (SVM). Experimental results on a publicly available data set demonstrate the superiority of the proposed approach.

Mutation rules and the evolution of sparseness and modularity in biological systems.

Directory of Open Access Journals (Sweden)

Tamar Friedlander

Full Text Available Biological systems exhibit two structural features on many levels of organization: sparseness, in which only a small fraction of possible interactions between components actually occur; and modularity--the near decomposability of the system into modules with distinct functionality. Recent work suggests that modularity can evolve in a variety of circumstances, including goals that vary in time such that they share the same subgoals (modularly varying goals, or when connections are costly. Here, we studied the origin of modularity and sparseness focusing on the nature of the mutation process, rather than on connection cost or variations in the goal. We use simulations of evolution with different mutation rules. We found that commonly used sum-rule mutations, in which interactions are mutated by adding random numbers, do not lead to modularity or sparseness except for in special situations. In contrast, product-rule mutations in which interactions are mutated by multiplying by random numbers--a better model for the effects of biological mutations--led to sparseness naturally. When the goals of evolution are modular, in the sense that specific groups of inputs affect specific groups of outputs, product-rule mutations also lead to modular structure; sum-rule mutations do not. Product-rule mutations generate sparseness and modularity because they tend to reduce interactions, and to keep small interaction terms small.

Natural image sequences constrain dynamic receptive fields and imply a sparse code.

Science.gov (United States)

Häusler, Chris; Susemihl, Alex; Nawrot, Martin P

2013-11-06

In their natural environment, animals experience a complex and dynamic visual scenery. Under such natural stimulus conditions, neurons in the visual cortex employ a spatially and temporally sparse code. For the input scenario of natural still images, previous work demonstrated that unsupervised feature learning combined with the constraint of sparse coding can predict physiologically measured receptive fields of simple cells in the primary visual cortex. This convincingly indicated that the mammalian visual system is adapted to the natural spatial input statistics. Here, we extend this approach to the time domain in order to predict dynamic receptive fields that can account for both spatial and temporal sparse activation in biological neurons. We rely on temporal restricted Boltzmann machines and suggest a novel temporal autoencoding training procedure. When tested on a dynamic multi-variate benchmark dataset this method outperformed existing models of this class. Learning features on a large dataset of natural movies allowed us to model spatio-temporal receptive fields for single neurons. They resemble temporally smooth transformations of previously obtained static receptive fields and are thus consistent with existing theories. A neuronal spike response model demonstrates how the dynamic receptive field facilitates temporal and population sparseness. We discuss the potential mechanisms and benefits of a spatially and temporally sparse representation of natural visual input. Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved.

Inferring Human Mobility from Sparse Low Accuracy Mobile Sensing Data

DEFF Research Database (Denmark)

Cuttone, Andrea; Jørgensen, Sune Lehmann; Larsen, Jakob Eg

2014-01-01

Understanding both collective and personal human mobility is a central topic in Computational Social Science. Smartphone sensing data is emerging as a promising source for studying human mobility. However, most literature focuses on high-precision GPS positioning and high-frequency sampling, which...... is not always feasible in a longitudinal study or for everyday applications because location sensing has a high battery cost. In this paper we study the feasibility of inferring human mobility from sparse, low accuracy mobile sensing data. We validate our results using participants' location diaries......, and analyze the inferred geographical networks, the time spent at different places, and the number of unique places over time. Our results suggest that low resolution data allows accurate inference of human mobility patterns....

The Roles of Sparse Direct Methods in Large-scale Simulations

Energy Technology Data Exchange (ETDEWEB)

Li, Xiaoye S.; Gao, Weiguo; Husbands, Parry J.R.; Yang, Chao; Ng, Esmond G.

2005-06-27

Sparse systems of linear equations and eigen-equations arise at the heart of many large-scale, vital simulations in DOE. Examples include the Accelerator Science and Technology SciDAC (Omega3P code, electromagnetic problem), the Center for Extended Magnetohydrodynamic Modeling SciDAC(NIMROD and M3D-C1 codes, fusion plasma simulation). The Terascale Optimal PDE Simulations (TOPS)is providing high-performance sparse direct solvers, which have had significant impacts on these applications. Over the past several years, we have been working closely with the other SciDAC teams to solve their large, sparse matrix problems arising from discretization of the partial differential equations. Most of these systems are very ill-conditioned, resulting in extremely poor convergence deployed our direct methods techniques in these applications, which achieved significant scientific results as well as performance gains. These successes were made possible through the SciDAC model of computer scientists and application scientists working together to take full advantage of terascale computing systems and new algorithms research.

The Roles of Sparse Direct Methods in Large-scale Simulations

International Nuclear Information System (INIS)

Li, Xiaoye S.; Gao, Weiguo; Husbands, Parry J.R.; Yang, Chao; Ng, Esmond G.

2005-01-01

Sparse systems of linear equations and eigen-equations arise at the heart of many large-scale, vital simulations in DOE. Examples include the Accelerator Science and Technology SciDAC (Omega3P code, electromagnetic problem), the Center for Extended Magnetohydrodynamic Modeling SciDAC(NIMROD and M3D-C1 codes, fusion plasma simulation). The Terascale Optimal PDE Simulations (TOPS)is providing high-performance sparse direct solvers, which have had significant impacts on these applications. Over the past several years, we have been working closely with the other SciDAC teams to solve their large, sparse matrix problems arising from discretization of the partial differential equations. Most of these systems are very ill-conditioned, resulting in extremely poor convergence deployed our direct methods techniques in these applications, which achieved significant scientific results as well as performance gains. These successes were made possible through the SciDAC model of computer scientists and application scientists working together to take full advantage of terascale computing systems and new algorithms research

Sparse logistic principal components analysis for binary data

KAUST Repository

Lee, Seokho

2010-09-01

We develop a new principal components analysis (PCA) type dimension reduction method for binary data. Different from the standard PCA which is defined on the observed data, the proposed PCA is defined on the logit transform of the success probabilities of the binary observations. Sparsity is introduced to the principal component (PC) loading vectors for enhanced interpretability and more stable extraction of the principal components. Our sparse PCA is formulated as solving an optimization problem with a criterion function motivated from a penalized Bernoulli likelihood. A Majorization-Minimization algorithm is developed to efficiently solve the optimization problem. The effectiveness of the proposed sparse logistic PCA method is illustrated by application to a single nucleotide polymorphism data set and a simulation study. © Institute ol Mathematical Statistics, 2010.

The Demographics and Properties of Wide-Orbit, Planetary-Mass Companions from PSF Fitting of Spitzer/IRAC Images

Science.gov (United States)

Martinez, Raquel; Kraus, Adam L.

2017-06-01

Over the past decade, a growing population of planetary-mass companions ( 100 AU) from their host stars, challenging existing models of both star and planet formation. It is unclear whether these systems represent the low-mass extreme of stellar binary formation or the high-mass and wide-orbit extreme of planet formation theories, as various proposed formation pathways inadequately explain the physical and orbital aspects of these systems. Even so, determining which scenario best reproduces the observed characteristics of the PMCs will come once a statistically robust sample of directly-imaged PMCs are found and studied.We are developing an automated pipeline to search for wide-orbit PMCs to young stars in Spitzer/IRAC images. A Markov Chain Monte Carlo (MCMC) algorithm is the backbone of our novel point spread function (PSF) subtraction routine that efficiently creates and subtracts χ2-minimizing instrumental PSFs, simultaneously measuring astrometry and infrared photometry of these systems across the four IRAC channels (3.6 μm, 4.5 μm, 5.8 μm, and 8 μm). In this work, we present the results of a Spitzer/IRAC archival imaging study of 11 young, low-mass (0.044-0.88 M⊙ K3.5-M7.5) stars known to have faint, low-mass companions in 3 nearby star-forming regions (Chameleon, Taurus, and Upper Scorpius). We characterize the systems found to have low-mass companions with non-zero [I1] - [I4] colors, potentially signifying the presence of a circum(sub?)stellar disk. Plans for future pipeline improvements and paths forward will also be discussed. Once this computational foundation is optimized, the stage is set to quickly scour the nearby star-forming regions already imaged by Spitzer, identify potential candidates for further characterization with ground- or space-based telescopes, and increase the number of widely-separated PMCs known.

Sparse linear systems: Theory of decomposition, methods, technology, applications and implementation in Wolfram Mathematica

Energy Technology Data Exchange (ETDEWEB)

Pilipchuk, L. A., E-mail: pilipchik@bsu.by [Belarussian State University, 220030 Minsk, 4, Nezavisimosti avenue, Republic of Belarus (Belarus); Pilipchuk, A. S., E-mail: an.pilipchuk@gmail.com [The Natural Resources and Environmental Protestion Ministry of the Republic of Belarus, 220004 Minsk, 10 Kollektornaya Street, Republic of Belarus (Belarus)

2015-11-30

In this paper we propose the theory of decomposition, methods, technologies, applications and implementation in Wol-fram Mathematica for the constructing the solutions of the sparse linear systems. One of the applications is the Sensor Location Problem for the symmetric graph in the case when split ratios of some arc flows can be zeros. The objective of that application is to minimize the number of sensors that are assigned to the nodes. We obtain a sparse system of linear algebraic equations and research its matrix rank. Sparse systems of these types appear in generalized network flow programming problems in the form of restrictions and can be characterized as systems with a large sparse sub-matrix representing the embedded network structure.

Sparse linear systems: Theory of decomposition, methods, technology, applications and implementation in Wolfram Mathematica

International Nuclear Information System (INIS)

Pilipchuk, L. A.; Pilipchuk, A. S.

2015-01-01

In this paper we propose the theory of decomposition, methods, technologies, applications and implementation in Wol-fram Mathematica for the constructing the solutions of the sparse linear systems. One of the applications is the Sensor Location Problem for the symmetric graph in the case when split ratios of some arc flows can be zeros. The objective of that application is to minimize the number of sensors that are assigned to the nodes. We obtain a sparse system of linear algebraic equations and research its matrix rank. Sparse systems of these types appear in generalized network flow programming problems in the form of restrictions and can be characterized as systems with a large sparse sub-matrix representing the embedded network structure

Multi-Layer Sparse Representation for Weighted LBP-Patches Based Facial Expression Recognition

Directory of Open Access Journals (Sweden)

Qi Jia

2015-03-01

Full Text Available In this paper, a novel facial expression recognition method based on sparse representation is proposed. Most contemporary facial expression recognition systems suffer from limited ability to handle image nuisances such as low resolution and noise. Especially for low intensity expression, most of the existing training methods have quite low recognition rates. Motivated by sparse representation, the problem can be solved by finding sparse coefficients of the test image by the whole training set. Deriving an effective facial representation from original face images is a vital step for successful facial expression recognition. We evaluate facial representation based on weighted local binary patterns, and Fisher separation criterion is used to calculate the weighs of patches. A multi-layer sparse representation framework is proposed for multi-intensity facial expression recognition, especially for low-intensity expressions and noisy expressions in reality, which is a critical problem but seldom addressed in the existing works. To this end, several experiments based on low-resolution and multi-intensity expressions are carried out. Promising results on publicly available databases demonstrate the potential of the proposed approach.

Low-rank sparse learning for robust visual tracking

KAUST Repository

Zhang, Tianzhu; Ghanem, Bernard; Liu, Si; Ahuja, Narendra

2012-01-01

In this paper, we propose a new particle-filter based tracking algorithm that exploits the relationship between particles (candidate targets). By representing particles as sparse linear combinations of dictionary templates, this algorithm

An Improved Information Hiding Method Based on Sparse Representation

Directory of Open Access Journals (Sweden)

Minghai Yao

2015-01-01

Full Text Available A novel biometric authentication information hiding method based on the sparse representation is proposed for enhancing the security of biometric information transmitted in the network. In order to make good use of abundant information of the cover image, the sparse representation method is adopted to exploit the correlation between the cover and biometric images. Thus, the biometric image is divided into two parts. The first part is the reconstructed image, and the other part is the residual image. The biometric authentication image cannot be restored by any one part. The residual image and sparse representation coefficients are embedded into the cover image. Then, for the sake of causing much less attention of attackers, the visual attention mechanism is employed to select embedding location and embedding sequence of secret information. Finally, the reversible watermarking algorithm based on histogram is utilized for embedding the secret information. For verifying the validity of the algorithm, the PolyU multispectral palmprint and the CASIA iris databases are used as biometric information. The experimental results show that the proposed method exhibits good security, invisibility, and high capacity.

Sparse reconstruction by means of the standard Tikhonov regularization

International Nuclear Information System (INIS)

Lu Shuai; Pereverzev, Sergei V

2008-01-01

It is a common belief that Tikhonov scheme with || · ||L 2 -penalty fails in sparse reconstruction. We are going to show, however, that this standard regularization can help if the stability measured in L 1 -norm will be properly taken into account in the choice of the regularization parameter. The crucial point is that now a stability bound may depend on the bases with respect to which the solution of the problem is assumed to be sparse. We discuss how such a stability can be estimated numerically and present the results of computational experiments giving the evidence of the reliability of our approach.

Sparse logistic principal components analysis for binary data

KAUST Repository

Lee, Seokho; Huang, Jianhua Z.; Hu, Jianhua

2010-01-01

with a criterion function motivated from a penalized Bernoulli likelihood. A Majorization-Minimization algorithm is developed to efficiently solve the optimization problem. The effectiveness of the proposed sparse logistic PCA method is illustrated

On Sparse Multi-Task Gaussian Process Priors for Music Preference Learning

DEFF Research Database (Denmark)

Nielsen, Jens Brehm; Jensen, Bjørn Sand; Larsen, Jan

In this paper we study pairwise preference learning in a music setting with multitask Gaussian processes and examine the effect of sparsity in the input space as well as in the actual judgments. To introduce sparsity in the inputs, we extend a classic pairwise likelihood model to support sparse...... simulation shows the performance on a real-world music preference dataset which motivates and demonstrates the potential of the sparse Gaussian process formulation for pairwise likelihoods....

Linear Regression on Sparse Features for Single-Channel Speech Separation

DEFF Research Database (Denmark)

Schmidt, Mikkel N.; Olsson, Rasmus Kongsgaard

2007-01-01

In this work we address the problem of separating multiple speakers from a single microphone recording. We formulate a linear regression model for estimating each speaker based on features derived from the mixture. The employed feature representation is a sparse, non-negative encoding of the speech...... mixture in terms of pre-learned speaker-dependent dictionaries. Previous work has shown that this feature representation by itself provides some degree of separation. We show that the performance is significantly improved when regression analysis is performed on the sparse, non-negative features, both...

Quasi optimal and adaptive sparse grids with control variates for PDEs with random diffusion coefficient

KAUST Repository

Tamellini, Lorenzo

2016-01-05

In this talk we discuss possible strategies to minimize the impact of the curse of dimensionality effect when building sparse-grid approximations of a multivariate function u = u(y1, ..., yN ). More precisely, we present a knapsack approach , in which we estimate the cost and the error reduction contribution of each possible component of the sparse grid, and then we choose the components with the highest error reduction /cost ratio. The estimates of the error reduction are obtained by either a mixed a-priori / a-posteriori approach, in which we first derive a theoretical bound and then tune it with some inexpensive auxiliary computations (resulting in the so-called quasi-optimal sparse grids ), or by a fully a-posteriori approach (obtaining the so-called adaptive sparse grids ). This framework is very general and can be used to build quasi-optimal/adaptive sparse grids on bounded and unbounded domains (e.g. u depending on uniform and normal random distributions for yn), using both nested and non-nested families of univariate collocation points. We present some theoretical convergence results as well as numerical results showing the efficiency of the proposed approach for the approximation of the solution of elliptic PDEs with random diffusion coefficients. In this context, to treat the case of rough permeability fields in which a sparse grid approach may not be suitable, we propose to use the sparse grids as a control variate in a Monte Carlo simulation.

Sparse Channel Estimation Including the Impact of the Transceiver Filters with Application to OFDM

DEFF Research Database (Denmark)

Barbu, Oana-Elena; Pedersen, Niels Lovmand; Manchón, Carles Navarro

2014-01-01

Traditionally, the dictionary matrices used in sparse wireless channel estimation have been based on the discrete Fourier transform, following the assumption that the channel frequency response (CFR) can be approximated as a linear combination of a small number of multipath components, each one......) and receive (demodulation) filters. Hence, the assumption of the CFR being sparse in the canonical Fourier dictionary may no longer hold. In this work, we derive a signal model and subsequently a novel dictionary matrix for sparse estimation that account for the impact of transceiver filters. Numerical...... results obtained in an OFDM transmission scenario demonstrate the superior accuracy of a sparse estimator that uses our proposed dictionary rather than the classical Fourier dictionary, and its robustness against a mismatch in the assumed transmit filter characteristics....

Sparse data structure design for wavelet-based methods

Directory of Open Access Journals (Sweden)

Latu Guillaume

2011-12-01

Full Text Available This course gives an introduction to the design of efficient datatypes for adaptive wavelet-based applications. It presents some code fragments and benchmark technics useful to learn about the design of sparse data structures and adaptive algorithms. Material and practical examples are given, and they provide good introduction for anyone involved in the development of adaptive applications. An answer will be given to the question: how to implement and efficiently use the discrete wavelet transform in computer applications? A focus will be made on time-evolution problems, and use of wavelet-based scheme for adaptively solving partial differential equations (PDE. One crucial issue is that the benefits of the adaptive method in term of algorithmic cost reduction can not be wasted by overheads associated to sparse data management.

The effects of surface-charged submicron polystyrene particles on the structure and performance of PSF forward osmosis membrane

Science.gov (United States)

Zuo, Hao-Ran; Fu, Jia-Bei; Cao, Gui-Ping; Hu, Nian; Lu, Hui; Liu, Hui-Qing; Chen, Peng-Peng; Yu, Jie

2018-04-01

Monodisperse surface-charged submicron polystyrene particles were designed, synthesized, and blended into polysulfone (PSF) support layer to prepare forward osmosis (FO) membrane with high performance. The membrane incorporated with particles were characterized with respect to morphology, porosity, and internal osmotic pressure (IOP). Results showed that the polymer particles not only increased the hydrophilicity and porosity of support layer, but also generated considerable IOP, which helped markedly decreasing the structure parameter from 1550 to 670 μm. The measured mass transfer parameters further confirmed the beneficial effects of the surface-charged submicron polymer particles on the performance of FO membrane. For instance, the water permeability coefficient (5.37 L m-2 h-1 bar-1) and water flux (49.7 L m-2 h-1) of the FO membrane incorporated with 5 wt% particles were almost twice as much as that of FO membrane without incorporation. This study suggests that monodisperse surface-charged submicron polymer particles are potential modifiers for improving the performance of FO membranes.

Preconditioned Inexact Newton for Nonlinear Sparse Electromagnetic Imaging

KAUST Repository

Desmal, Abdulla; Bagci, Hakan

2014-01-01

with smoothness promoting optimization/regularization schemes. However, this type of regularization schemes are known to perform poorly when applied in imagining domains with sparse content or sharp variations. In this work, an inexact Newton algorithm

Performance Comparison of Reconstruction Algorithms in Discrete Blind Multi-Coset Sampling

DEFF Research Database (Denmark)

Grigoryan, Ruben; Arildsen, Thomas; Tandur, Deepaknath

2012-01-01

This paper investigates the performance of different reconstruction algorithms in discrete blind multi-coset sampling. Multi-coset scheme is a promising compressed sensing architecture that can replace traditional Nyquist-rate sampling in the applications with multi-band frequency sparse signals...

Interferometric interpolation of sparse marine data

KAUST Repository

Hanafy, Sherif M.

2013-10-11

We present the theory and numerical results for interferometrically interpolating 2D and 3D marine surface seismic profiles data. For the interpolation of seismic data we use the combination of a recorded Green\\'s function and a model-based Green\\'s function for a water-layer model. Synthetic (2D and 3D) and field (2D) results show that the seismic data with sparse receiver intervals can be accurately interpolated to smaller intervals using multiples in the data. An up- and downgoing separation of both recorded and model-based Green\\'s functions can help in minimizing artefacts in a virtual shot gather. If the up- and downgoing separation is not possible, noticeable artefacts will be generated in the virtual shot gather. As a partial remedy we iteratively use a non-stationary 1D multi-channel matching filter with the interpolated data. Results suggest that a sparse marine seismic survey can yield more information about reflectors if traces are interpolated by interferometry. Comparing our results to those of f-k interpolation shows that the synthetic example gives comparable results while the field example shows better interpolation quality for the interferometric method. © 2013 European Association of Geoscientists & Engineers.

Dose-shaping using targeted sparse optimization

Energy Technology Data Exchange (ETDEWEB)

Sayre, George A.; Ruan, Dan [Department of Radiation Oncology, University of California - Los Angeles School of Medicine, 200 Medical Plaza, Los Angeles, California 90095 (United States)

2013-07-15

Purpose: Dose volume histograms (DVHs) are common tools in radiation therapy treatment planning to characterize plan quality. As statistical metrics, DVHs provide a compact summary of the underlying plan at the cost of losing spatial information: the same or similar dose-volume histograms can arise from substantially different spatial dose maps. This is exactly the reason why physicians and physicists scrutinize dose maps even after they satisfy all DVH endpoints numerically. However, up to this point, little has been done to control spatial phenomena, such as the spatial distribution of hot spots, which has significant clinical implications. To this end, the authors propose a novel objective function that enables a more direct tradeoff between target coverage, organ-sparing, and planning target volume (PTV) homogeneity, and presents our findings from four prostate cases, a pancreas case, and a head-and-neck case to illustrate the advantages and general applicability of our method.Methods: In designing the energy minimization objective (E{sub tot}{sup sparse}), the authors utilized the following robust cost functions: (1) an asymmetric linear well function to allow differential penalties for underdose, relaxation of prescription dose, and overdose in the PTV; (2) a two-piece linear function to heavily penalize high dose and mildly penalize low and intermediate dose in organs-at risk (OARs); and (3) a total variation energy, i.e., the L{sub 1} norm applied to the first-order approximation of the dose gradient in the PTV. By minimizing a weighted sum of these robust costs, general conformity to dose prescription and dose-gradient prescription is achieved while encouraging prescription violations to follow a Laplace distribution. In contrast, conventional quadratic objectives are associated with a Gaussian distribution of violations, which is less forgiving to large violations of prescription than the Laplace distribution. As a result, the proposed objective E{sub tot

High Order Tensor Formulation for Convolutional Sparse Coding

KAUST Repository

Bibi, Adel Aamer; Ghanem, Bernard

2017-01-01

Convolutional sparse coding (CSC) has gained attention for its successful role as a reconstruction and a classification tool in the computer vision and machine learning community. Current CSC methods can only reconstruct singlefeature 2D images

A Novel Design of Sparse Prototype Filter for Nearly Perfect Reconstruction Cosine-Modulated Filter Banks

Directory of Open Access Journals (Sweden)

Wei Xu

2018-05-01

Full Text Available Cosine-modulated filter banks play a major role in digital signal processing. Sparse FIR filter banks have lower implementation complexity than full filter banks, while keeping a good performance level. This paper presents a fast design paradigm for sparse nearly perfect-reconstruction (NPR cosine-modulated filter banks. First, an approximation function is introduced to reduce the non-convex quadratically constrained optimization problem to a linearly constrained optimization problem. Then, the desired sparse linear phase FIR prototype filter is derived through the orthogonal matching pursuit (OMP performed under the weighted l 2 norm. The simulation results demonstrate that the proposed scheme is an effective paradigm to design sparse NPR cosine-modulated filter banks.

Efficient Pseudorecursive Evaluation Schemes for Non-adaptive Sparse Grids

KAUST Repository

Buse, Gerrit

2014-01-01

In this work we propose novel algorithms for storing and evaluating sparse grid functions, operating on regular (not spatially adaptive), yet potentially dimensionally adaptive grid types. Besides regular sparse grids our approach includes truncated grids, both with and without boundary grid points. Similar to the implicit data structures proposed in Feuersänger (Dünngitterverfahren für hochdimensionale elliptische partielle Differntialgleichungen. Diploma Thesis, Institut für Numerische Simulation, Universität Bonn, 2005) and Murarasu et al. (Proceedings of the 16th ACM Symposium on Principles and Practice of Parallel Programming. Cambridge University Press, New York, 2011, pp. 25–34) we also define a bijective mapping from the multi-dimensional space of grid points to a contiguous index, such that the grid data can be stored in a simple array without overhead. Our approach is especially well-suited to exploit all levels of current commodity hardware, including cache-levels and vector extensions. Furthermore, this kind of data structure is extremely attractive for today’s real-time applications, as it gives direct access to the hierarchical structure of the grids, while outperforming other common sparse grid structures (hash maps, etc.) which do not match with modern compute platforms that well. For dimensionality d ≤ 10 we achieve good speedups on a 12 core Intel Westmere-EP NUMA platform compared to the results presented in Murarasu et al. (Proceedings of the International Conference on Computational Science—ICCS 2012. Procedia Computer Science, 2012). As we show, this also holds for the results obtained on Nvidia Fermi GPUs, for which we observe speedups over our own CPU implementation of up to 4.5 when dealing with moderate dimensionality. In high-dimensional settings, in the order of tens to hundreds of dimensions, our sparse grid evaluation kernels on the CPU outperform any other known implementation.

Noniterative MAP reconstruction using sparse matrix representations.

Science.gov (United States)

Cao, Guangzhi; Bouman, Charles A; Webb, Kevin J

2009-09-01

We present a method for noniterative maximum a posteriori (MAP) tomographic reconstruction which is based on the use of sparse matrix representations. Our approach is to precompute and store the inverse matrix required for MAP reconstruction. This approach has generally not been used in the past because the inverse matrix is typically large and fully populated (i.e., not sparse). In order to overcome this problem, we introduce two new ideas. The first idea is a novel theory for the lossy source coding of matrix transformations which we refer to as matrix source coding. This theory is based on a distortion metric that reflects the distortions produced in the final matrix-vector product, rather than the distortions in the coded matrix itself. The resulting algorithms are shown to require orthonormal transformations of both the measurement data and the matrix rows and columns before quantization and coding. The second idea is a method for efficiently storing and computing the required orthonormal transformations, which we call a sparse-matrix transform (SMT). The SMT is a generalization of the classical FFT in that it uses butterflies to compute an orthonormal transform; but unlike an FFT, the SMT uses the butterflies in an irregular pattern, and is numerically designed to best approximate the desired transforms. We demonstrate the potential of the noniterative MAP reconstruction with examples from optical tomography. The method requires offline computation to encode the inverse transform. However, once these offline computations are completed, the noniterative MAP algorithm is shown to reduce both storage and computation by well over two orders of magnitude, as compared to a linear iterative reconstruction methods.

On A Nonlinear Generalization of Sparse Coding and Dictionary Learning.

Science.gov (United States)

Xie, Yuchen; Ho, Jeffrey; Vemuri, Baba

2013-01-01

Existing dictionary learning algorithms are based on the assumption that the data are vectors in an Euclidean vector space ℝ d , and the dictionary is learned from the training data using the vector space structure of ℝ d and its Euclidean L 2 -metric. However, in many applications, features and data often originated from a Riemannian manifold that does not support a global linear (vector space) structure. Furthermore, the extrinsic viewpoint of existing dictionary learning algorithms becomes inappropriate for modeling and incorporating the intrinsic geometry of the manifold that is potentially important and critical to the application. This paper proposes a novel framework for sparse coding and dictionary learning for data on a Riemannian manifold, and it shows that the existing sparse coding and dictionary learning methods can be considered as special (Euclidean) cases of the more general framework proposed here. We show that both the dictionary and sparse coding can be effectively computed for several important classes of Riemannian manifolds, and we validate the proposed method using two well-known classification problems in computer vision and medical imaging analysis.

Hybrid kriging methods for interpolating sparse river bathymetry point data

Directory of Open Access Journals (Sweden)

Pedro Velloso Gomes Batista

Full Text Available ABSTRACT Terrain models that represent riverbed topography are used for analyzing geomorphologic changes, calculating water storage capacity, and making hydrologic simulations. These models are generated by interpolating bathymetry points. River bathymetry is usually surveyed through cross-sections, which may lead to a sparse sampling pattern. Hybrid kriging methods, such as regression kriging (RK and co-kriging (CK employ the correlation with auxiliary predictors, as well as inter-variable correlation, to improve the predictions of the target variable. In this study, we use the orthogonal distance of a (x, y point to the river centerline as a covariate for RK and CK. Given that riverbed elevation variability is abrupt transversely to the flow direction, it is expected that the greater the Euclidean distance of a point to the thalweg, the greater the bed elevation will be. The aim of this study was to evaluate if the use of the proposed covariate improves the spatial prediction of riverbed topography. In order to asses such premise, we perform an external validation. Transversal cross-sections are used to make the spatial predictions, and the point data surveyed between sections are used for testing. We compare the results from CK and RK to the ones obtained from ordinary kriging (OK. The validation indicates that RK yields the lowest RMSE among the interpolators. RK predictions represent the thalweg between cross-sections, whereas the other methods under-predict the river thalweg depth. Therefore, we conclude that RK provides a simple approach for enhancing the quality of the spatial prediction from sparse bathymetry data.

A Low Delay and Fast Converging Improved Proportionate Algorithm for Sparse System Identification

Directory of Open Access Journals (Sweden)

Benesty Jacob

2007-01-01

Full Text Available A sparse system identification algorithm for network echo cancellation is presented. This new approach exploits both the fast convergence of the improved proportionate normalized least mean square (IPNLMS algorithm and the efficient implementation of the multidelay adaptive filtering (MDF algorithm inheriting the beneficial properties of both. The proposed IPMDF algorithm is evaluated using impulse responses with various degrees of sparseness. Simulation results are also presented for both speech and white Gaussian noise input sequences. It has been shown that the IPMDF algorithm outperforms the MDF and IPNLMS algorithms for both sparse and dispersive echo path impulse responses. Computational complexity of the proposed algorithm is also discussed.

Robust visual tracking via multi-task sparse learning

KAUST Repository

Zhang, Tianzhu; Ghanem, Bernard; Liu, Si; Ahuja, Narendra

2012-01-01

In this paper, we formulate object tracking in a particle filter framework as a multi-task sparse learning problem, which we denote as Multi-Task Tracking (MTT). Since we model particles as linear combinations of dictionary templates

A Spectral Reconstruction Algorithm of Miniature Spectrometer Based on Sparse Optimization and Dictionary Learning.

Science.gov (United States)

Zhang, Shang; Dong, Yuhan; Fu, Hongyan; Huang, Shao-Lun; Zhang, Lin

2018-02-22

The miniaturization of spectrometer can broaden the application area of spectrometry, which has huge academic and industrial value. Among various miniaturization approaches, filter-based miniaturization is a promising implementation by utilizing broadband filters with distinct transmission functions. Mathematically, filter-based spectral reconstruction can be modeled as solving a system of linear equations. In this paper, we propose an algorithm of spectral reconstruction based on sparse optimization and dictionary learning. To verify the feasibility of the reconstruction algorithm, we design and implement a simple prototype of a filter-based miniature spectrometer. The experimental results demonstrate that sparse optimization is well applicable to spectral reconstruction whether the spectra are directly sparse or not. As for the non-directly sparse spectra, their sparsity can be enhanced by dictionary learning. In conclusion, the proposed approach has a bright application prospect in fabricating a practical miniature spectrometer.

Sparse Adaptive Iteratively-Weighted Thresholding Algorithm (SAITA for L p -Regularization Using the Multiple Sub-Dictionary Representation

Directory of Open Access Journals (Sweden)

Yunyi Li

2017-12-01

Full Text Available Both L 1 / 2 and L 2 / 3 are two typical non-convex regularizations of L p ( 0 < p < 1 , which can be employed to obtain a sparser solution than the L 1 regularization. Recently, the multiple-state sparse transformation strategy has been developed to exploit the sparsity in L 1 regularization for sparse signal recovery, which combines the iterative reweighted algorithms. To further exploit the sparse structure of signal and image, this paper adopts multiple dictionary sparse transform strategies for the two typical cases p ∈ { 1 / 2 ,   2 / 3 } based on an iterative L p thresholding algorithm and then proposes a sparse adaptive iterative-weighted L p thresholding algorithm (SAITA. Moreover, a simple yet effective regularization parameter is proposed to weight each sub-dictionary-based L p regularizer. Simulation results have shown that the proposed SAITA not only performs better than the corresponding L 1 algorithms but can also obtain a better recovery performance and achieve faster convergence than the conventional single-dictionary sparse transform-based L p case. Moreover, we conduct some applications about sparse image recovery and obtain good results by comparison with relative work.

Shape prior modeling using sparse representation and online dictionary learning.

Science.gov (United States)

Zhang, Shaoting; Zhan, Yiqiang; Zhou, Yan; Uzunbas, Mustafa; Metaxas, Dimitris N

2012-01-01

The recently proposed sparse shape composition (SSC) opens a new avenue for shape prior modeling. Instead of assuming any parametric model of shape statistics, SSC incorporates shape priors on-the-fly by approximating a shape instance (usually derived from appearance cues) by a sparse combination of shapes in a training repository. Theoretically, one can increase the modeling capability of SSC by including as many training shapes in the repository. However, this strategy confronts two limitations in practice. First, since SSC involves an iterative sparse optimization at run-time, the more shape instances contained in the repository, the less run-time efficiency SSC has. Therefore, a compact and informative shape dictionary is preferred to a large shape repository. Second, in medical imaging applications, training shapes seldom come in one batch. It is very time consuming and sometimes infeasible to reconstruct the shape dictionary every time new training shapes appear. In this paper, we propose an online learning method to address these two limitations. Our method starts from constructing an initial shape dictionary using the K-SVD algorithm. When new training shapes come, instead of re-constructing the dictionary from the ground up, we update the existing one using a block-coordinates descent approach. Using the dynamically updated dictionary, sparse shape composition can be gracefully scaled up to model shape priors from a large number of training shapes without sacrificing run-time efficiency. Our method is validated on lung localization in X-Ray and cardiac segmentation in MRI time series. Compared to the original SSC, it shows comparable performance while being significantly more efficient.

Mapping visual stimuli to perceptual decisions via sparse decoding of mesoscopic neural activity.

Science.gov (United States)

Sajda, Paul

2010-01-01

In this talk I will describe our work investigating sparse decoding of neural activity, given a realistic mapping of the visual scene to neuronal spike trains generated by a model of primary visual cortex (V1). We use a linear decoder which imposes sparsity via an L1 norm. The decoder can be viewed as a decoding neuron (linear summation followed by a sigmoidal nonlinearity) in which there are relatively few non-zero synaptic weights. We find: (1) the best decoding performance is for a representation that is sparse in both space and time, (2) decoding of a temporal code results in better performance than a rate code and is also a better fit to the psychophysical data, (3) the number of neurons required for decoding increases monotonically as signal-to-noise in the stimulus decreases, with as little as 1% of the neurons required for decoding at the highest signal-to-noise levels, and (4) sparse decoding results in a more accurate decoding of the stimulus and is a better fit to psychophysical performance than a distributed decoding, for example one imposed by an L2 norm. We conclude that sparse coding is well-justified from a decoding perspective in that it results in a minimum number of neurons and maximum accuracy when sparse representations can be decoded from the neural dynamics.

Multi-Frequency Polarimetric SAR Classification Based on Riemannian Manifold and Simultaneous Sparse Representation

Directory of Open Access Journals (Sweden)

Fan Yang

2015-07-01

Full Text Available Normally, polarimetric SAR classification is a high-dimensional nonlinear mapping problem. In the realm of pattern recognition, sparse representation is a very efficacious and powerful approach. As classical descriptors of polarimetric SAR, covariance and coherency matrices are Hermitian semidefinite and form a Riemannian manifold. Conventional Euclidean metrics are not suitable for a Riemannian manifold, and hence, normal sparse representation classification cannot be applied to polarimetric SAR directly. This paper proposes a new land cover classification approach for polarimetric SAR. There are two principal novelties in this paper. First, a Stein kernel on a Riemannian manifold instead of Euclidean metrics, combined with sparse representation, is employed for polarimetric SAR land cover classification. This approach is named Stein-sparse representation-based classification (SRC. Second, using simultaneous sparse representation and reasonable assumptions of the correlation of representation among different frequency bands, Stein-SRC is generalized to simultaneous Stein-SRC for multi-frequency polarimetric SAR classification. These classifiers are assessed using polarimetric SAR images from the Airborne Synthetic Aperture Radar (AIRSAR sensor of the Jet Propulsion Laboratory (JPL and the Electromagnetics Institute Synthetic Aperture Radar (EMISAR sensor of the Technical University of Denmark (DTU. Experiments on single-band and multi-band data both show that these approaches acquire more accurate classification results in comparison to many conventional and advanced classifiers.

Sparse canonical correlation analysis: new formulation and algorithm.

Science.gov (United States)

Chu, Delin; Liao, Li-Zhi; Ng, Michael K; Zhang, Xiaowei

2013-12-01

In this paper, we study canonical correlation analysis (CCA), which is a powerful tool in multivariate data analysis for finding the correlation between two sets of multidimensional variables. The main contributions of the paper are: 1) to reveal the equivalent relationship between a recursive formula and a trace formula for the multiple CCA problem, 2) to obtain the explicit characterization for all solutions of the multiple CCA problem even when the corresponding covariance matrices are singular, 3) to develop a new sparse CCA algorithm, and 4) to establish the equivalent relationship between the uncorrelated linear discriminant analysis and the CCA problem. We test several simulated and real-world datasets in gene classification and cross-language document retrieval to demonstrate the effectiveness of the proposed algorithm. The performance of the proposed method is competitive with the state-of-the-art sparse CCA algorithms.

System and method for acquiring and inverting sparse-frequency data

KAUST Repository

Alkhalifah, Tariq Ali

2017-01-01

A method of imaging an object includes generating a plurality of mono-frequency waveforms and applying the plurality of mono-frequency waveforms to the object to be modeled. In addition, sparse mono-frequency data is recorded in response to the plurality of mono-frequency waveforms applied to the object to be modeled. The sparse mono-frequency data is cross-correlated with one or more source functions each having a frequency approximately equal to each of the plurality of mono-frequency waveforms to obtain monochromatic frequency data. The monochromatic frequency data is utilized in an inversion to converge a model to a minimum value.

System and method for acquiring and inverting sparse-frequency data

KAUST Repository

Alkhalifah, Tariq Ali

2017-11-30

A method of imaging an object includes generating a plurality of mono-frequency waveforms and applying the plurality of mono-frequency waveforms to the object to be modeled. In addition, sparse mono-frequency data is recorded in response to the plurality of mono-frequency waveforms applied to the object to be modeled. The sparse mono-frequency data is cross-correlated with one or more source functions each having a frequency approximately equal to each of the plurality of mono-frequency waveforms to obtain monochromatic frequency data. The monochromatic frequency data is utilized in an inversion to converge a model to a minimum value.

Compressive Detection Using Sub-Nyquist Radars for Sparse Signals

Directory of Open Access Journals (Sweden)

Ying Sun

2016-01-01

Full Text Available This paper investigates the compression detection problem using sub-Nyquist radars, which is well suited to the scenario of high bandwidths in real-time processing because it would significantly reduce the computational burden and save power consumption and computation time. A compressive generalized likelihood ratio test (GLRT detector for sparse signals is proposed for sub-Nyquist radars without ever reconstructing the signal involved. The performance of the compressive GLRT detector is analyzed and the theoretical bounds are presented. The compressive GLRT detection performance of sub-Nyquist radars is also compared to the traditional GLRT detection performance of conventional radars, which employ traditional analog-to-digital conversion (ADC at Nyquist sampling rates. Simulation results demonstrate that the former can perform almost as well as the latter with a very small fraction of the number of measurements required by traditional detection in relatively high signal-to-noise ratio (SNR cases.

Dynamic Representations of Sparse Graphs

DEFF Research Database (Denmark)

Brodal, Gerth Stølting; Fagerberg, Rolf

1999-01-01

We present a linear space data structure for maintaining graphs with bounded arboricity—a large class of sparse graphs containing e.g. planar graphs and graphs of bounded treewidth—under edge insertions, edge deletions, and adjacency queries. The data structure supports adjacency queries in worst...... case O(c) time, and edge insertions and edge deletions in amortized O(1) and O(c+log n) time, respectively, where n is the number of nodes in the graph, and c is the bound on the arboricity....

Model methodology for estimating pesticide concentration extremes based on sparse monitoring data

Science.gov (United States)

Vecchia, Aldo V.

2018-03-22

This report describes a new methodology for using sparse (weekly or less frequent observations) and potentially highly censored pesticide monitoring data to simulate daily pesticide concentrations and associated quantities used for acute and chronic exposure assessments, such as the annual maximum daily concentration. The new methodology is based on a statistical model that expresses log-transformed daily pesticide concentration in terms of a seasonal wave, flow-related variability, long-term trend, and serially correlated errors. Methods are described for estimating the model parameters, generating conditional simulations of daily pesticide concentration given sparse (weekly or less frequent) and potentially highly censored observations, and estimating concentration extremes based on the conditional simulations. The model can be applied to datasets with as few as 3 years of record, as few as 30 total observations, and as few as 10 uncensored observations. The model was applied to atrazine, carbaryl, chlorpyrifos, and fipronil data for U.S. Geological Survey pesticide sampling sites with sufficient data for applying the model. A total of 112 sites were analyzed for atrazine, 38 for carbaryl, 34 for chlorpyrifos, and 33 for fipronil. The results are summarized in this report; and, R functions, described in this report and provided in an accompanying model archive, can be used to fit the model parameters and generate conditional simulations of daily concentrations for use in investigations involving pesticide exposure risk and uncertainty.

Recursive nearest neighbor search in a sparse and multiscale domain for comparing audio signals