Separation of fNIRS Signals into Functional and Systemic Components Based on Differences in Hemodynamic Modalities

PubMed Central

Yamada, Toru; Umeyama, Shinji; Matsuda, Keiji

2012-01-01

In conventional functional near-infrared spectroscopy (fNIRS), systemic physiological fluctuations evoked by a body's motion and psychophysiological changes often contaminate fNIRS signals. We propose a novel method for separating functional and systemic signals based on their hemodynamic differences. Considering their physiological origins, we assumed a negative and positive linear relationship between oxy- and deoxyhemoglobin changes of functional and systemic signals, respectively. Their coefficients are determined by an empirical procedure. The proposed method was compared to conventional and multi-distance NIRS. The results were as follows: (1) Nonfunctional tasks evoked substantial oxyhemoglobin changes, and comparatively smaller deoxyhemoglobin changes, in the same direction by conventional NIRS. The systemic components estimated by the proposed method were similar to the above finding. The estimated functional components were very small. (2) During finger-tapping tasks, laterality in the functional component was more distinctive using our proposed method than that by conventional fNIRS. The systemic component indicated task-evoked changes, regardless of the finger used to perform the task. (3) For all tasks, the functional components were highly coincident with signals estimated by multi-distance NIRS. These results strongly suggest that the functional component obtained by the proposed method originates in the cerebral cortical layer. We believe that the proposed method could improve the reliability of fNIRS measurements without any modification in commercially available instruments. PMID:23185590

Color preservation for tone reproduction and image enhancement

NASA Astrophysics Data System (ADS)

Hsin, Chengho; Lee, Zong Wei; Lee, Zheng Zhan; Shin, Shaw-Jyh

2014-01-01

Applications based on luminance processing often face the problem of recovering the original chrominance in the output color image. A common approach to reconstruct a color image from the luminance output is by preserving the original hue and saturation. However, this approach often produces a highly colorful image which is undesirable. We develop a color preservation method that not only retains the ratios of the input tri-chromatic values but also adjusts the output chroma in an appropriate way. Linearizing the output luminance is the key idea to realize this method. In addition, a lightness difference metric together with a colorfulness difference metric are proposed to evaluate the performance of the color preservation methods. It shows that the proposed method performs consistently better than the existing approaches.

An Adaptive Orientation Estimation Method for Magnetic and Inertial Sensors in the Presence of Magnetic Disturbances

PubMed Central

Fan, Bingfei; Li, Qingguo; Wang, Chao; Liu, Tao

2017-01-01

Magnetic and inertial sensors have been widely used to estimate the orientation of human segments due to their low cost, compact size and light weight. However, the accuracy of the estimated orientation is easily affected by external factors, especially when the sensor is used in an environment with magnetic disturbances. In this paper, we propose an adaptive method to improve the accuracy of orientation estimations in the presence of magnetic disturbances. The method is based on existing gradient descent algorithms, and it is performed prior to sensor fusion algorithms. The proposed method includes stationary state detection and magnetic disturbance severity determination. The stationary state detection makes this method immune to magnetic disturbances in stationary state, while the magnetic disturbance severity determination helps to determine the credibility of magnetometer data under dynamic conditions, so as to mitigate the negative effect of the magnetic disturbances. The proposed method was validated through experiments performed on a customized three-axis instrumented gimbal with known orientations. The error of the proposed method and the original gradient descent algorithms were calculated and compared. Experimental results demonstrate that in stationary state, the proposed method is completely immune to magnetic disturbances, and in dynamic conditions, the error caused by magnetic disturbance is reduced by 51.2% compared with original MIMU gradient descent algorithm. PMID:28534858

Efficient implementation of the 3D-DDA ray traversal algorithm on GPU and its application in radiation dose calculation.

PubMed

Xiao, Kai; Chen, Danny Z; Hu, X Sharon; Zhou, Bo

2012-12-01

The three-dimensional digital differential analyzer (3D-DDA) algorithm is a widely used ray traversal method, which is also at the core of many convolution∕superposition (C∕S) dose calculation approaches. However, porting existing C∕S dose calculation methods onto graphics processing unit (GPU) has brought challenges to retaining the efficiency of this algorithm. In particular, straightforward implementation of the original 3D-DDA algorithm inflicts a lot of branch divergence which conflicts with the GPU programming model and leads to suboptimal performance. In this paper, an efficient GPU implementation of the 3D-DDA algorithm is proposed, which effectively reduces such branch divergence and improves performance of the C∕S dose calculation programs running on GPU. The main idea of the proposed method is to convert a number of conditional statements in the original 3D-DDA algorithm into a set of simple operations (e.g., arithmetic, comparison, and logic) which are better supported by the GPU architecture. To verify and demonstrate the performance improvement, this ray traversal method was integrated into a GPU-based collapsed cone convolution∕superposition (CCCS) dose calculation program. The proposed method has been tested using a water phantom and various clinical cases on an NVIDIA GTX570 GPU. The CCCS dose calculation program based on the efficient 3D-DDA ray traversal implementation runs 1.42 ∼ 2.67× faster than the one based on the original 3D-DDA implementation, without losing any accuracy. The results show that the proposed method can effectively reduce branch divergence in the original 3D-DDA ray traversal algorithm and improve the performance of the CCCS program running on GPU. Considering the wide utilization of the 3D-DDA algorithm, various applications can benefit from this implementation method.

Blind compressed sensing image reconstruction based on alternating direction method

NASA Astrophysics Data System (ADS)

Liu, Qinan; Guo, Shuxu

2018-04-01

In order to solve the problem of how to reconstruct the original image under the condition of unknown sparse basis, this paper proposes an image reconstruction method based on blind compressed sensing model. In this model, the image signal is regarded as the product of a sparse coefficient matrix and a dictionary matrix. Based on the existing blind compressed sensing theory, the optimal solution is solved by the alternative minimization method. The proposed method solves the problem that the sparse basis in compressed sensing is difficult to represent, which restrains the noise and improves the quality of reconstructed image. This method ensures that the blind compressed sensing theory has a unique solution and can recover the reconstructed original image signal from a complex environment with a stronger self-adaptability. The experimental results show that the image reconstruction algorithm based on blind compressed sensing proposed in this paper can recover high quality image signals under the condition of under-sampling.

A graph decomposition-based approach for water distribution network optimization

NASA Astrophysics Data System (ADS)

Zheng, Feifei; Simpson, Angus R.; Zecchin, Aaron C.; Deuerlein, Jochen W.

2013-04-01

A novel optimization approach for water distribution network design is proposed in this paper. Using graph theory algorithms, a full water network is first decomposed into different subnetworks based on the connectivity of the network's components. The original whole network is simplified to a directed augmented tree, in which the subnetworks are substituted by augmented nodes and directed links are created to connect them. Differential evolution (DE) is then employed to optimize each subnetwork based on the sequence specified by the assigned directed links in the augmented tree. Rather than optimizing the original network as a whole, the subnetworks are sequentially optimized by the DE algorithm. A solution choice table is established for each subnetwork (except for the subnetwork that includes a supply node) and the optimal solution of the original whole network is finally obtained by use of the solution choice tables. Furthermore, a preconditioning algorithm is applied to the subnetworks to produce an approximately optimal solution for the original whole network. This solution specifies promising regions for the final optimization algorithm to further optimize the subnetworks. Five water network case studies are used to demonstrate the effectiveness of the proposed optimization method. A standard DE algorithm (SDE) and a genetic algorithm (GA) are applied to each case study without network decomposition to enable a comparison with the proposed method. The results show that the proposed method consistently outperforms the SDE and GA (both with tuned parameters) in terms of both the solution quality and efficiency.

Improving the time efficiency of the Fourier synthesis method for slice selection in magnetic resonance imaging.

PubMed

Tahayori, B; Khaneja, N; Johnston, L A; Farrell, P M; Mareels, I M Y

2016-01-01

The design of slice selective pulses for magnetic resonance imaging can be cast as an optimal control problem. The Fourier synthesis method is an existing approach to solve these optimal control problems. In this method the gradient field as well as the excitation field are switched rapidly and their amplitudes are calculated based on a Fourier series expansion. Here, we provide a novel insight into the Fourier synthesis method via representing the Bloch equation in spherical coordinates. Based on the spherical Bloch equation, we propose an alternative sequence of pulses that can be used for slice selection which is more time efficient compared to the original method. Simulation results demonstrate that while the performance of both methods is approximately the same, the required time for the proposed sequence of pulses is half of the original sequence of pulses. Furthermore, the slice selectivity of both sequences of pulses changes with radio frequency field inhomogeneities in a similar way. We also introduce a measure, referred to as gradient complexity, to compare the performance of both sequences of pulses. This measure indicates that for a desired level of uniformity in the excited slice, the gradient complexity for the proposed sequence of pulses is less than the original sequence. Copyright © 2015 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

A Novel Fault Diagnosis Method for Rotating Machinery Based on a Convolutional Neural Network

PubMed Central

Yang, Tao; Gao, Wei

2018-01-01

Fault diagnosis is critical to ensure the safety and reliable operation of rotating machinery. Most methods used in fault diagnosis of rotating machinery extract a few feature values from vibration signals for fault diagnosis, which is a dimensionality reduction from the original signal and may omit some important fault messages in the original signal. Thus, a novel diagnosis method is proposed involving the use of a convolutional neural network (CNN) to directly classify the continuous wavelet transform scalogram (CWTS), which is a time-frequency domain transform of the original signal and can contain most of the information of the vibration signals. In this method, CWTS is formed by discomposing vibration signals of rotating machinery in different scales using wavelet transform. Then the CNN is trained to diagnose faults, with CWTS as the input. A series of experiments is conducted on the rotor experiment platform using this method. The results indicate that the proposed method can diagnose the faults accurately. To verify the universality of this method, the trained CNN was also used to perform fault diagnosis for another piece of rotor equipment, and a good result was achieved. PMID:29734704

A Novel Fault Diagnosis Method for Rotating Machinery Based on a Convolutional Neural Network.

PubMed

Guo, Sheng; Yang, Tao; Gao, Wei; Zhang, Chen

2018-05-04

Fault diagnosis is critical to ensure the safety and reliable operation of rotating machinery. Most methods used in fault diagnosis of rotating machinery extract a few feature values from vibration signals for fault diagnosis, which is a dimensionality reduction from the original signal and may omit some important fault messages in the original signal. Thus, a novel diagnosis method is proposed involving the use of a convolutional neural network (CNN) to directly classify the continuous wavelet transform scalogram (CWTS), which is a time-frequency domain transform of the original signal and can contain most of the information of the vibration signals. In this method, CWTS is formed by discomposing vibration signals of rotating machinery in different scales using wavelet transform. Then the CNN is trained to diagnose faults, with CWTS as the input. A series of experiments is conducted on the rotor experiment platform using this method. The results indicate that the proposed method can diagnose the faults accurately. To verify the universality of this method, the trained CNN was also used to perform fault diagnosis for another piece of rotor equipment, and a good result was achieved.

An adaptive tensor voting algorithm combined with texture spectrum

NASA Astrophysics Data System (ADS)

Wang, Gang; Su, Qing-tang; Lü, Gao-huan; Zhang, Xiao-feng; Liu, Yu-huan; He, An-zhi

2015-01-01

An adaptive tensor voting algorithm combined with texture spectrum is proposed. The image texture spectrum is used to get the adaptive scale parameter of voting field. Then the texture information modifies both the attenuation coefficient and the attenuation field so that we can use this algorithm to create more significant and correct structures in the original image according to the human visual perception. At the same time, the proposed method can improve the edge extraction quality, which includes decreasing the flocculent region efficiently and making image clear. In the experiment for extracting pavement cracks, the original pavement image is processed by the proposed method which is combined with the significant curve feature threshold procedure, and the resulted image displays the faint crack signals submerged in the complicated background efficiently and clearly.

Sorted Index Numbers for Privacy Preserving Face Recognition

NASA Astrophysics Data System (ADS)

Wang, Yongjin; Hatzinakos, Dimitrios

2009-12-01

This paper presents a novel approach for changeable and privacy preserving face recognition. We first introduce a new method of biometric matching using the sorted index numbers (SINs) of feature vectors. Since it is impossible to recover any of the exact values of the original features, the transformation from original features to the SIN vectors is noninvertible. To address the irrevocable nature of biometric signals whilst obtaining stronger privacy protection, a random projection-based method is employed in conjunction with the SIN approach to generate changeable and privacy preserving biometric templates. The effectiveness of the proposed method is demonstrated on a large generic data set, which contains images from several well-known face databases. Extensive experimentation shows that the proposed solution may improve the recognition accuracy.

Fast dose kernel interpolation using Fourier transform with application to permanent prostate brachytherapy dosimetry.

PubMed

Liu, Derek; Sloboda, Ron S

2014-05-01

Boyer and Mok proposed a fast calculation method employing the Fourier transform (FT), for which calculation time is independent of the number of seeds but seed placement is restricted to calculation grid points. Here an interpolation method is described enabling unrestricted seed placement while preserving the computational efficiency of the original method. The Iodine-125 seed dose kernel was sampled and selected values were modified to optimize interpolation accuracy for clinically relevant doses. For each seed, the kernel was shifted to the nearest grid point via convolution with a unit impulse, implemented in the Fourier domain. The remaining fractional shift was performed using a piecewise third-order Lagrange filter. Implementation of the interpolation method greatly improved FT-based dose calculation accuracy. The dose distribution was accurate to within 2% beyond 3 mm from each seed. Isodose contours were indistinguishable from explicit TG-43 calculation. Dose-volume metric errors were negligible. Computation time for the FT interpolation method was essentially the same as Boyer's method. A FT interpolation method for permanent prostate brachytherapy TG-43 dose calculation was developed which expands upon Boyer's original method and enables unrestricted seed placement. The proposed method substantially improves the clinically relevant dose accuracy with negligible additional computation cost, preserving the efficiency of the original method.

Synthesizing Regression Results: A Factored Likelihood Method

ERIC Educational Resources Information Center

Wu, Meng-Jia; Becker, Betsy Jane

2013-01-01

Regression methods are widely used by researchers in many fields, yet methods for synthesizing regression results are scarce. This study proposes using a factored likelihood method, originally developed to handle missing data, to appropriately synthesize regression models involving different predictors. This method uses the correlations reported…

An efficient hybrid approach for multiobjective optimization of water distribution systems

NASA Astrophysics Data System (ADS)

Zheng, Feifei; Simpson, Angus R.; Zecchin, Aaron C.

2014-05-01

An efficient hybrid approach for the design of water distribution systems (WDSs) with multiple objectives is described in this paper. The objectives are the minimization of the network cost and maximization of the network resilience. A self-adaptive multiobjective differential evolution (SAMODE) algorithm has been developed, in which control parameters are automatically adapted by means of evolution instead of the presetting of fine-tuned parameter values. In the proposed method, a graph algorithm is first used to decompose a looped WDS into a shortest-distance tree (T) or forest, and chords (Ω). The original two-objective optimization problem is then approximated by a series of single-objective optimization problems of the T to be solved by nonlinear programming (NLP), thereby providing an approximate Pareto optimal front for the original whole network. Finally, the solutions at the approximate front are used to seed the SAMODE algorithm to find an improved front for the original entire network. The proposed approach is compared with two other conventional full-search optimization methods (the SAMODE algorithm and the NSGA-II) that seed the initial population with purely random solutions based on three case studies: a benchmark network and two real-world networks with multiple demand loading cases. Results show that (i) the proposed NLP-SAMODE method consistently generates better-quality Pareto fronts than the full-search methods with significantly improved efficiency; and (ii) the proposed SAMODE algorithm (no parameter tuning) exhibits better performance than the NSGA-II with calibrated parameter values in efficiently offering optimal fronts.

Perspective: researching the transition from non-living to the first microorganisms: methods and experiments are major challenges.

PubMed

Trevors, J T

2010-06-01

Methods to research the origin of microbial life are limited. However, microorganisms were the first organisms on the Earth capable of cell growth and division, and interactions with their environment, other microbial cells, and eventually with diverse eukaryotic organisms. The origin of microbial life and the supporting scientific evidence are both an enigma and a scientific priority. Numerous hypotheses have been proposed, scenarios imagined, speculations presented in papers, insights shared, and assumptions made without supporting experimentation, which have led to limited progress in understanding the origin of microbial life. The use of the human imagination to envision the origin of life events, without supporting experimentation, observation and independently replicated experiments required for science, is a significant constraint. The challenge remains how to better understand the origin of microbial life using observations and experimental methods as opposed to speculation, assumptions, scenarios, envisioning events and un-testable hypotheses. This is not an easy challenge as experimental design and plausible hypothesis testing are difficult. Since past approaches have been inconclusive in providing evidence for the origin of microbial life mechanisms and the manner in which genetic instructions was encoded into DNA/RNA, it is reasonable and logical to propose that progress will be made when testable, plausible hypotheses and methods are used in the origin of microbial life research, and the experimental observations are, or are not reproduced in independent laboratories. These perspectives will be discussed in this article as well as the possibility that a pre-biotic film preceded a microbial biofilm as a possible micro-location for the origin of microbial cells capable of growth and division. 2010 Elsevier B.V. All rights reserved.

Numerical solution of modified differential equations based on symmetry preservation.

PubMed

Ozbenli, Ersin; Vedula, Prakash

2017-12-01

In this paper, we propose a method to construct invariant finite-difference schemes for solution of partial differential equations (PDEs) via consideration of modified forms of the underlying PDEs. The invariant schemes, which preserve Lie symmetries, are obtained based on the method of equivariant moving frames. While it is often difficult to construct invariant numerical schemes for PDEs due to complicated symmetry groups associated with cumbersome discrete variable transformations, we note that symmetries associated with more convenient transformations can often be obtained by appropriately modifying the original PDEs. In some cases, modifications to the original PDEs are also found to be useful in order to avoid trivial solutions that might arise from particular selections of moving frames. In our proposed method, modified forms of PDEs can be obtained either by addition of perturbation terms to the original PDEs or through defect correction procedures. These additional terms, whose primary purpose is to enable symmetries with more convenient transformations, are then removed from the system by considering moving frames for which these specific terms go to zero. Further, we explore selection of appropriate moving frames that result in improvement in accuracy of invariant numerical schemes based on modified PDEs. The proposed method is tested using the linear advection equation (in one- and two-dimensions) and the inviscid Burgers' equation. Results obtained for these tests cases indicate that numerical schemes derived from the proposed method perform significantly better than existing schemes not only by virtue of improvement in numerical accuracy but also due to preservation of qualitative properties or symmetries of the underlying differential equations.

Efficient reversible data hiding in encrypted image with public key cryptosystem

NASA Astrophysics Data System (ADS)

Xiang, Shijun; Luo, Xinrong

2017-12-01

This paper proposes a new reversible data hiding scheme for encrypted images by using homomorphic and probabilistic properties of Paillier cryptosystem. The proposed method can embed additional data directly into encrypted image without any preprocessing operations on original image. By selecting two pixels as a group for encryption, data hider can retrieve the absolute differences of groups of two pixels by employing a modular multiplicative inverse method. Additional data can be embedded into encrypted image by shifting histogram of the absolute differences by using the homomorphic property in encrypted domain. On the receiver side, legal user can extract the marked histogram in encrypted domain in the same way as data hiding procedure. Then, the hidden data can be extracted from the marked histogram and the encrypted version of original image can be restored by using inverse histogram shifting operations. Besides, the marked absolute differences can be computed after decryption for extraction of additional data and restoration of original image. Compared with previous state-of-the-art works, the proposed scheme can effectively avoid preprocessing operations before encryption and can efficiently embed and extract data in encrypted domain. The experiments on the standard image files also certify the effectiveness of the proposed scheme.

3-D ultrasound volume reconstruction using the direct frame interpolation method.

PubMed

Scheipers, Ulrich; Koptenko, Sergei; Remlinger, Rachel; Falco, Tony; Lachaine, Martin

2010-11-01

A new method for 3-D ultrasound volume reconstruction using tracked freehand 3-D ultrasound is proposed. The method is based on solving the forward volume reconstruction problem using direct interpolation of high-resolution ultrasound B-mode image frames. A series of ultrasound B-mode image frames (an image series) is acquired using the freehand scanning technique and position sensing via optical tracking equipment. The proposed algorithm creates additional intermediate image frames by directly interpolating between two or more adjacent image frames of the original image series. The target volume is filled using the original frames in combination with the additionally constructed frames. Compared with conventional volume reconstruction methods, no additional filling of empty voxels or holes within the volume is required, because the whole extent of the volume is defined by the arrangement of the original and the additionally constructed B-mode image frames. The proposed direct frame interpolation (DFI) method was tested on two different data sets acquired while scanning the head and neck region of different patients. The first data set consisted of eight B-mode 2-D frame sets acquired under optimal laboratory conditions. The second data set consisted of 73 image series acquired during a clinical study. Sample volumes were reconstructed for all 81 image series using the proposed DFI method with four different interpolation orders, as well as with the pixel nearest-neighbor method using three different interpolation neighborhoods. In addition, volumes based on a reduced number of image frames were reconstructed for comparison of the different methods' accuracy and robustness in reconstructing image data that lies between the original image frames. The DFI method is based on a forward approach making use of a priori information about the position and shape of the B-mode image frames (e.g., masking information) to optimize the reconstruction procedure and to reduce computation times and memory requirements. The method is straightforward, independent of additional input or parameters, and uses the high-resolution B-mode image frames instead of usually lower-resolution voxel information for interpolation. The DFI method can be considered as a valuable alternative to conventional 3-D ultrasound reconstruction methods based on pixel or voxel nearest-neighbor approaches, offering better quality and competitive reconstruction time.

A multispectral photon-counting double random phase encoding scheme for image authentication.

PubMed

Yi, Faliu; Moon, Inkyu; Lee, Yeon H

2014-05-20

In this paper, we propose a new method for color image-based authentication that combines multispectral photon-counting imaging (MPCI) and double random phase encoding (DRPE) schemes. The sparsely distributed information from MPCI and the stationary white noise signal from DRPE make intruder attacks difficult. In this authentication method, the original multispectral RGB color image is down-sampled into a Bayer image. The three types of color samples (red, green and blue color) in the Bayer image are encrypted with DRPE and the amplitude part of the resulting image is photon counted. The corresponding phase information that has nonzero amplitude after photon counting is then kept for decryption. Experimental results show that the retrieved images from the proposed method do not visually resemble their original counterparts. Nevertheless, the original color image can be efficiently verified with statistical nonlinear correlations. Our experimental results also show that different interpolation algorithms applied to Bayer images result in different verification effects for multispectral RGB color images.

Adaptive error covariances estimation methods for ensemble Kalman filters

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

Zhen, Yicun, E-mail: zhen@math.psu.edu; Harlim, John, E-mail: jharlim@psu.edu

2015-08-01

This paper presents a computationally fast algorithm for estimating, both, the system and observation noise covariances of nonlinear dynamics, that can be used in an ensemble Kalman filtering framework. The new method is a modification of Belanger's recursive method, to avoid an expensive computational cost in inverting error covariance matrices of product of innovation processes of different lags when the number of observations becomes large. When we use only product of innovation processes up to one-lag, the computational cost is indeed comparable to a recently proposed method by Berry–Sauer's. However, our method is more flexible since it allows for usingmore » information from product of innovation processes of more than one-lag. Extensive numerical comparisons between the proposed method and both the original Belanger's and Berry–Sauer's schemes are shown in various examples, ranging from low-dimensional linear and nonlinear systems of SDEs and 40-dimensional stochastically forced Lorenz-96 model. Our numerical results suggest that the proposed scheme is as accurate as the original Belanger's scheme on low-dimensional problems and has a wider range of more accurate estimates compared to Berry–Sauer's method on L-96 example.« less

Dynamic GSCA (Generalized Structured Component Analysis) with Applications to the Analysis of Effective Connectivity in Functional Neuroimaging Data

ERIC Educational Resources Information Center

Jung, Kwanghee; Takane, Yoshio; Hwang, Heungsun; Woodward, Todd S.

2012-01-01

We propose a new method of structural equation modeling (SEM) for longitudinal and time series data, named Dynamic GSCA (Generalized Structured Component Analysis). The proposed method extends the original GSCA by incorporating a multivariate autoregressive model to account for the dynamic nature of data taken over time. Dynamic GSCA also…

Gray-world-assumption-based illuminant color estimation using color gamuts with high and low chroma

NASA Astrophysics Data System (ADS)

Kawamura, Harumi; Yonemura, Shunichi; Ohya, Jun; Kojima, Akira

2013-02-01

A new approach is proposed for estimating illuminant colors from color images under an unknown scene illuminant. The approach is based on a combination of a gray-world-assumption-based illuminant color estimation method and a method using color gamuts. The former method, which is one we had previously proposed, improved on the original method that hypothesizes that the average of all the object colors in a scene is achromatic. Since the original method estimates scene illuminant colors by calculating the average of all the image pixel values, its estimations are incorrect when certain image colors are dominant. Our previous method improves on it by choosing several colors on the basis of an opponent-color property, which is that the average color of opponent colors is achromatic, instead of using all colors. However, it cannot estimate illuminant colors when there are only a few image colors or when the image colors are unevenly distributed in local areas in the color space. The approach we propose in this paper combines our previous method and one using high chroma and low chroma gamuts, which makes it possible to find colors that satisfy the gray world assumption. High chroma gamuts are used for adding appropriate colors to the original image and low chroma gamuts are used for narrowing down illuminant color possibilities. Experimental results obtained using actual images show that even if the image colors are localized in a certain area in the color space, the illuminant colors are accurately estimated, with smaller estimation error average than that generated in the conventional method.

Tchebichef moment based restoration of Gaussian blurred images.

PubMed

Kumar, Ahlad; Paramesran, Raveendran; Lim, Chern-Loon; Dass, Sarat C

2016-11-10

With the knowledge of how edges vary in the presence of a Gaussian blur, a method that uses low-order Tchebichef moments is proposed to estimate the blur parameters: sigma (σ) and size (w). The difference between the Tchebichef moments of the original and the reblurred images is used as feature vectors to train an extreme learning machine for estimating the blur parameters (σ,w). The effectiveness of the proposed method to estimate the blur parameters is examined using cross-database validation. The estimated blur parameters from the proposed method are used in the split Bregman-based image restoration algorithm. A comparative analysis of the proposed method with three existing methods using all the images from the LIVE database is carried out. The results show that the proposed method in most of the cases performs better than the three existing methods in terms of the visual quality evaluated using the structural similarity index.

Discrimination of Medicine Radix Astragali from Different Geographic Origins Using Multiple Spectroscopies Combined with Data Fusion Methods

NASA Astrophysics Data System (ADS)

Wang, Hai-Yan; Song, Chao; Sha, Min; Liu, Jun; Li, Li-Ping; Zhang, Zheng-Yong

2018-05-01

Raman spectra and ultraviolet-visible absorption spectra of four different geographic origins of Radix Astragali were collected. These data were analyzed using kernel principal component analysis combined with sparse representation classification. The results showed that the recognition rate reached 70.44% using Raman spectra for data input and 90.34% using ultraviolet-visible absorption spectra for data input. A new fusion method based on Raman combined with ultraviolet-visible data was investigated and the recognition rate was increased to 96.43%. The experimental results suggested that the proposed data fusion method effectively improved the utilization rate of the original data.

An efficient study design to test parent-of-origin effects in family trios.

PubMed

Yu, Xiaobo; Chen, Gao; Feng, Rui

2017-11-01

Increasing evidence has shown that genes may cause prenatal, neonatal, and pediatric diseases depending on their parental origins. Statistical models that incorporate parent-of-origin effects (POEs) can improve the power of detecting disease-associated genes and help explain the missing heritability of diseases. In many studies, children have been sequenced for genome-wide association testing. But it may become unaffordable to sequence their parents and evaluate POEs. Motivated by the reality, we proposed a budget-friendly study design of sequencing children and only genotyping their parents through single nucleotide polymorphism array. We developed a powerful likelihood-based method, which takes into account both sequence reads and linkage disequilibrium to infer the parental origins of children's alleles and estimate their POEs on the outcome. We evaluated the performance of our proposed method and compared it with an existing method using only genotypes, through extensive simulations. Our method showed higher power than the genotype-based method. When either the mean read depth or the pair-end length was reasonably large, our method achieved ideal power. When single parents' genotypes were unavailable or parental genotypes at the testing locus were not typed, both methods lost power compared with when complete data were available; but the power loss from our method was smaller than the genotype-based method. We also extended our method to accommodate mixed genotype, low-, and high-coverage sequence data from children and their parents. At presence of sequence errors, low-coverage parental sequence data may lead to lower power than parental genotype data. © 2017 WILEY PERIODICALS, INC.

Topological charge number multiplexing for JTC multiple-image encryption

NASA Astrophysics Data System (ADS)

Chen, Qi; Shen, Xueju; Dou, Shuaifeng; Lin, Chao; Wang, Long

2018-04-01

We propose a method of topological charge number multiplexing based on the JTC encryption system to achieve multiple-image encryption. Using this method, multi-image can be encrypted into single ciphertext, and the original images can be recovered according to the authority level. The number of encrypted images is increased, moreover, the quality of decrypted images is improved. Results of computer simulation and initial experiment identify the validity of our proposed method.

An adaptive multi-feature segmentation model for infrared image

NASA Astrophysics Data System (ADS)

Zhang, Tingting; Han, Jin; Zhang, Yi; Bai, Lianfa

2016-04-01

Active contour models (ACM) have been extensively applied to image segmentation, conventional region-based active contour models only utilize global or local single feature information to minimize the energy functional to drive the contour evolution. Considering the limitations of original ACMs, an adaptive multi-feature segmentation model is proposed to handle infrared images with blurred boundaries and low contrast. In the proposed model, several essential local statistic features are introduced to construct a multi-feature signed pressure function (MFSPF). In addition, we draw upon the adaptive weight coefficient to modify the level set formulation, which is formed by integrating MFSPF with local statistic features and signed pressure function with global information. Experimental results demonstrate that the proposed method can make up for the inadequacy of the original method and get desirable results in segmenting infrared images.

Detection of Parent-of-Origin Effects Using General Pedigree Data

PubMed Central

Zhou, Ji-Yuan; Ding, Jie; Fung, Wing K.; Lin, Shili

2010-01-01

Genomic imprinting is an important epigenetic factor in complex traits study, which has generally been examined by testing for parent-of-origin effects of alleles. For a diallelic marker locus, the parental-asymmetry test (PAT) based on case-parents trios and its extensions to incomplete nuclear families (1-PAT and C-PAT) are simple and powerful for detecting parent-of-origin effects. However, these methods are suitable only for nuclear families and thus are not amenable to general pedigree data. Use of data from extended pedigrees, if available, may lead to more powerful methods than randomly selecting one two-generation nuclear family from each pedigree. In this study, we extend PAT to accommodate general pedigree data by proposing the pedigree PAT (PPAT) statistic, which uses all informative family trios from pedigrees. To fully utilize pedigrees with some missing genotypes, we further develop the Monte Carlo (MC) PPAT (MCPPAT) statistic based on MC sampling and estimation. Extensive simulations were carried out to evaluate the performance of the proposed methods. Under the assumption that the pedigrees and their associated affection patterns are randomly drawn from a population of pedigrees with at least one affected offspring, we demonstrated that MCPPAT is a valid test for parent-of-origin effects in the presence of association. Further, MCPPAT is much more powerful compared to PAT for trios or even PPAT for all informative family trios from the same pedigrees if there is missing data. Application of the proposed methods to a rheumatoid arthritis dataset further demonstrates the advantage of MCPPAT. PMID:19676055

Associative memory for online learning in noisy environments using self-organizing incremental neural network.

PubMed

Sudo, Akihito; Sato, Akihiro; Hasegawa, Osamu

2009-06-01

Associative memory operating in a real environment must perform well in online incremental learning and be robust to noisy data because noisy associative patterns are presented sequentially in a real environment. We propose a novel associative memory that satisfies these requirements. Using the proposed method, new associative pairs that are presented sequentially can be learned accurately without forgetting previously learned patterns. The memory size of the proposed method increases adaptively with learning patterns. Therefore, it suffers neither redundancy nor insufficiency of memory size, even in an environment in which the maximum number of associative pairs to be presented is unknown before learning. Noisy inputs in real environments are classifiable into two types: noise-added original patterns and faultily presented random patterns. The proposed method deals with two types of noise. To our knowledge, no conventional associative memory addresses noise of both types. The proposed associative memory performs as a bidirectional one-to-many or many-to-one associative memory and deals not only with bipolar data, but also with real-valued data. Results demonstrate that the proposed method's features are important for application to an intelligent robot operating in a real environment. The originality of our work consists of two points: employing a growing self-organizing network for an associative memory, and discussing what features are necessary for an associative memory for an intelligent robot and proposing an associative memory that satisfies those requirements.

Dynamic frame resizing with convolutional neural network for efficient video compression

NASA Astrophysics Data System (ADS)

Kim, Jaehwan; Park, Youngo; Choi, Kwang Pyo; Lee, JongSeok; Jeon, Sunyoung; Park, JeongHoon

2017-09-01

In the past, video codecs such as vc-1 and H.263 used a technique to encode reduced-resolution video and restore original resolution from the decoder for improvement of coding efficiency. The techniques of vc-1 and H.263 Annex Q are called dynamic frame resizing and reduced-resolution update mode, respectively. However, these techniques have not been widely used due to limited performance improvements that operate well only under specific conditions. In this paper, video frame resizing (reduced/restore) technique based on machine learning is proposed for improvement of coding efficiency. The proposed method features video of low resolution made by convolutional neural network (CNN) in encoder and reconstruction of original resolution using CNN in decoder. The proposed method shows improved subjective performance over all the high resolution videos which are dominantly consumed recently. In order to assess subjective quality of the proposed method, Video Multi-method Assessment Fusion (VMAF) which showed high reliability among many subjective measurement tools was used as subjective metric. Moreover, to assess general performance, diverse bitrates are tested. Experimental results showed that BD-rate based on VMAF was improved by about 51% compare to conventional HEVC. Especially, VMAF values were significantly improved in low bitrate. Also, when the method is subjectively tested, it had better subjective visual quality in similar bit rate.

Effective evaluation of privacy protection techniques in visible and thermal imagery

NASA Astrophysics Data System (ADS)

Nawaz, Tahir; Berg, Amanda; Ferryman, James; Ahlberg, Jörgen; Felsberg, Michael

2017-09-01

Privacy protection may be defined as replacing the original content in an image region with a (less intrusive) content having modified target appearance information to make it less recognizable by applying a privacy protection technique. Indeed, the development of privacy protection techniques also needs to be complemented with an established objective evaluation method to facilitate their assessment and comparison. Generally, existing evaluation methods rely on the use of subjective judgments or assume a specific target type in image data and use target detection and recognition accuracies to assess privacy protection. An annotation-free evaluation method that is neither subjective nor assumes a specific target type is proposed. It assesses two key aspects of privacy protection: "protection" and "utility." Protection is quantified as an appearance similarity, and utility is measured as a structural similarity between original and privacy-protected image regions. We performed an extensive experimentation using six challenging datasets (having 12 video sequences), including a new dataset (having six sequences) that contains visible and thermal imagery. The new dataset is made available online for the community. We demonstrate effectiveness of the proposed method by evaluating six image-based privacy protection techniques and also show comparisons of the proposed method over existing methods.

Half-unit weighted bilinear algorithm for image contrast enhancement in capsule endoscopy

NASA Astrophysics Data System (ADS)

Rukundo, Olivier

2018-04-01

This paper proposes a novel enhancement method based exclusively on the bilinear interpolation algorithm for capsule endoscopy images. The proposed method does not convert the original RBG image components to HSV or any other color space or model; instead, it processes directly RGB components. In each component, a group of four adjacent pixels and half-unit weight in the bilinear weighting function are used to calculate the average pixel value, identical for each pixel in that particular group. After calculations, groups of identical pixels are overlapped successively in horizontal and vertical directions to achieve a preliminary-enhanced image. The final-enhanced image is achieved by halving the sum of the original and preliminary-enhanced image pixels. Quantitative and qualitative experiments were conducted focusing on pairwise comparisons between original and enhanced images. Final-enhanced images have generally the best diagnostic quality and gave more details about the visibility of vessels and structures in capsule endoscopy images.

Dual Key Speech Encryption Algorithm Based Underdetermined BSS

PubMed Central

Zhao, Huan; Chen, Zuo; Zhang, Xixiang

2014-01-01

When the number of the mixed signals is less than that of the source signals, the underdetermined blind source separation (BSS) is a significant difficult problem. Due to the fact that the great amount data of speech communications and real-time communication has been required, we utilize the intractability of the underdetermined BSS problem to present a dual key speech encryption method. The original speech is mixed with dual key signals which consist of random key signals (one-time pad) generated by secret seed and chaotic signals generated from chaotic system. In the decryption process, approximate calculation is used to recover the original speech signals. The proposed algorithm for speech signals encryption can resist traditional attacks against the encryption system, and owing to approximate calculation, decryption becomes faster and more accurate. It is demonstrated that the proposed method has high level of security and can recover the original signals quickly and efficiently yet maintaining excellent audio quality. PMID:24955430

Biometrics encryption combining palmprint with two-layer error correction codes

NASA Astrophysics Data System (ADS)

Li, Hengjian; Qiu, Jian; Dong, Jiwen; Feng, Guang

2017-07-01

To bridge the gap between the fuzziness of biometrics and the exactitude of cryptography, based on combining palmprint with two-layer error correction codes, a novel biometrics encryption method is proposed. Firstly, the randomly generated original keys are encoded by convolutional and cyclic two-layer coding. The first layer uses a convolution code to correct burst errors. The second layer uses cyclic code to correct random errors. Then, the palmprint features are extracted from the palmprint images. Next, they are fused together by XORing operation. The information is stored in a smart card. Finally, the original keys extraction process is the information in the smart card XOR the user's palmprint features and then decoded with convolutional and cyclic two-layer code. The experimental results and security analysis show that it can recover the original keys completely. The proposed method is more secure than a single password factor, and has higher accuracy than a single biometric factor.

An improved parallel fuzzy connected image segmentation method based on CUDA.

PubMed

Wang, Liansheng; Li, Dong; Huang, Shaohui

2016-05-12

Fuzzy connectedness method (FC) is an effective method for extracting fuzzy objects from medical images. However, when FC is applied to large medical image datasets, its running time will be greatly expensive. Therefore, a parallel CUDA version of FC (CUDA-kFOE) was proposed by Ying et al. to accelerate the original FC. Unfortunately, CUDA-kFOE does not consider the edges between GPU blocks, which causes miscalculation of edge points. In this paper, an improved algorithm is proposed by adding a correction step on the edge points. The improved algorithm can greatly enhance the calculation accuracy. In the improved method, an iterative manner is applied. In the first iteration, the affinity computation strategy is changed and a look up table is employed for memory reduction. In the second iteration, the error voxels because of asynchronism are updated again. Three different CT sequences of hepatic vascular with different sizes were used in the experiments with three different seeds. NVIDIA Tesla C2075 is used to evaluate our improved method over these three data sets. Experimental results show that the improved algorithm can achieve a faster segmentation compared to the CPU version and higher accuracy than CUDA-kFOE. The calculation results were consistent with the CPU version, which demonstrates that it corrects the edge point calculation error of the original CUDA-kFOE. The proposed method has a comparable time cost and has less errors compared to the original CUDA-kFOE as demonstrated in the experimental results. In the future, we will focus on automatic acquisition method and automatic processing.

Design of tree structured matched wavelet for HRV signals of menstrual cycle.

PubMed

Rawal, Kirti; Saini, B S; Saini, Indu

2016-07-01

An algorithm is presented for designing a new class of wavelets matched to the Heart Rate Variability (HRV) signals of the menstrual cycle. The proposed wavelets are used to find HRV variations between phases of menstrual cycle. The method finds the signal matching characteristics by minimising the shape feature error using Least Mean Square method. The proposed filter banks are used for the decomposition of the HRV signal. For reconstructing the original signal, the tree structure method is used. In this approach, decomposed sub-bands are selected based upon their energy in each sub-band. Thus, instead of using all sub-bands for reconstruction, sub-bands having high energy content are used for the reconstruction of signal. Thus, a lower number of sub-bands are required for reconstruction of the original signal which shows the effectiveness of newly created filter coefficients. Results show that proposed wavelets are able to differentiate HRV variations between phases of the menstrual cycle accurately than standard wavelets.

Technical Note: Improving the VMERGE treatment planning algorithm for rotational radiotherapy

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

Gaddy, Melissa R., E-mail: mrgaddy@ncsu.edu; Papp,

2016-07-15

Purpose: The authors revisit the VMERGE treatment planning algorithm by Craft et al. [“Multicriteria VMAT optimization,” Med. Phys. 39, 686–696 (2012)] for arc therapy planning and propose two changes to the method that are aimed at improving the achieved trade-off between treatment time and plan quality at little additional planning time cost, while retaining other desirable properties of the original algorithm. Methods: The original VMERGE algorithm first computes an “ideal,” high quality but also highly time consuming treatment plan that irradiates the patient from all possible angles in a fine angular grid with a highly modulated beam and then makesmore » this plan deliverable within practical treatment time by an iterative fluence map merging and sequencing algorithm. We propose two changes to this method. First, we regularize the ideal plan obtained in the first step by adding an explicit constraint on treatment time. Second, we propose a different merging criterion that comprises of identifying and merging adjacent maps whose merging results in the least degradation of radiation dose. Results: The effect of both suggested modifications is evaluated individually and jointly on clinical prostate and paraspinal cases. Details of the two cases are reported. Conclusions: In the authors’ computational study they found that both proposed modifications, especially the regularization, yield noticeably improved treatment plans for the same treatment times than what can be obtained using the original VMERGE method. The resulting plans match the quality of 20-beam step-and-shoot IMRT plans with a delivery time of approximately 2 min.« less

Using false colors to protect visual privacy of sensitive content

NASA Astrophysics Data System (ADS)

Ćiftçi, Serdar; Korshunov, Pavel; Akyüz, Ahmet O.; Ebrahimi, Touradj

2015-03-01

Many privacy protection tools have been proposed for preserving privacy. Tools for protection of visual privacy available today lack either all or some of the important properties that are expected from such tools. Therefore, in this paper, we propose a simple yet effective method for privacy protection based on false color visualization, which maps color palette of an image into a different color palette, possibly after a compressive point transformation of the original pixel data, distorting the details of the original image. This method does not require any prior face detection or other sensitive regions detection and, hence, unlike typical privacy protection methods, it is less sensitive to inaccurate computer vision algorithms. It is also secure as the look-up tables can be encrypted, reversible as table look-ups can be inverted, flexible as it is independent of format or encoding, adjustable as the final result can be computed by interpolating the false color image with the original using different degrees of interpolation, less distracting as it does not create visually unpleasant artifacts, and selective as it preserves better semantic structure of the input. Four different color scales and four different compression functions, one which the proposed method relies, are evaluated via objective (three face recognition algorithms) and subjective (50 human subjects in an online-based study) assessments using faces from FERET public dataset. The evaluations demonstrate that DEF and RBS color scales lead to the strongest privacy protection, while compression functions add little to the strength of privacy protection. Statistical analysis also shows that recognition algorithms and human subjects perceive the proposed protection similarly

Improved l1-SPIRiT using 3D walsh transform-based sparsity basis.

PubMed

Feng, Zhen; Liu, Feng; Jiang, Mingfeng; Crozier, Stuart; Guo, He; Wang, Yuxin

2014-09-01

l1-SPIRiT is a fast magnetic resonance imaging (MRI) method which combines parallel imaging (PI) with compressed sensing (CS) by performing a joint l1-norm and l2-norm optimization procedure. The original l1-SPIRiT method uses two-dimensional (2D) Wavelet transform to exploit the intra-coil data redundancies and a joint sparsity model to exploit the inter-coil data redundancies. In this work, we propose to stack all the coil images into a three-dimensional (3D) matrix, and then a novel 3D Walsh transform-based sparsity basis is applied to simultaneously reduce the intra-coil and inter-coil data redundancies. Both the 2D Wavelet transform-based and the proposed 3D Walsh transform-based sparsity bases were investigated in the l1-SPIRiT method. The experimental results show that the proposed 3D Walsh transform-based l1-SPIRiT method outperformed the original l1-SPIRiT in terms of image quality and computational efficiency. Copyright © 2014 Elsevier Inc. All rights reserved.

Single Wall Carbon Nanotube Alignment Mechanisms for Non-Destructive Evaluation

NASA Technical Reports Server (NTRS)

Hong, Seunghun

2002-01-01

As proposed in our original proposal, we developed a new innovative method to assemble millions of single wall carbon nanotube (SWCNT)-based circuit components as fast as conventional microfabrication processes. This method is based on surface template assembly strategy. The new method solves one of the major bottlenecks in carbon nanotube based electrical applications and, potentially, may allow us to mass produce a large number of SWCNT-based integrated devices of critical interests to NASA.

Automatic tracking of labeled red blood cells in microchannels.

PubMed

Pinho, Diana; Lima, Rui; Pereira, Ana I; Gayubo, Fernando

2013-09-01

The current study proposes an automatic method for the segmentation and tracking of red blood cells flowing through a 100- μm glass capillary. The original images were obtained by means of a confocal system and then processed in MATLAB using the Image Processing Toolbox. The measurements obtained with the proposed automatic method were compared with the results determined by a manual tracking method. The comparison was performed by using both linear regressions and Bland-Altman analysis. The results have shown a good agreement between the two methods. Therefore, the proposed automatic method is a powerful way to provide rapid and accurate measurements for in vitro blood experiments in microchannels. Copyright © 2012 John Wiley & Sons, Ltd.

Observational methods for solar origin diagnostics of energetic protons

NASA Astrophysics Data System (ADS)

Miteva, Rositsa

2017-12-01

The aim of the present report is to outline the observational methods used to determine the solar origin - in terms of flares and coronal mass ejections (CMEs) - of the in situ observed solar energetic protons. Several widely used guidelines are given and different sources of uncertainties are summarized and discussed. In the present study, a new quality factor is proposed as a certainty check on the so-identified flare-CME pairs. In addition, the correlations between the proton peak intensity and the properties of their solar origin are evaluated as a function of the quality factor.

Matching by linear programming and successive convexification.

PubMed

Jiang, Hao; Drew, Mark S; Li, Ze-Nian

2007-06-01

We present a novel convex programming scheme to solve matching problems, focusing on the challenging problem of matching in a large search range and with cluttered background. Matching is formulated as metric labeling with L1 regularization terms, for which we propose a novel linear programming relaxation method and an efficient successive convexification implementation. The unique feature of the proposed relaxation scheme is that a much smaller set of basis labels is used to represent the original label space. This greatly reduces the size of the searching space. A successive convexification scheme solves the labeling problem in a coarse to fine manner. Importantly, the original cost function is reconvexified at each stage, in the new focus region only, and the focus region is updated so as to refine the searching result. This makes the method well-suited for large label set matching. Experiments demonstrate successful applications of the proposed matching scheme in object detection, motion estimation, and tracking.

Estimation of land-surface evaporation at four forest sites across Japan with the new nonlinear complementary method.

PubMed

Ai, Zhipin; Wang, Qinxue; Yang, Yonghui; Manevski, Kiril; Zhao, Xin; Eer, Deni

2017-12-19

Evaporation from land surfaces is a critical component of the Earth water cycle and of water management strategies. The complementary method originally proposed by Bouchet, which describes a linear relation between actual evaporation (E), potential evaporation (E po ) and apparent potential evaporation (E pa ) based on routinely measured weather data, is one of the various methods for evaporation calculation. This study evaluated the reformulated version of the original method, as proposed by Brutsaert, for forest land cover in Japan. The new complementary method is nonlinear and based on boundary conditions with strictly physical considerations. The only unknown parameter (α e ) was for the first time determined for various forest covers located from north to south across Japan. The values of α e ranged from 0.94 to 1.10, with a mean value of 1.01. Furthermore, the calculated evaporation with the new method showed a good fit with the eddy-covariance measured values, with a determination coefficient of 0.78 and a mean bias of 4%. Evaluation results revealed that the new nonlinear complementary relation performs better than the original linear relation in describing the relationship between E/E pa and E po /E pa , and also in depicting the asymmetry variation between E pa /E po and E/E po .

Nonlocal means-based speckle filtering for ultrasound images

PubMed Central

Coupé, Pierrick; Hellier, Pierre; Kervrann, Charles; Barillot, Christian

2009-01-01

In image processing, restoration is expected to improve the qualitative inspection of the image and the performance of quantitative image analysis techniques. In this paper, an adaptation of the Non Local (NL-) means filter is proposed for speckle reduction in ultrasound (US) images. Originally developed for additive white Gaussian noise, we propose to use a Bayesian framework to derive a NL-means filter adapted to a relevant ultrasound noise model. Quantitative results on synthetic data show the performances of the proposed method compared to well-established and state-of-the-art methods. Results on real images demonstrate that the proposed method is able to preserve accurately edges and structural details of the image. PMID:19482578

A weakly-compressible Cartesian grid approach for hydrodynamic flows

NASA Astrophysics Data System (ADS)

Bigay, P.; Oger, G.; Guilcher, P.-M.; Le Touzé, D.

2017-11-01

The present article aims at proposing an original strategy to solve hydrodynamic flows. In introduction, the motivations for this strategy are developed. It aims at modeling viscous and turbulent flows including complex moving geometries, while avoiding meshing constraints. The proposed approach relies on a weakly-compressible formulation of the Navier-Stokes equations. Unlike most hydrodynamic CFD (Computational Fluid Dynamics) solvers usually based on implicit incompressible formulations, a fully-explicit temporal scheme is used. A purely Cartesian grid is adopted for numerical accuracy and algorithmic simplicity purposes. This characteristic allows an easy use of Adaptive Mesh Refinement (AMR) methods embedded within a massively parallel framework. Geometries are automatically immersed within the Cartesian grid with an AMR compatible treatment. The method proposed uses an Immersed Boundary Method (IBM) adapted to the weakly-compressible formalism and imposed smoothly through a regularization function, which stands as another originality of this work. All these features have been implemented within an in-house solver based on this WCCH (Weakly-Compressible Cartesian Hydrodynamic) method which meets the above requirements whilst allowing the use of high-order (> 3) spatial schemes rarely used in existing hydrodynamic solvers. The details of this WCCH method are presented and validated in this article.

Hybrid active contour model for inhomogeneous image segmentation with background estimation

NASA Astrophysics Data System (ADS)

Sun, Kaiqiong; Li, Yaqin; Zeng, Shan; Wang, Jun

2018-03-01

This paper proposes a hybrid active contour model for inhomogeneous image segmentation. The data term of the energy function in the active contour consists of a global region fitting term in a difference image and a local region fitting term in the original image. The difference image is obtained by subtracting the background from the original image. The background image is dynamically estimated from a linear filtered result of the original image on the basis of the varying curve locations during the active contour evolution process. As in existing local models, fitting the image to local region information makes the proposed model robust against an inhomogeneous background and maintains the accuracy of the segmentation result. Furthermore, fitting the difference image to the global region information makes the proposed model robust against the initial contour location, unlike existing local models. Experimental results show that the proposed model can obtain improved segmentation results compared with related methods in terms of both segmentation accuracy and initial contour sensitivity.

Asymmetric color image encryption based on singular value decomposition

NASA Astrophysics Data System (ADS)

Yao, Lili; Yuan, Caojin; Qiang, Junjie; Feng, Shaotong; Nie, Shouping

2017-02-01

A novel asymmetric color image encryption approach by using singular value decomposition (SVD) is proposed. The original color image is encrypted into a ciphertext shown as an indexed image by using the proposed method. The red, green and blue components of the color image are subsequently encoded into a complex function which is then separated into U, S and V parts by SVD. The data matrix of the ciphertext is obtained by multiplying orthogonal matrices U and V while implementing phase-truncation. Diagonal entries of the three diagonal matrices of the SVD results are abstracted and scrambling combined to construct the colormap of the ciphertext. Thus, the encrypted indexed image covers less space than the original image. For decryption, the original color image cannot be recovered without private keys which are obtained from phase-truncation and the orthogonality of V. Computer simulations are presented to evaluate the performance of the proposed algorithm. We also analyze the security of the proposed system.

Design optimization of large-size format edge-lit light guide units

NASA Astrophysics Data System (ADS)

Hastanin, J.; Lenaerts, C.; Fleury-Frenette, K.

2016-04-01

In this paper, we present an original method of dot pattern generation dedicated to large-size format light guide plate (LGP) design optimization, such as photo-bioreactors, the number of dots greatly exceeds the maximum allowable number of optical objects supported by most common ray-tracing software. In the proposed method, in order to simplify the computational problem, the original optical system is replaced by an equivalent one. Accordingly, an original dot pattern is splitted into multiple small sections, inside which the dot size variation is less than the ink dots printing typical resolution. Then, these sections are replaced by equivalent cells with continuous diffusing film. After that, we adjust the TIS (Total Integrated Scatter) two-dimensional distribution over the grid of equivalent cells, using an iterative optimization procedure. Finally, the obtained optimal TIS distribution is converted into the dot size distribution by applying an appropriate conversion rule. An original semi-empirical equation dedicated to rectangular large-size LGPs is proposed for the initial guess of TIS distribution. It allows significantly reduce the total time needed to dot pattern optimization.

General properties of the Foldy-Wouthuysen transformation and applicability of the corrected original Foldy-Wouthuysen method

NASA Astrophysics Data System (ADS)

Silenko, Alexander J.

2016-02-01

General properties of the Foldy-Wouthuysen transformation which is widely used in quantum mechanics and quantum chemistry are considered. Merits and demerits of the original Foldy-Wouthuysen transformation method are analyzed. While this method does not satisfy the Eriksen condition of the Foldy-Wouthuysen transformation, it can be corrected with the use of the Baker-Campbell-Hausdorff formula. We show a possibility of such a correction and propose an appropriate algorithm of calculations. An applicability of the corrected Foldy-Wouthuysen method is restricted by the condition of convergence of a series of relativistic corrections.

Semantic transference for enriching multilingual biomedical knowledge resources.

PubMed

Pérez, María; Berlanga, Rafael

2015-12-01

Biomedical knowledge resources (KRs) are mainly expressed in English, and many applications using them suffer from the scarcity of knowledge in non-English languages. The goal of the present work is to take maximum profit from existing multilingual biomedical KRs lexicons to enrich their non-English counterparts. We propose to combine different automatic methods to generate pair-wise language alignments. More specifically, we use two well-known translation methods (GIZA++ and Moses), and we propose a new ad hoc method specially devised for multilingual KRs. Then, resulting alignments are used to transfer semantics between KRs across their languages. Transference quality is ensured by checking the semantic coherence of the generated alignments. Experiments have been carried out over the Spanish, French and German UMLS Metathesaurus counterparts. As a result, the enriched Spanish KR can grow up to 1,514,217 concepts (originally 286,659), the French KR up to 1,104,968 concepts (originally 83,119), and the German KR up to 1,136,020 concepts (originally 86,842). Copyright © 2015 Elsevier Inc. All rights reserved.

Recent developments in the Dorfman-Berbaum-Metz procedure for multireader ROC study analysis.

PubMed

Hillis, Stephen L; Berbaum, Kevin S; Metz, Charles E

2008-05-01

The Dorfman-Berbaum-Metz (DBM) method has been one of the most popular methods for analyzing multireader receiver-operating characteristic (ROC) studies since it was proposed in 1992. Despite its popularity, the original procedure has several drawbacks: it is limited to jackknife accuracy estimates, it is substantially conservative, and it is not based on a satisfactory conceptual or theoretical model. Recently, solutions to these problems have been presented in three papers. Our purpose is to summarize and provide an overview of these recent developments. We present and discuss the recently proposed solutions for the various drawbacks of the original DBM method. We compare the solutions in a simulation study and find that they result in improved performance for the DBM procedure. We also compare the solutions using two real data studies and find that the modified DBM procedure that incorporates these solutions yields more significant results and clearer interpretations of the variance component parameters than the original DBM procedure. We recommend using the modified DBM procedure that incorporates the recent developments.

Reliable femoral frame construction based on MRI dedicated to muscles position follow-up.

PubMed

Dubois, G; Bonneau, D; Lafage, V; Rouch, P; Skalli, W

2015-10-01

In vivo follow-up of muscle shape variation represents a challenge when evaluating muscle development due to disease or treatment. Recent developments in muscles reconstruction techniques indicate MRI as a clinical tool for the follow-up of the thigh muscles. The comparison of 3D muscles shape from two different sequences is not easy because there is no common frame. This study proposes an innovative method for the reconstruction of a reliable femoral frame based on the femoral head and both condyles centers. In order to robustify the definition of condylar spheres, an original method was developed to combine the estimation of diameters of both condyles from the lateral antero-posterior distance and the estimation of the spheres center from an optimization process. The influence of spacing between MR slices and of origin positions was studied. For all axes, the proposed method presented an angular error lower than 1° with spacing between slice of 10 mm and the optimal position of the origin was identified at 56 % of the distance between the femoral head center and the barycenter of both condyles. The high reliability of this method provides a robust frame for clinical follow-up based on MRI .

An Improved Teaching-Learning-Based Optimization with the Social Character of PSO for Global Optimization.

PubMed

Zou, Feng; Chen, Debao; Wang, Jiangtao

2016-01-01

An improved teaching-learning-based optimization with combining of the social character of PSO (TLBO-PSO), which is considering the teacher's behavior influence on the students and the mean grade of the class, is proposed in the paper to find the global solutions of function optimization problems. In this method, the teacher phase of TLBO is modified; the new position of the individual is determined by the old position, the mean position, and the best position of current generation. The method overcomes disadvantage that the evolution of the original TLBO might stop when the mean position of students equals the position of the teacher. To decrease the computation cost of the algorithm, the process of removing the duplicate individual in original TLBO is not adopted in the improved algorithm. Moreover, the probability of local convergence of the improved method is decreased by the mutation operator. The effectiveness of the proposed method is tested on some benchmark functions, and the results are competitive with respect to some other methods.

Real-Space Analysis of Scanning Tunneling Microscopy Topography Datasets Using Sparse Modeling Approach

NASA Astrophysics Data System (ADS)

Miyama, Masamichi J.; Hukushima, Koji

2018-04-01

A sparse modeling approach is proposed for analyzing scanning tunneling microscopy topography data, which contain numerous peaks originating from the electron density of surface atoms and/or impurities. The method, based on the relevance vector machine with L1 regularization and k-means clustering, enables separation of the peaks and peak center positioning with accuracy beyond the resolution of the measurement grid. The validity and efficiency of the proposed method are demonstrated using synthetic data in comparison with the conventional least-squares method. An application of the proposed method to experimental data of a metallic oxide thin-film clearly indicates the existence of defects and corresponding local lattice distortions.

Gene selection for microarray data classification via subspace learning and manifold regularization.

PubMed

Tang, Chang; Cao, Lijuan; Zheng, Xiao; Wang, Minhui

2017-12-19

With the rapid development of DNA microarray technology, large amount of genomic data has been generated. Classification of these microarray data is a challenge task since gene expression data are often with thousands of genes but a small number of samples. In this paper, an effective gene selection method is proposed to select the best subset of genes for microarray data with the irrelevant and redundant genes removed. Compared with original data, the selected gene subset can benefit the classification task. We formulate the gene selection task as a manifold regularized subspace learning problem. In detail, a projection matrix is used to project the original high dimensional microarray data into a lower dimensional subspace, with the constraint that the original genes can be well represented by the selected genes. Meanwhile, the local manifold structure of original data is preserved by a Laplacian graph regularization term on the low-dimensional data space. The projection matrix can serve as an importance indicator of different genes. An iterative update algorithm is developed for solving the problem. Experimental results on six publicly available microarray datasets and one clinical dataset demonstrate that the proposed method performs better when compared with other state-of-the-art methods in terms of microarray data classification. Graphical Abstract The graphical abstract of this work.

Compressive sensing method for recognizing cat-eye effect targets.

PubMed

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

2013-10-01

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

Online approximation of the multichannel Wiener filter with preservation of interaural level difference for binaural hearing-aids.

PubMed

Marques do Carmo, Diego; Costa, Márcio Holsbach

2018-04-01

This work presents an online approximation method for the multichannel Wiener filter (MWF) noise reduction technique with preservation of the noise interaural level difference (ILD) for binaural hearing-aids. The steepest descent method is applied to a previously proposed MWF-ILD cost function to both approximate the optimal linear estimator of the desired speech and keep the subjective perception of the original acoustic scenario. The computational cost of the resulting algorithm is estimated in terms of multiply and accumulate operations, whose number can be controlled by setting the number of iterations at each time frame. Simulation results for the particular case of one speech and one-directional noise source show that the proposed method increases the signal-to-noise ratio SNR of the originally acquired speech by up to 16.9 dB in the assessed scenarios. As compared to the online implementation of the conventional MWF technique, the proposed technique provides a reduction of up to 7 dB in the noise ILD error at the price of a reduction of up 3 dB in the output SNR. Subjective experiments with volunteers complement these objective measures with psychoacoustic results, which corroborate the expected spatial preservation of the original acoustic scenario. The proposed method allows practical online implementation of the MWF-ILD noise reduction technique under constrained computational resources. Predicted SNR improvements from 12 dB to 16.9 dB can be obtained in application-specific integrated circuits for hearing-aids and state-of-the-art digital signal processors. Copyright © 2018 Elsevier Ltd. All rights reserved.

[A graph cuts-based interactive method for segmentation of magnetic resonance images of meningioma].

PubMed

Li, Shuan-qiang; Feng, Qian-jin; Chen, Wu-fan; Lin, Ya-zhong

2011-06-01

For accurate segmentation of the magnetic resonance (MR) images of meningioma, we propose a novel interactive segmentation method based on graph cuts. The high dimensional image features was extracted, and for each pixel, the probabilities of its origin, either the tumor or the background regions, were estimated by exploiting the weighted K-nearest neighborhood classifier. Based on these probabilities, a new energy function was proposed. Finally, a graph cut optimal framework was used for the solution of the energy function. The proposed method was evaluated by application in the segmentation of MR images of meningioma, and the results showed that the method significantly improved the segmentation accuracy compared with the gray level information-based graph cut method.

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

PubMed Central

Sun, Chenglu; Li, Wei; Chen, Wei

2017-01-01

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

Robust rotational-velocity-Verlet integration methods.

PubMed

Rozmanov, Dmitri; Kusalik, Peter G

2010-05-01

Two rotational integration algorithms for rigid-body dynamics are proposed in velocity-Verlet formulation. The first method uses quaternion dynamics and was derived from the original rotational leap-frog method by Svanberg [Mol. Phys. 92, 1085 (1997)]; it produces time consistent positions and momenta. The second method is also formulated in terms of quaternions but it is not quaternion specific and can be easily adapted for any other orientational representation. Both the methods are tested extensively and compared to existing rotational integrators. The proposed integrators demonstrated performance at least at the level of previously reported rotational algorithms. The choice of simulation parameters is also discussed.

Robust rotational-velocity-Verlet integration methods

NASA Astrophysics Data System (ADS)

Rozmanov, Dmitri; Kusalik, Peter G.

2010-05-01

Two rotational integration algorithms for rigid-body dynamics are proposed in velocity-Verlet formulation. The first method uses quaternion dynamics and was derived from the original rotational leap-frog method by Svanberg [Mol. Phys. 92, 1085 (1997)]; it produces time consistent positions and momenta. The second method is also formulated in terms of quaternions but it is not quaternion specific and can be easily adapted for any other orientational representation. Both the methods are tested extensively and compared to existing rotational integrators. The proposed integrators demonstrated performance at least at the level of previously reported rotational algorithms. The choice of simulation parameters is also discussed.

An Improved Pansharpening Method for Misaligned Panchromatic and Multispectral Data

PubMed Central

Jing, Linhai; Tang, Yunwei; Ding, Haifeng

2018-01-01

Numerous pansharpening methods were proposed in recent decades for fusing low-spatial-resolution multispectral (MS) images with high-spatial-resolution (HSR) panchromatic (PAN) bands to produce fused HSR MS images, which are widely used in various remote sensing tasks. The effect of misregistration between MS and PAN bands on quality of fused products has gained much attention in recent years. An improved method for misaligned MS and PAN imagery is proposed, through two improvements made on a previously published method named RMI (reduce misalignment impact). The performance of the proposed method was assessed by comparing with some outstanding fusion methods, such as adaptive Gram-Schmidt and generalized Laplacian pyramid. Experimental results show that the improved version can reduce spectral distortions of fused dark pixels and sharpen boundaries between different image objects, as well as obtain similar quality indexes with the original RMI method. In addition, the proposed method was evaluated with respect to its sensitivity to misalignments between MS and PAN bands. It is certified that the proposed method is more robust to misalignments between MS and PAN bands than the other methods. PMID:29439502

An Improved Pansharpening Method for Misaligned Panchromatic and Multispectral Data.

PubMed

Li, Hui; Jing, Linhai; Tang, Yunwei; Ding, Haifeng

2018-02-11

Numerous pansharpening methods were proposed in recent decades for fusing low-spatial-resolution multispectral (MS) images with high-spatial-resolution (HSR) panchromatic (PAN) bands to produce fused HSR MS images, which are widely used in various remote sensing tasks. The effect of misregistration between MS and PAN bands on quality of fused products has gained much attention in recent years. An improved method for misaligned MS and PAN imagery is proposed, through two improvements made on a previously published method named RMI (reduce misalignment impact). The performance of the proposed method was assessed by comparing with some outstanding fusion methods, such as adaptive Gram-Schmidt and generalized Laplacian pyramid. Experimental results show that the improved version can reduce spectral distortions of fused dark pixels and sharpen boundaries between different image objects, as well as obtain similar quality indexes with the original RMI method. In addition, the proposed method was evaluated with respect to its sensitivity to misalignments between MS and PAN bands. It is certified that the proposed method is more robust to misalignments between MS and PAN bands than the other methods.

Mal-Xtract: Hidden Code Extraction using Memory Analysis

NASA Astrophysics Data System (ADS)

Lim, Charles; Syailendra Kotualubun, Yohanes; Suryadi; Ramli, Kalamullah

2017-01-01

Software packer has been used effectively to hide the original code inside a binary executable, making it more difficult for existing signature based anti malware software to detect malicious code inside the executable. A new method of written and rewritten memory section is introduced to to detect the exact end time of unpacking routine and extract original code from packed binary executable using Memory Analysis running in an software emulated environment. Our experiment results show that at least 97% of the original code from the various binary executable packed with different software packers could be extracted. The proposed method has also been successfully extracted hidden code from recent malware family samples.

Development of a Compound Optimization Approach Based on Imperialist Competitive Algorithm

NASA Astrophysics Data System (ADS)

Wang, Qimei; Yang, Zhihong; Wang, Yong

In this paper, an improved novel approach is developed for the imperialist competitive algorithm to achieve a greater performance. The Nelder-Meand simplex method is applied to execute alternately with the original procedures of the algorithm. The approach is tested on twelve widely-used benchmark functions and is also compared with other relative studies. It is shown that the proposed approach has a faster convergence rate, better search ability, and higher stability than the original algorithm and other relative methods.

Optical image encryption using QR code and multilevel fingerprints in gyrator transform domains

NASA Astrophysics Data System (ADS)

Wei, Yang; Yan, Aimin; Dong, Jiabin; Hu, Zhijuan; Zhang, Jingtao

2017-11-01

A new concept of GT encryption scheme is proposed in this paper. We present a novel optical image encryption method by using quick response (QR) code and multilevel fingerprint keys in gyrator transform (GT) domains. In this method, an original image is firstly transformed into a QR code, which is placed in the input plane of cascaded GTs. Subsequently, the QR code is encrypted into the cipher-text by using multilevel fingerprint keys. The original image can be obtained easily by reading the high-quality retrieved QR code with hand-held devices. The main parameters used as private keys are GTs' rotation angles and multilevel fingerprints. Biometrics and cryptography are integrated with each other to improve data security. Numerical simulations are performed to demonstrate the validity and feasibility of the proposed encryption scheme. In the future, the method of applying QR codes and fingerprints in GT domains possesses much potential for information security.

The use of fatigue tests in the manufacture of automotive steel wheels.

NASA Astrophysics Data System (ADS)

Drozyner, P.; Rychlik, A.

2016-08-01

Production for the automotive industry must be particularly sensitive to the aspect of safety and reliability of manufactured components. One of such element is the rim, where durability is a feature which significantly affects the safety of transport. Customer complaints regarding this element are particularly painful for the manufacturer because it is almost always associated with the event of accident or near-accident. Authors propose original comprehensive method of quality control at selected stages of rims production: supply of materials, production and pre-shipment inspections. Tests by the proposed method are carried out on the originally designed inertial fatigue machine The machine allows bending fatigue tests in the frequency range of 0 to 50 Hz at controlled increments of vibration amplitude. The method has been positively verified in one of rims factory in Poland. Implementation resulted in an almost complete elimination of complaints resulting from manufacturing and material errors.

A Fixed-Pattern Noise Correction Method Based on Gray Value Compensation for TDI CMOS Image Sensor.

PubMed

Liu, Zhenwang; Xu, Jiangtao; Wang, Xinlei; Nie, Kaiming; Jin, Weimin

2015-09-16

In order to eliminate the fixed-pattern noise (FPN) in the output image of time-delay-integration CMOS image sensor (TDI-CIS), a FPN correction method based on gray value compensation is proposed. One hundred images are first captured under uniform illumination. Then, row FPN (RFPN) and column FPN (CFPN) are estimated based on the row-mean vector and column-mean vector of all collected images, respectively. Finally, RFPN are corrected by adding the estimated RFPN gray value to the original gray values of pixels in the corresponding row, and CFPN are corrected by subtracting the estimated CFPN gray value from the original gray values of pixels in the corresponding column. Experimental results based on a 128-stage TDI-CIS show that, after correcting the FPN in the image captured under uniform illumination with the proposed method, the standard-deviation of row-mean vector decreases from 5.6798 to 0.4214 LSB, and the standard-deviation of column-mean vector decreases from 15.2080 to 13.4623 LSB. Both kinds of FPN in the real images captured by TDI-CIS are eliminated effectively with the proposed method.

Determining Predictor Importance in Hierarchical Linear Models Using Dominance Analysis

ERIC Educational Resources Information Center

Luo, Wen; Azen, Razia

2013-01-01

Dominance analysis (DA) is a method used to evaluate the relative importance of predictors that was originally proposed for linear regression models. This article proposes an extension of DA that allows researchers to determine the relative importance of predictors in hierarchical linear models (HLM). Commonly used measures of model adequacy in…

Sea Ice Detection Based on an Improved Similarity Measurement Method Using Hyperspectral Data.

PubMed

Han, Yanling; Li, Jue; Zhang, Yun; Hong, Zhonghua; Wang, Jing

2017-05-15

Hyperspectral remote sensing technology can acquire nearly continuous spectrum information and rich sea ice image information, thus providing an important means of sea ice detection. However, the correlation and redundancy among hyperspectral bands reduce the accuracy of traditional sea ice detection methods. Based on the spectral characteristics of sea ice, this study presents an improved similarity measurement method based on linear prediction (ISMLP) to detect sea ice. First, the first original band with a large amount of information is determined based on mutual information theory. Subsequently, a second original band with the least similarity is chosen by the spectral correlation measuring method. Finally, subsequent bands are selected through the linear prediction method, and a support vector machine classifier model is applied to classify sea ice. In experiments performed on images of Baffin Bay and Bohai Bay, comparative analyses were conducted to compare the proposed method and traditional sea ice detection methods. Our proposed ISMLP method achieved the highest classification accuracies (91.18% and 94.22%) in both experiments. From these results the ISMLP method exhibits better performance overall than other methods and can be effectively applied to hyperspectral sea ice detection.

Sea Ice Detection Based on an Improved Similarity Measurement Method Using Hyperspectral Data

PubMed Central

Han, Yanling; Li, Jue; Zhang, Yun; Hong, Zhonghua; Wang, Jing

2017-01-01

Hyperspectral remote sensing technology can acquire nearly continuous spectrum information and rich sea ice image information, thus providing an important means of sea ice detection. However, the correlation and redundancy among hyperspectral bands reduce the accuracy of traditional sea ice detection methods. Based on the spectral characteristics of sea ice, this study presents an improved similarity measurement method based on linear prediction (ISMLP) to detect sea ice. First, the first original band with a large amount of information is determined based on mutual information theory. Subsequently, a second original band with the least similarity is chosen by the spectral correlation measuring method. Finally, subsequent bands are selected through the linear prediction method, and a support vector machine classifier model is applied to classify sea ice. In experiments performed on images of Baffin Bay and Bohai Bay, comparative analyses were conducted to compare the proposed method and traditional sea ice detection methods. Our proposed ISMLP method achieved the highest classification accuracies (91.18% and 94.22%) in both experiments. From these results the ISMLP method exhibits better performance overall than other methods and can be effectively applied to hyperspectral sea ice detection. PMID:28505135

A simple mass-conserved level set method for simulation of multiphase flows

NASA Astrophysics Data System (ADS)

Yuan, H.-Z.; Shu, C.; Wang, Y.; Shu, S.

2018-04-01

In this paper, a modified level set method is proposed for simulation of multiphase flows with large density ratio and high Reynolds number. The present method simply introduces a source or sink term into the level set equation to compensate the mass loss or offset the mass increase. The source or sink term is derived analytically by applying the mass conservation principle with the level set equation and the continuity equation of flow field. Since only a source term is introduced, the application of the present method is as simple as the original level set method, but it can guarantee the overall mass conservation. To validate the present method, the vortex flow problem is first considered. The simulation results are compared with those from the original level set method, which demonstrates that the modified level set method has the capability of accurately capturing the interface and keeping the mass conservation. Then, the proposed method is further validated by simulating the Laplace law, the merging of two bubbles, a bubble rising with high density ratio, and Rayleigh-Taylor instability with high Reynolds number. Numerical results show that the mass is a well-conserved by the present method.

An improved sampling method of complex network

NASA Astrophysics Data System (ADS)

Gao, Qi; Ding, Xintong; Pan, Feng; Li, Weixing

2014-12-01

Sampling subnet is an important topic of complex network research. Sampling methods influence the structure and characteristics of subnet. Random multiple snowball with Cohen (RMSC) process sampling which combines the advantages of random sampling and snowball sampling is proposed in this paper. It has the ability to explore global information and discover the local structure at the same time. The experiments indicate that this novel sampling method could keep the similarity between sampling subnet and original network on degree distribution, connectivity rate and average shortest path. This method is applicable to the situation where the prior knowledge about degree distribution of original network is not sufficient.

Learning binary code via PCA of angle projection for image retrieval

NASA Astrophysics Data System (ADS)

Yang, Fumeng; Ye, Zhiqiang; Wei, Xueqi; Wu, Congzhong

2018-01-01

With benefits of low storage costs and high query speeds, binary code representation methods are widely researched for efficiently retrieving large-scale data. In image hashing method, learning hashing function to embed highdimensions feature to Hamming space is a key step for accuracy retrieval. Principal component analysis (PCA) technical is widely used in compact hashing methods, and most these hashing methods adopt PCA projection functions to project the original data into several dimensions of real values, and then each of these projected dimensions is quantized into one bit by thresholding. The variances of different projected dimensions are different, and with real-valued projection produced more quantization error. To avoid the real-valued projection with large quantization error, in this paper we proposed to use Cosine similarity projection for each dimensions, the angle projection can keep the original structure and more compact with the Cosine-valued. We used our method combined the ITQ hashing algorithm, and the extensive experiments on the public CIFAR-10 and Caltech-256 datasets validate the effectiveness of the proposed method.

Detecting text in natural scenes with multi-level MSER and SWT

NASA Astrophysics Data System (ADS)

Lu, Tongwei; Liu, Renjun

2018-04-01

The detection of the characters in the natural scene is susceptible to factors such as complex background, variable viewing angle and diverse forms of language, which leads to poor detection results. Aiming at these problems, a new text detection method was proposed, which consisted of two main stages, candidate region extraction and text region detection. At first stage, the method used multiple scale transformations of original image and multiple thresholds of maximally stable extremal regions (MSER) to detect the text regions which could detect character regions comprehensively. At second stage, obtained SWT maps by using the stroke width transform (SWT) algorithm to compute the candidate regions, then using cascaded classifiers to propose non-text regions. The proposed method was evaluated on the standard benchmark datasets of ICDAR2011 and the datasets that we made our own data sets. The experiment results showed that the proposed method have greatly improved that compared to other text detection methods.

Multilayer Extreme Learning Machine With Subnetwork Nodes for Representation Learning.

PubMed

Yang, Yimin; Wu, Q M Jonathan

2016-11-01

The extreme learning machine (ELM), which was originally proposed for "generalized" single-hidden layer feedforward neural networks, provides efficient unified learning solutions for the applications of clustering, regression, and classification. It presents competitive accuracy with superb efficiency in many applications. However, ELM with subnetwork nodes architecture has not attracted much research attentions. Recently, many methods have been proposed for supervised/unsupervised dimension reduction or representation learning, but these methods normally only work for one type of problem. This paper studies the general architecture of multilayer ELM (ML-ELM) with subnetwork nodes, showing that: 1) the proposed method provides a representation learning platform with unsupervised/supervised and compressed/sparse representation learning and 2) experimental results on ten image datasets and 16 classification datasets show that, compared to other conventional feature learning methods, the proposed ML-ELM with subnetwork nodes performs competitively or much better than other feature learning methods.

FCDECOMP: decomposition of metabolic networks based on flux coupling relations.

PubMed

Rezvan, Abolfazl; Marashi, Sayed-Amir; Eslahchi, Changiz

2014-10-01

A metabolic network model provides a computational framework to study the metabolism of a cell at the system level. Due to their large sizes and complexity, rational decomposition of these networks into subsystems is a strategy to obtain better insight into the metabolic functions. Additionally, decomposing metabolic networks paves the way to use computational methods that will be otherwise very slow when run on the original genome-scale network. In the present study, we propose FCDECOMP decomposition method based on flux coupling relations (FCRs) between pairs of reaction fluxes. This approach utilizes a genetic algorithm (GA) to obtain subsystems that can be analyzed in isolation, i.e. without considering the reactions of the original network in the analysis. Therefore, we propose that our method is useful for discovering biologically meaningful modules in metabolic networks. As a case study, we show that when this method is applied to the metabolic networks of barley seeds and yeast, the modules are in good agreement with the biological compartments of these networks.

Robustness of S1 statistic with Hodges-Lehmann for skewed distributions

NASA Astrophysics Data System (ADS)

Ahad, Nor Aishah; Yahaya, Sharipah Soaad Syed; Yin, Lee Ping

2016-10-01

Analysis of variance (ANOVA) is a common use parametric method to test the differences in means for more than two groups when the populations are normally distributed. ANOVA is highly inefficient under the influence of non- normal and heteroscedastic settings. When the assumptions are violated, researchers are looking for alternative such as Kruskal-Wallis under nonparametric or robust method. This study focused on flexible method, S1 statistic for comparing groups using median as the location estimator. S1 statistic was modified by substituting the median with Hodges-Lehmann and the default scale estimator with the variance of Hodges-Lehmann and MADn to produce two different test statistics for comparing groups. Bootstrap method was used for testing the hypotheses since the sampling distributions of these modified S1 statistics are unknown. The performance of the proposed statistic in terms of Type I error was measured and compared against the original S1 statistic, ANOVA and Kruskal-Wallis. The propose procedures show improvement compared to the original statistic especially under extremely skewed distribution.

Multiscale Reconstruction for Magnetic Resonance Fingerprinting

PubMed Central

Pierre, Eric Y.; Ma, Dan; Chen, Yong; Badve, Chaitra; Griswold, Mark A.

2015-01-01

Purpose To reduce acquisition time needed to obtain reliable parametric maps with Magnetic Resonance Fingerprinting. Methods An iterative-denoising algorithm is initialized by reconstructing the MRF image series at low image resolution. For subsequent iterations, the method enforces pixel-wise fidelity to the best-matching dictionary template then enforces fidelity to the acquired data at slightly higher spatial resolution. After convergence, parametric maps with desirable spatial resolution are obtained through template matching of the final image series. The proposed method was evaluated on phantom and in-vivo data using the highly-undersampled, variable-density spiral trajectory and compared with the original MRF method. The benefits of additional sparsity constraints were also evaluated. When available, gold standard parameter maps were used to quantify the performance of each method. Results The proposed approach allowed convergence to accurate parametric maps with as few as 300 time points of acquisition, as compared to 1000 in the original MRF work. Simultaneous quantification of T1, T2, proton density (PD) and B0 field variations in the brain was achieved in vivo for a 256×256 matrix for a total acquisition time of 10.2s, representing a 3-fold reduction in acquisition time. Conclusions The proposed iterative multiscale reconstruction reliably increases MRF acquisition speed and accuracy. PMID:26132462

Kalman Filtered Bio Heat Transfer Model Based Self-adaptive Hybrid Magnetic Resonance Thermometry.

PubMed

Zhang, Yuxin; Chen, Shuo; Deng, Kexin; Chen, Bingyao; Wei, Xing; Yang, Jiafei; Wang, Shi; Ying, Kui

2017-01-01

To develop a self-adaptive and fast thermometry method by combining the original hybrid magnetic resonance thermometry method and the bio heat transfer equation (BHTE) model. The proposed Kalman filtered Bio Heat Transfer Model Based Self-adaptive Hybrid Magnetic Resonance Thermometry, abbreviated as KalBHT hybrid method, introduced the BHTE model to synthesize a window on the regularization term of the hybrid algorithm, which leads to a self-adaptive regularization both spatially and temporally with change of temperature. Further, to decrease the sensitivity to accuracy of the BHTE model, Kalman filter is utilized to update the window at each iteration time. To investigate the effect of the proposed model, computer heating simulation, phantom microwave heating experiment and dynamic in-vivo model validation of liver and thoracic tumor were conducted in this study. The heating simulation indicates that the KalBHT hybrid algorithm achieves more accurate results without adjusting λ to a proper value in comparison to the hybrid algorithm. The results of the phantom heating experiment illustrate that the proposed model is able to follow temperature changes in the presence of motion and the temperature estimated also shows less noise in the background and surrounding the hot spot. The dynamic in-vivo model validation with heating simulation demonstrates that the proposed model has a higher convergence rate, more robustness to susceptibility problem surrounding the hot spot and more accuracy of temperature estimation. In the healthy liver experiment with heating simulation, the RMSE of the hot spot of the proposed model is reduced to about 50% compared to the RMSE of the original hybrid model and the convergence time becomes only about one fifth of the hybrid model. The proposed model is able to improve the accuracy of the original hybrid algorithm and accelerate the convergence rate of MR temperature estimation.

Opacity annotation of diffuse lung diseases using deep convolutional neural network with multi-channel information

NASA Astrophysics Data System (ADS)

Mabu, Shingo; Kido, Shoji; Hashimoto, Noriaki; Hirano, Yasushi; Kuremoto, Takashi

2018-02-01

This research proposes a multi-channel deep convolutional neural network (DCNN) for computer-aided diagnosis (CAD) that classifies normal and abnormal opacities of diffuse lung diseases in Computed Tomography (CT) images. Because CT images are gray scale, DCNN usually uses one channel for inputting image data. On the other hand, this research uses multi-channel DCNN where each channel corresponds to the original raw image or the images transformed by some preprocessing techniques. In fact, the information obtained only from raw images is limited and some conventional research suggested that preprocessing of images contributes to improving the classification accuracy. Thus, the combination of the original and preprocessed images is expected to show higher accuracy. The proposed method realizes region of interest (ROI)-based opacity annotation. We used lung CT images taken in Yamaguchi University Hospital, Japan, and they are divided into 32 × 32 ROI images. The ROIs contain six kinds of opacities: consolidation, ground-glass opacity (GGO), emphysema, honeycombing, nodular, and normal. The aim of the proposed method is to classify each ROI into one of the six opacities (classes). The DCNN structure is based on VGG network that secured the first and second places in ImageNet ILSVRC-2014. From the experimental results, the classification accuracy of the proposed method was better than the conventional method with single channel, and there was a significant difference between them.

A Lossless hybrid wavelet-fractal compression for welding radiographic images.

PubMed

Mekhalfa, Faiza; Avanaki, Mohammad R N; Berkani, Daoud

2016-01-01

In this work a lossless wavelet-fractal image coder is proposed. The process starts by compressing and decompressing the original image using wavelet transformation and fractal coding algorithm. The decompressed image is removed from the original one to obtain a residual image which is coded by using Huffman algorithm. Simulation results show that with the proposed scheme, we achieve an infinite peak signal to noise ratio (PSNR) with higher compression ratio compared to typical lossless method. Moreover, the use of wavelet transform speeds up the fractal compression algorithm by reducing the size of the domain pool. The compression results of several welding radiographic images using the proposed scheme are evaluated quantitatively and compared with the results of Huffman coding algorithm.

Spectral-Spatial Shared Linear Regression for Hyperspectral Image Classification.

PubMed

Haoliang Yuan; Yuan Yan Tang

2017-04-01

Classification of the pixels in hyperspectral image (HSI) is an important task and has been popularly applied in many practical applications. Its major challenge is the high-dimensional small-sized problem. To deal with this problem, lots of subspace learning (SL) methods are developed to reduce the dimension of the pixels while preserving the important discriminant information. Motivated by ridge linear regression (RLR) framework for SL, we propose a spectral-spatial shared linear regression method (SSSLR) for extracting the feature representation. Comparing with RLR, our proposed SSSLR has the following two advantages. First, we utilize a convex set to explore the spatial structure for computing the linear projection matrix. Second, we utilize a shared structure learning model, which is formed by original data space and a hidden feature space, to learn a more discriminant linear projection matrix for classification. To optimize our proposed method, an efficient iterative algorithm is proposed. Experimental results on two popular HSI data sets, i.e., Indian Pines and Salinas demonstrate that our proposed methods outperform many SL methods.

Pizza Again? On the Division of Polygons into Sections with a Common Origin

ERIC Educational Resources Information Center

Sinitsky, Ilya; Stupel, Moshe; Sinitsky, Marina

2018-01-01

The paper explores the division of a polygon into equal-area pieces using line segments originating at a common point. The mathematical background of the proposed method is very simple and belongs to secondary school geometry. Simple examples dividing a square into two, four or eight congruent pieces provide a starting point to discovering how to…

Stationarity conditions for physicochemical processes in the interior ballistics of a gun

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

Lipanov, A.M.

1995-09-01

An original method is proposed for ensuring time-invariant (stationary) interior ballistic parameters in the postprojectile space of a gun barrel. Stationarity of the parameters is achieved by investing the solid-propellant charge with highly original structures that produce the required pressure condition and linear growth of the projectile velocity. Simple relations are obtained for calculating the principal characteristics.

A transformed path integral approach for solution of the Fokker-Planck equation

NASA Astrophysics Data System (ADS)

Subramaniam, Gnana M.; Vedula, Prakash

2017-10-01

A novel path integral (PI) based method for solution of the Fokker-Planck equation is presented. The proposed method, termed the transformed path integral (TPI) method, utilizes a new formulation for the underlying short-time propagator to perform the evolution of the probability density function (PDF) in a transformed computational domain where a more accurate representation of the PDF can be ensured. The new formulation, based on a dynamic transformation of the original state space with the statistics of the PDF as parameters, preserves the non-negativity of the PDF and incorporates short-time properties of the underlying stochastic process. New update equations for the state PDF in a transformed space and the parameters of the transformation (including mean and covariance) that better accommodate nonlinearities in drift and non-Gaussian behavior in distributions are proposed (based on properties of the SDE). Owing to the choice of transformation considered, the proposed method maps a fixed grid in transformed space to a dynamically adaptive grid in the original state space. The TPI method, in contrast to conventional methods such as Monte Carlo simulations and fixed grid approaches, is able to better represent the distributions (especially the tail information) and better address challenges in processes with large diffusion, large drift and large concentration of PDF. Additionally, in the proposed TPI method, error bounds on the probability in the computational domain can be obtained using the Chebyshev's inequality. The benefits of the TPI method over conventional methods are illustrated through simulations of linear and nonlinear drift processes in one-dimensional and multidimensional state spaces. The effects of spatial and temporal grid resolutions as well as that of the diffusion coefficient on the error in the PDF are also characterized.

Ab initio gene identification in metagenomic sequences

PubMed Central

Zhu, Wenhan; Lomsadze, Alexandre; Borodovsky, Mark

2010-01-01

We describe an algorithm for gene identification in DNA sequences derived from shotgun sequencing of microbial communities. Accurate ab initio gene prediction in a short nucleotide sequence of anonymous origin is hampered by uncertainty in model parameters. While several machine learning approaches could be proposed to bypass this difficulty, one effective method is to estimate parameters from dependencies, formed in evolution, between frequencies of oligonucleotides in protein-coding regions and genome nucleotide composition. Original version of the method was proposed in 1999 and has been used since for (i) reconstructing codon frequency vector needed for gene finding in viral genomes and (ii) initializing parameters of self-training gene finding algorithms. With advent of new prokaryotic genomes en masse it became possible to enhance the original approach by using direct polynomial and logistic approximations of oligonucleotide frequencies, as well as by separating models for bacteria and archaea. These advances have increased the accuracy of model reconstruction and, subsequently, gene prediction. We describe the refined method and assess its accuracy on known prokaryotic genomes split into short sequences. Also, we show that as a result of application of the new method, several thousands of new genes could be added to existing annotations of several human and mouse gut metagenomes. PMID:20403810

Setting Cut-Scores: A Critical Review of the Angoff and Modified Angoff Methods

ERIC Educational Resources Information Center

Ricker, Kathryn L.

2006-01-01

The purpose of this article is to review critically the Angoff (1971) and modified Angoff methods for setting cut-scores. The criteria used in this review were originally proposed by Berk (1986). The assumptions of the Angoff method and other current issues surrounding this method are also discussed. Recommendations are made for using the Angoff…

Utility-preserving anonymization for health data publishing.

PubMed

Lee, Hyukki; Kim, Soohyung; Kim, Jong Wook; Chung, Yon Dohn

2017-07-11

Publishing raw electronic health records (EHRs) may be considered as a breach of the privacy of individuals because they usually contain sensitive information. A common practice for the privacy-preserving data publishing is to anonymize the data before publishing, and thus satisfy privacy models such as k-anonymity. Among various anonymization techniques, generalization is the most commonly used in medical/health data processing. Generalization inevitably causes information loss, and thus, various methods have been proposed to reduce information loss. However, existing generalization-based data anonymization methods cannot avoid excessive information loss and preserve data utility. We propose a utility-preserving anonymization for privacy preserving data publishing (PPDP). To preserve data utility, the proposed method comprises three parts: (1) utility-preserving model, (2) counterfeit record insertion, (3) catalog of the counterfeit records. We also propose an anonymization algorithm using the proposed method. Our anonymization algorithm applies full-domain generalization algorithm. We evaluate our method in comparison with existence method on two aspects, information loss measured through various quality metrics and error rate of analysis result. With all different types of quality metrics, our proposed method show the lower information loss than the existing method. In the real-world EHRs analysis, analysis results show small portion of error between the anonymized data through the proposed method and original data. We propose a new utility-preserving anonymization method and an anonymization algorithm using the proposed method. Through experiments on various datasets, we show that the utility of EHRs anonymized by the proposed method is significantly better than those anonymized by previous approaches.

[A research in speech endpoint detection based on boxes-coupling generalization dimension].

PubMed

Wang, Zimei; Yang, Cuirong; Wu, Wei; Fan, Yingle

2008-06-01

In this paper, a new calculating method of generalized dimension, based on boxes-coupling principle, is proposed to overcome the edge effects and to improve the capability of the speech endpoint detection which is based on the original calculating method of generalized dimension. This new method has been applied to speech endpoint detection. Firstly, the length of overlapping border was determined, and through calculating the generalized dimension by covering the speech signal with overlapped boxes, three-dimension feature vectors including the box dimension, the information dimension and the correlation dimension were obtained. Secondly, in the light of the relation between feature distance and similarity degree, feature extraction was conducted by use of common distance. Lastly, bi-threshold method was used to classify the speech signals. The results of experiment indicated that, by comparison with the original generalized dimension (OGD) and the spectral entropy (SE) algorithm, the proposed method is more robust and effective for detecting the speech signals which contain different kinds of noise in different signal noise ratio (SNR), especially in low SNR.

An image hiding method based on cascaded iterative Fourier transform and public-key encryption algorithm

NASA Astrophysics Data System (ADS)

Zhang, B.; Sang, Jun; Alam, Mohammad S.

2013-03-01

An image hiding method based on cascaded iterative Fourier transform and public-key encryption algorithm was proposed. Firstly, the original secret image was encrypted into two phase-only masks M1 and M2 via cascaded iterative Fourier transform (CIFT) algorithm. Then, the public-key encryption algorithm RSA was adopted to encrypt M2 into M2' . Finally, a host image was enlarged by extending one pixel into 2×2 pixels and each element in M1 and M2' was multiplied with a superimposition coefficient and added to or subtracted from two different elements in the 2×2 pixels of the enlarged host image. To recover the secret image from the stego-image, the two masks were extracted from the stego-image without the original host image. By applying public-key encryption algorithm, the key distribution was facilitated, and also compared with the image hiding method based on optical interference, the proposed method may reach higher robustness by employing the characteristics of the CIFT algorithm. Computer simulations show that this method has good robustness against image processing.

Kernel Method Based Human Model for Enhancing Interactive Evolutionary Optimization

PubMed Central

Zhao, Qiangfu; Liu, Yong

2015-01-01

A fitness landscape presents the relationship between individual and its reproductive success in evolutionary computation (EC). However, discrete and approximate landscape in an original search space may not support enough and accurate information for EC search, especially in interactive EC (IEC). The fitness landscape of human subjective evaluation in IEC is very difficult and impossible to model, even with a hypothesis of what its definition might be. In this paper, we propose a method to establish a human model in projected high dimensional search space by kernel classification for enhancing IEC search. Because bivalent logic is a simplest perceptual paradigm, the human model is established by considering this paradigm principle. In feature space, we design a linear classifier as a human model to obtain user preference knowledge, which cannot be supported linearly in original discrete search space. The human model is established by this method for predicting potential perceptual knowledge of human. With the human model, we design an evolution control method to enhance IEC search. From experimental evaluation results with a pseudo-IEC user, our proposed model and method can enhance IEC search significantly. PMID:25879050

A novel high-frequency encoding algorithm for image compression

NASA Astrophysics Data System (ADS)

Siddeq, Mohammed M.; Rodrigues, Marcos A.

2017-12-01

In this paper, a new method for image compression is proposed whose quality is demonstrated through accurate 3D reconstruction from 2D images. The method is based on the discrete cosine transform (DCT) together with a high-frequency minimization encoding algorithm at compression stage and a new concurrent binary search algorithm at decompression stage. The proposed compression method consists of five main steps: (1) divide the image into blocks and apply DCT to each block; (2) apply a high-frequency minimization method to the AC-coefficients reducing each block by 2/3 resulting in a minimized array; (3) build a look up table of probability data to enable the recovery of the original high frequencies at decompression stage; (4) apply a delta or differential operator to the list of DC-components; and (5) apply arithmetic encoding to the outputs of steps (2) and (4). At decompression stage, the look up table and the concurrent binary search algorithm are used to reconstruct all high-frequency AC-coefficients while the DC-components are decoded by reversing the arithmetic coding. Finally, the inverse DCT recovers the original image. We tested the technique by compressing and decompressing 2D images including images with structured light patterns for 3D reconstruction. The technique is compared with JPEG and JPEG2000 through 2D and 3D RMSE. Results demonstrate that the proposed compression method is perceptually superior to JPEG with equivalent quality to JPEG2000. Concerning 3D surface reconstruction from images, it is demonstrated that the proposed method is superior to both JPEG and JPEG2000.

Implementing Sympson-Hetter Item-Exposure Control in a Shadow-Test Approach to Constrained Adaptive Testing

ERIC Educational Resources Information Center

Veldkamp, Bernard P.; van der Linden, Wim J.

2008-01-01

In most operational computerized adaptive testing (CAT) programs, the Sympson-Hetter (SH) method is used to control the exposure of the items. Several modifications and improvements of the original method have been proposed. The Stocking and Lewis (1998) version of the method uses a multinomial experiment to select items. For severely constrained…

Remote sensing fusion based on guided image filtering

NASA Astrophysics Data System (ADS)

Zhao, Wenfei; Dai, Qinling; Wang, Leiguang

2015-12-01

In this paper, we propose a novel remote sensing fusion approach based on guided image filtering. The fused images can well preserve the spectral features of the original multispectral (MS) images, meanwhile, enhance the spatial details information. Four quality assessment indexes are also introduced to evaluate the fusion effect when compared with other fusion methods. Experiments carried out on Gaofen-2, QuickBird, WorldView-2 and Landsat-8 images. And the results show an excellent performance of the proposed method.

Assessing Clinical Significance: Does it Matter which Method we Use?

ERIC Educational Resources Information Center

Atkins, David C.; Bedics, Jamie D.; Mcglinchey, Joseph B.; Beauchaine, Theodore P.

2005-01-01

Measures of clinical significance are frequently used to evaluate client change during therapy. Several alternatives to the original method devised by N. S. Jacobson, W. C. Follette, & D. Revenstorf (1984) have been proposed, each purporting to increase accuracy. However, researchers have had little systematic guidance in choosing among…

Reliability of Test Scores in Nonparametric Item Response Theory.

ERIC Educational Resources Information Center

Sijtsma, Klaas; Molenaar, Ivo W.

1987-01-01

Three methods for estimating reliability are studied within the context of nonparametric item response theory. Two were proposed originally by Mokken and a third is developed in this paper. Using a Monte Carlo strategy, these three estimation methods are compared with four "classical" lower bounds to reliability. (Author/JAZ)

Contour integral method for obtaining the self-energy matrices of electrodes in electron transport calculations

NASA Astrophysics Data System (ADS)

Iwase, Shigeru; Futamura, Yasunori; Imakura, Akira; Sakurai, Tetsuya; Tsukamoto, Shigeru; Ono, Tomoya

2018-05-01

We propose an efficient computational method for evaluating the self-energy matrices of electrodes to study ballistic electron transport properties in nanoscale systems. To reduce the high computational cost incurred in large systems, a contour integral eigensolver based on the Sakurai-Sugiura method combined with the shifted biconjugate gradient method is developed to solve an exponential-type eigenvalue problem for complex wave vectors. A remarkable feature of the proposed algorithm is that the numerical procedure is very similar to that of conventional band structure calculations. We implement the developed method in the framework of the real-space higher-order finite-difference scheme with nonlocal pseudopotentials. Numerical tests for a wide variety of materials validate the robustness, accuracy, and efficiency of the proposed method. As an illustration of the method, we present the electron transport property of the freestanding silicene with the line defect originating from the reversed buckled phases.

A nonlinear quality-related fault detection approach based on modified kernel partial least squares.

PubMed

Jiao, Jianfang; Zhao, Ning; Wang, Guang; Yin, Shen

2017-01-01

In this paper, a new nonlinear quality-related fault detection method is proposed based on kernel partial least squares (KPLS) model. To deal with the nonlinear characteristics among process variables, the proposed method maps these original variables into feature space in which the linear relationship between kernel matrix and output matrix is realized by means of KPLS. Then the kernel matrix is decomposed into two orthogonal parts by singular value decomposition (SVD) and the statistics for each part are determined appropriately for the purpose of quality-related fault detection. Compared with relevant existing nonlinear approaches, the proposed method has the advantages of simple diagnosis logic and stable performance. A widely used literature example and an industrial process are used for the performance evaluation for the proposed method. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Evaluating user reputation in online rating systems via an iterative group-based ranking method

NASA Astrophysics Data System (ADS)

Gao, Jian; Zhou, Tao

2017-05-01

Reputation is a valuable asset in online social lives and it has drawn increased attention. Due to the existence of noisy ratings and spamming attacks, how to evaluate user reputation in online rating systems is especially significant. However, most of the previous ranking-based methods either follow a debatable assumption or have unsatisfied robustness. In this paper, we propose an iterative group-based ranking method by introducing an iterative reputation-allocation process into the original group-based ranking method. More specifically, the reputation of users is calculated based on the weighted sizes of the user rating groups after grouping all users by their rating similarities, and the high reputation users' ratings have larger weights in dominating the corresponding user rating groups. The reputation of users and the user rating group sizes are iteratively updated until they become stable. Results on two real data sets with artificial spammers suggest that the proposed method has better performance than the state-of-the-art methods and its robustness is considerably improved comparing with the original group-based ranking method. Our work highlights the positive role of considering users' grouping behaviors towards a better online user reputation evaluation.

Optical multiple-image authentication based on cascaded phase filtering structure

NASA Astrophysics Data System (ADS)

Wang, Q.; Alfalou, A.; Brosseau, C.

2016-10-01

In this study, we report on the recent developments of optical image authentication algorithms. Compared with conventional optical encryption, optical image authentication achieves more security strength because such methods do not need to recover information of plaintext totally during the decryption period. Several recently proposed authentication systems are briefly introduced. We also propose a novel multiple-image authentication system, where multiple original images are encoded into a photon-limited encoded image by using a triple-plane based phase retrieval algorithm and photon counting imaging (PCI) technique. One can only recover a noise-like image using correct keys. To check authority of multiple images, a nonlinear fractional correlation is employed to recognize the original information hidden in the decrypted results. The proposal can be implemented optically using a cascaded phase filtering configuration. Computer simulation results are presented to evaluate the performance of this proposal and its effectiveness.

Good Practices for Learning to Recognize Actions Using FV and VLAD.

PubMed

Wu, Jianxin; Zhang, Yu; Lin, Weiyao

2016-12-01

High dimensional representations such as Fisher vectors (FV) and vectors of locally aggregated descriptors (VLAD) have shown state-of-the-art accuracy for action recognition in videos. The high dimensionality, on the other hand, also causes computational difficulties when scaling up to large-scale video data. This paper makes three lines of contributions to learning to recognize actions using high dimensional representations. First, we reviewed several existing techniques that improve upon FV or VLAD in image classification, and performed extensive empirical evaluations to assess their applicability for action recognition. Our analyses of these empirical results show that normality and bimodality are essential to achieve high accuracy. Second, we proposed a new pooling strategy for VLAD and three simple, efficient, and effective transformations for both FV and VLAD. Both proposed methods have shown higher accuracy than the original FV/VLAD method in extensive evaluations. Third, we proposed and evaluated new feature selection and compression methods for the FV and VLAD representations. This strategy uses only 4% of the storage of the original representation, but achieves comparable or even higher accuracy. Based on these contributions, we recommend a set of good practices for action recognition in videos for practitioners in this field.

Steganographic optical image encryption system based on reversible data hiding and double random phase encoding

NASA Astrophysics Data System (ADS)

Chuang, Cheng-Hung; Chen, Yen-Lin

2013-02-01

This study presents a steganographic optical image encryption system based on reversible data hiding and double random phase encoding (DRPE) techniques. Conventional optical image encryption systems can securely transmit valuable images using an encryption method for possible application in optical transmission systems. The steganographic optical image encryption system based on the DRPE technique has been investigated to hide secret data in encrypted images. However, the DRPE techniques vulnerable to attacks and many of the data hiding methods in the DRPE system can distort the decrypted images. The proposed system, based on reversible data hiding, uses a JBIG2 compression scheme to achieve lossless decrypted image quality and perform a prior encryption process. Thus, the DRPE technique enables a more secured optical encryption process. The proposed method extracts and compresses the bit planes of the original image using the lossless JBIG2 technique. The secret data are embedded in the remaining storage space. The RSA algorithm can cipher the compressed binary bits and secret data for advanced security. Experimental results show that the proposed system achieves a high data embedding capacity and lossless reconstruction of the original images.

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

Shumway, R.H.; McQuarrie, A.D.

Robust statistical approaches to the problem of discriminating between regional earthquakes and explosions are developed. We compare linear discriminant analysis using descriptive features like amplitude and spectral ratios with signal discrimination techniques using the original signal waveforms and spectral approximations to the log likelihood function. Robust information theoretic techniques are proposed and all methods are applied to 8 earthquakes and 8 mining explosions in Scandinavia and to an event from Novaya Zemlya of unknown origin. It is noted that signal discrimination approaches based on discrimination information and Renyi entropy perform better in the test sample than conventional methods based onmore » spectral ratios involving the P and S phases. Two techniques for identifying the ripple-firing pattern for typical mining explosions are proposed and shown to work well on simulated data and on several Scandinavian earthquakes and explosions. We use both cepstral analysis in the frequency domain and a time domain method based on the autocorrelation and partial autocorrelation functions. The proposed approach strips off underlying smooth spectral and seasonal spectral components corresponding to the echo pattern induced by two simple ripple-fired models. For two mining explosions, a pattern is identified whereas for two earthquakes, no pattern is evident.« less

A polynomial primal-dual Dikin-type algorithm for linear programming

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

Jansen, B.; Roos, R.; Terlaky, T.

1994-12-31

We present a new primal-dual affine scaling method for linear programming. The search direction is obtained by using Dikin`s original idea: minimize the objective function (which is the duality gap in a primal-dual algorithm) over a suitable ellipsoid. The search direction has no obvious relationship with the directions proposed in the literature so far. It guarantees a significant decrease in the duality gap in each iteration, and at the same time drives the iterates to the central path. The method admits a polynomial complexity bound that is better than the one for Monteiro et al.`s original primal-dual affine scaling method.

Program of Research in Flight Dynamics, The George Washington University at NASA Langley Research Center

NASA Technical Reports Server (NTRS)

Murphy, Patrick C. (Technical Monitor); Klein, Vladislav

2005-01-01

The program objectives are fully defined in the original proposal entitled Program of Research in Flight Dynamics in GW at NASA Langley Research Center, which was originated March 20, 1975, and in the renewals of the research program from January 1, 2003 to September 30, 2005. The program in its present form includes three major topics: 1. the improvement of existing methods and development of new methods for wind tunnel and flight data analysis, 2. the application of these methods to wind tunnel and flight test data obtained from advanced airplanes, 3. the correlation of flight results with wind tunnel measurements, and theoretical predictions.

A Chaotic Ordered Hierarchies Consistency Analysis Performance Evaluation Model

NASA Astrophysics Data System (ADS)

Yeh, Wei-Chang

2013-02-01

The Hierarchies Consistency Analysis (HCA) is proposed by Guh in-cooperated along with some case study on a Resort to reinforce the weakness of Analytical Hierarchy Process (AHP). Although the results obtained enabled aid for the Decision Maker to make more reasonable and rational verdicts, the HCA itself is flawed. In this paper, our objective is to indicate the problems of HCA, and then propose a revised method called chaotic ordered HCA (COH in short) which can avoid problems. Since the COH is based upon Guh's method, the Decision Maker establishes decisions in a way similar to that of the original method.

Chaotic Signal Denoising Based on Hierarchical Threshold Synchrosqueezed Wavelet Transform

NASA Astrophysics Data System (ADS)

Wang, Wen-Bo; Jing, Yun-yu; Zhao, Yan-chao; Zhang, Lian-Hua; Wang, Xiang-Li

2017-12-01

In order to overcoming the shortcoming of single threshold synchrosqueezed wavelet transform(SWT) denoising method, an adaptive hierarchical threshold SWT chaotic signal denoising method is proposed. Firstly, a new SWT threshold function is constructed based on Stein unbiased risk estimation, which is two order continuous derivable. Then, by using of the new threshold function, a threshold process based on the minimum mean square error was implemented, and the optimal estimation value of each layer threshold in SWT chaotic denoising is obtained. The experimental results of the simulating chaotic signal and measured sunspot signals show that, the proposed method can filter the noise of chaotic signal well, and the intrinsic chaotic characteristic of the original signal can be recovered very well. Compared with the EEMD denoising method and the single threshold SWT denoising method, the proposed method can obtain better denoising result for the chaotic signal.

Approximation-based common principal component for feature extraction in multi-class brain-computer interfaces.

PubMed

Hoang, Tuan; Tran, Dat; Huang, Xu

2013-01-01

Common Spatial Pattern (CSP) is a state-of-the-art method for feature extraction in Brain-Computer Interface (BCI) systems. However it is designed for 2-class BCI classification problems. Current extensions of this method to multiple classes based on subspace union and covariance matrix similarity do not provide a high performance. This paper presents a new approach to solving multi-class BCI classification problems by forming a subspace resembled from original subspaces and the proposed method for this approach is called Approximation-based Common Principal Component (ACPC). We perform experiments on Dataset 2a used in BCI Competition IV to evaluate the proposed method. This dataset was designed for motor imagery classification with 4 classes. Preliminary experiments show that the proposed ACPC feature extraction method when combining with Support Vector Machines outperforms CSP-based feature extraction methods on the experimental dataset.

A multi-scale tensor voting approach for small retinal vessel segmentation in high resolution fundus images.

PubMed

Christodoulidis, Argyrios; Hurtut, Thomas; Tahar, Houssem Ben; Cheriet, Farida

2016-09-01

Segmenting the retinal vessels from fundus images is a prerequisite for many CAD systems for the automatic detection of diabetic retinopathy lesions. So far, research efforts have concentrated mainly on the accurate localization of the large to medium diameter vessels. However, failure to detect the smallest vessels at the segmentation step can lead to false positive lesion detection counts in a subsequent lesion analysis stage. In this study, a new hybrid method for the segmentation of the smallest vessels is proposed. Line detection and perceptual organization techniques are combined in a multi-scale scheme. Small vessels are reconstructed from the perceptual-based approach via tracking and pixel painting. The segmentation was validated in a high resolution fundus image database including healthy and diabetic subjects using pixel-based as well as perceptual-based measures. The proposed method achieves 85.06% sensitivity rate, while the original multi-scale line detection method achieves 81.06% sensitivity rate for the corresponding images (p<0.05). The improvement in the sensitivity rate for the database is 6.47% when only the smallest vessels are considered (p<0.05). For the perceptual-based measure, the proposed method improves the detection of the vasculature by 7.8% against the original multi-scale line detection method (p<0.05). Copyright © 2016 Elsevier Ltd. All rights reserved.

Projections onto Convex Sets Super-Resolution Reconstruction Based on Point Spread Function Estimation of Low-Resolution Remote Sensing Images

PubMed Central

Fan, Chong; Wu, Chaoyun; Li, Grand; Ma, Jun

2017-01-01

To solve the problem on inaccuracy when estimating the point spread function (PSF) of the ideal original image in traditional projection onto convex set (POCS) super-resolution (SR) reconstruction, this paper presents an improved POCS SR algorithm based on PSF estimation of low-resolution (LR) remote sensing images. The proposed algorithm can improve the spatial resolution of the image and benefit agricultural crop visual interpolation. The PSF of the high-resolution (HR) image is unknown in reality. Therefore, analysis of the relationship between the PSF of the HR image and the PSF of the LR image is important to estimate the PSF of the HR image by using multiple LR images. In this study, the linear relationship between the PSFs of the HR and LR images can be proven. In addition, the novel slant knife-edge method is employed, which can improve the accuracy of the PSF estimation of LR images. Finally, the proposed method is applied to reconstruct airborne digital sensor 40 (ADS40) three-line array images and the overlapped areas of two adjacent GF-2 images by embedding the estimated PSF of the HR image to the original POCS SR algorithm. Experimental results show that the proposed method yields higher quality of reconstructed images than that produced by the blind SR method and the bicubic interpolation method. PMID:28208837

Region-based multifocus image fusion for the precise acquisition of Pap smear images.

PubMed

Tello-Mijares, Santiago; Bescós, Jesús

2018-05-01

A multifocus image fusion method to obtain a single focused image from a sequence of microscopic high-magnification Papanicolau source (Pap smear) images is presented. These images, captured each in a different position of the microscope lens, frequently show partially focused cells or parts of cells, which makes them unpractical for the direct application of image analysis techniques. The proposed method obtains a focused image with a high preservation of original pixels information while achieving a negligible visibility of the fusion artifacts. The method starts by identifying the best-focused image of the sequence; then, it performs a mean-shift segmentation over this image; the focus level of the segmented regions is evaluated in all the images of the sequence, and best-focused regions are merged in a single combined image; finally, this image is processed with an adaptive artifact removal process. The combination of a region-oriented approach, instead of block-based approaches, and a minimum modification of the value of focused pixels in the original images achieve a highly contrasted image with no visible artifacts, which makes this method especially convenient for the medical imaging domain. The proposed method is compared with several state-of-the-art alternatives over a representative dataset. The experimental results show that our proposal obtains the best and more stable quality indicators. (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Novel disturbance-observer-based control for systems with high-order mismatched disturbances

NASA Astrophysics Data System (ADS)

Fang, Xing; Liu, Fei; Wang, Zhiguo; Dong, Na

2018-01-01

A novel disturbance-observer-based control method is investigated to attenuate the high-order mismatched disturbances. First, a finite-time disturbance observer (FTDO) is proposed to estimate the disturbances as well as the derivatives. By incorporating the outputs of FTDO, the original system is then reconstructed, where the mismatched disturbances are transformed to the matched ones that are compensated by feed-forward algorithm. Moreover, a feedback control law is developed to achieve the stability and tracking performance requirements for the systems. Finally, the proposed composite control method is applied to an unmanned helicopter system. The simulation results demonstrate that the proposed control method exhibits excellent control performance in the presence of high-order matched and mismatched disturbances.

Topological Vulnerability Evaluation Model Based on Fractal Dimension of Complex Networks.

PubMed

Gou, Li; Wei, Bo; Sadiq, Rehan; Sadiq, Yong; Deng, Yong

2016-01-01

With an increasing emphasis on network security, much more attentions have been attracted to the vulnerability of complex networks. In this paper, the fractal dimension, which can reflect space-filling capacity of networks, is redefined as the origin moment of the edge betweenness to obtain a more reasonable evaluation of vulnerability. The proposed model combining multiple evaluation indexes not only overcomes the shortage of average edge betweenness's failing to evaluate vulnerability of some special networks, but also characterizes the topological structure and highlights the space-filling capacity of networks. The applications to six US airline networks illustrate the practicality and effectiveness of our proposed method, and the comparisons with three other commonly used methods further validate the superiority of our proposed method.

Enhancing the pictorial content of digital holograms at 100 frames per second.

PubMed

Tsang, P W M; Poon, T-C; Cheung, K W K

2012-06-18

We report a low complexity, non-iterative method for enhancing the sharpness, brightness, and contrast of the pictorial content that is recorded in a digital hologram, without the need of re-generating the latter from the original object scene. In our proposed method, the hologram is first back-projected to a 2-D virtual diffraction plane (VDP) which is located at close proximity to the original object points. Next the field distribution on the VDP, which shares similar optical properties as the object scene, is enhanced. Subsequently, the processed VDP is expanded into a full hologram. We demonstrate two types of enhancement: a modified histogram equalization to improve the brightness and contrast, and localized high-boost-filtering (LHBF) to increase the sharpness. Experiment results have demonstrated that our proposed method is capable of enhancing a 2048x2048 hologram at a rate of around 100 frames per second. To the best of our knowledge, this is the first time real-time image enhancement is considered in the context of digital holography.

SHOP: a method for structure-based fragment and scaffold hopping.

PubMed

Fontaine, Fabien; Cross, Simon; Plasencia, Guillem; Pastor, Manuel; Zamora, Ismael

2009-03-01

A new method for fragment and scaffold replacement is presented that generates new families of compounds with biological activity, using GRID molecular interaction fields (MIFs) and the crystal structure of the targets. In contrast to virtual screening strategies, this methodology aims only to replace a fragment of the original molecule, maintaining the other structural elements that are known or suspected to have a critical role in ligand binding. First, we report a validation of the method, recovering up to 95% of the original fragments searched among the top-five proposed solutions, using 164 fragment queries from 11 diverse targets. Second, six key customizable parameters are investigated, concluding that filtering the receptor MIF using the co-crystallized ligand atom type has the greatest impact on the ranking of the proposed solutions. Finally, 11 examples using more realistic scenarios have been performed; diverse chemotypes are returned, including some that are similar to compounds that are known to bind to similar targets.

EEG-based emergency braking intention prediction for brain-controlled driving considering one electrode falling-off.

PubMed

Huikang Wang; Luzheng Bi; Teng Teng

2017-07-01

This paper proposes a novel method of electroencephalography (EEG)-based driver emergency braking intention detection system for brain-controlled driving considering one electrode falling-off. First, whether one electrode falls off is discriminated based on EEG potentials. Then, the missing signals are estimated by using the signals collected from other channels based on multivariate linear regression. Finally, a linear decoder is applied to classify driver intentions. Experimental results show that the falling-off discrimination accuracy is 99.63% on average and the correlation coefficient and root mean squared error (RMSE) between the estimated and experimental data are 0.90 and 11.43 μV, respectively, on average. Given one electrode falls off, the system accuracy of the proposed intention prediction method is significantly higher than that of the original method (95.12% VS 79.11%) and is close to that (95.95%) of the original system under normal situations (i. e., no electrode falling-off).

Stress and PTSD Mechanisms as Targets for Pharmacotherapy of Alcohol Abuse, Addiction, and Relapse

DTIC Science & Technology

2014-10-01

hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed , and...new staff person needed for the proposed studies and have also implemented new methods that were not in the original proposal but that make the...in year 3, due to the urgent need for new therapies to prevent PTSD in response to trauma. The remaining proposed studies using rat models to

Restoration of Monotonicity Respecting in Dynamic Regression

PubMed Central

Huang, Yijian

2017-01-01

Dynamic regression models, including the quantile regression model and Aalen’s additive hazards model, are widely adopted to investigate evolving covariate effects. Yet lack of monotonicity respecting with standard estimation procedures remains an outstanding issue. Advances have recently been made, but none provides a complete resolution. In this article, we propose a novel adaptive interpolation method to restore monotonicity respecting, by successively identifying and then interpolating nearest monotonicity-respecting points of an original estimator. Under mild regularity conditions, the resulting regression coefficient estimator is shown to be asymptotically equivalent to the original. Our numerical studies have demonstrated that the proposed estimator is much more smooth and may have better finite-sample efficiency than the original as well as, when available as only in special cases, other competing monotonicity-respecting estimators. Illustration with a clinical study is provided. PMID:29430068

Unsupervised background-constrained tank segmentation of infrared images in complex background based on the Otsu method.

PubMed

Zhou, Yulong; Gao, Min; Fang, Dan; Zhang, Baoquan

2016-01-01

In an effort to implement fast and effective tank segmentation from infrared images in complex background, the threshold of the maximum between-class variance method (i.e., the Otsu method) is analyzed and the working mechanism of the Otsu method is discussed. Subsequently, a fast and effective method for tank segmentation from infrared images in complex background is proposed based on the Otsu method via constraining the complex background of the image. Considering the complexity of background, the original image is firstly divided into three classes of target region, middle background and lower background via maximizing the sum of their between-class variances. Then, the unsupervised background constraint is implemented based on the within-class variance of target region and hence the original image can be simplified. Finally, the Otsu method is applied to simplified image for threshold selection. Experimental results on a variety of tank infrared images (880 × 480 pixels) in complex background demonstrate that the proposed method enjoys better segmentation performance and even could be comparative with the manual segmentation in segmented results. In addition, its average running time is only 9.22 ms, implying the new method with good performance in real time processing.

Adaptive pseudo-color enhancement method of weld radiographic images based on HSI color space and self-transformation of pixels.

PubMed

Jiang, Hongquan; Zhao, Yalin; Gao, Jianmin; Gao, Zhiyong

2017-06-01

The radiographic testing (RT) image of a steam turbine manufacturing enterprise has the characteristics of low gray level, low contrast, and blurriness, which lead to a substandard image quality. Moreover, it is not conducive for human eyes to detect and evaluate defects. This study proposes an adaptive pseudo-color enhancement method for weld radiographic images based on the hue, saturation, and intensity (HSI) color space and the self-transformation of pixels to solve these problems. First, the pixel's self-transformation is performed to the pixel value of the original RT image. The function value after the pixel's self-transformation is assigned to the HSI components in the HSI color space. Thereafter, the average intensity of the enhanced image is adaptively adjusted to 0.5 according to the intensity of the original image. Moreover, the hue range and interval can be adjusted according to personal habits. Finally, the HSI components after the adaptive adjustment can be transformed to display in the red, green, and blue color space. Numerous weld radiographic images from a steam turbine manufacturing enterprise are used to validate the proposed method. The experimental results show that the proposed pseudo-color enhancement method can improve image definition and make the target and background areas distinct in weld radiographic images. The enhanced images will be more conducive for defect recognition. Moreover, the image enhanced using the proposed method conforms to the human eye visual properties, and the effectiveness of defect recognition and evaluation can be ensured.

Adaptive pseudo-color enhancement method of weld radiographic images based on HSI color space and self-transformation of pixels

NASA Astrophysics Data System (ADS)

Jiang, Hongquan; Zhao, Yalin; Gao, Jianmin; Gao, Zhiyong

2017-06-01

The radiographic testing (RT) image of a steam turbine manufacturing enterprise has the characteristics of low gray level, low contrast, and blurriness, which lead to a substandard image quality. Moreover, it is not conducive for human eyes to detect and evaluate defects. This study proposes an adaptive pseudo-color enhancement method for weld radiographic images based on the hue, saturation, and intensity (HSI) color space and the self-transformation of pixels to solve these problems. First, the pixel's self-transformation is performed to the pixel value of the original RT image. The function value after the pixel's self-transformation is assigned to the HSI components in the HSI color space. Thereafter, the average intensity of the enhanced image is adaptively adjusted to 0.5 according to the intensity of the original image. Moreover, the hue range and interval can be adjusted according to personal habits. Finally, the HSI components after the adaptive adjustment can be transformed to display in the red, green, and blue color space. Numerous weld radiographic images from a steam turbine manufacturing enterprise are used to validate the proposed method. The experimental results show that the proposed pseudo-color enhancement method can improve image definition and make the target and background areas distinct in weld radiographic images. The enhanced images will be more conducive for defect recognition. Moreover, the image enhanced using the proposed method conforms to the human eye visual properties, and the effectiveness of defect recognition and evaluation can be ensured.

A novel strategy for exploring the reassortment origins of newly emerging influenza virus.

PubMed

Tian, Deqiao; Wang, Yumin; Zheng, Tao

2011-01-01

In early 2009, new swine-origin influenza A (H1N1) virus emerged in Mexico and the United States. The emerging influenza virus had made global influenza pandemic for nearly one year. To every emerging pathogen, exploring the origin sources is vital for viral control and clearance. Influenza virus is different from other virus in that it has 8 segments, making the segment reassortment a main drive in virus evolution. In exploring reassortment evolution origins of a newly emerging influenza virus, integrated comparing of the origin sources of all the segments is necessary. If some segments have high homologous with one parental strain, lower homologous with another parental strain, while other segments are reverse, can we proposed that this emerging influenza virus may re-assort from the two parental strains. Here we try to explore the multilevel reassortment evolution origins of 2009 H1N1 influenza virus using this method. By further validating the fidelity of this strategy, this method might be useful in judging the reassortment origins of newly emerging influenza virus.

The backward phase flow and FBI-transform-based Eulerian Gaussian beams for the Schroedinger equation

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

Leung Shingyu, E-mail: masyleung@ust.h; Qian Jianliang, E-mail: qian@math.msu.ed

2010-11-20

We propose the backward phase flow method to implement the Fourier-Bros-Iagolnitzer (FBI)-transform-based Eulerian Gaussian beam method for solving the Schroedinger equation in the semi-classical regime. The idea of Eulerian Gaussian beams has been first proposed in . In this paper we aim at two crucial computational issues of the Eulerian Gaussian beam method: how to carry out long-time beam propagation and how to compute beam ingredients rapidly in phase space. By virtue of the FBI transform, we address the first issue by introducing the reinitialization strategy into the Eulerian Gaussian beam framework. Essentially we reinitialize beam propagation by applying themore » FBI transform to wavefields at intermediate time steps when the beams become too wide. To address the second issue, inspired by the original phase flow method, we propose the backward phase flow method which allows us to compute beam ingredients rapidly. Numerical examples demonstrate the efficiency and accuracy of the proposed algorithms.« less

The backward phase flow and FBI-transform-based Eulerian Gaussian beams for the Schrödinger equation

NASA Astrophysics Data System (ADS)

Leung, Shingyu; Qian, Jianliang

2010-11-01

We propose the backward phase flow method to implement the Fourier-Bros-Iagolnitzer (FBI)-transform-based Eulerian Gaussian beam method for solving the Schrödinger equation in the semi-classical regime. The idea of Eulerian Gaussian beams has been first proposed in [12]. In this paper we aim at two crucial computational issues of the Eulerian Gaussian beam method: how to carry out long-time beam propagation and how to compute beam ingredients rapidly in phase space. By virtue of the FBI transform, we address the first issue by introducing the reinitialization strategy into the Eulerian Gaussian beam framework. Essentially we reinitialize beam propagation by applying the FBI transform to wavefields at intermediate time steps when the beams become too wide. To address the second issue, inspired by the original phase flow method, we propose the backward phase flow method which allows us to compute beam ingredients rapidly. Numerical examples demonstrate the efficiency and accuracy of the proposed algorithms.

Security Analysis and Improvements to the PsychoPass Method

PubMed Central

2013-01-01

Background In a recent paper, Pietro Cipresso et al proposed the PsychoPass method, a simple way to create strong passwords that are easy to remember. However, the method has some security issues that need to be addressed. Objective To perform a security analysis on the PsychoPass method and outline the limitations of and possible improvements to the method. Methods We used the brute force analysis and dictionary attack analysis of the PsychoPass method to outline its weaknesses. Results The first issue with the Psychopass method is that it requires the password reproduction on the same keyboard layout as was used to generate the password. The second issue is a security weakness: although the produced password is 24 characters long, the password is still weak. We elaborate on the weakness and propose a solution that produces strong passwords. The proposed version first requires the use of the SHIFT and ALT-GR keys in combination with other keys, and second, the keys need to be 1-2 distances apart. Conclusions The proposed improved PsychoPass method yields passwords that can be broken only in hundreds of years based on current computing powers. The proposed PsychoPass method requires 10 keys, as opposed to 20 keys in the original method, for comparable password strength. PMID:23942458

Cryptography Would Reveal Alterations In Photographs

NASA Technical Reports Server (NTRS)

Friedman, Gary L.

1995-01-01

Public-key decryption method proposed to guarantee authenticity of photographic images represented in form of digital files. In method, digital camera generates original data from image in standard public format; also produces coded signature to verify standard-format image data. Scheme also helps protect against other forms of lying, such as attaching false captions.

The Method behind the Madness: Acquiring Online Journals and a Solution to Provide Access

ERIC Educational Resources Information Center

Skekel, Donna

2005-01-01

Libraries are seeking the best possible solution for integrating online journals into their collections. While exploring the different methods and technology available, many libraries still strive to fulfill the original "library mission" proposed by Charles Cutter in his "Rules for a Dictionary Catalog". Providing comprehensive access to…

Design and analysis of a flux intensifying permanent magnet embedded salient pole wind generator

NASA Astrophysics Data System (ADS)

Guo, Yujing; Jin, Ping; Lin, Heyun; Yang, Hui; Lyu, Shukang

2018-05-01

This paper presents an improved flux intensifying permanent magnet embedded salient pole wind generator (FI-PMESPWG) with mirror symmetrical magnetizing directions permanent magnet (PM) for improving generator's performances. The air-gap flux densities, the output voltage, the cogging torque and the d- and q-axis inductances of FI-PMESPWG are all calculated and analyzed by using the finite element method (FEM). To highlight the advantages of the proposed FI-PMESPWG, an original permanent magnet embedded salient pole wind generator (PMESPWG) model is adopted for comparison under the same operating conditions. The calculating results show that the air-gap flux densities of FI-PMESPWG are intensified with the same magnet amounts because the PMs are set in a form of V shape in each pole. The difference between d-axis inductance and q-axis inductance of the proposed FI-PMESPWG is reduced. Thus, the output power of the proposed FI-PMESPWG reaches a higher value than that of the original PMESPWG at the same current phase angle. The cogging torque is diminished because the flux path is changed. All the analysis results indicate that the electromagnetic characteristics of the proposed FI-PMESPWG are significantly better than that of the original PMESPWG.

[The Bridgelok system for additional retention of bonded prostheses].

PubMed

Maroto García, J; Gutiérrez Molero, F; López Montero, M V

1989-04-01

The partial protesis with metal carving and cemented with composite resin is used frequently as a reversible method in order to substitute teeth, due to the light preparation necessary for the original teeth. Recently, a new system (Bridgelok) with pins has been proposed as safer for this kind of protesis. This paper studies if this new system originates a higher resistance compared to other system without pins.

Color filter array pattern identification using variance of color difference image

NASA Astrophysics Data System (ADS)

Shin, Hyun Jun; Jeon, Jong Ju; Eom, Il Kyu

2017-07-01

A color filter array is placed on the image sensor of a digital camera to acquire color images. Each pixel uses only one color, since the image sensor can measure only one color per pixel. Therefore, empty pixels are filled using an interpolation process called demosaicing. The original and the interpolated pixels have different statistical characteristics. If the image is modified by manipulation or forgery, the color filter array pattern is altered. This pattern change can be a clue for image forgery detection. However, most forgery detection algorithms have the disadvantage of assuming the color filter array pattern. We present an identification method of the color filter array pattern. Initially, the local mean is eliminated to remove the background effect. Subsequently, the color difference block is constructed to emphasize the difference between the original pixel and the interpolated pixel. The variance measure of the color difference image is proposed as a means of estimating the color filter array configuration. The experimental results show that the proposed method is effective in identifying the color filter array pattern. Compared with conventional methods, our method provides superior performance.

Optical Double Image Hiding in the Fractional Hartley Transform Using Structured Phase Filter and Arnold Transform

NASA Astrophysics Data System (ADS)

Yadav, Poonam Lata; Singh, Hukum

2018-06-01

To maintain the security of the image encryption and to protect the image from intruders, a new asymmetric cryptosystem based on fractional Hartley Transform (FrHT) and the Arnold transform (AT) is proposed. AT is a method of image cropping and edging in which pixels of the image are reorganized. In this cryptosystem we have used AT so as to extent the information content of the two original images onto the encrypted images so as to increase the safety of the encoded images. We have even used Structured Phase Mask (SPM) and Hybrid Mask (HM) as the encryption keys. The original image is first multiplied with the SPM and HM and then transformed with direct and inverse fractional Hartley transform so as to obtain the encrypted image. The fractional orders of the FrHT and the parameters of the AT correspond to the keys of encryption and decryption methods. If both the keys are correctly used only then the original image would be retrieved. Recommended method helps in strengthening the safety of DRPE by growing the key space and the number of parameters and the method is robust against various attacks. By using MATLAB 8.3.0.52 (R2014a) we calculate the strength of the recommended cryptosystem. A set of simulated results shows the power of the proposed asymmetric cryptosystem.

Robust kernel collaborative representation for face recognition

NASA Astrophysics Data System (ADS)

Huang, Wei; Wang, Xiaohui; Ma, Yanbo; Jiang, Yuzheng; Zhu, Yinghui; Jin, Zhong

2015-05-01

One of the greatest challenges of representation-based face recognition is that the training samples are usually insufficient. In other words, the training set usually does not include enough samples to show varieties of high-dimensional face images caused by illuminations, facial expressions, and postures. When the test sample is significantly different from the training samples of the same subject, the recognition performance will be sharply reduced. We propose a robust kernel collaborative representation based on virtual samples for face recognition. We think that the virtual training set conveys some reasonable and possible variations of the original training samples. Hence, we design a new object function to more closely match the representation coefficients generated from the original and virtual training sets. In order to further improve the robustness, we implement the corresponding representation-based face recognition in kernel space. It is noteworthy that any kind of virtual training samples can be used in our method. We use noised face images to obtain virtual face samples. The noise can be approximately viewed as a reflection of the varieties of illuminations, facial expressions, and postures. Our work is a simple and feasible way to obtain virtual face samples to impose Gaussian noise (and other types of noise) specifically to the original training samples to obtain possible variations of the original samples. Experimental results on the FERET, Georgia Tech, and ORL face databases show that the proposed method is more robust than two state-of-the-art face recognition methods, such as CRC and Kernel CRC.

A simple modification of the Baermann method for diagnosis of strongyloidiasis.

PubMed

Hernández-Chavarría, F; Avendaño, L

2001-08-01

The diagnosis of Strongyloides stercoralis infections is routinely made by microscopic observation of larvae in stool samples, a low sensitivity method, or by other, most effective methods, such as the Baermann or agar culture plate methods. We propose in this paper a practical modification of Baermann method. One hundred and six stool samples from alcoholic patients were analyzed using the direct smear test, agar culture plate method, the standard Baermann method, and its proposed modification. For this modification the funnel used in the original version of the method is substituted by a test tube with a rubber stopper, perforated to allow insertion of a pipette tip. The tube with a fecal suspension is inverted over another tube containing 6 ml of saline solution and incubated at 37 degrees C for at least 2 h. The saline solution from the second tube is centrifuged and the pellet is observed microscopically. Larva of S. stercoralis were detected in six samples (5.7%) by the two versions of the Baermann method. Five samples were positive using the agar culture plate method, and only in two samples the larva were observed using direct microscopic observation of fecal smears. Cysts of Endolimax nana and Entamoeba histolytica/dyspar were also detected in the modification of Baermann method. Data obtained by the modified Baermann method suggest that this methodology may helps concentrate larvae of S. stercoralis as efficiently as the original method.

Security analysis and improvements to the PsychoPass method.

PubMed

Brumen, Bostjan; Heričko, Marjan; Rozman, Ivan; Hölbl, Marko

2013-08-13

In a recent paper, Pietro Cipresso et al proposed the PsychoPass method, a simple way to create strong passwords that are easy to remember. However, the method has some security issues that need to be addressed. To perform a security analysis on the PsychoPass method and outline the limitations of and possible improvements to the method. We used the brute force analysis and dictionary attack analysis of the PsychoPass method to outline its weaknesses. The first issue with the Psychopass method is that it requires the password reproduction on the same keyboard layout as was used to generate the password. The second issue is a security weakness: although the produced password is 24 characters long, the password is still weak. We elaborate on the weakness and propose a solution that produces strong passwords. The proposed version first requires the use of the SHIFT and ALT-GR keys in combination with other keys, and second, the keys need to be 1-2 distances apart. The proposed improved PsychoPass method yields passwords that can be broken only in hundreds of years based on current computing powers. The proposed PsychoPass method requires 10 keys, as opposed to 20 keys in the original method, for comparable password strength.

Equivalency principle for magnetoelectroelastic multiferroics with arbitrary microstructure: The phase field approach

NASA Astrophysics Data System (ADS)

Ni, Yong; He, Linghui; Khachaturyan, Armen G.

2010-07-01

A phase field method is proposed to determine the equilibrium fields of a magnetoelectroelastic multiferroic with arbitrarily distributed constitutive constants under applied loadings. This method is based on a developed generalized Eshelby's equivalency principle, in which the elastic strain, electrostatic, and magnetostatic fields at the equilibrium in the original heterogeneous system are exactly the same as those in an equivalent homogeneous magnetoelectroelastic coupled or uncoupled system with properly chosen distributed effective eigenstrain, polarization, and magnetization fields. Finding these effective fields fully solves the equilibrium elasticity, electrostatics, and magnetostatics in the original heterogeneous multiferroic. The paper formulates a variational principle proving that the effective fields are minimizers of appropriate close-form energy functional. The proposed phase field approach produces the energy minimizing effective fields (and thus solving the general multiferroic problem) as a result of artificial relaxation process described by the Ginzburg-Landau-Khalatnikov kinetic equations.

Design of an optimal preview controller for linear discrete-time descriptor systems with state delay

NASA Astrophysics Data System (ADS)

Cao, Mengjuan; Liao, Fucheng

2015-04-01

In this paper, the linear discrete-time descriptor system with state delay is studied, and a design method for an optimal preview controller is proposed. First, by using the discrete lifting technique, the original system is transformed into a general descriptor system without state delay in form. Then, taking advantage of the first-order forward difference operator, we construct a descriptor augmented error system, including the state vectors of the lifted system, error vectors, and desired target signals. Rigorous mathematical proofs are given for the regularity, stabilisability, causal controllability, and causal observability of the descriptor augmented error system. Based on these, the optimal preview controller with preview feedforward compensation for the original system is obtained by using the standard optimal regulator theory of the descriptor system. The effectiveness of the proposed method is shown by numerical simulation.

Structural reliability calculation method based on the dual neural network and direct integration method.

PubMed

Li, Haibin; He, Yun; Nie, Xiaobo

2018-01-01

Structural reliability analysis under uncertainty is paid wide attention by engineers and scholars due to reflecting the structural characteristics and the bearing actual situation. The direct integration method, started from the definition of reliability theory, is easy to be understood, but there are still mathematics difficulties in the calculation of multiple integrals. Therefore, a dual neural network method is proposed for calculating multiple integrals in this paper. Dual neural network consists of two neural networks. The neural network A is used to learn the integrand function, and the neural network B is used to simulate the original function. According to the derivative relationships between the network output and the network input, the neural network B is derived from the neural network A. On this basis, the performance function of normalization is employed in the proposed method to overcome the difficulty of multiple integrations and to improve the accuracy for reliability calculations. The comparisons between the proposed method and Monte Carlo simulation method, Hasofer-Lind method, the mean value first-order second moment method have demonstrated that the proposed method is an efficient and accurate reliability method for structural reliability problems.

A bat algorithm with mutation for UCAV path planning.

PubMed

Wang, Gaige; Guo, Lihong; Duan, Hong; Liu, Luo; Wang, Heqi

2012-01-01

Path planning for uninhabited combat air vehicle (UCAV) is a complicated high dimension optimization problem, which mainly centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. Original bat algorithm (BA) is used to solve the UCAV path planning problem. Furthermore, a new bat algorithm with mutation (BAM) is proposed to solve the UCAV path planning problem, and a modification is applied to mutate between bats during the process of the new solutions updating. Then, the UCAV can find the safe path by connecting the chosen nodes of the coordinates while avoiding the threat areas and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic BA. The realization procedure for original BA and this improved metaheuristic approach BAM is also presented. To prove the performance of this proposed metaheuristic method, BAM is compared with BA and other population-based optimization methods, such as ACO, BBO, DE, ES, GA, PBIL, PSO, and SGA. The experiment shows that the proposed approach is more effective and feasible in UCAV path planning than the other models.

Optimal Price Decision Problem for Simultaneous Multi-article Auction and Its Optimal Price Searching Method by Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Masuda, Kazuaki; Aiyoshi, Eitaro

We propose a method for solving optimal price decision problems for simultaneous multi-article auctions. An auction problem, originally formulated as a combinatorial problem, determines both every seller's whether or not to sell his/her article and every buyer's which article(s) to buy, so that the total utility of buyers and sellers will be maximized. Due to the duality theory, we transform it equivalently into a dual problem in which Lagrange multipliers are interpreted as articles' transaction price. As the dual problem is a continuous optimization problem with respect to the multipliers (i.e., the transaction prices), we propose a numerical method to solve it by applying heuristic global search methods. In this paper, Particle Swarm Optimization (PSO) is used to solve the dual problem, and experimental results are presented to show the validity of the proposed method.

Determination of Optimal Heat-Storage Thickness of Layer for “Smart Wall” by Methods of Nonlinear Heat Conduction Equations for Phase-transition Materials

NASA Astrophysics Data System (ADS)

Pospelova, I.

2017-11-01

The article suggests an original way of keeping heat load and its compensation for a microclimate system by proposing the “Smart Wall”. The construction consists of specially combined composite materials including phase-transition materials. The method for determination of the layer thickness is proposed for a certain accumulation time. Varying the thickness and composition of the layer it is possible to achieve a low amount of the thermal conductivity coefficient and to obtain various functional characteristics of fences.

Jack Healy Remembers - Anecdotal Evidence for the Origin of the Approximate 24-hour Urine Sampling Protocol Used for Worker Bioassay Monitoring

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

Carbaugh, Eugene H.

2008-10-01

The origin of the approximate 24-hour urine sampling protocol used at Hanford for routine bioassay is attributed to an informal study done in the mid-1940s. While the actual data were never published and have been lost, anecdotal recollections by staff involved in the initial bioassay program design and administration suggest that the sampling protocol had a solid scientific basis. Numerous alternate methods for normalizing partial day samples to represent a total 24-hour collection have since been proposed and used, but no one method is obviously preferred.

An improved design method of a tuned mass damper for an in-service footbridge

NASA Astrophysics Data System (ADS)

Shi, Weixing; Wang, Liangkun; Lu, Zheng

2018-03-01

Tuned mass damper (TMD) has a wide range of applications in the vibration control of footbridges. However, the traditional engineering design method may lead to a mistuned TMD. In this paper, an improved TMD design method based on the model updating is proposed. Firstly, the original finite element model (FEM) is studied and the natural characteristics of the in-service or newly built footbridge is identified by field test, and then the original FEM is updated. TMD is designed according to the new updated FEM, and it is optimized according to the simulation on vibration control effects. Finally, the installation and field measurement of TMD are carried out. The improved design method can be applied to both in-service and newly built footbridges. This paper illustrates the improved design method with an engineering example. The frequency identification results of field test and original FEM show that there is a relatively large difference between them. The TMD designed according to the updated FEM has better vibration control effect than the TMD designed according to the original FEM. The site test results show that TMD has good effect on controlling human-induced vibrations.

Towards breaking the spatial resolution barriers: An optical flow and super-resolution approach for sea ice motion estimation

NASA Astrophysics Data System (ADS)

Petrou, Zisis I.; Xian, Yang; Tian, YingLi

2018-04-01

Estimation of sea ice motion at fine scales is important for a number of regional and local level applications, including modeling of sea ice distribution, ocean-atmosphere and climate dynamics, as well as safe navigation and sea operations. In this study, we propose an optical flow and super-resolution approach to accurately estimate motion from remote sensing images at a higher spatial resolution than the original data. First, an external example learning-based super-resolution method is applied on the original images to generate higher resolution versions. Then, an optical flow approach is applied on the higher resolution images, identifying sparse correspondences and interpolating them to extract a dense motion vector field with continuous values and subpixel accuracies. Our proposed approach is successfully evaluated on passive microwave, optical, and Synthetic Aperture Radar data, proving appropriate for multi-sensor applications and different spatial resolutions. The approach estimates motion with similar or higher accuracy than the original data, while increasing the spatial resolution of up to eight times. In addition, the adopted optical flow component outperforms a state-of-the-art pattern matching method. Overall, the proposed approach results in accurate motion vectors with unprecedented spatial resolutions of up to 1.5 km for passive microwave data covering the entire Arctic and 20 m for radar data, and proves promising for numerous scientific and operational applications.

Supervised multiblock sparse multivariable analysis with application to multimodal brain imaging genetics.

PubMed

Kawaguchi, Atsushi; Yamashita, Fumio

2017-10-01

This article proposes a procedure for describing the relationship between high-dimensional data sets, such as multimodal brain images and genetic data. We propose a supervised technique to incorporate the clinical outcome to determine a score, which is a linear combination of variables with hieratical structures to multimodalities. This approach is expected to obtain interpretable and predictive scores. The proposed method was applied to a study of Alzheimer's disease (AD). We propose a diagnostic method for AD that involves using whole-brain magnetic resonance imaging (MRI) and positron emission tomography (PET), and we select effective brain regions for the diagnostic probability and investigate the genome-wide association with the regions using single nucleotide polymorphisms (SNPs). The two-step dimension reduction method, which we previously introduced, was considered applicable to such a study and allows us to partially incorporate the proposed method. We show that the proposed method offers classification functions with feasibility and reasonable prediction accuracy based on the receiver operating characteristic (ROC) analysis and reasonable regions of the brain and genomes. Our simulation study based on the synthetic structured data set showed that the proposed method outperformed the original method and provided the characteristic for the supervised feature. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Metabolomics study of Populus type propolis.

PubMed

Anđelković, Boban; Vujisić, Ljubodrag; Vučković, Ivan; Tešević, Vele; Vajs, Vlatka; Gođevac, Dejan

2017-02-20

Herein, we propose rapid and simple spectroscopic methods to determine the chemical composition of propolis derived from various Populus species using a metabolomics approach. In order to correlate variability in Populus type propolis composition with the altitude of its collection, NMR, IR, and UV spectroscopy followed by OPLS was conducted. The botanical origin of propolis was established by comparing propolis spectral data to those of buds of various Populus species. An O2PLS method was utilized to integrate two blocks of data. According to OPLS and O2PLS, the major compounds in propolis samples, collected from temperate continental climate above 500m, were phenolic glycerides originating from P. tremula buds. Flavonoids were predominant in propolis samples collected below 400m, originating from P. nigra and P. x euramericana buds. Samples collected at 400-500m were of mixed origin, with variable amounts of all detected metabolites. Copyright © 2016 Elsevier B.V. All rights reserved.

Pseudo-orthogonalization of memory patterns for associative memory.

PubMed

Oku, Makito; Makino, Takaki; Aihara, Kazuyuki

2013-11-01

A new method for improving the storage capacity of associative memory models on a neural network is proposed. The storage capacity of the network increases in proportion to the network size in the case of random patterns, but, in general, the capacity suffers from correlation among memory patterns. Numerous solutions to this problem have been proposed so far, but their high computational cost limits their scalability. In this paper, we propose a novel and simple solution that is locally computable without any iteration. Our method involves XNOR masking of the original memory patterns with random patterns, and the masked patterns and masks are concatenated. The resulting decorrelated patterns allow higher storage capacity at the cost of the pattern length. Furthermore, the increase in the pattern length can be reduced through blockwise masking, which results in a small amount of capacity loss. Movie replay and image recognition are presented as examples to demonstrate the scalability of the proposed method.

A Lyapunov method for stability analysis of piecewise-affine systems over non-invariant domains

NASA Astrophysics Data System (ADS)

Rubagotti, Matteo; Zaccarian, Luca; Bemporad, Alberto

2016-05-01

This paper analyses stability of discrete-time piecewise-affine systems, defined on possibly non-invariant domains, taking into account the possible presence of multiple dynamics in each of the polytopic regions of the system. An algorithm based on linear programming is proposed, in order to prove exponential stability of the origin and to find a positively invariant estimate of its region of attraction. The results are based on the definition of a piecewise-affine Lyapunov function, which is in general discontinuous on the boundaries of the regions. The proposed method is proven to lead to feasible solutions in a broader range of cases as compared to a previously proposed approach. Two numerical examples are shown, among which a case where the proposed method is applied to a closed-loop system, to which model predictive control was applied without a-priori guarantee of stability.

An improved multi-paths optimization method for video stabilization

NASA Astrophysics Data System (ADS)

Qin, Tao; Zhong, Sheng

2018-03-01

For video stabilization, the difference between original camera motion path and the optimized one is proportional to the cropping ratio and warping ratio. A good optimized path should preserve the moving tendency of the original one meanwhile the cropping ratio and warping ratio of each frame should be kept in a proper range. In this paper we use an improved warping-based motion representation model, and propose a gauss-based multi-paths optimization method to get a smoothing path and obtain a stabilized video. The proposed video stabilization method consists of two parts: camera motion path estimation and path smoothing. We estimate the perspective transform of adjacent frames according to warping-based motion representation model. It works well on some challenging videos where most previous 2D methods or 3D methods fail for lacking of long features trajectories. The multi-paths optimization method can deal well with parallax, as we calculate the space-time correlation of the adjacent grid, and then a kernel of gauss is used to weigh the motion of adjacent grid. Then the multi-paths are smoothed while minimize the crop ratio and the distortion. We test our method on a large variety of consumer videos, which have casual jitter and parallax, and achieve good results.

Mapping replication origins in yeast chromosomes.

PubMed

Brewer, B J; Fangman, W L

1991-07-01

The replicon hypothesis, first proposed in 1963 by Jacob and Brenner, states that DNA replication is controlled at sites called origins. Replication origins have been well studied in prokaryotes. However, the study of eukaryotic chromosomal origins has lagged behind, because until recently there has been no method for reliably determining the identity and location of origins from eukaryotic chromosomes. Here, we review a technique we developed with the yeast Saccharomyces cerevisiae that allows both the mapping of replication origins and an assessment of their activity. Two-dimensional agarose gel electrophoresis and Southern hybridization with total genomic DNA are used to determine whether a particular restriction fragment acquires the branched structure diagnostic of replication initiation. The technique has been used to localize origins in yeast chromosomes and assess their initiation efficiency. In some cases, origin activation is dependent upon the surrounding context. The technique is also being applied to a variety of eukaryotic organisms.

Research on Image Encryption Based on DNA Sequence and Chaos Theory

NASA Astrophysics Data System (ADS)

Tian Zhang, Tian; Yan, Shan Jun; Gu, Cheng Yan; Ren, Ran; Liao, Kai Xin

2018-04-01

Nowadays encryption is a common technique to protect image data from unauthorized access. In recent years, many scientists have proposed various encryption algorithms based on DNA sequence to provide a new idea for the design of image encryption algorithm. Therefore, a new method of image encryption based on DNA computing technology is proposed in this paper, whose original image is encrypted by DNA coding and 1-D logistic chaotic mapping. First, the algorithm uses two modules as the encryption key. The first module uses the real DNA sequence, and the second module is made by one-dimensional logistic chaos mapping. Secondly, the algorithm uses DNA complementary rules to encode original image, and uses the key and DNA computing technology to compute each pixel value of the original image, so as to realize the encryption of the whole image. Simulation results show that the algorithm has good encryption effect and security.

HUMAN EYE OPTICS: Determination of positions of optical elements of the human eye

NASA Astrophysics Data System (ADS)

Galetskii, S. O.; Cherezova, T. Yu

2009-02-01

An original method for noninvasive determining the positions of elements of intraocular optics is proposed. The analytic dependence of the measurement error on the optical-scheme parameters and the restriction in distance from the element being measured are determined within the framework of the method proposed. It is shown that the method can be efficiently used for determining the position of elements in the classical Gullstrand eye model and personalised eye models. The positions of six optical surfaces of the Gullstrand eye model and four optical surfaces of the personalised eye model can be determined with an error of less than 0.25 mm.

Alignment of chirped-pulse compressor

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

Yakovlev, I V

2012-11-30

An original method of alignment of grating compressors for ultrahigh-power CPA laser systems is proposed. The use of this method for adjustment of the grating compressor of a PEARL subpetawatt laser complex made it possible to align the diffraction gratings with a second accuracy in all three angular degrees of freedom, including alignment of the grooves, and to adjust the angles of beam incidence on the grating with a high accuracy. A simple method for measuring the difference in the groove densities of gratings with accuracy better than 0.005 lines mm{sup -1} is proposed and tested. (control of laser radiationmore » parameters)« less

Phase unwrapping with a virtual Hartmann-Shack wavefront sensor.

PubMed

Akondi, Vyas; Falldorf, Claas; Marcos, Susana; Vohnsen, Brian

2015-10-05

The use of a spatial light modulator for implementing a digital phase-shifting (PS) point diffraction interferometer (PDI) allows tunability in fringe spacing and in achieving PS without the need for mechanically moving parts. However, a small amount of detector or scatter noise could affect the accuracy of wavefront sensing. Here, a novel method of wavefront reconstruction incorporating a virtual Hartmann-Shack (HS) wavefront sensor is proposed that allows easy tuning of several wavefront sensor parameters. The proposed method was tested and compared with a Fourier unwrapping method implemented on a digital PS PDI. The rewrapping of the Fourier reconstructed wavefronts resulted in phase maps that matched well the original wrapped phase and the performance was found to be more stable and accurate than conventional methods. Through simulation studies, the superiority of the proposed virtual HS phase unwrapping method is shown in comparison with the Fourier unwrapping method in the presence of noise. Further, combining the two methods could improve accuracy when the signal-to-noise ratio is sufficiently high.

Multiscale reconstruction for MR fingerprinting.

PubMed

Pierre, Eric Y; Ma, Dan; Chen, Yong; Badve, Chaitra; Griswold, Mark A

2016-06-01

To reduce the acquisition time needed to obtain reliable parametric maps with Magnetic Resonance Fingerprinting. An iterative-denoising algorithm is initialized by reconstructing the MRF image series at low image resolution. For subsequent iterations, the method enforces pixel-wise fidelity to the best-matching dictionary template then enforces fidelity to the acquired data at slightly higher spatial resolution. After convergence, parametric maps with desirable spatial resolution are obtained through template matching of the final image series. The proposed method was evaluated on phantom and in vivo data using the highly undersampled, variable-density spiral trajectory and compared with the original MRF method. The benefits of additional sparsity constraints were also evaluated. When available, gold standard parameter maps were used to quantify the performance of each method. The proposed approach allowed convergence to accurate parametric maps with as few as 300 time points of acquisition, as compared to 1000 in the original MRF work. Simultaneous quantification of T1, T2, proton density (PD), and B0 field variations in the brain was achieved in vivo for a 256 × 256 matrix for a total acquisition time of 10.2 s, representing a three-fold reduction in acquisition time. The proposed iterative multiscale reconstruction reliably increases MRF acquisition speed and accuracy. Magn Reson Med 75:2481-2492, 2016. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Enhanced image fusion using directional contrast rules in fuzzy transform domain.

PubMed

Nandal, Amita; Rosales, Hamurabi Gamboa

2016-01-01

In this paper a novel image fusion algorithm based on directional contrast in fuzzy transform (FTR) domain is proposed. Input images to be fused are first divided into several non-overlapping blocks. The components of these sub-blocks are fused using directional contrast based fuzzy fusion rule in FTR domain. The fused sub-blocks are then transformed into original size blocks using inverse-FTR. Further, these inverse transformed blocks are fused according to select maximum based fusion rule for reconstructing the final fused image. The proposed fusion algorithm is both visually and quantitatively compared with other standard and recent fusion algorithms. Experimental results demonstrate that the proposed method generates better results than the other methods.

Learning locality preserving graph from data.

PubMed

Zhang, Yan-Ming; Huang, Kaizhu; Hou, Xinwen; Liu, Cheng-Lin

2014-11-01

Machine learning based on graph representation, or manifold learning, has attracted great interest in recent years. As the discrete approximation of data manifold, the graph plays a crucial role in these kinds of learning approaches. In this paper, we propose a novel learning method for graph construction, which is distinct from previous methods in that it solves an optimization problem with the aim of directly preserving the local information of the original data set. We show that the proposed objective has close connections with the popular Laplacian Eigenmap problem, and is hence well justified. The optimization turns out to be a quadratic programming problem with n(n-1)/2 variables (n is the number of data points). Exploiting the sparsity of the graph, we further propose a more efficient cutting plane algorithm to solve the problem, making the method better scalable in practice. In the context of clustering and semi-supervised learning, we demonstrated the advantages of our proposed method by experiments.

An Efficient Audio Watermarking Algorithm in Frequency Domain for Copyright Protection

NASA Astrophysics Data System (ADS)

Dhar, Pranab Kumar; Khan, Mohammad Ibrahim; Kim, Cheol-Hong; Kim, Jong-Myon

Digital Watermarking plays an important role for copyright protection of multimedia data. This paper proposes a new watermarking system in frequency domain for copyright protection of digital audio. In our proposed watermarking system, the original audio is segmented into non-overlapping frames. Watermarks are then embedded into the selected prominent peaks in the magnitude spectrum of each frame. Watermarks are extracted by performing the inverse operation of watermark embedding process. Simulation results indicate that the proposed watermarking system is highly robust against various kinds of attacks such as noise addition, cropping, re-sampling, re-quantization, MP3 compression, and low-pass filtering. Our proposed watermarking system outperforms Cox's method in terms of imperceptibility, while keeping comparable robustness with the Cox's method. Our proposed system achieves SNR (signal-to-noise ratio) values ranging from 20 dB to 28 dB, in contrast to Cox's method which achieves SNR values ranging from only 14 dB to 23 dB.

Contact angle adjustment in equation-of-state-based pseudopotential model.

PubMed

Hu, Anjie; Li, Longjian; Uddin, Rizwan; Liu, Dong

2016-05-01

The single component pseudopotential lattice Boltzmann model has been widely applied in multiphase simulation due to its simplicity and stability. In many studies, it has been claimed that this model can be stable for density ratios larger than 1000. However, the application of the model is still limited to small density ratios when the contact angle is considered. The reason is that the original contact angle adjustment method influences the stability of the model. Moreover, simulation results in the present work show that, by applying the original contact angle adjustment method, the density distribution near the wall is artificially changed, and the contact angle is dependent on the surface tension. Hence, it is very inconvenient to apply this method with a fixed contact angle, and the accuracy of the model cannot be guaranteed. To solve these problems, a contact angle adjustment method based on the geometry analysis is proposed and numerically compared with the original method. Simulation results show that, with our contact angle adjustment method, the stability of the model is highly improved when the density ratio is relatively large, and it is independent of the surface tension.

Contact angle adjustment in equation-of-state-based pseudopotential model

NASA Astrophysics Data System (ADS)

Hu, Anjie; Li, Longjian; Uddin, Rizwan; Liu, Dong

2016-05-01

The single component pseudopotential lattice Boltzmann model has been widely applied in multiphase simulation due to its simplicity and stability. In many studies, it has been claimed that this model can be stable for density ratios larger than 1000. However, the application of the model is still limited to small density ratios when the contact angle is considered. The reason is that the original contact angle adjustment method influences the stability of the model. Moreover, simulation results in the present work show that, by applying the original contact angle adjustment method, the density distribution near the wall is artificially changed, and the contact angle is dependent on the surface tension. Hence, it is very inconvenient to apply this method with a fixed contact angle, and the accuracy of the model cannot be guaranteed. To solve these problems, a contact angle adjustment method based on the geometry analysis is proposed and numerically compared with the original method. Simulation results show that, with our contact angle adjustment method, the stability of the model is highly improved when the density ratio is relatively large, and it is independent of the surface tension.

Perceptual video quality assessment in H.264 video coding standard using objective modeling.

PubMed

Karthikeyan, Ramasamy; Sainarayanan, Gopalakrishnan; Deepa, Subramaniam Nachimuthu

2014-01-01

Since usage of digital video is wide spread nowadays, quality considerations have become essential, and industry demand for video quality measurement is rising. This proposal provides a method of perceptual quality assessment in H.264 standard encoder using objective modeling. For this purpose, quality impairments are calculated and a model is developed to compute the perceptual video quality metric based on no reference method. Because of the shuttle difference between the original video and the encoded video the quality of the encoded picture gets degraded, this quality difference is introduced by the encoding process like Intra and Inter prediction. The proposed model takes into account of the artifacts introduced by these spatial and temporal activities in the hybrid block based coding methods and an objective modeling of these artifacts into subjective quality estimation is proposed. The proposed model calculates the objective quality metric using subjective impairments; blockiness, blur and jerkiness compared to the existing bitrate only calculation defined in the ITU G 1070 model. The accuracy of the proposed perceptual video quality metrics is compared against popular full reference objective methods as defined by VQEG.

Probabilistic durability assessment of concrete structures in marine environments: Reliability and sensitivity analysis

NASA Astrophysics Data System (ADS)

Yu, Bo; Ning, Chao-lie; Li, Bing

2017-03-01

A probabilistic framework for durability assessment of concrete structures in marine environments was proposed in terms of reliability and sensitivity analysis, which takes into account the uncertainties under the environmental, material, structural and executional conditions. A time-dependent probabilistic model of chloride ingress was established first to consider the variations in various governing parameters, such as the chloride concentration, chloride diffusion coefficient, and age factor. Then the Nataf transformation was adopted to transform the non-normal random variables from the original physical space into the independent standard Normal space. After that the durability limit state function and its gradient vector with respect to the original physical parameters were derived analytically, based on which the first-order reliability method was adopted to analyze the time-dependent reliability and parametric sensitivity of concrete structures in marine environments. The accuracy of the proposed method was verified by comparing with the second-order reliability method and the Monte Carlo simulation. Finally, the influences of environmental conditions, material properties, structural parameters and execution conditions on the time-dependent reliability of concrete structures in marine environments were also investigated. The proposed probabilistic framework can be implemented in the decision-making algorithm for the maintenance and repair of deteriorating concrete structures in marine environments.

A novel on-line spatial-temporal k-anonymity method for location privacy protection from sequence rules-based inference attacks.

PubMed

Zhang, Haitao; Wu, Chenxue; Chen, Zewei; Liu, Zhao; Zhu, Yunhong

2017-01-01

Analyzing large-scale spatial-temporal k-anonymity datasets recorded in location-based service (LBS) application servers can benefit some LBS applications. However, such analyses can allow adversaries to make inference attacks that cannot be handled by spatial-temporal k-anonymity methods or other methods for protecting sensitive knowledge. In response to this challenge, first we defined a destination location prediction attack model based on privacy-sensitive sequence rules mined from large scale anonymity datasets. Then we proposed a novel on-line spatial-temporal k-anonymity method that can resist such inference attacks. Our anti-attack technique generates new anonymity datasets with awareness of privacy-sensitive sequence rules. The new datasets extend the original sequence database of anonymity datasets to hide the privacy-sensitive rules progressively. The process includes two phases: off-line analysis and on-line application. In the off-line phase, sequence rules are mined from an original sequence database of anonymity datasets, and privacy-sensitive sequence rules are developed by correlating privacy-sensitive spatial regions with spatial grid cells among the sequence rules. In the on-line phase, new anonymity datasets are generated upon LBS requests by adopting specific generalization and avoidance principles to hide the privacy-sensitive sequence rules progressively from the extended sequence anonymity datasets database. We conducted extensive experiments to test the performance of the proposed method, and to explore the influence of the parameter K value. The results demonstrated that our proposed approach is faster and more effective for hiding privacy-sensitive sequence rules in terms of hiding sensitive rules ratios to eliminate inference attacks. Our method also had fewer side effects in terms of generating new sensitive rules ratios than the traditional spatial-temporal k-anonymity method, and had basically the same side effects in terms of non-sensitive rules variation ratios with the traditional spatial-temporal k-anonymity method. Furthermore, we also found the performance variation tendency from the parameter K value, which can help achieve the goal of hiding the maximum number of original sensitive rules while generating a minimum of new sensitive rules and affecting a minimum number of non-sensitive rules.

A novel on-line spatial-temporal k-anonymity method for location privacy protection from sequence rules-based inference attacks

PubMed Central

Wu, Chenxue; Liu, Zhao; Zhu, Yunhong

2017-01-01

Analyzing large-scale spatial-temporal k-anonymity datasets recorded in location-based service (LBS) application servers can benefit some LBS applications. However, such analyses can allow adversaries to make inference attacks that cannot be handled by spatial-temporal k-anonymity methods or other methods for protecting sensitive knowledge. In response to this challenge, first we defined a destination location prediction attack model based on privacy-sensitive sequence rules mined from large scale anonymity datasets. Then we proposed a novel on-line spatial-temporal k-anonymity method that can resist such inference attacks. Our anti-attack technique generates new anonymity datasets with awareness of privacy-sensitive sequence rules. The new datasets extend the original sequence database of anonymity datasets to hide the privacy-sensitive rules progressively. The process includes two phases: off-line analysis and on-line application. In the off-line phase, sequence rules are mined from an original sequence database of anonymity datasets, and privacy-sensitive sequence rules are developed by correlating privacy-sensitive spatial regions with spatial grid cells among the sequence rules. In the on-line phase, new anonymity datasets are generated upon LBS requests by adopting specific generalization and avoidance principles to hide the privacy-sensitive sequence rules progressively from the extended sequence anonymity datasets database. We conducted extensive experiments to test the performance of the proposed method, and to explore the influence of the parameter K value. The results demonstrated that our proposed approach is faster and more effective for hiding privacy-sensitive sequence rules in terms of hiding sensitive rules ratios to eliminate inference attacks. Our method also had fewer side effects in terms of generating new sensitive rules ratios than the traditional spatial-temporal k-anonymity method, and had basically the same side effects in terms of non-sensitive rules variation ratios with the traditional spatial-temporal k-anonymity method. Furthermore, we also found the performance variation tendency from the parameter K value, which can help achieve the goal of hiding the maximum number of original sensitive rules while generating a minimum of new sensitive rules and affecting a minimum number of non-sensitive rules. PMID:28767687

Response prediction techniques and case studies of a path blocking system based on Global Transmissibility Direct Transmissibility method

NASA Astrophysics Data System (ADS)

Wang, Zengwei; Zhu, Ping; Zhao, Jianxuan

2017-02-01

In this paper, the prediction capabilities of the Global Transmissibility Direct Transmissibility (GTDT) method are further developed. Two path blocking techniques solely using the easily measured variables of the original system to predict the response of a path blocking system are generalized to finite element models of continuous systems. The proposed techniques are derived theoretically in a general form for the scenarios of setting the response of a subsystem to zero and of removing the link between two directly connected subsystems. The objective of this paper is to verify the reliability of the proposed techniques by finite element simulations. Two typical cases, the structural vibration transmission case and the structure-borne sound case, in two different configurations are employed to illustrate the validity of proposed techniques. The points of attention for each case have been discussed, and conclusions are given. It is shown that for the two cases of blocking a subsystem the proposed techniques are able to predict the new response using measured variables of the original system, even though operational forces are unknown. For the structural vibration transmission case of removing a connector between two components, the proposed techniques are available only when the rotational component responses of the connector are very small. The proposed techniques offer relative path measures and provide an alternative way to deal with NVH problems. The work in this paper provides guidance and reference for the engineering application of the GTDT prediction techniques.

Least-Squares Support Vector Machine Approach to Viral Replication Origin Prediction

PubMed Central

Cruz-Cano, Raul; Chew, David S.H.; Kwok-Pui, Choi; Ming-Ying, Leung

2010-01-01

Replication of their DNA genomes is a central step in the reproduction of many viruses. Procedures to find replication origins, which are initiation sites of the DNA replication process, are therefore of great importance for controlling the growth and spread of such viruses. Existing computational methods for viral replication origin prediction have mostly been tested within the family of herpesviruses. This paper proposes a new approach by least-squares support vector machines (LS-SVMs) and tests its performance not only on the herpes family but also on a collection of caudoviruses coming from three viral families under the order of caudovirales. The LS-SVM approach provides sensitivities and positive predictive values superior or comparable to those given by the previous methods. When suitably combined with previous methods, the LS-SVM approach further improves the prediction accuracy for the herpesvirus replication origins. Furthermore, by recursive feature elimination, the LS-SVM has also helped find the most significant features of the data sets. The results suggest that the LS-SVMs will be a highly useful addition to the set of computational tools for viral replication origin prediction and illustrate the value of optimization-based computing techniques in biomedical applications. PMID:20729987

Least-Squares Support Vector Machine Approach to Viral Replication Origin Prediction.

PubMed

Cruz-Cano, Raul; Chew, David S H; Kwok-Pui, Choi; Ming-Ying, Leung

2010-06-01

Replication of their DNA genomes is a central step in the reproduction of many viruses. Procedures to find replication origins, which are initiation sites of the DNA replication process, are therefore of great importance for controlling the growth and spread of such viruses. Existing computational methods for viral replication origin prediction have mostly been tested within the family of herpesviruses. This paper proposes a new approach by least-squares support vector machines (LS-SVMs) and tests its performance not only on the herpes family but also on a collection of caudoviruses coming from three viral families under the order of caudovirales. The LS-SVM approach provides sensitivities and positive predictive values superior or comparable to those given by the previous methods. When suitably combined with previous methods, the LS-SVM approach further improves the prediction accuracy for the herpesvirus replication origins. Furthermore, by recursive feature elimination, the LS-SVM has also helped find the most significant features of the data sets. The results suggest that the LS-SVMs will be a highly useful addition to the set of computational tools for viral replication origin prediction and illustrate the value of optimization-based computing techniques in biomedical applications.

Enhancing multiple-point geostatistical modeling: 1. Graph theory and pattern adjustment

NASA Astrophysics Data System (ADS)

Tahmasebi, Pejman; Sahimi, Muhammad

2016-03-01

In recent years, higher-order geostatistical methods have been used for modeling of a wide variety of large-scale porous media, such as groundwater aquifers and oil reservoirs. Their popularity stems from their ability to account for qualitative data and the great flexibility that they offer for conditioning the models to hard (quantitative) data, which endow them with the capability for generating realistic realizations of porous formations with very complex channels, as well as features that are mainly a barrier to fluid flow. One group of such models consists of pattern-based methods that use a set of data points for generating stochastic realizations by which the large-scale structure and highly-connected features are reproduced accurately. The cross correlation-based simulation (CCSIM) algorithm, proposed previously by the authors, is a member of this group that has been shown to be capable of simulating multimillion cell models in a matter of a few CPU seconds. The method is, however, sensitive to pattern's specifications, such as boundaries and the number of replicates. In this paper the original CCSIM algorithm is reconsidered and two significant improvements are proposed for accurately reproducing large-scale patterns of heterogeneities in porous media. First, an effective boundary-correction method based on the graph theory is presented by which one identifies the optimal cutting path/surface for removing the patchiness and discontinuities in the realization of a porous medium. Next, a new pattern adjustment method is proposed that automatically transfers the features in a pattern to one that seamlessly matches the surrounding patterns. The original CCSIM algorithm is then combined with the two methods and is tested using various complex two- and three-dimensional examples. It should, however, be emphasized that the methods that we propose in this paper are applicable to other pattern-based geostatistical simulation methods.

Fruit fly optimization based least square support vector regression for blind image restoration

NASA Astrophysics Data System (ADS)

Zhang, Jiao; Wang, Rui; Li, Junshan; Yang, Yawei

2014-11-01

The goal of image restoration is to reconstruct the original scene from a degraded observation. It is a critical and challenging task in image processing. Classical restorations require explicit knowledge of the point spread function and a description of the noise as priors. However, it is not practical for many real image processing. The recovery processing needs to be a blind image restoration scenario. Since blind deconvolution is an ill-posed problem, many blind restoration methods need to make additional assumptions to construct restrictions. Due to the differences of PSF and noise energy, blurring images can be quite different. It is difficult to achieve a good balance between proper assumption and high restoration quality in blind deconvolution. Recently, machine learning techniques have been applied to blind image restoration. The least square support vector regression (LSSVR) has been proven to offer strong potential in estimating and forecasting issues. Therefore, this paper proposes a LSSVR-based image restoration method. However, selecting the optimal parameters for support vector machine is essential to the training result. As a novel meta-heuristic algorithm, the fruit fly optimization algorithm (FOA) can be used to handle optimization problems, and has the advantages of fast convergence to the global optimal solution. In the proposed method, the training samples are created from a neighborhood in the degraded image to the central pixel in the original image. The mapping between the degraded image and the original image is learned by training LSSVR. The two parameters of LSSVR are optimized though FOA. The fitness function of FOA is calculated by the restoration error function. With the acquired mapping, the degraded image can be recovered. Experimental results show the proposed method can obtain satisfactory restoration effect. Compared with BP neural network regression, SVR method and Lucy-Richardson algorithm, it speeds up the restoration rate and performs better. Both objective and subjective restoration performances are studied in the comparison experiments.

Cancelable ECG biometrics using GLRT and performance improvement using guided filter with irreversible guide signal.

PubMed

Kim, Hanvit; Minh Phuong Nguyen; Se Young Chun

2017-07-01

Biometrics such as ECG provides a convenient and powerful security tool to verify or identify an individual. However, one important drawback of biometrics is that it is irrevocable. In other words, biometrics cannot be re-used practically once it is compromised. Cancelable biometrics has been investigated to overcome this drawback. In this paper, we propose a cancelable ECG biometrics by deriving a generalized likelihood ratio test (GLRT) detector from a composite hypothesis testing in randomly projected domain. Since it is common to observe performance degradation for cancelable biometrics, we also propose a guided filtering (GF) with irreversible guide signal that is a non-invertibly transformed signal of ECG authentication template. We evaluated our proposed method using ECG-ID database with 89 subjects. Conventional Euclidean detector with original ECG template yielded 93.9% PD1 (detection probability at 1% FAR) while Euclidean detector with 10% compressed ECG (1/10 of the original data size) yielded 90.8% PD1. Our proposed GLRT detector with 10% compressed ECG yielded 91.4%, which is better than Euclidean with the same compressed ECG. GF with our proposed irreversible ECG template further improved the performance of our GLRT with 10% compressed ECG up to 94.3%, which is higher than Euclidean detector with original ECG. Lastly, we showed that our proposed cancelable ECG biometrics practically met cancelable biometrics criteria such as efficiency, re-usability, diversity and non-invertibility.

Kernel-aligned multi-view canonical correlation analysis for image recognition

NASA Astrophysics Data System (ADS)

Su, Shuzhi; Ge, Hongwei; Yuan, Yun-Hao

2016-09-01

Existing kernel-based correlation analysis methods mainly adopt a single kernel in each view. However, only a single kernel is usually insufficient to characterize nonlinear distribution information of a view. To solve the problem, we transform each original feature vector into a 2-dimensional feature matrix by means of kernel alignment, and then propose a novel kernel-aligned multi-view canonical correlation analysis (KAMCCA) method on the basis of the feature matrices. Our proposed method can simultaneously employ multiple kernels to better capture the nonlinear distribution information of each view, so that correlation features learned by KAMCCA can have well discriminating power in real-world image recognition. Extensive experiments are designed on five real-world image datasets, including NIR face images, thermal face images, visible face images, handwritten digit images, and object images. Promising experimental results on the datasets have manifested the effectiveness of our proposed method.

Petri Net controller synthesis based on decomposed manufacturing models.

PubMed

Dideban, Abbas; Zeraatkar, Hashem

2018-06-01

Utilizing of supervisory control theory on the real systems in many modeling tools such as Petri Net (PN) becomes challenging in recent years due to the significant states in the automata models or uncontrollable events. The uncontrollable events initiate the forbidden states which might be removed by employing some linear constraints. Although there are many methods which have been proposed to reduce these constraints, enforcing them to a large-scale system is very difficult and complicated. This paper proposes a new method for controller synthesis based on PN modeling. In this approach, the original PN model is broken down into some smaller models in which the computational cost reduces significantly. Using this method, it is easy to reduce and enforce the constraints to a Petri net model. The appropriate results of our proposed method on the PN models denote worthy controller synthesis for the large scale systems. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

[Up-to-date methods of cell therapy in treatment of burns].

PubMed

Smirnov, S V; Kiselev, I V; Vasil'ev, A V; Terskikh, V V

2003-01-01

Complex of methods for repair of wounded and burned skin with transplantation of keratinocytes and fibroblasts grown in vitro is proposed. Five different tissue constructions may be used. Original method of skin recovery based on use of alive equivalent of skin is developed. Results of its clinical application are presented. It is concluded that cell constructions may be used in combined treatment of wounds and burns.

Clustered Multi-Task Learning for Automatic Radar Target Recognition

PubMed Central

Li, Cong; Bao, Weimin; Xu, Luping; Zhang, Hua

2017-01-01

Model training is a key technique for radar target recognition. Traditional model training algorithms in the framework of single task leaning ignore the relationships among multiple tasks, which degrades the recognition performance. In this paper, we propose a clustered multi-task learning, which can reveal and share the multi-task relationships for radar target recognition. To further make full use of these relationships, the latent multi-task relationships in the projection space are taken into consideration. Specifically, a constraint term in the projection space is proposed, the main idea of which is that multiple tasks within a close cluster should be close to each other in the projection space. In the proposed method, the cluster structures and multi-task relationships can be autonomously learned and utilized in both of the original and projected space. In view of the nonlinear characteristics of radar targets, the proposed method is extended to a non-linear kernel version and the corresponding non-linear multi-task solving method is proposed. Comprehensive experimental studies on simulated high-resolution range profile dataset and MSTAR SAR public database verify the superiority of the proposed method to some related algorithms. PMID:28953267

A novel method to infer the origin of polyploids from Amplified Fragment Length Polymorphism data reveals that the alpine polyploid complex of Senecio carniolicus (Asteraceae) evolved mainly via autopolyploidy.

PubMed

Winkler, Manuela; Escobar García, Pedro; Gattringer, Andreas; Sonnleitner, Michaela; Hülber, Karl; Schönswetter, Peter; Schneeweiss, Gerald M

2017-09-01

Despite its evolutionary and ecological relevance, the mode of polyploid origin has been notoriously difficult to be reconstructed from molecular data. Here, we present a method to identify the putative parents of polyploids and thus to infer the mode of their origin (auto- vs. allopolyploidy) from Amplified Fragment Length Polymorphism (AFLP) data. To this end, we use Cohen's d of distances between in silico polyploids, generated within a priori defined scenarios of origin from a priori delimited putative parental entities (e.g. taxa, genetic lineages), and natural polyploids. Simulations show that the discriminatory power of the proposed method increases mainly with increasing divergence between the lower-ploid putative ancestors and less so with increasing delay of polyploidization relative to the time of divergence. We apply the new method to the Senecio carniolicus aggregate, distributed in the European Alps and comprising two diploid, one tetraploid and one hexaploid species. In the eastern part of its distribution, the S. carniolicus aggregate was inferred to comprise an autopolyploid series, whereas for western populations of the tetraploid species, an allopolyploid origin involving the two diploid species was the most likely scenario. Although this suggests that the tetraploid species has two independent origins, other evidence (ribotype distribution, morphology) is consistent with the hypothesis of an autopolyploid origin with subsequent introgression by the second diploid species. Altogether, identifying the best among alternative scenarios using Cohen's d can be straightforward, but particular scenarios, such as allopolyploid origin vs. autopolyploid origin with subsequent introgression, remain difficult to be distinguished. © 2016 John Wiley & Sons Ltd.

A manipulative instrument with simultaneous gesture and end-effector trajectory planning and controlling

NASA Astrophysics Data System (ADS)

Lin, Hsien-I.; Nguyen, Xuan-Anh

2017-05-01

To operate a redundant manipulator to accomplish the end-effector trajectory planning and simultaneously control its gesture in online programming, incorporating the human motion is a useful and flexible option. This paper focuses on a manipulative instrument that can simultaneously control its arm gesture and end-effector trajectory via human teleoperation. The instrument can be classified by two parts; first, for the human motion capture and data processing, marker systems are proposed to capture human gesture. Second, the manipulator kinematics control is implemented by an augmented multi-tasking method, and forward and backward reaching inverse kinematics, respectively. Especially, the local-solution and divergence problems of a multi-tasking method are resolved by the proposed augmented multi-tasking method. Computer simulations and experiments with a 7-DOF (degree of freedom) redundant manipulator were used to validate the proposed method. Comparison among the single-tasking, original multi-tasking, and augmented multi-tasking algorithms were performed and the result showed that the proposed augmented method had a good end-effector position accuracy and the most similar gesture to the human gesture. Additionally, the experimental results showed that the proposed instrument was realized online.

A blind dual color images watermarking based on IWT and state coding

NASA Astrophysics Data System (ADS)

Su, Qingtang; Niu, Yugang; Liu, Xianxi; Zhu, Yu

2012-04-01

In this paper, a state-coding based blind watermarking algorithm is proposed to embed color image watermark to color host image. The technique of state coding, which makes the state code of data set be equal to the hiding watermark information, is introduced in this paper. When embedding watermark, using Integer Wavelet Transform (IWT) and the rules of state coding, these components, R, G and B, of color image watermark are embedded to these components, Y, Cr and Cb, of color host image. Moreover, the rules of state coding are also used to extract watermark from the watermarked image without resorting to the original watermark or original host image. Experimental results show that the proposed watermarking algorithm cannot only meet the demand on invisibility and robustness of the watermark, but also have well performance compared with other proposed methods considered in this work.

Adaptive compressive learning for prediction of protein-protein interactions from primary sequence.

PubMed

Zhang, Ya-Nan; Pan, Xiao-Yong; Huang, Yan; Shen, Hong-Bin

2011-08-21

Protein-protein interactions (PPIs) play an important role in biological processes. Although much effort has been devoted to the identification of novel PPIs by integrating experimental biological knowledge, there are still many difficulties because of lacking enough protein structural and functional information. It is highly desired to develop methods based only on amino acid sequences for predicting PPIs. However, sequence-based predictors are often struggling with the high-dimensionality causing over-fitting and high computational complexity problems, as well as the redundancy of sequential feature vectors. In this paper, a novel computational approach based on compressed sensing theory is proposed to predict yeast Saccharomyces cerevisiae PPIs from primary sequence and has achieved promising results. The key advantage of the proposed compressed sensing algorithm is that it can compress the original high-dimensional protein sequential feature vector into a much lower but more condensed space taking the sparsity property of the original signal into account. What makes compressed sensing much more attractive in protein sequence analysis is its compressed signal can be reconstructed from far fewer measurements than what is usually considered necessary in traditional Nyquist sampling theory. Experimental results demonstrate that proposed compressed sensing method is powerful for analyzing noisy biological data and reducing redundancy in feature vectors. The proposed method represents a new strategy of dealing with high-dimensional protein discrete model and has great potentiality to be extended to deal with many other complicated biological systems. Copyright © 2011 Elsevier Ltd. All rights reserved.

Chaotification of complex networks with impulsive control.

PubMed

Guan, Zhi-Hong; Liu, Feng; Li, Juan; Wang, Yan-Wu

2012-06-01

This paper investigates the chaotification problem of complex dynamical networks (CDN) with impulsive control. Both the discrete and continuous cases are studied. The method is presented to drive all states of every node in CDN to chaos. The proposed impulsive control strategy is effective for both the originally stable and unstable CDN. The upper bound of the impulse intervals for originally stable networks is derived. Finally, the effectiveness of the theoretical results is verified by numerical examples.

A novel weight determination method for time series data aggregation

NASA Astrophysics Data System (ADS)

Xu, Paiheng; Zhang, Rong; Deng, Yong

2017-09-01

Aggregation in time series is of great importance in time series smoothing, predicting and other time series analysis process, which makes it crucial to address the weights in times series correctly and reasonably. In this paper, a novel method to obtain the weights in time series is proposed, in which we adopt induced ordered weighted aggregation (IOWA) operator and visibility graph averaging (VGA) operator and linearly combine the weights separately generated by the two operator. The IOWA operator is introduced to the weight determination of time series, through which the time decay factor is taken into consideration. The VGA operator is able to generate weights with respect to the degree distribution in the visibility graph constructed from the corresponding time series, which reflects the relative importance of vertices in time series. The proposed method is applied to two practical datasets to illustrate its merits. The aggregation of Construction Cost Index (CCI) demonstrates the ability of proposed method to smooth time series, while the aggregation of The Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) illustrate how proposed method maintain the variation tendency of original data.

Color transfer method preserving perceived lightness

NASA Astrophysics Data System (ADS)

Ueda, Chiaki; Azetsu, Tadahiro; Suetake, Noriaki; Uchino, Eiji

2016-06-01

Color transfer originally proposed by Reinhard et al. is a method to change the color appearance of an input image by using the color information of a reference image. The purpose of this study is to modify color transfer so that it works well even when the scenes of the input and reference images are not similar. Concretely, a color transfer method with lightness correction and color gamut adjustment is proposed. The lightness correction is applied to preserve the perceived lightness which is explained by the Helmholtz-Kohlrausch (H-K) effect. This effect is the phenomenon that vivid colors are perceived as brighter than dull colors with the same lightness. Hence, when the chroma is changed by image processing, the perceived lightness is also changed even if the physical lightness is preserved after the image processing. In the proposed method, by considering the H-K effect, color transfer that preserves the perceived lightness after processing is realized. Furthermore, color gamut adjustment is introduced to address the color gamut problem, which is caused by color space conversion. The effectiveness of the proposed method is verified by performing some experiments.

Non-destructive geographical traceability of sea cucumber (Apostichopus japonicus) using near infrared spectroscopy combined with chemometric methods

PubMed Central

Cai, Rui; Wang, Shisheng; Tang, Bo; Li, Yueqing; Zhao, Weijie

2018-01-01

Sea cucumber is the major tonic seafood worldwide, and geographical origin traceability is an important part of its quality and safety control. In this work, a non-destructive method for origin traceability of sea cucumber (Apostichopus japonicus) from northern China Sea and East China Sea using near infrared spectroscopy (NIRS) and multivariate analysis methods was proposed. Total fat contents of 189 fresh sea cucumber samples were determined and partial least-squares (PLS) regression was used to establish the quantitative NIRS model. The ordered predictor selection algorithm was performed to select feasible wavelength regions for the construction of PLS and identification models. The identification model was developed by principal component analysis combined with Mahalanobis distance and scaling to the first range algorithms. In the test set of the optimum PLS models, the root mean square error of prediction was 0.45, and correlation coefficient was 0.90. The correct classification rates of 100% were obtained in both identification calibration model and test model. The overall results indicated that NIRS method combined with chemometric analysis was a suitable tool for origin traceability and identification of fresh sea cucumber samples from nine origins in China. PMID:29410795

Non-destructive geographical traceability of sea cucumber (Apostichopus japonicus) using near infrared spectroscopy combined with chemometric methods.

PubMed

Guo, Xiuhan; Cai, Rui; Wang, Shisheng; Tang, Bo; Li, Yueqing; Zhao, Weijie

2018-01-01

Sea cucumber is the major tonic seafood worldwide, and geographical origin traceability is an important part of its quality and safety control. In this work, a non-destructive method for origin traceability of sea cucumber ( Apostichopus japonicus ) from northern China Sea and East China Sea using near infrared spectroscopy (NIRS) and multivariate analysis methods was proposed. Total fat contents of 189 fresh sea cucumber samples were determined and partial least-squares (PLS) regression was used to establish the quantitative NIRS model. The ordered predictor selection algorithm was performed to select feasible wavelength regions for the construction of PLS and identification models. The identification model was developed by principal component analysis combined with Mahalanobis distance and scaling to the first range algorithms. In the test set of the optimum PLS models, the root mean square error of prediction was 0.45, and correlation coefficient was 0.90. The correct classification rates of 100% were obtained in both identification calibration model and test model. The overall results indicated that NIRS method combined with chemometric analysis was a suitable tool for origin traceability and identification of fresh sea cucumber samples from nine origins in China.

Numerical solution of the general coupled nonlinear Schrödinger equations on unbounded domains.

PubMed

Li, Hongwei; Guo, Yue

2017-12-01

The numerical solution of the general coupled nonlinear Schrödinger equations on unbounded domains is considered by applying the artificial boundary method in this paper. In order to design the local absorbing boundary conditions for the coupled nonlinear Schrödinger equations, we generalize the unified approach previously proposed [J. Zhang et al., Phys. Rev. E 78, 026709 (2008)PLEEE81539-375510.1103/PhysRevE.78.026709]. Based on the methodology underlying the unified approach, the original problem is split into two parts, linear and nonlinear terms, and we then achieve a one-way operator to approximate the linear term to make the wave out-going, and finally we combine the one-way operator with the nonlinear term to derive the local absorbing boundary conditions. Then we reduce the original problem into an initial boundary value problem on the bounded domain, which can be solved by the finite difference method. The stability of the reduced problem is also analyzed by introducing some auxiliary variables. Ample numerical examples are presented to verify the accuracy and effectiveness of our proposed method.

Robust and efficient method for matching features in omnidirectional images

NASA Astrophysics Data System (ADS)

Zhu, Qinyi; Zhang, Zhijiang; Zeng, Dan

2018-04-01

Binary descriptors have been widely used in many real-time applications due to their efficiency. These descriptors are commonly designed for perspective images but perform poorly on omnidirectional images, which are severely distorted. To address this issue, this paper proposes tangent plane BRIEF (TPBRIEF) and adapted log polar grid-based motion statistics (ALPGMS). TPBRIEF projects keypoints to a unit sphere and applies the fixed test set in BRIEF descriptor on the tangent plane of the unit sphere. The fixed test set is then backprojected onto the original distorted images to construct the distortion invariant descriptor. TPBRIEF directly enables keypoint detecting and feature describing on original distorted images, whereas other approaches correct the distortion through image resampling, which introduces artifacts and adds time cost. With ALPGMS, omnidirectional images are divided into circular arches named adapted log polar grids. Whether a match is true or false is then determined by simply thresholding the match numbers in a grid pair where the two matched points located. Experiments show that TPBRIEF greatly improves the feature matching accuracy and ALPGMS robustly removes wrong matches. Our proposed method outperforms the state-of-the-art methods.

Inference of median difference based on the Box-Cox model in randomized clinical trials.

PubMed

Maruo, K; Isogawa, N; Gosho, M

2015-05-10

In randomized clinical trials, many medical and biological measurements are not normally distributed and are often skewed. The Box-Cox transformation is a powerful procedure for comparing two treatment groups for skewed continuous variables in terms of a statistical test. However, it is difficult to directly estimate and interpret the location difference between the two groups on the original scale of the measurement. We propose a helpful method that infers the difference of the treatment effect on the original scale in a more easily interpretable form. We also provide statistical analysis packages that consistently include an estimate of the treatment effect, covariance adjustments, standard errors, and statistical hypothesis tests. The simulation study that focuses on randomized parallel group clinical trials with two treatment groups indicates that the performance of the proposed method is equivalent to or better than that of the existing non-parametric approaches in terms of the type-I error rate and power. We illustrate our method with cluster of differentiation 4 data in an acquired immune deficiency syndrome clinical trial. Copyright © 2015 John Wiley & Sons, Ltd.

A bulk viscosity approach for shock capturing on unstructured grids

NASA Astrophysics Data System (ADS)

Shoeybi, Mohammad; Larsson, Nils Johan; Ham, Frank; Moin, Parviz

2008-11-01

The bulk viscosity approach for shock capturing (Cook and Cabot, JCP, 2005) augments the bulk part of the viscous stress tensor. The intention is to capture shock waves without dissipating turbulent structures. The present work extends and modifies this method for unstructured grids. We propose a method that properly scales the bulk viscosity with the grid spacing normal to the shock for unstructured grid for which the shock is not necessarily aligned with the grid. The magnitude of the strain rate tensor used in the original formulation is replaced with the dilatation, which appears to be more appropriate in the vortical turbulent flow regions (Mani et al., 2008). The original form of the model is found to have an impact on dilatational motions away form the shock wave, which is eliminated by a proposed localization of the bulk viscosity. Finally, to allow for grid adaptation around shock waves, an explicit/implicit time advancement scheme has been developed that adaptively identifies the stiff regions. The full method has been verified with several test cases, including 2D shock-vorticity entropy interaction, homogenous isotropic turbulence, and turbulent flow over a cylinder.

Modified Method of Adaptive Artificial Viscosity for Solution of Gas Dynamics Problems on Parallel Computer Systems

NASA Astrophysics Data System (ADS)

Popov, Igor; Sukov, Sergey

2018-02-01

A modification of the adaptive artificial viscosity (AAV) method is considered. This modification is based on one stage time approximation and is adopted to calculation of gasdynamics problems on unstructured grids with an arbitrary type of grid elements. The proposed numerical method has simplified logic, better performance and parallel efficiency compared to the implementation of the original AAV method. Computer experiments evidence the robustness and convergence of the method to difference solution.

CancerLocator: non-invasive cancer diagnosis and tissue-of-origin prediction using methylation profiles of cell-free DNA.

PubMed

Kang, Shuli; Li, Qingjiao; Chen, Quan; Zhou, Yonggang; Park, Stacy; Lee, Gina; Grimes, Brandon; Krysan, Kostyantyn; Yu, Min; Wang, Wei; Alber, Frank; Sun, Fengzhu; Dubinett, Steven M; Li, Wenyuan; Zhou, Xianghong Jasmine

2017-03-24

We propose a probabilistic method, CancerLocator, which exploits the diagnostic potential of cell-free DNA by determining not only the presence but also the location of tumors. CancerLocator simultaneously infers the proportions and the tissue-of-origin of tumor-derived cell-free DNA in a blood sample using genome-wide DNA methylation data. CancerLocator outperforms two established multi-class classification methods on simulations and real data, even with the low proportion of tumor-derived DNA in the cell-free DNA scenarios. CancerLocator also achieves promising results on patient plasma samples with low DNA methylation sequencing coverage.

Assessing the Availability of Fertility Regulation Methods: Report on a Methodological Study. Scientific Reports, No. 1, February 1977.

ERIC Educational Resources Information Center

Rodriguez, German

The report investigates the problems of assessing the availability of fertility regulation methods in the household and the community. The study originated from the need to evaluate a number of proposed additions to the data collection instruments used by the World Fertility Survey (WFS). The core questionnaire was modified to add the following…

Assessing the Resolution Adaptability of the Zhang-McFarlane Cumulus Parameterization With Spatial and Temporal Averaging: RESOLUTION ADAPTABILITY OF ZM SCHEME

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

Yun, Yuxing; Fan, Jiwen; Xiao, Heng

Realistic modeling of cumulus convection at fine model resolutions (a few to a few tens of km) is problematic since it requires the cumulus scheme to adapt to higher resolution than they were originally designed for (~100 km). To solve this problem, we implement the spatial averaging method proposed in Xiao et al. (2015) and also propose a temporal averaging method for the large-scale convective available potential energy (CAPE) tendency in the Zhang-McFarlane (ZM) cumulus parameterization. The resolution adaptability of the original ZM scheme, the scheme with spatial averaging, and the scheme with both spatial and temporal averaging at 4-32more » km resolution is assessed using the Weather Research and Forecasting (WRF) model, by comparing with Cloud Resolving Model (CRM) results. We find that the original ZM scheme has very poor resolution adaptability, with sub-grid convective transport and precipitation increasing significantly as the resolution increases. The spatial averaging method improves the resolution adaptability of the ZM scheme and better conserves the total transport of moist static energy and total precipitation. With the temporal averaging method, the resolution adaptability of the scheme is further improved, with sub-grid convective precipitation becoming smaller than resolved precipitation for resolution higher than 8 km, which is consistent with the results from the CRM simulation. Both the spatial distribution and time series of precipitation are improved with the spatial and temporal averaging methods. The results may be helpful for developing resolution adaptability for other cumulus parameterizations that are based on quasi-equilibrium assumption.« less

Research on High Accuracy Detection of Red Tide Hyperspecrral Based on Deep Learning Cnn

NASA Astrophysics Data System (ADS)

Hu, Y.; Ma, Y.; An, J.

2018-04-01

Increasing frequency in red tide outbreaks has been reported around the world. It is of great concern due to not only their adverse effects on human health and marine organisms, but also their impacts on the economy of the affected areas. this paper put forward a high accuracy detection method based on a fully-connected deep CNN detection model with 8-layers to monitor red tide in hyperspectral remote sensing images, then make a discussion of the glint suppression method for improving the accuracy of red tide detection. The results show that the proposed CNN hyperspectral detection model can detect red tide accurately and effectively. The red tide detection accuracy of the proposed CNN model based on original image and filter-image is 95.58 % and 97.45 %, respectively, and compared with the SVM method, the CNN detection accuracy is increased by 7.52 % and 2.25 %. Compared with SVM method base on original image, the red tide CNN detection accuracy based on filter-image increased by 8.62 % and 6.37 %. It also indicates that the image glint affects the accuracy of red tide detection seriously.

Near-Optimal Guidance Method for Maximizing the Reachable Domain of Gliding Aircraft

NASA Astrophysics Data System (ADS)

Tsuchiya, Takeshi

This paper proposes a guidance method for gliding aircraft by using onboard computers to calculate a near-optimal trajectory in real-time, and thereby expanding the reachable domain. The results are applicable to advanced aircraft and future space transportation systems that require high safety. The calculation load of the optimal control problem that is used to maximize the reachable domain is too large for current computers to calculate in real-time. Thus the optimal control problem is divided into two problems: a gliding distance maximization problem in which the aircraft motion is limited to a vertical plane, and an optimal turning flight problem in a horizontal direction. First, the former problem is solved using a shooting method. It can be solved easily because its scale is smaller than that of the original problem, and because some of the features of the optimal solution are obtained in the first part of this paper. Next, in the latter problem, the optimal bank angle is computed from the solution of the former; this is an analytical computation, rather than an iterative computation. Finally, the reachable domain obtained from the proposed near-optimal guidance method is compared with that obtained from the original optimal control problem.

Combining Correlation Matrices: Simulation Analysis of Improved Fixed-Effects Methods

ERIC Educational Resources Information Center

Hafdahl, Adam R.

2007-01-01

The originally proposed multivariate meta-analysis approach for correlation matrices--analyze Pearson correlations, with each study's observed correlations replacing their population counterparts in its conditional-covariance matrix--performs poorly. Two refinements are considered: Analyze Fisher Z-transformed correlations, and substitute better…

Proposal of a punch biopsy protocol as a pre-requisite for the establishment of a tissue bank from resected esophageal tumors.

PubMed

Bonavina, Luigi; Laface, Letizia; Picozzi, Stefano; Nencioni, Marco; Siboni, Stefano; Bona, Davide; Sironi, Andrea; Sorba, Francesca; Clemente, Claudio

2010-09-01

With the development of tissue banking, a need for homogeneous methods of collection, processing, and storage of tissue has emerged. We describe the implementation of a biological bank in a high-volume, tertiary care University referral center for esophageal cancer surgery. We also propose an original punch biopsy technique of the surgical specimen. The method proved to be simple, reproducible, and not expensive. Unified standards for specimen collection are necessary to improve results of specimen-based diagnostic testing and research in surgical oncology.

A non-Hertzian method for solving wheel-rail normal contact problem taking into account the effect of yaw

NASA Astrophysics Data System (ADS)

Liu, Binbin; Bruni, Stefano; Vollebregt, Edwin

2016-09-01

A novel approach is proposed in this paper to deal with non-Hertzian normal contact in wheel-rail interface, extending the widely used Kik-Piotrowski method. The new approach is able to consider the effect of the yaw angle of the wheelset against the rail on the shape of the contact patch and on pressure distribution. Furthermore, the method considers the variation of profile curvature across the contact patch, enhancing the correspondence to CONTACT for highly non-Hertzian contact conditions. The simulation results show that the proposed method can provide more accurate estimation than the original algorithm compared to Kalker's CONTACT, and that the influence of yaw on the contact results is significant under certain circumstances.

A method for reducing the order of nonlinear dynamic systems

NASA Astrophysics Data System (ADS)

Masri, S. F.; Miller, R. K.; Sassi, H.; Caughey, T. K.

1984-06-01

An approximate method that uses conventional condensation techniques for linear systems together with the nonparametric identification of the reduced-order model generalized nonlinear restoring forces is presented for reducing the order of discrete multidegree-of-freedom dynamic systems that possess arbitrary nonlinear characteristics. The utility of the proposed method is demonstrated by considering a redundant three-dimensional finite-element model half of whose elements incorporate hysteretic properties. A nonlinear reduced-order model, of one-third the order of the original model, is developed on the basis of wideband stationary random excitation and the validity of the reduced-order model is subsequently demonstrated by its ability to predict with adequate accuracy the transient response of the original nonlinear model under a different nonstationary random excitation.

Fuji apple storage time rapid determination method using Vis/NIR spectroscopy.

PubMed

Liu, Fuqi; Tang, Xuxiang

2015-01-01

Fuji apple storage time rapid determination method using visible/near-infrared (Vis/NIR) spectroscopy was studied in this paper. Vis/NIR diffuse reflection spectroscopy responses to samples were measured for 6 days. Spectroscopy data were processed by stochastic resonance (SR). Principal component analysis (PCA) was utilized to analyze original spectroscopy data and SNR eigen value. Results demonstrated that PCA could not totally discriminate Fuji apples using original spectroscopy data. Signal-to-noise ratio (SNR) spectrum clearly classified all apple samples. PCA using SNR spectrum successfully discriminated apple samples. Therefore, Vis/NIR spectroscopy was effective for Fuji apple storage time rapid discrimination. The proposed method is also promising in condition safety control and management for food and environmental laboratories.

Medical image security using modified chaos-based cryptography approach

NASA Astrophysics Data System (ADS)

Talib Gatta, Methaq; Al-latief, Shahad Thamear Abd

2018-05-01

The progressive development in telecommunication and networking technologies have led to the increased popularity of telemedicine usage which involve storage and transfer of medical images and related information so security concern is emerged. This paper presents a method to provide the security to the medical images since its play a major role in people healthcare organizations. The main idea in this work based on the chaotic sequence in order to provide efficient encryption method that allows reconstructing the original image from the encrypted image with high quality and minimum distortion in its content and doesn’t effect in human treatment and diagnosing. Experimental results prove the efficiency of the proposed method using some of statistical measures and robust correlation between original image and decrypted image.

Recurrence Density Enhanced Complex Networks for Nonlinear Time Series Analysis

NASA Astrophysics Data System (ADS)

Costa, Diego G. De B.; Reis, Barbara M. Da F.; Zou, Yong; Quiles, Marcos G.; Macau, Elbert E. N.

We introduce a new method, which is entitled Recurrence Density Enhanced Complex Network (RDE-CN), to properly analyze nonlinear time series. Our method first transforms a recurrence plot into a figure of a reduced number of points yet preserving the main and fundamental recurrence properties of the original plot. This resulting figure is then reinterpreted as a complex network, which is further characterized by network statistical measures. We illustrate the computational power of RDE-CN approach by time series by both the logistic map and experimental fluid flows, which show that our method distinguishes different dynamics sufficiently well as the traditional recurrence analysis. Therefore, the proposed methodology characterizes the recurrence matrix adequately, while using a reduced set of points from the original recurrence plots.

Fuji apple storage time rapid determination method using Vis/NIR spectroscopy

PubMed Central

Liu, Fuqi; Tang, Xuxiang

2015-01-01

Fuji apple storage time rapid determination method using visible/near-infrared (Vis/NIR) spectroscopy was studied in this paper. Vis/NIR diffuse reflection spectroscopy responses to samples were measured for 6 days. Spectroscopy data were processed by stochastic resonance (SR). Principal component analysis (PCA) was utilized to analyze original spectroscopy data and SNR eigen value. Results demonstrated that PCA could not totally discriminate Fuji apples using original spectroscopy data. Signal-to-noise ratio (SNR) spectrum clearly classified all apple samples. PCA using SNR spectrum successfully discriminated apple samples. Therefore, Vis/NIR spectroscopy was effective for Fuji apple storage time rapid discrimination. The proposed method is also promising in condition safety control and management for food and environmental laboratories. PMID:25874818

Color image watermarking against fog effects

NASA Astrophysics Data System (ADS)

Chotikawanid, Piyanart; Amornraksa, Thumrongrat

2017-07-01

Fog effects in various computer and camera software can partially or fully damage the watermark information within the watermarked image. In this paper, we propose a color image watermarking based on the modification of reflectance component against fog effects. The reflectance component is extracted from the blue color channel in the RGB color space of a host image, and then used to carry a watermark signal. The watermark extraction is blindly achieved by subtracting the estimation of the original reflectance component from the watermarked component. The performance of the proposed watermarking method in terms of wPSNR and NC is evaluated, and then compared with the previous method. The experimental results on robustness against various levels of fog effect, from both computer software and mobile application, demonstrated a higher robustness of our proposed method, compared to the previous one.

Lightness modification of color image for protanopia and deuteranopia

NASA Astrophysics Data System (ADS)

Tanaka, Go; Suetake, Noriaki; Uchino, Eiji

2010-01-01

In multimedia content, colors play important roles in conveying visual information. However, color information cannot always be perceived uniformly by all people. People with a color vision deficiency, such as dichromacy, cannot recognize and distinguish certain color combinations. In this paper, an effective lightness modification method, which enables barrier-free color vision for people with dichromacy, especially protanopia or deuteranopia, while preserving the color information in the original image for people with standard color vision, is proposed. In the proposed method, an optimization problem concerning lightness components is first defined by considering color differences in an input image. Then a perceptible and comprehensible color image for both protanopes and viewers with no color vision deficiency or both deuteranopes and viewers with no color vision deficiency is obtained by solving the optimization problem. Through experiments, the effectiveness of the proposed method is illustrated.

Feedback linearization of singularly perturbed systems based on canonical similarity transformations

NASA Astrophysics Data System (ADS)

Kabanov, A. A.

2018-05-01

This paper discusses the problem of feedback linearization of a singularly perturbed system in a state-dependent coefficient form. The result is based on the introduction of a canonical similarity transformation. The transformation matrix is constructed from separate blocks for fast and slow part of an original singularly perturbed system. The transformed singular perturbed system has a linear canonical form that significantly simplifies a control design problem. Proposed similarity transformation allows accomplishing linearization of the system without considering the virtual output (as it is needed for normal form method), a technique of a transition from phase coordinates of the transformed system to state variables of the original system is simpler. The application of the proposed approach is illustrated through example.

Image dehazing based on non-local saturation

NASA Astrophysics Data System (ADS)

Wang, Linlin; Zhang, Qian; Yang, Deyun; Hou, Yingkun; He, Xiaoting

2018-04-01

In this paper, a method based on non-local saturation algorithm is proposed to avoid block and halo effect for single image dehazing with dark channel prior. First we convert original image from RGB color space into HSV color space with the idea of non-local method. Image saturation is weighted equally by the size of fixed window according to image resolution. Second we utilize the saturation to estimate the atmospheric light value and transmission rate. Then through the function of saturation and transmission, the haze-free image is obtained based on the atmospheric scattering model. Comparing the results of existing methods, our method can restore image color and enhance contrast. We guarantee the proposed method with quantitative and qualitative evaluation respectively. Experiments show the better visual effect with high efficiency.

[Feature extraction for breast cancer data based on geometric algebra theory and feature selection using differential evolution].

PubMed

Li, Jing; Hong, Wenxue

2014-12-01

The feature extraction and feature selection are the important issues in pattern recognition. Based on the geometric algebra representation of vector, a new feature extraction method using blade coefficient of geometric algebra was proposed in this study. At the same time, an improved differential evolution (DE) feature selection method was proposed to solve the elevated high dimension issue. The simple linear discriminant analysis was used as the classifier. The result of the 10-fold cross-validation (10 CV) classification of public breast cancer biomedical dataset was more than 96% and proved superior to that of the original features and traditional feature extraction method.

An equivalent method of mixed dielectric constant in passive microwave/millimeter radiometric measurement

NASA Astrophysics Data System (ADS)

Su, Jinlong; Tian, Yan; Hu, Fei; Gui, Liangqi; Cheng, Yayun; Peng, Xiaohui

2017-10-01

Dielectric constant is an important role to describe the properties of matter. This paper proposes This paper proposes the concept of mixed dielectric constant(MDC) in passive microwave radiometric measurement. In addition, a MDC inversion method is come up, Ratio of Angle-Polarization Difference(RAPD) is utilized in this method. The MDC of several materials are investigated using RAPD. Brightness temperatures(TBs) which calculated by MDC and original dielectric constant are compared. Random errors are added to the simulation to test the robustness of the algorithm. Keywords: Passive detection, microwave/millimeter, radiometric measurement, ratio of angle-polarization difference (RAPD), mixed dielectric constant (MDC), brightness temperatures, remote sensing, target recognition.

Weighted least squares phase unwrapping based on the wavelet transform

NASA Astrophysics Data System (ADS)

Chen, Jiafeng; Chen, Haiqin; Yang, Zhengang; Ren, Haixia

2007-01-01

The weighted least squares phase unwrapping algorithm is a robust and accurate method to solve phase unwrapping problem. This method usually leads to a large sparse linear equation system. Gauss-Seidel relaxation iterative method is usually used to solve this large linear equation. However, this method is not practical due to its extremely slow convergence. The multigrid method is an efficient algorithm to improve convergence rate. However, this method needs an additional weight restriction operator which is very complicated. For this reason, the multiresolution analysis method based on the wavelet transform is proposed. By applying the wavelet transform, the original system is decomposed into its coarse and fine resolution levels and an equivalent equation system with better convergence condition can be obtained. Fast convergence in separate coarse resolution levels speeds up the overall system convergence rate. The simulated experiment shows that the proposed method converges faster and provides better result than the multigrid method.

Generalized Gilat-Raubenheimer method for density-of-states calculation in photonic crystals

NASA Astrophysics Data System (ADS)

Liu, Boyuan; Johnson, Steven G.; Joannopoulos, John D.; Lu, Ling

2018-04-01

An efficient numerical algorithm is the key for accurate evaluation of density of states (DOS) in band theory. The Gilat-Raubenheimer (GR) method proposed in 1966 is an efficient linear extrapolation method which was limited in specific lattices. Here, using an affine transformation, we provide a new generalization of the original GR method to any Bravais lattices and show that it is superior to the tetrahedron method and the adaptive Gaussian broadening method. Finally, we apply our generalized GR method to compute DOS of various gyroid photonic crystals of topological degeneracies.

A Bat Algorithm with Mutation for UCAV Path Planning

PubMed Central

Wang, Gaige; Guo, Lihong; Duan, Hong; Liu, Luo; Wang, Heqi

2012-01-01

Path planning for uninhabited combat air vehicle (UCAV) is a complicated high dimension optimization problem, which mainly centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. Original bat algorithm (BA) is used to solve the UCAV path planning problem. Furthermore, a new bat algorithm with mutation (BAM) is proposed to solve the UCAV path planning problem, and a modification is applied to mutate between bats during the process of the new solutions updating. Then, the UCAV can find the safe path by connecting the chosen nodes of the coordinates while avoiding the threat areas and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic BA. The realization procedure for original BA and this improved metaheuristic approach BAM is also presented. To prove the performance of this proposed metaheuristic method, BAM is compared with BA and other population-based optimization methods, such as ACO, BBO, DE, ES, GA, PBIL, PSO, and SGA. The experiment shows that the proposed approach is more effective and feasible in UCAV path planning than the other models. PMID:23365518

Component-based subspace linear discriminant analysis method for face recognition with one training sample

NASA Astrophysics Data System (ADS)

Huang, Jian; Yuen, Pong C.; Chen, Wen-Sheng; Lai, J. H.

2005-05-01

Many face recognition algorithms/systems have been developed in the last decade and excellent performances have also been reported when there is a sufficient number of representative training samples. In many real-life applications such as passport identification, only one well-controlled frontal sample image is available for training. Under this situation, the performance of existing algorithms will degrade dramatically or may not even be implemented. We propose a component-based linear discriminant analysis (LDA) method to solve the one training sample problem. The basic idea of the proposed method is to construct local facial feature component bunches by moving each local feature region in four directions. In this way, we not only generate more samples with lower dimension than the original image, but also consider the face detection localization error while training. After that, we propose a subspace LDA method, which is tailor-made for a small number of training samples, for the local feature projection to maximize the discrimination power. Theoretical analysis and experiment results show that our proposed subspace LDA is efficient and overcomes the limitations in existing LDA methods. Finally, we combine the contributions of each local component bunch with a weighted combination scheme to draw the recognition decision. A FERET database is used for evaluating the proposed method and results are encouraging.

[Artistic anatomy of the nose: proposals for a simplified project of rhinoplasty].

PubMed

Polselli, R; Saban, Y

2007-01-01

The authors developed an original and simple method of evaluation of the aesthetic lines of the nose adapted to the harmony of the face. Initially based on their experience, the authors propose an evaluation of the nose in 2 stages and 5 sequencies based on the construction of single circuit lines according to various incidences. They checked thereafter the validity of this method on the operative project and on the appreciation of the results of the rhinoplasties. Controlled on several types of faces, the method suggested by the authors proved to be reliable, simple, reproducible. The authors proposed a method of evaluation of the aesthetic lines of the nose integrated to the harmony of the face. This method relies on the construction, in 5 stages, of single circuit lines not requiring any particular material. The artistic method of evaluation of the nose proposed by the authors is very simple. Rapid and immediately usable, it makes it possible to schedule a rhinoplasty in a few minutes. The evaluation of the aesthetic results of the rhinoplasties is also very simple and reproducible. It has moreover the merit to propose a model of teaching making it possible to the rhinoplastician to criticize his results and thus to progress in its technical training and its operational indications.

Affinity learning with diffusion on tensor product graph.

PubMed

Yang, Xingwei; Prasad, Lakshman; Latecki, Longin Jan

2013-01-01

In many applications, we are given a finite set of data points sampled from a data manifold and represented as a graph with edge weights determined by pairwise similarities of the samples. Often the pairwise similarities (which are also called affinities) are unreliable due to noise or due to intrinsic difficulties in estimating similarity values of the samples. As observed in several recent approaches, more reliable similarities can be obtained if the original similarities are diffused in the context of other data points, where the context of each point is a set of points most similar to it. Compared to the existing methods, our approach differs in two main aspects. First, instead of diffusing the similarity information on the original graph, we propose to utilize the tensor product graph (TPG) obtained by the tensor product of the original graph with itself. Since TPG takes into account higher order information, it is not a surprise that we obtain more reliable similarities. However, it comes at the price of higher order computational complexity and storage requirement. The key contribution of the proposed approach is that the information propagation on TPG can be computed with the same computational complexity and the same amount of storage as the propagation on the original graph. We prove that a graph diffusion process on TPG is equivalent to a novel iterative algorithm on the original graph, which is guaranteed to converge. After its convergence we obtain new edge weights that can be interpreted as new, learned affinities. We stress that the affinities are learned in an unsupervised setting. We illustrate the benefits of the proposed approach for data manifolds composed of shapes, images, and image patches on two very different tasks of image retrieval and image segmentation. With learned affinities, we achieve the bull's eye retrieval score of 99.99 percent on the MPEG-7 shape dataset, which is much higher than the state-of-the-art algorithms. When the data- points are image patches, the NCut with the learned affinities not only significantly outperforms the NCut with the original affinities, but it also outperforms state-of-the-art image segmentation methods.

Automatic identification and removal of ocular artifacts in EEG--improved adaptive predictor filtering for portable applications.

PubMed

Zhao, Qinglin; Hu, Bin; Shi, Yujun; Li, Yang; Moore, Philip; Sun, Minghou; Peng, Hong

2014-06-01

Electroencephalogram (EEG) signals have a long history of use as a noninvasive approach to measure brain function. An essential component in EEG-based applications is the removal of Ocular Artifacts (OA) from the EEG signals. In this paper we propose a hybrid de-noising method combining Discrete Wavelet Transformation (DWT) and an Adaptive Predictor Filter (APF). A particularly novel feature of the proposed method is the use of the APF based on an adaptive autoregressive model for prediction of the waveform of signals in the ocular artifact zones. In our test, based on simulated data, the accuracy of noise removal in the proposed model was significantly increased when compared to existing methods including: Wavelet Packet Transform (WPT) and Independent Component Analysis (ICA), Discrete Wavelet Transform (DWT) and Adaptive Noise Cancellation (ANC). The results demonstrate that the proposed method achieved a lower mean square error and higher correlation between the original and corrected EEG. The proposed method has also been evaluated using data from calibration trials for the Online Predictive Tools for Intervention in Mental Illness (OPTIMI) project. The results of this evaluation indicate an improvement in performance in terms of the recovery of true EEG signals with EEG tracking and computational speed in the analysis. The proposed method is well suited to applications in portable environments where the constraints with respect to acceptable wearable sensor attachments usually dictate single channel devices.

A strategy for evaluating pathway analysis methods.

PubMed

Yu, Chenggang; Woo, Hyung Jun; Yu, Xueping; Oyama, Tatsuya; Wallqvist, Anders; Reifman, Jaques

2017-10-13

Researchers have previously developed a multitude of methods designed to identify biological pathways associated with specific clinical or experimental conditions of interest, with the aim of facilitating biological interpretation of high-throughput data. Before practically applying such pathway analysis (PA) methods, we must first evaluate their performance and reliability, using datasets where the pathways perturbed by the conditions of interest have been well characterized in advance. However, such 'ground truths' (or gold standards) are often unavailable. Furthermore, previous evaluation strategies that have focused on defining 'true answers' are unable to systematically and objectively assess PA methods under a wide range of conditions. In this work, we propose a novel strategy for evaluating PA methods independently of any gold standard, either established or assumed. The strategy involves the use of two mutually complementary metrics, recall and discrimination. Recall measures the consistency of the perturbed pathways identified by applying a particular analysis method to an original large dataset and those identified by the same method to a sub-dataset of the original dataset. In contrast, discrimination measures specificity-the degree to which the perturbed pathways identified by a particular method to a dataset from one experiment differ from those identifying by the same method to a dataset from a different experiment. We used these metrics and 24 datasets to evaluate six widely used PA methods. The results highlighted the common challenge in reliably identifying significant pathways from small datasets. Importantly, we confirmed the effectiveness of our proposed dual-metric strategy by showing that previous comparative studies corroborate the performance evaluations of the six methods obtained by our strategy. Unlike any previously proposed strategy for evaluating the performance of PA methods, our dual-metric strategy does not rely on any ground truth, either established or assumed, of the pathways perturbed by a specific clinical or experimental condition. As such, our strategy allows researchers to systematically and objectively evaluate pathway analysis methods by employing any number of datasets for a variety of conditions.

Human ear detection in the thermal infrared spectrum

NASA Astrophysics Data System (ADS)

Abaza, Ayman; Bourlai, Thirimachos

2012-06-01

In this paper the problem of human ear detection in the thermal infrared (IR) spectrum is studied in order to illustrate the advantages and limitations of the most important steps of ear-based biometrics that can operate in day and night time environments. The main contributions of this work are two-fold: First, a dual-band database is assembled that consists of visible and thermal profile face images. The thermal data was collected using a high definition middle-wave infrared (3-5 microns) camera that is capable of acquiring thermal imprints of human skin. Second, a fully automated, thermal imaging based ear detection method is developed for real-time segmentation of human ears in either day or night time environments. The proposed method is based on Haar features forming a cascaded AdaBoost classifier (our modified version of the original Viola-Jones approach1 that was designed to be applied mainly in visible band images). The main advantage of the proposed method, applied on our profile face image data set collected in the thermal-band, is that it is designed to reduce the learning time required by the original Viola-Jones method from several weeks to several hours. Unlike other approaches reported in the literature, which have been tested but not designed to operate in the thermal band, our method yields a high detection accuracy that reaches ~ 91.5%. Further analysis on our data set yielded that: (a) photometric normalization techniques do not directly improve ear detection performance. However, when using a certain photometric normalization technique (CLAHE) on falsely detected images, the detection rate improved by ~ 4%; (b) the high detection accuracy of our method did not degrade when we lowered down the original spatial resolution of thermal ear images. For example, even after using one third of the original spatial resolution (i.e. ~ 20% of the original computational time) of the thermal profile face images, the high ear detection accuracy of our method remained unaffected. This resulted also in speeding up the detection time of an ear image from 265 to 17 milliseconds per image. To the best of our knowledge this is the first time that the problem of human ear detection in the thermal band is being investigated in the open literature.

Double-tick realization of binary control program

NASA Astrophysics Data System (ADS)

Kobylecki, Michał; Kania, Dariusz

2016-12-01

This paper presents a procedure for the implementation of control algorithms for hardware-bit compatible with the standard IEC61131-3. The described transformation based on the sets of calculus and graphs, allows translation of the original form of the control program to the form in full compliance with the original, giving the architecture represented by two tick. The proposed method enables the efficient implementation of the control bits in the FPGA with the use of a standardized programming language LD.

A Proposal to Plan and Develop a Sample Set of Drill and Testing Materials, Based on Audio and Visual Environmental and Situational Stimuli, Aimed at Training and Testing in the Creation of Original Utterances by Foreign Language Students at the Secondary and College Levels.

ERIC Educational Resources Information Center

Obrecht, Dean H.

This report contrasts the results of a rigidly specified, pattern-oriented approach to learning Spanish with an approach that emphasizes the origination of sentences by the learner in direct response to stimuli. Pretesting and posttesting statistics are presented and conclusions are discussed. The experimental method, which required the student to…

Edge enhancement and image equalization by unsharp masking using self-adaptive photochromic filters.

PubMed

Ferrari, José A; Flores, Jorge L; Perciante, César D; Frins, Erna

2009-07-01

A new method for real-time edge enhancement and image equalization using photochromic filters is presented. The reversible self-adaptive capacity of photochromic materials is used for creating an unsharp mask of the original image. This unsharp mask produces a kind of self filtering of the original image. Unlike the usual Fourier (coherent) image processing, the technique we propose can also be used with incoherent illumination. Validation experiments with Bacteriorhodopsin and photochromic glass are presented.

Identification and Characterization of Intercritical Heat-Affected Zone in As-Welded Grade 91 Weldment

NASA Astrophysics Data System (ADS)

Wang, Yiyu; Kannan, Rangasayee; Li, Leijun

2016-12-01

A metallurgical method is proposed for locating the intercritical heat-affected zone in the as-welded Grade 91 steel. New austenitic grains, preferentially formed along the original prior austenite grain boundaries, are characterized to contain finer M23C6 carbides and higher strain levels than the original prior austenite grains. Kurdjumov-Sachs Group 1 variant pairs, with a low misorientation of 7 deg within a martensitic block, are identified as the dominant variants in the new PAGs.

Recommendation for the review of biological reference intervals in medical laboratories.

PubMed

Henny, Joseph; Vassault, Anne; Boursier, Guilaine; Vukasovic, Ines; Mesko Brguljan, Pika; Lohmander, Maria; Ghita, Irina; Andreu, Francisco A Bernabeu; Kroupis, Christos; Sprongl, Ludek; Thelen, Marc H M; Vanstapel, Florent J L A; Vodnik, Tatjana; Huisman, Willem; Vaubourdolle, Michel

2016-12-01

This document is based on the original recommendation of the Expert Panel on the Theory of Reference Values of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC), updated guidelines were recently published under the auspices of the IFCC and the Clinical and Laboratory Standards Institute (CLSI). This document summarizes proposals for recommendations on: (i) The terminology, which is often confusing, noticeably concerning the terms of reference limits and decision limits. (ii) The method for the determination of reference limits according to the original procedure and the conditions, which should be used. (iii) A simple procedure allowing the medical laboratories to fulfill the requirements of the regulation and standards. The updated document proposes to verify that published reference limits are applicable to the laboratory involved. Finally, the strengths and limits of the revised recommendations (especially the selection of the reference population, the maintenance of the analytical quality, the choice of the statistical method used…) will be briefly discussed.

Exploiting Fractional Order PID Controller Methods in Improving the Performance of Integer Order PID Controllers: A GA Based Approach

NASA Astrophysics Data System (ADS)

Mukherjee, Bijoy K.; Metia, Santanu

2009-10-01

The paper is divided into three parts. The first part gives a brief introduction to the overall paper, to fractional order PID (PIλDμ) controllers and to Genetic Algorithm (GA). In the second part, first it has been studied how the performance of an integer order PID controller deteriorates when implemented with lossy capacitors in its analog realization. Thereafter it has been shown that the lossy capacitors can be effectively modeled by fractional order terms. Then, a novel GA based method has been proposed to tune the controller parameters such that the original performance is retained even though realized with the same lossy capacitors. Simulation results have been presented to validate the usefulness of the method. Some Ziegler-Nichols type tuning rules for design of fractional order PID controllers have been proposed in the literature [11]. In the third part, a novel GA based method has been proposed which shows how equivalent integer order PID controllers can be obtained which will give performance level similar to those of the fractional order PID controllers thereby removing the complexity involved in the implementation of the latter. It has been shown with extensive simulation results that the equivalent integer order PID controllers more or less retain the robustness and iso-damping properties of the original fractional order PID controllers. Simulation results also show that the equivalent integer order PID controllers are more robust than the normal Ziegler-Nichols tuned PID controllers.

Speckle noise reduction in ultrasound images using a discrete wavelet transform-based image fusion technique.

PubMed

Choi, Hyun Ho; Lee, Ju Hwan; Kim, Sung Min; Park, Sung Yun

2015-01-01

Here, the speckle noise in ultrasonic images is removed using an image fusion-based denoising method. To optimize the denoising performance, each discrete wavelet transform (DWT) and filtering technique was analyzed and compared. In addition, the performances were compared in order to derive the optimal input conditions. To evaluate the speckle noise removal performance, an image fusion algorithm was applied to the ultrasound images, and comparatively analyzed with the original image without the algorithm. As a result, applying DWT and filtering techniques caused information loss and noise characteristics, and did not represent the most significant noise reduction performance. Conversely, an image fusion method applying SRAD-original conditions preserved the key information in the original image, and the speckle noise was removed. Based on such characteristics, the input conditions of SRAD-original had the best denoising performance with the ultrasound images. From this study, the best denoising technique proposed based on the results was confirmed to have a high potential for clinical application.

An archaeal origin of eukaryotes supports only two primary domains of life.

PubMed

Williams, Tom A; Foster, Peter G; Cox, Cymon J; Embley, T Martin

2013-12-12

The discovery of the Archaea and the proposal of the three-domains 'universal' tree, based on ribosomal RNA and core genes mainly involved in protein translation, catalysed new ideas for cellular evolution and eukaryotic origins. However, accumulating evidence suggests that the three-domains tree may be incorrect: evolutionary trees made using newer methods place eukaryotic core genes within the Archaea, supporting hypotheses in which an archaeon participated in eukaryotic origins by founding the host lineage for the mitochondrial endosymbiont. These results provide support for only two primary domains of life--Archaea and Bacteria--because eukaryotes arose through partnership between them.

Gene regulatory network identification from the yeast cell cycle based on a neuro-fuzzy system.

PubMed

Wang, B H; Lim, J W; Lim, J S

2016-08-30

Many studies exist for reconstructing gene regulatory networks (GRNs). In this paper, we propose a method based on an advanced neuro-fuzzy system, for gene regulatory network reconstruction from microarray time-series data. This approach uses a neural network with a weighted fuzzy function to model the relationships between genes. Fuzzy rules, which determine the regulators of genes, are very simplified through this method. Additionally, a regulator selection procedure is proposed, which extracts the exact dynamic relationship between genes, using the information obtained from the weighted fuzzy function. Time-series related features are extracted from the original data to employ the characteristics of temporal data that are useful for accurate GRN reconstruction. The microarray dataset of the yeast cell cycle was used for our study. We measured the mean squared prediction error for the efficiency of the proposed approach and evaluated the accuracy in terms of precision, sensitivity, and F-score. The proposed method outperformed the other existing approaches.

Estimating conditional proportion curves by regression residuals.

PubMed

Han, Bing; Lim, Nelson

2010-06-15

Researchers often derive a categorical outcome from an observed continuous measurement y. For example, human obesity status can be defined by the body mass index. They proceed to estimate the conditional proportion curve p(x) = P(y

Systematic review of proposed definitions of nocturnal polyuria and population-based evidence of their diagnostic accuracy.

PubMed

Olesen, Tine Kold; Denys, Marie-Astrid; Vande Walle, Johan; Everaert, Karel

2018-02-06

Background Evidence of diagnostic accuracy for proposed definitions of nocturnal polyuria is currently unclear. Purpose Systematic review to determine population-based evidence of the diagnostic accuracy of proposed definitions of nocturnal polyuria based on data from frequency-volume charts. Methods Seventeen pre-specified search terms identified 351 unique investigations published from 1990 to 2016 in BIOSIS, Embase, Embase Alerts, International Pharmaceutical Abstract, Medline, and Cochrane. Thirteen original communications were included in this review based on pre-specified exclusion criteria. Data were extracted from each paper regarding subject age, sex, ethnicity, health status, sample size, data collection methods, and diagnostic discrimination of proposed definitions including sensitivity, specificity, positive and negative predictive value. Results The sample size of study cohorts, participant age, sex, ethnicity, and health status varied considerably in 13 studies reporting on the diagnostic performance of seven different definitions of nocturnal polyuria using frequency-volume chart data from 4968 participants. Most study cohorts were small, mono-ethnic, including only Caucasian males aged 50 or higher with primary or secondary polyuria that were compared to a control group of healthy men without nocturia in prospective or retrospective settings. Proposed definitions had poor discriminatory accuracy in evaluations based on data from subjects independent from the original study cohorts with findings being similar regarding the most widely evaluated definition endorsed by ICS. Conclusions Diagnostic performance characteristics for proposed definitions of nocturnal polyuria show poor to modest discrimination and are not based on sufficient level of evidence from representative, multi-ethnic population-based data from both females and males of all adult ages.

Crack resistance determination of material by wedge splitting a chevron-notched specimen

NASA Astrophysics Data System (ADS)

Deryugin, Ye. Ye.

2017-12-01

An original method is proposed for the crack resistance determination of a material by wedge splitting of a chevron-notched specimen. It was developed at the Institute of Strength Physics and Materials Science SB RAS in the laboratory of Physical Mesomechanics and Nondestructive Methods of Control. An example of the crack resistance test of technical titanium VT1-0 is considered.

A study on scattering correction for γ-photon 3D imaging test method

NASA Astrophysics Data System (ADS)

Xiao, Hui; Zhao, Min; Liu, Jiantang; Chen, Hao

2018-03-01

A pair of 511KeV γ-photons is generated during a positron annihilation. Their directions differ by 180°. The moving path and energy information can be utilized to form the 3D imaging test method in industrial domain. However, the scattered γ-photons are the major factors influencing the imaging precision of the test method. This study proposes a γ-photon single scattering correction method from the perspective of spatial geometry. The method first determines possible scattering points when the scattered γ-photon pair hits the detector pair. The range of scattering angle can then be calculated according to the energy window. Finally, the number of scattered γ-photons denotes the attenuation of the total scattered γ-photons along its moving path. The corrected γ-photons are obtained by deducting the scattered γ-photons from the original ones. Two experiments are conducted to verify the effectiveness of the proposed scattering correction method. The results concluded that the proposed scattering correction method can efficiently correct scattered γ-photons and improve the test accuracy.

Comparison of QuEChERS sample preparation methods for the analysis of pesticide residues in canned and fresh peach.

PubMed

Costa, Fabiane Pinho; Caldas, Sergiane Souza; Primel, Ednei Gilberto

2014-12-15

Original, citrate and acetate QuEChERS methods were studied in order to evaluate the extraction efficiency and the matrix effect in the extraction of pesticides from canned peach samples. Determinations were performed by gas chromatography coupled to mass spectrometry (GC-MS). The proposed method with extraction using the original QuEChERS method and determination by GC-MS was validated. LOQs ranged between 1 and 10 μg kg(-1) and all analytical curves showed r values higher than 0.99. Recovery values varied from 69% to 125% with RSDs less than 20%. The matrix effect was evaluated and most compounds showed signal enrichment. Robustness was demonstrated using fresh peaches, which provided recovery values within acceptable limits. The applicability of the method was verified and residues of tebuconazole and dimethoate were found in the samples. Copyright © 2014 Elsevier Ltd. All rights reserved.

Reply to comment by Fred L. Ogden et al. on "Beyond the SCS-CN method: A theoretical framework for spatially lumped rainfall-runoff response"

NASA Astrophysics Data System (ADS)

Bartlett, M. S.; Parolari, A. J.; McDonnell, J. J.; Porporato, A.

2017-07-01

Though Ogden et al. list several shortcomings of the original SCS-CN method, fit for purpose is a key consideration in hydrological modelling, as shown by the adoption of SCS-CN method in many design standards. The theoretical framework of Bartlett et al. [2016a] reveals a family of semidistributed models, of which the SCS-CN method is just one member. Other members include event-based versions of the Variable Infiltration Capacity (VIC) model and TOPMODEL. This general model allows us to move beyond the limitations of the original SCS-CN method under different rainfall-runoff mechanisms and distributions for soil and rainfall variability. Future research should link this general model approach to different hydrogeographic settings, in line with the call for action proposed by Ogden et al.

The Coordinate Transformation Method of High Resolution dem Data

NASA Astrophysics Data System (ADS)

Yan, Chaode; Guo, Wang; Li, Aimin

2018-04-01

Coordinate transformation methods of DEM data can be divided into two categories. One reconstruct based on original vector elevation data. The other transforms DEM data blocks by transforming parameters. But the former doesn't work in the absence of original vector data, and the later may cause errors at joint places between adjoining blocks of high resolution DEM data. In view of this problem, a method dealing with high resolution DEM data coordinate transformation is proposed. The method transforms DEM data into discrete vector elevation points, and then adjusts positions of points by bi-linear interpolation respectively. Finally, a TIN is generated by transformed points, and the new DEM data in target coordinate system is reconstructed based on TIN. An algorithm which can find blocks and transform automatically is given in this paper. The method is tested in different terrains and proved to be feasible and valid.

An iterative shrinkage approach to total-variation image restoration.

PubMed

Michailovich, Oleg V

2011-05-01

The problem of restoration of digital images from their degraded measurements plays a central role in a multitude of practically important applications. A particularly challenging instance of this problem occurs in the case when the degradation phenomenon is modeled by an ill-conditioned operator. In such a situation, the presence of noise makes it impossible to recover a valuable approximation of the image of interest without using some a priori information about its properties. Such a priori information--commonly referred to as simply priors--is essential for image restoration, rendering it stable and robust to noise. Moreover, using the priors makes the recovered images exhibit some plausible features of their original counterpart. Particularly, if the original image is known to be a piecewise smooth function, one of the standard priors used in this case is defined by the Rudin-Osher-Fatemi model, which results in total variation (TV) based image restoration. The current arsenal of algorithms for TV-based image restoration is vast. In this present paper, a different approach to the solution of the problem is proposed based upon the method of iterative shrinkage (aka iterated thresholding). In the proposed method, the TV-based image restoration is performed through a recursive application of two simple procedures, viz. linear filtering and soft thresholding. Therefore, the method can be identified as belonging to the group of first-order algorithms which are efficient in dealing with images of relatively large sizes. Another valuable feature of the proposed method consists in its working directly with the TV functional, rather then with its smoothed versions. Moreover, the method provides a single solution for both isotropic and anisotropic definitions of the TV functional, thereby establishing a useful connection between the two formulae. Finally, a number of standard examples of image deblurring are demonstrated, in which the proposed method can provide restoration results of superior quality as compared to the case of sparse-wavelet deconvolution.

3D craniofacial registration using thin-plate spline transform and cylindrical surface projection

PubMed Central

Chen, Yucong; Deng, Qingqiong; Duan, Fuqing

2017-01-01

Craniofacial registration is used to establish the point-to-point correspondence in a unified coordinate system among human craniofacial models. It is the foundation of craniofacial reconstruction and other craniofacial statistical analysis research. In this paper, a non-rigid 3D craniofacial registration method using thin-plate spline transform and cylindrical surface projection is proposed. First, the gradient descent optimization is utilized to improve a cylindrical surface fitting (CSF) for the reference craniofacial model. Second, the thin-plate spline transform (TPST) is applied to deform a target craniofacial model to the reference model. Finally, the cylindrical surface projection (CSP) is used to derive the point correspondence between the reference and deformed target models. To accelerate the procedure, the iterative closest point ICP algorithm is used to obtain a rough correspondence, which can provide a possible intersection area of the CSP. Finally, the inverse TPST is used to map the obtained corresponding points from the deformed target craniofacial model to the original model, and it can be realized directly by the correspondence between the original target model and the deformed target model. Three types of registration, namely, reflexive, involutive and transitive registration, are carried out to verify the effectiveness of the proposed craniofacial registration algorithm. Comparison with the methods in the literature shows that the proposed method is more accurate. PMID:28982117

3D craniofacial registration using thin-plate spline transform and cylindrical surface projection.

PubMed

Chen, Yucong; Zhao, Junli; Deng, Qingqiong; Duan, Fuqing

2017-01-01

Craniofacial registration is used to establish the point-to-point correspondence in a unified coordinate system among human craniofacial models. It is the foundation of craniofacial reconstruction and other craniofacial statistical analysis research. In this paper, a non-rigid 3D craniofacial registration method using thin-plate spline transform and cylindrical surface projection is proposed. First, the gradient descent optimization is utilized to improve a cylindrical surface fitting (CSF) for the reference craniofacial model. Second, the thin-plate spline transform (TPST) is applied to deform a target craniofacial model to the reference model. Finally, the cylindrical surface projection (CSP) is used to derive the point correspondence between the reference and deformed target models. To accelerate the procedure, the iterative closest point ICP algorithm is used to obtain a rough correspondence, which can provide a possible intersection area of the CSP. Finally, the inverse TPST is used to map the obtained corresponding points from the deformed target craniofacial model to the original model, and it can be realized directly by the correspondence between the original target model and the deformed target model. Three types of registration, namely, reflexive, involutive and transitive registration, are carried out to verify the effectiveness of the proposed craniofacial registration algorithm. Comparison with the methods in the literature shows that the proposed method is more accurate.

A beam hardening and dispersion correction for x-ray dark-field radiography.

PubMed

Pelzer, Georg; Anton, Gisela; Horn, Florian; Rieger, Jens; Ritter, André; Wandner, Johannes; Weber, Thomas; Michel, Thilo

2016-06-01

X-ray dark-field imaging promises information on the small angle scattering properties even of large samples. However, the dark-field image is correlated with the object's attenuation and phase-shift if a polychromatic x-ray spectrum is used. A method to remove part of these correlations is proposed. The experimental setup for image acquisition was modeled in a wave-field simulation to quantify the dark-field signals originating solely from a material's attenuation and phase-shift. A calibration matrix was simulated for ICRU46 breast tissue. Using the simulated data, a dark-field image of a human mastectomy sample was corrected for the finger print of attenuation- and phase-image. Comparing the simulated, attenuation-based dark-field values to a phantom measurement, a good agreement was found. Applying the proposed method to mammographic dark-field data, a reduction of the dark-field background and anatomical noise was achieved. The contrast between microcalcifications and their surrounding background was increased. The authors show that the influence of and dispersion can be quantified by simulation and, thus, measured image data can be corrected. The simulation allows to determine the corresponding dark-field artifacts for a wide range of setup parameters, like tube-voltage and filtration. The application of the proposed method to mammographic dark-field data shows an increase in contrast compared to the original image, which might simplify a further image-based diagnosis.

Multi-objective based spectral unmixing for hyperspectral images

NASA Astrophysics Data System (ADS)

Xu, Xia; Shi, Zhenwei

2017-02-01

Sparse hyperspectral unmixing assumes that each observed pixel can be expressed by a linear combination of several pure spectra in a priori library. Sparse unmixing is challenging, since it is usually transformed to a NP-hard l0 norm based optimization problem. Existing methods usually utilize a relaxation to the original l0 norm. However, the relaxation may bring in sensitive weighted parameters and additional calculation error. In this paper, we propose a novel multi-objective based algorithm to solve the sparse unmixing problem without any relaxation. We transform sparse unmixing to a multi-objective optimization problem, which contains two correlative objectives: minimizing the reconstruction error and controlling the endmember sparsity. To improve the efficiency of multi-objective optimization, a population-based randomly flipping strategy is designed. Moreover, we theoretically prove that the proposed method is able to recover a guaranteed approximate solution from the spectral library within limited iterations. The proposed method can directly deal with l0 norm via binary coding for the spectral signatures in the library. Experiments on both synthetic and real hyperspectral datasets demonstrate the effectiveness of the proposed method.

A Hybrid Vehicle Detection Method Based on Viola-Jones and HOG + SVM from UAV Images

PubMed Central

Xu, Yongzheng; Yu, Guizhen; Wang, Yunpeng; Wu, Xinkai; Ma, Yalong

2016-01-01

A new hybrid vehicle detection scheme which integrates the Viola-Jones (V-J) and linear SVM classifier with HOG feature (HOG + SVM) methods is proposed for vehicle detection from low-altitude unmanned aerial vehicle (UAV) images. As both V-J and HOG + SVM are sensitive to on-road vehicles’ in-plane rotation, the proposed scheme first adopts a roadway orientation adjustment method, which rotates each UAV image to align the roads with the horizontal direction so the original V-J or HOG + SVM method can be directly applied to achieve fast detection and high accuracy. To address the issue of descending detection speed for V-J and HOG + SVM, the proposed scheme further develops an adaptive switching strategy which sophistically integrates V-J and HOG + SVM methods based on their different descending trends of detection speed to improve detection efficiency. A comprehensive evaluation shows that the switching strategy, combined with the road orientation adjustment method, can significantly improve the efficiency and effectiveness of the vehicle detection from UAV images. The results also show that the proposed vehicle detection method is competitive compared with other existing vehicle detection methods. Furthermore, since the proposed vehicle detection method can be performed on videos captured from moving UAV platforms without the need of image registration or additional road database, it has great potentials of field applications. Future research will be focusing on expanding the current method for detecting other transportation modes such as buses, trucks, motors, bicycles, and pedestrians. PMID:27548179

A Hybrid Vehicle Detection Method Based on Viola-Jones and HOG + SVM from UAV Images.

PubMed

Xu, Yongzheng; Yu, Guizhen; Wang, Yunpeng; Wu, Xinkai; Ma, Yalong

2016-08-19

A new hybrid vehicle detection scheme which integrates the Viola-Jones (V-J) and linear SVM classifier with HOG feature (HOG + SVM) methods is proposed for vehicle detection from low-altitude unmanned aerial vehicle (UAV) images. As both V-J and HOG + SVM are sensitive to on-road vehicles' in-plane rotation, the proposed scheme first adopts a roadway orientation adjustment method, which rotates each UAV image to align the roads with the horizontal direction so the original V-J or HOG + SVM method can be directly applied to achieve fast detection and high accuracy. To address the issue of descending detection speed for V-J and HOG + SVM, the proposed scheme further develops an adaptive switching strategy which sophistically integrates V-J and HOG + SVM methods based on their different descending trends of detection speed to improve detection efficiency. A comprehensive evaluation shows that the switching strategy, combined with the road orientation adjustment method, can significantly improve the efficiency and effectiveness of the vehicle detection from UAV images. The results also show that the proposed vehicle detection method is competitive compared with other existing vehicle detection methods. Furthermore, since the proposed vehicle detection method can be performed on videos captured from moving UAV platforms without the need of image registration or additional road database, it has great potentials of field applications. Future research will be focusing on expanding the current method for detecting other transportation modes such as buses, trucks, motors, bicycles, and pedestrians.

Manual of praying mantis morphology, nomenclature, and practices (Insecta, Mantodea)

PubMed Central

Brannoch, Sydney K.; Wieland, Frank; Rivera, Julio; Klass, Klaus-Dieter; Olivier Béthoux; Svenson, Gavin J.

2017-01-01

Abstract This study provides a comprehensive review of historical morphological nomenclature used for praying mantis (Mantodea) morphology, which includes citations, original use, and assignment of homology. All referenced structures across historical works correspond to a proposed standard term for use in all subsequent works pertaining to praying mantis morphology and systematics. The new standards are presented with a verbal description in a glossary as well as indicated on illustrations and images. In the vast majority of cases, originally used terms were adopted as the new standard. In addition, historical morphological topographical homology conjectures are considered with discussion on modern interpretations. A new standardized formulation to present foreleg femoral and tibial spines is proposed for clarity based on previous works. In addition, descriptions for methods of collection, curation, genital complex dissection, and labeling are provided to aid in the proper preservation and storage of specimens for longevity and ease of study. Due to the lack of consistent linear morphometric measurement practices in the literature, we have proposed a series of measurements for taxonomic and morphological research. These measurements are presented with figures to provide visual aids with homologous landmarks to ensure compatibility and comparability across the Order. Finally, our proposed method of pinning mantises is presented with a photographical example as well as a video tutorial available at http://mantodearesearch.com. PMID:29200926

Research of facial feature extraction based on MMC

NASA Astrophysics Data System (ADS)

Xue, Donglin; Zhao, Jiufen; Tang, Qinhong; Shi, Shaokun

2017-07-01

Based on the maximum margin criterion (MMC), a new algorithm of statistically uncorrelated optimal discriminant vectors and a new algorithm of orthogonal optimal discriminant vectors for feature extraction were proposed. The purpose of the maximum margin criterion is to maximize the inter-class scatter while simultaneously minimizing the intra-class scatter after the projection. Compared with original MMC method and principal component analysis (PCA) method, the proposed methods are better in terms of reducing or eliminating the statistically correlation between features and improving recognition rate. The experiment results on Olivetti Research Laboratory (ORL) face database shows that the new feature extraction method of statistically uncorrelated maximum margin criterion (SUMMC) are better in terms of recognition rate and stability. Besides, the relations between maximum margin criterion and Fisher criterion for feature extraction were revealed.

Sunspot drawings handwritten character recognition method based on deep learning

NASA Astrophysics Data System (ADS)

Zheng, Sheng; Zeng, Xiangyun; Lin, Ganghua; Zhao, Cui; Feng, Yongli; Tao, Jinping; Zhu, Daoyuan; Xiong, Li

2016-05-01

High accuracy scanned sunspot drawings handwritten characters recognition is an issue of critical importance to analyze sunspots movement and store them in the database. This paper presents a robust deep learning method for scanned sunspot drawings handwritten characters recognition. The convolution neural network (CNN) is one algorithm of deep learning which is truly successful in training of multi-layer network structure. CNN is used to train recognition model of handwritten character images which are extracted from the original sunspot drawings. We demonstrate the advantages of the proposed method on sunspot drawings provided by Chinese Academy Yunnan Observatory and obtain the daily full-disc sunspot numbers and sunspot areas from the sunspot drawings. The experimental results show that the proposed method achieves a high recognition accurate rate.

Automatic Identification of Artifact-Related Independent Components for Artifact Removal in EEG Recordings.

PubMed

Zou, Yuan; Nathan, Viswam; Jafari, Roozbeh

2016-01-01

Electroencephalography (EEG) is the recording of electrical activity produced by the firing of neurons within the brain. These activities can be decoded by signal processing techniques. However, EEG recordings are always contaminated with artifacts which hinder the decoding process. Therefore, identifying and removing artifacts is an important step. Researchers often clean EEG recordings with assistance from independent component analysis (ICA), since it can decompose EEG recordings into a number of artifact-related and event-related potential (ERP)-related independent components. However, existing ICA-based artifact identification strategies mostly restrict themselves to a subset of artifacts, e.g., identifying eye movement artifacts only, and have not been shown to reliably identify artifacts caused by nonbiological origins like high-impedance electrodes. In this paper, we propose an automatic algorithm for the identification of general artifacts. The proposed algorithm consists of two parts: 1) an event-related feature-based clustering algorithm used to identify artifacts which have physiological origins; and 2) the electrode-scalp impedance information employed for identifying nonbiological artifacts. The results on EEG data collected from ten subjects show that our algorithm can effectively detect, separate, and remove both physiological and nonbiological artifacts. Qualitative evaluation of the reconstructed EEG signals demonstrates that our proposed method can effectively enhance the signal quality, especially the quality of ERPs, even for those that barely display ERPs in the raw EEG. The performance results also show that our proposed method can effectively identify artifacts and subsequently enhance the classification accuracies compared to four commonly used automatic artifact removal methods.

Automatic Identification of Artifact-related Independent Components for Artifact Removal in EEG Recordings

PubMed Central

Zou, Yuan; Nathan, Viswam; Jafari, Roozbeh

2017-01-01

Electroencephalography (EEG) is the recording of electrical activity produced by the firing of neurons within the brain. These activities can be decoded by signal processing techniques. However, EEG recordings are always contaminated with artifacts which hinder the decoding process. Therefore, identifying and removing artifacts is an important step. Researchers often clean EEG recordings with assistance from Independent Component Analysis (ICA), since it can decompose EEG recordings into a number of artifact-related and event related potential (ERP)-related independent components (ICs). However, existing ICA-based artifact identification strategies mostly restrict themselves to a subset of artifacts, e.g. identifying eye movement artifacts only, and have not been shown to reliably identify artifacts caused by non-biological origins like high-impedance electrodes. In this paper, we propose an automatic algorithm for the identification of general artifacts. The proposed algorithm consists of two parts: 1) an event-related feature based clustering algorithm used to identify artifacts which have physiological origins and 2) the electrode-scalp impedance information employed for identifying non-biological artifacts. The results on EEG data collected from 10 subjects show that our algorithm can effectively detect, separate, and remove both physiological and non-biological artifacts. Qualitative evaluation of the reconstructed EEG signals demonstrates that our proposed method can effectively enhance the signal quality, especially the quality of ERPs, even for those that barely display ERPs in the raw EEG. The performance results also show that our proposed method can effectively identify artifacts and subsequently enhance the classification accuracies compared to four commonly used automatic artifact removal methods. PMID:25415992

Adaptive correlation filter-based video stabilization without accumulative global motion estimation

NASA Astrophysics Data System (ADS)

Koh, Eunjin; Lee, Chanyong; Jeong, Dong Gil

2014-12-01

We present a digital video stabilization approach that provides both robustness and efficiency for practical applications. In this approach, we adopt a stabilization model that maintains spatio-temporal information of past input frames efficiently and can track original stabilization position. Because of the stabilization model, the proposed method does not need accumulative global motion estimation and can recover the original position even if there is a failure in interframe motion estimation. It can also intelligently overcome the situation of damaged or interrupted video sequences. Moreover, because it is simple and suitable to parallel scheme, we implement it on a commercial field programmable gate array and a graphics processing unit board with compute unified device architecture in a breeze. Experimental results show that the proposed approach is both fast and robust.

Combination of Sharing Matrix and Image Encryption for Lossless $(k,n)$ -Secret Image Sharing.

PubMed

Bao, Long; Yi, Shuang; Zhou, Yicong

2017-12-01

This paper first introduces a (k,n) -sharing matrix S (k, n) and its generation algorithm. Mathematical analysis is provided to show its potential for secret image sharing. Combining sharing matrix with image encryption, we further propose a lossless (k,n) -secret image sharing scheme (SMIE-SIS). Only with no less than k shares, all the ciphertext information and security key can be reconstructed, which results in a lossless recovery of original information. This can be proved by the correctness and security analysis. Performance evaluation and security analysis demonstrate that the proposed SMIE-SIS with arbitrary settings of k and n has at least five advantages: 1) it is able to fully recover the original image without any distortion; 2) it has much lower pixel expansion than many existing methods; 3) its computation cost is much lower than the polynomial-based secret image sharing methods; 4) it is able to verify and detect a fake share; and 5) even using the same original image with the same initial settings of parameters, every execution of SMIE-SIS is able to generate completely different secret shares that are unpredictable and non-repetitive. This property offers SMIE-SIS a high level of security to withstand many different attacks.

Defeaturing CAD models using a geometry-based size field and facet-based reduction operators.

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

Quadros, William Roshan; Owen, Steven James

2010-04-01

We propose a method to automatically defeature a CAD model by detecting irrelevant features using a geometry-based size field and a method to remove the irrelevant features via facet-based operations on a discrete representation. A discrete B-Rep model is first created by obtaining a faceted representation of the CAD entities. The candidate facet entities are then marked for reduction by using a geometry-based size field. This is accomplished by estimating local mesh sizes based on geometric criteria. If the field value at a facet entity goes below a user specified threshold value then it is identified as an irrelevant featuremore » and is marked for reduction. The reduction of marked facet entities is primarily performed using an edge collapse operator. Care is taken to retain a valid geometry and topology of the discrete model throughout the procedure. The original model is not altered as the defeaturing is performed on a separate discrete model. Associativity between the entities of the discrete model and that of original CAD model is maintained in order to decode the attributes and boundary conditions applied on the original CAD entities onto the mesh via the entities of the discrete model. Example models are presented to illustrate the effectiveness of the proposed approach.« less

Tensor Rank Preserving Discriminant Analysis for Facial Recognition.

PubMed

Tao, Dapeng; Guo, Yanan; Li, Yaotang; Gao, Xinbo

2017-10-12

Facial recognition, one of the basic topics in computer vision and pattern recognition, has received substantial attention in recent years. However, for those traditional facial recognition algorithms, the facial images are reshaped to a long vector, thereby losing part of the original spatial constraints of each pixel. In this paper, a new tensor-based feature extraction algorithm termed tensor rank preserving discriminant analysis (TRPDA) for facial image recognition is proposed; the proposed method involves two stages: in the first stage, the low-dimensional tensor subspace of the original input tensor samples was obtained; in the second stage, discriminative locality alignment was utilized to obtain the ultimate vector feature representation for subsequent facial recognition. On the one hand, the proposed TRPDA algorithm fully utilizes the natural structure of the input samples, and it applies an optimization criterion that can directly handle the tensor spectral analysis problem, thereby decreasing the computation cost compared those traditional tensor-based feature selection algorithms. On the other hand, the proposed TRPDA algorithm extracts feature by finding a tensor subspace that preserves most of the rank order information of the intra-class input samples. Experiments on the three facial databases are performed here to determine the effectiveness of the proposed TRPDA algorithm.

Simple Common Plane contact detection algorithm for FE/FD methods

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

Vorobiev, O

2006-07-19

Common-plane (CP) algorithm is widely used in Discrete Element Method (DEM) to model contact forces between interacting particles or blocks. A new simple contact detection algorithm is proposed to model contacts in FE/FD methods which is similar to the CP algorithm. The CP is defined as a plane separating interacting faces of FE/FD mesh instead of blocks or particles in the original CP method. The method does not require iterations. It is very robust and easy to implement both in 2D and 3D case.

Dynamic baseline detection method for power data network service

NASA Astrophysics Data System (ADS)

Chen, Wei

2017-08-01

This paper proposes a dynamic baseline Traffic detection Method which is based on the historical traffic data for the Power data network. The method uses Cisco's NetFlow acquisition tool to collect the original historical traffic data from network element at fixed intervals. This method uses three dimensions information including the communication port, time, traffic (number of bytes or number of packets) t. By filtering, removing the deviation value, calculating the dynamic baseline value, comparing the actual value with the baseline value, the method can detect whether the current network traffic is abnormal.

Joint Dictionary Learning-Based Non-Negative Matrix Factorization for Voice Conversion to Improve Speech Intelligibility After Oral Surgery.

PubMed

Fu, Szu-Wei; Li, Pei-Chun; Lai, Ying-Hui; Yang, Cheng-Chien; Hsieh, Li-Chun; Tsao, Yu

2017-11-01

Objective: This paper focuses on machine learning based voice conversion (VC) techniques for improving the speech intelligibility of surgical patients who have had parts of their articulators removed. Because of the removal of parts of the articulator, a patient's speech may be distorted and difficult to understand. To overcome this problem, VC methods can be applied to convert the distorted speech such that it is clear and more intelligible. To design an effective VC method, two key points must be considered: 1) the amount of training data may be limited (because speaking for a long time is usually difficult for postoperative patients); 2) rapid conversion is desirable (for better communication). Methods: We propose a novel joint dictionary learning based non-negative matrix factorization (JD-NMF) algorithm. Compared to conventional VC techniques, JD-NMF can perform VC efficiently and effectively with only a small amount of training data. Results: The experimental results demonstrate that the proposed JD-NMF method not only achieves notably higher short-time objective intelligibility (STOI) scores (a standardized objective intelligibility evaluation metric) than those obtained using the original unconverted speech but is also significantly more efficient and effective than a conventional exemplar-based NMF VC method. Conclusion: The proposed JD-NMF method may outperform the state-of-the-art exemplar-based NMF VC method in terms of STOI scores under the desired scenario. Significance: We confirmed the advantages of the proposed joint training criterion for the NMF-based VC. Moreover, we verified that the proposed JD-NMF can effectively improve the speech intelligibility scores of oral surgery patients. Objective: This paper focuses on machine learning based voice conversion (VC) techniques for improving the speech intelligibility of surgical patients who have had parts of their articulators removed. Because of the removal of parts of the articulator, a patient's speech may be distorted and difficult to understand. To overcome this problem, VC methods can be applied to convert the distorted speech such that it is clear and more intelligible. To design an effective VC method, two key points must be considered: 1) the amount of training data may be limited (because speaking for a long time is usually difficult for postoperative patients); 2) rapid conversion is desirable (for better communication). Methods: We propose a novel joint dictionary learning based non-negative matrix factorization (JD-NMF) algorithm. Compared to conventional VC techniques, JD-NMF can perform VC efficiently and effectively with only a small amount of training data. Results: The experimental results demonstrate that the proposed JD-NMF method not only achieves notably higher short-time objective intelligibility (STOI) scores (a standardized objective intelligibility evaluation metric) than those obtained using the original unconverted speech but is also significantly more efficient and effective than a conventional exemplar-based NMF VC method. Conclusion: The proposed JD-NMF method may outperform the state-of-the-art exemplar-based NMF VC method in terms of STOI scores under the desired scenario. Significance: We confirmed the advantages of the proposed joint training criterion for the NMF-based VC. Moreover, we verified that the proposed JD-NMF can effectively improve the speech intelligibility scores of oral surgery patients.

Labeled RFS-Based Track-Before-Detect for Multiple Maneuvering Targets in the Infrared Focal Plane Array.

PubMed

Li, Miao; Li, Jun; Zhou, Yiyu

2015-12-08

The problem of jointly detecting and tracking multiple targets from the raw observations of an infrared focal plane array is a challenging task, especially for the case with uncertain target dynamics. In this paper a multi-model labeled multi-Bernoulli (MM-LMB) track-before-detect method is proposed within the labeled random finite sets (RFS) framework. The proposed track-before-detect method consists of two parts-MM-LMB filter and MM-LMB smoother. For the MM-LMB filter, original LMB filter is applied to track-before-detect based on target and measurement models, and is integrated with the interacting multiple models (IMM) approach to accommodate the uncertainty of target dynamics. For the MM-LMB smoother, taking advantage of the track labels and posterior model transition probability, the single-model single-target smoother is extended to a multi-model multi-target smoother. A Sequential Monte Carlo approach is also presented to implement the proposed method. Simulation results show the proposed method can effectively achieve tracking continuity for multiple maneuvering targets. In addition, compared with the forward filtering alone, our method is more robust due to its combination of forward filtering and backward smoothing.

Labeled RFS-Based Track-Before-Detect for Multiple Maneuvering Targets in the Infrared Focal Plane Array

PubMed Central

Li, Miao; Li, Jun; Zhou, Yiyu

2015-01-01

The problem of jointly detecting and tracking multiple targets from the raw observations of an infrared focal plane array is a challenging task, especially for the case with uncertain target dynamics. In this paper a multi-model labeled multi-Bernoulli (MM-LMB) track-before-detect method is proposed within the labeled random finite sets (RFS) framework. The proposed track-before-detect method consists of two parts—MM-LMB filter and MM-LMB smoother. For the MM-LMB filter, original LMB filter is applied to track-before-detect based on target and measurement models, and is integrated with the interacting multiple models (IMM) approach to accommodate the uncertainty of target dynamics. For the MM-LMB smoother, taking advantage of the track labels and posterior model transition probability, the single-model single-target smoother is extended to a multi-model multi-target smoother. A Sequential Monte Carlo approach is also presented to implement the proposed method. Simulation results show the proposed method can effectively achieve tracking continuity for multiple maneuvering targets. In addition, compared with the forward filtering alone, our method is more robust due to its combination of forward filtering and backward smoothing. PMID:26670234

Optical asymmetric cryptography based on amplitude reconstruction of elliptically polarized light

NASA Astrophysics Data System (ADS)

Cai, Jianjun; Shen, Xueju; Lei, Ming

2017-11-01

We propose a novel optical asymmetric image encryption method based on amplitude reconstruction of elliptically polarized light, which is free from silhouette problem. The original image is analytically separated into two phase-only masks firstly, and then the two masks are encoded into amplitudes of the orthogonal polarization components of an elliptically polarized light. Finally, the elliptically polarized light propagates through a linear polarizer, and the output intensity distribution is recorded by a CCD camera to obtain the ciphertext. The whole encryption procedure could be implemented by using commonly used optical elements, and it combines diffusion process and confusion process. As a result, the proposed method achieves high robustness against iterative-algorithm-based attacks. Simulation results are presented to prove the validity of the proposed cryptography.

Comparative study of methods for recognition of an unknown person's action from a video sequence

NASA Astrophysics Data System (ADS)

Hori, Takayuki; Ohya, Jun; Kurumisawa, Jun

2009-02-01

This paper proposes a Tensor Decomposition Based method that can recognize an unknown person's action from a video sequence, where the unknown person is not included in the database (tensor) used for the recognition. The tensor consists of persons, actions and time-series image features. For the observed unknown person's action, one of the actions stored in the tensor is assumed. Using the motion signature obtained from the assumption, the unknown person's actions are synthesized. The actions of one of the persons in the tensor are replaced by the synthesized actions. Then, the core tensor for the replaced tensor is computed. This process is repeated for the actions and persons. For each iteration, the difference between the replaced and original core tensors is computed. The assumption that gives the minimal difference is the action recognition result. For the time-series image features to be stored in the tensor and to be extracted from the observed video sequence, the human body silhouette's contour shape based feature is used. To show the validity of our proposed method, our proposed method is experimentally compared with Nearest Neighbor rule and Principal Component analysis based method. Experiments using 33 persons' seven kinds of action show that our proposed method achieves better recognition accuracies for the seven actions than the other methods.

Robust High-Capacity Audio Watermarking Based on FFT Amplitude Modification

NASA Astrophysics Data System (ADS)

Fallahpour, Mehdi; Megías, David

This paper proposes a novel robust audio watermarking algorithm to embed data and extract it in a bit-exact manner based on changing the magnitudes of the FFT spectrum. The key point is selecting a frequency band for embedding based on the comparison between the original and the MP3 compressed/decompressed signal and on a suitable scaling factor. The experimental results show that the method has a very high capacity (about 5kbps), without significant perceptual distortion (ODG about -0.25) and provides robustness against common audio signal processing such as added noise, filtering and MPEG compression (MP3). Furthermore, the proposed method has a larger capacity (number of embedded bits to number of host bits rate) than recent image data hiding methods.

Sub-pattern based multi-manifold discriminant analysis for face recognition

NASA Astrophysics Data System (ADS)

Dai, Jiangyan; Guo, Changlu; Zhou, Wei; Shi, Yanjiao; Cong, Lin; Yi, Yugen

2018-04-01

In this paper, we present a Sub-pattern based Multi-manifold Discriminant Analysis (SpMMDA) algorithm for face recognition. Unlike existing Multi-manifold Discriminant Analysis (MMDA) approach which is based on holistic information of face image for recognition, SpMMDA operates on sub-images partitioned from the original face image and then extracts the discriminative local feature from the sub-images separately. Moreover, the structure information of different sub-images from the same face image is considered in the proposed method with the aim of further improve the recognition performance. Extensive experiments on three standard face databases (Extended YaleB, CMU PIE and AR) demonstrate that the proposed method is effective and outperforms some other sub-pattern based face recognition methods.

Simplified Computation for Nonparametric Windows Method of Probability Density Function Estimation.

PubMed

Joshi, Niranjan; Kadir, Timor; Brady, Michael

2011-08-01

Recently, Kadir and Brady proposed a method for estimating probability density functions (PDFs) for digital signals which they call the Nonparametric (NP) Windows method. The method involves constructing a continuous space representation of the discrete space and sampled signal by using a suitable interpolation method. NP Windows requires only a small number of observed signal samples to estimate the PDF and is completely data driven. In this short paper, we first develop analytical formulae to obtain the NP Windows PDF estimates for 1D, 2D, and 3D signals, for different interpolation methods. We then show that the original procedure to calculate the PDF estimate can be significantly simplified and made computationally more efficient by a judicious choice of the frame of reference. We have also outlined specific algorithmic details of the procedures enabling quick implementation. Our reformulation of the original concept has directly demonstrated a close link between the NP Windows method and the Kernel Density Estimator.

Naturalness preservation image contrast enhancement via histogram modification

NASA Astrophysics Data System (ADS)

Tian, Qi-Chong; Cohen, Laurent D.

2018-04-01

Contrast enhancement is a technique for enhancing image contrast to obtain better visual quality. Since many existing contrast enhancement algorithms usually produce over-enhanced results, the naturalness preservation is needed to be considered in the framework of image contrast enhancement. This paper proposes a naturalness preservation contrast enhancement method, which adopts the histogram matching to improve the contrast and uses the image quality assessment to automatically select the optimal target histogram. The contrast improvement and the naturalness preservation are both considered in the target histogram, so this method can avoid the over-enhancement problem. In the proposed method, the optimal target histogram is a weighted sum of the original histogram, the uniform histogram, and the Gaussian-shaped histogram. Then the structural metric and the statistical naturalness metric are used to determine the weights of corresponding histograms. At last, the contrast-enhanced image is obtained via matching the optimal target histogram. The experiments demonstrate the proposed method outperforms the compared histogram-based contrast enhancement algorithms.

Metaheuristic Algorithms for Convolution Neural Network

PubMed Central

Fanany, Mohamad Ivan; Arymurthy, Aniati Murni

2016-01-01

A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN), a famous deep learning method, is still rarely investigated. Deep learning relates to a type of machine learning technique, where its aim is to move closer to the goal of artificial intelligence of creating a machine that could successfully perform any intellectual tasks that can be carried out by a human. In this paper, we propose the implementation strategy of three popular metaheuristic approaches, that is, simulated annealing, differential evolution, and harmony search, to optimize CNN. The performances of these metaheuristic methods in optimizing CNN on classifying MNIST and CIFAR dataset were evaluated and compared. Furthermore, the proposed methods are also compared with the original CNN. Although the proposed methods show an increase in the computation time, their accuracy has also been improved (up to 7.14 percent). PMID:27375738

Metaheuristic Algorithms for Convolution Neural Network.

PubMed

Rere, L M Rasdi; Fanany, Mohamad Ivan; Arymurthy, Aniati Murni

2016-01-01

A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN), a famous deep learning method, is still rarely investigated. Deep learning relates to a type of machine learning technique, where its aim is to move closer to the goal of artificial intelligence of creating a machine that could successfully perform any intellectual tasks that can be carried out by a human. In this paper, we propose the implementation strategy of three popular metaheuristic approaches, that is, simulated annealing, differential evolution, and harmony search, to optimize CNN. The performances of these metaheuristic methods in optimizing CNN on classifying MNIST and CIFAR dataset were evaluated and compared. Furthermore, the proposed methods are also compared with the original CNN. Although the proposed methods show an increase in the computation time, their accuracy has also been improved (up to 7.14 percent).

Singular boundary method for wave propagation analysis in periodic structures

NASA Astrophysics Data System (ADS)

Fu, Zhuojia; Chen, Wen; Wen, Pihua; Zhang, Chuanzeng

2018-07-01

A strong-form boundary collocation method, the singular boundary method (SBM), is developed in this paper for the wave propagation analysis at low and moderate wavenumbers in periodic structures. The SBM is of several advantages including mathematically simple, easy-to-program, meshless with the application of the concept of origin intensity factors in order to eliminate the singularity of the fundamental solutions and avoid the numerical evaluation of the singular integrals in the boundary element method. Due to the periodic behaviors of the structures, the SBM coefficient matrix can be represented as a block Toeplitz matrix. By employing three different fast Toeplitz-matrix solvers, the computational time and storage requirements are significantly reduced in the proposed SBM analysis. To demonstrate the effectiveness of the proposed SBM formulation for wave propagation analysis in periodic structures, several benchmark examples are presented and discussed The proposed SBM results are compared with the analytical solutions, the reference results and the COMSOL software.

Color image segmentation with support vector machines: applications to road signs detection.

PubMed

Cyganek, Bogusław

2008-08-01

In this paper we propose efficient color segmentation method which is based on the Support Vector Machine classifier operating in a one-class mode. The method has been developed especially for the road signs recognition system, although it can be used in other applications. The main advantage of the proposed method comes from the fact that the segmentation of characteristic colors is performed not in the original but in the higher dimensional feature space. By this a better data encapsulation with a linear hypersphere can be usually achieved. Moreover, the classifier does not try to capture the whole distribution of the input data which is often difficult to achieve. Instead, the characteristic data samples, called support vectors, are selected which allow construction of the tightest hypersphere that encloses majority of the input data. Then classification of a test data simply consists in a measurement of its distance to a centre of the found hypersphere. The experimental results show high accuracy and speed of the proposed method.

Mathematical model and metaheuristics for simultaneous balancing and sequencing of a robotic mixed-model assembly line

NASA Astrophysics Data System (ADS)

Li, Zixiang; Janardhanan, Mukund Nilakantan; Tang, Qiuhua; Nielsen, Peter

2018-05-01

This article presents the first method to simultaneously balance and sequence robotic mixed-model assembly lines (RMALB/S), which involves three sub-problems: task assignment, model sequencing and robot allocation. A new mixed-integer programming model is developed to minimize makespan and, using CPLEX solver, small-size problems are solved for optimality. Two metaheuristics, the restarted simulated annealing algorithm and co-evolutionary algorithm, are developed and improved to address this NP-hard problem. The restarted simulated annealing method replaces the current temperature with a new temperature to restart the search process. The co-evolutionary method uses a restart mechanism to generate a new population by modifying several vectors simultaneously. The proposed algorithms are tested on a set of benchmark problems and compared with five other high-performing metaheuristics. The proposed algorithms outperform their original editions and the benchmarked methods. The proposed algorithms are able to solve the balancing and sequencing problem of a robotic mixed-model assembly line effectively and efficiently.

Solvothermal method as a green chemistry solution for micro-encapsulation of phase change materials for high temperature thermal energy storage

NASA Astrophysics Data System (ADS)

Tudor, Albert Ioan; Motoc, Adrian Mihail; Ciobota, Cristina Florentina; Ciobota, Dan. Nastase; Piticescu, Radu Robert; Romero-Sanchez, Maria Dolores

2018-05-01

Thermal energy storage systems using phase change materials (PCMs) as latent heat storage are one of the main challenges at European level in improving the performances and efficiency of concentrated solar power energy generation due to their high energy density. PCM with high working temperatures in the temperature range 300-500 °C are required for these purposes. However their use is still limited due to the problems raised by the corrosion of the majority of high temperature PCMs and lower thermal transfer properties. Micro-encapsulation was proposed as one method to overcome these problems. Different micro-encapsulation methods proposed in the literature are presented and discussed. An original process for the micro-encapsulation of potassium nitrate as PCM in inorganic zinc oxide shells based on a solvothermal method followed by spray drying to produce microcapsules with controlled phase composition and distribution is proposed and their transformation temperatures and enthalpies measured by differential scanning calorimetry are presented.

Parameters Identification for Photovoltaic Module Based on an Improved Artificial Fish Swarm Algorithm

PubMed Central

Wang, Hong-Hua

2014-01-01

A precise mathematical model plays a pivotal role in the simulation, evaluation, and optimization of photovoltaic (PV) power systems. Different from the traditional linear model, the model of PV module has the features of nonlinearity and multiparameters. Since conventional methods are incapable of identifying the parameters of PV module, an excellent optimization algorithm is required. Artificial fish swarm algorithm (AFSA), originally inspired by the simulation of collective behavior of real fish swarms, is proposed to fast and accurately extract the parameters of PV module. In addition to the regular operation, a mutation operator (MO) is designed to enhance the searching performance of the algorithm. The feasibility of the proposed method is demonstrated by various parameters of PV module under different environmental conditions, and the testing results are compared with other studied methods in terms of final solutions and computational time. The simulation results show that the proposed method is capable of obtaining higher parameters identification precision. PMID:25243233

Equating with Miditests Using IRT

ERIC Educational Resources Information Center

Fitzpatrick, Joseph; Skorupski, William P.

2016-01-01

The equating performance of two internal anchor test structures--miditests and minitests--is studied for four IRT equating methods using simulated data. Originally proposed by Sinharay and Holland, miditests are anchors that have the same mean difficulty as the overall test but less variance in item difficulties. Four popular IRT equating methods…

How does new evidence change our estimates of probabilities? Carnap's formula revisited

NASA Technical Reports Server (NTRS)

Kreinovich, Vladik; Quintana, Chris

1992-01-01

The formula originally proposed by R. Carnap in his analysis of induction is reviewed and its natural generalization is presented. A situation is considered where the probability of a certain event is determined without using standard statistical methods due to the lack of observation.

Characteristic fingerprint based on gingerol derivative analysis for discrimination of ginger (Zingiber officinale) according to geographical origin using HPLC-DAD combined with chemometrics.

PubMed

Yudthavorasit, Soparat; Wongravee, Kanet; Leepipatpiboon, Natchanun

2014-09-01

Chromatographic fingerprints of gingers from five different ginger-producing countries (China, India, Malaysia, Thailand and Vietnam) were newly established to discriminate the origin of ginger. The pungent bioactive principles of ginger, gingerols and six other gingerol-related compounds were determined and identified. Their variations in HPLC profiles create the characteristic pattern of each origin by employing similarity analysis, hierarchical cluster analysis (HCA), principal component analysis (PCA) and linear discriminant analysis (LDA). As results, the ginger profiles tended to be grouped and separated on the basis of the geographical closeness of the countries of origin. An effective mathematical model with high predictive ability was obtained and chemical markers for each origin were also identified as the characteristic active compounds to differentiate the ginger origin. The proposed method is useful for quality control of ginger in case of origin labelling and to assess food authenticity issues. Copyright © 2014 Elsevier Ltd. All rights reserved.

Pseudo CT estimation from MRI using patch-based random forest

NASA Astrophysics Data System (ADS)

Yang, Xiaofeng; Lei, Yang; Shu, Hui-Kuo; Rossi, Peter; Mao, Hui; Shim, Hyunsuk; Curran, Walter J.; Liu, Tian

2017-02-01

Recently, MR simulators gain popularity because of unnecessary radiation exposure of CT simulators being used in radiation therapy planning. We propose a method for pseudo CT estimation from MR images based on a patch-based random forest. Patient-specific anatomical features are extracted from the aligned training images and adopted as signatures for each voxel. The most robust and informative features are identified using feature selection to train the random forest. The well-trained random forest is used to predict the pseudo CT of a new patient. This prediction technique was tested with human brain images and the prediction accuracy was assessed using the original CT images. Peak signal-to-noise ratio (PSNR) and feature similarity (FSIM) indexes were used to quantify the differences between the pseudo and original CT images. The experimental results showed the proposed method could accurately generate pseudo CT images from MR images. In summary, we have developed a new pseudo CT prediction method based on patch-based random forest, demonstrated its clinical feasibility, and validated its prediction accuracy. This pseudo CT prediction technique could be a useful tool for MRI-based radiation treatment planning and attenuation correction in a PET/MRI scanner.

Feature Transformation Detection Method with Best Spectral Band Selection Process for Hyper-spectral Imaging

NASA Astrophysics Data System (ADS)

Chen, Hai-Wen; McGurr, Mike; Brickhouse, Mark

2015-11-01

We present a newly developed feature transformation (FT) detection method for hyper-spectral imagery (HSI) sensors. In essence, the FT method, by transforming the original features (spectral bands) to a different feature domain, may considerably increase the statistical separation between the target and background probability density functions, and thus may significantly improve the target detection and identification performance, as evidenced by the test results in this paper. We show that by differentiating the original spectral, one can completely separate targets from the background using a single spectral band, leading to perfect detection results. In addition, we have proposed an automated best spectral band selection process with a double-threshold scheme that can rank the available spectral bands from the best to the worst for target detection. Finally, we have also proposed an automated cross-spectrum fusion process to further improve the detection performance in lower spectral range (<1000 nm) by selecting the best spectral band pair with multivariate analysis. Promising detection performance has been achieved using a small background material signature library for concept-proving, and has then been further evaluated and verified using a real background HSI scene collected by a HYDICE sensor.

High-quality compressive ghost imaging

NASA Astrophysics Data System (ADS)

Huang, Heyan; Zhou, Cheng; Tian, Tian; Liu, Dongqi; Song, Lijun

2018-04-01

We propose a high-quality compressive ghost imaging method based on projected Landweber regularization and guided filter, which effectively reduce the undersampling noise and improve the resolution. In our scheme, the original object is reconstructed by decomposing of regularization and denoising steps instead of solving a minimization problem in compressive reconstruction process. The simulation and experimental results show that our method can obtain high ghost imaging quality in terms of PSNR and visual observation.

Archaeal phylogenomics provides evidence in support of a methanogenic origin of the Archaea and a thaumarchaeal origin for the eukaryotes.

PubMed

Kelly, S; Wickstead, B; Gull, K

2011-04-07

We have developed a machine-learning approach to identify 3537 discrete orthologue protein sequence groups distributed across all available archaeal genomes. We show that treating these orthologue groups as binary detection/non-detection data is sufficient to capture the majority of archaeal phylogeny. We subsequently use the sequence data from these groups to infer a method and substitution-model-independent phylogeny. By holding this phylogeny constrained and interrogating the intersection of this large dataset with both the Eukarya and the Bacteria using Bayesian and maximum-likelihood approaches, we propose and provide evidence for a methanogenic origin of the Archaea. By the same criteria, we also provide evidence in support of an origin for Eukarya either within or as sisters to the Thaumarchaea.

A novel alignment-free method for detection of lateral genetic transfer based on TF-IDF.

PubMed

Cong, Yingnan; Chan, Yao-Ban; Ragan, Mark A

2016-07-25

Lateral genetic transfer (LGT) plays an important role in the evolution of microbes. Existing computational methods for detecting genomic regions of putative lateral origin scale poorly to large data. Here, we propose a novel method based on TF-IDF (Term Frequency-Inverse Document Frequency) statistics to detect not only regions of lateral origin, but also their origin and direction of transfer, in sets of hierarchically structured nucleotide or protein sequences. This approach is based on the frequency distributions of k-mers in the sequences. If a set of contiguous k-mers appears sufficiently more frequently in another phyletic group than in its own, we infer that they have been transferred from the first group to the second. We performed rigorous tests of TF-IDF using simulated and empirical datasets. With the simulated data, we tested our method under different parameter settings for sequence length, substitution rate between and within groups and post-LGT, deletion rate, length of transferred region and k size, and found that we can detect LGT events with high precision and recall. Our method performs better than an established method, ALFY, which has high recall but low precision. Our method is efficient, with runtime increasing approximately linearly with sequence length.

A half-blind color image hiding and encryption method in fractional Fourier domains

NASA Astrophysics Data System (ADS)

Ge, Fan; Chen, Linfei; Zhao, Daomu

2008-09-01

We have proposed a new technique for digital image encryption and hiding based on fractional Fourier transforms with double random phases. An original hidden image is encrypted two times and the keys are increased to strengthen information protection. Color image hiding and encryption with wavelength multiplexing is proposed by embedding and encryption in R, G and B three channels. The robustness against occlusion attacks and noise attacks are analyzed. And computer simulations are presented with the corresponding results.

On a modification method of Lefschetz thimbles

NASA Astrophysics Data System (ADS)

Tsutsui, Shoichiro; Doi, Takahiro M.

2018-03-01

The QCD at finite density is not well understood yet, where standard Monte Carlo simulation suffers from the sign problem. In order to overcome the sign problem, the method of Lefschetz thimble has been explored. Basically, the original sign problem can be less severe in a complexified theory due to the constancy of the imaginary part of an action on each thimble. However, global phase factors assigned on each thimble still remain. Their interference is not negligible in a situation where a large number of thimbles contribute to the partition function, and this could also lead to a sign problem. In this study, we propose a method to resolve this problem by modifying the structure of Lefschetz thimbles such that only a single thimble is relevant to the partition function. It can be shown that observables measured in the original and modified theories are connected by a simple identity. We exemplify that our method works well in a toy model.

High spatial resolution compressed sensing (HSPARSE) functional MRI.

PubMed

Fang, Zhongnan; Van Le, Nguyen; Choy, ManKin; Lee, Jin Hyung

2016-08-01

To propose a novel compressed sensing (CS) high spatial resolution functional MRI (fMRI) method and demonstrate the advantages and limitations of using CS for high spatial resolution fMRI. A randomly undersampled variable density spiral trajectory enabling an acceleration factor of 5.3 was designed with a balanced steady state free precession sequence to achieve high spatial resolution data acquisition. A modified k-t SPARSE method was then implemented and applied with a strategy to optimize regularization parameters for consistent, high quality CS reconstruction. The proposed method improves spatial resolution by six-fold with 12 to 47% contrast-to-noise ratio (CNR), 33 to 117% F-value improvement and maintains the same temporal resolution. It also achieves high sensitivity of 69 to 99% compared the original ground-truth, small false positive rate of less than 0.05 and low hemodynamic response function distortion across a wide range of CNRs. The proposed method is robust to physiological noise and enables detection of layer-specific activities in vivo, which cannot be resolved using the highest spatial resolution Nyquist acquisition. The proposed method enables high spatial resolution fMRI that can resolve layer-specific brain activity and demonstrates the significant improvement that CS can bring to high spatial resolution fMRI. Magn Reson Med 76:440-455, 2016. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.

Regularized spherical polar fourier diffusion MRI with optimal dictionary learning.

PubMed

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

2013-01-01

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

Unsupervised malaria parasite detection based on phase spectrum.

PubMed

Fang, Yuming; Xiong, Wei; Lin, Weisi; Chen, Zhenzhong

2011-01-01

In this paper, we propose a novel method for malaria parasite detection based on phase spectrum. The method first obtains the amplitude spectrum and phase spectrum for blood smear images through Quaternion Fourier Transform (QFT). Then it gets the reconstructed image based on Inverse Quaternion Fourier transform (IQFT) on a constant amplitude spectrum and the original phase spectrum. The malaria parasite areas can be detected easily from the reconstructed blood smear images. Extensive experiments have demonstrated the effectiveness of this novel method.

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

Smith, R.; Harrison, D. E. Jr.

A variable time step integration algorithm for carrying out molecular dynamics simulations of atomic collision cascades is proposed which evaluates the interaction forces only once per time step. The algorithm is tested on some model problems which have exact solutions and is compared against other common methods. These comparisons show that the method has good stability and accuracy. Applications to Ar/sup +/ bombardment of Cu and Si show good accuracy and improved speed to the original method (D. E. Harrison, W. L. Gay, and H. M. Effron, J. Math. Phys. /bold 10/, 1179 (1969)).

Approximating the Basset force by optimizing the method of van Hinsberg et al.

NASA Astrophysics Data System (ADS)

Casas, G.; Ferrer, A.; Oñate, E.

2018-01-01

In this work we put the method proposed by van Hinsberg et al. [29] to the test, highlighting its accuracy and efficiency in a sequence of benchmarks of increasing complexity. Furthermore, we explore the possibility of systematizing the way in which the method's free parameters are determined by generalizing the optimization problem that was considered originally. Finally, we provide a list of worked-out values, ready for implementation in large-scale particle-laden flow simulations.

Beyond single-stream with the Schrödinger method

NASA Astrophysics Data System (ADS)

Uhlemann, Cora; Kopp, Michael

2016-10-01

We investigate large scale structure formation of collisionless dark matter in the phase space description based on the Vlasov-Poisson equation. We present the Schrödinger method, originally proposed by \\cite{WK93} as numerical technique based on the Schrödinger Poisson equation, as an analytical tool which is superior to the common standard pressureless fluid model. Whereas the dust model fails and develops singularities at shell crossing the Schrödinger method encompasses multi-streaming and even virialization.

[Determination of wine original regions using information fusion of NIR and MIR spectroscopy].

PubMed

Xiang, Ling-Li; Li, Meng-Hua; Li, Jing-Mingz; Li, Jun-Hui; Zhang, Lu-Da; Zhao, Long-Lian

2014-10-01

Geographical origins of wine grapes are significant factors affecting wine quality and wine prices. Tasters' evaluation is a good method but has some limitations. It is important to discriminate different wine original regions quickly and accurately. The present paper proposed a method to determine wine original regions based on Bayesian information fusion that fused near-infrared (NIR) transmission spectra information and mid-infrared (MIR) ATR spectra information of wines. This method improved the determination results by expanding the sources of analysis information. NIR spectra and MIR spectra of 153 wine samples from four different regions of grape growing were collected by near-infrared and mid-infrared Fourier transform spe trometer separately. These four different regions are Huailai, Yantai, Gansu and Changli, which areall typical geographical originals for Chinese wines. NIR and MIR discriminant models for wine regions were established using partial least squares discriminant analysis (PLS-DA) based on NIR spectra and MIR spectra separately. In PLS-DA, the regions of wine samples are presented in group of binary code. There are four wine regions in this paper, thereby using four nodes standing for categorical variables. The output nodes values for each sample in NIR and MIR models were normalized first. These values stand for the probabilities of each sample belonging to each category. They seemed as the input to the Bayesian discriminant formula as a priori probability value. The probabilities were substituteed into the Bayesian formula to get posterior probabilities, by which we can judge the new class characteristics of these samples. Considering the stability of PLS-DA models, all the wine samples were divided into calibration sets and validation sets randomly for ten times. The results of NIR and MIR discriminant models of four wine regions were as follows: the average accuracy rates of calibration sets were 78.21% (NIR) and 82.57% (MIR), and the average accuracy rates of validation sets were 82.50% (NIR) and 81.98% (MIR). After using the method proposed in this paper, the accuracy rates of calibration and validation changed to 87.11% and 90.87% separately, which all achieved better results of determination than individual spectroscopy. These results suggest that Bayesian information fusion of NIR and MIR spectra is feasible for fast identification of wine original regions.

[The identification investigation of the material evidence of biological origin after multifactorial exposure].

PubMed

Iakovlev, D Iu; Solodun, Iu V; Proskurin, V N

1999-01-01

The possibility of investigating pieces of material evidence of biological origin after exposure to various factors is evaluated. The possibility of detecting proteins of liquid media of human organism by electrophoresis in polyacrylamide denatured gel is investigated. The method is intended for identification of biological material in a state of grave destruction. Methodology of such studies is proposed. The data indicate that the structural integrity and qualitative composition of the spectrum of main serum proteins are retained after combined exposure to damaging factors and complete destruction of blood cells.

Noise estimation for hyperspectral imagery using spectral unmixing and synthesis

NASA Astrophysics Data System (ADS)

Demirkesen, C.; Leloglu, Ugur M.

2014-10-01

Most hyperspectral image (HSI) processing algorithms assume a signal to noise ratio model in their formulation which makes them dependent on accurate noise estimation. Many techniques have been proposed to estimate the noise. A very comprehensive comparative study on the subject is done by Gao et al. [1]. In a nut-shell, most techniques are based on the idea of calculating standard deviation from assumed-to-be homogenous regions in the image. Some of these algorithms work on a regular grid parameterized with a window size w, while others make use of image segmentation in order to obtain homogenous regions. This study focuses not only to the statistics of the noise but to the estimation of the noise itself. A noise estimation technique motivated from a recent HSI de-noising approach [2] is proposed in this study. The denoising algorithm is based on estimation of the end-members and their fractional abundances using non-negative least squares method. The end-members are extracted using the well-known simplex volume optimization technique called NFINDR after manual selection of number of end-members and the image is reconstructed using the estimated endmembers and abundances. Actually, image de-noising and noise estimation are two sides of the same coin: Once we denoise an image, we can estimate the noise by calculating the difference of the de-noised image and the original noisy image. In this study, the noise is estimated as described above. To assess the accuracy of this method, the methodology in [1] is followed, i.e., synthetic images are created by mixing end-member spectra and noise. Since best performing method for noise estimation was spectral and spatial de-correlation (SSDC) originally proposed in [3], the proposed method is compared to SSDC. The results of the experiments conducted with synthetic HSIs suggest that the proposed noise estimation strategy outperforms the existing techniques in terms of mean and standard deviation of absolute error of the estimated noise. Finally, it is shown that the proposed technique demonstrated a robust behavior to the change of its single parameter, namely the number of end-members.

Pixel-based speckle adjustment for noise reduction in Fourier-domain OCT images.

PubMed

Zhang, Anqi; Xi, Jiefeng; Sun, Jitao; Li, Xingde

2017-03-01

Speckle resides in OCT signals and inevitably effects OCT image quality. In this work, we present a novel method for speckle noise reduction in Fourier-domain OCT images, which utilizes the phase information of complex OCT data. In this method, speckle area is pre-delineated pixelwise based on a phase-domain processing method and then adjusted by the results of wavelet shrinkage of the original image. Coefficient shrinkage method such as wavelet or contourlet is applied afterwards for further suppressing the speckle noise. Compared with conventional methods without speckle adjustment, the proposed method demonstrates significant improvement of image quality.

Extension of the ratio method to low energy

DOE PAGES

Colomer, Frederic; Capel, Pierre; Nunes, F. M.; ...

2016-05-25

The ratio method has been proposed as a means to remove the reaction model dependence in the study of halo nuclei. Originally, it was developed for higher energies but given the potential interest in applying the method at lower energy, in this work we explore its validity at 20 MeV/nucleon. The ratio method takes the ratio of the breakup angular distribution and the summed angular distribution (which includes elastic, inelastic and breakup) and uses this observable to constrain the features of the original halo wave function. In this work we use the Continuum Discretized Coupled Channel method and the Coulomb-correctedmore » Dynamical Eikonal Approximation for the study. We study the reactions of 11Be on 12C, 40Ca and 208Pb at 20 MeV/nucleon. We compare the various theoretical descriptions and explore the dependence of our result on the core-target interaction. Lastly, our study demonstrates that the ratio method is valid at these lower beam energies.« less

Underwater image enhancement based on the dark channel prior and attenuation compensation

NASA Astrophysics Data System (ADS)

Guo, Qingwen; Xue, Lulu; Tang, Ruichun; Guo, Lingrui

2017-10-01

Aimed at the two problems of underwater imaging, fog effect and color cast, an Improved Segmentation Dark Channel Prior (ISDCP) defogging method is proposed to solve the fog effects caused by physical properties of water. Due to mass refraction of light in the process of underwater imaging, fog effects would lead to image blurring. And color cast is closely related to different degree of attenuation while light with different wavelengths is traveling in water. The proposed method here integrates the ISDCP and quantitative histogram stretching techniques into the image enhancement procedure. Firstly, the threshold value is set during the refinement process of the transmission maps to identify the original mismatching, and to conduct the differentiated defogging process further. Secondly, a method of judging the propagating distance of light is adopted to get the attenuation degree of energy during the propagation underwater. Finally, the image histogram is stretched quantitatively in Red-Green-Blue channel respectively according to the degree of attenuation in each color channel. The proposed method ISDCP can reduce the computational complexity and improve the efficiency in terms of defogging effect to meet the real-time requirements. Qualitative and quantitative comparison for several different underwater scenes reveals that the proposed method can significantly improve the visibility compared with previous methods.

Conditional Monthly Weather Resampling Procedure for Operational Seasonal Water Resources Forecasting

NASA Astrophysics Data System (ADS)

Beckers, J.; Weerts, A.; Tijdeman, E.; Welles, E.; McManamon, A.

2013-12-01

To provide reliable and accurate seasonal streamflow forecasts for water resources management several operational hydrologic agencies and hydropower companies around the world use the Extended Streamflow Prediction (ESP) procedure. The ESP in its original implementation does not accommodate for any additional information that the forecaster may have about expected deviations from climatology in the near future. Several attempts have been conducted to improve the skill of the ESP forecast, especially for areas which are affected by teleconnetions (e,g. ENSO, PDO) via selection (Hamlet and Lettenmaier, 1999) or weighting schemes (Werner et al., 2004; Wood and Lettenmaier, 2006; Najafi et al., 2012). A disadvantage of such schemes is that they lead to a reduction of the signal to noise ratio of the probabilistic forecast. To overcome this, we propose a resampling method conditional on climate indices to generate meteorological time series to be used in the ESP. The method can be used to generate a large number of meteorological ensemble members in order to improve the statistical properties of the ensemble. The effectiveness of the method was demonstrated in a real-time operational hydrologic seasonal forecasts system for the Columbia River basin operated by the Bonneville Power Administration. The forecast skill of the k-nn resampler was tested against the original ESP for three basins at the long-range seasonal time scale. The BSS and CRPSS were used to compare the results to those of the original ESP method. Positive forecast skill scores were found for the resampler method conditioned on different indices for the prediction of spring peak flows in the Dworshak and Hungry Horse basin. For the Libby Dam basin however, no improvement of skill was found. The proposed resampling method is a promising practical approach that can add skill to ESP forecasts at the seasonal time scale. Further improvement is possible by fine tuning the method and selecting the most informative climate indices for the region of interest.

Fast sweeping methods for hyperbolic systems of conservation laws at steady state II

NASA Astrophysics Data System (ADS)

Engquist, Björn; Froese, Brittany D.; Tsai, Yen-Hsi Richard

2015-04-01

The idea of using fast sweeping methods for solving stationary systems of conservation laws has previously been proposed for efficiently computing solutions with sharp shocks. We further develop these methods to allow for a more challenging class of problems including problems with sonic points, shocks originating in the interior of the domain, rarefaction waves, and two-dimensional systems. We show that fast sweeping methods can produce higher-order accuracy. Computational results validate the claims of accuracy, sharp shock curves, and optimal computational efficiency.

A coherent discrete variable representation method on a sphere

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

Yu, Hua -Gen

Here, the coherent discrete variable representation (ZDVR) has been extended for construct- ing a multidimensional potential-optimized DVR basis on a sphere. In order to deal with the non-constant Jacobian in spherical angles, two direct product primitive basis methods are proposed so that the original ZDVR technique can be properly implemented. The method has been demonstrated by computing the lowest states of a two dimensional (2D) vibrational model. Results show that the extended ZDVR method gives accurate eigenval- ues and exponential convergence with increasing ZDVR basis size.

A coherent discrete variable representation method on a sphere

DOE PAGES

Yu, Hua -Gen

2017-09-05

Here, the coherent discrete variable representation (ZDVR) has been extended for construct- ing a multidimensional potential-optimized DVR basis on a sphere. In order to deal with the non-constant Jacobian in spherical angles, two direct product primitive basis methods are proposed so that the original ZDVR technique can be properly implemented. The method has been demonstrated by computing the lowest states of a two dimensional (2D) vibrational model. Results show that the extended ZDVR method gives accurate eigenval- ues and exponential convergence with increasing ZDVR basis size.

Frequency Compounded Imaging with a High-Frequency Dual Element Transducer

PubMed Central

Chang, Jin Ho; Kim, Hyung Ham; Lee, Jungwoo; Shung, K. Kirk

2014-01-01

This paper proposes a frequency compounding method to reduce speckle interferences, where a concentric annular type high-frequency dual element transducer is used to broaden the bandwidth of an imaging system. In frequency compounding methods, frequency division is carried out to obtain sub-band images containing uncorrelated speckles, which sacrifices axial resolution. Therefore, frequency compounding often deteriorates the target-detecting capability, quantified by the total signal-to-noise ratio (SNR), when the speckle’s SNR (SSNR) is not improved as much as the degraded axial resolution. However, this could be avoided if the effective bandwidth required for frequency compounding is increased. The primary goal of the proposed approach, hence, is to improve SSNR by a factor of two under the condition where axial resolution is degraded by a factor of less than two, which indicates the total SNR improvement to higher than 40% compared to that of an original image. Since the method here employs a dual element transducer operating at 20 and 40 MHz, the effective bandwidth necessary for frequency compounding becomes broadened. By dividing each spectrum of RF samples from both elements into two sub-bands, this method eventually enables four sets of the sub-band samples to contain uncorrelated speckles. This causes the axial resolution to be reduced by a factor of as low as 1.85, which means that this method would improve total SNR by at least 47 %. An in vitro experiment on an excised pig eye was performed to validate the proposed approach, and the results showed that the SSNR was improved from 2.081±0.365 in the original image to 4.206±0.635 in the final compounding image. PMID:19914674

Frequency compounded imaging with a high-frequency dual element transducer.

PubMed

Chang, Jin Ho; Kim, Hyung Ham; Lee, Jungwoo; Shung, K Kirk

2010-04-01

This paper proposes a frequency compounding method to reduce speckle interferences, where a concentric annular type high-frequency dual element transducer is used to broaden the bandwidth of an imaging system. In frequency compounding methods, frequency division is carried out to obtain sub-band images containing uncorrelated speckles, which sacrifices axial resolution. Therefore, frequency compounding often deteriorates the target-detecting capability, quantified by the total signal-to-noise ratio (SNR), when the speckle's SNR (SSNR) is not improved as much as the degraded axial resolution. However, this could be avoided if the effective bandwidth required for frequency compounding is increased. The primary goal of the proposed approach, hence, is to improve SSNR by a factor of two under the condition where axial resolution is degraded by a factor of less than two, which indicates the total SNR improvement to higher than 40% compared to that of an original image. Since the method here employs a dual element transducer operating at 20 and 40MHz, the effective bandwidth necessary for frequency compounding becomes broadened. By dividing each spectrum of RF samples from both elements into two sub-bands, this method eventually enables four sets of the sub-band samples to contain uncorrelated speckles. This causes the axial resolution to be reduced by a factor of as low as 1.85, which means that this method would improve total SNR by at least 47%. An in vitro experiment on an excised pig eye was performed to validate the proposed approach, and the results showed that the SSNR was improved from 2.081+/-0.365 in the original image to 4.206+/-0.635 in the final compounding image. Copyright 2009 Elsevier B.V. All rights reserved.

A novel tree-based procedure for deciphering the genomic spectrum of clinical disease entities.

PubMed

Mbogning, Cyprien; Perdry, Hervé; Toussile, Wilson; Broët, Philippe

2014-01-01

Dissecting the genomic spectrum of clinical disease entities is a challenging task. Recursive partitioning (or classification trees) methods provide powerful tools for exploring complex interplay among genomic factors, with respect to a main factor, that can reveal hidden genomic patterns. To take confounding variables into account, the partially linear tree-based regression (PLTR) model has been recently published. It combines regression models and tree-based methodology. It is however computationally burdensome and not well suited for situations for which a large number of exploratory variables is expected. We developed a novel procedure that represents an alternative to the original PLTR procedure, and considered different selection criteria. A simulation study with different scenarios has been performed to compare the performances of the proposed procedure to the original PLTR strategy. The proposed procedure with a Bayesian Information Criterion (BIC) achieved good performances to detect the hidden structure as compared to the original procedure. The novel procedure was used for analyzing patterns of copy-number alterations in lung adenocarcinomas, with respect to Kirsten Rat Sarcoma Viral Oncogene Homolog gene (KRAS) mutation status, while controlling for a cohort effect. Results highlight two subgroups of pure or nearly pure wild-type KRAS tumors with particular copy-number alteration patterns. The proposed procedure with a BIC criterion represents a powerful and practical alternative to the original procedure. Our procedure performs well in a general framework and is simple to implement.

Multi-label spacecraft electrical signal classification method based on DBN and random forest

PubMed Central

Li, Ke; Yu, Nan; Li, Pengfei; Song, Shimin; Wu, Yalei; Li, Yang; Liu, Meng

2017-01-01

In spacecraft electrical signal characteristic data, there exists a large amount of data with high-dimensional features, a high computational complexity degree, and a low rate of identification problems, which causes great difficulty in fault diagnosis of spacecraft electronic load systems. This paper proposes a feature extraction method that is based on deep belief networks (DBN) and a classification method that is based on the random forest (RF) algorithm; The proposed algorithm mainly employs a multi-layer neural network to reduce the dimension of the original data, and then, classification is applied. Firstly, we use the method of wavelet denoising, which was used to pre-process the data. Secondly, the deep belief network is used to reduce the feature dimension and improve the rate of classification for the electrical characteristics data. Finally, we used the random forest algorithm to classify the data and comparing it with other algorithms. The experimental results show that compared with other algorithms, the proposed method shows excellent performance in terms of accuracy, computational efficiency, and stability in addressing spacecraft electrical signal data. PMID:28486479

Multi-label spacecraft electrical signal classification method based on DBN and random forest.

PubMed

Li, Ke; Yu, Nan; Li, Pengfei; Song, Shimin; Wu, Yalei; Li, Yang; Liu, Meng

2017-01-01

In spacecraft electrical signal characteristic data, there exists a large amount of data with high-dimensional features, a high computational complexity degree, and a low rate of identification problems, which causes great difficulty in fault diagnosis of spacecraft electronic load systems. This paper proposes a feature extraction method that is based on deep belief networks (DBN) and a classification method that is based on the random forest (RF) algorithm; The proposed algorithm mainly employs a multi-layer neural network to reduce the dimension of the original data, and then, classification is applied. Firstly, we use the method of wavelet denoising, which was used to pre-process the data. Secondly, the deep belief network is used to reduce the feature dimension and improve the rate of classification for the electrical characteristics data. Finally, we used the random forest algorithm to classify the data and comparing it with other algorithms. The experimental results show that compared with other algorithms, the proposed method shows excellent performance in terms of accuracy, computational efficiency, and stability in addressing spacecraft electrical signal data.

Pruning artificial neural networks using neural complexity measures.

PubMed

Jorgensen, Thomas D; Haynes, Barry P; Norlund, Charlotte C F

2008-10-01

This paper describes a new method for pruning artificial neural networks, using a measure of the neural complexity of the neural network. This measure is used to determine the connections that should be pruned. The measure computes the information-theoretic complexity of a neural network, which is similar to, yet different from previous research on pruning. The method proposed here shows how overly large and complex networks can be reduced in size, whilst retaining learnt behaviour and fitness. The technique proposed here helps to discover a network topology that matches the complexity of the problem it is meant to solve. This novel pruning technique is tested in a robot control domain, simulating a racecar. It is shown, that the proposed pruning method is a significant improvement over the most commonly used pruning method Magnitude Based Pruning. Furthermore, some of the pruned networks prove to be faster learners than the benchmark network that they originate from. This means that this pruning method can also help to unleash hidden potential in a network, because the learning time decreases substantially for a pruned a network, due to the reduction of dimensionality of the network.

Full-frame video stabilization with motion inpainting.

PubMed

Matsushita, Yasuyuki; Ofek, Eyal; Ge, Weina; Tang, Xiaoou; Shum, Heung-Yeung

2006-07-01

Video stabilization is an important video enhancement technology which aims at removing annoying shaky motion from videos. We propose a practical and robust approach of video stabilization that produces full-frame stabilized videos with good visual quality. While most previous methods end up with producing smaller size stabilized videos, our completion method can produce full-frame videos by naturally filling in missing image parts by locally aligning image data of neighboring frames. To achieve this, motion inpainting is proposed to enforce spatial and temporal consistency of the completion in both static and dynamic image areas. In addition, image quality in the stabilized video is enhanced with a new practical deblurring algorithm. Instead of estimating point spread functions, our method transfers and interpolates sharper image pixels of neighboring frames to increase the sharpness of the frame. The proposed video completion and deblurring methods enabled us to develop a complete video stabilizer which can naturally keep the original image quality in the stabilized videos. The effectiveness of our method is confirmed by extensive experiments over a wide variety of videos.

Morphology filter bank for extracting nodular and linear patterns in medical images.

PubMed

Hashimoto, Ryutaro; Uchiyama, Yoshikazu; Uchimura, Keiichi; Koutaki, Gou; Inoue, Tomoki

2017-04-01

Using image processing to extract nodular or linear shadows is a key technique of computer-aided diagnosis schemes. This study proposes a new method for extracting nodular and linear patterns of various sizes in medical images. We have developed a morphology filter bank that creates multiresolution representations of an image. Analysis bank of this filter bank produces nodular and linear patterns at each resolution level. Synthesis bank can then be used to perfectly reconstruct the original image from these decomposed patterns. Our proposed method shows better performance based on a quantitative evaluation using a synthesized image compared with a conventional method based on a Hessian matrix, often used to enhance nodular and linear patterns. In addition, experiments show that our method can be applied to the followings: (1) microcalcifications of various sizes in mammograms can be extracted, (2) blood vessels of various sizes in retinal fundus images can be extracted, and (3) thoracic CT images can be reconstructed while removing normal vessels. Our proposed method is useful for extracting nodular and linear shadows or removing normal structures in medical images.

A study for watermark methods appropriate to medical images.

PubMed

Cho, Y; Ahn, B; Kim, J S; Kim, I Y; Kim, S I

2001-06-01

The network system, including the picture archiving and communication system (PACS), is essential in hospital and medical imaging fields these days. Many medical images are accessed and processed on the web, as well as in PACS. Therefore, any possible accidents caused by the illegal modification of medical images must be prevented. Digital image watermark techniques have been proposed as a method to protect against illegal copying or modification of copyrighted material. Invisible signatures made by a digital image watermarking technique can be a solution to these problems. However, medical images have some different characteristics from normal digital images in that one must not corrupt the information contained in the original medical images. In this study, we suggest modified watermark methods appropriate for medical image processing and communication system that prevent clinically important data contained in original images from being corrupted.

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

Tuo, Rui; Jeff Wu, C. F.

Calibration parameters in deterministic computer experiments are those attributes that cannot be measured or available in physical experiments. Here, an approach to estimate them by using data from physical experiments and computer simulations. A theoretical framework is given which allows us to study the issues of parameter identifiability and estimation. We define the L 2-consistency for calibration as a justification for calibration methods. It is shown that a simplified version of the original KO method leads to asymptotically L 2-inconsistent calibration. This L 2-inconsistency can be remedied by modifying the original estimation procedure. A novel calibration method, called the Lmore » 2 calibration, is proposed and proven to be L 2-consistent and enjoys optimal convergence rate. Furthermore a numerical example and some mathematical analysis are used to illustrate the source of the L 2-inconsistency problem.« less

A fast referenceless PRFS-based MR thermometry by phase finite difference

NASA Astrophysics Data System (ADS)

Zou, Chao; Shen, Huan; He, Mengyue; Tie, Changjun; Chung, Yiu-Cho; Liu, Xin

2013-08-01

Proton resonance frequency shift-based MR thermometry is a promising temperature monitoring approach for thermotherapy but its accuracy is vulnerable to inter-scan motion. Model-based referenceless thermometry has been proposed to address this problem but phase unwrapping is usually needed before the model fitting process. In this paper, a referenceless MR thermometry method using phase finite difference that avoids the time consuming phase unwrapping procedure is proposed. Unlike the previously proposed phase gradient technique, the use of finite difference in the new method reduces the fitting error resulting from the ringing artifacts associated with phase discontinuity in the calculation of the phase gradient image. The new method takes into account the values at the perimeter of the region of interest because of their direct relevance to the extrapolated baseline phase of the region of interest (where temperature increase takes place). In simulation study, in vivo and ex vivo experiments, the new method has a root-mean-square temperature error of 0.35 °C, 1.02 °C and 1.73 °C compared to 0.83 °C, 2.81 °C, and 3.76 °C from the phase gradient method, respectively. The method also demonstrated a slightly higher, albeit small, temperature accuracy than the original referenceless MR thermometry method. The proposed method is computationally efficient (∼0.1 s per image), making it very suitable for the real time temperature monitoring.

An adaptive band selection method for dimension reduction of hyper-spectral remote sensing image

NASA Astrophysics Data System (ADS)

Yu, Zhijie; Yu, Hui; Wang, Chen-sheng

2014-11-01

Hyper-spectral remote sensing data can be acquired by imaging the same area with multiple wavelengths, and it normally consists of hundreds of band-images. Hyper-spectral images can not only provide spatial information but also high resolution spectral information, and it has been widely used in environment monitoring, mineral investigation and military reconnaissance. However, because of the corresponding large data volume, it is very difficult to transmit and store Hyper-spectral images. Hyper-spectral image dimensional reduction technique is desired to resolve this problem. Because of the High relation and high redundancy of the hyper-spectral bands, it is very feasible that applying the dimensional reduction method to compress the data volume. This paper proposed a novel band selection-based dimension reduction method which can adaptively select the bands which contain more information and details. The proposed method is based on the principal component analysis (PCA), and then computes the index corresponding to every band. The indexes obtained are then ranked in order of magnitude from large to small. Based on the threshold, system can adaptively and reasonably select the bands. The proposed method can overcome the shortcomings induced by transform-based dimension reduction method and prevent the original spectral information from being lost. The performance of the proposed method has been validated by implementing several experiments. The experimental results show that the proposed algorithm can reduce the dimensions of hyper-spectral image with little information loss by adaptively selecting the band images.

Computer-aided diagnosis system: a Bayesian hybrid classification method.

PubMed

Calle-Alonso, F; Pérez, C J; Arias-Nicolás, J P; Martín, J

2013-10-01

A novel method to classify multi-class biomedical objects is presented. The method is based on a hybrid approach which combines pairwise comparison, Bayesian regression and the k-nearest neighbor technique. It can be applied in a fully automatic way or in a relevance feedback framework. In the latter case, the information obtained from both an expert and the automatic classification is iteratively used to improve the results until a certain accuracy level is achieved, then, the learning process is finished and new classifications can be automatically performed. The method has been applied in two biomedical contexts by following the same cross-validation schemes as in the original studies. The first one refers to cancer diagnosis, leading to an accuracy of 77.35% versus 66.37%, originally obtained. The second one considers the diagnosis of pathologies of the vertebral column. The original method achieves accuracies ranging from 76.5% to 96.7%, and from 82.3% to 97.1% in two different cross-validation schemes. Even with no supervision, the proposed method reaches 96.71% and 97.32% in these two cases. By using a supervised framework the achieved accuracy is 97.74%. Furthermore, all abnormal cases were correctly classified. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Joint Chroma Subsampling and Distortion-Minimization-Based Luma Modification for RGB Color Images With Application.

PubMed

Chung, Kuo-Liang; Hsu, Tsu-Chun; Huang, Chi-Chao

2017-10-01

In this paper, we propose a novel and effective hybrid method, which joins the conventional chroma subsampling and the distortion-minimization-based luma modification together, to improve the quality of the reconstructed RGB full-color image. Assume the input RGB full-color image has been transformed to a YUV image, prior to compression. For each 2×2 UV block, one 4:2:0 subsampling is applied to determine the one subsampled U and V components, U s and V s . Based on U s , V s , and the corresponding 2×2 original RGB block, a main theorem is provided to determine the ideally modified 2×2 luma block in constant time such that the color peak signal-to-noise ratio (CPSNR) quality distortion between the original 2×2 RGB block and the reconstructed 2×2 RGB block can be minimized in a globally optimal sense. Furthermore, the proposed hybrid method and the delivered theorem are adjusted to tackle the digital time delay integration images and the Bayer mosaic images whose Bayer CFA structure has been widely used in modern commercial digital cameras. Based on the IMAX, Kodak, and screen content test image sets, the experimental results demonstrate that in high efficiency video coding, the proposed hybrid method has substantial quality improvement, in terms of the CPSNR quality, visual effect, CPSNR-bitrate trade-off, and Bjøntegaard delta PSNR performance, of the reconstructed RGB images when compared with existing chroma subsampling schemes.

a New Multimodal Multi-Criteria Route Planning Model by Integrating a Fuzzy-Ahp Weighting Method and a Simulated Annealing Algorithm

NASA Astrophysics Data System (ADS)

Ghaderi, F.; Pahlavani, P.

2015-12-01

A multimodal multi-criteria route planning (MMRP) system provides an optimal multimodal route from an origin point to a destination point considering two or more criteria in a way this route can be a combination of public and private transportation modes. In this paper, the simulate annealing (SA) and the fuzzy analytical hierarchy process (fuzzy AHP) were combined in order to find this route. In this regard, firstly, the effective criteria that are significant for users in their trip were determined. Then the weight of each criterion was calculated using the fuzzy AHP weighting method. The most important characteristic of this weighting method is the use of fuzzy numbers that aids the users to consider their uncertainty in pairwise comparison of criteria. After determining the criteria weights, the proposed SA algorithm were used for determining an optimal route from an origin to a destination. One of the most important problems in a meta-heuristic algorithm is trapping in local minima. In this study, five transportation modes, including subway, bus rapid transit (BRT), taxi, walking, and bus were considered for moving between nodes. Also, the fare, the time, the user's bother, and the length of the path were considered as effective criteria for solving the problem. The proposed model was implemented in an area in centre of Tehran in a GUI MATLAB programming language. The results showed a high efficiency and speed of the proposed algorithm that support our analyses.

Manipulation of the swirling flow instability in hydraulic turbine diffuser by different methods of water injection

NASA Astrophysics Data System (ADS)

Rudolf, Pavel; Litera, Jiří; Alejandro Ibarra Bolanos, Germán; Štefan, David

2018-06-01

Vortex rope, which induces substantial pressure pulsations, arises in the draft tube (diffuser) of Francis turbine for off-design operating conditions. Present paper focuses on mitigation of those pulsations using active water jet injection control. Several modifications of the original Susan-Resiga's idea were proposed. All modifications are driven by manipulation of the shear layer region, which is believed to play important role in swirling flow instability. While some of the methods provide results close to the original one, none of them works in such a wide range. Series of numerical experiments support the idea that the necessary condition for vortex rope pulsation mitigation is increasing the fluid momentum along the draft tube axis.

Infrared fix pattern noise reduction method based on Shearlet Transform

NASA Astrophysics Data System (ADS)

Rong, Shenghui; Zhou, Huixin; Zhao, Dong; Cheng, Kuanhong; Qian, Kun; Qin, Hanlin

2018-06-01

The non-uniformity correction (NUC) is an effective way to reduce fix pattern noise (FPN) and improve infrared image quality. The temporal high-pass NUC method is a kind of practical NUC method because of its simple implementation. However, traditional temporal high-pass NUC methods rely deeply on the scene motion and suffer image ghosting and blurring. Thus, this paper proposes an improved NUC method based on Shearlet Transform (ST). First, the raw infrared image is decomposed into multiscale and multi-orientation subbands by ST and the FPN component mainly exists in some certain high-frequency subbands. Then, high-frequency subbands are processed by the temporal filter to extract the FPN due to its low-frequency characteristics. Besides, each subband has a confidence parameter to determine the degree of FPN, which is estimated by the variance of subbands adaptively. At last, the process of NUC is achieved by subtracting the estimated FPN component from the original subbands and the corrected infrared image can be obtained by the inverse ST. The performance of the proposed method is evaluated with real and synthetic infrared image sequences thoroughly. Experimental results indicate that the proposed method can reduce heavily FPN with less roughness and RMSE.

Andragogy and Motivation: An Examination of the Principles of Andragogy through Two Motivation Theories

ERIC Educational Resources Information Center

Houde, Joseph

2006-01-01

Andragogy, originally proposed by Malcolm Knowles, has been criticized as an atheoretical model. Validation of andragogy has been advocated by scholars, and this paper explores one method for that process. Current motivation theory, specifically socioemotional selectivity and self-determination theory correspond with aspects of andragogy. In…

"Rallying" to the Cause: Colleges, Politics and Where to Draw the Line.

ERIC Educational Resources Information Center

Gould, Jonathan B.

1993-01-01

The Federal Election Commission (FEC) is considering a rule that would prohibit federal candidates from holding political events on private college campuses. Explains the proposal's origins and content, and argues against the measure, explaining both its redundancy and internal inconsistencies. Suggests a less intrusive method to accomplish FEC's…

Ethical and Unethical Methods of Plagiarism Prevention in Academic Writing

ERIC Educational Resources Information Center

Bakhtiyari, Kaveh; Salehi, Hadi; Embi, Mohamed Amin; Shakiba, Masoud; Zavvari, Azam; Shahbazi-Moghadam, Masoomeh; Ebrahim, Nader Ale; Mohammadjafari, Marjan

2014-01-01

This paper discusses plagiarism origins, and the ethical solutions to prevent it. It also reviews some unethical approaches, which may be used to decrease the plagiarism rate in academic writings. We propose eight ethical techniques to avoid unconscious and accidental plagiarism in manuscripts without using online systems such as Turnitin and/or…

Closure to new results for an approximate method for calculating two-dimensional furrow infiltration

USDA-ARS?s Scientific Manuscript database

In a discussion paper, Ebrahimian and Noury (2015) raised several concerns about an approximate solution to the two-dimensional Richards equation presented by Bautista et al (2014). The solution is based on a procedure originally proposed by Warrick et al. (2007). Such a solution is of practical i...

Adaptive distance metric learning for diffusion tensor image segmentation.

PubMed

Kong, Youyong; Wang, Defeng; Shi, Lin; Hui, Steve C N; Chu, Winnie C W

2014-01-01

High quality segmentation of diffusion tensor images (DTI) is of key interest in biomedical research and clinical application. In previous studies, most efforts have been made to construct predefined metrics for different DTI segmentation tasks. These methods require adequate prior knowledge and tuning parameters. To overcome these disadvantages, we proposed to automatically learn an adaptive distance metric by a graph based semi-supervised learning model for DTI segmentation. An original discriminative distance vector was first formulated by combining both geometry and orientation distances derived from diffusion tensors. The kernel metric over the original distance and labels of all voxels were then simultaneously optimized in a graph based semi-supervised learning approach. Finally, the optimization task was efficiently solved with an iterative gradient descent method to achieve the optimal solution. With our approach, an adaptive distance metric could be available for each specific segmentation task. Experiments on synthetic and real brain DTI datasets were performed to demonstrate the effectiveness and robustness of the proposed distance metric learning approach. The performance of our approach was compared with three classical metrics in the graph based semi-supervised learning framework.

Adaptive Distance Metric Learning for Diffusion Tensor Image Segmentation

PubMed Central

Kong, Youyong; Wang, Defeng; Shi, Lin; Hui, Steve C. N.; Chu, Winnie C. W.

2014-01-01

High quality segmentation of diffusion tensor images (DTI) is of key interest in biomedical research and clinical application. In previous studies, most efforts have been made to construct predefined metrics for different DTI segmentation tasks. These methods require adequate prior knowledge and tuning parameters. To overcome these disadvantages, we proposed to automatically learn an adaptive distance metric by a graph based semi-supervised learning model for DTI segmentation. An original discriminative distance vector was first formulated by combining both geometry and orientation distances derived from diffusion tensors. The kernel metric over the original distance and labels of all voxels were then simultaneously optimized in a graph based semi-supervised learning approach. Finally, the optimization task was efficiently solved with an iterative gradient descent method to achieve the optimal solution. With our approach, an adaptive distance metric could be available for each specific segmentation task. Experiments on synthetic and real brain DTI datasets were performed to demonstrate the effectiveness and robustness of the proposed distance metric learning approach. The performance of our approach was compared with three classical metrics in the graph based semi-supervised learning framework. PMID:24651858

Perceptually controlled doping for audio source separation

NASA Astrophysics Data System (ADS)

Mahé, Gaël; Nadalin, Everton Z.; Suyama, Ricardo; Romano, João MT

2014-12-01

The separation of an underdetermined audio mixture can be performed through sparse component analysis (SCA) that relies however on the strong hypothesis that source signals are sparse in some domain. To overcome this difficulty in the case where the original sources are available before the mixing process, the informed source separation (ISS) embeds in the mixture a watermark, which information can help a further separation. Though powerful, this technique is generally specific to a particular mixing setup and may be compromised by an additional bitrate compression stage. Thus, instead of watermarking, we propose a `doping' method that makes the time-frequency representation of each source more sparse, while preserving its audio quality. This method is based on an iterative decrease of the distance between the distribution of the signal and a target sparse distribution, under a perceptual constraint. We aim to show that the proposed approach is robust to audio coding and that the use of the sparsified signals improves the source separation, in comparison with the original sources. In this work, the analysis is made only in instantaneous mixtures and focused on voice sources.

Convolutional neural networks based on augmented training samples for synthetic aperture radar target recognition

NASA Astrophysics Data System (ADS)

Yan, Yue

2018-03-01

A synthetic aperture radar (SAR) automatic target recognition (ATR) method based on the convolutional neural networks (CNN) trained by augmented training samples is proposed. To enhance the robustness of CNN to various extended operating conditions (EOCs), the original training images are used to generate the noisy samples at different signal-to-noise ratios (SNRs), multiresolution representations, and partially occluded images. Then, the generated images together with the original ones are used to train a designed CNN for target recognition. The augmented training samples can contrapuntally improve the robustness of the trained CNN to the covered EOCs, i.e., the noise corruption, resolution variance, and partial occlusion. Moreover, the significantly larger training set effectively enhances the representation capability for other conditions, e.g., the standard operating condition (SOC), as well as the stability of the network. Therefore, better performance can be achieved by the proposed method for SAR ATR. For experimental evaluation, extensive experiments are conducted on the Moving and Stationary Target Acquisition and Recognition dataset under SOC and several typical EOCs.

Joint sparse coding based spatial pyramid matching for classification of color medical image.

PubMed

Shi, Jun; Li, Yi; Zhu, Jie; Sun, Haojie; Cai, Yin

2015-04-01

Although color medical images are important in clinical practice, they are usually converted to grayscale for further processing in pattern recognition, resulting in loss of rich color information. The sparse coding based linear spatial pyramid matching (ScSPM) and its variants are popular for grayscale image classification, but cannot extract color information. In this paper, we propose a joint sparse coding based SPM (JScSPM) method for the classification of color medical images. A joint dictionary can represent both the color information in each color channel and the correlation between channels. Consequently, the joint sparse codes calculated from a joint dictionary can carry color information, and therefore this method can easily transform a feature descriptor originally designed for grayscale images to a color descriptor. A color hepatocellular carcinoma histological image dataset was used to evaluate the performance of the proposed JScSPM algorithm. Experimental results show that JScSPM provides significant improvements as compared with the majority voting based ScSPM and the original ScSPM for color medical image classification. Copyright © 2014 Elsevier Ltd. All rights reserved.

No-Reference Image Quality Assessment by Wide-Perceptual-Domain Scorer Ensemble Method.

PubMed

Liu, Tsung-Jung; Liu, Kuan-Hsien

2018-03-01

A no-reference (NR) learning-based approach to assess image quality is presented in this paper. The devised features are extracted from wide perceptual domains, including brightness, contrast, color, distortion, and texture. These features are used to train a model (scorer) which can predict scores. The scorer selection algorithms are utilized to help simplify the proposed system. In the final stage, the ensemble method is used to combine the prediction results from selected scorers. Two multiple-scale versions of the proposed approach are also presented along with the single-scale one. They turn out to have better performances than the original single-scale method. Because of having features from five different domains at multiple image scales and using the outputs (scores) from selected score prediction models as features for multi-scale or cross-scale fusion (i.e., ensemble), the proposed NR image quality assessment models are robust with respect to more than 24 image distortion types. They also can be used on the evaluation of images with authentic distortions. The extensive experiments on three well-known and representative databases confirm the performance robustness of our proposed model.

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

Litvinenko,V.; Yakimenko, V.

We propose undertaking a demonstration experiment on suppressing spontaneous undulator radiation from an electron beam at BNL's Accelerator Test Facility (ATF). We describe the method, the proposed layout, and a possible schedule. There are several advantages in strongly suppressing shot noise in the electron beam, and the corresponding spontaneous radiation. The self-amplified spontaneous (SASE) emission originating from shot noise in the electron beam is the main source of noise in high-gain FEL amplifiers. It may negatively affect several HG FEL applications ranging from single- to multi-stage HGHG FELs. SASE saturation also imposes a fundamental hard limit on the gain ofmore » an FEL amplifier in a coherent electron-cooling scheme. A novel active method for suppressing shot noise in relativistic electron beams by many orders-of-magnitude was recently proposed. While theoretically such strong suppression appears feasible, the performance and applicability of this novel method must be evaluated experimentally. Several practical questions about the proposed noise suppressor, such as 3D effects and/or sensitivity to the e-beam parameters also require experimental clarification. To do this, we propose here a proof-of-principle experiment using elements of the VISA FEL at BNL's Accelerator Test Facility.« less

A novel hybrid decomposition-and-ensemble model based on CEEMD and GWO for short-term PM2.5 concentration forecasting

NASA Astrophysics Data System (ADS)

Niu, Mingfei; Wang, Yufang; Sun, Shaolong; Li, Yongwu

2016-06-01

To enhance prediction reliability and accuracy, a hybrid model based on the promising principle of "decomposition and ensemble" and a recently proposed meta-heuristic called grey wolf optimizer (GWO) is introduced for daily PM2.5 concentration forecasting. Compared with existing PM2.5 forecasting methods, this proposed model has improved the prediction accuracy and hit rates of directional prediction. The proposed model involves three main steps, i.e., decomposing the original PM2.5 series into several intrinsic mode functions (IMFs) via complementary ensemble empirical mode decomposition (CEEMD) for simplifying the complex data; individually predicting each IMF with support vector regression (SVR) optimized by GWO; integrating all predicted IMFs for the ensemble result as the final prediction by another SVR optimized by GWO. Seven benchmark models, including single artificial intelligence (AI) models, other decomposition-ensemble models with different decomposition methods and models with the same decomposition-ensemble method but optimized by different algorithms, are considered to verify the superiority of the proposed hybrid model. The empirical study indicates that the proposed hybrid decomposition-ensemble model is remarkably superior to all considered benchmark models for its higher prediction accuracy and hit rates of directional prediction.

Identification of FOPDT and SOPDT process dynamics using closed loop test.

PubMed

Bajarangbali, Raghunath; Majhi, Somanath; Pandey, Saurabh

2014-07-01

In this paper, identification of stable and unstable first order, second order overdamped and underdamped process dynamics with time delay is presented. Relay with hysteresis is used to induce a limit cycle output and using this information, unknown process model parameters are estimated. State space based generalized analytical expressions are derived to achieve accurate results. To show the performance of the proposed method expressions are also derived for systems with a zero. In real time systems, measurement noise is an important issue during identification of process dynamics. A relay with hysteresis reduces the effect of measurement noise, in addition a new multiloop control strategy is proposed to recover the original limit cycle. Simulation results are included to validate the effectiveness of the proposed method. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Visual improvement for bad handwriting based on Monte-Carlo method

NASA Astrophysics Data System (ADS)

Shi, Cao; Xiao, Jianguo; Xu, Canhui; Jia, Wenhua

2014-03-01

A visual improvement algorithm based on Monte Carlo simulation is proposed in this paper, in order to enhance visual effects for bad handwriting. The whole improvement process is to use well designed typeface so as to optimize bad handwriting image. In this process, a series of linear operators for image transformation are defined for transforming typeface image to approach handwriting image. And specific parameters of linear operators are estimated by Monte Carlo method. Visual improvement experiments illustrate that the proposed algorithm can effectively enhance visual effect for handwriting image as well as maintain the original handwriting features, such as tilt, stroke order and drawing direction etc. The proposed visual improvement algorithm, in this paper, has a huge potential to be applied in tablet computer and Mobile Internet, in order to improve user experience on handwriting.

Centralized PI control for high dimensional multivariable systems based on equivalent transfer function.

PubMed

Luan, Xiaoli; Chen, Qiang; Liu, Fei

2014-09-01

This article presents a new scheme to design full matrix controller for high dimensional multivariable processes based on equivalent transfer function (ETF). Differing from existing ETF method, the proposed ETF is derived directly by exploiting the relationship between the equivalent closed-loop transfer function and the inverse of open-loop transfer function. Based on the obtained ETF, the full matrix controller is designed utilizing the existing PI tuning rules. The new proposed ETF model can more accurately represent the original processes. Furthermore, the full matrix centralized controller design method proposed in this paper is applicable to high dimensional multivariable systems with satisfactory performance. Comparison with other multivariable controllers shows that the designed ETF based controller is superior with respect to design-complexity and obtained performance. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

A data-driven approach for denoising GNSS position time series

NASA Astrophysics Data System (ADS)

Li, Yanyan; Xu, Caijun; Yi, Lei; Fang, Rongxin

2017-12-01

Global navigation satellite system (GNSS) datasets suffer from common mode error (CME) and other unmodeled errors. To decrease the noise level in GNSS positioning, we propose a new data-driven adaptive multiscale denoising method in this paper. Both synthetic and real-world long-term GNSS datasets were employed to assess the performance of the proposed method, and its results were compared with those of stacking filtering, principal component analysis (PCA) and the recently developed multiscale multiway PCA. It is found that the proposed method can significantly eliminate the high-frequency white noise and remove the low-frequency CME. Furthermore, the proposed method is more precise for denoising GNSS signals than the other denoising methods. For example, in the real-world example, our method reduces the mean standard deviation of the north, east and vertical components from 1.54 to 0.26, 1.64 to 0.21 and 4.80 to 0.72 mm, respectively. Noise analysis indicates that for the original signals, a combination of power-law plus white noise model can be identified as the best noise model. For the filtered time series using our method, the generalized Gauss-Markov model is the best noise model with the spectral indices close to - 3, indicating that flicker walk noise can be identified. Moreover, the common mode error in the unfiltered time series is significantly reduced by the proposed method. After filtering with our method, a combination of power-law plus white noise model is the best noise model for the CMEs in the study region.

Entropyology: the application of bioinformatics and data modeling to digital virus and malware recognition

NASA Astrophysics Data System (ADS)

Jaenisch, Holger M.; Handley, James W.

2010-04-01

Malware are analogs of viruses. Viruses are comprised of large numbers of polypeptide proteins. The shape and function of the protein strands determines the functionality of the segment, similar to a subroutine in malware. The full combination of subroutines is the malware organism, in analogous fashion as a collection of polypeptides forms protein structures that are information bearing. We propose to apply the methods of Bioinformatics to analyze malware to provide a rich feature set for creating a unique and novel detection and classification scheme that is originally applied to Bioinformatics amino acid sequencing. Our proposed methods enable real time in situ (in contrast to in vivo) detection applications.

Particle tracking by using single coefficient of Wigner-Ville distribution

NASA Astrophysics Data System (ADS)

Widjaja, J.; Dawprateep, S.; Chuamchaitrakool, P.; Meemon, P.

2016-11-01

A new method for extracting information from particle holograms by using a single coefficient of Wigner-Ville distribution (WVD) is proposed to obviate drawbacks of conventional numerical reconstructions. Our previous study found that analysis of the holograms by using the WVD gives output coefficients which are mainly confined along a diagonal direction intercepted at the origin of the WVD plane. The slope of this diagonal direction is inversely proportional to the particle position. One of these coefficients always has minimum amplitude, regardless of the particle position. By detecting position of the coefficient with minimum amplitude in the WVD plane, the particle position can be accurately measured. The proposed method is verified through computer simulations.

Subband directional vector quantization in radiological image compression

NASA Astrophysics Data System (ADS)

Akrout, Nabil M.; Diab, Chaouki; Prost, Remy; Goutte, Robert; Amiel, Michel

1992-05-01

The aim of this paper is to propose a new scheme for image compression. The method is very efficient for images which have directional edges such as the tree-like structure of the coronary vessels in digital angiograms. This method involves two steps. First, the original image is decomposed at different resolution levels using a pyramidal subband decomposition scheme. For decomposition/reconstruction of the image, free of aliasing and boundary errors, we use an ideal band-pass filter bank implemented in the Discrete Cosine Transform domain (DCT). Second, the high-frequency subbands are vector quantized using a multiresolution codebook with vertical and horizontal codewords which take into account the edge orientation of each subband. The proposed method reduces the blocking effect encountered at low bit rates in conventional vector quantization.

Compression-RSA: New approach of encryption and decryption method

NASA Astrophysics Data System (ADS)

Hung, Chang Ee; Mandangan, Arif

2013-04-01

Rivest-Shamir-Adleman (RSA) cryptosystem is a well known asymmetric cryptosystem and it has been applied in a very wide area. Many researches with different approaches have been carried out in order to improve the security and performance of RSA cryptosystem. The enhancement of the performance of RSA cryptosystem is our main interest. In this paper, we propose a new method to increase the efficiency of RSA by shortening the number of plaintext before it goes under encryption process without affecting the original content of the plaintext. Concept of simple Continued Fraction and the new special relationship between it and Euclidean Algorithm have been applied on this newly proposed method. By reducing the number of plaintext-ciphertext, the encryption-decryption processes of a secret message can be accelerated.

Separation of specular and diffuse components using tensor voting in color images.

PubMed

Nguyen, Tam; Vo, Quang Nhat; Yang, Hyung-Jeong; Kim, Soo-Hyung; Lee, Guee-Sang

2014-11-20

Most methods for the detection and removal of specular reflections suffer from nonuniform highlight regions and/or nonconverged artifacts induced by discontinuities in the surface colors, especially when dealing with highly textured, multicolored images. In this paper, a novel noniterative and predefined constraint-free method based on tensor voting is proposed to detect and remove the highlight components of a single color image. The distribution of diffuse and specular pixels in the original image is determined using tensors' saliency analysis, instead of comparing color information among neighbor pixels. The achieved diffuse reflectance distribution is used to remove specularity components. The proposed method is evaluated quantitatively and qualitatively over a dataset of highly textured, multicolor images. The experimental results show that our result outperforms other state-of-the-art techniques.

Low-dimensional approximation searching strategy for transfer entropy from non-uniform embedding

PubMed Central

2018-01-01

Transfer entropy from non-uniform embedding is a popular tool for the inference of causal relationships among dynamical subsystems. In this study we present an approach that makes use of low-dimensional conditional mutual information quantities to decompose the original high-dimensional conditional mutual information in the searching procedure of non-uniform embedding for significant variables at different lags. We perform a series of simulation experiments to assess the sensitivity and specificity of our proposed method to demonstrate its advantage compared to previous algorithms. The results provide concrete evidence that low-dimensional approximations can help to improve the statistical accuracy of transfer entropy in multivariate causality analysis and yield a better performance over other methods. The proposed method is especially efficient as the data length grows. PMID:29547669

Simulation tests of the optimization method of Hopfield and Tank using neural networks

NASA Technical Reports Server (NTRS)

Paielli, Russell A.

1988-01-01

The method proposed by Hopfield and Tank for using the Hopfield neural network with continuous valued neurons to solve the traveling salesman problem is tested by simulation. Several researchers have apparently been unable to successfully repeat the numerical simulation documented by Hopfield and Tank. However, as suggested to the author by Adams, it appears that the reason for those difficulties is that a key parameter value is reported erroneously (by four orders of magnitude) in the original paper. When a reasonable value is used for that parameter, the network performs generally as claimed. Additionally, a new method of using feedback to control the input bias currents to the amplifiers is proposed and successfully tested. This eliminates the need to set the input currents by trial and error.

Dual energy approach for cone beam artifacts correction

NASA Astrophysics Data System (ADS)

Han, Chulhee; Choi, Shinkook; Lee, Changwoo; Baek, Jongduk

2017-03-01

Cone beam computed tomography systems generate 3D volumetric images, which provide further morphological information compared to radiography and tomosynthesis systems. However, reconstructed images by FDK algorithm contain cone beam artifacts when a cone angle is large. To reduce the cone beam artifacts, two-pass algorithm has been proposed. The two-pass algorithm considers the cone beam artifacts are mainly caused by high density materials, and proposes an effective method to estimate error images (i.e., cone beam artifacts images) by the high density materials. While this approach is simple and effective with a small cone angle (i.e., 5 - 7 degree), the correction performance is degraded as the cone angle increases. In this work, we propose a new method to reduce the cone beam artifacts using a dual energy technique. The basic idea of the proposed method is to estimate the error images generated by the high density materials more reliably. To do this, projection data of the high density materials are extracted from dual energy CT projection data using a material decomposition technique, and then reconstructed by iterative reconstruction using total-variation regularization. The reconstructed high density materials are used to estimate the error images from the original FDK images. The performance of the proposed method is compared with the two-pass algorithm using root mean square errors. The results show that the proposed method reduces the cone beam artifacts more effectively, especially with a large cone angle.

A method of power analysis based on piecewise discrete Fourier transform

NASA Astrophysics Data System (ADS)

Xin, Miaomiao; Zhang, Yanchi; Xie, Da

2018-04-01

The paper analyzes the existing feature extraction methods. The characteristics of discrete Fourier transform and piecewise aggregation approximation are analyzed. Combining with the advantages of the two methods, a new piecewise discrete Fourier transform is proposed. And the method is used to analyze the lighting power of a large customer in this paper. The time series feature maps of four different cases are compared with the original data, discrete Fourier transform, piecewise aggregation approximation and piecewise discrete Fourier transform. This new method can reflect both the overall trend of electricity change and its internal changes in electrical analysis.

Simple Common Plane contact algorithm for explicit FE/FD methods

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

Vorobiev, O

2006-12-18

Common-plane (CP) algorithm is widely used in Discrete Element Method (DEM) to model contact forces between interacting particles or blocks. A new simple contact algorithm is proposed to model contacts in FE/FD methods which is similar to the CP algorithm. The CP is defined as a plane separating interacting faces of FE/FD mesh instead of blocks or particles used in the original CP method. The new method does not require iterations even for very stiff contacts. It is very robust and easy to implement both in 2D and 3D parallel codes.

Novel approaches to estimating the turbulent kinetic energy dissipation rate from low- and moderate-resolution velocity fluctuation time series

NASA Astrophysics Data System (ADS)

Wacławczyk, Marta; Ma, Yong-Feng; Kopeć, Jacek M.; Malinowski, Szymon P.

2017-11-01

In this paper we propose two approaches to estimating the turbulent kinetic energy (TKE) dissipation rate, based on the zero-crossing method by Sreenivasan et al. (1983). The original formulation requires a fine resolution of the measured signal, down to the smallest dissipative scales. However, due to finite sampling frequency, as well as measurement errors, velocity time series obtained from airborne experiments are characterized by the presence of effective spectral cutoffs. In contrast to the original formulation the new approaches are suitable for use with signals originating from airborne experiments. The suitability of the new approaches is tested using measurement data obtained during the Physics of Stratocumulus Top (POST) airborne research campaign as well as synthetic turbulence data. They appear useful and complementary to existing methods. We show the number-of-crossings-based approaches respond differently to errors due to finite sampling and finite averaging than the classical power spectral method. Hence, their application for the case of short signals and small sampling frequencies is particularly interesting, as it can increase the robustness of turbulent kinetic energy dissipation rate retrieval.

Asteroid orbital inversion using uniform phase-space sampling

NASA Astrophysics Data System (ADS)

Muinonen, K.; Pentikäinen, H.; Granvik, M.; Oszkiewicz, D.; Virtanen, J.

2014-07-01

We review statistical inverse methods for asteroid orbit computation from a small number of astrometric observations and short time intervals of observations. With the help of Markov-chain Monte Carlo methods (MCMC), we present a novel inverse method that utilizes uniform sampling of the phase space for the orbital elements. The statistical orbital ranging method (Virtanen et al. 2001, Muinonen et al. 2001) was set out to resolve the long-lasting challenges in the initial computation of orbits for asteroids. The ranging method starts from the selection of a pair of astrometric observations. Thereafter, the topocentric ranges and angular deviations in R.A. and Decl. are randomly sampled. The two Cartesian positions allow for the computation of orbital elements and, subsequently, the computation of ephemerides for the observation dates. Candidate orbital elements are included in the sample of accepted elements if the χ^2-value between the observed and computed observations is within a pre-defined threshold. The sample orbital elements obtain weights based on a certain debiasing procedure. When the weights are available, the full sample of orbital elements allows the probabilistic assessments for, e.g., object classification and ephemeris computation as well as the computation of collision probabilities. The MCMC ranging method (Oszkiewicz et al. 2009; see also Granvik et al. 2009) replaces the original sampling algorithm described above with a proposal probability density function (p.d.f.), and a chain of sample orbital elements results in the phase space. MCMC ranging is based on a bivariate Gaussian p.d.f. for the topocentric ranges, and allows for the sampling to focus on the phase-space domain with most of the probability mass. In the virtual-observation MCMC method (Muinonen et al. 2012), the proposal p.d.f. for the orbital elements is chosen to mimic the a posteriori p.d.f. for the elements: first, random errors are simulated for each observation, resulting in a set of virtual observations; second, corresponding virtual least-squares orbital elements are derived using the Nelder-Mead downhill simplex method; third, repeating the procedure two times allows for a computation of a difference for two sets of virtual orbital elements; and, fourth, this orbital-element difference constitutes a symmetric proposal in a random-walk Metropolis-Hastings algorithm, avoiding the explicit computation of the proposal p.d.f. In a discrete approximation, the allowed proposals coincide with the differences that are based on a large number of pre-computed sets of virtual least-squares orbital elements. The virtual-observation MCMC method is thus based on the characterization of the relevant volume in the orbital-element phase space. Here we utilize MCMC to map the phase-space domain of acceptable solutions. We can make use of the proposal p.d.f.s from the MCMC ranging and virtual-observation methods. The present phase-space mapping produces, upon convergence, a uniform sampling of the solution space within a pre-defined χ^2-value. The weights of the sampled orbital elements are then computed on the basis of the corresponding χ^2-values. The present method resembles the original ranging method. On one hand, MCMC mapping is insensitive to local extrema in the phase space and efficiently maps the solution space. This is somewhat contrary to the MCMC methods described above. On the other hand, MCMC mapping can suffer from producing a small number of sample elements with small χ^2-values, in resemblance to the original ranging method. We apply the methods to example near-Earth, main-belt, and transneptunian objects, and highlight the utilization of the methods in the data processing and analysis pipeline of the ESA Gaia space mission.

Archaeal phylogenomics provides evidence in support of a methanogenic origin of the Archaea and a thaumarchaeal origin for the eukaryotes

PubMed Central

Kelly, S.; Wickstead, B.; Gull, K.

2011-01-01

We have developed a machine-learning approach to identify 3537 discrete orthologue protein sequence groups distributed across all available archaeal genomes. We show that treating these orthologue groups as binary detection/non-detection data is sufficient to capture the majority of archaeal phylogeny. We subsequently use the sequence data from these groups to infer a method and substitution-model-independent phylogeny. By holding this phylogeny constrained and interrogating the intersection of this large dataset with both the Eukarya and the Bacteria using Bayesian and maximum-likelihood approaches, we propose and provide evidence for a methanogenic origin of the Archaea. By the same criteria, we also provide evidence in support of an origin for Eukarya either within or as sisters to the Thaumarchaea. PMID:20880885

Reassessment of roles of oxygen and ultraviolet light in Precambrian evolution

NASA Technical Reports Server (NTRS)

Margulis, L.; Rambler, M.; Walker, J. C. G.

1976-01-01

It is argued that the transition to an oxidizing atmosphere preceded the origin of eukaryotic cells, which in turn must have preceded the origin of metazoa. Moreover, the number of methods by which organisms can protect themselves from harmful UV radiation is sufficiently large to suggest that solar UV, even when the atmosphere was anaerobic, was not such as to control the distribution and diversification of life. An alternative explanation for the late and sudden appearance of metazoa in lower Cambrian sediments is proposed, which is related to the mechanisms by which fully mature eukaryotic cells probably originated. There was probably a protracted evolution of modern genetic systems based on mitosis in cells which acquired organelles (e.g., plastids and mitochondria) by hereditary endosymbiosis. The origin of hard parts underlies the Cambrian explosion of metazoans.

[A correction method of baseline drift of discrete spectrum of NIR].

PubMed

Hu, Ai-Qin; Yuan, Hong-Fu; Song, Chun-Feng; Li, Xiao-Yu

2014-10-01

In the present paper, a new correction method of baseline drift of discrete spectrum is proposed by combination of cubic spline interpolation and first order derivative. A fitting spectrum is constructed by cubic spline interpolation, using the datum in discrete spectrum as interpolation nodes. The fitting spectrum is differentiable. First order derivative is applied to the fitting spectrum to calculate derivative spectrum. The spectral wavelengths which are the same as the original discrete spectrum were taken out from the derivative spectrum to constitute the first derivative spectra of the discrete spectra, thereby to correct the baseline drift of the discrete spectra. The effects of the new method were demonstrated by comparison of the performances of multivariate models built using original spectra, direct differential spectra and the spectra pretreated by the new method. The results show that negative effects on the performance of multivariate model caused by baseline drift of discrete spectra can be effectively eliminated by the new method.

Multirate sampled-data yaw-damper and modal suppression system design

NASA Technical Reports Server (NTRS)

Berg, Martin C.; Mason, Gregory S.

1990-01-01

A multirate control law synthesized algorithm based on an infinite-time quadratic cost function, was developed along with a method for analyzing the robustness of multirate systems. A generalized multirate sampled-data control law structure (GMCLS) was introduced. A new infinite-time-based parameter optimization multirate sampled-data control law synthesis method and solution algorithm were developed. A singular-value-based method for determining gain and phase margins for multirate systems was also developed. The finite-time-based parameter optimization multirate sampled-data control law synthesis algorithm originally intended to be applied to the aircraft problem was instead demonstrated by application to a simpler problem involving the control of the tip position of a two-link robot arm. The GMCLS, the infinite-time-based parameter optimization multirate control law synthesis method and solution algorithm, and the singular-value based method for determining gain and phase margins were all demonstrated by application to the aircraft control problem originally proposed for this project.

Pelvic Arterial Anatomy Relevant to Prostatic Artery Embolisation and Proposal for Angiographic Classification

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

Assis, André Moreira de, E-mail: andre.maa@gmail.com; Moreira, Airton Mota, E-mail: motamoreira@gmail.com; Paula Rodrigues, Vanessa Cristina de, E-mail: vanessapaular@yahoo.com.br

PurposeTo describe and categorize the angiographic findings regarding prostatic vascularization, propose an anatomic classification, and discuss its implications for the PAE procedure.MethodsAngiographic findings from 143 PAE procedures were reviewed retrospectively, and the origin of the inferior vesical artery (IVA) was classified into five subtypes as follows: type I: IVA originating from the anterior division of the internal iliac artery (IIA), from a common trunk with the superior vesical artery (SVA); type II: IVA originating from the anterior division of the IIA, inferior to the SVA origin; type III: IVA originating from the obturator artery; type IV: IVA originating from themore » internal pudendal artery; and type V: less common origins of the IVA. Incidences were calculated by percentage.ResultsTwo hundred eighty-six pelvic sides (n = 286) were analyzed, and 267 (93.3 %) were classified into I–IV types. Among them, the most common origin was type IV (n = 89, 31.1 %), followed by type I (n = 82, 28.7 %), type III (n = 54, 18.9 %), and type II (n = 42, 14.7 %). Type V anatomy was seen in 16 cases (5.6 %). Double vascularization, defined as two independent prostatic branches in one pelvic side, was seen in 23 cases (8.0 %).ConclusionsDespite the large number of possible anatomical variations of male pelvis, four main patterns corresponded to almost 95 % of the cases. Evaluation of anatomy in a systematic fashion, following a standard classification, will make PAE a faster, safer, and more effective procedure.« less

On the origin of the driving force in the Marangoni propelled gas bubble trapping mechanism.

PubMed

Miniewicz, A; Quintard, C; Orlikowska, H; Bartkiewicz, S

2017-07-19

Gas bubbles can be trapped and then manipulated with laser light. In this report, we propose the detailed optical trapping mechanism of gas bubbles confined inside a thin light-absorbing liquid layer between two glass plates. The necessary condition of bubble trapping in this case is the direct absorption of light by the solution containing a dye. Due to heat release, fluid whirls propelled by the surface Marangoni effect at the liquid/gas interface emerge and extend to large distances. We report the experimental microscopic observation of the origin of whirls at an initially flat liquid/air interface as well as at the curved interface of a liquid/gas bubble and support this finding with advanced numerical simulations using the finite element method within the COMSOL Multiphysics platform. The simulation results were in good agreement with the observations, which allowed us to propose a simple physical model for this particular trapping mechanism, to establish the origin of forces attracting bubbles toward a laser beam and to predict other phenomena related to this effect.

Resolution recovery for Compton camera using origin ensemble algorithm.

PubMed

Andreyev, A; Celler, A; Ozsahin, I; Sitek, A

2016-08-01

Compton cameras (CCs) use electronic collimation to reconstruct the images of activity distribution. Although this approach can greatly improve imaging efficiency, due to complex geometry of the CC principle, image reconstruction with the standard iterative algorithms, such as ordered subset expectation maximization (OSEM), can be very time-consuming, even more so if resolution recovery (RR) is implemented. We have previously shown that the origin ensemble (OE) algorithm can be used for the reconstruction of the CC data. Here we propose a method of extending our OE algorithm to include RR. To validate the proposed algorithm we used Monte Carlo simulations of a CC composed of multiple layers of pixelated CZT detectors and designed for imaging small animals. A series of CC acquisitions of small hot spheres and the Derenzo phantom placed in air were simulated. Images obtained from (a) the exact data, (b) blurred data but reconstructed without resolution recovery, and (c) blurred and reconstructed with resolution recovery were compared. Furthermore, the reconstructed contrast-to-background ratios were investigated using the phantom with nine spheres placed in a hot background. Our simulations demonstrate that the proposed method allows for the recovery of the resolution loss that is due to imperfect accuracy of event detection. Additionally, tests of camera sensitivity corresponding to different detector configurations demonstrate that the proposed CC design has sensitivity comparable to PET. When the same number of events were considered, the computation time per iteration increased only by a factor of 2 when OE reconstruction with the resolution recovery correction was performed relative to the original OE algorithm. We estimate that the addition of resolution recovery to the OSEM would increase reconstruction times by 2-3 orders of magnitude per iteration. The results of our tests demonstrate the improvement of image resolution provided by the OE reconstructions with resolution recovery. The quality of images and their contrast are similar to those obtained from the OE reconstructions from scans simulated with perfect energy and spatial resolutions.

Development and evaluation of a monitoring-aid system for a nuclear power plant in control room system manipulation.

PubMed

Lin, Jhih-Tsong; Chen, Yan-Cheng; Wu, Shih-Chieh; Hwang, Sheue-Ling

2017-01-01

In an advanced nuclear power plant (NPP), the operators are responsible for monitoring a massive number of alarm parameters. To assist the operators, a monitoring-aid system (MAS), that applies four quality control chart methods, was proposed and evaluated. Two types of MAS, namely, text and graph marks, were proposed and compared with the original display. To validate the proposed MAS, 17 professional engineers and operators were invited to join an experiment. Two different system states, normal and abnormal, were simulated. The operators were asked to manipulate the system, monitor the critical parameters, search for operational procedures, and deal with other secondary tasks. The primary and secondary task performance and heart rate were measured. After each task was conducted, three subjective rating questionnaires, namely, mental workload, situation awareness, and preference ratings, were implemented for the proposed MAS and the original system. With the assistance of the MAS, the alarm detection rate, secondary task performance, and subjective mental workload demonstrate significant improvements. The proposed MAS helps the operators monitor critical parameters. Therefore, the MAS should be considered for implementation with the control panel to increase the safety of NPPs. Furthermore, the MAS could reduce the mental workload might decrease the health hazard of the operators.

Performance analysis of AES-Blowfish hybrid algorithm for security of patient medical record data

NASA Astrophysics Data System (ADS)

Mahmud H, Amir; Angga W, Bayu; Tommy; Marwan E, Andi; Siregar, Rosyidah

2018-04-01

A file security is one method to protect data confidentiality, integrity and information security. Cryptography is one of techniques used to secure and guarantee data confidentiality by doing conversion to the plaintext (original message) to cipher text (hidden message) with two important processes, they are encrypt and decrypt. Some researchers proposed a hybrid method to improve data security. In this research we proposed hybrid method of AES-blowfish (BF) to secure the patient’s medical report data into the form PDF file that sources from database. Generation method of private and public key uses two ways of approach, those are RSA method f RSA and ECC. We will analyze impact of these two ways of approach for hybrid method at AES-blowfish based on time and Throughput. Based on testing results, BF method is faster than AES and AES-BF hybrid, however AES-BF hybrid is better for throughput compared with AES and BF is higher.

Nonlinear Model Reduction in Power Systems by Balancing of Empirical Controllability and Observability Covariances

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

Qi, Junjian; Wang, Jianhui; Liu, Hui

Abstract: In this paper, nonlinear model reduction for power systems is performed by the balancing of empirical controllability and observability covariances that are calculated around the operating region. Unlike existing model reduction methods, the external system does not need to be linearized but is directly dealt with as a nonlinear system. A transformation is found to balance the controllability and observability covariances in order to determine which states have the greatest contribution to the input-output behavior. The original system model is then reduced by Galerkin projection based on this transformation. The proposed method is tested and validated on a systemmore » comprised of a 16-machine 68-bus system and an IEEE 50-machine 145-bus system. The results show that by using the proposed model reduction the calculation efficiency can be greatly improved; at the same time, the obtained state trajectories are close to those for directly simulating the whole system or partitioning the system while not performing reduction. Compared with the balanced truncation method based on a linearized model, the proposed nonlinear model reduction method can guarantee higher accuracy and similar calculation efficiency. It is shown that the proposed method is not sensitive to the choice of the matrices for calculating the empirical covariances.« less

Convex blind image deconvolution with inverse filtering

NASA Astrophysics Data System (ADS)

Lv, Xiao-Guang; Li, Fang; Zeng, Tieyong

2018-03-01

Blind image deconvolution is the process of estimating both the original image and the blur kernel from the degraded image with only partial or no information about degradation and the imaging system. It is a bilinear ill-posed inverse problem corresponding to the direct problem of convolution. Regularization methods are used to handle the ill-posedness of blind deconvolution and get meaningful solutions. In this paper, we investigate a convex regularized inverse filtering method for blind deconvolution of images. We assume that the support region of the blur object is known, as has been done in a few existing works. By studying the inverse filters of signal and image restoration problems, we observe the oscillation structure of the inverse filters. Inspired by the oscillation structure of the inverse filters, we propose to use the star norm to regularize the inverse filter. Meanwhile, we use the total variation to regularize the resulting image obtained by convolving the inverse filter with the degraded image. The proposed minimization model is shown to be convex. We employ the first-order primal-dual method for the solution of the proposed minimization model. Numerical examples for blind image restoration are given to show that the proposed method outperforms some existing methods in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), visual quality and time consumption.

A Novel Method Using Abstract Convex Underestimation in Ab-Initio Protein Structure Prediction for Guiding Search in Conformational Feature Space.

PubMed

Hao, Xiao-Hu; Zhang, Gui-Jun; Zhou, Xiao-Gen; Yu, Xu-Feng

2016-01-01

To address the searching problem of protein conformational space in ab-initio protein structure prediction, a novel method using abstract convex underestimation (ACUE) based on the framework of evolutionary algorithm was proposed. Computing such conformations, essential to associate structural and functional information with gene sequences, is challenging due to the high-dimensionality and rugged energy surface of the protein conformational space. As a consequence, the dimension of protein conformational space should be reduced to a proper level. In this paper, the high-dimensionality original conformational space was converted into feature space whose dimension is considerably reduced by feature extraction technique. And, the underestimate space could be constructed according to abstract convex theory. Thus, the entropy effect caused by searching in the high-dimensionality conformational space could be avoided through such conversion. The tight lower bound estimate information was obtained to guide the searching direction, and the invalid searching area in which the global optimal solution is not located could be eliminated in advance. Moreover, instead of expensively calculating the energy of conformations in the original conformational space, the estimate value is employed to judge if the conformation is worth exploring to reduce the evaluation time, thereby making computational cost lower and the searching process more efficient. Additionally, fragment assembly and the Monte Carlo method are combined to generate a series of metastable conformations by sampling in the conformational space. The proposed method provides a novel technique to solve the searching problem of protein conformational space. Twenty small-to-medium structurally diverse proteins were tested, and the proposed ACUE method was compared with It Fix, HEA, Rosetta and the developed method LEDE without underestimate information. Test results show that the ACUE method can more rapidly and more efficiently obtain the near-native protein structure.

Object Detection and Classification by Decision-Level Fusion for Intelligent Vehicle Systems.

PubMed

Oh, Sang-Il; Kang, Hang-Bong

2017-01-22

To understand driving environments effectively, it is important to achieve accurate detection and classification of objects detected by sensor-based intelligent vehicle systems, which are significantly important tasks. Object detection is performed for the localization of objects, whereas object classification recognizes object classes from detected object regions. For accurate object detection and classification, fusing multiple sensor information into a key component of the representation and perception processes is necessary. In this paper, we propose a new object-detection and classification method using decision-level fusion. We fuse the classification outputs from independent unary classifiers, such as 3D point clouds and image data using a convolutional neural network (CNN). The unary classifiers for the two sensors are the CNN with five layers, which use more than two pre-trained convolutional layers to consider local to global features as data representation. To represent data using convolutional layers, we apply region of interest (ROI) pooling to the outputs of each layer on the object candidate regions generated using object proposal generation to realize color flattening and semantic grouping for charge-coupled device and Light Detection And Ranging (LiDAR) sensors. We evaluate our proposed method on a KITTI benchmark dataset to detect and classify three object classes: cars, pedestrians and cyclists. The evaluation results show that the proposed method achieves better performance than the previous methods. Our proposed method extracted approximately 500 proposals on a 1226 × 370 image, whereas the original selective search method extracted approximately 10 6 × n proposals. We obtained classification performance with 77.72% mean average precision over the entirety of the classes in the moderate detection level of the KITTI benchmark dataset.

Object Detection and Classification by Decision-Level Fusion for Intelligent Vehicle Systems

PubMed Central

Oh, Sang-Il; Kang, Hang-Bong

2017-01-01

To understand driving environments effectively, it is important to achieve accurate detection and classification of objects detected by sensor-based intelligent vehicle systems, which are significantly important tasks. Object detection is performed for the localization of objects, whereas object classification recognizes object classes from detected object regions. For accurate object detection and classification, fusing multiple sensor information into a key component of the representation and perception processes is necessary. In this paper, we propose a new object-detection and classification method using decision-level fusion. We fuse the classification outputs from independent unary classifiers, such as 3D point clouds and image data using a convolutional neural network (CNN). The unary classifiers for the two sensors are the CNN with five layers, which use more than two pre-trained convolutional layers to consider local to global features as data representation. To represent data using convolutional layers, we apply region of interest (ROI) pooling to the outputs of each layer on the object candidate regions generated using object proposal generation to realize color flattening and semantic grouping for charge-coupled device and Light Detection And Ranging (LiDAR) sensors. We evaluate our proposed method on a KITTI benchmark dataset to detect and classify three object classes: cars, pedestrians and cyclists. The evaluation results show that the proposed method achieves better performance than the previous methods. Our proposed method extracted approximately 500 proposals on a 1226×370 image, whereas the original selective search method extracted approximately 106×n proposals. We obtained classification performance with 77.72% mean average precision over the entirety of the classes in the moderate detection level of the KITTI benchmark dataset. PMID:28117742

Full-field optical coherence tomography image restoration based on Hilbert transformation

NASA Astrophysics Data System (ADS)

Na, Jihoon; Choi, Woo June; Choi, Eun Seo; Ryu, Seon Young; Lee, Byeong Ha

2007-02-01

We propose the envelope detection method that is based on Hilbert transform for image restoration in full-filed optical coherence tomography (FF-OCT). The FF-OCT system presenting a high-axial resolution of 0.9 μm was implemented with a Kohler illuminator based on Linnik interferometer configuration. A 250 W customized quartz tungsten halogen lamp was used as a broadband light source and a CCD camera was used as a 2-dimentional detector array. The proposed image restoration method for FF-OCT requires only single phase-shifting. By using both the original and the phase-shifted images, we could remove the offset and the background signals from the interference fringe images. The desired coherent envelope image was obtained by applying Hilbert transform. With the proposed image restoration method, we demonstrate en-face imaging performance of the implemented FF-OCT system by presenting a tilted mirror surface, an integrated circuit chip, and a piece of onion epithelium.

Automatic markerless registration of point clouds with semantic-keypoint-based 4-points congruent sets

NASA Astrophysics Data System (ADS)

Ge, Xuming

2017-08-01

The coarse registration of point clouds from urban building scenes has become a key topic in applications of terrestrial laser scanning technology. Sampling-based algorithms in the random sample consensus (RANSAC) model have emerged as mainstream solutions to address coarse registration problems. In this paper, we propose a novel combined solution to automatically align two markerless point clouds from building scenes. Firstly, the method segments non-ground points from ground points. Secondly, the proposed method detects feature points from each cross section and then obtains semantic keypoints by connecting feature points with specific rules. Finally, the detected semantic keypoints from two point clouds act as inputs to a modified 4PCS algorithm. Examples are presented and the results compared with those of K-4PCS to demonstrate the main contributions of the proposed method, which are the extension of the original 4PCS to handle heavy datasets and the use of semantic keypoints to improve K-4PCS in relation to registration accuracy and computational efficiency.

Image encryption with chaotic map and Arnold transform in the gyrator transform domains

NASA Astrophysics Data System (ADS)

Sang, Jun; Luo, Hongling; Zhao, Jun; Alam, Mohammad S.; Cai, Bin

2017-05-01

An image encryption method combing chaotic map and Arnold transform in the gyrator transform domains was proposed. Firstly, the original secret image is XOR-ed with a random binary sequence generated by a logistic map. Then, the gyrator transform is performed. Finally, the amplitude and phase of the gyrator transform are permutated by Arnold transform. The decryption procedure is the inverse operation of encryption. The secret keys used in the proposed method include the control parameter and the initial value of the logistic map, the rotation angle of the gyrator transform, and the transform number of the Arnold transform. Therefore, the key space is large, while the key data volume is small. The numerical simulation was conducted to demonstrate the effectiveness of the proposed method and the security analysis was performed in terms of the histogram of the encrypted image, the sensitiveness to the secret keys, decryption upon ciphertext loss, and resistance to the chosen-plaintext attack.

Quantile Regression Models for Current Status Data

PubMed Central

Ou, Fang-Shu; Zeng, Donglin; Cai, Jianwen

2016-01-01

Current status data arise frequently in demography, epidemiology, and econometrics where the exact failure time cannot be determined but is only known to have occurred before or after a known observation time. We propose a quantile regression model to analyze current status data, because it does not require distributional assumptions and the coefficients can be interpreted as direct regression effects on the distribution of failure time in the original time scale. Our model assumes that the conditional quantile of failure time is a linear function of covariates. We assume conditional independence between the failure time and observation time. An M-estimator is developed for parameter estimation which is computed using the concave-convex procedure and its confidence intervals are constructed using a subsampling method. Asymptotic properties for the estimator are derived and proven using modern empirical process theory. The small sample performance of the proposed method is demonstrated via simulation studies. Finally, we apply the proposed method to analyze data from the Mayo Clinic Study of Aging. PMID:27994307

Data-driven mono-component feature identification via modified nonlocal means and MEWT for mechanical drivetrain fault diagnosis

NASA Astrophysics Data System (ADS)

Pan, Jun; Chen, Jinglong; Zi, Yanyang; Yuan, Jing; Chen, Binqiang; He, Zhengjia

2016-12-01

It is significant to perform condition monitoring and fault diagnosis on rolling mills in steel-making plant to ensure economic benefit. However, timely fault identification of key parts in a complicated industrial system under operating condition is still a challenging task since acquired condition signals are usually multi-modulated and inevitably mixed with strong noise. Therefore, a new data-driven mono-component identification method is proposed in this paper for diagnostic purpose. First, the modified nonlocal means algorithm (NLmeans) is proposed to reduce noise in vibration signals without destroying its original Fourier spectrum structure. During the modified NLmeans, two modifications are investigated and performed to improve denoising effect. Then, the modified empirical wavelet transform (MEWT) is applied on the de-noised signal to adaptively extract empirical mono-component modes. Finally, the modes are analyzed for mechanical fault identification based on Hilbert transform. The results show that the proposed data-driven method owns superior performance during system operation compared with the MEWT method.

Acoustic imaging of a duct spinning mode by the use of an in-duct circular microphone array.

PubMed

Wei, Qingkai; Huang, Xun; Peers, Edward

2013-06-01

An imaging method of acoustic spinning modes propagating within a circular duct simply with surface pressure information is introduced in this paper. The proposed method is developed in a theoretical way and is demonstrated by a numerical simulation case. Nowadays, the measurements within a duct have to be conducted using in-duct microphone array, which is unable to provide information of complete acoustic solutions across the test section. The proposed method can estimate immeasurable information by forming a so-called observer. The fundamental idea behind the testing method was originally developed in control theory for ordinary differential equations. Spinning mode propagation, however, is formulated in partial differential equations. A finite difference technique is used to reduce the associated partial differential equations to a classical form in control. The observer method can thereafter be applied straightforwardly. The algorithm is recursive and, thus, could be operated in real-time. A numerical simulation for a straight circular duct is conducted. The acoustic solutions on the test section can be reconstructed with good agreement to analytical solutions. The results suggest the potential and applications of the proposed method.

Improved measurements of RNA structure conservation with generalized centroid estimators.

PubMed

Okada, Yohei; Saito, Yutaka; Sato, Kengo; Sakakibara, Yasubumi

2011-01-01

Identification of non-protein-coding RNAs (ncRNAs) in genomes is a crucial task for not only molecular cell biology but also bioinformatics. Secondary structures of ncRNAs are employed as a key feature of ncRNA analysis since biological functions of ncRNAs are deeply related to their secondary structures. Although the minimum free energy (MFE) structure of an RNA sequence is regarded as the most stable structure, MFE alone could not be an appropriate measure for identifying ncRNAs since the free energy is heavily biased by the nucleotide composition. Therefore, instead of MFE itself, several alternative measures for identifying ncRNAs have been proposed such as the structure conservation index (SCI) and the base pair distance (BPD), both of which employ MFE structures. However, these measurements are unfortunately not suitable for identifying ncRNAs in some cases including the genome-wide search and incur high false discovery rate. In this study, we propose improved measurements based on SCI and BPD, applying generalized centroid estimators to incorporate the robustness against low quality multiple alignments. Our experiments show that our proposed methods achieve higher accuracy than the original SCI and BPD for not only human-curated structural alignments but also low quality alignments produced by CLUSTAL W. Furthermore, the centroid-based SCI on CLUSTAL W alignments is more accurate than or comparable with that of the original SCI on structural alignments generated with RAF, a high quality structural aligner, for which twofold expensive computational time is required on average. We conclude that our methods are more suitable for genome-wide alignments which are of low quality from the point of view on secondary structures than the original SCI and BPD.

Seizing opportunities for change at the operational level.

PubMed

Restrepo, Diana; Charron-Latour, Julie; Pourmonet, Hugo; Bassetto, Samuel

2016-04-18

Purpose - This paper presents a method for handling everyday opportunities for improvement, led by floor staff in healthcare institutions. More than 400,000 incidents and accidents were recorded in Quebec healthcare institutions in 2013. The burden of treatment falls on hospital floor staff. The purpose of this paper is to raise the visibility of this problem and support staff better in their efforts to handle opportunities for improvement. Design/methodology/approach - Based on issues identified in the literature, which have been found to exist in various organizations, the method involved reviewing practices in the field, proposing a solution, and testing it to assess its relevance and limitations. The method was tested in partnership with the Centre Hospitalier de l'Université de Montréal, in the internal medicine unit at Hôtel-Dieu campus. The test lasted three months. Indicators from this test have been compared to results in the literature. Findings - The proposed method presents a 68 per cent increase in ideas generated per person and per week compared to the reference study. The mean time for closing actions was about 41 per cent better (lower) than in the reference case. Research limitations/implications - The test lasted 15 weeks; a longer test is needed to collect more data. Practical implications - The first practical implication of this study was the creation of a method allowing employees to seize opportunities for improvement in their daily work. The application of this method revealed: first, the operational nature of the proposal (empowerment of the work team); second, the operationalization of continuous improvement (71 per cent of ideas were finalized while the initiative was monitored); third, the smooth operation of the mechanism for facilitating continuous improvement (organization of weekly meetings and team participation in these meetings in 90 per cent of cases); and fourth, a shared feeling that intra- and inter-team communication had been strengthened. Originality/value - The main value of this paper is that it proposes a simple problem-solving process that gives employees an opportunity to improve their daily work. The originality of this paper resides in comparing results to a standard case and observing an improvement. This paper proposes a new problem-solving structure and tests it scientifically.

Improving the complementary methods to estimate evapotranspiration under diverse climatic and physical conditions

NASA Astrophysics Data System (ADS)

Anayah, F. M.; Kaluarachchi, J. J.

2014-06-01

Reliable estimation of evapotranspiration (ET) is important for the purpose of water resources planning and management. Complementary methods, including complementary relationship areal evapotranspiration (CRAE), advection aridity (AA) and Granger and Gray (GG), have been used to estimate ET because these methods are simple and practical in estimating regional ET using meteorological data only. However, prior studies have found limitations in these methods especially in contrasting climates. This study aims to develop a calibration-free universal method using the complementary relationships to compute regional ET in contrasting climatic and physical conditions with meteorological data only. The proposed methodology consists of a systematic sensitivity analysis using the existing complementary methods. This work used 34 global FLUXNET sites where eddy covariance (EC) fluxes of ET are available for validation. A total of 33 alternative model variations from the original complementary methods were proposed. Further analysis using statistical methods and simplified climatic class definitions produced one distinctly improved GG-model-based alternative. The proposed model produced a single-step ET formulation with results equal to or better than the recent studies using data-intensive, classical methods. Average root mean square error (RMSE), mean absolute bias (BIAS) and R2 (coefficient of determination) across 34 global sites were 20.57 mm month-1, 10.55 mm month-1 and 0.64, respectively. The proposed model showed a step forward toward predicting ET in large river basins with limited data and requiring no calibration.

Motion capture based identification of the human body inertial parameters.

PubMed

Venture, Gentiane; Ayusawa, Ko; Nakamura, Yoshihiko

2008-01-01

Identification of body inertia, masses and center of mass is an important data to simulate, monitor and understand dynamics of motion, to personalize rehabilitation programs. This paper proposes an original method to identify the inertial parameters of the human body, making use of motion capture data and contact forces measurements. It allows in-vivo painless estimation and monitoring of the inertial parameters. The method is described and then obtained experimental results are presented and discussed.

Atlas-based fuzzy connectedness segmentation and intensity nonuniformity correction applied to brain MRI.

PubMed

Zhou, Yongxin; Bai, Jing

2007-01-01

A framework that combines atlas registration, fuzzy connectedness (FC) segmentation, and parametric bias field correction (PABIC) is proposed for the automatic segmentation of brain magnetic resonance imaging (MRI). First, the atlas is registered onto the MRI to initialize the following FC segmentation. Original techniques are proposed to estimate necessary initial parameters of FC segmentation. Further, the result of the FC segmentation is utilized to initialize a following PABIC algorithm. Finally, we re-apply the FC technique on the PABIC corrected MRI to get the final segmentation. Thus, we avoid expert human intervention and provide a fully automatic method for brain MRI segmentation. Experiments on both simulated and real MRI images demonstrate the validity of the method, as well as the limitation of the method. Being a fully automatic method, it is expected to find wide applications, such as three-dimensional visualization, radiation therapy planning, and medical database construction.

Astigmatism error modification for absolute shape reconstruction using Fourier transform method

NASA Astrophysics Data System (ADS)

He, Yuhang; Li, Qiang; Gao, Bo; Liu, Ang; Xu, Kaiyuan; Wei, Xiaohong; Chai, Liqun

2014-12-01

A method is proposed to modify astigmatism errors in absolute shape reconstruction of optical plane using Fourier transform method. If a transmission and reflection flat are used in an absolute test, two translation measurements lead to obtain the absolute shapes by making use of the characteristic relationship between the differential and original shapes in spatial frequency domain. However, because the translation device cannot guarantee the test and reference flats rigidly parallel to each other after the translations, a tilt error exists in the obtained differential data, which caused power and astigmatism errors in the reconstructed shapes. In order to modify the astigmatism errors, a rotation measurement is added. Based on the rotation invariability of the form of Zernike polynomial in circular domain, the astigmatism terms are calculated by solving polynomial coefficient equations related to the rotation differential data, and subsequently the astigmatism terms including error are modified. Computer simulation proves the validity of the proposed method.

Reproducing Quantum Probability Distributions at the Speed of Classical Dynamics: A New Approach for Developing Force-Field Functors.

PubMed

Sundar, Vikram; Gelbwaser-Klimovsky, David; Aspuru-Guzik, Alán

2018-04-05

Modeling nuclear quantum effects is required for accurate molecular dynamics (MD) simulations of molecules. The community has paid special attention to water and other biomolecules that show hydrogen bonding. Standard methods of modeling nuclear quantum effects like Ring Polymer Molecular Dynamics (RPMD) are computationally costlier than running classical trajectories. A force-field functor (FFF) is an alternative method that computes an effective force field that replicates quantum properties of the original force field. In this work, we propose an efficient method of computing FFF using the Wigner-Kirkwood expansion. As a test case, we calculate a range of thermodynamic properties of Neon, obtaining the same level of accuracy as RPMD, but with the shorter runtime of classical simulations. By modifying existing MD programs, the proposed method could be used in the future to increase the efficiency and accuracy of MD simulations involving water and proteins.

Domain Regeneration for Cross-Database Micro-Expression Recognition

NASA Astrophysics Data System (ADS)

Zong, Yuan; Zheng, Wenming; Huang, Xiaohua; Shi, Jingang; Cui, Zhen; Zhao, Guoying

2018-05-01

In this paper, we investigate the cross-database micro-expression recognition problem, where the training and testing samples are from two different micro-expression databases. Under this setting, the training and testing samples would have different feature distributions and hence the performance of most existing micro-expression recognition methods may decrease greatly. To solve this problem, we propose a simple yet effective method called Target Sample Re-Generator (TSRG) in this paper. By using TSRG, we are able to re-generate the samples from target micro-expression database and the re-generated target samples would share same or similar feature distributions with the original source samples. For this reason, we can then use the classifier learned based on the labeled source samples to accurately predict the micro-expression categories of the unlabeled target samples. To evaluate the performance of the proposed TSRG method, extensive cross-database micro-expression recognition experiments designed based on SMIC and CASME II databases are conducted. Compared with recent state-of-the-art cross-database emotion recognition methods, the proposed TSRG achieves more promising results.

Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network

PubMed Central

Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing

2016-01-01

A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method. PMID:26761006

Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network.

PubMed

Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing

2016-01-08

A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method.

Discriminative non-negative matrix factorization (DNMF) and its application to the fault diagnosis of diesel engine

NASA Astrophysics Data System (ADS)

Yang, Yong-sheng; Ming, An-bo; Zhang, You-yun; Zhu, Yong-sheng

2017-10-01

Diesel engines, widely used in engineering, are very important for the running of equipments and their fault diagnosis have attracted much attention. In the past several decades, the image based fault diagnosis methods have provided efficient ways for the diesel engine fault diagnosis. By introducing the class information into the traditional non-negative matrix factorization (NMF), an improved NMF algorithm named as discriminative NMF (DNMF) was developed and a novel imaged based fault diagnosis method was proposed by the combination of the DNMF and the KNN classifier. Experiments performed on the fault diagnosis of diesel engine were used to validate the efficacy of the proposed method. It is shown that the fault conditions of diesel engine can be efficiently classified by the proposed method using the coefficient matrix obtained by DNMF. Compared with the original NMF (ONMF) and principle component analysis (PCA), the DNMF can represent the class information more efficiently because the class characters of basis matrices obtained by the DNMF are more visible than those in the basis matrices obtained by the ONMF and PCA.

On better estimating and normalizing the relationship between clinical parameters: comparing respiratory modulations in the photoplethysmogram and blood pressure signal (DPOP versus PPV).

PubMed

Addison, Paul S; Wang, Rui; Uribe, Alberto A; Bergese, Sergio D

2015-01-01

DPOP (ΔPOP or Delta-POP) is a noninvasive parameter which measures the strength of respiratory modulations present in the pulse oximeter waveform. It has been proposed as a noninvasive alternative to pulse pressure variation (PPV) used in the prediction of the response to volume expansion in hypovolemic patients. We considered a number of simple techniques for better determining the underlying relationship between the two parameters. It was shown numerically that baseline-induced signal errors were asymmetric in nature, which corresponded to observation, and we proposed a method which combines a least-median-of-squares estimator with the requirement that the relationship passes through the origin (the LMSO method). We further developed a method of normalization of the parameters through rescaling DPOP using the inverse gradient of the linear fitted relationship. We propose that this normalization method (LMSO-N) is applicable to the matching of a wide range of clinical parameters. It is also generally applicable to the self-normalizing of parameters whose behaviour may change slightly due to algorithmic improvements.

On Better Estimating and Normalizing the Relationship between Clinical Parameters: Comparing Respiratory Modulations in the Photoplethysmogram and Blood Pressure Signal (DPOP versus PPV)

PubMed Central

Addison, Paul S.; Wang, Rui; Uribe, Alberto A.; Bergese, Sergio D.

2015-01-01

DPOP (ΔPOP or Delta-POP) is a noninvasive parameter which measures the strength of respiratory modulations present in the pulse oximeter waveform. It has been proposed as a noninvasive alternative to pulse pressure variation (PPV) used in the prediction of the response to volume expansion in hypovolemic patients. We considered a number of simple techniques for better determining the underlying relationship between the two parameters. It was shown numerically that baseline-induced signal errors were asymmetric in nature, which corresponded to observation, and we proposed a method which combines a least-median-of-squares estimator with the requirement that the relationship passes through the origin (the LMSO method). We further developed a method of normalization of the parameters through rescaling DPOP using the inverse gradient of the linear fitted relationship. We propose that this normalization method (LMSO-N) is applicable to the matching of a wide range of clinical parameters. It is also generally applicable to the self-normalizing of parameters whose behaviour may change slightly due to algorithmic improvements. PMID:25691912

Drawing Road Networks with Mental Maps.

PubMed

Lin, Shih-Syun; Lin, Chao-Hung; Hu, Yan-Jhang; Lee, Tong-Yee

2014-09-01

Tourist and destination maps are thematic maps designed to represent specific themes in maps. The road network topologies in these maps are generally more important than the geometric accuracy of roads. A road network warping method is proposed to facilitate map generation and improve theme representation in maps. The basic idea is deforming a road network to meet a user-specified mental map while an optimization process is performed to propagate distortions originating from road network warping. To generate a map, the proposed method includes algorithms for estimating road significance and for deforming a road network according to various geometric and aesthetic constraints. The proposed method can produce an iconic mark of a theme from a road network and meet a user-specified mental map. Therefore, the resulting map can serve as a tourist or destination map that not only provides visual aids for route planning and navigation tasks, but also visually emphasizes the presentation of a theme in a map for the purpose of advertising. In the experiments, the demonstrations of map generations show that our method enables map generation systems to generate deformed tourist and destination maps efficiently.

Pixel-based speckle adjustment for noise reduction in Fourier-domain OCT images

PubMed Central

Zhang, Anqi; Xi, Jiefeng; Sun, Jitao; Li, Xingde

2017-01-01

Speckle resides in OCT signals and inevitably effects OCT image quality. In this work, we present a novel method for speckle noise reduction in Fourier-domain OCT images, which utilizes the phase information of complex OCT data. In this method, speckle area is pre-delineated pixelwise based on a phase-domain processing method and then adjusted by the results of wavelet shrinkage of the original image. Coefficient shrinkage method such as wavelet or contourlet is applied afterwards for further suppressing the speckle noise. Compared with conventional methods without speckle adjustment, the proposed method demonstrates significant improvement of image quality. PMID:28663860

Electropherogram of capillary zone electrophoresis with effective mobility axis as a transverse axis and its analytical utility. I. Transformation applying the hypothetical electroosmotic flow.

PubMed

Ikuta, N; Yamada, Y; Hirokawa, T

2000-01-01

For capillary zone electrophoresis, a new method of transformation from migration time to effective mobility was proposed, in which the mobility increase due to Joule heating and the relaxation effect of the potential gradient were eliminated successfully. The precision of the mobility evaluated by the proposed transformation was discussed in relation to the analysis of rare earth ions. By using the transformation, almost the same pherograms could be obtained even from the pherograms obtained originally at different applied voltages.

Kernel PLS-SVC for Linear and Nonlinear Discrimination

NASA Technical Reports Server (NTRS)

Rosipal, Roman; Trejo, Leonard J.; Matthews, Bryan

2003-01-01

A new methodology for discrimination is proposed. This is based on kernel orthonormalized partial least squares (PLS) dimensionality reduction of the original data space followed by support vector machines for classification. Close connection of orthonormalized PLS and Fisher's approach to linear discrimination or equivalently with canonical correlation analysis is described. This gives preference to use orthonormalized PLS over principal component analysis. Good behavior of the proposed method is demonstrated on 13 different benchmark data sets and on the real world problem of the classification finger movement periods versus non-movement periods based on electroencephalogram.

Comments on "Image denoising by sparse 3-D transform-domain collaborative filtering".

PubMed

Hou, Yingkun; Zhao, Chunxia; Yang, Deyun; Cheng, Yong

2011-01-01

In order to resolve the problem that the denoising performance has a sharp drop when noise standard deviation reaches 40, proposed to replace the wavelet transform by the DCT. In this comment, we argue that this replacement is unnecessary, and that the problem can be solved by adjusting some numerical parameters. We also present this parameter modification approach here. Experimental results demonstrate that the proposed modification achieves better results in terms of both peak signal-to-noise ratio and subjective visual quality than the original method for strong noise.

Performance evaluation of Olympic weightlifters.

PubMed

Garhammer, J

1979-01-01

The comparison of weights lifted by athletes in different bodyweight categories is a continuing problem for the sport of olympic weightlifting. An objective mechanical evaluation procedure was developed using basic ideas from a model proposed by Ranta in 1975. This procedure was based on more realistic assumptions than the original model and considered both vertical and horizontal bar movements. Utilization of data obtained from film of national caliber lifters indicated that the proposed method was workable, and that the evaluative indices ranked lifters in reasonable order relative to other comparative techniques.

A Theoretical Framework for Calibration in Computer Models: Parametrization, Estimation and Convergence Properties

DOE PAGES

Tuo, Rui; Jeff Wu, C. F.

2016-07-19

Calibration parameters in deterministic computer experiments are those attributes that cannot be measured or available in physical experiments. Here, an approach to estimate them by using data from physical experiments and computer simulations. A theoretical framework is given which allows us to study the issues of parameter identifiability and estimation. We define the L 2-consistency for calibration as a justification for calibration methods. It is shown that a simplified version of the original KO method leads to asymptotically L 2-inconsistent calibration. This L 2-inconsistency can be remedied by modifying the original estimation procedure. A novel calibration method, called the Lmore » 2 calibration, is proposed and proven to be L 2-consistent and enjoys optimal convergence rate. Furthermore a numerical example and some mathematical analysis are used to illustrate the source of the L 2-inconsistency problem.« less

Uncovering the spatial structure of mobility networks

NASA Astrophysics Data System (ADS)

Louail, Thomas; Lenormand, Maxime; Picornell, Miguel; García Cantú, Oliva; Herranz, Ricardo; Frias-Martinez, Enrique; Ramasco, José J.; Barthelemy, Marc

2015-01-01

The extraction of a clear and simple footprint of the structure of large, weighted and directed networks is a general problem that has relevance for many applications. An important example is seen in origin-destination matrices, which contain the complete information on commuting flows, but are difficult to analyze and compare. We propose here a versatile method, which extracts a coarse-grained signature of mobility networks, under the form of a 2 × 2 matrix that separates the flows into four categories. We apply this method to origin-destination matrices extracted from mobile phone data recorded in 31 Spanish cities. We show that these cities essentially differ by their proportion of two types of flows: integrated (between residential and employment hotspots) and random flows, whose importance increases with city size. Finally, the method allows the determination of categories of networks, and in the mobility case, the classification of cities according to their commuting structure.

Creation of heterogeneous materials on the basis of B4C and Ni powders by the method of cold spraying with subsequent layer-by-layer laser treatment

NASA Astrophysics Data System (ADS)

Fomin, V. M.; Golyshev, A. A.; Kosarev, V. F.; Malikov, A. G.; Orishich, A. M.; Ryashin, N. S.; Filippov, A. A.; Shikalov, V. S.

2017-09-01

A method is proposed for creating principally new functionally graded heterogeneous materials on the basis of B4C ceramic powders with different mass fractions in the original mixture and plastic metallic additive of Ni by a combined method of cold spraying with subsequent layer-by-layer laser treatment. Mechanical properties of the resultant tracks are examined. It is shown that the track microhardness increases with increasing B4C concentration in the original mixture. The track structure is found to depend on the size of ceramic particles in the interval from 3 to 75 μm. Reduction of the B4C particle size (approximately by a factor of 2-3) inside the track owing to fragmentation under the action of the laser beam is observed for the first time.

Reduction of metal artifacts in x-ray CT images using a convolutional neural network

NASA Astrophysics Data System (ADS)

Zhang, Yanbo; Chu, Ying; Yu, Hengyong

2017-09-01

Patients usually contain various metallic implants (e.g. dental fillings, prostheses), causing severe artifacts in the x-ray CT images. Although a large number of metal artifact reduction (MAR) methods have been proposed in the past four decades, MAR is still one of the major problems in clinical x-ray CT. In this work, we develop a convolutional neural network (CNN) based MAR framework, which combines the information from the original and corrected images to suppress artifacts. Before the MAR, we generate a group of data and train a CNN. First, we numerically simulate various metal artifacts cases and build a dataset, which includes metal-free images (used as references), metal-inserted images and various MAR methods corrected images. Then, ten thousands patches are extracted from the databased to train the metal artifact reduction CNN. In the MAR stage, the original image and two corrected images are stacked as a three-channel input image for CNN, and a CNN image is generated with less artifacts. The water equivalent regions in the CNN image are set to a uniform value to yield a CNN prior, whose forward projections are used to replace the metal affected projections, followed by the FBP reconstruction. Experimental results demonstrate the superior metal artifact reduction capability of the proposed method to its competitors.

Global universe anisotropy probed by the alignment of structures in the cosmic microwave background.

PubMed

Wiaux, Y; Vielva, P; Martínez-González, E; Vandergheynst, P

2006-04-21

We question the global universe isotropy by probing the alignment of local structures in the cosmic microwave background (CMB) radiation. The original method proposed relies on a steerable wavelet decomposition of the CMB signal on the sphere. The analysis of the first-year Wilkinson Microwave Anisotropy Probe data identifies a mean preferred plane with a normal direction close to the CMB dipole axis, and a mean preferred direction in this plane, very close to the ecliptic poles axis. Previous statistical anisotropy results are thereby synthesized, but further analyses are still required to establish their origin.

A Method for Estimating Zero-Flow Pressure and Intracranial Pressure

PubMed Central

Caren, Marzban; Paul, Raymond Illian; David, Morison; Anne, Moore; Michel, Kliot; Marek, Czosnyka; Pierre, Mourad

2012-01-01

Background It has been hypothesized that critical closing pressure of cerebral circulation, or zero-flow pressure (ZFP), can estimate intracranial pressure (ICP). One ZFP estimation method employs extrapolation of arterial blood pressure versus blood-flow velocity. The aim of this study is to improve ICP predictions. Methods Two revisions are considered: 1) The linear model employed for extrapolation is extended to a nonlinear equation, and 2) the parameters of the model are estimated by an alternative criterion (not least-squares). The method is applied to data on transcranial Doppler measurements of blood-flow velocity, arterial blood pressure, and ICP, from 104 patients suffering from closed traumatic brain injury, sampled across the United States and England. Results The revisions lead to qualitative (e.g., precluding negative ICP) and quantitative improvements in ICP prediction. In going from the original to the revised method, the ±2 standard deviation of error is reduced from 33 to 24 mm Hg; the root-mean-squared error (RMSE) is reduced from 11 to 8.2 mm Hg. The distribution of RMSE is tighter as well; for the revised method the 25th and 75th percentiles are 4.1 and 13.7 mm Hg, respectively, as compared to 5.1 and 18.8 mm Hg for the original method. Conclusions Proposed alterations to a procedure for estimating ZFP lead to more accurate and more precise estimates of ICP, thereby offering improved means of estimating it noninvasively. The quality of the estimates is inadequate for many applications, but further work is proposed which may lead to clinically useful results. PMID:22824923

[Method for evaluating the positional accuracy of a six-degrees-of-freedom radiotherapy couch using high definition digital cameras].

PubMed

Takemura, Akihiro; Ueda, Shinichi; Noto, Kimiya; Kurata, Yuichi; Shoji, Saori

2011-01-01

In this study, we proposed and evaluated a positional accuracy assessment method with two high-resolution digital cameras for add-on six-degrees-of-freedom radiotherapy (6D) couches. Two high resolution digital cameras (D5000, Nikon Co.) were used in this accuracy assessment method. These cameras were placed on two orthogonal axes of a linear accelerator (LINAC) coordinate system and focused on the isocenter of the LINAC. Pictures of a needle that was fixed on the 6D couch were taken by the cameras during couch motions of translation and rotation of each axis. The coordinates of the needle in the pictures were obtained using manual measurement, and the coordinate error of the needle was calculated. The accuracy of a HexaPOD evo (Elekta AB, Sweden) was evaluated using this method. All of the mean values of the X, Y, and Z coordinate errors in the translation tests were within ±0.1 mm. However, the standard deviation of the Z coordinate errors in the Z translation test was 0.24 mm, which is higher than the others. In the X rotation test, we found that the X coordinate of the rotational origin of the 6D couch was shifted. We proposed an accuracy assessment method for a 6D couch. The method was able to evaluate the accuracy of the motion of only the 6D couch and revealed the deviation of the origin of the couch rotation. This accuracy assessment method is effective for evaluating add-on 6D couch positioning.

Load influence on gear noise. [mathematical model for determining acoustic pressure level as function of load

NASA Technical Reports Server (NTRS)

Merticaru, V.

1974-01-01

An original mathematical model is proposed to derive equations for calculation of gear noise. These equations permit the acoustic pressure level to be determined as a function of load. Application of this method to three parallel gears is reported. The logical calculation scheme is given, as well as the results obtained.

Durbin-Watson partial least-squares regression applied to MIR data on adulteration with edible oils of different origins.

PubMed

Jović, Ozren

2016-12-15

A novel method for quantitative prediction and variable-selection on spectroscopic data, called Durbin-Watson partial least-squares regression (dwPLS), is proposed in this paper. The idea is to inspect serial correlation in infrared data that is known to consist of highly correlated neighbouring variables. The method selects only those variables whose intervals have a lower Durbin-Watson statistic (dw) than a certain optimal cutoff. For each interval, dw is calculated on a vector of regression coefficients. Adulteration of cold-pressed linseed oil (L), a well-known nutrient beneficial to health, is studied in this work by its being mixed with cheaper oils: rapeseed oil (R), sesame oil (Se) and sunflower oil (Su). The samples for each botanical origin of oil vary with respect to producer, content and geographic origin. The results obtained indicate that MIR-ATR, combined with dwPLS could be implemented to quantitative determination of edible-oil adulteration. Copyright © 2016 Elsevier Ltd. All rights reserved.

Learning SVM in Kreĭn Spaces.

PubMed

Loosli, Gaelle; Canu, Stephane; Ong, Cheng Soon

2016-06-01

This paper presents a theoretical foundation for an SVM solver in Kreĭn spaces. Up to now, all methods are based either on the matrix correction, or on non-convex minimization, or on feature-space embedding. Here we justify and evaluate a solution that uses the original (indefinite) similarity measure, in the original Kreĭn space. This solution is the result of a stabilization procedure. We establish the correspondence between the stabilization problem (which has to be solved) and a classical SVM based on minimization (which is easy to solve). We provide simple equations to go from one to the other (in both directions). This link between stabilization and minimization problems is the key to obtain a solution in the original Kreĭn space. Using KSVM, one can solve SVM with usually troublesome kernels (large negative eigenvalues or large numbers of negative eigenvalues). We show experiments showing that our algorithm KSVM outperforms all previously proposed approaches to deal with indefinite matrices in SVM-like kernel methods.

Singularity-sensitive gauge-based radar rainfall adjustment methods for urban hydrological applications

NASA Astrophysics Data System (ADS)

Wang, L.-P.; Ochoa-Rodríguez, S.; Onof, C.; Willems, P.

2015-09-01

Gauge-based radar rainfall adjustment techniques have been widely used to improve the applicability of radar rainfall estimates to large-scale hydrological modelling. However, their use for urban hydrological applications is limited as they were mostly developed based upon Gaussian approximations and therefore tend to smooth off so-called "singularities" (features of a non-Gaussian field) that can be observed in the fine-scale rainfall structure. Overlooking the singularities could be critical, given that their distribution is highly consistent with that of local extreme magnitudes. This deficiency may cause large errors in the subsequent urban hydrological modelling. To address this limitation and improve the applicability of adjustment techniques at urban scales, a method is proposed herein which incorporates a local singularity analysis into existing adjustment techniques and allows the preservation of the singularity structures throughout the adjustment process. In this paper the proposed singularity analysis is incorporated into the Bayesian merging technique and the performance of the resulting singularity-sensitive method is compared with that of the original Bayesian (non singularity-sensitive) technique and the commonly used mean field bias adjustment. This test is conducted using as case study four storm events observed in the Portobello catchment (53 km2) (Edinburgh, UK) during 2011 and for which radar estimates, dense rain gauge and sewer flow records, as well as a recently calibrated urban drainage model were available. The results suggest that, in general, the proposed singularity-sensitive method can effectively preserve the non-normality in local rainfall structure, while retaining the ability of the original adjustment techniques to generate nearly unbiased estimates. Moreover, the ability of the singularity-sensitive technique to preserve the non-normality in rainfall estimates often leads to better reproduction of the urban drainage system's dynamics, particularly of peak runoff flows.

75 FR 8279 - Airworthiness Directives; The Boeing Company Model 747 Airplanes

Federal Register 2010, 2011, 2012, 2013, 2014

2010-02-24

... airplanes. The original NPRM would have superseded an existing AD that currently requires repetitive... inspection of the modified area. The original NPRM proposed to continue to require those actions using revised service information. For certain airplanes, the original NPRM proposed to require new repetitive...

75 FR 34516 - Bureau of Educational and Cultural Affairs; Edmund S. Muskie Graduate Fellowship Program

Federal Register 2010, 2011, 2012, 2013, 2014

2010-06-17

.... Muskie Graduate Fellowship Program Notice: Correction to original Request for Grant Proposals. SUMMARY... revision to the original Request for Grant Proposals (RFGP) for the Edmund S. Muskie Graduate Fellowship... the original announcement remain the same. Additional Information Interested organizations should...

76 FR 69328 - Proposed Collection; Comment Request; Race and National Origin Identification

Federal Register 2010, 2011, 2012, 2013, 2014

2011-11-08

... DEPARTMENT OF THE TREASURY Proposed Collection; Comment Request; Race and National Origin... INFORMATION: OMB Number: 1505-0195. Type of Review: Revision of a currently approved collection. Title: Race...Connector, is used to capture race and national origin information electronically from an applicant. The...

Image superresolution of cytology images using wavelet based patch search

NASA Astrophysics Data System (ADS)

Vargas, Carlos; García-Arteaga, Juan D.; Romero, Eduardo

2015-01-01

Telecytology is a new research area that holds the potential of significantly reducing the number of deaths due to cervical cancer in developing countries. This work presents a novel super-resolution technique that couples high and low frequency information in order to reduce the bandwidth consumption of cervical image transmission. The proposed approach starts by decomposing into wavelets the high resolution images and transmitting only the lower frequency coefficients. The transmitted coefficients are used to reconstruct an image of the original size. Additional details are added by iteratively replacing patches of the wavelet reconstructed image with equivalent high resolution patches from a previously acquired image database. Finally, the original transmitted low frequency coefficients are used to correct the final image. Results show a higher signal to noise ratio in the proposed method over simply discarding high frequency wavelet coefficients or replacing directly down-sampled patches from the image-database.

Concept analysis and validation of the nursing diagnosis, delayed surgical recovery.

PubMed

Appoloni, Aline Helena; Herdman, T Heather; Napoleão, Anamaria Alves; Campos de Carvalho, Emilia; Hortense, Priscilla

2013-10-01

To analyze the human response of delayed surgical recovery, approved by NANDA-I, and to validate its defining characteristics (DCs) and related factors (RFs). This was a two-part study using a concept analysis based on the method of Walker and Avant, and diagnostic content validation based on Fehring's model. Three of the original DCs, and three proposed DCs identified from the concept analysis, were validated in this study; five of the original RFs and four proposed RFs were validated. A revision of the concept studied is suggested, incorporating the validation of some of the DCs and RFs presented by NANDA-I, and the insertion of new, validated DCs and RFs. This study may enable the extension of the use of this diagnosis and contribute to quality surgical care of clients. © 2013, The Authors. International Journal of Nursing Knowledge © 2013, NANDA International.

Toward optimal feature and time segment selection by divergence method for EEG signals classification.

PubMed

Wang, Jie; Feng, Zuren; Lu, Na; Luo, Jing

2018-06-01

Feature selection plays an important role in the field of EEG signals based motor imagery pattern classification. It is a process that aims to select an optimal feature subset from the original set. Two significant advantages involved are: lowering the computational burden so as to speed up the learning procedure and removing redundant and irrelevant features so as to improve the classification performance. Therefore, feature selection is widely employed in the classification of EEG signals in practical brain-computer interface systems. In this paper, we present a novel statistical model to select the optimal feature subset based on the Kullback-Leibler divergence measure, and automatically select the optimal subject-specific time segment. The proposed method comprises four successive stages: a broad frequency band filtering and common spatial pattern enhancement as preprocessing, features extraction by autoregressive model and log-variance, the Kullback-Leibler divergence based optimal feature and time segment selection and linear discriminate analysis classification. More importantly, this paper provides a potential framework for combining other feature extraction models and classification algorithms with the proposed method for EEG signals classification. Experiments on single-trial EEG signals from two public competition datasets not only demonstrate that the proposed method is effective in selecting discriminative features and time segment, but also show that the proposed method yields relatively better classification results in comparison with other competitive methods. Copyright © 2018 Elsevier Ltd. All rights reserved.

Manifold Learning by Preserving Distance Orders.

PubMed

Ataer-Cansizoglu, Esra; Akcakaya, Murat; Orhan, Umut; Erdogmus, Deniz

2014-03-01

Nonlinear dimensionality reduction is essential for the analysis and the interpretation of high dimensional data sets. In this manuscript, we propose a distance order preserving manifold learning algorithm that extends the basic mean-squared error cost function used mainly in multidimensional scaling (MDS)-based methods. We develop a constrained optimization problem by assuming explicit constraints on the order of distances in the low-dimensional space. In this optimization problem, as a generalization of MDS, instead of forcing a linear relationship between the distances in the high-dimensional original and low-dimensional projection space, we learn a non-decreasing relation approximated by radial basis functions. We compare the proposed method with existing manifold learning algorithms using synthetic datasets based on the commonly used residual variance and proposed percentage of violated distance orders metrics. We also perform experiments on a retinal image dataset used in Retinopathy of Prematurity (ROP) diagnosis.

Intelligent fault recognition strategy based on adaptive optimized multiple centers

NASA Astrophysics Data System (ADS)

Zheng, Bo; Li, Yan-Feng; Huang, Hong-Zhong

2018-06-01

For the recognition principle based optimized single center, one important issue is that the data with nonlinear separatrix cannot be recognized accurately. In order to solve this problem, a novel recognition strategy based on adaptive optimized multiple centers is proposed in this paper. This strategy recognizes the data sets with nonlinear separatrix by the multiple centers. Meanwhile, the priority levels are introduced into the multi-objective optimization, including recognition accuracy, the quantity of optimized centers, and distance relationship. According to the characteristics of various data, the priority levels are adjusted to ensure the quantity of optimized centers adaptively and to keep the original accuracy. The proposed method is compared with other methods, including support vector machine (SVM), neural network, and Bayesian classifier. The results demonstrate that the proposed strategy has the same or even better recognition ability on different distribution characteristics of data.

Reversible integer wavelet transform for blind image hiding method

PubMed Central

Bibi, Nargis; Mahmood, Zahid; Akram, Tallha; Naqvi, Syed Rameez

2017-01-01

In this article, a blind data hiding reversible methodology to embed the secret data for hiding purpose into cover image is proposed. The key advantage of this research work is to resolve the privacy and secrecy issues raised during the data transmission over the internet. Firstly, data is decomposed into sub-bands using the integer wavelets. For decomposition, the Fresnelet transform is utilized which encrypts the secret data by choosing a unique key parameter to construct a dummy pattern. The dummy pattern is then embedded into an approximated sub-band of the cover image. Our proposed method reveals high-capacity and great imperceptibility of the secret embedded data. With the utilization of family of integer wavelets, the proposed novel approach becomes more efficient for hiding and retrieving process. It retrieved the secret hidden data from the embedded data blindly, without the requirement of original cover image. PMID:28498855

Composite fuzzy sliding mode control of nonlinear singularly perturbed systems.

PubMed

Nagarale, Ravindrakumar M; Patre, B M

2014-05-01

This paper deals with the robust asymptotic stabilization for a class of nonlinear singularly perturbed systems using the fuzzy sliding mode control technique. In the proposed approach the original system is decomposed into two subsystems as slow and fast models by the singularly perturbed method. The composite fuzzy sliding mode controller is designed for stabilizing the full order system by combining separately designed slow and fast fuzzy sliding mode controllers. The two-time scale design approach minimizes the effect of boundary layer system on the full order system. A stability analysis allows us to provide sufficient conditions for the asymptotic stability of the full order closed-loop system. The simulation results show improved system performance of the proposed controller as compared to existing methods. The experimentation results validate the effectiveness of the proposed controller. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Evaluation of speaker de-identification based on voice gender and age conversion

NASA Astrophysics Data System (ADS)

Přibil, Jiří; Přibilová, Anna; Matoušek, Jindřich

2018-03-01

Two basic tasks are covered in this paper. The first one consists in the design and practical testing of a new method for voice de-identification that changes the apparent age and/or gender of a speaker by multi-segmental frequency scale transformation combined with prosody modification. The second task is aimed at verification of applicability of a classifier based on Gaussian mixture models (GMM) to detect the original Czech and Slovak speakers after applied voice deidentification. The performed experiments confirm functionality of the developed gender and age conversion for all selected types of de-identification which can be objectively evaluated by the GMM-based open-set classifier. The original speaker detection accuracy was compared also for sentences uttered by German and English speakers showing language independence of the proposed method.

Quality Tetrahedral Mesh Smoothing via Boundary-Optimized Delaunay Triangulation

PubMed Central

Gao, Zhanheng; Yu, Zeyun; Holst, Michael

2012-01-01

Despite its great success in improving the quality of a tetrahedral mesh, the original optimal Delaunay triangulation (ODT) is designed to move only inner vertices and thus cannot handle input meshes containing “bad” triangles on boundaries. In the current work, we present an integrated approach called boundary-optimized Delaunay triangulation (B-ODT) to smooth (improve) a tetrahedral mesh. In our method, both inner and boundary vertices are repositioned by analytically minimizing the error between a paraboloid function and its piecewise linear interpolation over the neighborhood of each vertex. In addition to the guaranteed volume-preserving property, the proposed algorithm can be readily adapted to preserve sharp features in the original mesh. A number of experiments are included to demonstrate the performance of our method. PMID:23144522

25 CFR 224.62 - May a final proposed TERA differ from the original proposed TERA?

Code of Federal Regulations, 2010 CFR

2010-04-01

... 25 Indians 1 2010-04-01 2010-04-01 false May a final proposed TERA differ from the original proposed TERA? 224.62 Section 224.62 Indians BUREAU OF INDIAN AFFAIRS, DEPARTMENT OF THE INTERIOR ENERGY AND MINERALS TRIBAL ENERGY RESOURCE AGREEMENTS UNDER THE INDIAN TRIBAL ENERGY DEVELOPMENT AND SELF...

SU-E-J-122: The CBCT Dose Calculation Using a Patient Specific CBCT Number to Mass Density Conversion Curve Based On a Novel Image Registration and Organ Mapping Method in Head-And-Neck Radiation Therapy

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

Zhou, J; Lasio, G; Chen, S

2015-06-15

Purpose: To develop a CBCT HU correction method using a patient specific HU to mass density conversion curve based on a novel image registration and organ mapping method for head-and-neck radiation therapy. Methods: There are three steps to generate a patient specific CBCT HU to mass density conversion curve. First, we developed a novel robust image registration method based on sparseness analysis to register the planning CT (PCT) and the CBCT. Second, a novel organ mapping method was developed to transfer the organs at risk (OAR) contours from the PCT to the CBCT and corresponding mean HU values of eachmore » OAR were measured in both the PCT and CBCT volumes. Third, a set of PCT and CBCT HU to mass density conversion curves were created based on the mean HU values of OARs and the corresponding mass density of the OAR in the PCT. Then, we compared our proposed conversion curve with the traditional Catphan phantom based CBCT HU to mass density calibration curve. Both curves were input into the treatment planning system (TPS) for dose calculation. Last, the PTV and OAR doses, DVH and dose distributions of CBCT plans are compared to the original treatment plan. Results: One head-and-neck cases which contained a pair of PCT and CBCT was used. The dose differences between the PCT and CBCT plans using the proposed method are −1.33% for the mean PTV, 0.06% for PTV D95%, and −0.56% for the left neck. The dose differences between plans of PCT and CBCT corrected using the CATPhan based method are −4.39% for mean PTV, 4.07% for PTV D95%, and −2.01% for the left neck. Conclusion: The proposed CBCT HU correction method achieves better agreement with the original treatment plan compared to the traditional CATPhan based calibration method.« less

An item-oriented recommendation algorithm on cold-start problem

NASA Astrophysics Data System (ADS)

Qiu, Tian; Chen, Guang; Zhang, Zi-Ke; Zhou, Tao

2011-09-01

Based on a hybrid algorithm incorporating the heat conduction and probability spreading processes (Proc. Natl. Acad. Sci. U.S.A., 107 (2010) 4511), in this letter, we propose an improved method by introducing an item-oriented function, focusing on solving the dilemma of the recommendation accuracy between the cold and popular items. Differently from previous works, the present algorithm does not require any additional information (e.g., tags). Further experimental results obtained in three real datasets, RYM, Netflix and MovieLens, show that, compared with the original hybrid method, the proposed algorithm significantly enhances the recommendation accuracy of the cold items, while it keeps the recommendation accuracy of the overall and the popular items. This work might shed some light on both understanding and designing effective methods for long-tailed online applications of recommender systems.

Phase aberration compensation of digital holographic microscopy based on least squares surface fitting

NASA Astrophysics Data System (ADS)

Di, Jianglei; Zhao, Jianlin; Sun, Weiwei; Jiang, Hongzhen; Yan, Xiaobo

2009-10-01

Digital holographic microscopy allows the numerical reconstruction of the complex wavefront of samples, especially biological samples such as living cells. In digital holographic microscopy, a microscope objective is introduced to improve the transverse resolution of the sample; however a phase aberration in the object wavefront is also brought along, which will affect the phase distribution of the reconstructed image. We propose here a numerical method to compensate for the phase aberration of thin transparent objects with a single hologram. The least squares surface fitting with points number less than the matrix of the original hologram is performed on the unwrapped phase distribution to remove the unwanted wavefront curvature. The proposed method is demonstrated with the samples of the cicada wings and epidermal cells of garlic, and the experimental results are consistent with that of the double exposure method.

Hylemetry versus Biometry: a new method to certificate the lithography authenticity

NASA Astrophysics Data System (ADS)

Schirripa Spagnolo, Giuseppe; Cozzella, Lorenzo; Simonetti, Carla

2011-06-01

When we buy an artwork object a certificate of authenticity contain specific details about the artwork. Unfortunately, these certificates are often exchanged between similar artworks: the same document is supplied by the seller to certificate the originality. In this way the buyer will have a copy of an original certificate to attest that the "not original artwork" is an original one. A solution for this problem would be to insert a system that links together the certificate and a specific artwork. To do this it is necessary, for a single artwork, to find unique, unrepeatable, and unchangeable characteristics. In this paper we propose a new lithography certification based on the color spots distribution, which compose the lithography itself. Due to the high resolution acquisition media available today, it is possible using analysis method typical of speckle metrology. In particular, in verification phase it is only necessary acquiring the same portion of lithography, extracting the verification information, using the private key to obtain the same information from the certificate and confronting the two information using a comparison threshold. Due to the possible rotation and translation it is applied image correlation solutions, used in speckle metrology, to determine translation and rotation error and correct allow to verifying extracted and acquired images in the best situation, for granting correct originality verification.

PDEs on moving surfaces via the closest point method and a modified grid based particle method

NASA Astrophysics Data System (ADS)

Petras, A.; Ruuth, S. J.

2016-05-01

Partial differential equations (PDEs) on surfaces arise in a wide range of applications. The closest point method (Ruuth and Merriman (2008) [20]) is a recent embedding method that has been used to solve a variety of PDEs on smooth surfaces using a closest point representation of the surface and standard Cartesian grid methods in the embedding space. The original closest point method (CPM) was designed for problems posed on static surfaces, however the solution of PDEs on moving surfaces is of considerable interest as well. Here we propose solving PDEs on moving surfaces using a combination of the CPM and a modification of the grid based particle method (Leung and Zhao (2009) [12]). The grid based particle method (GBPM) represents and tracks surfaces using meshless particles and an Eulerian reference grid. Our modification of the GBPM introduces a reconstruction step into the original method to ensure that all the grid points within a computational tube surrounding the surface are active. We present a number of examples to illustrate the numerical convergence properties of our combined method. Experiments for advection-diffusion equations that are strongly coupled to the velocity of the surface are also presented.

Efficient image enhancement using sparse source separation in the Retinex theory

NASA Astrophysics Data System (ADS)

Yoon, Jongsu; Choi, Jangwon; Choe, Yoonsik

2017-11-01

Color constancy is the feature of the human vision system (HVS) that ensures the relative constancy of the perceived color of objects under varying illumination conditions. The Retinex theory of machine vision systems is based on the HVS. Among Retinex algorithms, the physics-based algorithms are efficient; however, they generally do not satisfy the local characteristics of the original Retinex theory because they eliminate global illumination from their optimization. We apply the sparse source separation technique to the Retinex theory to present a physics-based algorithm that satisfies the locality characteristic of the original Retinex theory. Previous Retinex algorithms have limited use in image enhancement because the total variation Retinex results in an overly enhanced image and the sparse source separation Retinex cannot completely restore the original image. In contrast, our proposed method preserves the image edge and can very nearly replicate the original image without any special operation.

Simultaneous Tumor Segmentation, Image Restoration, and Blur Kernel Estimation in PET Using Multiple Regularizations

PubMed Central

Li, Laquan; Wang, Jian; Lu, Wei; Tan, Shan

2016-01-01

Accurate tumor segmentation from PET images is crucial in many radiation oncology applications. Among others, partial volume effect (PVE) is recognized as one of the most important factors degrading imaging quality and segmentation accuracy in PET. Taking into account that image restoration and tumor segmentation are tightly coupled and can promote each other, we proposed a variational method to solve both problems simultaneously in this study. The proposed method integrated total variation (TV) semi-blind de-convolution and Mumford-Shah segmentation with multiple regularizations. Unlike many existing energy minimization methods using either TV or L2 regularization, the proposed method employed TV regularization over tumor edges to preserve edge information, and L2 regularization inside tumor regions to preserve the smooth change of the metabolic uptake in a PET image. The blur kernel was modeled as anisotropic Gaussian to address the resolution difference in transverse and axial directions commonly seen in a clinic PET scanner. The energy functional was rephrased using the Γ-convergence approximation and was iteratively optimized using the alternating minimization (AM) algorithm. The performance of the proposed method was validated on a physical phantom and two clinic datasets with non-Hodgkin’s lymphoma and esophageal cancer, respectively. Experimental results demonstrated that the proposed method had high performance for simultaneous image restoration, tumor segmentation and scanner blur kernel estimation. Particularly, the recovery coefficients (RC) of the restored images of the proposed method in the phantom study were close to 1, indicating an efficient recovery of the original blurred images; for segmentation the proposed method achieved average dice similarity indexes (DSIs) of 0.79 and 0.80 for two clinic datasets, respectively; and the relative errors of the estimated blur kernel widths were less than 19% in the transversal direction and 7% in the axial direction. PMID:28603407

Diagnosing a Strong-Fault Model by Conflict and Consistency

PubMed Central

Zhou, Gan; Feng, Wenquan

2018-01-01

The diagnosis method for a weak-fault model with only normal behaviors of each component has evolved over decades. However, many systems now demand a strong-fault models, the fault modes of which have specific behaviors as well. It is difficult to diagnose a strong-fault model due to its non-monotonicity. Currently, diagnosis methods usually employ conflicts to isolate possible fault and the process can be expedited when some observed output is consistent with the model’s prediction where the consistency indicates probably normal components. This paper solves the problem of efficiently diagnosing a strong-fault model by proposing a novel Logic-based Truth Maintenance System (LTMS) with two search approaches based on conflict and consistency. At the beginning, the original a strong-fault model is encoded by Boolean variables and converted into Conjunctive Normal Form (CNF). Then the proposed LTMS is employed to reason over CNF and find multiple minimal conflicts and maximal consistencies when there exists fault. The search approaches offer the best candidate efficiency based on the reasoning result until the diagnosis results are obtained. The completeness, coverage, correctness and complexity of the proposals are analyzed theoretically to show their strength and weakness. Finally, the proposed approaches are demonstrated by applying them to a real-world domain—the heat control unit of a spacecraft—where the proposed methods are significantly better than best first and conflict directly with A* search methods. PMID:29596302

Three-dimensional image acquisition and reconstruction system on a mobile device based on computer-generated integral imaging.

PubMed

Erdenebat, Munkh-Uchral; Kim, Byeong-Jun; Piao, Yan-Ling; Park, Seo-Yeon; Kwon, Ki-Chul; Piao, Mei-Lan; Yoo, Kwan-Hee; Kim, Nam

2017-10-01

A mobile three-dimensional image acquisition and reconstruction system using a computer-generated integral imaging technique is proposed. A depth camera connected to the mobile device acquires the color and depth data of a real object simultaneously, and an elemental image array is generated based on the original three-dimensional information for the object, with lens array specifications input into the mobile device. The three-dimensional visualization of the real object is reconstructed on the mobile display through optical or digital reconstruction methods. The proposed system is implemented successfully and the experimental results certify that the system is an effective and interesting method of displaying real three-dimensional content on a mobile device.

Fast ITTBC using pattern code on subband segmentation

NASA Astrophysics Data System (ADS)

Koh, Sung S.; Kim, Hanchil; Lee, Kooyoung; Kim, Hongbin; Jeong, Hun; Cho, Gangseok; Kim, Chunghwa

2000-06-01

Iterated Transformation Theory-Based Coding suffers from very high computational complexity in encoding phase. This is due to its exhaustive search. In this paper, our proposed image coding algorithm preprocess an original image to subband segmentation image by wavelet transform before image coding to reduce encoding complexity. A similar block is searched by using the 24 block pattern codes which are coded by the edge information in the image block on the domain pool of the subband segmentation. As a result, numerical data shows that the encoding time of the proposed coding method can be reduced to 98.82% of that of Joaquin's method, while the loss in quality relative to the Jacquin's is about 0.28 dB in PSNR, which is visually negligible.

Continuous-variable measurement-device-independent quantum key distribution with photon subtraction

NASA Astrophysics Data System (ADS)

Ma, Hong-Xin; Huang, Peng; Bai, Dong-Yun; Wang, Shi-Yu; Bao, Wan-Su; Zeng, Gui-Hua

2018-04-01

It has been found that non-Gaussian operations can be applied to increase and distill entanglement between Gaussian entangled states. We show the successful use of the non-Gaussian operation, in particular, photon subtraction operation, on the continuous-variable measurement-device-independent quantum key distribution (CV-MDI-QKD) protocol. The proposed method can be implemented based on existing technologies. Security analysis shows that the photon subtraction operation can remarkably increase the maximal transmission distance of the CV-MDI-QKD protocol, which precisely make up for the shortcoming of the original CV-MDI-QKD protocol, and one-photon subtraction operation has the best performance. Moreover, the proposed protocol provides a feasible method for the experimental implementation of the CV-MDI-QKD protocol.

Online Sensor Fault Detection Based on an Improved Strong Tracking Filter

PubMed Central

Wang, Lijuan; Wu, Lifeng; Guan, Yong; Wang, Guohui

2015-01-01

We propose a method for online sensor fault detection that is based on the evolving Strong Tracking Filter (STCKF). The cubature rule is used to estimate states to improve the accuracy of making estimates in a nonlinear case. A residual is the difference in value between an estimated value and the true value. A residual will be regarded as a signal that includes fault information. The threshold is set at a reasonable level, and will be compared with residuals to determine whether or not the sensor is faulty. The proposed method requires only a nominal plant model and uses STCKF to estimate the original state vector. The effectiveness of the algorithm is verified by simulation on a drum-boiler model. PMID:25690553

Research on Fault Rate Prediction Method of T/R Component

NASA Astrophysics Data System (ADS)

Hou, Xiaodong; Yang, Jiangping; Bi, Zengjun; Zhang, Yu

2017-07-01

T/R component is an important part of the large phased array radar antenna array, because of its large numbers, high fault rate, it has important significance for fault prediction. Aiming at the problems of traditional grey model GM(1,1) in practical operation, the discrete grey model is established based on the original model in this paper, and the optimization factor is introduced to optimize the background value, and the linear form of the prediction model is added, the improved discrete grey model of linear regression is proposed, finally, an example is simulated and compared with other models. The results show that the method proposed in this paper has higher accuracy and the solution is simple and the application scope is more extensive.

LCD denoise and the vector mutual information method in the application of the gear fault diagnosis under different working conditions

NASA Astrophysics Data System (ADS)

Xiangfeng, Zhang; Hong, Jiang

2018-03-01

In this paper, the full vector LCD method is proposed to solve the misjudgment problem caused by the change of the working condition. First, the signal from different working condition is decomposed by LCD, to obtain the Intrinsic Scale Component (ISC)whose instantaneous frequency with physical significance. Then, calculate of the cross correlation coefficient between ISC and the original signal, signal denoising based on the principle of mutual information minimum. At last, calculate the sum of absolute Vector mutual information of the sample under different working condition and the denoised ISC as the characteristics to classify by use of Support vector machine (SVM). The wind turbines vibration platform gear box experiment proves that this method can identify fault characteristics under different working conditions. The advantage of this method is that it reduce dependence of man’s subjective experience, identify fault directly from the original data of vibration signal. It will has high engineering value.

Support vector machine based classification of fast Fourier transform spectroscopy of proteins

NASA Astrophysics Data System (ADS)

Lazarevic, Aleksandar; Pokrajac, Dragoljub; Marcano, Aristides; Melikechi, Noureddine

2009-02-01

Fast Fourier transform spectroscopy has proved to be a powerful method for study of the secondary structure of proteins since peak positions and their relative amplitude are affected by the number of hydrogen bridges that sustain this secondary structure. However, to our best knowledge, the method has not been used yet for identification of proteins within a complex matrix like a blood sample. The principal reason is the apparent similarity of protein infrared spectra with actual differences usually masked by the solvent contribution and other interactions. In this paper, we propose a novel machine learning based method that uses protein spectra for classification and identification of such proteins within a given sample. The proposed method uses principal component analysis (PCA) to identify most important linear combinations of original spectral components and then employs support vector machine (SVM) classification model applied on such identified combinations to categorize proteins into one of given groups. Our experiments have been performed on the set of four different proteins, namely: Bovine Serum Albumin, Leptin, Insulin-like Growth Factor 2 and Osteopontin. Our proposed method of applying principal component analysis along with support vector machines exhibits excellent classification accuracy when identifying proteins using their infrared spectra.

A Modified Empirical Wavelet Transform for Acoustic Emission Signal Decomposition in Structural Health Monitoring.

PubMed

Dong, Shaopeng; Yuan, Mei; Wang, Qiusheng; Liang, Zhiling

2018-05-21

The acoustic emission (AE) method is useful for structural health monitoring (SHM) of composite structures due to its high sensitivity and real-time capability. The main challenge, however, is how to classify the AE data into different failure mechanisms because the detected signals are affected by various factors. Empirical wavelet transform (EWT) is a solution for analyzing the multi-component signals and has been used to process the AE data. In order to solve the spectrum separation problem of the AE signals, this paper proposes a novel modified separation method based on local window maxima (LWM) algorithm. It searches the local maxima of the Fourier spectrum in a proper window, and automatically determines the boundaries of spectrum segmentations, which helps to eliminate the impact of noise interference or frequency dispersion in the detected signal and obtain the meaningful empirical modes that are more related to the damage characteristics. Additionally, both simulation signal and AE signal from the composite structures are used to verify the effectiveness of the proposed method. Finally, the experimental results indicate that the proposed method performs better than the original EWT method in identifying different damage mechanisms of composite structures.

A Modified Empirical Wavelet Transform for Acoustic Emission Signal Decomposition in Structural Health Monitoring

PubMed Central

Dong, Shaopeng; Yuan, Mei; Wang, Qiusheng; Liang, Zhiling

2018-01-01

The acoustic emission (AE) method is useful for structural health monitoring (SHM) of composite structures due to its high sensitivity and real-time capability. The main challenge, however, is how to classify the AE data into different failure mechanisms because the detected signals are affected by various factors. Empirical wavelet transform (EWT) is a solution for analyzing the multi-component signals and has been used to process the AE data. In order to solve the spectrum separation problem of the AE signals, this paper proposes a novel modified separation method based on local window maxima (LWM) algorithm. It searches the local maxima of the Fourier spectrum in a proper window, and automatically determines the boundaries of spectrum segmentations, which helps to eliminate the impact of noise interference or frequency dispersion in the detected signal and obtain the meaningful empirical modes that are more related to the damage characteristics. Additionally, both simulation signal and AE signal from the composite structures are used to verify the effectiveness of the proposed method. Finally, the experimental results indicate that the proposed method performs better than the original EWT method in identifying different damage mechanisms of composite structures. PMID:29883411

Prompt identification of tsunamigenic earthquakes from 3-component seismic data

NASA Astrophysics Data System (ADS)

Kundu, Ajit; Bhadauria, Y. S.; Basu, S.; Mukhopadhyay, S.

2016-10-01

An Artificial Neural Network (ANN) based algorithm for prompt identification of shallow focus (depth < 70 km) tsunamigenic earthquakes at a regional distance is proposed in the paper. The promptness here refers to decision making as fast as 5 min after the arrival of LR phase in the seismogram. The root mean square amplitudes of seismic phases recorded by a single 3-component station have been considered as inputs besides location and magnitude. The trained ANN has been found to categorize 100% of the new earthquakes successfully as tsunamigenic or non-tsunamigenic. The proposed method has been corroborated by an alternate mapping technique of earthquake category estimation. The second method involves computation of focal parameters, estimation of water volume displaced at the source and eventually deciding category of the earthquake. The method has been found to identify 95% of the new earthquakes successfully. Both the methods have been tested using three component broad band seismic data recorded at PALK (Pallekele, Sri Lanka) station provided by IRIS for earthquakes originating from Sumatra region of magnitude 6 and above. The fair agreement between the methods ensures that a prompt alert system could be developed based on proposed method. The method would prove to be extremely useful for the regions that are not adequately instrumented for azimuthal coverage.

Constrained Total Generalized p-Variation Minimization for Few-View X-Ray Computed Tomography Image Reconstruction.

PubMed

Zhang, Hanming; Wang, Linyuan; Yan, Bin; Li, Lei; Cai, Ailong; Hu, Guoen

2016-01-01

Total generalized variation (TGV)-based computed tomography (CT) image reconstruction, which utilizes high-order image derivatives, is superior to total variation-based methods in terms of the preservation of edge information and the suppression of unfavorable staircase effects. However, conventional TGV regularization employs l1-based form, which is not the most direct method for maximizing sparsity prior. In this study, we propose a total generalized p-variation (TGpV) regularization model to improve the sparsity exploitation of TGV and offer efficient solutions to few-view CT image reconstruction problems. To solve the nonconvex optimization problem of the TGpV minimization model, we then present an efficient iterative algorithm based on the alternating minimization of augmented Lagrangian function. All of the resulting subproblems decoupled by variable splitting admit explicit solutions by applying alternating minimization method and generalized p-shrinkage mapping. In addition, approximate solutions that can be easily performed and quickly calculated through fast Fourier transform are derived using the proximal point method to reduce the cost of inner subproblems. The accuracy and efficiency of the simulated and real data are qualitatively and quantitatively evaluated to validate the efficiency and feasibility of the proposed method. Overall, the proposed method exhibits reasonable performance and outperforms the original TGV-based method when applied to few-view problems.

A Mixed Integer Linear Programming Approach to Electrical Stimulation Optimization Problems.

PubMed

Abouelseoud, Gehan; Abouelseoud, Yasmine; Shoukry, Amin; Ismail, Nour; Mekky, Jaidaa

2018-02-01

Electrical stimulation optimization is a challenging problem. Even when a single region is targeted for excitation, the problem remains a constrained multi-objective optimization problem. The constrained nature of the problem results from safety concerns while its multi-objectives originate from the requirement that non-targeted regions should remain unaffected. In this paper, we propose a mixed integer linear programming formulation that can successfully address the challenges facing this problem. Moreover, the proposed framework can conclusively check the feasibility of the stimulation goals. This helps researchers to avoid wasting time trying to achieve goals that are impossible under a chosen stimulation setup. The superiority of the proposed framework over alternative methods is demonstrated through simulation examples.

Pirogow's Amputation: A Modification of the Operation Method

PubMed Central

Bueschges, M.; Muehlberger, T.; Mauss, K. L.; Bruck, J. C.; Ottomann, C.

2013-01-01

Introduction. Pirogow's amputation at the ankle presents a valuable alternative to lower leg amputation for patients with the corresponding indications. Although this method offers the ability to stay mobile without the use of a prosthesis, it is rarely performed. This paper proposes a modification regarding the operation method of the Pirogow amputation. The results of the modified operation method on ten patients were objectified 12 months after the operation using a patient questionnaire (Ankle Score). Material and Methods. We modified the original method by rotating the calcaneus. To fix the calcaneus to the tibia, Kirschner wire and a 3/0 spongiosa tension screw as well as a Fixateur externe were used. Results. 70% of those questioned who were amputated following the modified Pirogow method indicated an excellent or very good result in total points whereas in the control group (original Pirogow's amputation) only 40% reported excellent or very good result. In addition, the level of pain experienced one year after the completed operation showed different results in favour of the group being operated with the modified way. Furthermore, patients in both groups showed differences in radiological results, postoperative leg length difference, and postoperative mobility. Conclusion. The modified Pirogow amputation presents a valuable alternative to the original amputation method for patients with the corresponding indications. The benefits are found in the significantly reduced pain, difference in reduced radiological complications, the increase in mobility without a prosthesis, and the reduction of postoperative leg length difference. PMID:23606976

A System Approach to Navy Medical Education and Training. Appendix 36. Competency Curriculum for Operating Room Assistant and Operating Room Technician.

DTIC Science & Technology

1974-08-31

These methods and curriculum materials constituted a third (instructional) sub-system. Thus, as originally proposed, a system capability has been...NODAL and its associated indexing techniques, it is possible to assemble modified or completely different inventories than those used in this research...covering all hair as a source of infection Method by which synthetic material causes static electricity Danger of static electricity in O.R. suite I

[Estimation of efficacy of the ozone-quant therapy in prophylaxis and treatment of the purulent-necrotic complications of pancreatic pseudocysts].

PubMed

Hazdiuk, P V

2009-01-01

There were analyzed the tactics and the results of 132 patients complex surgical treatment , suffering pancreatic gland pseudocysts (PGP). The original method of the ozone-quant therapy (OQTH), using biophysical factors, such as the ozonized solution of sodium chloride and low-intensity laser irradiation, was applied for prophylaxis and treatment of PGP purulent-necrotic complications (PNC). The data obtained, witness about efficacy of the proposed OQTH method in prophylaxis and treatment of PGP PNC.

STOCK Mechanics:. a General Theory and Method of Energy Conservation with Applications on Djia

NASA Astrophysics Data System (ADS)

Tuncay, Çağlar

A new method, based on the original theory of conservation of sum of kinetic and potential energy defined for prices is proposed and applied on the Dow Jones Industrials Average (DJIA). The general trends averaged over months or years gave a roughly conserved total energy, with three different potential energies, i.e., positive definite quadratic, negative definite quadratic and linear potential energy for exponential rises (and falls), sinusoidal oscillations and parabolic trajectories, respectively. Corresponding expressions for force (impact) are also given.

Implementation details of the coupled QMR algorithm

NASA Technical Reports Server (NTRS)

Freund, Roland W.; Nachtigal, Noel M.

1992-01-01

The original quasi-minimal residual method (QMR) relies on the three-term look-ahead Lanczos process, to generate basis vectors for the underlying Krylov subspaces. However, empirical observations indicate that, in finite precision arithmetic, three-term vector recurrences are less robust than mathematically equivalent coupled two-term recurrences. Therefore, we recently proposed a new implementation of the QMR method based on a coupled two-term look-ahead Lanczos procedure. In this paper, we describe implementation details of this coupled QMR algorithm, and we present results of numerical experiments.

Automated real time peg and tool detection for the FLS trainer box.

PubMed

Nemani, Arun; Sankaranarayanan, Ganesh

2012-01-01

This study proposes a method that effectively tracks trocar tool and peg positions in real time to allow real time assessment of the peg transfer task of the Fundamentals of Laparoscopic Surgery (FLS). By utilizing custom code along with OpenCV libraries, tool and peg positions can be accurately tracked without altering the original setup conditions of the FLS trainer box. This is achieved via a series of image filtration sequences, thresholding functions, and Haar training methods.

A new linear back projection algorithm to electrical tomography based on measuring data decomposition

NASA Astrophysics Data System (ADS)

Sun, Benyuan; Yue, Shihong; Cui, Ziqiang; Wang, Huaxiang

2015-12-01

As an advanced measurement technique of non-radiant, non-intrusive, rapid response, and low cost, the electrical tomography (ET) technique has developed rapidly in recent decades. The ET imaging algorithm plays an important role in the ET imaging process. Linear back projection (LBP) is the most used ET algorithm due to its advantages of dynamic imaging process, real-time response, and easy realization. But the LBP algorithm is of low spatial resolution due to the natural ‘soft field’ effect and ‘ill-posed solution’ problems; thus its applicable ranges are greatly limited. In this paper, an original data decomposition method is proposed, and every ET measuring data are decomposed into two independent new data based on the positive and negative sensing areas of the measuring data. Consequently, the number of total measuring data is extended to twice as many as the number of the original data, thus effectively reducing the ‘ill-posed solution’. On the other hand, an index to measure the ‘soft field’ effect is proposed. The index shows that the decomposed data can distinguish between different contributions of various units (pixels) for any ET measuring data, and can efficiently reduce the ‘soft field’ effect of the ET imaging process. In light of the data decomposition method, a new linear back projection algorithm is proposed to improve the spatial resolution of the ET image. A series of simulations and experiments are applied to validate the proposed algorithm by the real-time performances and the progress of spatial resolutions.

Histogram equalization with Bayesian estimation for noise robust speech recognition.

PubMed

Suh, Youngjoo; Kim, Hoirin

2018-02-01

The histogram equalization approach is an efficient feature normalization technique for noise robust automatic speech recognition. However, it suffers from performance degradation when some fundamental conditions are not satisfied in the test environment. To remedy these limitations of the original histogram equalization methods, class-based histogram equalization approach has been proposed. Although this approach showed substantial performance improvement under noise environments, it still suffers from performance degradation due to the overfitting problem when test data are insufficient. To address this issue, the proposed histogram equalization technique employs the Bayesian estimation method in the test cumulative distribution function estimation. It was reported in a previous study conducted on the Aurora-4 task that the proposed approach provided substantial performance gains in speech recognition systems based on the acoustic modeling of the Gaussian mixture model-hidden Markov model. In this work, the proposed approach was examined in speech recognition systems with deep neural network-hidden Markov model (DNN-HMM), the current mainstream speech recognition approach where it also showed meaningful performance improvement over the conventional maximum likelihood estimation-based method. The fusion of the proposed features with the mel-frequency cepstral coefficients provided additional performance gains in DNN-HMM systems, which otherwise suffer from performance degradation in the clean test condition.

Multisource Transfer Learning With Convolutional Neural Networks for Lung Pattern Analysis.

PubMed

Christodoulidis, Stergios; Anthimopoulos, Marios; Ebner, Lukas; Christe, Andreas; Mougiakakou, Stavroula

2017-01-01

Early diagnosis of interstitial lung diseases is crucial for their treatment, but even experienced physicians find it difficult, as their clinical manifestations are similar. In order to assist with the diagnosis, computer-aided diagnosis systems have been developed. These commonly rely on a fixed scale classifier that scans CT images, recognizes textural lung patterns, and generates a map of pathologies. In a previous study, we proposed a method for classifying lung tissue patterns using a deep convolutional neural network (CNN), with an architecture designed for the specific problem. In this study, we present an improved method for training the proposed network by transferring knowledge from the similar domain of general texture classification. Six publicly available texture databases are used to pretrain networks with the proposed architecture, which are then fine-tuned on the lung tissue data. The resulting CNNs are combined in an ensemble and their fused knowledge is compressed back to a network with the original architecture. The proposed approach resulted in an absolute increase of about 2% in the performance of the proposed CNN. The results demonstrate the potential of transfer learning in the field of medical image analysis, indicate the textural nature of the problem and show that the method used for training a network can be as important as designing its architecture.

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

MacFarlane, Michael; Battista, Jerry; Chen, Jeff

Purpose: To develop a radiotherapy dose tracking and plan evaluation technique using cone-beam computed tomography (CBCT) images. Methods: We developed a patient-specific method of calibrating CBCT image sets for dose calculation. The planning CT was first registered with the CBCT using deformable image registration (DIR). A scatter plot was generated between the CT numbers of the planning CT and CBCT for each slice. The CBCT calibration curve was obtained by least-square fitting of the data, and applied to each CBCT slice. The calibrated CBCT was then merged with original planning CT to extend the small field of view of CBCT.more » Finally, the treatment plan was copied to the merged CT for dose tracking and plan evaluation. The proposed patient-specific calibration method was also compared to two methods proposed in literature. To evaluate the accuracy of each technique, 15 head-and-neck patients requiring plan adaptation were arbitrarily selected from our institution. The original plan was calculated on each method’s data set, including a second planning CT acquired within 48 hours of the CBCT (serving as gold standard). Clinically relevant dose metrics and 3D gamma analysis of dose distributions were compared between the different techniques. Results: Compared to the gold standard of using planning CTs, the patient-specific CBCT calibration method was shown to provide promising results with gamma pass rates above 95% and average dose metric agreement within 2.5%. Conclusions: The patient-specific CBCT calibration method could potentially be used for on-line dose tracking and plan evaluation, without requiring a re-planning CT session.« less

Simple automatic strategy for background drift correction in chromatographic data analysis.

PubMed

Fu, Hai-Yan; Li, He-Dong; Yu, Yong-Jie; Wang, Bing; Lu, Peng; Cui, Hua-Peng; Liu, Ping-Ping; She, Yuan-Bin

2016-06-03

Chromatographic background drift correction, which influences peak detection and time shift alignment results, is a critical stage in chromatographic data analysis. In this study, an automatic background drift correction methodology was developed. Local minimum values in a chromatogram were initially detected and organized as a new baseline vector. Iterative optimization was then employed to recognize outliers, which belong to the chromatographic peaks, in this vector, and update the outliers in the baseline until convergence. The optimized baseline vector was finally expanded into the original chromatogram, and linear interpolation was employed to estimate background drift in the chromatogram. The principle underlying the proposed method was confirmed using a complex gas chromatographic dataset. Finally, the proposed approach was applied to eliminate background drift in liquid chromatography quadrupole time-of-flight samples used in the metabolic study of Escherichia coli samples. The proposed method was comparable with three classical techniques: morphological weighted penalized least squares, moving window minimum value strategy and background drift correction by orthogonal subspace projection. The proposed method allows almost automatic implementation of background drift correction, which is convenient for practical use. Copyright © 2016 Elsevier B.V. All rights reserved.

Image re-sampling detection through a novel interpolation kernel.

PubMed

Hilal, Alaa

2018-06-01

Image re-sampling involved in re-size and rotation transformations is an essential element block in a typical digital image alteration. Fortunately, traces left from such processes are detectable, proving that the image has gone a re-sampling transformation. Within this context, we present in this paper two original contributions. First, we propose a new re-sampling interpolation kernel. It depends on five independent parameters that controls its amplitude, angular frequency, standard deviation, and duration. Then, we demonstrate its capacity to imitate the same behavior of the most frequent interpolation kernels used in digital image re-sampling applications. Secondly, the proposed model is used to characterize and detect the correlation coefficients involved in re-sampling transformations. The involved process includes a minimization of an error function using the gradient method. The proposed method is assessed over a large database of 11,000 re-sampled images. Additionally, it is implemented within an algorithm in order to assess images that had undergone complex transformations. Obtained results demonstrate better performance and reduced processing time when compared to a reference method validating the suitability of the proposed approaches. Copyright © 2018 Elsevier B.V. All rights reserved.

Confidence intervals for predicting lumber strength properties based on ratios of percentiles from two Weibull populations.

Treesearch

Richard A. Johnson; James W. Evans; David W. Green

2003-01-01

Ratios of strength properties of lumber are commonly used to calculate property values for standards. Although originally proposed in terms of means, ratios are being applied without regard to position in the distribution. It is now known that lumber strength properties are generally not normally distributed. Therefore, nonparametric methods are often used to derive...

Array measurements adapted to the number of available sensors: Theoretical and practical approach for ESAC method

NASA Astrophysics Data System (ADS)

Galiana-Merino, J. J.; Rosa-Cintas, S.; Rosa-Herranz, J.; Garrido, J.; Peláez, J. A.; Martino, S.; Delgado, J.

2016-05-01

Array measurements of ambient noise have become a useful technique to estimate the surface wave dispersion curves and subsequently the subsurface elastic parameters that characterize the studied soil. One of the logistical handicaps associated with this kind of measurements is the requirement of several stations recording at the same time, which limits their applicability in the case of research groups without enough infrastructure resources. In this paper, we describe the theoretical basis of the ESAC method and we deduce how the number of stations needed to implement any array layout can be reduced to only two stations. In this way, we propose a new methodology to implement an N stations array layout by using only M stations (M < N), which will be recording in different positions of the original prearranged N stations geometry at different times. We also provide some practical guidelines to implement the proposed approach and we show different examples where the obtained results confirm the theoretical foundations. Thus, the study carried out reflects that we can use a minimum of 2 stations to deploy any array layout originally designed for higher number of sensors.

Advance study of fiber-reinforced self-compacting concrete

NASA Astrophysics Data System (ADS)

Mironova, M.; Ivanova, M.; Naidenov, V.; Georgiev, I.; Stary, J.

2015-10-01

Incorporation in concrete composition of steel macro- and micro - fiber reinforcement with structural function increases the degree of ductility of typically brittle cement-containing composites, which in some cases can replace completely or partially conventional steel reinforcement in the form of rods and meshes. Thus, that can reduce manufacturing, detailing and placement of conventional reinforcement, which enhances productivity and economic efficiency of the building process. In this paper, six fiber-reinforced with different amounts of steel fiber cement-containing self-compacting compositions are investigated. The results of some of their main strength-deformation characteristics are presented. Advance approach for the study of structural and material properties of these type composites is proposed by using the methods of industrial computed tomography. The obtained original tomography results about the microstructure and characteristics of individual structural components make it possible to analyze the effective macro-characteristics of the studied composites. The resulting analytical data are relevant for the purposes of multi-dimensional modeling of these systems. Multifactor structure-mechanical analysis of the obtained with different methods original scientific results is proposed. It is presented a conclusion of the capabilities and effectiveness of complex analysis in the studies to characterize the properties of self-compacting fiber-reinforced concrete.

Elephants, people, parks and development: the case of the Luangwa Valley, Zambia

NASA Astrophysics Data System (ADS)

Abel, Nick; Blaikie, Piers

1986-11-01

New ideas about conserving wildlife are emerging to compete with conventional national park policies. But methods of analyzing wildlife conservation problems in Africa are inadequate for the analysis of complex issues of policy. Much of the analysis of conservation policy attempts to be ‘apolitical’ on issues charged with social conflict. Analyses are too often ahistorical when history can say a great deal about the origins of present-day ecological problems. Further-more, problems are commonly analyzed within narrow discilinary frameworks which predetermine the nature of conclusions and lead to professionally biased proposals. This case study of the Luangwa Valley, Zambia, is used to demonstrate a method which attempts to remedy these weaknesses, In the first part of the article we examine the role of the Luangwa National Parks in the context of the Zambian political economy, and identify social groups which compete for the resources of the national parks. Next we trace the historical origins of present-day ecological changes. These analyses lead toward a model of the Parks and some of their relationships with the national economy. We end with a proposal for communal use of wildlife which attempts to resolve some of the contradictions inherent in current policy.

Unsupervised Ensemble Anomaly Detection Using Time-Periodic Packet Sampling

NASA Astrophysics Data System (ADS)

Uchida, Masato; Nawata, Shuichi; Gu, Yu; Tsuru, Masato; Oie, Yuji

We propose an anomaly detection method for finding patterns in network traffic that do not conform to legitimate (i.e., normal) behavior. The proposed method trains a baseline model describing the normal behavior of network traffic without using manually labeled traffic data. The trained baseline model is used as the basis for comparison with the audit network traffic. This anomaly detection works in an unsupervised manner through the use of time-periodic packet sampling, which is used in a manner that differs from its intended purpose — the lossy nature of packet sampling is used to extract normal packets from the unlabeled original traffic data. Evaluation using actual traffic traces showed that the proposed method has false positive and false negative rates in the detection of anomalies regarding TCP SYN packets comparable to those of a conventional method that uses manually labeled traffic data to train the baseline model. Performance variation due to the probabilistic nature of sampled traffic data is mitigated by using ensemble anomaly detection that collectively exploits multiple baseline models in parallel. Alarm sensitivity is adjusted for the intended use by using maximum- and minimum-based anomaly detection that effectively take advantage of the performance variations among the multiple baseline models. Testing using actual traffic traces showed that the proposed anomaly detection method performs as well as one using manually labeled traffic data and better than one using randomly sampled (unlabeled) traffic data.

Security-Enhanced Push Button Configuration for Home Smart Control.

PubMed

Han, Junghee; Park, Taejoon

2017-06-08

With the emergence of smart and converged home services, the need for the secure and easy interplay of various devices has been increased. Push Button Configuration (PBC) is one of the technologies proposed for easy set-up of a secure session between IT and consumer devices. Although the Wi-Fi Direct specification explicitly states that all devices must support the PBC method, its applicability is very limited. This is because the security vulnerability of PBC can be maliciously exploited so that attackers can make illegitimate sessions with consumer devices. To address this problem, this paper proposes a novel Security-enhanced PBC (SePBC) scheme with which we can uncover suspicious or malicious devices. The proposed mechanism has several unique features. First, we develop a secure handshake distance measurement protocol by preventing an adversary sitting outside the region from maliciously manipulating its distance to be fake. Second, it is compatible with the original Wi-Fi PBC without introducing a brand-new methodology. Finally, SePBC uses lightweight operations without CPU-intensive cryptography computation and employs inexpensive H/W. Moreover, it needs to incur little overhead when there is no attack. This paper also designs and implements the proposed SePBC in the real world. Our experimental results and analysis show that the proposed SePBC scheme effectively defeats attacks on PBC while minimizing the modification of the original PBC equipment.

Security-Enhanced Push Button Configuration for Home Smart Control †

PubMed Central

Han, Junghee; Park, Taejoon

2017-01-01

With the emergence of smart and converged home services, the need for the secure and easy interplay of various devices has been increased. Push Button Configuration (PBC) is one of the technologies proposed for easy set-up of a secure session between IT and consumer devices. Although the Wi-Fi Direct specification explicitly states that all devices must support the PBC method, its applicability is very limited. This is because the security vulnerability of PBC can be maliciously exploited so that attackers can make illegitimate sessions with consumer devices. To address this problem, this paper proposes a novel Security-enhanced PBC (SePBC) scheme with which we can uncover suspicious or malicious devices. The proposed mechanism has several unique features. First, we develop a secure handshake distance measurement protocol by preventing an adversary sitting outside the region from maliciously manipulating its distance to be fake. Second, it is compatible with the original Wi-Fi PBC without introducing a brand-new methodology. Finally, SePBC uses lightweight operations without CPU-intensive cryptography computation and employs inexpensive H/W. Moreover, it needs to incur little overhead when there is no attack. This paper also designs and implements the proposed SePBC in the real world. Our experimental results and analysis show that the proposed SePBC scheme effectively defeats attacks on PBC while minimizing the modification of the original PBC equipment. PMID:28594370

A Two-Stage Algorithm for Origin-Destination Matrices Estimation Considering Dynamic Dispersion Parameter for Route Choice

PubMed Central

Wang, Yong; Ma, Xiaolei; Liu, Yong; Gong, Ke; Henricakson, Kristian C.; Xu, Maozeng; Wang, Yinhai

2016-01-01

This paper proposes a two-stage algorithm to simultaneously estimate origin-destination (OD) matrix, link choice proportion, and dispersion parameter using partial traffic counts in a congested network. A non-linear optimization model is developed which incorporates a dynamic dispersion parameter, followed by a two-stage algorithm in which Generalized Least Squares (GLS) estimation and a Stochastic User Equilibrium (SUE) assignment model are iteratively applied until the convergence is reached. To evaluate the performance of the algorithm, the proposed approach is implemented in a hypothetical network using input data with high error, and tested under a range of variation coefficients. The root mean squared error (RMSE) of the estimated OD demand and link flows are used to evaluate the model estimation results. The results indicate that the estimated dispersion parameter theta is insensitive to the choice of variation coefficients. The proposed approach is shown to outperform two established OD estimation methods and produce parameter estimates that are close to the ground truth. In addition, the proposed approach is applied to an empirical network in Seattle, WA to validate the robustness and practicality of this methodology. In summary, this study proposes and evaluates an innovative computational approach to accurately estimate OD matrices using link-level traffic flow data, and provides useful insight for optimal parameter selection in modeling travelers’ route choice behavior. PMID:26761209

Rebuilding DEMATEL threshold value: an example of a food and beverage information system.

PubMed

Hsieh, Yi-Fang; Lee, Yu-Cheng; Lin, Shao-Bin

2016-01-01

This study demonstrates how a decision-making trial and evaluation laboratory (DEMATEL) threshold value can be quickly and reasonably determined in the process of combining DEMATEL and decomposed theory of planned behavior (DTPB) models. Models are combined to identify the key factors of a complex problem. This paper presents a case study of a food and beverage information system as an example. The analysis of the example indicates that, given direct and indirect relationships among variables, if a traditional DTPB model only simulates the effects of the variables without considering that the variables will affect the original cause-and-effect relationships among the variables, then the original DTPB model variables cannot represent a complete relationship. For the food and beverage example, a DEMATEL method was employed to reconstruct a DTPB model and, more importantly, to calculate reasonable DEMATEL threshold value for determining additional relationships of variables in the original DTPB model. This study is method-oriented, and the depth of investigation into any individual case is limited. Therefore, the methods proposed in various fields of study should ideally be used to identify deeper and more practical implications.

Automated segmentation and isolation of touching cell nuclei in cytopathology smear images of pleural effusion using distance transform watershed method

NASA Astrophysics Data System (ADS)

Win, Khin Yadanar; Choomchuay, Somsak; Hamamoto, Kazuhiko

2017-06-01

The automated segmentation of cell nuclei is an essential stage in the quantitative image analysis of cell nuclei extracted from smear cytology images of pleural fluid. Cell nuclei can indicate cancer as the characteristics of cell nuclei are associated with cells proliferation and malignancy in term of size, shape and the stained color. Nevertheless, automatic nuclei segmentation has remained challenging due to the artifacts caused by slide preparation, nuclei heterogeneity such as the poor contrast, inconsistent stained color, the cells variation, and cells overlapping. In this paper, we proposed a watershed-based method that is capable to segment the nuclei of the variety of cells from cytology pleural fluid smear images. Firstly, the original image is preprocessed by converting into the grayscale image and enhancing by adjusting and equalizing the intensity using histogram equalization. Next, the cell nuclei are segmented using OTSU thresholding as the binary image. The undesirable artifacts are eliminated using morphological operations. Finally, the distance transform based watershed method is applied to isolate the touching and overlapping cell nuclei. The proposed method is tested with 25 Papanicolaou (Pap) stained pleural fluid images. The accuracy of our proposed method is 92%. The method is relatively simple, and the results are very promising.

Planetary Gears Feature Extraction and Fault Diagnosis Method Based on VMD and CNN.

PubMed

Liu, Chang; Cheng, Gang; Chen, Xihui; Pang, Yusong

2018-05-11

Given local weak feature information, a novel feature extraction and fault diagnosis method for planetary gears based on variational mode decomposition (VMD), singular value decomposition (SVD), and convolutional neural network (CNN) is proposed. VMD was used to decompose the original vibration signal to mode components. The mode matrix was partitioned into a number of submatrices and local feature information contained in each submatrix was extracted as a singular value vector using SVD. The singular value vector matrix corresponding to the current fault state was constructed according to the location of each submatrix. Finally, by training a CNN using singular value vector matrices as inputs, planetary gear fault state identification and classification was achieved. The experimental results confirm that the proposed method can successfully extract local weak feature information and accurately identify different faults. The singular value vector matrices of different fault states have a distinct difference in element size and waveform. The VMD-based partition extraction method is better than ensemble empirical mode decomposition (EEMD), resulting in a higher CNN total recognition rate of 100% with fewer training times (14 times). Further analysis demonstrated that the method can also be applied to the degradation recognition of planetary gears. Thus, the proposed method is an effective feature extraction and fault diagnosis technique for planetary gears.

Planetary Gears Feature Extraction and Fault Diagnosis Method Based on VMD and CNN

PubMed Central

Cheng, Gang; Chen, Xihui

2018-01-01

Given local weak feature information, a novel feature extraction and fault diagnosis method for planetary gears based on variational mode decomposition (VMD), singular value decomposition (SVD), and convolutional neural network (CNN) is proposed. VMD was used to decompose the original vibration signal to mode components. The mode matrix was partitioned into a number of submatrices and local feature information contained in each submatrix was extracted as a singular value vector using SVD. The singular value vector matrix corresponding to the current fault state was constructed according to the location of each submatrix. Finally, by training a CNN using singular value vector matrices as inputs, planetary gear fault state identification and classification was achieved. The experimental results confirm that the proposed method can successfully extract local weak feature information and accurately identify different faults. The singular value vector matrices of different fault states have a distinct difference in element size and waveform. The VMD-based partition extraction method is better than ensemble empirical mode decomposition (EEMD), resulting in a higher CNN total recognition rate of 100% with fewer training times (14 times). Further analysis demonstrated that the method can also be applied to the degradation recognition of planetary gears. Thus, the proposed method is an effective feature extraction and fault diagnosis technique for planetary gears. PMID:29751671

On some methods for assessing earthquake predictions

NASA Astrophysics Data System (ADS)

Molchan, G.; Romashkova, L.; Peresan, A.

2017-09-01

A regional approach to the problem of assessing earthquake predictions inevitably faces a deficit of data. We point out some basic limits of assessment methods reported in the literature, considering the practical case of the performance of the CN pattern recognition method in the prediction of large Italian earthquakes. Along with the classical hypothesis testing, a new game approach, the so-called parimutuel gambling (PG) method, is examined. The PG, originally proposed for the evaluation of the probabilistic earthquake forecast, has been recently adapted for the case of 'alarm-based' CN prediction. The PG approach is a non-standard method; therefore it deserves careful examination and theoretical analysis. We show that the PG alarm-based version leads to an almost complete loss of information about predicted earthquakes (even for a large sample). As a result, any conclusions based on the alarm-based PG approach are not to be trusted. We also show that the original probabilistic PG approach does not necessarily identifies the genuine forecast correctly among competing seismicity rate models, even when applied to extensive data.

One step linear reconstruction method for continuous wave diffuse optical tomography

NASA Astrophysics Data System (ADS)

Ukhrowiyah, N.; Yasin, M.

2017-09-01

The method one step linear reconstruction method for continuous wave diffuse optical tomography is proposed and demonstrated for polyvinyl chloride based material and breast phantom. Approximation which used in this method is selecting regulation coefficient and evaluating the difference between two states that corresponding to the data acquired without and with a change in optical properties. This method is used to recovery of optical parameters from measured boundary data of light propagation in the object. The research is demonstrated by simulation and experimental data. Numerical object is used to produce simulation data. Chloride based material and breast phantom sample is used to produce experimental data. Comparisons of results between experiment and simulation data are conducted to validate the proposed method. The results of the reconstruction image which is produced by the one step linear reconstruction method show that the image reconstruction almost same as the original object. This approach provides a means of imaging that is sensitive to changes in optical properties, which may be particularly useful for functional imaging used continuous wave diffuse optical tomography of early diagnosis of breast cancer.

Cultivating Advanced Technical Writing Skills through a Graduate-Level Course on Writing Research Proposals

ERIC Educational Resources Information Center

McCarthy, Brian D.; Dempsey, Jillian L.

2017-01-01

A graduate-level course focused on original research proposals is introduced to address the uneven preparation in technical writing of new chemistry graduate students. This course focuses on writing original research proposals. The general course structure features extensive group discussions, small-group activities, and regular in-class…

Gauge-origin dependence in electronic g-tensor calculations

NASA Astrophysics Data System (ADS)

Glasbrenner, Michael; Vogler, Sigurd; Ochsenfeld, Christian

2018-06-01

We present a benchmark study on the gauge-origin dependence of the electronic g-tensor using data from unrestricted density functional theory calculations with the spin-orbit mean field ansatz. Our data suggest in accordance with previous studies that g-tensor calculations employing a common gauge-origin are sufficiently accurate for small molecules; however, for extended molecules, the introduced errors can become relevant and significantly exceed the basis set error. Using calculations with the spin-orbit mean field ansatz and gauge-including atomic orbitals as a reference, we furthermore show that the accuracy and reliability of common gauge-origin approaches in larger molecules depends strongly on the locality of the spin density distribution. We propose a new pragmatic ansatz for choosing the gauge-origin which takes the spin density distribution into account and gives reasonably accurate values for molecules with a single localized spin center. For more general cases like molecules with several spatially distant spin centers, common gauge-origin approaches are shown to be insufficient for consistently achieving high accuracy. Therefore the computation of g-tensors using distributed gauge-origin methods like gauge-including atomic orbitals is considered as the ideal approach and is recommended for larger molecular systems.

Information theoretic comparisons of original and transformed data from Landsat MSS and TM

NASA Technical Reports Server (NTRS)

Malila, W. A.

1985-01-01

The dispersion and concentration of signal values in transformed data from the Landsat-4 MSS and TM instruments are analyzed using a communications theory approach. The definition of entropy of Shannon was used to quantify information, and the concept of mutual information was employed to develop a measure of information contained in several subsets of variables. Several comparisons of information content are made on the basis of the information content measure, including: system design capacities; data volume occupied by agricultural data; and the information content of original bands and Tasseled Cap variables. A method for analyzing noise effects in MSS and TM data is proposed.

Modularization of gradient-index optical design using wavefront matching enabled optimization.

PubMed

Nagar, Jogender; Brocker, Donovan E; Campbell, Sawyer D; Easum, John A; Werner, Douglas H

2016-05-02

This paper proposes a new design paradigm which allows for a modular approach to replacing a homogeneous optical lens system with a higher-performance GRadient-INdex (GRIN) lens system using a WaveFront Matching (WFM) method. In multi-lens GRIN systems, a full-system-optimization approach can be challenging due to the large number of design variables. The proposed WFM design paradigm enables optimization of each component independently by explicitly matching the WaveFront Error (WFE) of the original homogeneous component at the exit pupil, resulting in an efficient design procedure for complex multi-lens systems.

A parameterized logarithmic image processing method with Laplacian of Gaussian filtering for lung nodule enhancement in chest radiographs.

PubMed

Chen, Sheng; Yao, Liping; Chen, Bao

2016-11-01

The enhancement of lung nodules in chest radiographs (CXRs) plays an important role in the manual as well as computer-aided detection (CADe) lung cancer. In this paper, we proposed a parameterized logarithmic image processing (PLIP) method combined with the Laplacian of a Gaussian (LoG) filter to enhance lung nodules in CXRs. We first applied several LoG filters with varying parameters to an original CXR to enhance the nodule-like structures as well as the edges in the image. We then applied the PLIP model, which can enhance lung nodule images with high contrast and was beneficial in extracting effective features for nodule detection in the CADe scheme. Our method combined the advantages of both the PLIP algorithm and the LoG algorithm, which can enhance lung nodules in chest radiographs with high contrast. To test our nodule enhancement method, we tested a CADe scheme, with a relatively high performance in nodule detection, using a publically available database containing 140 nodules in 140 CXRs enhanced through our nodule enhancement method. The CADe scheme attained a sensitivity of 81 and 70 % with an average of 5.0 frame rate (FP) and 2.0 FP, respectively, in a leave-one-out cross-validation test. By contrast, the CADe scheme based on the original image recorded a sensitivity of 77 and 63 % at 5.0 FP and 2.0 FP, respectively. We introduced the measurement of enhancement by entropy evaluation to objectively assess our method. Experimental results show that the proposed method obtains an effective enhancement of lung nodules in CXRs for both radiologists and CADe schemes.

Sinusoidal Analysis-Synthesis of Audio Using Perceptual Criteria

NASA Astrophysics Data System (ADS)

Painter, Ted; Spanias, Andreas

2003-12-01

This paper presents a new method for the selection of sinusoidal components for use in compact representations of narrowband audio. The method consists of ranking and selecting the most perceptually relevant sinusoids. The idea behind the method is to maximize the matching between the auditory excitation pattern associated with the original signal and the corresponding auditory excitation pattern associated with the modeled signal that is being represented by a small set of sinusoidal parameters. The proposed component-selection methodology is shown to outperform the maximum signal-to-mask ratio selection strategy in terms of subjective quality.

Asymptotic approximation method of force reconstruction: Application and analysis of stationary random forces

NASA Astrophysics Data System (ADS)

Sanchez, J.

2018-06-01

In this paper, the application and analysis of the asymptotic approximation method to a single degree-of-freedom has recently been produced. The original concepts are summarized, and the necessary probabilistic concepts are developed and applied to single degree-of-freedom systems. Then, these concepts are united, and the theoretical and computational models are developed. To determine the viability of the proposed method in a probabilistic context, numerical experiments are conducted, and consist of a frequency analysis, analysis of the effects of measurement noise, and a statistical analysis. In addition, two examples are presented and discussed.

Signal-to-noise ratio estimation using adaptive tuning on the piecewise cubic Hermite interpolation model for images.

PubMed

Sim, K S; Yeap, Z X; Tso, C P

2016-11-01

An improvement to the existing technique of quantifying signal-to-noise ratio (SNR) of scanning electron microscope (SEM) images using piecewise cubic Hermite interpolation (PCHIP) technique is proposed. The new technique uses an adaptive tuning onto the PCHIP, and is thus named as ATPCHIP. To test its accuracy, 70 images are corrupted with noise and their autocorrelation functions are then plotted. The ATPCHIP technique is applied to estimate the uncorrupted noise-free zero offset point from a corrupted image. Three existing methods, the nearest neighborhood, first order interpolation and original PCHIP, are used to compare with the performance of the proposed ATPCHIP method, with respect to their calculated SNR values. Results show that ATPCHIP is an accurate and reliable method to estimate SNR values from SEM images. SCANNING 38:502-514, 2016. © 2015 Wiley Periodicals, Inc. © Wiley Periodicals, Inc.

Swarm based mean-variance mapping optimization (MVMOS) for solving economic dispatch

NASA Astrophysics Data System (ADS)

Khoa, T. H.; Vasant, P. M.; Singh, M. S. Balbir; Dieu, V. N.

2014-10-01

The economic dispatch (ED) is an essential optimization task in the power generation system. It is defined as the process of allocating the real power output of generation units to meet required load demand so as their total operating cost is minimized while satisfying all physical and operational constraints. This paper introduces a novel optimization which named as Swarm based Mean-variance mapping optimization (MVMOS). The technique is the extension of the original single particle mean-variance mapping optimization (MVMO). Its features make it potentially attractive algorithm for solving optimization problems. The proposed method is implemented for three test power systems, including 3, 13 and 20 thermal generation units with quadratic cost function and the obtained results are compared with many other methods available in the literature. Test results have indicated that the proposed method can efficiently implement for solving economic dispatch.

Three-Dimensional Navier-Stokes Calculations Using the Modified Space-Time CESE Method

NASA Technical Reports Server (NTRS)

Chang, Chau-lyan

2007-01-01

The space-time conservation element solution element (CESE) method is modified to address the robustness issues of high-aspect-ratio, viscous, near-wall meshes. In this new approach, the dependent variable gradients are evaluated using element edges and the corresponding neighboring solution elements while keeping the original flux integration procedure intact. As such, the excellent flux conservation property is retained and the new edge-based gradients evaluation significantly improves the robustness for high-aspect ratio meshes frequently encountered in three-dimensional, Navier-Stokes calculations. The order of accuracy of the proposed method is demonstrated for oblique acoustic wave propagation, shock-wave interaction, and hypersonic flows over a blunt body. The confirmed second-order convergence along with the enhanced robustness in handling hypersonic blunt body flow calculations makes the proposed approach a very competitive CFD framework for 3D Navier-Stokes simulations.

A Noise-Filtered Under-Sampling Scheme for Imbalanced Classification.

PubMed

Kang, Qi; Chen, XiaoShuang; Li, SiSi; Zhou, MengChu

2017-12-01

Under-sampling is a popular data preprocessing method in dealing with class imbalance problems, with the purposes of balancing datasets to achieve a high classification rate and avoiding the bias toward majority class examples. It always uses full minority data in a training dataset. However, some noisy minority examples may reduce the performance of classifiers. In this paper, a new under-sampling scheme is proposed by incorporating a noise filter before executing resampling. In order to verify the efficiency, this scheme is implemented based on four popular under-sampling methods, i.e., Undersampling + Adaboost, RUSBoost, UnderBagging, and EasyEnsemble through benchmarks and significance analysis. Furthermore, this paper also summarizes the relationship between algorithm performance and imbalanced ratio. Experimental results indicate that the proposed scheme can improve the original undersampling-based methods with significance in terms of three popular metrics for imbalanced classification, i.e., the area under the curve, -measure, and -mean.

Research on simulation based material delivery system for an automobile company with multi logistics center

NASA Astrophysics Data System (ADS)

Luo, D.; Guan, Z.; Wang, C.; Yue, L.; Peng, L.

2017-06-01

Distribution of different parts to the assembly lines is significant for companies to improve production. Current research investigates the problem of distribution method optimization of a logistics system in a third party logistic company that provide professional services to an automobile manufacturing case company in China. Current research investigates the logistics leveling the material distribution and unloading platform of the automobile logistics enterprise and proposed logistics distribution strategy, material classification method, as well as logistics scheduling. Moreover, the simulation technology Simio is employed on assembly line logistics system which helps to find and validate an optimization distribution scheme through simulation experiments. Experimental results indicate that the proposed scheme can solve the logistic balance and levels the material problem and congestion of the unloading pattern in an efficient way as compared to the original method employed by the case company.

Bias correction for magnetic resonance images via joint entropy regularization.

PubMed

Wang, Shanshan; Xia, Yong; Dong, Pei; Luo, Jianhua; Huang, Qiu; Feng, Dagan; Li, Yuanxiang

2014-01-01

Due to the imperfections of the radio frequency (RF) coil or object-dependent electrodynamic interactions, magnetic resonance (MR) images often suffer from a smooth and biologically meaningless bias field, which causes severe troubles for subsequent processing and quantitative analysis. To effectively restore the original signal, this paper simultaneously exploits the spatial and gradient features of the corrupted MR images for bias correction via the joint entropy regularization. With both isotropic and anisotropic total variation (TV) considered, two nonparametric bias correction algorithms have been proposed, namely IsoTVBiasC and AniTVBiasC. These two methods have been applied to simulated images under various noise levels and bias field corruption and also tested on real MR data. The test results show that the proposed two methods can effectively remove the bias field and also present comparable performance compared to the state-of-the-art methods.

An Exact Formula for Calculating Inverse Radial Lens Distortions

PubMed Central

Drap, Pierre; Lefèvre, Julien

2016-01-01

This article presents a new approach to calculating the inverse of radial distortions. The method presented here provides a model of reverse radial distortion, currently modeled by a polynomial expression, that proposes another polynomial expression where the new coefficients are a function of the original ones. After describing the state of the art, the proposed method is developed. It is based on a formal calculus involving a power series used to deduce a recursive formula for the new coefficients. We present several implementations of this method and describe the experiments conducted to assess the validity of the new approach. Such an approach, non-iterative, using another polynomial expression, able to be deduced from the first one, can actually be interesting in terms of performance, reuse of existing software, or bridging between different existing software tools that do not consider distortion from the same point of view. PMID:27258288

Using State Estimation Residuals to Detect Abnormal SCADA Data

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

Ma, Jian; Chen, Yousu; Huang, Zhenyu

2010-04-30

Detection of abnormal supervisory control and data acquisition (SCADA) data is critically important for safe and secure operation of modern power systems. In this paper, a methodology of abnormal SCADA data detection based on state estimation residuals is presented. Preceded with a brief overview of outlier detection methods and bad SCADA data detection for state estimation, the framework of the proposed methodology is described. Instead of using original SCADA measurements as the bad data sources, the residuals calculated based on the results of the state estimator are used as the input for the outlier detection algorithm. The BACON algorithm ismore » applied to the outlier detection task. The IEEE 118-bus system is used as a test base to evaluate the effectiveness of the proposed methodology. The accuracy of the BACON method is compared with that of the 3-σ method for the simulated SCADA measurements and residuals.« less

A Hybrid of Deep Network and Hidden Markov Model for MCI Identification with Resting-State fMRI.

PubMed

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

2015-10-01

In this paper, we propose a novel method for modelling functional dynamics in resting-state fMRI (rs-fMRI) for Mild Cognitive Impairment (MCI) identification. Specifically, we devise a hybrid architecture by combining Deep Auto-Encoder (DAE) and Hidden Markov Model (HMM). The roles of DAE and HMM are, respectively, to discover hierarchical non-linear relations among features, by which we transform the original features into a lower dimension space, and to model dynamic characteristics inherent in rs-fMRI, i.e. , internal state changes. By building a generative model with HMMs for each class individually, we estimate the data likelihood of a test subject as MCI or normal healthy control, based on which we identify the clinical label. In our experiments, we achieved the maximal accuracy of 81.08% with the proposed method, outperforming state-of-the-art methods in the literature.

A Hybrid of Deep Network and Hidden Markov Model for MCI Identification with Resting-State fMRI

PubMed Central

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

2015-01-01

In this paper, we propose a novel method for modelling functional dynamics in resting-state fMRI (rs-fMRI) for Mild Cognitive Impairment (MCI) identification. Specifically, we devise a hybrid architecture by combining Deep Auto-Encoder (DAE) and Hidden Markov Model (HMM). The roles of DAE and HMM are, respectively, to discover hierarchical non-linear relations among features, by which we transform the original features into a lower dimension space, and to model dynamic characteristics inherent in rs-fMRI, i.e., internal state changes. By building a generative model with HMMs for each class individually, we estimate the data likelihood of a test subject as MCI or normal healthy control, based on which we identify the clinical label. In our experiments, we achieved the maximal accuracy of 81.08% with the proposed method, outperforming state-of-the-art methods in the literature. PMID:27054199

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

Jing, Yaqi; Meng, Qinghao, E-mail: qh-meng@tju.edu.cn; Qi, Peifeng

An electronic nose (e-nose) was designed to classify Chinese liquors of the same aroma style. A new method of feature reduction which combined feature selection with feature extraction was proposed. Feature selection method used 8 feature-selection algorithms based on information theory and reduced the dimension of the feature space to 41. Kernel entropy component analysis was introduced into the e-nose system as a feature extraction method and the dimension of feature space was reduced to 12. Classification of Chinese liquors was performed by using back propagation artificial neural network (BP-ANN), linear discrimination analysis (LDA), and a multi-linear classifier. The classificationmore » rate of the multi-linear classifier was 97.22%, which was higher than LDA and BP-ANN. Finally the classification of Chinese liquors according to their raw materials and geographical origins was performed using the proposed multi-linear classifier and classification rate was 98.75% and 100%, respectively.« less

A general framework of noise suppression in material decomposition for dual-energy CT

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

Petrongolo, Michael; Dong, Xue; Zhu, Lei, E-mail: leizhu@gatech.edu

Purpose: As a general problem of dual-energy CT (DECT), noise amplification in material decomposition severely reduces the signal-to-noise ratio on the decomposed images compared to that on the original CT images. In this work, the authors propose a general framework of noise suppression in material decomposition for DECT. The method is based on an iterative algorithm recently developed in their group for image-domain decomposition of DECT, with an extension to include nonlinear decomposition models. The generalized framework of iterative DECT decomposition enables beam-hardening correction with simultaneous noise suppression, which improves the clinical benefits of DECT. Methods: The authors propose tomore » suppress noise on the decomposed images of DECT using convex optimization, which is formulated in the form of least-squares estimation with smoothness regularization. Based on the design principles of a best linear unbiased estimator, the authors include the inverse of the estimated variance–covariance matrix of the decomposed images as the penalty weight in the least-squares term. Analytical formulas are derived to compute the variance–covariance matrix for decomposed images with general-form numerical or analytical decomposition. As a demonstration, the authors implement the proposed algorithm on phantom data using an empirical polynomial function of decomposition measured on a calibration scan. The polynomial coefficients are determined from the projection data acquired on a wedge phantom, and the signal decomposition is performed in the projection domain. Results: On the Catphan{sup ®}600 phantom, the proposed noise suppression method reduces the average noise standard deviation of basis material images by one to two orders of magnitude, with a superior performance on spatial resolution as shown in comparisons of line-pair images and modulation transfer function measurements. On the synthesized monoenergetic CT images, the noise standard deviation is reduced by a factor of 2–3. By using nonlinear decomposition on projections, the authors’ method effectively suppresses the streaking artifacts of beam hardening and obtains more uniform images than their previous approach based on a linear model. Similar performance of noise suppression is observed in the results of an anthropomorphic head phantom and a pediatric chest phantom generated by the proposed method. With beam-hardening correction enabled by their approach, the image spatial nonuniformity on the head phantom is reduced from around 10% on the original CT images to 4.9% on the synthesized monoenergetic CT image. On the pediatric chest phantom, their method suppresses image noise standard deviation by a factor of around 7.5, and compared with linear decomposition, it reduces the estimation error of electron densities from 33.3% to 8.6%. Conclusions: The authors propose a general framework of noise suppression in material decomposition for DECT. Phantom studies have shown the proposed method improves the image uniformity and the accuracy of electron density measurements by effective beam-hardening correction and reduces noise level without noticeable resolution loss.« less

Identification of influential spreaders in online social networks using interaction weighted K-core decomposition method

NASA Astrophysics Data System (ADS)

Al-garadi, Mohammed Ali; Varathan, Kasturi Dewi; Ravana, Sri Devi

2017-02-01

Online social networks (OSNs) have become a vital part of everyday living. OSNs provide researchers and scientists with unique prospects to comprehend individuals on a scale and to analyze human behavioral patterns. Influential spreaders identification is an important subject in understanding the dynamics of information diffusion in OSNs. Targeting these influential spreaders is significant in planning the techniques for accelerating the propagation of information that is useful for various applications, such as viral marketing applications or blocking the diffusion of annoying information (spreading of viruses, rumors, online negative behaviors, and cyberbullying). Existing K-core decomposition methods consider links equally when calculating the influential spreaders for unweighted networks. Alternatively, the proposed link weights are based only on the degree of nodes. Thus, if a node is linked to high-degree nodes, then this node will receive high weight and is treated as an important node. Conversely, the degree of nodes in OSN context does not always provide accurate influence of users. In the present study, we improve the K-core method for OSNs by proposing a novel link-weighting method based on the interaction among users. The proposed method is based on the observation that the interaction of users is a significant factor in quantifying the spreading capability of user in OSNs. The tracking of diffusion links in the real spreading dynamics of information verifies the effectiveness of our proposed method for identifying influential spreaders in OSNs as compared with degree centrality, PageRank, and original K-core.

Studying the dynamics of interbeat interval time series of healthy and congestive heart failure subjects using scale based symbolic entropy analysis

PubMed Central

Awan, Imtiaz; Aziz, Wajid; Habib, Nazneen; Alowibdi, Jalal S.; Saeed, Sharjil; Nadeem, Malik Sajjad Ahmed; Shah, Syed Ahsin Ali

2018-01-01

Considerable interest has been devoted for developing a deeper understanding of the dynamics of healthy biological systems and how these dynamics are affected due to aging and disease. Entropy based complexity measures have widely been used for quantifying the dynamics of physical and biological systems. These techniques have provided valuable information leading to a fuller understanding of the dynamics of these systems and underlying stimuli that are responsible for anomalous behavior. The single scale based traditional entropy measures yielded contradictory results about the dynamics of real world time series data of healthy and pathological subjects. Recently the multiscale entropy (MSE) algorithm was introduced for precise description of the complexity of biological signals, which was used in numerous fields since its inception. The original MSE quantified the complexity of coarse-grained time series using sample entropy. The original MSE may be unreliable for short signals because the length of the coarse-grained time series decreases with increasing scaling factor τ, however, MSE works well for long signals. To overcome the drawback of original MSE, various variants of this method have been proposed for evaluating complexity efficiently. In this study, we have proposed multiscale normalized corrected Shannon entropy (MNCSE), in which instead of using sample entropy, symbolic entropy measure NCSE has been used as an entropy estimate. The results of the study are compared with traditional MSE. The effectiveness of the proposed approach is demonstrated using noise signals as well as interbeat interval signals from healthy and pathological subjects. The preliminary results of the study indicate that MNCSE values are more stable and reliable than original MSE values. The results show that MNCSE based features lead to higher classification accuracies in comparison with the MSE based features. PMID:29771977

Studying the dynamics of interbeat interval time series of healthy and congestive heart failure subjects using scale based symbolic entropy analysis.

PubMed

Awan, Imtiaz; Aziz, Wajid; Shah, Imran Hussain; Habib, Nazneen; Alowibdi, Jalal S; Saeed, Sharjil; Nadeem, Malik Sajjad Ahmed; Shah, Syed Ahsin Ali

2018-01-01

Considerable interest has been devoted for developing a deeper understanding of the dynamics of healthy biological systems and how these dynamics are affected due to aging and disease. Entropy based complexity measures have widely been used for quantifying the dynamics of physical and biological systems. These techniques have provided valuable information leading to a fuller understanding of the dynamics of these systems and underlying stimuli that are responsible for anomalous behavior. The single scale based traditional entropy measures yielded contradictory results about the dynamics of real world time series data of healthy and pathological subjects. Recently the multiscale entropy (MSE) algorithm was introduced for precise description of the complexity of biological signals, which was used in numerous fields since its inception. The original MSE quantified the complexity of coarse-grained time series using sample entropy. The original MSE may be unreliable for short signals because the length of the coarse-grained time series decreases with increasing scaling factor τ, however, MSE works well for long signals. To overcome the drawback of original MSE, various variants of this method have been proposed for evaluating complexity efficiently. In this study, we have proposed multiscale normalized corrected Shannon entropy (MNCSE), in which instead of using sample entropy, symbolic entropy measure NCSE has been used as an entropy estimate. The results of the study are compared with traditional MSE. The effectiveness of the proposed approach is demonstrated using noise signals as well as interbeat interval signals from healthy and pathological subjects. The preliminary results of the study indicate that MNCSE values are more stable and reliable than original MSE values. The results show that MNCSE based features lead to higher classification accuracies in comparison with the MSE based features.

Measures of lexical distance between languages

NASA Astrophysics Data System (ADS)

Petroni, Filippo; Serva, Maurizio

2010-06-01

The idea of measuring distance between languages seems to have its roots in the work of the French explorer Dumont D’Urville (1832) [13]. He collected comparative word lists for various languages during his voyages aboard the Astrolabe from 1826 to 1829 and, in his work concerning the geographical division of the Pacific, he proposed a method for measuring the degree of relation among languages. The method used by modern glottochronology, developed by Morris Swadesh in the 1950s, measures distances from the percentage of shared cognates, which are words with a common historical origin. Recently, we proposed a new automated method which uses the normalized Levenshtein distances among words with the same meaning and averages on the words contained in a list. Recently another group of scholars, Bakker et al. (2009) [8] and Holman et al. (2008) [9], proposed a refined version of our definition including a second normalization. In this paper we compare the information content of our definition with the refined version in order to decide which of the two can be applied with greater success to resolve relationships among languages.

Finger crease pattern recognition using Legendre moments and principal component analysis

NASA Astrophysics Data System (ADS)

Luo, Rongfang; Lin, Tusheng

2007-03-01

The finger joint lines defined as finger creases and its distribution can identify a person. In this paper, we propose a new finger crease pattern recognition method based on Legendre moments and principal component analysis (PCA). After obtaining the region of interest (ROI) for each finger image in the pre-processing stage, Legendre moments under Radon transform are applied to construct a moment feature matrix from the ROI, which greatly decreases the dimensionality of ROI and can represent principal components of the finger creases quite well. Then, an approach to finger crease pattern recognition is designed based on Karhunen-Loeve (K-L) transform. The method applies PCA to a moment feature matrix rather than the original image matrix to achieve the feature vector. The proposed method has been tested on a database of 824 images from 103 individuals using the nearest neighbor classifier. The accuracy up to 98.584% has been obtained when using 4 samples per class for training. The experimental results demonstrate that our proposed approach is feasible and effective in biometrics.

Consistent lattice Boltzmann methods for incompressible axisymmetric flows

NASA Astrophysics Data System (ADS)

Zhang, Liangqi; Yang, Shiliang; Zeng, Zhong; Yin, Linmao; Zhao, Ya; Chew, Jia Wei

2016-08-01

In this work, consistent lattice Boltzmann (LB) methods for incompressible axisymmetric flows are developed based on two efficient axisymmetric LB models available in the literature. In accord with their respective original models, the proposed axisymmetric models evolve within the framework of the standard LB method and the source terms contain no gradient calculations. Moreover, the incompressibility conditions are realized with the Hermite expansion, thus the compressibility errors arising in the existing models are expected to be reduced by the proposed incompressible models. In addition, an extra relaxation parameter is added to the Bhatnagar-Gross-Krook collision operator to suppress the effect of the ghost variable and thus the numerical stability of the present models is significantly improved. Theoretical analyses, based on the Chapman-Enskog expansion and the equivalent moment system, are performed to derive the macroscopic equations from the LB models and the resulting truncation terms (i.e., the compressibility errors) are investigated. In addition, numerical validations are carried out based on four well-acknowledged benchmark tests and the accuracy and applicability of the proposed incompressible axisymmetric LB models are verified.

Effective Alternating Direction Optimization Methods for Sparsity-Constrained Blind Image Deblurring.

PubMed

Xiong, Naixue; Liu, Ryan Wen; Liang, Maohan; Wu, Di; Liu, Zhao; Wu, Huisi

2017-01-18

Single-image blind deblurring for imaging sensors in the Internet of Things (IoT) is a challenging ill-conditioned inverse problem, which requires regularization techniques to stabilize the image restoration process. The purpose is to recover the underlying blur kernel and latent sharp image from only one blurred image. Under many degraded imaging conditions, the blur kernel could be considered not only spatially sparse, but also piecewise smooth with the support of a continuous curve. By taking advantage of the hybrid sparse properties of the blur kernel, a hybrid regularization method is proposed in this paper to robustly and accurately estimate the blur kernel. The effectiveness of the proposed blur kernel estimation method is enhanced by incorporating both the L 1 -norm of kernel intensity and the squared L 2 -norm of the intensity derivative. Once the accurate estimation of the blur kernel is obtained, the original blind deblurring can be simplified to the direct deconvolution of blurred images. To guarantee robust non-blind deconvolution, a variational image restoration model is presented based on the L 1 -norm data-fidelity term and the total generalized variation (TGV) regularizer of second-order. All non-smooth optimization problems related to blur kernel estimation and non-blind deconvolution are effectively handled by using the alternating direction method of multipliers (ADMM)-based numerical methods. Comprehensive experiments on both synthetic and realistic datasets have been implemented to compare the proposed method with several state-of-the-art methods. The experimental comparisons have illustrated the satisfactory imaging performance of the proposed method in terms of quantitative and qualitative evaluations.

Multilabel learning via random label selection for protein subcellular multilocations prediction.

PubMed

Wang, Xiao; Li, Guo-Zheng

2013-01-01

Prediction of protein subcellular localization is an important but challenging problem, particularly when proteins may simultaneously exist at, or move between, two or more different subcellular location sites. Most of the existing protein subcellular localization methods are only used to deal with the single-location proteins. In the past few years, only a few methods have been proposed to tackle proteins with multiple locations. However, they only adopt a simple strategy, that is, transforming the multilocation proteins to multiple proteins with single location, which does not take correlations among different subcellular locations into account. In this paper, a novel method named random label selection (RALS) (multilabel learning via RALS), which extends the simple binary relevance (BR) method, is proposed to learn from multilocation proteins in an effective and efficient way. RALS does not explicitly find the correlations among labels, but rather implicitly attempts to learn the label correlations from data by augmenting original feature space with randomly selected labels as its additional input features. Through the fivefold cross-validation test on a benchmark data set, we demonstrate our proposed method with consideration of label correlations obviously outperforms the baseline BR method without consideration of label correlations, indicating correlations among different subcellular locations really exist and contribute to improvement of prediction performance. Experimental results on two benchmark data sets also show that our proposed methods achieve significantly higher performance than some other state-of-the-art methods in predicting subcellular multilocations of proteins. The prediction web server is available at >http://levis.tongji.edu.cn:8080/bioinfo/MLPred-Euk/ for the public usage.

A Novel Method of Autonomous Inspection for Transmission Line based on Cable Inspection Robot LiDAR Data

PubMed Central

Qin, Xinyan; Wu, Gongping; Fan, Fei; Ye, Xuhui; Mei, Quanjie

2018-01-01

With the growth of the national economy, there is increasing demand for electricity, which forces transmission line corridors to become structurally complicated and extend to complex environments (e.g., mountains, forests). It is a great challenge to inspect transmission line in these regions. To address these difficulties, a novel method of autonomous inspection for transmission line is proposed based on cable inspection robot (CIR) LiDAR data, which mainly includes two steps: preliminary inspection and autonomous inspection. In preliminary inspection, the position and orientation system (POS) data is used for original point cloud dividing, ground point filtering, and structured partition. A hierarchical classification strategy is established to identify the classes and positions of the abnormal points. In autonomous inspection, CIR can autonomously reach the specified points through inspection planning. These inspection targets are imaged with PTZ (pan, tilt, zoom) cameras by coordinate transformation. The feasibility and effectiveness of the proposed method are verified by test site experiments and actual line experiments, respectively. The proposed method greatly reduces manpower and improves inspection accuracy, providing a theoretical basis for intelligent inspection of transmission lines in the future. PMID:29462865

Impacts of heterogeneous organic matter on phenanthrene sorption--Different soil and sediment samples

USGS Publications Warehouse

Karapanagioti, Hrissi K.; Childs, Jeffrey; Sabatini, David A.

2001-01-01

Organic petrography has been proposed as a tool for characterizing the heterogeneous organic matter present in soil and sediment samples. A new simplified method is proposed as a quantitative means of interpreting observed sorption behavior for phenanthrene and different soils and sediments based on their organic petrographical characterization. This method is tested under singe solute conditions and at phenanthrene concentration of 1 μg/L. Since the opaque organic matter fraction dominates the sorption process, we propose that by quantifying this fraction one can interpret organic content normalized sorption distribution coefficient (Koc) values for a sample. While this method was developed and tested for various samples within the same aquifer, in the current study the method is validated for soil and sediment samples from different sites that cover a wide range of organic matter origin, age, and organic content. All 10 soil and sediment samples studied had log Koc values for the opaque particles between 5.6 and 6.8. This range of Koc values illustrates the heterogeneity of opaque particles between sites and geological formations and thus the need to characterize the opaque fraction of materials on a site-by-site basis.

Fast clustering using adaptive density peak detection.

PubMed

Wang, Xiao-Feng; Xu, Yifan

2017-12-01

Common limitations of clustering methods include the slow algorithm convergence, the instability of the pre-specification on a number of intrinsic parameters, and the lack of robustness to outliers. A recent clustering approach proposed a fast search algorithm of cluster centers based on their local densities. However, the selection of the key intrinsic parameters in the algorithm was not systematically investigated. It is relatively difficult to estimate the "optimal" parameters since the original definition of the local density in the algorithm is based on a truncated counting measure. In this paper, we propose a clustering procedure with adaptive density peak detection, where the local density is estimated through the nonparametric multivariate kernel estimation. The model parameter is then able to be calculated from the equations with statistical theoretical justification. We also develop an automatic cluster centroid selection method through maximizing an average silhouette index. The advantage and flexibility of the proposed method are demonstrated through simulation studies and the analysis of a few benchmark gene expression data sets. The method only needs to perform in one single step without any iteration and thus is fast and has a great potential to apply on big data analysis. A user-friendly R package ADPclust is developed for public use.

Three dimensional range geometry and texture data compression with space-filling curves.

PubMed

Chen, Xia; Zhang, Song

2017-10-16

This paper presents a novel method to effectively store three-dimensional (3D) data and 2D texture data into a regular 24-bit image. The proposed method uses the Hilbert space-filling curve to map the normalized unwrapped phase map to two 8-bit color channels, and saves the third color channel for 2D texture storage. By further leveraging existing 2D image and video compression techniques, the proposed method can achieve high compression ratios while effectively preserving data quality. Since the encoding and decoding processes can be applied to most of the current 2D media platforms, this proposed compression method can make 3D data storage and transmission available for many electrical devices without requiring special hardware changes. Experiments demonstrate that if a lossless 2D image/video format is used, both original 3D geometry and 2D color texture can be accurately recovered; if lossy image/video compression is used, only black-and-white or grayscale texture can be properly recovered, but much higher compression ratios (e.g., 1543:1 against the ASCII OBJ format) are achieved with slight loss of 3D geometry quality.

Design equations for the assessment and FRP-strengthening of reinforced rectangular concrete columns under combined biaxial bending and axial loads

NASA Astrophysics Data System (ADS)

Alessandri, S.; Monti, G.

2008-05-01

A simple procedure is proposed for the assessment of reinforced rectangular concrete columns under combined biaxial bending and axial loads and for the design of a correct amount of FRP-strengthening for underdesigned concrete sections. Approximate closed-form equations are developed based on the load contour method originally proposed by Bresler for reinforced concrete sections. The 3D failure surface is approximated along its contours, at a constant axial load, by means of equations given as the sum of the acting/resisting moment ratio in the directions of principal axes of the sections, raised to a power depending on the axial load, the steel reinforcement ratio, and the section shape. The method is extended to FRP-strengthened sections. Moreover, to make it possible to apply the load contour method in a more practical way, simple closed-form equations are developed for rectangular reinforced concrete sections with a two-way steel reinforcement and FRP strengthenings on each side. A comparison between the approach proposed and the fiber method (which is considered exact) shows that the simplified equations correctly represent the section interaction diagram.

Non-rigid ultrasound image registration using generalized relaxation labeling process

NASA Astrophysics Data System (ADS)

Lee, Jong-Ha; Seong, Yeong Kyeong; Park, MoonHo; Woo, Kyoung-Gu; Ku, Jeonghun; Park, Hee-Jun

2013-03-01

This research proposes a novel non-rigid registration method for ultrasound images. The most predominant anatomical features in medical images are tissue boundaries, which appear as edges. In ultrasound images, however, other features can be identified as well due to the specular reflections that appear as bright lines superimposed on the ideal edge location. In this work, an image's local phase information (via the frequency domain) is used to find the ideal edge location. The generalized relaxation labeling process is then formulated to align the feature points extracted from the ideal edge location. In this work, the original relaxation labeling method was generalized by taking n compatibility coefficient values to improve non-rigid registration performance. This contextual information combined with a relaxation labeling process is used to search for a correspondence. Then the transformation is calculated by the thin plate spline (TPS) model. These two processes are iterated until the optimal correspondence and transformation are found. We have tested our proposed method and the state-of-the-art algorithms with synthetic data and bladder ultrasound images of in vivo human subjects. Experiments show that the proposed method improves registration performance significantly, as compared to other state-of-the-art non-rigid registration algorithms.